[11-22 14:34:22] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:34:24] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:34:24] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 12 --codebook_size 4096 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 4096 codebook_embed_dim : 12 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 branch : main } [11-22 14:34:24] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:34:27] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:34:27] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:34:22] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:34:24] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:34:24] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 12 --codebook_size 4096 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 4096 codebook_embed_dim : 12 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 branch : main commit_msg : add } [11-22 14:34:24] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:34:27] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:34:27] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:34:22] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:34:24] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:34:24] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 12 --codebook_size 4096 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 4096 codebook_embed_dim : 12 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 commit_msg : add branch : main } [11-22 14:34:24] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:34:27] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:34:27] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:34:22] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:34:24] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:34:24] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 12 --codebook_size 4096 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 4096 codebook_embed_dim : 12 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_msg : add commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 } [11-22 14:34:24] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:34:27] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:34:27] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:34:22] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:34:24] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:34:24] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 12 --codebook_size 4096 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 4096 codebook_embed_dim : 12 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 branch : main } [11-22 14:34:24] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:34:27] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:34:27] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:34:22] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:34:24] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:34:24] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 12 --codebook_size 4096 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 4096 codebook_embed_dim : 12 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 branch : main commit_msg : add } [11-22 14:34:24] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:34:27] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:34:27] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:34:22] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:34:24] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:34:24] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 12 --codebook_size 4096 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 4096 codebook_embed_dim : 12 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 commit_msg : add } [11-22 14:34:24] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:34:27] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:34:27] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:34:22] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:34:22] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:34:24] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:34:24] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 12 --codebook_size 4096 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 4096 codebook_embed_dim : 12 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add branch : main commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 } [11-22 14:34:24] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:34:27] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:34:27] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:34:27] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:34:27] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:34:27] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ... [dataloader multi processing](*) finished! (46.70s) [dataloader multi processing](*) finished! (47.26s) [dataloader multi processing](*) finished! (48.44s) [dataloader multi processing](*) finished! (48.64s) [dataloader multi processing](*) finished! (49.46s) [11-22 14:35:14] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [dataloader multi processing](*) finished! (50.69s) [dataloader multi processing](*) finished! (52.31s) [11-22 14:35:16] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:35:19] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:19] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:20] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [dataloader multi processing](*) finished! (53.76s) [11-22 14:35:14] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:35:19] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:19] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:21] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:35:20] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:20] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:21] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:35:15] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:35:20] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:20] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:21] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:35:15] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:35:20] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:20] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:21] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:35:18] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:35:22] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:22] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:24] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:35:19] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:35:25] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:25] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:26] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:35:21] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:35:25] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:25] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887, 0.2887]) [11-22 14:35:26] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:35:24] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:35:27] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:35:22] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:35:22] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:35:21] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:35:22] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:35:21] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:35:27] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 ======================================================= RESTART [11-22 14:36:54] ======================================================= ======================================================= RESTART [11-22 14:36:54] ======================================================= ======================================================= RESTART [11-22 14:36:54] ======================================================= ======================================================= RESTART [11-22 14:36:54] ======================================================= ======================================================= RESTART [11-22 14:36:54] ======================================================= ======================================================= RESTART [11-22 14:36:54] ======================================================= ======================================================= RESTART [11-22 14:36:54] ======================================================= ======================================================= RESTART [11-22 14:36:54] ======================================================= [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:36:55] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:36:55] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 branch : main commit_msg : add } [11-22 14:36:55] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:36:58] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:36:58] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:36:54] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:36:55] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:36:55] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_msg : add commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 } [11-22 14:36:55] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:36:58] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:36:58] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:36:54] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:36:55] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:36:55] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_msg : add commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 } [11-22 14:36:55] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:36:58] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:36:58] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:36:54] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:36:55] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:36:55] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_msg : add commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 } [11-22 14:36:55] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:36:58] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:36:58] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:36:54] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:36:55] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:36:55] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 commit_msg : add branch : main } [11-22 14:36:55] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:36:58] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:36:58] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:36:54] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:36:55] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:36:55] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 branch : main } [11-22 14:36:55] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:36:58] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:36:58] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:36:54] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:36:55] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:36:55] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 branch : main } [11-22 14:36:55] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:36:58] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:36:58] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:36:54] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:36:54] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:36:55] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:36:55] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_id : 9bc7c3673d0c9a4fbec8f6cc776e35d59d8d4b97 commit_msg : add } [11-22 14:36:55] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:36:58] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:36:58] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:36:58] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:36:58] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:36:58] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ... [dataloader multi processing](*) finished! (48.18s) [dataloader multi processing](*) finished! (48.30s) [dataloader multi processing](*) finished! (48.57s) [dataloader multi processing](*) finished! (48.61s) [dataloader multi processing](*) finished! (48.65s) [dataloader multi processing](*) finished! (49.39s) [dataloader multi processing](*) finished! (49.97s) [dataloader multi processing](*) finished! (49.97s) [11-22 14:37:46] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:37:51] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:51] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:52] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:37:48] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:37:51] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:51] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:52] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:37:46] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:37:51] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:51] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:52] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:37:47] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:37:51] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:51] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:53] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:37:47] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:37:51] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:51] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:53] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:37:47] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:37:51] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:51] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:53] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:37:48] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:37:52] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:52] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:53] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:37:48] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:37:53] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:53] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:37:54] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:37:53] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:38:03] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:38:03] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:38:03] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:38:03] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:38:03] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank0] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:37:53] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:38:03] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:38:03] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:38:03] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:38:03] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:38:03] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank8] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:37:54] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:38:03] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:38:03] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:38:03] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:38:03] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:38:03] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank16] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:37:54] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:38:03] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:38:03] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:38:03] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:38:03] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:38:03] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank24] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:37:53] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:38:03] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:38:03] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:38:03] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:38:03] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:38:04] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank32] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:37:53] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:38:03] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:38:03] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:38:03] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:38:03] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:38:04] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank40] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:37:53] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:38:03] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:38:03] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:38:03] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:38:03] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:38:04] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank48] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:37:53] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:38:03] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:38:03] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:38:03] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:38:03] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:38:04] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank56] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:38:04] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:38:04] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 14:38:04] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:38:04] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 14:38:04] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:38:04] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 14:38:04] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:38:04] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 14:38:04] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:38:04] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 14:38:04] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:38:04] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 14:38:04] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:38:04] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 14:38:04] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:38:04] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} ======================================================= RESTART [11-22 14:40:03] ======================================================= ======================================================= RESTART [11-22 14:40:03] ======================================================= ======================================================= RESTART [11-22 14:40:03] ======================================================= ======================================================= RESTART [11-22 14:40:03] ======================================================= ======================================================= RESTART [11-22 14:40:03] ======================================================= ======================================================= RESTART [11-22 14:40:03] ======================================================= ======================================================= RESTART [11-22 14:40:03] ======================================================= ======================================================= RESTART [11-22 14:40:03] ======================================================= [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:40:04] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:40:04] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 branch : main } [11-22 14:40:04] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:40:07] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:40:07] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:40:03] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:40:04] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:40:04] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 commit_msg : add } [11-22 14:40:04] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:40:07] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:40:07] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:40:03] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:40:04] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:40:04] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 branch : main commit_msg : add } [11-22 14:40:04] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:40:07] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:40:07] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:40:03] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:40:04] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:40:04] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 branch : main commit_msg : add } [11-22 14:40:04] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:40:07] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:40:07] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:40:03] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:40:04] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:40:04] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 branch : main } [11-22 14:40:04] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:40:07] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:40:07] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:40:03] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:40:04] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:40:04] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 commit_msg : add branch : main } [11-22 14:40:04] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:40:07] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:40:07] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:40:03] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:40:04] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:40:04] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add branch : main commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 } [11-22 14:40:04] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:40:07] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:40:07] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 14:40:03] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 14:40:03] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 14:40:04] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 14:40:04] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 17 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=17 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd17__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 branch : main commit_msg : add } [11-22 14:40:04] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 14:40:07] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 14:40:07] (e/user/VAR/utils/data.py, line 51)=> [11-22 14:40:07] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 14:40:07] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 14:40:07] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ... [dataloader multi processing](*) finished! (46.10s) [dataloader multi processing](*) finished! (46.88s) [dataloader multi processing](*) finished! (47.47s) [dataloader multi processing](*) finished! (47.69s) [dataloader multi processing](*) finished! (47.83s) [dataloader multi processing](*) finished! (48.33s) [dataloader multi processing](*) finished! (48.36s) [dataloader multi processing](*) finished! (48.82s) [11-22 14:40:53] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:40:58] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:40:58] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:40:59] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:40:54] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:40:59] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:40:59] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:41:00] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:40:56] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:40:59] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:40:59] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:41:00] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:40:55] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:40:59] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:40:59] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:41:01] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:40:55] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:41:00] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:41:00] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:41:01] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:40:55] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:41:00] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:41:00] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:41:01] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:40:56] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:41:00] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:41:00] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:41:01] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:40:56] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 14:41:00] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:41:00] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 14:41:01] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/17), fused_if_available=True (fusing_add_ln=0/17, fusing_mlp=0/17) ==== [VAR config ] embed_dim=1088, num_heads=17, depth=17, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0708333 (tensor([0.0000, 0.0044, 0.0089, 0.0133, 0.0177, 0.0221, 0.0266, 0.0310, 0.0354, 0.0398, 0.0443, 0.0487, 0.0531, 0.0576, 0.0620, 0.0664, 0.0708])) [11-22 14:41:01] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:41:11] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:41:11] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:41:11] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:41:11] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:41:11] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank0] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:41:01] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:41:11] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:41:11] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:41:11] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:41:11] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:41:11] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank8] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:41:02] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:41:11] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:41:11] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:41:11] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:41:11] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:41:11] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank16] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:41:01] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:41:11] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:41:11] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:41:11] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:41:11] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:41:11] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank24] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:41:00] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:41:11] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:41:11] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:41:11] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:41:11] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:41:11] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank32] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:41:02] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:41:11] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:41:11] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:41:11] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:41:11] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:41:11] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank40] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:41:02] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:41:11] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:41:11] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:41:11] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:41:11] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:41:11] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank48] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:41:02] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0175035 [11-22 14:41:11] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.0708333 (word_embed): Linear(in_features=28, out_features=1088, bias=False) (class_emb): Embedding(1001, 1088) (lvl_embed): Embedding(10, 1088) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) (1-16): 16 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1088, out_features=3264, bias=False) (proj): Linear(in_features=1088, out_features=1088, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1088, out_features=4352, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=4352, out_features=1088, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=6528, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1088,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1088, out_features=2176, bias=True) ) ) (head): Linear(in_features=1088, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1088, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 14:41:11] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 14:41:11] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=409.98 [11-22 14:41:11] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 14:41:11] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank56] type(model).__name__='OptimizedModule' count=219, numel=409981857 [11-22 14:41:12] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:41:12] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 14:53:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 14 days, 12:56:31 tlr: 1.2e-06 tnm: 0.20 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.03 (0.03) Acct: 0.00 (0.00) proj_loss: 0.0028 (0.0028) time: 752.6614 data: 0.0006 [11-22 14:41:12] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:41:12] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 14:53:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 14 days, 13:17:39 tlr: 1.2e-06 tnm: 0.20 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.01 (0.01) Acct: 0.03 (0.03) proj_loss: 0.0005 (0.0005) time: 753.4211 data: 0.0005 [11-22 14:41:12] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:41:12] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 14:53:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 14 days, 13:17:46 tlr: 1.2e-06 tnm: 0.20 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.01 (0.01) Acct: 0.00 (0.00) proj_loss: 0.0030 (0.0030) time: 753.4253 data: 0.0004 [11-22 14:41:12] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:41:12] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 14:53:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 14 days, 13:17:52 tlr: 1.2e-06 tnm: 0.20 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: 0.0003 (0.0003) time: 753.4286 data: 0.0005 [11-22 14:41:12] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:41:12] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 14:53:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 14 days, 13:17:53 tlr: 1.2e-06 tnm: 0.20 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: 0.0033 (0.0033) time: 753.4293 data: 0.0006 [11-22 14:41:12] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:41:12] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 14:53:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 14 days, 13:17:53 tlr: 1.2e-06 tnm: 0.20 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.03 (0.03) Acct: 0.00 (0.00) proj_loss: 0.0012 (0.0012) time: 753.4293 data: 0.0005 [11-22 14:41:12] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:41:12] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 14:53:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 14 days, 13:19:07 tlr: 1.2e-06 tnm: 0.20 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: 0.0021 (0.0021) time: 753.4733 data: 0.0005 [11-22 14:41:12] (/VAR/utils/lr_control.py, line 105)=> [11-22 14:41:12] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 14:53:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 14 days, 13:20:13 tlr: 1.2e-06 tnm: 0.20 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.01 (0.01) Acct: 0.00 (0.00) proj_loss: -0.0009 (-0.0009) time: 753.5129 data: 0.0005 [11-22 14:57:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 0:50:12 tlr: 9.7e-06 tnm: 0.05 Lm: 9.701 (9.701) Lt: 9.699 (9.699) Accm: 0.02 (0.02) Acct: 0.02 (0.02) proj_loss: -0.1210 (-0.1210) time: 0.3575 data: 0.0002 [11-22 14:57:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 0:50:09 tlr: 9.7e-06 tnm: 0.05 Lm: 9.702 (9.702) Lt: 9.701 (9.701) Accm: 0.01 (0.01) Acct: 0.00 (0.00) proj_loss: -0.1201 (-0.1201) time: 0.3575 data: 0.0003 [11-22 14:57:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 0:50:12 tlr: 9.7e-06 tnm: 0.05 Lm: 9.702 (9.702) Lt: 9.702 (9.702) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: -0.1141 (-0.1141) time: 0.3575 data: 0.0002 [11-22 14:57:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 0:50:12 tlr: 9.7e-06 tnm: 0.05 Lm: 9.702 (9.702) Lt: 9.702 (9.702) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: -0.1193 (-0.1193) time: 0.3575 data: 0.0003 [11-22 14:57:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 0:50:12 tlr: 9.7e-06 tnm: 0.05 Lm: 9.701 (9.701) Lt: 9.701 (9.701) Accm: 0.01 (0.01) Acct: 0.00 (0.00) proj_loss: -0.1162 (-0.1162) time: 0.3575 data: 0.0002 [11-22 14:57:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 0:50:12 tlr: 9.7e-06 tnm: 0.05 Lm: 9.700 (9.700) Lt: 9.700 (9.700) Accm: 0.01 (0.01) Acct: 0.02 (0.02) proj_loss: -0.1291 (-0.1291) time: 0.3575 data: 0.0002 [11-22 14:57:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 0:50:12 tlr: 9.7e-06 tnm: 0.05 Lm: 9.702 (9.702) Lt: 9.702 (9.702) Accm: 0.01 (0.01) Acct: 0.02 (0.02) proj_loss: -0.1160 (-0.1160) time: 0.3575 data: 0.0003 [11-22 14:57:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 0:50:12 tlr: 9.7e-06 tnm: 0.05 Lm: 9.701 (9.701) Lt: 9.700 (9.700) Accm: 0.01 (0.01) Acct: 0.00 (0.00) proj_loss: -0.1116 (-0.1116) time: 0.3575 data: 0.0003 [11-22 15:00:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:19:15 tlr: 1.8e-05 tnm: 0.09 Lm: 9.698 (9.689) Lt: 9.696 (9.689) Accm: 0.01 (0.01) Acct: 0.00 (0.00) proj_loss: -0.2245 (-0.1841) time: 0.3610 data: 0.0003 [11-22 15:00:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:19:15 tlr: 1.8e-05 tnm: 0.09 Lm: 9.700 (9.692) Lt: 9.699 (9.691) Accm: 0.03 (0.02) Acct: 0.00 (0.00) proj_loss: -0.2429 (-0.1887) time: 0.3610 data: 0.0003 [11-22 15:00:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:19:15 tlr: 1.8e-05 tnm: 0.09 Lm: 9.699 (9.691) Lt: 9.697 (9.691) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: -0.2354 (-0.1865) time: 0.3610 data: 0.0003 [11-22 15:00:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:19:15 tlr: 1.8e-05 tnm: 0.09 Lm: 9.700 (9.690) Lt: 9.699 (9.689) Accm: 0.00 (0.01) Acct: 0.00 (0.00) proj_loss: -0.2388 (-0.1895) time: 0.3610 data: 0.0002 [11-22 15:00:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:19:15 tlr: 1.8e-05 tnm: 0.09 Lm: 9.700 (9.692) Lt: 9.700 (9.690) Accm: 0.00 (0.01) Acct: 0.00 (0.00) proj_loss: -0.2316 (-0.1843) time: 0.3610 data: 0.0002 [11-22 15:00:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:19:15 tlr: 1.8e-05 tnm: 0.09 Lm: 9.697 (9.689) Lt: 9.694 (9.688) Accm: 0.03 (0.02) Acct: 0.00 (0.01) proj_loss: -0.2410 (-0.1904) time: 0.3610 data: 0.0002 [11-22 15:00:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:19:15 tlr: 1.8e-05 tnm: 0.09 Lm: 9.699 (9.691) Lt: 9.699 (9.689) Accm: 0.01 (0.01) Acct: 0.00 (0.01) proj_loss: -0.2340 (-0.1868) time: 0.3610 data: 0.0003 [11-22 15:00:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:19:15 tlr: 1.8e-05 tnm: 0.09 Lm: 9.696 (9.691) Lt: 9.696 (9.691) Accm: 0.01 (0.01) Acct: 0.00 (0.01) proj_loss: -0.2586 (-0.1935) time: 0.3610 data: 0.0004 ======================================================= RESTART [11-22 15:09:28] ======================================================= ======================================================= RESTART [11-22 15:09:28] ======================================================= ======================================================= RESTART [11-22 15:09:28] ======================================================= ======================================================= RESTART [11-22 15:09:28] ======================================================= [11-22 15:09:28] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 15:09:28] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 15:09:28] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 15:09:29] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-22 15:09:29] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add branch : main commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 } [11-22 15:09:29] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 15:09:32] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 15:09:32] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:09:32] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:09:32] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:09:32] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:09:32] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 15:09:32] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 15:09:32] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 15:09:28] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 15:09:28] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 15:09:28] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 15:09:29] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-22 15:09:29] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 commit_msg : add branch : main } [11-22 15:09:29] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 15:09:32] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 15:09:32] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:09:32] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:09:32] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:09:32] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:09:32] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 15:09:32] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 15:09:32] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 15:09:28] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 15:09:28] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 15:09:28] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 15:09:29] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-22 15:09:29] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_msg : add commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 } [11-22 15:09:29] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 15:09:32] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 15:09:32] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:09:32] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:09:32] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:09:32] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:09:32] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 15:09:32] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 15:09:32] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 15:09:28] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 15:09:28] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 15:09:28] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 15:09:29] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-22 15:09:29] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 branch : main } [11-22 15:09:29] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 15:09:32] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 15:09:32] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:09:32] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:09:32] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:09:32] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:09:32] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:09:32] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 15:09:32] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 15:09:32] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ... [dataloader multi processing](*) finished! (47.11s) [dataloader multi processing](*) finished! (47.65s) [dataloader multi processing](*) finished! (48.80s) [11-22 15:10:21] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 15:10:24] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:10:24] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:10:25] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-22 15:10:19] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 15:10:24] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:10:24] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:10:25] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [dataloader multi processing](*) finished! (53.42s) [11-22 15:10:20] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 15:10:25] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:10:25] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:10:26] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-22 15:10:26] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 15:10:30] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:10:30] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:10:32] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-22 15:10:27] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-22 15:10:55] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 15:10:55] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 15:10:55] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-22 15:10:55] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 15:10:55] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank0] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-22 15:10:27] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-22 15:10:55] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 15:10:55] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 15:10:55] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-22 15:10:55] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 15:10:55] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank8] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-22 15:10:33] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-22 15:10:55] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 15:10:55] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 15:10:55] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-22 15:10:55] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 15:10:55] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank16] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-22 15:10:27] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-22 15:10:55] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 15:10:55] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 15:10:55] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-22 15:10:55] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 15:10:55] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank24] type(model).__name__='OptimizedModule' count=303, numel=1085773120 ======================================================= RESTART [11-22 15:18:25] ======================================================= ======================================================= RESTART [11-22 15:18:25] ======================================================= ======================================================= RESTART [11-22 15:18:25] ======================================================= ======================================================= RESTART [11-22 15:18:25] ======================================================= ======================================================= RESTART [11-22 15:18:25] ======================================================= ======================================================= RESTART [11-22 15:18:25] ======================================================= ======================================================= RESTART [11-22 15:18:25] ======================================================= ======================================================= RESTART [11-22 15:18:25] ======================================================= [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 15:18:27] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 15:18:27] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 commit_msg : add branch : main } [11-22 15:18:27] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 15:18:30] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 15:18:30] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 15:18:25] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 15:18:27] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 15:18:27] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add branch : main commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 } [11-22 15:18:27] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 15:18:30] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 15:18:30] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 15:18:25] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 15:18:27] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 15:18:27] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add branch : main commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 } [11-22 15:18:27] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 15:18:30] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 15:18:30] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 15:18:25] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 15:18:27] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 15:18:27] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 commit_msg : add } [11-22 15:18:27] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 15:18:30] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 15:18:30] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 15:18:25] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 15:18:27] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 15:18:27] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 commit_msg : add branch : main } [11-22 15:18:27] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 15:18:30] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 15:18:30] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 15:18:25] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 15:18:27] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 15:18:27] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 commit_msg : add } [11-22 15:18:27] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 15:18:30] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 15:18:30] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 15:18:25] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 15:18:27] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 15:18:27] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 commit_msg : add branch : main } [11-22 15:18:27] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 15:18:30] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 15:18:30] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-22 15:18:25] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-22 15:18:25] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-22 15:18:27] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-22 15:18:27] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 commit_msg : add branch : main } [11-22 15:18:27] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-22 15:18:30] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-22 15:18:30] (e/user/VAR/utils/data.py, line 51)=> [11-22 15:18:30] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume] no ckpt found @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt*.pth [11-22 15:18:30] (/home/user/VAR/train.py , line 65)=> [auto_resume quit] [11-22 15:18:30] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ... [dataloader multi processing](*) finished! (47.56s) [dataloader multi processing](*) finished! (47.60s) [dataloader multi processing](*) finished! (47.62s) [dataloader multi processing](*) finished! (48.92s) [dataloader multi processing](*) finished! (49.06s) [dataloader multi processing](*) finished! (49.50s) [dataloader multi processing](*) finished! (49.97s) [dataloader multi processing](*) finished! (51.38s) [11-22 15:19:19] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 15:19:21] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:21] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:23] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-22 15:19:17] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 15:19:22] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:22] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:23] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-22 15:19:17] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 15:19:22] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:22] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:23] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-22 15:19:17] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 15:19:22] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:22] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:23] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-22 15:19:19] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 15:19:23] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:23] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:25] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-22 15:19:19] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 15:19:24] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:24] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:25] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-22 15:19:20] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 15:19:24] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:24] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:25] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-22 15:19:21] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-22 15:19:26] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:26] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-22 15:19:27] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-22 15:19:24] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-22 15:19:49] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 15:19:49] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 15:19:49] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-22 15:19:49] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 15:19:49] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank0] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-22 15:19:26] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-22 15:19:49] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 15:19:49] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 15:19:49] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-22 15:19:49] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 15:19:50] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank8] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-22 15:19:25] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-22 15:19:49] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 15:19:49] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 15:19:49] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-22 15:19:49] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 15:19:50] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank16] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-22 15:19:25] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-22 15:19:49] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 15:19:49] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 15:19:49] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-22 15:19:49] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 15:19:50] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank24] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-22 15:19:29] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-22 15:19:49] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 15:19:49] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 15:19:49] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-22 15:19:49] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 15:19:50] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank32] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-22 15:19:27] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-22 15:19:49] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 15:19:49] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 15:19:49] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-22 15:19:49] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 15:19:50] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank40] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-22 15:19:25] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-22 15:19:49] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 15:19:49] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 15:19:49] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-22 15:19:49] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 15:19:50] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank48] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-22 15:19:27] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-22 15:19:49] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-22 15:19:49] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-22 15:19:49] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-22 15:19:49] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-22 15:19:50] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank56] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-22 15:19:50] (/VAR/utils/lr_control.py, line 105)=> [11-22 15:19:50] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 15:33:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 16 days, 9:27:54 tlr: 1.2e-06 tnm: 0.31 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: 0.0036 (0.0036) time: 848.6966 data: 0.0005 [11-22 15:19:50] (/VAR/utils/lr_control.py, line 105)=> [11-22 15:19:50] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 15:33:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 16 days, 9:27:59 tlr: 1.2e-06 tnm: 0.31 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: 0.0033 (0.0033) time: 848.6996 data: 0.0004 [11-22 15:19:50] (/VAR/utils/lr_control.py, line 105)=> [11-22 15:19:50] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 15:33:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 16 days, 9:28:05 tlr: 1.2e-06 tnm: 0.31 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: 0.0028 (0.0028) time: 848.7032 data: 0.0005 [11-22 15:19:50] (/VAR/utils/lr_control.py, line 105)=> [11-22 15:19:50] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 15:33:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 16 days, 9:28:06 tlr: 1.2e-06 tnm: 0.31 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: 0.0034 (0.0034) time: 848.7039 data: 0.0005 [11-22 15:19:50] (/VAR/utils/lr_control.py, line 105)=> [11-22 15:19:50] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 15:33:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 16 days, 9:28:09 tlr: 1.2e-06 tnm: 0.31 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: 0.0004 (0.0004) time: 848.7055 data: 0.0006 [11-22 15:19:50] (/VAR/utils/lr_control.py, line 105)=> [11-22 15:19:50] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 15:33:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 16 days, 9:29:08 tlr: 1.2e-06 tnm: 0.31 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: 0.0021 (0.0021) time: 848.7410 data: 0.0005 [11-22 15:19:50] (/VAR/utils/lr_control.py, line 105)=> [11-22 15:19:50] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 15:33:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 16 days, 9:29:11 tlr: 1.2e-06 tnm: 0.31 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.01 (0.01) Acct: 0.00 (0.00) proj_loss: 0.0035 (0.0035) time: 848.7429 data: 0.0006 [11-22 15:19:50] (/VAR/utils/lr_control.py, line 105)=> [11-22 15:19:50] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-22 15:33:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 0/1669] eta: 16 days, 9:09:29 tlr: 1.2e-06 tnm: 0.31 Lm: 9.704 (9.704) Lt: 9.704 (9.704) Accm: 0.01 (0.01) Acct: 0.00 (0.00) proj_loss: 0.0016 (0.0016) time: 848.0346 data: 0.0006 [11-22 15:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 1:15:14 tlr: 9.7e-06 tnm: 0.07 Lm: 9.700 (9.700) Lt: 9.700 (9.700) Accm: 0.01 (0.01) Acct: 0.00 (0.00) proj_loss: -0.1395 (-0.1395) time: 0.7520 data: 0.0003 [11-22 15:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 1:15:16 tlr: 9.7e-06 tnm: 0.07 Lm: 9.700 (9.700) Lt: 9.699 (9.699) Accm: 0.01 (0.01) Acct: 0.02 (0.02) proj_loss: -0.1314 (-0.1314) time: 0.7520 data: 0.0003 [11-22 15:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 1:15:16 tlr: 9.7e-06 tnm: 0.07 Lm: 9.701 (9.701) Lt: 9.699 (9.699) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: -0.1293 (-0.1293) time: 0.7520 data: 0.0003 [11-22 15:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 1:15:16 tlr: 9.7e-06 tnm: 0.07 Lm: 9.700 (9.700) Lt: 9.700 (9.700) Accm: 0.01 (0.01) Acct: 0.00 (0.00) proj_loss: -0.1465 (-0.1465) time: 0.7520 data: 0.0003 [11-22 15:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 1:15:16 tlr: 9.7e-06 tnm: 0.07 Lm: 9.701 (9.701) Lt: 9.701 (9.701) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: -0.1263 (-0.1263) time: 0.7520 data: 0.0003 [11-22 15:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 1:15:16 tlr: 9.7e-06 tnm: 0.07 Lm: 9.702 (9.702) Lt: 9.701 (9.701) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: -0.1370 (-0.1370) time: 0.7520 data: 0.0003 [11-22 15:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 1:15:16 tlr: 9.7e-06 tnm: 0.07 Lm: 9.700 (9.700) Lt: 9.699 (9.699) Accm: 0.01 (0.01) Acct: 0.02 (0.02) proj_loss: -0.1352 (-0.1352) time: 0.7520 data: 0.0003 [11-22 15:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 417/1669] eta: 1:15:16 tlr: 9.7e-06 tnm: 0.07 Lm: 9.702 (9.702) Lt: 9.700 (9.700) Accm: 0.01 (0.01) Acct: 0.00 (0.00) proj_loss: -0.1275 (-0.1275) time: 0.7520 data: 0.0003 [11-22 15:50:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:30:20 tlr: 1.8e-05 tnm: 0.20 Lm: 9.699 (9.682) Lt: 9.696 (9.683) Accm: 0.03 (0.02) Acct: 0.00 (0.01) proj_loss: -0.2554 (-0.1939) time: 0.7506 data: 0.0003 [11-22 15:50:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:30:20 tlr: 1.8e-05 tnm: 0.20 Lm: 9.699 (9.682) Lt: 9.698 (9.684) Accm: 0.00 (0.01) Acct: 0.00 (0.01) proj_loss: -0.2560 (-0.1941) time: 0.7505 data: 0.0002 [11-22 15:50:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:30:20 tlr: 1.8e-05 tnm: 0.20 Lm: 9.699 (9.682) Lt: 9.699 (9.684) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: -0.2761 (-0.2015) time: 0.7505 data: 0.0003 [11-22 15:50:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:30:20 tlr: 1.8e-05 tnm: 0.20 Lm: 9.697 (9.681) Lt: 9.696 (9.684) Accm: 0.01 (0.02) Acct: 0.00 (0.00) proj_loss: -0.2806 (-0.2054) time: 0.7506 data: 0.0003 [11-22 15:50:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:30:20 tlr: 1.8e-05 tnm: 0.20 Lm: 9.697 (9.682) Lt: 9.695 (9.682) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: -0.2614 (-0.1944) time: 0.7505 data: 0.0003 [11-22 15:50:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:30:20 tlr: 1.8e-05 tnm: 0.20 Lm: 9.696 (9.682) Lt: 9.694 (9.683) Accm: 0.01 (0.01) Acct: 0.03 (0.02) proj_loss: -0.2664 (-0.1977) time: 0.7505 data: 0.0003 [11-22 15:50:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:30:20 tlr: 1.8e-05 tnm: 0.20 Lm: 9.697 (9.682) Lt: 9.694 (9.684) Accm: 0.01 (0.01) Acct: 0.03 (0.02) proj_loss: -0.2737 (-0.2003) time: 0.7505 data: 0.0003 [11-22 15:50:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [ 834/1669] eta: 0:30:20 tlr: 1.8e-05 tnm: 0.20 Lm: 9.696 (9.682) Lt: 9.695 (9.684) Accm: 0.01 (0.01) Acct: 0.00 (0.00) proj_loss: -0.2965 (-0.2038) time: 0.7505 data: 0.0003 [11-22 15:55:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1251/1669] eta: 0:11:52 tlr: 2.7e-05 tnm: 0.57 Lm: 9.670 (9.654) Lt: 9.674 (9.662) Accm: 0.01 (0.01) Acct: 0.00 (0.00) proj_loss: -0.3074 (-0.2371) time: 0.7517 data: 0.0003 [11-22 15:55:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1251/1669] eta: 0:11:52 tlr: 2.7e-05 tnm: 0.57 Lm: 9.672 (9.652) Lt: 9.673 (9.660) Accm: 0.01 (0.01) Acct: 0.00 (0.01) proj_loss: -0.2857 (-0.2244) time: 0.7517 data: 0.0003 [11-22 15:55:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1251/1669] eta: 0:11:52 tlr: 2.7e-05 tnm: 0.57 Lm: 9.671 (9.657) Lt: 9.673 (9.666) Accm: 0.00 (0.01) Acct: 0.00 (0.01) proj_loss: -0.2991 (-0.2316) time: 0.7517 data: 0.0002 [11-22 15:55:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1251/1669] eta: 0:11:52 tlr: 2.7e-05 tnm: 0.57 Lm: 9.671 (9.653) Lt: 9.671 (9.661) Accm: 0.00 (0.00) Acct: 0.00 (0.00) proj_loss: -0.2929 (-0.2322) time: 0.7517 data: 0.0002 [11-22 15:55:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1251/1669] eta: 0:11:52 tlr: 2.7e-05 tnm: 0.57 Lm: 9.670 (9.654) Lt: 9.674 (9.665) Accm: 0.01 (0.02) Acct: 0.00 (0.01) proj_loss: -0.2995 (-0.2337) time: 0.7517 data: 0.0002 [11-22 15:55:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1251/1669] eta: 0:11:52 tlr: 2.7e-05 tnm: 0.57 Lm: 9.671 (9.655) Lt: 9.672 (9.660) Accm: 0.01 (0.01) Acct: 0.02 (0.02) proj_loss: -0.2983 (-0.2310) time: 0.7517 data: 0.0002 [11-22 15:55:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1251/1669] eta: 0:11:52 tlr: 2.7e-05 tnm: 0.57 Lm: 9.671 (9.656) Lt: 9.673 (9.666) Accm: 0.01 (0.02) Acct: 0.00 (0.01) proj_loss: -0.2910 (-0.2288) time: 0.7517 data: 0.0003 [11-22 15:55:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1251/1669] eta: 0:11:52 tlr: 2.7e-05 tnm: 0.57 Lm: 9.671 (9.655) Lt: 9.674 (9.663) Accm: 0.01 (0.02) Acct: 0.03 (0.03) proj_loss: -0.3004 (-0.2320) time: 0.7517 data: 0.0003 [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1668/1669] eta: 0:00:01 tlr: 3.5e-05 tnm: 1.03 Lm: 9.644 (9.619) Lt: 9.649 (9.636) Accm: 0.03 (0.02) Acct: 0.00 (0.02) proj_loss: -0.3153 (-0.2469) time: 0.7538 data: 0.0021 [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1668/1669] eta: 0:00:01 tlr: 3.5e-05 tnm: 1.03 Lm: 9.645 (9.619) Lt: 9.651 (9.632) Accm: 0.01 (0.01) Acct: 0.03 (0.02) proj_loss: -0.3303 (-0.2538) time: 0.7538 data: 0.0016 [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1668/1669] eta: 0:00:01 tlr: 3.5e-05 tnm: 1.03 Lm: 9.643 (9.621) Lt: 9.651 (9.640) Accm: 0.01 (0.02) Acct: 0.00 (0.01) proj_loss: -0.3184 (-0.2550) time: 0.7538 data: 0.0018 [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1668/1669] eta: 0:00:01 tlr: 3.5e-05 tnm: 1.03 Lm: 9.644 (9.622) Lt: 9.648 (9.639) Accm: 0.00 (0.02) Acct: 0.00 (0.01) proj_loss: -0.3221 (-0.2530) time: 0.7538 data: 0.0017 [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1668/1669] eta: 0:00:01 tlr: 3.5e-05 tnm: 1.03 Lm: 9.645 (9.623) Lt: 9.654 (9.640) Accm: 0.01 (0.02) Acct: 0.03 (0.03) proj_loss: -0.3272 (-0.2546) time: 0.7538 data: 0.0018 [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1668/1669] eta: 0:00:01 tlr: 3.5e-05 tnm: 1.03 Lm: 9.645 (9.619) Lt: 9.653 (9.636) Accm: 0.01 (0.02) Acct: 0.00 (0.01) proj_loss: -0.3136 (-0.2524) time: 0.7538 data: 0.0016 [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1668/1669] eta: 0:00:01 tlr: 3.5e-05 tnm: 1.03 Lm: 9.645 (9.620) Lt: 9.648 (9.635) Accm: 0.00 (0.01) Acct: 0.00 (0.02) proj_loss: -0.3245 (-0.2523) time: 0.7538 data: 0.0017 [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 0/350] [1668/1669] eta: 0:00:01 tlr: 3.5e-05 tnm: 1.03 Lm: 9.644 (9.622) Lt: 9.650 (9.637) Accm: 0.03 (0.03) Acct: 0.00 (0.01) proj_loss: -0.3266 (-0.2489) time: 0.7538 data: 0.0017 [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 0/350] Total time: 0:40:47 (1.466 s / it) [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 0/350] Total time: 0:40:47 (1.466 s / it) [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 0/350] Total time: 0:40:46 (1.466 s / it) [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 0/350] Total time: 0:40:47 (1.466 s / it) [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 0/350] Total time: 0:40:47 (1.466 s / it) [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 0/350] Total time: 0:40:47 (1.466 s / it) [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 0/350] Total time: 0:40:47 (1.466 s / it) [11-22 16:00:38] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 0/350] Total time: 0:40:47 (1.466 s / it) [11-22 16:00:38] (/home/user/VAR/train.py , line 276)=> [ep0] (training ) Lm: 9.622 (9.622), Lt: 9.638 (9.638), Acc m&t: 0.02 0.02, Remain: 5 days, 2:43:05, Finish: 2024-11-27 02:43 [11-22 16:00:38] (/home/user/VAR/train.py , line 276)=> [ep0] (training ) Lm: 9.622 (9.622), Lt: 9.638 (9.638), Acc m&t: 0.02 0.02, Remain: 5 days, 2:42:38, Finish: 2024-11-27 02:43 [11-22 16:00:38] (/home/user/VAR/train.py , line 276)=> [ep0] (training ) Lm: 9.622 (9.622), Lt: 9.638 (9.638), Acc m&t: 0.02 0.02, Remain: 5 days, 2:43:08, Finish: 2024-11-27 02:43 [11-22 16:00:38] (/home/user/VAR/train.py , line 276)=> [ep0] (training ) Lm: 9.622 (9.622), Lt: 9.638 (9.638), Acc m&t: 0.02 0.02, Remain: 5 days, 2:43:58, Finish: 2024-11-27 02:44 [11-22 16:00:38] (/home/user/VAR/train.py , line 276)=> [ep0] (training ) Lm: 9.622 (9.622), Lt: 9.638 (9.638), Acc m&t: 0.02 0.02, Remain: 5 days, 2:41:15, Finish: 2024-11-27 02:41 [11-22 16:00:38] (/home/user/VAR/train.py , line 276)=> [ep0] (training ) Lm: 9.622 (9.622), Lt: 9.638 (9.638), Acc m&t: 0.02 0.02, Remain: 5 days, 2:43:25, Finish: 2024-11-27 02:44 [11-22 16:00:38] (/home/user/VAR/train.py , line 276)=> [ep0] (training ) Lm: 9.622 (9.622), Lt: 9.638 (9.638), Acc m&t: 0.02 0.02, Remain: 5 days, 2:43:47, Finish: 2024-11-27 02:44 [11-22 16:00:38] (/home/user/VAR/train.py , line 276)=> [ep0] (training ) Lm: 9.622 (9.622), Lt: 9.638 (9.638), Acc m&t: 0.02 0.02, Remain: 5 days, 2:43:47, Finish: 2024-11-27 02:44 [11-22 16:00:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 0/1669] eta: 0:19:56 tlr: 3.5e-05 tnm: 1.01 Lm: 9.487 (9.487) Lt: 9.534 (9.534) Accm: 0.01 (0.01) Acct: 0.00 (0.00) proj_loss: -0.3331 (-0.3331) time: 0.7168 data: 0.0004 [11-22 16:00:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 0/1669] eta: 0:20:55 tlr: 3.5e-05 tnm: 1.01 Lm: 9.487 (9.487) Lt: 9.517 (9.517) Accm: 0.06 (0.06) Acct: 0.00 (0.00) proj_loss: -0.3095 (-0.3095) time: 0.7523 data: 0.0003 [11-22 16:00:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 0/1669] eta: 0:21:06 tlr: 3.5e-05 tnm: 1.01 Lm: 9.477 (9.477) Lt: 9.531 (9.531) Accm: 0.04 (0.04) Acct: 0.00 (0.00) proj_loss: -0.3386 (-0.3386) time: 0.7591 data: 0.0004 [11-22 16:00:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 0/1669] eta: 0:19:58 tlr: 3.5e-05 tnm: 1.01 Lm: 9.480 (9.480) Lt: 9.506 (9.506) Accm: 0.06 (0.06) Acct: 0.10 (0.10) proj_loss: -0.3413 (-0.3413) time: 0.7182 data: 0.0003 [11-22 16:00:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 0/1669] eta: 0:19:57 tlr: 3.5e-05 tnm: 1.01 Lm: 9.483 (9.483) Lt: 9.534 (9.534) Accm: 0.04 (0.04) Acct: 0.03 (0.03) proj_loss: -0.3386 (-0.3386) time: 0.7173 data: 0.0003 [11-22 16:00:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 0/1669] eta: 0:21:35 tlr: 3.5e-05 tnm: 1.01 Lm: 9.495 (9.495) Lt: 9.532 (9.532) Accm: 0.03 (0.03) Acct: 0.00 (0.00) proj_loss: -0.3193 (-0.3193) time: 0.7763 data: 0.0004 [11-22 16:00:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 0/1669] eta: 0:20:01 tlr: 3.5e-05 tnm: 1.01 Lm: 9.495 (9.495) Lt: 9.542 (9.542) Accm: 0.07 (0.07) Acct: 0.07 (0.07) proj_loss: -0.3264 (-0.3264) time: 0.7197 data: 0.0003 [11-22 16:00:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 0/1669] eta: 0:20:13 tlr: 3.5e-05 tnm: 1.01 Lm: 9.484 (9.484) Lt: 9.524 (9.524) Accm: 0.03 (0.03) Acct: 0.07 (0.07) proj_loss: -0.3247 (-0.3247) time: 0.7268 data: 0.0003 [11-22 16:06:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 417/1669] eta: 0:17:04 tlr: 4.4e-05 tnm: 3.32 Lm: 9.304 (9.304) Lt: 9.362 (9.362) Accm: 0.10 (0.10) Acct: 0.07 (0.07) proj_loss: -0.3344 (-0.3344) time: 1.3079 data: 0.0003 [11-22 16:06:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 417/1669] eta: 0:17:04 tlr: 4.4e-05 tnm: 3.32 Lm: 9.304 (9.304) Lt: 9.342 (9.342) Accm: 0.08 (0.08) Acct: 0.07 (0.07) proj_loss: -0.3219 (-0.3219) time: 1.3079 data: 0.0003 [11-22 16:06:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 417/1669] eta: 0:17:04 tlr: 4.4e-05 tnm: 3.32 Lm: 9.299 (9.299) Lt: 9.343 (9.343) Accm: 0.08 (0.08) Acct: 0.12 (0.12) proj_loss: -0.3418 (-0.3418) time: 1.3079 data: 0.0003 [11-22 16:06:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 417/1669] eta: 0:17:04 tlr: 4.4e-05 tnm: 3.32 Lm: 9.316 (9.316) Lt: 9.366 (9.366) Accm: 0.05 (0.05) Acct: 0.05 (0.05) proj_loss: -0.3182 (-0.3182) time: 1.3079 data: 0.0003 [11-22 16:06:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 417/1669] eta: 0:17:04 tlr: 4.4e-05 tnm: 3.32 Lm: 9.330 (9.330) Lt: 9.375 (9.375) Accm: 0.10 (0.10) Acct: 0.03 (0.03) proj_loss: -0.3111 (-0.3111) time: 1.3079 data: 0.0002 [11-22 16:06:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 417/1669] eta: 0:17:04 tlr: 4.4e-05 tnm: 3.32 Lm: 9.329 (9.329) Lt: 9.384 (9.384) Accm: 0.05 (0.05) Acct: 0.05 (0.05) proj_loss: -0.3329 (-0.3329) time: 1.3079 data: 0.0003 [11-22 16:06:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 417/1669] eta: 0:17:04 tlr: 4.4e-05 tnm: 3.32 Lm: 9.296 (9.296) Lt: 9.359 (9.359) Accm: 0.08 (0.08) Acct: 0.07 (0.07) proj_loss: -0.3427 (-0.3427) time: 1.3079 data: 0.0003 [11-22 16:06:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 417/1669] eta: 0:17:04 tlr: 4.4e-05 tnm: 3.32 Lm: 9.316 (9.316) Lt: 9.366 (9.366) Accm: 0.11 (0.11) Acct: 0.12 (0.12) proj_loss: -0.3306 (-0.3306) time: 1.3079 data: 0.0003 [11-22 16:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 834/1669] eta: 0:11:16 tlr: 5.2e-05 tnm: 1.99 Lm: 9.137 (9.004) Lt: 9.190 (8.967) Accm: 0.15 (0.23) Acct: 0.17 (0.30) proj_loss: -0.3264 (-0.3215) time: 0.7487 data: 0.0003 [11-22 16:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 834/1669] eta: 0:11:16 tlr: 5.2e-05 tnm: 1.99 Lm: 9.131 (9.020) Lt: 9.193 (8.996) Accm: 0.16 (0.21) Acct: 0.14 (0.18) proj_loss: -0.3302 (-0.3285) time: 0.7487 data: 0.0002 [11-22 16:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 834/1669] eta: 0:11:16 tlr: 5.2e-05 tnm: 1.99 Lm: 9.113 (9.021) Lt: 9.152 (8.980) Accm: 0.13 (0.21) Acct: 0.14 (0.23) proj_loss: -0.3231 (-0.3223) time: 0.7487 data: 0.0003 [11-22 16:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 834/1669] eta: 0:11:16 tlr: 5.2e-05 tnm: 1.99 Lm: 9.105 (9.015) Lt: 9.184 (8.987) Accm: 0.15 (0.21) Acct: 0.14 (0.24) proj_loss: -0.3331 (-0.3357) time: 0.7487 data: 0.0003 [11-22 16:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 834/1669] eta: 0:11:16 tlr: 5.2e-05 tnm: 1.99 Lm: 9.174 (9.036) Lt: 9.233 (8.997) Accm: 0.15 (0.22) Acct: 0.07 (0.30) proj_loss: -0.3127 (-0.3192) time: 0.7487 data: 0.0002 [11-22 16:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 834/1669] eta: 0:11:16 tlr: 5.2e-05 tnm: 1.99 Lm: 9.117 (8.999) Lt: 9.180 (8.954) Accm: 0.10 (0.23) Acct: 0.14 (0.34) proj_loss: -0.3413 (-0.3379) time: 0.7487 data: 0.0003 [11-22 16:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 834/1669] eta: 0:11:16 tlr: 5.2e-05 tnm: 1.99 Lm: 9.175 (9.033) Lt: 9.235 (9.014) Accm: 0.06 (0.17) Acct: 0.07 (0.18) proj_loss: -0.3274 (-0.3310) time: 0.7487 data: 0.0003 [11-22 16:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [ 834/1669] eta: 0:11:16 tlr: 5.2e-05 tnm: 1.99 Lm: 9.148 (9.027) Lt: 9.209 (9.006) Accm: 0.07 (0.15) Acct: 0.07 (0.18) proj_loss: -0.3247 (-0.3238) time: 0.7487 data: 0.0003 [11-22 16:17:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1251/1669] eta: 0:05:30 tlr: 6.1e-05 tnm: 1.92 Lm: 8.784 (8.830) Lt: 8.704 (8.737) Accm: 0.31 (0.32) Acct: 0.34 (0.40) proj_loss: -0.3239 (-0.3253) time: 0.7482 data: 0.0002 [11-22 16:17:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1251/1669] eta: 0:05:30 tlr: 6.1e-05 tnm: 1.92 Lm: 8.791 (8.841) Lt: 8.728 (8.756) Accm: 0.29 (0.27) Acct: 0.28 (0.26) proj_loss: -0.3293 (-0.3285) time: 0.7482 data: 0.0002 [11-22 16:17:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1251/1669] eta: 0:05:30 tlr: 6.1e-05 tnm: 1.92 Lm: 8.811 (8.849) Lt: 8.737 (8.746) Accm: 0.31 (0.33) Acct: 0.45 (0.46) proj_loss: -0.3223 (-0.3224) time: 0.7482 data: 0.0003 [11-22 16:17:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1251/1669] eta: 0:05:30 tlr: 6.1e-05 tnm: 1.92 Lm: 8.759 (8.816) Lt: 8.678 (8.716) Accm: 0.31 (0.32) Acct: 0.41 (0.43) proj_loss: -0.3403 (-0.3383) time: 0.7482 data: 0.0003 [11-22 16:17:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1251/1669] eta: 0:05:30 tlr: 6.1e-05 tnm: 1.92 Lm: 8.759 (8.812) Lt: 8.679 (8.714) Accm: 0.31 (0.29) Acct: 0.40 (0.38) proj_loss: -0.3260 (-0.3226) time: 0.7482 data: 0.0002 [11-22 16:17:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1251/1669] eta: 0:05:30 tlr: 6.1e-05 tnm: 1.92 Lm: 8.778 (8.821) Lt: 8.714 (8.728) Accm: 0.31 (0.38) Acct: 0.36 (0.53) proj_loss: -0.3278 (-0.3324) time: 0.7482 data: 0.0002 [11-22 16:17:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1251/1669] eta: 0:05:30 tlr: 6.1e-05 tnm: 1.92 Lm: 8.807 (8.830) Lt: 8.754 (8.757) Accm: 0.23 (0.28) Acct: 0.26 (0.35) proj_loss: -0.3306 (-0.3318) time: 0.7482 data: 0.0002 [11-22 16:17:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1251/1669] eta: 0:05:30 tlr: 6.1e-05 tnm: 1.92 Lm: 8.798 (8.838) Lt: 8.747 (8.747) Accm: 0.20 (0.24) Acct: 0.26 (0.34) proj_loss: -0.3233 (-0.3233) time: 0.7482 data: 0.0003 [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 1.59 Lm: 8.451 (8.715) Lt: 8.264 (8.598) Accm: 0.42 (0.34) Acct: 0.41 (0.37) proj_loss: -0.3302 (-0.3292) time: 0.7567 data: 0.0017 [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 1.59 Lm: 8.451 (8.699) Lt: 8.244 (8.562) Accm: 0.47 (0.45) Acct: 0.59 (0.64) proj_loss: -0.3225 (-0.3293) time: 0.7567 data: 0.0018 [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 1.59 Lm: 8.448 (8.719) Lt: 8.240 (8.582) Accm: 0.47 (0.39) Acct: 0.83 (0.56) proj_loss: -0.3262 (-0.3231) time: 0.7567 data: 0.0016 [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 1.59 Lm: 8.440 (8.705) Lt: 8.273 (8.585) Accm: 0.41 (0.35) Acct: 0.45 (0.45) proj_loss: -0.3320 (-0.3318) time: 0.7567 data: 0.0019 [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 1.59 Lm: 8.449 (8.720) Lt: 8.286 (8.593) Accm: 0.34 (0.32) Acct: 0.45 (0.45) proj_loss: -0.3247 (-0.3264) time: 0.7567 data: 0.0017 [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 1.59 Lm: 8.401 (8.694) Lt: 8.176 (8.567) Accm: 0.51 (0.36) Acct: 0.62 (0.47) proj_loss: -0.3394 (-0.3363) time: 0.7567 data: 0.0018 [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 1.59 Lm: 8.455 (8.709) Lt: 8.255 (8.581) Accm: 0.48 (0.43) Acct: 0.55 (0.59) proj_loss: -0.3234 (-0.3249) time: 0.7567 data: 0.0016 [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 1/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 1.59 Lm: 8.382 (8.689) Lt: 8.169 (8.569) Accm: 0.47 (0.34) Acct: 0.62 (0.47) proj_loss: -0.3264 (-0.3289) time: 0.7567 data: 0.0019 [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 1/350] Total time: 0:21:43 (0.781 s / it) [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 1/350] Total time: 0:21:43 (0.781 s / it) [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 1/350] Total time: 0:21:43 (0.781 s / it) [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 1/350] Total time: 0:21:43 (0.781 s / it) [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 1/350] Total time: 0:21:43 (0.781 s / it) [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 1/350] Total time: 0:21:43 (0.781 s / it) [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 1/350] Total time: 0:21:43 (0.781 s / it) [11-22 16:22:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 1/350] Total time: 0:21:43 (0.781 s / it) [11-22 16:22:21] (/home/user/VAR/train.py , line 276)=> [ep1] (training ) Lm: 8.704 (8.704), Lt: 8.578 (8.578), Acc m&t: 0.36 0.47, Remain: 5 days, 2:57:30, Finish: 2024-11-27 03:19 [11-22 16:22:21] (/home/user/VAR/train.py , line 276)=> [ep1] (training ) Lm: 8.704 (8.704), Lt: 8.578 (8.578), Acc m&t: 0.36 0.47, Remain: 5 days, 2:57:38, Finish: 2024-11-27 03:20 [11-22 16:22:21] (/home/user/VAR/train.py , line 276)=> [ep1] (training ) Lm: 8.704 (8.704), Lt: 8.578 (8.578), Acc m&t: 0.36 0.47, Remain: 5 days, 2:57:26, Finish: 2024-11-27 03:19 [11-22 16:22:21] (/home/user/VAR/train.py , line 276)=> [ep1] (training ) Lm: 8.704 (8.704), Lt: 8.578 (8.578), Acc m&t: 0.36 0.47, Remain: 5 days, 2:56:06, Finish: 2024-11-27 03:18 [11-22 16:22:21] (/home/user/VAR/train.py , line 276)=> [ep1] (training ) Lm: 8.704 (8.704), Lt: 8.578 (8.578), Acc m&t: 0.36 0.47, Remain: 5 days, 2:56:49, Finish: 2024-11-27 03:19 [11-22 16:22:21] (/home/user/VAR/train.py , line 276)=> [ep1] (training ) Lm: 8.704 (8.704), Lt: 8.578 (8.578), Acc m&t: 0.36 0.47, Remain: 5 days, 2:56:26, Finish: 2024-11-27 03:18 [11-22 16:22:21] (/home/user/VAR/train.py , line 276)=> [ep1] (training ) Lm: 8.704 (8.704), Lt: 8.578 (8.578), Acc m&t: 0.36 0.47, Remain: 5 days, 2:52:05, Finish: 2024-11-27 03:14 [11-22 16:22:21] (/home/user/VAR/train.py , line 276)=> [ep1] (training ) Lm: 8.704 (8.704), Lt: 8.578 (8.578), Acc m&t: 0.36 0.47, Remain: 5 days, 2:57:48, Finish: 2024-11-27 03:20 [11-22 16:22:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 0/1669] eta: 0:21:11 tlr: 6.9e-05 tnm: 1.62 Lm: 8.170 (8.170) Lt: 7.859 (7.859) Accm: 0.63 (0.63) Acct: 0.86 (0.86) proj_loss: -0.3369 (-0.3369) time: 0.7619 data: 0.0004 [11-22 16:22:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 0/1669] eta: 0:21:12 tlr: 6.9e-05 tnm: 1.62 Lm: 8.270 (8.270) Lt: 8.019 (8.019) Accm: 0.47 (0.47) Acct: 0.55 (0.55) proj_loss: -0.3185 (-0.3185) time: 0.7624 data: 0.0004 [11-22 16:22:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 0/1669] eta: 0:21:12 tlr: 6.9e-05 tnm: 1.62 Lm: 8.271 (8.271) Lt: 8.015 (8.015) Accm: 0.52 (0.52) Acct: 0.76 (0.76) proj_loss: -0.3234 (-0.3234) time: 0.7625 data: 0.0004 [11-22 16:22:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 0/1669] eta: 0:21:13 tlr: 6.9e-05 tnm: 1.62 Lm: 8.205 (8.205) Lt: 7.893 (7.893) Accm: 0.60 (0.60) Acct: 0.76 (0.76) proj_loss: -0.3185 (-0.3185) time: 0.7631 data: 0.0004 [11-22 16:22:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 0/1669] eta: 0:21:12 tlr: 6.9e-05 tnm: 1.62 Lm: 8.272 (8.272) Lt: 8.045 (8.045) Accm: 0.50 (0.50) Acct: 0.62 (0.62) proj_loss: -0.3283 (-0.3283) time: 0.7626 data: 0.0004 [11-22 16:22:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 0/1669] eta: 0:21:13 tlr: 6.9e-05 tnm: 1.62 Lm: 8.170 (8.170) Lt: 7.886 (7.886) Accm: 0.68 (0.68) Acct: 1.03 (1.03) proj_loss: -0.3276 (-0.3276) time: 0.7628 data: 0.0003 [11-22 16:22:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 0/1669] eta: 0:21:14 tlr: 6.9e-05 tnm: 1.62 Lm: 8.158 (8.158) Lt: 7.918 (7.918) Accm: 0.76 (0.76) Acct: 0.83 (0.83) proj_loss: -0.3423 (-0.3423) time: 0.7634 data: 0.0004 [11-22 16:22:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 0/1669] eta: 0:21:14 tlr: 6.9e-05 tnm: 1.62 Lm: 8.202 (8.202) Lt: 7.884 (7.884) Accm: 0.45 (0.45) Acct: 0.76 (0.76) proj_loss: -0.3447 (-0.3447) time: 0.7635 data: 0.0003 [11-22 16:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 417/1669] eta: 0:15:41 tlr: 7.8e-05 tnm: 1.43 Lm: 8.133 (8.133) Lt: 7.859 (7.859) Accm: 0.65 (0.65) Acct: 0.84 (0.84) proj_loss: -0.3371 (-0.3371) time: 0.7511 data: 0.0002 [11-22 16:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 417/1669] eta: 0:15:41 tlr: 7.8e-05 tnm: 1.43 Lm: 8.207 (8.207) Lt: 7.934 (7.934) Accm: 0.63 (0.63) Acct: 0.88 (0.88) proj_loss: -0.3339 (-0.3339) time: 0.7511 data: 0.0003 [11-22 16:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 417/1669] eta: 0:15:41 tlr: 7.8e-05 tnm: 1.43 Lm: 8.176 (8.176) Lt: 7.856 (7.856) Accm: 0.55 (0.55) Acct: 0.86 (0.86) proj_loss: -0.3331 (-0.3331) time: 0.7511 data: 0.0003 [11-22 16:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 417/1669] eta: 0:15:41 tlr: 7.8e-05 tnm: 1.43 Lm: 8.161 (8.161) Lt: 7.884 (7.884) Accm: 0.63 (0.63) Acct: 0.91 (0.91) proj_loss: -0.3338 (-0.3338) time: 0.7511 data: 0.0003 [11-22 16:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 417/1669] eta: 0:15:41 tlr: 7.8e-05 tnm: 1.43 Lm: 8.219 (8.219) Lt: 7.965 (7.965) Accm: 0.49 (0.49) Acct: 0.60 (0.60) proj_loss: -0.3323 (-0.3323) time: 0.7511 data: 0.0003 [11-22 16:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 417/1669] eta: 0:15:41 tlr: 7.8e-05 tnm: 1.43 Lm: 8.176 (8.176) Lt: 7.884 (7.884) Accm: 0.49 (0.49) Acct: 0.74 (0.74) proj_loss: -0.3498 (-0.3498) time: 0.7511 data: 0.0003 [11-22 16:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 417/1669] eta: 0:15:41 tlr: 7.8e-05 tnm: 1.43 Lm: 8.214 (8.214) Lt: 7.968 (7.968) Accm: 0.51 (0.51) Acct: 0.72 (0.72) proj_loss: -0.3319 (-0.3319) time: 0.7511 data: 0.0003 [11-22 16:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 417/1669] eta: 0:15:41 tlr: 7.8e-05 tnm: 1.43 Lm: 8.140 (8.140) Lt: 7.881 (7.881) Accm: 0.67 (0.67) Acct: 0.81 (0.81) proj_loss: -0.3375 (-0.3375) time: 0.7511 data: 0.0003 [11-22 16:33:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 834/1669] eta: 0:10:43 tlr: 8.6e-05 tnm: 1.37 Lm: 8.122 (8.121) Lt: 7.844 (7.858) Accm: 0.64 (0.66) Acct: 0.83 (0.84) proj_loss: -0.3416 (-0.3389) time: 1.2022 data: 0.0003 [11-22 16:33:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 834/1669] eta: 0:10:43 tlr: 8.6e-05 tnm: 1.37 Lm: 8.096 (8.111) Lt: 7.859 (7.828) Accm: 0.67 (0.68) Acct: 0.86 (0.85) proj_loss: -0.3373 (-0.3401) time: 1.2022 data: 0.0002 [11-22 16:33:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 834/1669] eta: 0:10:43 tlr: 8.6e-05 tnm: 1.37 Lm: 8.143 (8.180) Lt: 7.853 (7.893) Accm: 0.57 (0.61) Acct: 0.96 (0.91) proj_loss: -0.3444 (-0.3384) time: 1.2023 data: 0.0003 [11-22 16:33:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 834/1669] eta: 0:10:43 tlr: 8.6e-05 tnm: 1.37 Lm: 8.147 (8.152) Lt: 7.819 (7.830) Accm: 0.60 (0.57) Acct: 0.90 (0.87) proj_loss: -0.3478 (-0.3391) time: 1.2022 data: 0.0003 [11-22 16:33:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 834/1669] eta: 0:10:43 tlr: 8.6e-05 tnm: 1.37 Lm: 8.157 (8.171) Lt: 7.890 (7.918) Accm: 0.52 (0.59) Acct: 0.83 (0.76) proj_loss: -0.3355 (-0.3356) time: 1.2022 data: 0.0003 [11-22 16:33:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 834/1669] eta: 0:10:43 tlr: 8.6e-05 tnm: 1.37 Lm: 8.169 (8.195) Lt: 7.910 (7.928) Accm: 0.51 (0.57) Acct: 0.65 (0.77) proj_loss: -0.3461 (-0.3390) time: 1.1640 data: 0.0003 [11-22 16:33:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 834/1669] eta: 0:10:43 tlr: 8.6e-05 tnm: 1.37 Lm: 8.153 (8.148) Lt: 7.883 (7.875) Accm: 0.63 (0.63) Acct: 0.79 (0.87) proj_loss: -0.3401 (-0.3368) time: 1.2023 data: 0.0003 [11-22 16:33:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [ 834/1669] eta: 0:10:43 tlr: 8.6e-05 tnm: 1.37 Lm: 8.185 (8.179) Lt: 7.884 (7.886) Accm: 0.52 (0.52) Acct: 0.76 (0.76) proj_loss: -0.3548 (-0.3532) time: 1.2023 data: 0.0003 [11-22 16:39:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1251/1669] eta: 0:05:34 tlr: 9.5e-05 tnm: 1.24 Lm: 8.106 (8.112) Lt: 7.829 (7.821) Accm: 0.66 (0.67) Acct: 0.86 (0.85) proj_loss: -0.3401 (-0.3408) time: 0.7491 data: 0.0003 [11-22 16:39:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1251/1669] eta: 0:05:34 tlr: 9.5e-05 tnm: 1.24 Lm: 8.126 (8.135) Lt: 7.799 (7.817) Accm: 0.60 (0.58) Acct: 0.90 (0.88) proj_loss: -0.3494 (-0.3430) time: 0.7491 data: 0.0002 [11-22 16:39:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1251/1669] eta: 0:05:34 tlr: 9.5e-05 tnm: 1.24 Lm: 8.146 (8.163) Lt: 7.877 (7.905) Accm: 0.59 (0.60) Acct: 0.83 (0.83) proj_loss: -0.3392 (-0.3390) time: 0.7491 data: 0.0003 [11-22 16:39:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1251/1669] eta: 0:05:34 tlr: 9.5e-05 tnm: 1.24 Lm: 8.152 (8.149) Lt: 7.880 (7.875) Accm: 0.60 (0.59) Acct: 0.79 (0.84) proj_loss: -0.3415 (-0.3397) time: 0.7491 data: 0.0003 [11-22 16:39:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1251/1669] eta: 0:05:34 tlr: 9.5e-05 tnm: 1.24 Lm: 8.157 (8.167) Lt: 7.882 (7.896) Accm: 0.59 (0.59) Acct: 0.86 (0.84) proj_loss: -0.3493 (-0.3446) time: 0.7491 data: 0.0003 [11-22 16:39:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1251/1669] eta: 0:05:34 tlr: 9.5e-05 tnm: 1.24 Lm: 8.168 (8.153) Lt: 7.884 (7.860) Accm: 0.54 (0.53) Acct: 0.77 (0.80) proj_loss: -0.3498 (-0.3495) time: 0.7491 data: 0.0003 [11-22 16:39:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1251/1669] eta: 0:05:34 tlr: 9.5e-05 tnm: 1.24 Lm: 8.103 (8.112) Lt: 7.828 (7.834) Accm: 0.61 (0.62) Acct: 0.81 (0.75) proj_loss: -0.3420 (-0.3448) time: 0.7492 data: 0.0002 [11-22 16:39:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1251/1669] eta: 0:05:34 tlr: 9.5e-05 tnm: 1.24 Lm: 8.139 (8.169) Lt: 7.838 (7.876) Accm: 0.58 (0.60) Acct: 0.86 (0.86) proj_loss: -0.3459 (-0.3446) time: 0.7491 data: 0.0002 [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 1.18 Lm: 8.134 (8.158) Lt: 7.850 (7.871) Accm: 0.57 (0.59) Acct: 0.76 (0.81) proj_loss: -0.3474 (-0.3470) time: 0.7534 data: 0.0017 [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 2/350] Total time: 0:21:55 (0.788 s / it) [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 1.18 Lm: 8.096 (8.086) Lt: 7.800 (7.792) Accm: 0.67 (0.68) Acct: 0.86 (0.92) proj_loss: -0.3429 (-0.3449) time: 0.7534 data: 0.0018 [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 1.18 Lm: 8.152 (8.127) Lt: 7.877 (7.851) Accm: 0.63 (0.61) Acct: 0.79 (0.85) proj_loss: -0.3429 (-0.3424) time: 0.7534 data: 0.0016 [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 1.18 Lm: 8.145 (8.141) Lt: 7.853 (7.868) Accm: 0.55 (0.59) Acct: 0.76 (0.83) proj_loss: -0.3521 (-0.3461) time: 0.7534 data: 0.0019 [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 1.18 Lm: 8.104 (8.129) Lt: 7.779 (7.808) Accm: 0.60 (0.63) Acct: 0.90 (0.97) proj_loss: -0.3478 (-0.3433) time: 0.7534 data: 0.0015 [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 1.18 Lm: 8.136 (8.145) Lt: 7.864 (7.877) Accm: 0.66 (0.62) Acct: 0.83 (0.86) proj_loss: -0.3429 (-0.3422) time: 0.7534 data: 0.0016 [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 1.18 Lm: 8.110 (8.111) Lt: 7.844 (7.836) Accm: 0.58 (0.60) Acct: 0.79 (0.76) proj_loss: -0.3417 (-0.3442) time: 0.7534 data: 0.0015 [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 2/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 1.18 Lm: 8.151 (8.139) Lt: 7.884 (7.834) Accm: 0.55 (0.56) Acct: 0.79 (0.81) proj_loss: -0.3520 (-0.3500) time: 0.7534 data: 0.0019 [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 2/350] Total time: 0:21:55 (0.788 s / it) [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 2/350] Total time: 0:21:55 (0.788 s / it) [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 2/350] Total time: 0:21:55 (0.788 s / it) [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 2/350] Total time: 0:21:55 (0.788 s / it) [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 2/350] Total time: 0:21:55 (0.788 s / it) [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 2/350] Total time: 0:21:55 (0.788 s / it) [11-22 16:44:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 2/350] Total time: 0:21:55 (0.788 s / it) [11-22 16:44:17] (/home/user/VAR/train.py , line 276)=> [ep2] (training ) Lm: 8.113 (8.113), Lt: 7.825 (7.825), Acc m&t: 0.65 0.90, Remain: 5 days, 2:16:08, Finish: 2024-11-27 03:00 [11-22 16:44:17] (/home/user/VAR/train.py , line 276)=> [ep2] (training ) Lm: 8.113 (8.113), Lt: 7.825 (7.825), Acc m&t: 0.65 0.90, Remain: 5 days, 2:15:50, Finish: 2024-11-27 03:00 [11-22 16:44:17] (/home/user/VAR/train.py , line 276)=> [ep2] (training ) Lm: 8.113 (8.113), Lt: 7.825 (7.825), Acc m&t: 0.65 0.90, Remain: 5 days, 2:08:27, Finish: 2024-11-27 02:52 [11-22 16:44:17] (/home/user/VAR/train.py , line 276)=> [ep2] (training ) Lm: 8.113 (8.113), Lt: 7.825 (7.825), Acc m&t: 0.65 0.90, Remain: 5 days, 2:14:39, Finish: 2024-11-27 02:58 [11-22 16:44:17] (/home/user/VAR/train.py , line 276)=> [ep2] (training ) Lm: 8.113 (8.113), Lt: 7.825 (7.825), Acc m&t: 0.65 0.90, Remain: 5 days, 2:14:13, Finish: 2024-11-27 02:58 [11-22 16:44:17] (/home/user/VAR/train.py , line 276)=> [ep2] (training ) Lm: 8.113 (8.113), Lt: 7.825 (7.825), Acc m&t: 0.65 0.90, Remain: 5 days, 2:15:26, Finish: 2024-11-27 02:59 [11-22 16:44:17] (/home/user/VAR/train.py , line 276)=> [ep2] (training ) Lm: 8.113 (8.113), Lt: 7.825 (7.825), Acc m&t: 0.65 0.90, Remain: 5 days, 2:16:05, Finish: 2024-11-27 03:00 [11-22 16:44:17] (/home/user/VAR/train.py , line 276)=> [ep2] (training ) Lm: 8.113 (8.113), Lt: 7.825 (7.825), Acc m&t: 0.65 0.90, Remain: 5 days, 2:14:08, Finish: 2024-11-27 02:58 [11-22 16:44:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 0/1669] eta: 0:20:11 tlr: 0.0001 tnm: 1.16 Lm: 8.060 (8.060) Lt: 7.755 (7.755) Accm: 0.76 (0.76) Acct: 1.14 (1.14) proj_loss: -0.3689 (-0.3689) time: 0.7258 data: 0.0003 [11-22 16:44:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 0/1669] eta: 0:20:12 tlr: 0.0001 tnm: 1.16 Lm: 8.015 (8.015) Lt: 7.705 (7.705) Accm: 0.60 (0.60) Acct: 0.59 (0.59) proj_loss: -0.3673 (-0.3673) time: 0.7262 data: 0.0004 [11-22 16:44:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 0/1669] eta: 0:20:13 tlr: 0.0001 tnm: 1.16 Lm: 8.037 (8.037) Lt: 7.766 (7.766) Accm: 0.77 (0.77) Acct: 1.14 (1.14) proj_loss: -0.3583 (-0.3583) time: 0.7268 data: 0.0003 [11-22 16:44:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 0/1669] eta: 0:20:12 tlr: 0.0001 tnm: 1.16 Lm: 8.055 (8.055) Lt: 7.778 (7.778) Accm: 0.77 (0.77) Acct: 0.86 (0.86) proj_loss: -0.3523 (-0.3523) time: 0.7264 data: 0.0004 [11-22 16:44:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 0/1669] eta: 0:20:05 tlr: 0.0001 tnm: 1.16 Lm: 8.063 (8.063) Lt: 7.794 (7.794) Accm: 0.73 (0.73) Acct: 1.07 (1.07) proj_loss: -0.3570 (-0.3570) time: 0.7223 data: 0.0003 [11-22 16:44:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 0/1669] eta: 0:20:12 tlr: 0.0001 tnm: 1.16 Lm: 7.936 (7.936) Lt: 7.603 (7.603) Accm: 0.68 (0.68) Acct: 0.93 (0.93) proj_loss: -0.3634 (-0.3634) time: 0.7265 data: 0.0004 [11-22 16:44:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 0/1669] eta: 0:20:13 tlr: 0.0001 tnm: 1.16 Lm: 8.047 (8.047) Lt: 7.738 (7.738) Accm: 0.70 (0.70) Acct: 0.96 (0.96) proj_loss: -0.3551 (-0.3551) time: 0.7272 data: 0.0004 [11-22 16:44:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 0/1669] eta: 0:20:14 tlr: 0.0001 tnm: 1.16 Lm: 8.045 (8.045) Lt: 7.717 (7.717) Accm: 0.80 (0.80) Acct: 0.90 (0.90) proj_loss: -0.3440 (-0.3440) time: 0.7276 data: 0.0004 [11-22 16:49:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 417/1669] eta: 0:15:40 tlr: 0.00011 tnm: 1.03 Lm: 7.991 (7.991) Lt: 7.664 (7.664) Accm: 0.74 (0.74) Acct: 1.00 (1.00) proj_loss: -0.3517 (-0.3517) time: 0.7512 data: 0.0003 [11-22 16:49:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 417/1669] eta: 0:15:40 tlr: 0.00011 tnm: 1.03 Lm: 8.049 (8.049) Lt: 7.742 (7.742) Accm: 0.76 (0.76) Acct: 1.14 (1.14) proj_loss: -0.3664 (-0.3664) time: 0.7512 data: 0.0003 [11-22 16:49:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 417/1669] eta: 0:15:40 tlr: 0.00011 tnm: 1.03 Lm: 8.019 (8.019) Lt: 7.716 (7.716) Accm: 0.68 (0.68) Acct: 0.77 (0.77) proj_loss: -0.3627 (-0.3627) time: 0.7512 data: 0.0003 [11-22 16:49:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 417/1669] eta: 0:15:40 tlr: 0.00011 tnm: 1.03 Lm: 8.009 (8.009) Lt: 7.718 (7.718) Accm: 0.66 (0.66) Acct: 0.96 (0.96) proj_loss: -0.3672 (-0.3672) time: 0.7512 data: 0.0003 [11-22 16:49:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 417/1669] eta: 0:15:40 tlr: 0.00011 tnm: 1.03 Lm: 8.043 (8.043) Lt: 7.736 (7.736) Accm: 0.71 (0.71) Acct: 0.84 (0.84) proj_loss: -0.3543 (-0.3543) time: 0.7512 data: 0.0003 [11-22 16:49:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 417/1669] eta: 0:15:40 tlr: 0.00011 tnm: 1.03 Lm: 8.065 (8.065) Lt: 7.790 (7.790) Accm: 0.66 (0.66) Acct: 0.96 (0.96) proj_loss: -0.3634 (-0.3634) time: 0.7512 data: 0.0002 [11-22 16:49:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 417/1669] eta: 0:15:40 tlr: 0.00011 tnm: 1.03 Lm: 8.024 (8.024) Lt: 7.722 (7.722) Accm: 0.68 (0.68) Acct: 0.86 (0.86) proj_loss: -0.3566 (-0.3566) time: 0.7512 data: 0.0002 [11-22 16:49:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 417/1669] eta: 0:15:40 tlr: 0.00011 tnm: 1.03 Lm: 8.030 (8.030) Lt: 7.730 (7.730) Accm: 0.73 (0.73) Acct: 1.14 (1.14) proj_loss: -0.3496 (-0.3496) time: 0.7512 data: 0.0003 [11-22 16:54:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 834/1669] eta: 0:10:27 tlr: 0.00012 tnm: 1.26 Lm: 8.023 (7.966) Lt: 7.693 (7.659) Accm: 0.77 (0.80) Acct: 1.14 (1.21) proj_loss: -0.3583 (-0.3568) time: 0.7498 data: 0.0003 [11-22 16:54:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 834/1669] eta: 0:10:27 tlr: 0.00012 tnm: 1.26 Lm: 8.063 (8.029) Lt: 7.787 (7.746) Accm: 0.73 (0.74) Acct: 1.07 (1.14) proj_loss: -0.3699 (-0.3659) time: 0.7498 data: 0.0002 [11-22 16:54:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 834/1669] eta: 0:10:27 tlr: 0.00012 tnm: 1.26 Lm: 8.015 (8.003) Lt: 7.705 (7.711) Accm: 0.67 (0.68) Acct: 0.96 (0.84) proj_loss: -0.3647 (-0.3634) time: 0.7498 data: 0.0002 [11-22 16:54:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 834/1669] eta: 0:10:27 tlr: 0.00012 tnm: 1.26 Lm: 8.039 (7.974) Lt: 7.730 (7.675) Accm: 0.77 (0.77) Acct: 1.14 (1.11) proj_loss: -0.3689 (-0.3703) time: 0.7498 data: 0.0003 [11-22 16:54:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 834/1669] eta: 0:10:27 tlr: 0.00012 tnm: 1.26 Lm: 8.001 (8.000) Lt: 7.707 (7.715) Accm: 0.70 (0.75) Acct: 0.96 (0.95) proj_loss: -0.3581 (-0.3635) time: 0.7498 data: 0.0002 [11-22 16:54:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 834/1669] eta: 0:10:27 tlr: 0.00012 tnm: 1.26 Lm: 8.031 (8.003) Lt: 7.695 (7.693) Accm: 0.77 (0.80) Acct: 0.86 (1.04) proj_loss: -0.3564 (-0.3606) time: 0.7498 data: 0.0002 [11-22 16:54:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 834/1669] eta: 0:10:27 tlr: 0.00012 tnm: 1.26 Lm: 7.941 (7.987) Lt: 7.603 (7.667) Accm: 0.68 (0.73) Acct: 1.00 (1.02) proj_loss: -0.3634 (-0.3657) time: 0.7498 data: 0.0003 [11-22 16:54:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [ 834/1669] eta: 0:10:27 tlr: 0.00012 tnm: 1.26 Lm: 8.035 (8.005) Lt: 7.717 (7.689) Accm: 0.77 (0.75) Acct: 1.03 (1.01) proj_loss: -0.3594 (-0.3554) time: 0.7498 data: 0.0003 [11-22 16:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1251/1669] eta: 0:05:14 tlr: 0.00013 tnm: 0.91 Lm: 8.011 (7.993) Lt: 7.721 (7.707) Accm: 0.80 (0.77) Acct: 1.08 (1.13) proj_loss: -0.3704 (-0.3695) time: 0.7502 data: 0.0002 [11-22 16:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1251/1669] eta: 0:05:14 tlr: 0.00013 tnm: 0.91 Lm: 7.947 (7.942) Lt: 7.624 (7.633) Accm: 0.77 (0.79) Acct: 1.14 (1.17) proj_loss: -0.3648 (-0.3631) time: 0.7502 data: 0.0003 [11-22 16:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1251/1669] eta: 0:05:14 tlr: 0.00013 tnm: 0.91 Lm: 7.940 (7.974) Lt: 7.638 (7.669) Accm: 0.78 (0.80) Acct: 1.07 (1.15) proj_loss: -0.3666 (-0.3667) time: 0.7502 data: 0.0003 [11-22 16:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1251/1669] eta: 0:05:14 tlr: 0.00013 tnm: 0.91 Lm: 8.005 (8.001) Lt: 7.703 (7.704) Accm: 0.71 (0.72) Acct: 0.96 (0.90) proj_loss: -0.3660 (-0.3691) time: 0.7502 data: 0.0002 [11-22 16:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1251/1669] eta: 0:05:14 tlr: 0.00013 tnm: 0.91 Lm: 8.014 (7.978) Lt: 7.691 (7.669) Accm: 0.77 (0.77) Acct: 1.10 (1.08) proj_loss: -0.3735 (-0.3730) time: 0.7502 data: 0.0002 [11-22 16:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1251/1669] eta: 0:05:14 tlr: 0.00013 tnm: 0.91 Lm: 8.034 (8.012) Lt: 7.696 (7.694) Accm: 0.72 (0.77) Acct: 0.84 (0.96) proj_loss: -0.3557 (-0.3592) time: 0.7502 data: 0.0002 [11-22 16:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1251/1669] eta: 0:05:14 tlr: 0.00013 tnm: 0.91 Lm: 7.985 (7.986) Lt: 7.673 (7.674) Accm: 0.79 (0.79) Acct: 1.07 (1.07) proj_loss: -0.3610 (-0.3620) time: 0.7502 data: 0.0003 [11-22 16:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1251/1669] eta: 0:05:14 tlr: 0.00013 tnm: 0.91 Lm: 8.013 (8.006) Lt: 7.712 (7.716) Accm: 0.71 (0.75) Acct: 1.05 (1.03) proj_loss: -0.3639 (-0.3650) time: 0.7502 data: 0.0002 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.78 Lm: 7.964 (7.987) Lt: 7.692 (7.704) Accm: 0.73 (0.73) Acct: 1.07 (1.03) proj_loss: -0.3710 (-0.3752) time: 0.7549 data: 0.0016 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.78 Lm: 7.989 (7.979) Lt: 7.676 (7.671) Accm: 0.77 (0.77) Acct: 1.14 (1.10) proj_loss: -0.3781 (-0.3790) time: 0.7549 data: 0.0016 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.78 Lm: 8.031 (7.993) Lt: 7.695 (7.680) Accm: 0.67 (0.75) Acct: 0.83 (0.92) proj_loss: -0.3564 (-0.3643) time: 0.7549 data: 0.0016 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.78 Lm: 7.878 (7.929) Lt: 7.593 (7.625) Accm: 0.77 (0.80) Acct: 1.14 (1.18) proj_loss: -0.3713 (-0.3717) time: 0.7549 data: 0.0014 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.78 Lm: 7.938 (7.959) Lt: 7.603 (7.654) Accm: 0.87 (0.84) Acct: 1.14 (1.20) proj_loss: -0.3698 (-0.3741) time: 0.7549 data: 0.0016 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.78 Lm: 7.995 (7.996) Lt: 7.701 (7.702) Accm: 0.76 (0.75) Acct: 0.96 (0.92) proj_loss: -0.3673 (-0.3738) time: 0.7549 data: 0.0018 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.78 Lm: 8.001 (7.986) Lt: 7.707 (7.686) Accm: 0.70 (0.74) Acct: 0.96 (1.01) proj_loss: -0.3697 (-0.3694) time: 0.7549 data: 0.0014 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 3/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.78 Lm: 7.936 (7.969) Lt: 7.629 (7.651) Accm: 0.77 (0.77) Acct: 1.03 (1.05) proj_loss: -0.3627 (-0.3626) time: 0.7549 data: 0.0021 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 3/350] Total time: 0:21:55 (0.788 s / it) [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 3/350] Total time: 0:21:55 (0.788 s / it) [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 3/350] Total time: 0:21:55 (0.788 s / it) [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 3/350] Total time: 0:21:55 (0.788 s / it) [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 3/350] Total time: 0:21:55 (0.788 s / it) [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 3/350] Total time: 0:21:55 (0.788 s / it) [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 3/350] Total time: 0:21:55 (0.788 s / it) [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 3/350] Total time: 0:21:55 (0.788 s / it) [11-22 17:06:13] (/home/user/VAR/train.py , line 276)=> [ep3] (training ) Lm: 7.975 (7.975), Lt: 7.673 (7.673), Acc m&t: 0.76 1.04, Remain: 5 days, 1:54:14, Finish: 2024-11-27 03:00 [11-22 17:06:13] (/home/user/VAR/train.py , line 276)=> [ep3] (training ) Lm: 7.975 (7.975), Lt: 7.673 (7.673), Acc m&t: 0.76 1.04, Remain: 5 days, 1:55:48, Finish: 2024-11-27 03:02 [11-22 17:06:13] (/home/user/VAR/train.py , line 276)=> [ep3] (training ) Lm: 7.975 (7.975), Lt: 7.673 (7.673), Acc m&t: 0.76 1.04, Remain: 5 days, 1:54:33, Finish: 2024-11-27 03:00 [11-22 17:06:13] (/home/user/VAR/train.py , line 276)=> [ep3] (training ) Lm: 7.975 (7.975), Lt: 7.673 (7.673), Acc m&t: 0.76 1.04, Remain: 5 days, 1:54:59, Finish: 2024-11-27 03:01 [11-22 17:06:13] (/home/user/VAR/train.py , line 276)=> [ep3] (training ) Lm: 7.975 (7.975), Lt: 7.673 (7.673), Acc m&t: 0.76 1.04, Remain: 5 days, 1:54:41, Finish: 2024-11-27 03:00 [11-22 17:06:13] (/home/user/VAR/train.py , line 276)=> [ep3] (training ) Lm: 7.975 (7.975), Lt: 7.673 (7.673), Acc m&t: 0.76 1.04, Remain: 5 days, 1:54:44, Finish: 2024-11-27 03:00 [11-22 17:06:13] (/home/user/VAR/train.py , line 276)=> [ep3] (training ) Lm: 7.975 (7.975), Lt: 7.673 (7.673), Acc m&t: 0.76 1.04, Remain: 5 days, 1:55:00, Finish: 2024-11-27 03:01 [11-22 17:06:13] (/home/user/VAR/train.py , line 276)=> [ep3] (training ) Lm: 7.975 (7.975), Lt: 7.673 (7.673), Acc m&t: 0.76 1.04, Remain: 5 days, 1:56:34, Finish: 2024-11-27 03:02 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 0/1669] eta: 0:19:58 tlr: 0.00014 tnm: 0.97 Lm: 7.919 (7.919) Lt: 7.614 (7.614) Accm: 0.79 (0.79) Acct: 1.07 (1.07) proj_loss: -0.3947 (-0.3947) time: 0.7183 data: 0.0004 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 0/1669] eta: 0:19:59 tlr: 0.00014 tnm: 0.97 Lm: 7.954 (7.954) Lt: 7.671 (7.671) Accm: 1.02 (1.02) Acct: 1.31 (1.31) proj_loss: -0.4166 (-0.4166) time: 0.7189 data: 0.0003 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 0/1669] eta: 0:19:59 tlr: 0.00014 tnm: 0.97 Lm: 7.814 (7.814) Lt: 7.457 (7.457) Accm: 0.87 (0.87) Acct: 1.17 (1.17) proj_loss: -0.3799 (-0.3799) time: 0.7189 data: 0.0003 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 0/1669] eta: 0:19:59 tlr: 0.00014 tnm: 0.97 Lm: 7.999 (7.999) Lt: 7.675 (7.675) Accm: 0.82 (0.82) Acct: 1.27 (1.27) proj_loss: -0.3834 (-0.3834) time: 0.7188 data: 0.0005 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 0/1669] eta: 0:20:00 tlr: 0.00014 tnm: 0.97 Lm: 7.924 (7.924) Lt: 7.665 (7.665) Accm: 0.70 (0.70) Acct: 1.00 (1.00) proj_loss: -0.3814 (-0.3814) time: 0.7194 data: 0.0004 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 0/1669] eta: 0:20:01 tlr: 0.00014 tnm: 0.97 Lm: 7.983 (7.983) Lt: 7.719 (7.719) Accm: 0.63 (0.63) Acct: 0.93 (0.93) proj_loss: -0.3686 (-0.3686) time: 0.7196 data: 0.0004 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 0/1669] eta: 0:20:02 tlr: 0.00014 tnm: 0.97 Lm: 7.918 (7.918) Lt: 7.585 (7.585) Accm: 0.77 (0.77) Acct: 1.14 (1.14) proj_loss: -0.3890 (-0.3890) time: 0.7202 data: 0.0004 [11-22 17:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 0/1669] eta: 0:20:01 tlr: 0.00014 tnm: 0.97 Lm: 8.034 (8.034) Lt: 7.711 (7.711) Accm: 0.63 (0.63) Acct: 0.86 (0.86) proj_loss: -0.4047 (-0.4047) time: 0.7198 data: 0.0004 [11-22 17:11:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 417/1669] eta: 0:15:39 tlr: 0.00015 tnm: 0.92 Lm: 7.963 (7.963) Lt: 7.676 (7.676) Accm: 0.74 (0.74) Acct: 0.98 (0.98) proj_loss: -0.4077 (-0.4077) time: 0.7508 data: 0.0003 [11-22 17:11:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 417/1669] eta: 0:15:39 tlr: 0.00015 tnm: 0.92 Lm: 7.929 (7.929) Lt: 7.643 (7.643) Accm: 0.89 (0.89) Acct: 1.12 (1.12) proj_loss: -0.4159 (-0.4159) time: 0.7508 data: 0.0002 [11-22 17:11:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 417/1669] eta: 0:15:39 tlr: 0.00015 tnm: 0.92 Lm: 7.955 (7.955) Lt: 7.643 (7.643) Accm: 0.90 (0.90) Acct: 1.31 (1.31) proj_loss: -0.4069 (-0.4069) time: 0.7508 data: 0.0002 [11-22 17:11:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 417/1669] eta: 0:15:39 tlr: 0.00015 tnm: 0.92 Lm: 7.957 (7.957) Lt: 7.658 (7.658) Accm: 0.84 (0.84) Acct: 1.03 (1.03) proj_loss: -0.4013 (-0.4013) time: 0.7508 data: 0.0002 [11-22 17:11:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 417/1669] eta: 0:15:39 tlr: 0.00015 tnm: 0.92 Lm: 7.933 (7.933) Lt: 7.649 (7.649) Accm: 0.80 (0.80) Acct: 1.10 (1.10) proj_loss: -0.3860 (-0.3860) time: 0.7508 data: 0.0003 [11-22 17:11:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 417/1669] eta: 0:15:39 tlr: 0.00015 tnm: 0.92 Lm: 7.890 (7.890) Lt: 7.635 (7.635) Accm: 0.82 (0.82) Acct: 1.21 (1.21) proj_loss: -0.3841 (-0.3841) time: 0.7508 data: 0.0003 [11-22 17:11:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 417/1669] eta: 0:15:39 tlr: 0.00015 tnm: 0.92 Lm: 7.856 (7.856) Lt: 7.528 (7.528) Accm: 0.81 (0.81) Acct: 1.07 (1.07) proj_loss: -0.3850 (-0.3850) time: 0.7508 data: 0.0002 [11-22 17:11:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 417/1669] eta: 0:15:39 tlr: 0.00015 tnm: 0.92 Lm: 7.865 (7.865) Lt: 7.523 (7.523) Accm: 0.94 (0.94) Acct: 1.31 (1.31) proj_loss: -0.3968 (-0.3968) time: 0.7508 data: 0.0003 [11-22 17:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 834/1669] eta: 0:10:26 tlr: 0.00015 tnm: 0.81 Lm: 7.946 (7.934) Lt: 7.670 (7.652) Accm: 0.92 (0.90) Acct: 0.93 (1.04) proj_loss: -0.4166 (-0.4163) time: 0.7514 data: 0.0002 [11-22 17:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 834/1669] eta: 0:10:26 tlr: 0.00015 tnm: 0.81 Lm: 7.919 (7.885) Lt: 7.614 (7.579) Accm: 0.89 (0.87) Acct: 1.07 (1.09) proj_loss: -0.4079 (-0.4104) time: 0.7514 data: 0.0003 [11-22 17:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 834/1669] eta: 0:10:26 tlr: 0.00015 tnm: 0.81 Lm: 7.934 (7.933) Lt: 7.634 (7.641) Accm: 0.82 (0.81) Acct: 1.00 (1.06) proj_loss: -0.3907 (-0.3963) time: 0.7513 data: 0.0003 [11-22 17:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 834/1669] eta: 0:10:26 tlr: 0.00015 tnm: 0.81 Lm: 7.898 (7.899) Lt: 7.598 (7.569) Accm: 0.74 (0.73) Acct: 0.96 (1.00) proj_loss: -0.3858 (-0.3852) time: 0.7514 data: 0.0003 [11-22 17:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 834/1669] eta: 0:10:26 tlr: 0.00015 tnm: 0.81 Lm: 7.854 (7.878) Lt: 7.564 (7.611) Accm: 0.71 (0.78) Acct: 0.93 (1.11) proj_loss: -0.3997 (-0.3916) time: 0.7514 data: 0.0003 [11-22 17:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 834/1669] eta: 0:10:26 tlr: 0.00015 tnm: 0.81 Lm: 7.911 (7.910) Lt: 7.611 (7.598) Accm: 0.82 (0.81) Acct: 1.27 (1.16) proj_loss: -0.3940 (-0.4026) time: 0.7514 data: 0.0003 [11-22 17:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 834/1669] eta: 0:10:26 tlr: 0.00015 tnm: 0.81 Lm: 7.915 (7.882) Lt: 7.585 (7.547) Accm: 0.77 (0.84) Acct: 1.14 (1.11) proj_loss: -0.3977 (-0.3971) time: 0.7514 data: 0.0003 [11-22 17:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [ 834/1669] eta: 0:10:26 tlr: 0.00015 tnm: 0.81 Lm: 7.928 (7.952) Lt: 7.641 (7.656) Accm: 0.84 (0.77) Acct: 1.10 (1.07) proj_loss: -0.4108 (-0.4115) time: 0.7514 data: 0.0002 [11-22 17:21:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1251/1669] eta: 0:05:13 tlr: 0.00016 tnm: 0.86 Lm: 7.929 (7.926) Lt: 7.628 (7.630) Accm: 0.84 (0.82) Acct: 1.08 (1.08) proj_loss: -0.3943 (-0.3967) time: 0.7512 data: 0.0002 [11-22 17:21:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1251/1669] eta: 0:05:13 tlr: 0.00016 tnm: 0.86 Lm: 7.930 (7.929) Lt: 7.642 (7.636) Accm: 0.88 (0.89) Acct: 1.08 (1.09) proj_loss: -0.4159 (-0.4146) time: 0.7512 data: 0.0003 [11-22 17:21:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1251/1669] eta: 0:05:13 tlr: 0.00016 tnm: 0.86 Lm: 7.845 (7.857) Lt: 7.529 (7.545) Accm: 0.91 (0.90) Acct: 1.14 (1.12) proj_loss: -0.4025 (-0.4071) time: 0.7512 data: 0.0003 [11-22 17:21:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1251/1669] eta: 0:05:13 tlr: 0.00016 tnm: 0.86 Lm: 7.895 (7.903) Lt: 7.615 (7.603) Accm: 0.76 (0.78) Acct: 1.15 (1.13) proj_loss: -0.4042 (-0.4055) time: 0.7512 data: 0.0002 [11-22 17:21:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1251/1669] eta: 0:05:13 tlr: 0.00016 tnm: 0.86 Lm: 7.910 (7.924) Lt: 7.628 (7.607) Accm: 0.84 (0.80) Acct: 1.17 (1.11) proj_loss: -0.4077 (-0.4092) time: 0.7512 data: 0.0002 [11-22 17:21:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1251/1669] eta: 0:05:13 tlr: 0.00016 tnm: 0.86 Lm: 7.899 (7.899) Lt: 7.604 (7.579) Accm: 0.81 (0.77) Acct: 1.07 (1.04) proj_loss: -0.3879 (-0.3924) time: 0.7512 data: 0.0003 [11-22 17:21:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1251/1669] eta: 0:05:13 tlr: 0.00016 tnm: 0.86 Lm: 7.825 (7.856) Lt: 7.557 (7.582) Accm: 0.86 (0.87) Acct: 1.21 (1.27) proj_loss: -0.4031 (-0.3984) time: 0.7512 data: 0.0003 [11-22 17:21:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1251/1669] eta: 0:05:13 tlr: 0.00016 tnm: 0.86 Lm: 7.864 (7.856) Lt: 7.523 (7.522) Accm: 0.87 (0.87) Acct: 1.31 (1.23) proj_loss: -0.4011 (-0.4035) time: 0.7512 data: 0.0003 [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.65 Lm: 7.924 (7.898) Lt: 7.623 (7.600) Accm: 0.86 (0.85) Acct: 1.17 (1.12) proj_loss: -0.3979 (-0.4007) time: 0.7515 data: 0.0020 [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.65 Lm: 7.914 (7.895) Lt: 7.615 (7.606) Accm: 0.90 (0.89) Acct: 1.10 (1.10) proj_loss: -0.4166 (-0.4164) time: 0.7515 data: 0.0015 [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.65 Lm: 7.803 (7.846) Lt: 7.458 (7.528) Accm: 0.93 (0.92) Acct: 1.21 (1.18) proj_loss: -0.4079 (-0.4095) time: 0.7515 data: 0.0020 [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.65 Lm: 7.898 (7.846) Lt: 7.598 (7.524) Accm: 0.87 (0.81) Acct: 1.17 (1.12) proj_loss: -0.3900 (-0.3958) time: 0.7515 data: 0.0015 [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.65 Lm: 7.880 (7.893) Lt: 7.611 (7.589) Accm: 0.82 (0.80) Acct: 1.21 (1.14) proj_loss: -0.4098 (-0.4064) time: 0.7515 data: 0.0016 [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.65 Lm: 7.813 (7.836) Lt: 7.477 (7.513) Accm: 0.87 (0.87) Acct: 1.17 (1.22) proj_loss: -0.4046 (-0.4085) time: 0.7515 data: 0.0016 [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.65 Lm: 7.893 (7.880) Lt: 7.615 (7.553) Accm: 0.84 (0.84) Acct: 1.10 (1.10) proj_loss: -0.4108 (-0.4127) time: 0.7515 data: 0.0015 [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 4/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.65 Lm: 7.854 (7.856) Lt: 7.550 (7.575) Accm: 0.93 (0.88) Acct: 1.34 (1.28) proj_loss: -0.4017 (-0.3990) time: 0.7515 data: 0.0016 [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 4/350] Total time: 0:20:54 (0.752 s / it) [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 4/350] Total time: 0:20:54 (0.752 s / it) [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 4/350] Total time: 0:20:54 (0.752 s / it) [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 4/350] Total time: 0:20:54 (0.752 s / it) [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 4/350] Total time: 0:20:54 (0.752 s / it) [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 4/350] Total time: 0:20:54 (0.752 s / it) [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 4/350] Total time: 0:20:54 (0.752 s / it) [11-22 17:27:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 4/350] Total time: 0:20:54 (0.752 s / it) [11-22 17:27:07] (/home/user/VAR/train.py , line 276)=> [ep4] (training ) Lm: 7.860 (7.860), Lt: 7.553 (7.553), Acc m&t: 0.85 1.14, Remain: 5 days, 1:01:18, Finish: 2024-11-27 02:28 [11-22 17:27:07] (/home/user/VAR/train.py , line 276)=> [ep4] (training ) Lm: 7.860 (7.860), Lt: 7.553 (7.553), Acc m&t: 0.85 1.14, Remain: 5 days, 1:01:43, Finish: 2024-11-27 02:28 [11-22 17:27:07] (/home/user/VAR/train.py , line 276)=> [ep4] (training ) Lm: 7.860 (7.860), Lt: 7.553 (7.553), Acc m&t: 0.85 1.14, Remain: 5 days, 1:01:15, Finish: 2024-11-27 02:28 [11-22 17:27:07] (/home/user/VAR/train.py , line 276)=> [ep4] (training ) Lm: 7.860 (7.860), Lt: 7.553 (7.553), Acc m&t: 0.85 1.14, Remain: 5 days, 1:00:29, Finish: 2024-11-27 02:27 [11-22 17:27:07] (/home/user/VAR/train.py , line 276)=> [ep4] (training ) Lm: 7.860 (7.860), Lt: 7.553 (7.553), Acc m&t: 0.85 1.14, Remain: 5 days, 1:00:37, Finish: 2024-11-27 02:27 [11-22 17:27:07] (/home/user/VAR/train.py , line 276)=> [ep4] (training ) Lm: 7.860 (7.860), Lt: 7.553 (7.553), Acc m&t: 0.85 1.14, Remain: 5 days, 1:00:39, Finish: 2024-11-27 02:27 [11-22 17:27:07] (/home/user/VAR/train.py , line 276)=> [ep4] (training ) Lm: 7.860 (7.860), Lt: 7.553 (7.553), Acc m&t: 0.85 1.14, Remain: 5 days, 1:00:24, Finish: 2024-11-27 02:27 [11-22 17:27:07] (/home/user/VAR/train.py , line 276)=> [ep4] (training ) Lm: 7.860 (7.860), Lt: 7.553 (7.553), Acc m&t: 0.85 1.14, Remain: 5 days, 1:00:03, Finish: 2024-11-27 02:27 [11-22 17:27:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 0/1669] eta: 0:19:56 tlr: 0.00017 tnm: 0.80 Lm: 7.770 (7.770) Lt: 7.435 (7.435) Accm: 0.96 (0.96) Acct: 1.21 (1.21) proj_loss: -0.4351 (-0.4351) time: 0.7168 data: 0.0003 [11-22 17:27:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 0/1669] eta: 0:19:59 tlr: 0.00017 tnm: 0.80 Lm: 7.638 (7.638) Lt: 7.317 (7.317) Accm: 0.93 (0.93) Acct: 1.38 (1.38) proj_loss: -0.4206 (-0.4206) time: 0.7184 data: 0.0003 [11-22 17:27:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 0/1669] eta: 0:19:57 tlr: 0.00017 tnm: 0.80 Lm: 7.828 (7.828) Lt: 7.514 (7.514) Accm: 0.90 (0.90) Acct: 1.21 (1.21) proj_loss: -0.4213 (-0.4213) time: 0.7173 data: 0.0004 [11-22 17:27:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 0/1669] eta: 0:19:57 tlr: 0.00017 tnm: 0.80 Lm: 7.791 (7.791) Lt: 7.454 (7.454) Accm: 0.83 (0.83) Acct: 1.17 (1.17) proj_loss: -0.3832 (-0.3832) time: 0.7177 data: 0.0004 [11-22 17:27:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 0/1669] eta: 0:19:57 tlr: 0.00017 tnm: 0.80 Lm: 7.763 (7.763) Lt: 7.469 (7.469) Accm: 0.80 (0.80) Acct: 0.96 (0.96) proj_loss: -0.4481 (-0.4481) time: 0.7176 data: 0.0003 [11-22 17:27:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 0/1669] eta: 0:20:07 tlr: 0.00017 tnm: 0.80 Lm: 7.891 (7.891) Lt: 7.623 (7.623) Accm: 0.64 (0.64) Acct: 0.83 (0.83) proj_loss: -0.4100 (-0.4100) time: 0.7235 data: 0.0003 [11-22 17:27:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 0/1669] eta: 0:19:59 tlr: 0.00017 tnm: 0.80 Lm: 7.728 (7.728) Lt: 7.400 (7.400) Accm: 1.09 (1.09) Acct: 1.48 (1.48) proj_loss: -0.4250 (-0.4250) time: 0.7185 data: 0.0003 [11-22 17:27:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 0/1669] eta: 0:19:59 tlr: 0.00017 tnm: 0.80 Lm: 7.854 (7.854) Lt: 7.530 (7.530) Accm: 0.74 (0.74) Acct: 1.14 (1.14) proj_loss: -0.4018 (-0.4018) time: 0.7188 data: 0.0004 [11-22 17:33:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 417/1669] eta: 0:18:47 tlr: 0.00018 tnm: 0.74 Lm: 7.831 (7.831) Lt: 7.523 (7.523) Accm: 0.84 (0.84) Acct: 1.15 (1.15) proj_loss: -0.4245 (-0.4245) time: 0.7501 data: 0.0002 [11-22 17:33:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 417/1669] eta: 0:18:47 tlr: 0.00018 tnm: 0.74 Lm: 7.795 (7.795) Lt: 7.490 (7.490) Accm: 0.91 (0.91) Acct: 1.21 (1.21) proj_loss: -0.4379 (-0.4379) time: 0.7500 data: 0.0003 [11-22 17:33:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 417/1669] eta: 0:18:47 tlr: 0.00018 tnm: 0.74 Lm: 7.782 (7.782) Lt: 7.454 (7.454) Accm: 0.85 (0.85) Acct: 1.21 (1.21) proj_loss: -0.4012 (-0.4012) time: 0.7500 data: 0.0003 [11-22 17:33:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 417/1669] eta: 0:18:47 tlr: 0.00018 tnm: 0.74 Lm: 7.671 (7.671) Lt: 7.352 (7.352) Accm: 0.91 (0.91) Acct: 1.31 (1.31) proj_loss: -0.4192 (-0.4192) time: 0.7500 data: 0.0002 [11-22 17:33:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 417/1669] eta: 0:18:47 tlr: 0.00018 tnm: 0.74 Lm: 7.762 (7.762) Lt: 7.461 (7.461) Accm: 0.81 (0.81) Acct: 0.90 (0.90) proj_loss: -0.4360 (-0.4360) time: 0.7500 data: 0.0003 [11-22 17:33:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 417/1669] eta: 0:18:47 tlr: 0.00018 tnm: 0.74 Lm: 7.788 (7.788) Lt: 7.444 (7.444) Accm: 0.77 (0.77) Acct: 1.05 (1.05) proj_loss: -0.4098 (-0.4098) time: 0.7500 data: 0.0003 [11-22 17:33:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 417/1669] eta: 0:18:47 tlr: 0.00018 tnm: 0.74 Lm: 7.768 (7.768) Lt: 7.442 (7.442) Accm: 0.94 (0.94) Acct: 1.27 (1.27) proj_loss: -0.4264 (-0.4264) time: 0.7501 data: 0.0003 [11-22 17:33:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 417/1669] eta: 0:18:47 tlr: 0.00018 tnm: 0.74 Lm: 7.805 (7.805) Lt: 7.506 (7.506) Accm: 0.79 (0.79) Acct: 1.07 (1.07) proj_loss: -0.4234 (-0.4234) time: 0.7501 data: 0.0003 [11-22 17:38:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 834/1669] eta: 0:11:29 tlr: 0.00019 tnm: 0.64 Lm: 7.747 (7.785) Lt: 7.389 (7.465) Accm: 0.95 (0.88) Acct: 1.31 (1.21) proj_loss: -0.4155 (-0.4207) time: 0.7500 data: 0.0003 [11-22 17:38:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 834/1669] eta: 0:11:29 tlr: 0.00019 tnm: 0.64 Lm: 7.770 (7.762) Lt: 7.436 (7.472) Accm: 0.90 (0.91) Acct: 1.21 (1.12) proj_loss: -0.4407 (-0.4448) time: 0.7500 data: 0.0002 [11-22 17:38:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 834/1669] eta: 0:11:29 tlr: 0.00019 tnm: 0.64 Lm: 7.828 (7.793) Lt: 7.514 (7.495) Accm: 0.90 (0.88) Acct: 1.21 (1.22) proj_loss: -0.4213 (-0.4164) time: 0.7500 data: 0.0002 [11-22 17:38:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 834/1669] eta: 0:11:29 tlr: 0.00019 tnm: 0.64 Lm: 7.791 (7.788) Lt: 7.454 (7.470) Accm: 0.83 (0.84) Acct: 1.24 (1.24) proj_loss: -0.4191 (-0.4140) time: 0.7500 data: 0.0002 [11-22 17:38:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 834/1669] eta: 0:11:29 tlr: 0.00019 tnm: 0.64 Lm: 7.638 (7.659) Lt: 7.317 (7.329) Accm: 0.93 (1.00) Acct: 1.38 (1.41) proj_loss: -0.4206 (-0.4199) time: 0.7500 data: 0.0002 [11-22 17:38:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 834/1669] eta: 0:11:29 tlr: 0.00019 tnm: 0.64 Lm: 7.763 (7.769) Lt: 7.469 (7.486) Accm: 0.82 (0.90) Acct: 0.96 (1.17) proj_loss: -0.4281 (-0.4333) time: 0.7500 data: 0.0003 [11-22 17:38:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 834/1669] eta: 0:11:29 tlr: 0.00019 tnm: 0.64 Lm: 7.741 (7.759) Lt: 7.407 (7.430) Accm: 0.92 (0.93) Acct: 1.27 (1.27) proj_loss: -0.4277 (-0.4289) time: 0.7500 data: 0.0002 [11-22 17:38:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [ 834/1669] eta: 0:11:29 tlr: 0.00019 tnm: 0.64 Lm: 7.743 (7.773) Lt: 7.430 (7.439) Accm: 0.80 (0.82) Acct: 1.14 (1.09) proj_loss: -0.4178 (-0.4156) time: 0.7500 data: 0.0003 [11-22 17:43:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1251/1669] eta: 0:05:34 tlr: 0.0002 tnm: 0.69 Lm: 7.733 (7.721) Lt: 7.394 (7.388) Accm: 0.85 (0.95) Acct: 1.15 (1.27) proj_loss: -0.4225 (-0.4224) time: 0.7516 data: 0.0003 [11-22 17:43:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1251/1669] eta: 0:05:34 tlr: 0.0002 tnm: 0.69 Lm: 7.763 (7.767) Lt: 7.482 (7.488) Accm: 0.81 (0.86) Acct: 0.90 (1.06) proj_loss: -0.4328 (-0.4344) time: 0.7516 data: 0.0003 [11-22 17:43:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1251/1669] eta: 0:05:34 tlr: 0.0002 tnm: 0.69 Lm: 7.735 (7.737) Lt: 7.404 (7.385) Accm: 0.92 (0.93) Acct: 1.21 (1.24) proj_loss: -0.4272 (-0.4284) time: 0.7516 data: 0.0002 [11-22 17:43:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1251/1669] eta: 0:05:34 tlr: 0.0002 tnm: 0.69 Lm: 7.733 (7.757) Lt: 7.388 (7.446) Accm: 1.01 (0.99) Acct: 1.39 (1.39) proj_loss: -0.4261 (-0.4261) time: 0.7516 data: 0.0003 [11-22 17:43:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1251/1669] eta: 0:05:34 tlr: 0.0002 tnm: 0.69 Lm: 7.772 (7.742) Lt: 7.476 (7.440) Accm: 0.94 (0.93) Acct: 1.22 (1.22) proj_loss: -0.4245 (-0.4201) time: 0.7516 data: 0.0003 [11-22 17:43:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1251/1669] eta: 0:05:34 tlr: 0.0002 tnm: 0.69 Lm: 7.657 (7.663) Lt: 7.343 (7.340) Accm: 0.93 (0.98) Acct: 1.33 (1.38) proj_loss: -0.4209 (-0.4292) time: 0.7516 data: 0.0003 [11-22 17:43:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1251/1669] eta: 0:05:34 tlr: 0.0002 tnm: 0.69 Lm: 7.782 (7.784) Lt: 7.454 (7.447) Accm: 0.85 (0.86) Acct: 1.26 (1.25) proj_loss: -0.4177 (-0.4146) time: 0.7516 data: 0.0003 [11-22 17:43:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1251/1669] eta: 0:05:34 tlr: 0.0002 tnm: 0.69 Lm: 7.732 (7.728) Lt: 7.435 (7.432) Accm: 0.93 (1.00) Acct: 1.21 (1.32) proj_loss: -0.4379 (-0.4404) time: 0.7516 data: 0.0002 [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.69 Lm: 7.752 (7.733) Lt: 7.435 (7.426) Accm: 0.96 (1.00) Acct: 1.21 (1.32) proj_loss: -0.4351 (-0.4388) time: 0.7526 data: 0.0018 [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 5/350] Total time: 0:21:56 (0.789 s / it) [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.69 Lm: 7.717 (7.718) Lt: 7.438 (7.400) Accm: 0.98 (0.95) Acct: 1.21 (1.20) proj_loss: -0.4213 (-0.4195) time: 0.7526 data: 0.0017 [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.69 Lm: 7.763 (7.730) Lt: 7.469 (7.434) Accm: 0.82 (0.93) Acct: 0.96 (1.16) proj_loss: -0.4376 (-0.4404) time: 0.7526 data: 0.0017 [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.69 Lm: 7.676 (7.670) Lt: 7.370 (7.346) Accm: 0.93 (0.98) Acct: 1.38 (1.38) proj_loss: -0.4206 (-0.4259) time: 0.7526 data: 0.0015 [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.69 Lm: 7.773 (7.763) Lt: 7.453 (7.419) Accm: 0.87 (0.92) Acct: 1.27 (1.38) proj_loss: -0.4164 (-0.4144) time: 0.7526 data: 0.0017 [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.69 Lm: 7.719 (7.734) Lt: 7.387 (7.415) Accm: 0.95 (0.97) Acct: 1.34 (1.38) proj_loss: -0.4360 (-0.4281) time: 0.7526 data: 0.0019 [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.69 Lm: 7.728 (7.690) Lt: 7.400 (7.330) Accm: 0.92 (0.99) Acct: 1.27 (1.36) proj_loss: -0.4277 (-0.4311) time: 0.7526 data: 0.0015 [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 5/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.69 Lm: 7.723 (7.672) Lt: 7.358 (7.323) Accm: 0.90 (1.02) Acct: 1.17 (1.38) proj_loss: -0.4272 (-0.4251) time: 0.7526 data: 0.0020 [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 5/350] Total time: 0:21:56 (0.789 s / it) [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 5/350] Total time: 0:21:56 (0.789 s / it) [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 5/350] Total time: 0:21:56 (0.789 s / it) [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 5/350] Total time: 0:21:56 (0.789 s / it) [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 5/350] Total time: 0:21:56 (0.789 s / it) [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 5/350] Total time: 0:21:56 (0.789 s / it) [11-22 17:49:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 5/350] Total time: 0:21:56 (0.789 s / it) [11-22 17:49:04] (/home/user/VAR/train.py , line 276)=> [ep5] (training ) Lm: 7.721 (7.721), Lt: 7.386 (7.386), Acc m&t: 1.00 1.36, Remain: 5 days, 1:02:02, Finish: 2024-11-27 02:51 [11-22 17:49:04] (/home/user/VAR/train.py , line 276)=> [ep5] (training ) Lm: 7.721 (7.721), Lt: 7.386 (7.386), Acc m&t: 1.00 1.36, Remain: 5 days, 1:02:31, Finish: 2024-11-27 02:51 [11-22 17:49:04] (/home/user/VAR/train.py , line 276)=> [ep5] (training ) Lm: 7.721 (7.721), Lt: 7.386 (7.386), Acc m&t: 1.00 1.36, Remain: 5 days, 1:06:22, Finish: 2024-11-27 02:55 [11-22 17:49:04] (/home/user/VAR/train.py , line 276)=> [ep5] (training ) Lm: 7.721 (7.721), Lt: 7.386 (7.386), Acc m&t: 1.00 1.36, Remain: 5 days, 1:01:36, Finish: 2024-11-27 02:50 [11-22 17:49:04] (/home/user/VAR/train.py , line 276)=> [ep5] (training ) Lm: 7.721 (7.721), Lt: 7.386 (7.386), Acc m&t: 1.00 1.36, Remain: 5 days, 1:01:07, Finish: 2024-11-27 02:50 [11-22 17:49:04] (/home/user/VAR/train.py , line 276)=> [ep5] (training ) Lm: 7.721 (7.721), Lt: 7.386 (7.386), Acc m&t: 1.00 1.36, Remain: 5 days, 1:02:19, Finish: 2024-11-27 02:51 [11-22 17:49:04] (/home/user/VAR/train.py , line 276)=> [ep5] (training ) Lm: 7.721 (7.721), Lt: 7.386 (7.386), Acc m&t: 1.00 1.36, Remain: 5 days, 1:01:59, Finish: 2024-11-27 02:51 [11-22 17:49:04] (/home/user/VAR/train.py , line 276)=> [ep5] (training ) Lm: 7.721 (7.721), Lt: 7.386 (7.386), Acc m&t: 1.00 1.36, Remain: 5 days, 1:02:09, Finish: 2024-11-27 02:51 [11-22 17:49:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 0/1669] eta: 0:19:49 tlr: 0.00021 tnm: 0.70 Lm: 7.624 (7.624) Lt: 7.273 (7.273) Accm: 1.03 (1.03) Acct: 1.31 (1.31) proj_loss: -0.4138 (-0.4138) time: 0.7128 data: 0.0004 [11-22 17:49:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 0/1669] eta: 0:19:51 tlr: 0.00021 tnm: 0.70 Lm: 7.707 (7.707) Lt: 7.307 (7.307) Accm: 1.01 (1.01) Acct: 1.10 (1.10) proj_loss: -0.4105 (-0.4105) time: 0.7142 data: 0.0004 [11-22 17:49:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 0/1669] eta: 0:19:52 tlr: 0.00021 tnm: 0.70 Lm: 7.605 (7.605) Lt: 7.245 (7.245) Accm: 1.27 (1.27) Acct: 2.00 (2.00) proj_loss: -0.4126 (-0.4126) time: 0.7143 data: 0.0003 [11-22 17:49:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 0/1669] eta: 0:19:52 tlr: 0.00021 tnm: 0.70 Lm: 7.710 (7.710) Lt: 7.386 (7.386) Accm: 1.09 (1.09) Acct: 1.52 (1.52) proj_loss: -0.4052 (-0.4052) time: 0.7144 data: 0.0003 [11-22 17:49:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 0/1669] eta: 0:19:52 tlr: 0.00021 tnm: 0.70 Lm: 7.673 (7.673) Lt: 7.327 (7.327) Accm: 1.06 (1.06) Acct: 1.31 (1.31) proj_loss: -0.4338 (-0.4338) time: 0.7143 data: 0.0003 [11-22 17:49:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 0/1669] eta: 0:19:52 tlr: 0.00021 tnm: 0.70 Lm: 7.559 (7.559) Lt: 7.208 (7.208) Accm: 1.09 (1.09) Acct: 1.58 (1.58) proj_loss: -0.4375 (-0.4375) time: 0.7146 data: 0.0003 [11-22 17:49:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 0/1669] eta: 0:19:53 tlr: 0.00021 tnm: 0.70 Lm: 7.614 (7.614) Lt: 7.261 (7.261) Accm: 1.33 (1.33) Acct: 1.55 (1.55) proj_loss: -0.4285 (-0.4285) time: 0.7148 data: 0.0004 [11-22 17:49:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 0/1669] eta: 0:19:54 tlr: 0.00021 tnm: 0.70 Lm: 7.609 (7.609) Lt: 7.298 (7.298) Accm: 1.11 (1.11) Acct: 1.52 (1.52) proj_loss: -0.4130 (-0.4130) time: 0.7157 data: 0.0003 [11-22 17:54:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 417/1669] eta: 0:15:39 tlr: 0.00021 tnm: 0.59 Lm: 7.597 (7.597) Lt: 7.215 (7.215) Accm: 1.19 (1.19) Acct: 1.65 (1.65) proj_loss: -0.4214 (-0.4214) time: 0.7544 data: 0.0002 [11-22 17:54:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 417/1669] eta: 0:15:39 tlr: 0.00021 tnm: 0.59 Lm: 7.569 (7.569) Lt: 7.193 (7.193) Accm: 1.31 (1.31) Acct: 1.95 (1.95) proj_loss: -0.4227 (-0.4227) time: 0.7544 data: 0.0003 [11-22 17:54:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 417/1669] eta: 0:15:39 tlr: 0.00021 tnm: 0.59 Lm: 7.475 (7.475) Lt: 7.115 (7.115) Accm: 1.25 (1.25) Acct: 1.67 (1.67) proj_loss: -0.4245 (-0.4245) time: 0.7544 data: 0.0003 [11-22 17:54:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 417/1669] eta: 0:15:39 tlr: 0.00021 tnm: 0.59 Lm: 7.596 (7.596) Lt: 7.241 (7.241) Accm: 1.14 (1.14) Acct: 1.53 (1.53) proj_loss: -0.4330 (-0.4330) time: 0.7544 data: 0.0002 [11-22 17:54:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 417/1669] eta: 0:15:39 tlr: 0.00021 tnm: 0.59 Lm: 7.646 (7.646) Lt: 7.304 (7.304) Accm: 1.14 (1.14) Acct: 1.69 (1.69) proj_loss: -0.4228 (-0.4228) time: 0.7544 data: 0.0002 [11-22 17:54:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 417/1669] eta: 0:15:39 tlr: 0.00021 tnm: 0.59 Lm: 7.625 (7.625) Lt: 7.302 (7.302) Accm: 1.19 (1.19) Acct: 1.52 (1.52) proj_loss: -0.4271 (-0.4271) time: 0.7544 data: 0.0003 [11-22 17:54:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 417/1669] eta: 0:15:39 tlr: 0.00021 tnm: 0.59 Lm: 7.553 (7.553) Lt: 7.172 (7.172) Accm: 1.19 (1.19) Acct: 1.64 (1.64) proj_loss: -0.4376 (-0.4376) time: 0.7544 data: 0.0003 [11-22 17:54:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 417/1669] eta: 0:15:39 tlr: 0.00021 tnm: 0.59 Lm: 7.601 (7.601) Lt: 7.204 (7.204) Accm: 1.11 (1.11) Acct: 1.39 (1.39) proj_loss: -0.4246 (-0.4246) time: 0.7544 data: 0.0003 [11-22 18:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 834/1669] eta: 0:11:29 tlr: 0.00022 tnm: 0.72 Lm: 7.518 (7.573) Lt: 7.101 (7.165) Accm: 1.21 (1.15) Acct: 1.69 (1.53) proj_loss: -0.4381 (-0.4291) time: 0.7521 data: 0.0002 [11-22 18:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 834/1669] eta: 0:11:29 tlr: 0.00022 tnm: 0.72 Lm: 7.570 (7.565) Lt: 7.157 (7.180) Accm: 1.24 (1.20) Acct: 1.83 (1.71) proj_loss: -0.4291 (-0.4265) time: 0.7520 data: 0.0003 [11-22 18:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 834/1669] eta: 0:11:29 tlr: 0.00022 tnm: 0.72 Lm: 7.673 (7.630) Lt: 7.308 (7.263) Accm: 1.12 (1.13) Acct: 1.52 (1.53) proj_loss: -0.4322 (-0.4311) time: 0.7521 data: 0.0002 [11-22 18:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 834/1669] eta: 0:11:29 tlr: 0.00022 tnm: 0.72 Lm: 7.582 (7.569) Lt: 7.221 (7.198) Accm: 1.18 (1.28) Acct: 1.86 (1.93) proj_loss: -0.4402 (-0.4286) time: 0.7521 data: 0.0002 [11-22 18:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 834/1669] eta: 0:11:29 tlr: 0.00022 tnm: 0.72 Lm: 7.547 (7.562) Lt: 7.141 (7.147) Accm: 1.35 (1.33) Acct: 1.89 (1.93) proj_loss: -0.4164 (-0.4206) time: 0.7520 data: 0.0002 [11-22 18:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 834/1669] eta: 0:11:29 tlr: 0.00022 tnm: 0.72 Lm: 7.614 (7.565) Lt: 7.261 (7.223) Accm: 1.33 (1.25) Acct: 1.55 (1.57) proj_loss: -0.4285 (-0.4332) time: 0.7521 data: 0.0003 [11-22 18:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 834/1669] eta: 0:11:29 tlr: 0.00022 tnm: 0.72 Lm: 7.559 (7.586) Lt: 7.208 (7.203) Accm: 1.09 (1.11) Acct: 1.58 (1.53) proj_loss: -0.4378 (-0.4389) time: 0.7521 data: 0.0003 [11-22 18:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [ 834/1669] eta: 0:11:29 tlr: 0.00022 tnm: 0.72 Lm: 7.609 (7.535) Lt: 7.290 (7.173) Accm: 1.11 (1.18) Acct: 1.52 (1.61) proj_loss: -0.4361 (-0.4327) time: 0.7521 data: 0.0003 [11-22 18:05:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1251/1669] eta: 0:05:34 tlr: 0.00023 tnm: 0.60 Lm: 7.527 (7.512) Lt: 7.151 (7.133) Accm: 1.25 (1.24) Acct: 1.67 (1.70) proj_loss: -0.4345 (-0.4327) time: 0.7514 data: 0.0003 [11-22 18:05:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1251/1669] eta: 0:05:34 tlr: 0.00023 tnm: 0.60 Lm: 7.577 (7.570) Lt: 7.158 (7.175) Accm: 1.22 (1.20) Acct: 1.70 (1.68) proj_loss: -0.4328 (-0.4334) time: 0.7514 data: 0.0002 [11-22 18:05:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1251/1669] eta: 0:05:34 tlr: 0.00023 tnm: 0.60 Lm: 7.576 (7.577) Lt: 7.169 (7.159) Accm: 1.31 (1.25) Acct: 1.89 (1.84) proj_loss: -0.4246 (-0.4241) time: 0.7514 data: 0.0003 [11-22 18:05:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1251/1669] eta: 0:05:34 tlr: 0.00023 tnm: 0.60 Lm: 7.506 (7.550) Lt: 7.094 (7.121) Accm: 1.22 (1.21) Acct: 1.74 (1.59) proj_loss: -0.4306 (-0.4276) time: 0.7514 data: 0.0002 [11-22 18:05:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1251/1669] eta: 0:05:34 tlr: 0.00023 tnm: 0.60 Lm: 7.530 (7.528) Lt: 7.164 (7.159) Accm: 1.33 (1.27) Acct: 1.62 (1.65) proj_loss: -0.4370 (-0.4374) time: 0.7514 data: 0.0003 [11-22 18:05:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1251/1669] eta: 0:05:34 tlr: 0.00023 tnm: 0.60 Lm: 7.582 (7.591) Lt: 7.194 (7.197) Accm: 1.09 (1.10) Acct: 1.46 (1.48) proj_loss: -0.4376 (-0.4284) time: 0.7514 data: 0.0003 [11-22 18:05:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1251/1669] eta: 0:05:34 tlr: 0.00023 tnm: 0.60 Lm: 7.498 (7.528) Lt: 7.103 (7.130) Accm: 1.37 (1.35) Acct: 2.07 (2.01) proj_loss: -0.4403 (-0.4346) time: 0.7514 data: 0.0003 [11-22 18:05:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1251/1669] eta: 0:05:34 tlr: 0.00023 tnm: 0.60 Lm: 7.596 (7.588) Lt: 7.231 (7.212) Accm: 1.17 (1.21) Acct: 1.64 (1.66) proj_loss: -0.4330 (-0.4319) time: 0.7514 data: 0.0003 [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.65 Lm: 7.519 (7.569) Lt: 7.155 (7.188) Accm: 1.18 (1.20) Acct: 1.69 (1.67) proj_loss: -0.4338 (-0.4378) time: 0.7519 data: 0.0019 [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 6/350] Total time: 0:21:56 (0.789 s / it) [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.65 Lm: 7.495 (7.523) Lt: 7.087 (7.089) Accm: 1.24 (1.29) Acct: 1.79 (1.77) proj_loss: -0.4362 (-0.4293) time: 0.7519 data: 0.0015 [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.65 Lm: 7.570 (7.556) Lt: 7.157 (7.161) Accm: 1.24 (1.22) Acct: 1.69 (1.68) proj_loss: -0.4365 (-0.4403) time: 0.7520 data: 0.0018 [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.65 Lm: 7.614 (7.547) Lt: 7.207 (7.169) Accm: 1.33 (1.25) Acct: 1.58 (1.64) proj_loss: -0.4455 (-0.4406) time: 0.7519 data: 0.0019 [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.65 Lm: 7.547 (7.555) Lt: 7.141 (7.137) Accm: 1.27 (1.25) Acct: 1.89 (1.78) proj_loss: -0.4212 (-0.4235) time: 0.7519 data: 0.0020 [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.65 Lm: 7.415 (7.503) Lt: 6.985 (7.084) Accm: 1.31 (1.34) Acct: 1.86 (1.96) proj_loss: -0.4404 (-0.4369) time: 0.7520 data: 0.0014 [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.65 Lm: 7.559 (7.547) Lt: 7.180 (7.131) Accm: 1.09 (1.24) Acct: 1.58 (1.71) proj_loss: -0.4375 (-0.4287) time: 0.7519 data: 0.0014 [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 6/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.65 Lm: 7.445 (7.498) Lt: 7.013 (7.096) Accm: 1.38 (1.29) Acct: 1.83 (1.79) proj_loss: -0.4361 (-0.4366) time: 0.7520 data: 0.0016 [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 6/350] Total time: 0:21:56 (0.789 s / it) [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 6/350] Total time: 0:21:56 (0.789 s / it) [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 6/350] Total time: 0:21:56 (0.789 s / it) [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 6/350] Total time: 0:21:56 (0.789 s / it) [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 6/350] Total time: 0:21:56 (0.789 s / it) [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 6/350] Total time: 0:21:56 (0.789 s / it) [11-22 18:11:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 6/350] Total time: 0:21:56 (0.789 s / it) [11-22 18:11:00] (/home/user/VAR/train.py , line 276)=> [ep6] (training ) Lm: 7.538 (7.538), Lt: 7.124 (7.124), Acc m&t: 1.23 1.74, Remain: 5 days, 0:21:04, Finish: 2024-11-27 02:32 [11-22 18:11:00] (/home/user/VAR/train.py , line 276)=> [ep6] (training ) Lm: 7.538 (7.538), Lt: 7.124 (7.124), Acc m&t: 1.23 1.74, Remain: 5 days, 0:19:21, Finish: 2024-11-27 02:30 [11-22 18:11:00] (/home/user/VAR/train.py , line 276)=> [ep6] (training ) Lm: 7.538 (7.538), Lt: 7.124 (7.124), Acc m&t: 1.23 1.74, Remain: 5 days, 0:21:06, Finish: 2024-11-27 02:32 [11-22 18:11:00] (/home/user/VAR/train.py , line 276)=> [ep6] (training ) Lm: 7.538 (7.538), Lt: 7.124 (7.124), Acc m&t: 1.23 1.74, Remain: 5 days, 0:19:04, Finish: 2024-11-27 02:30 [11-22 18:11:00] (/home/user/VAR/train.py , line 276)=> [ep6] (training ) Lm: 7.538 (7.538), Lt: 7.124 (7.124), Acc m&t: 1.23 1.74, Remain: 5 days, 0:20:09, Finish: 2024-11-27 02:31 [11-22 18:11:00] (/home/user/VAR/train.py , line 276)=> [ep6] (training ) Lm: 7.538 (7.538), Lt: 7.124 (7.124), Acc m&t: 1.23 1.74, Remain: 5 days, 0:21:51, Finish: 2024-11-27 02:32 [11-22 18:11:00] (/home/user/VAR/train.py , line 276)=> [ep6] (training ) Lm: 7.538 (7.538), Lt: 7.124 (7.124), Acc m&t: 1.23 1.74, Remain: 5 days, 0:21:37, Finish: 2024-11-27 02:32 [11-22 18:11:00] (/home/user/VAR/train.py , line 276)=> [ep6] (training ) Lm: 7.538 (7.538), Lt: 7.124 (7.124), Acc m&t: 1.23 1.74, Remain: 5 days, 0:19:37, Finish: 2024-11-27 02:30 [11-22 18:11:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 0/1669] eta: 0:20:00 tlr: 0.00024 tnm: 0.60 Lm: 7.441 (7.441) Lt: 6.978 (6.978) Accm: 1.85 (1.85) Acct: 2.44 (2.44) proj_loss: -0.4831 (-0.4831) time: 0.7191 data: 0.0003 [11-22 18:11:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 0/1669] eta: 0:20:03 tlr: 0.00024 tnm: 0.60 Lm: 7.578 (7.578) Lt: 7.136 (7.136) Accm: 1.06 (1.06) Acct: 1.55 (1.55) proj_loss: -0.4135 (-0.4135) time: 0.7210 data: 0.0004 [11-22 18:11:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 0/1669] eta: 0:20:01 tlr: 0.00024 tnm: 0.60 Lm: 7.432 (7.432) Lt: 6.952 (6.952) Accm: 1.46 (1.46) Acct: 2.31 (2.31) proj_loss: -0.4572 (-0.4572) time: 0.7199 data: 0.0003 [11-22 18:11:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 0/1669] eta: 0:20:03 tlr: 0.00024 tnm: 0.60 Lm: 7.426 (7.426) Lt: 7.014 (7.014) Accm: 1.25 (1.25) Acct: 1.83 (1.83) proj_loss: -0.4436 (-0.4436) time: 0.7209 data: 0.0003 [11-22 18:11:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 0/1669] eta: 0:20:03 tlr: 0.00024 tnm: 0.60 Lm: 7.496 (7.496) Lt: 7.063 (7.063) Accm: 1.24 (1.24) Acct: 1.86 (1.86) proj_loss: -0.4517 (-0.4517) time: 0.7211 data: 0.0004 [11-22 18:11:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 0/1669] eta: 0:20:03 tlr: 0.00024 tnm: 0.60 Lm: 7.303 (7.303) Lt: 6.840 (6.840) Accm: 1.49 (1.49) Acct: 1.89 (1.89) proj_loss: -0.4364 (-0.4364) time: 0.7213 data: 0.0003 [11-22 18:11:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 0/1669] eta: 0:20:02 tlr: 0.00024 tnm: 0.60 Lm: 7.418 (7.418) Lt: 6.881 (6.881) Accm: 1.52 (1.52) Acct: 2.24 (2.24) proj_loss: -0.4294 (-0.4294) time: 0.7207 data: 0.0004 [11-22 18:11:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 0/1669] eta: 0:20:03 tlr: 0.00024 tnm: 0.60 Lm: 7.306 (7.306) Lt: 6.818 (6.818) Accm: 1.59 (1.59) Acct: 2.24 (2.24) proj_loss: -0.4425 (-0.4425) time: 0.7213 data: 0.0004 [11-22 18:16:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 417/1669] eta: 0:15:38 tlr: 0.00024 tnm: 0.68 Lm: 7.478 (7.478) Lt: 7.038 (7.038) Accm: 1.30 (1.30) Acct: 2.03 (2.03) proj_loss: -0.4623 (-0.4623) time: 0.7508 data: 0.0002 [11-22 18:16:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 417/1669] eta: 0:15:38 tlr: 0.00024 tnm: 0.68 Lm: 7.474 (7.474) Lt: 7.006 (7.006) Accm: 1.62 (1.62) Acct: 2.15 (2.15) proj_loss: -0.4674 (-0.4674) time: 0.7508 data: 0.0002 [11-22 18:16:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 417/1669] eta: 0:15:38 tlr: 0.00024 tnm: 0.68 Lm: 7.341 (7.341) Lt: 6.848 (6.848) Accm: 1.60 (1.60) Acct: 2.27 (2.27) proj_loss: -0.4356 (-0.4356) time: 0.7508 data: 0.0003 [11-22 18:16:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 417/1669] eta: 0:15:38 tlr: 0.00024 tnm: 0.68 Lm: 7.444 (7.444) Lt: 6.920 (6.920) Accm: 1.44 (1.44) Acct: 2.20 (2.20) proj_loss: -0.4405 (-0.4405) time: 0.7508 data: 0.0002 [11-22 18:16:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 417/1669] eta: 0:15:38 tlr: 0.00024 tnm: 0.68 Lm: 7.514 (7.514) Lt: 7.066 (7.066) Accm: 1.20 (1.20) Acct: 1.72 (1.72) proj_loss: -0.4173 (-0.4173) time: 0.7508 data: 0.0002 [11-22 18:16:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 417/1669] eta: 0:15:38 tlr: 0.00024 tnm: 0.68 Lm: 7.471 (7.471) Lt: 6.999 (6.999) Accm: 1.26 (1.26) Acct: 1.79 (1.79) proj_loss: -0.4516 (-0.4516) time: 0.7508 data: 0.0002 [11-22 18:16:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 417/1669] eta: 0:15:38 tlr: 0.00024 tnm: 0.68 Lm: 7.440 (7.440) Lt: 6.974 (6.974) Accm: 1.22 (1.22) Acct: 1.69 (1.69) proj_loss: -0.4259 (-0.4259) time: 0.7508 data: 0.0002 [11-22 18:16:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 417/1669] eta: 0:15:38 tlr: 0.00024 tnm: 0.68 Lm: 7.458 (7.458) Lt: 7.029 (7.029) Accm: 1.34 (1.34) Acct: 2.01 (2.01) proj_loss: -0.4440 (-0.4440) time: 0.7508 data: 0.0003 [11-22 18:21:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.57 Lm: 7.426 (7.432) Lt: 7.014 (6.991) Accm: 1.43 (1.39) Acct: 2.20 (2.08) proj_loss: -0.4443 (-0.4525) time: 0.7507 data: 0.0003 [11-22 18:21:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.57 Lm: 7.447 (7.437) Lt: 6.935 (6.966) Accm: 1.28 (1.39) Acct: 1.86 (1.99) proj_loss: -0.4515 (-0.4429) time: 0.7506 data: 0.0002 [11-22 18:21:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.57 Lm: 7.441 (7.381) Lt: 6.978 (6.904) Accm: 1.85 (1.72) Acct: 2.44 (2.32) proj_loss: -0.4520 (-0.4623) time: 0.7507 data: 0.0003 [11-22 18:21:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.57 Lm: 7.460 (7.472) Lt: 6.958 (7.011) Accm: 1.19 (1.27) Acct: 1.76 (1.93) proj_loss: -0.4572 (-0.4558) time: 0.7507 data: 0.0002 [11-22 18:21:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.57 Lm: 7.451 (7.445) Lt: 6.996 (6.977) Accm: 1.34 (1.34) Acct: 1.89 (1.92) proj_loss: -0.4211 (-0.4221) time: 0.7507 data: 0.0002 [11-22 18:21:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.57 Lm: 7.471 (7.462) Lt: 6.959 (6.970) Accm: 1.37 (1.41) Acct: 2.17 (2.18) proj_loss: -0.4515 (-0.4489) time: 0.7507 data: 0.0003 [11-22 18:21:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.57 Lm: 7.423 (7.435) Lt: 6.964 (6.971) Accm: 1.46 (1.30) Acct: 1.89 (1.79) proj_loss: -0.4334 (-0.4284) time: 0.7507 data: 0.0002 [11-22 18:21:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.57 Lm: 7.306 (7.323) Lt: 6.861 (6.853) Accm: 1.59 (1.59) Acct: 2.31 (2.30) proj_loss: -0.4425 (-0.4453) time: 0.7507 data: 0.0003 [11-22 18:27:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1251/1669] eta: 0:05:35 tlr: 0.00024 tnm: 0.55 Lm: 7.341 (7.341) Lt: 6.870 (6.864) Accm: 1.58 (1.57) Acct: 2.29 (2.29) proj_loss: -0.4392 (-0.4430) time: 0.7504 data: 0.0003 [11-22 18:27:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1251/1669] eta: 0:05:35 tlr: 0.00024 tnm: 0.55 Lm: 7.439 (7.435) Lt: 6.936 (6.959) Accm: 1.38 (1.41) Acct: 1.96 (2.01) proj_loss: -0.4516 (-0.4495) time: 0.7503 data: 0.0003 [11-22 18:27:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1251/1669] eta: 0:05:35 tlr: 0.00024 tnm: 0.55 Lm: 7.446 (7.460) Lt: 6.957 (6.997) Accm: 1.29 (1.30) Acct: 2.00 (2.01) proj_loss: -0.4604 (-0.4578) time: 0.7504 data: 0.0003 [11-22 18:27:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1251/1669] eta: 0:05:35 tlr: 0.00024 tnm: 0.55 Lm: 7.462 (7.452) Lt: 6.995 (6.982) Accm: 1.38 (1.35) Acct: 1.84 (1.89) proj_loss: -0.4264 (-0.4275) time: 0.7503 data: 0.0002 [11-22 18:27:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1251/1669] eta: 0:05:35 tlr: 0.00024 tnm: 0.55 Lm: 7.400 (7.420) Lt: 6.935 (6.955) Accm: 1.43 (1.32) Acct: 1.76 (1.75) proj_loss: -0.4349 (-0.4335) time: 0.7504 data: 0.0003 [11-22 18:27:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1251/1669] eta: 0:05:35 tlr: 0.00024 tnm: 0.55 Lm: 7.444 (7.426) Lt: 6.920 (6.937) Accm: 1.44 (1.49) Acct: 2.20 (2.28) proj_loss: -0.4503 (-0.4490) time: 0.7503 data: 0.0003 [11-22 18:27:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1251/1669] eta: 0:05:35 tlr: 0.00024 tnm: 0.55 Lm: 7.403 (7.402) Lt: 6.964 (6.944) Accm: 1.46 (1.45) Acct: 2.20 (2.19) proj_loss: -0.4440 (-0.4452) time: 0.7504 data: 0.0003 [11-22 18:27:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1251/1669] eta: 0:05:35 tlr: 0.00024 tnm: 0.55 Lm: 7.448 (7.400) Lt: 6.977 (6.922) Accm: 1.62 (1.61) Acct: 2.15 (2.20) proj_loss: -0.4518 (-0.4545) time: 0.7503 data: 0.0003 [11-22 18:32:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.57 Lm: 7.441 (7.400) Lt: 6.975 (6.899) Accm: 1.40 (1.56) Acct: 2.24 (2.21) proj_loss: -0.4517 (-0.4457) time: 0.7545 data: 0.0018 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 7/350] Total time: 0:21:57 (0.789 s / it) [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.57 Lm: 7.418 (7.414) Lt: 6.892 (6.928) Accm: 1.37 (1.46) Acct: 2.17 (2.16) proj_loss: -0.4490 (-0.4459) time: 0.7545 data: 0.0018 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.57 Lm: 7.431 (7.400) Lt: 6.935 (6.912) Accm: 1.49 (1.44) Acct: 2.07 (2.12) proj_loss: -0.4517 (-0.4537) time: 0.7545 data: 0.0018 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.57 Lm: 7.432 (7.413) Lt: 6.955 (6.951) Accm: 1.38 (1.38) Acct: 2.07 (2.02) proj_loss: -0.4572 (-0.4543) time: 0.7545 data: 0.0018 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.57 Lm: 7.380 (7.358) Lt: 6.913 (6.879) Accm: 1.50 (1.50) Acct: 2.20 (2.29) proj_loss: -0.4443 (-0.4465) time: 0.7545 data: 0.0018 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.57 Lm: 7.451 (7.447) Lt: 6.995 (6.970) Accm: 1.41 (1.37) Acct: 1.89 (1.90) proj_loss: -0.4317 (-0.4318) time: 0.7545 data: 0.0016 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.57 Lm: 7.362 (7.345) Lt: 6.861 (6.848) Accm: 1.59 (1.61) Acct: 2.31 (2.33) proj_loss: -0.4425 (-0.4437) time: 0.7545 data: 0.0020 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 7/350] Total time: 0:21:57 (0.789 s / it) [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 7/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.57 Lm: 7.377 (7.392) Lt: 6.906 (6.914) Accm: 1.46 (1.43) Acct: 1.89 (1.94) proj_loss: -0.4337 (-0.4335) time: 0.7546 data: 0.0016 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 7/350] Total time: 0:21:57 (0.789 s / it) [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 7/350] Total time: 0:21:57 (0.789 s / it) [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 7/350] Total time: 0:21:57 (0.789 s / it) [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 7/350] Total time: 0:21:57 (0.789 s / it) [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 7/350] Total time: 0:21:57 (0.789 s / it) [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 7/350] Total time: 0:21:57 (0.789 s / it) [11-22 18:32:58] (/home/user/VAR/train.py , line 276)=> [ep7] (training ) Lm: 7.390 (7.390), Lt: 6.904 (6.904), Acc m&t: 1.47 2.15, Remain: 5 days, 0:13:19, Finish: 2024-11-27 02:46 [11-22 18:32:58] (/home/user/VAR/train.py , line 276)=> [ep7] (training ) Lm: 7.390 (7.390), Lt: 6.904 (6.904), Acc m&t: 1.47 2.15, Remain: 5 days, 0:12:27, Finish: 2024-11-27 02:45 [11-22 18:32:58] (/home/user/VAR/train.py , line 276)=> [ep7] (training ) Lm: 7.390 (7.390), Lt: 6.904 (6.904), Acc m&t: 1.47 2.15, Remain: 5 days, 0:13:34, Finish: 2024-11-27 02:46 [11-22 18:32:58] (/home/user/VAR/train.py , line 276)=> [ep7] (training ) Lm: 7.390 (7.390), Lt: 6.904 (6.904), Acc m&t: 1.47 2.15, Remain: 5 days, 0:11:56, Finish: 2024-11-27 02:44 [11-22 18:32:58] (/home/user/VAR/train.py , line 276)=> [ep7] (training ) Lm: 7.390 (7.390), Lt: 6.904 (6.904), Acc m&t: 1.47 2.15, Remain: 5 days, 0:12:55, Finish: 2024-11-27 02:45 [11-22 18:32:58] (/home/user/VAR/train.py , line 276)=> [ep7] (training ) Lm: 7.390 (7.390), Lt: 6.904 (6.904), Acc m&t: 1.47 2.15, Remain: 5 days, 0:12:59, Finish: 2024-11-27 02:45 [11-22 18:32:58] (/home/user/VAR/train.py , line 276)=> [ep7] (training ) Lm: 7.390 (7.390), Lt: 6.904 (6.904), Acc m&t: 1.47 2.15, Remain: 5 days, 0:13:54, Finish: 2024-11-27 02:46 [11-22 18:32:58] (/home/user/VAR/train.py , line 276)=> [ep7] (training ) Lm: 7.390 (7.390), Lt: 6.904 (6.904), Acc m&t: 1.47 2.15, Remain: 5 days, 0:13:06, Finish: 2024-11-27 02:46 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 0/1669] eta: 0:20:28 tlr: 0.00024 tnm: 0.60 Lm: 7.364 (7.364) Lt: 6.890 (6.890) Accm: 1.40 (1.40) Acct: 1.96 (1.96) proj_loss: -0.4693 (-0.4693) time: 0.7359 data: 0.0003 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 0/1669] eta: 0:20:30 tlr: 0.00024 tnm: 0.60 Lm: 7.454 (7.454) Lt: 7.011 (7.011) Accm: 1.70 (1.70) Acct: 1.93 (1.93) proj_loss: -0.4412 (-0.4412) time: 0.7371 data: 0.0003 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 0/1669] eta: 0:20:32 tlr: 0.00024 tnm: 0.60 Lm: 7.301 (7.301) Lt: 6.769 (6.769) Accm: 1.62 (1.62) Acct: 2.20 (2.20) proj_loss: -0.4440 (-0.4440) time: 0.7383 data: 0.0004 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 0/1669] eta: 0:21:01 tlr: 0.00024 tnm: 0.60 Lm: 7.416 (7.416) Lt: 6.905 (6.905) Accm: 1.65 (1.65) Acct: 2.48 (2.48) proj_loss: -0.4281 (-0.4281) time: 0.7558 data: 0.0004 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 0/1669] eta: 0:20:29 tlr: 0.00024 tnm: 0.60 Lm: 7.310 (7.310) Lt: 6.822 (6.822) Accm: 1.88 (1.88) Acct: 2.41 (2.41) proj_loss: -0.4784 (-0.4784) time: 0.7367 data: 0.0004 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 0/1669] eta: 0:20:30 tlr: 0.00024 tnm: 0.60 Lm: 7.384 (7.384) Lt: 6.882 (6.882) Accm: 1.44 (1.44) Acct: 2.07 (2.07) proj_loss: -0.4614 (-0.4614) time: 0.7373 data: 0.0004 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 0/1669] eta: 0:20:31 tlr: 0.00024 tnm: 0.60 Lm: 7.286 (7.286) Lt: 6.772 (6.772) Accm: 1.65 (1.65) Acct: 2.38 (2.38) proj_loss: -0.4502 (-0.4502) time: 0.7380 data: 0.0003 [11-22 18:32:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 0/1669] eta: 0:20:33 tlr: 0.00024 tnm: 0.60 Lm: 7.306 (7.306) Lt: 6.756 (6.756) Accm: 1.54 (1.54) Acct: 2.41 (2.41) proj_loss: -0.4376 (-0.4376) time: 0.7390 data: 0.0004 [11-22 18:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.58 Lm: 7.326 (7.326) Lt: 6.847 (6.847) Accm: 1.54 (1.54) Acct: 2.19 (2.19) proj_loss: -0.4555 (-0.4555) time: 0.7512 data: 0.0003 [11-22 18:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.58 Lm: 7.344 (7.344) Lt: 6.843 (6.843) Accm: 1.65 (1.65) Acct: 2.20 (2.20) proj_loss: -0.4560 (-0.4560) time: 0.7512 data: 0.0002 [11-22 18:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.58 Lm: 7.324 (7.324) Lt: 6.793 (6.793) Accm: 1.60 (1.60) Acct: 2.36 (2.36) proj_loss: -0.4638 (-0.4638) time: 0.7512 data: 0.0003 [11-22 18:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.58 Lm: 7.297 (7.297) Lt: 6.734 (6.734) Accm: 1.58 (1.58) Acct: 2.41 (2.41) proj_loss: -0.4371 (-0.4371) time: 0.7512 data: 0.0003 [11-22 18:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.58 Lm: 7.208 (7.208) Lt: 6.664 (6.664) Accm: 1.89 (1.89) Acct: 2.69 (2.69) proj_loss: -0.4639 (-0.4639) time: 0.7512 data: 0.0002 [11-22 18:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.58 Lm: 7.162 (7.162) Lt: 6.620 (6.620) Accm: 2.06 (2.06) Acct: 2.88 (2.88) proj_loss: -0.4638 (-0.4638) time: 0.7512 data: 0.0003 [11-22 18:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.58 Lm: 7.201 (7.201) Lt: 6.686 (6.686) Accm: 1.80 (1.80) Acct: 2.65 (2.65) proj_loss: -0.4613 (-0.4613) time: 0.7512 data: 0.0002 [11-22 18:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.58 Lm: 7.412 (7.412) Lt: 6.895 (6.895) Accm: 1.73 (1.73) Acct: 2.36 (2.36) proj_loss: -0.4499 (-0.4499) time: 0.7511 data: 0.0003 [11-22 18:43:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.52 Lm: 7.408 (7.287) Lt: 6.885 (6.752) Accm: 1.81 (1.81) Acct: 2.48 (2.48) proj_loss: -0.4531 (-0.4510) time: 0.7490 data: 0.0003 [11-22 18:43:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.52 Lm: 7.287 (7.305) Lt: 6.804 (6.824) Accm: 1.56 (1.55) Acct: 2.13 (2.17) proj_loss: -0.4416 (-0.4504) time: 0.7490 data: 0.0003 [11-22 18:43:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.52 Lm: 7.100 (7.162) Lt: 6.603 (6.631) Accm: 1.98 (1.90) Acct: 2.96 (2.75) proj_loss: -0.4575 (-0.4601) time: 0.7490 data: 0.0002 [11-22 18:43:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.52 Lm: 7.309 (7.333) Lt: 6.742 (6.810) Accm: 1.62 (1.64) Acct: 2.48 (2.33) proj_loss: -0.4707 (-0.4678) time: 0.7491 data: 0.0002 [11-22 18:43:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.52 Lm: 7.384 (7.371) Lt: 6.882 (6.853) Accm: 1.44 (1.47) Acct: 2.07 (2.18) proj_loss: -0.4614 (-0.4583) time: 0.7490 data: 0.0003 [11-22 18:43:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.52 Lm: 7.220 (7.181) Lt: 6.718 (6.653) Accm: 1.88 (1.94) Acct: 2.44 (2.73) proj_loss: -0.4493 (-0.4516) time: 0.7491 data: 0.0003 [11-22 18:43:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.52 Lm: 7.129 (7.171) Lt: 6.555 (6.627) Accm: 1.73 (1.84) Acct: 2.62 (2.66) proj_loss: -0.4624 (-0.4634) time: 0.7491 data: 0.0002 [11-22 18:43:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [ 834/1669] eta: 0:10:26 tlr: 0.00024 tnm: 0.52 Lm: 7.298 (7.297) Lt: 6.738 (6.735) Accm: 1.62 (1.66) Acct: 2.41 (2.61) proj_loss: -0.4376 (-0.4527) time: 0.7491 data: 0.0003 [11-22 18:48:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1251/1669] eta: 0:05:13 tlr: 0.00024 tnm: 0.53 Lm: 7.276 (7.263) Lt: 6.791 (6.776) Accm: 1.62 (1.63) Acct: 2.27 (2.41) proj_loss: -0.4456 (-0.4502) time: 0.7524 data: 0.0002 [11-22 18:48:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1251/1669] eta: 0:05:13 tlr: 0.00024 tnm: 0.53 Lm: 7.124 (7.159) Lt: 6.585 (6.615) Accm: 1.84 (1.85) Acct: 2.79 (2.72) proj_loss: -0.4655 (-0.4634) time: 0.7524 data: 0.0003 [11-22 18:48:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1251/1669] eta: 0:05:13 tlr: 0.00024 tnm: 0.53 Lm: 7.331 (7.279) Lt: 6.837 (6.761) Accm: 1.73 (1.74) Acct: 2.36 (2.40) proj_loss: -0.4501 (-0.4500) time: 0.7524 data: 0.0003 [11-22 18:48:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1251/1669] eta: 0:05:13 tlr: 0.00024 tnm: 0.53 Lm: 7.387 (7.376) Lt: 6.878 (6.858) Accm: 1.45 (1.46) Acct: 2.12 (2.18) proj_loss: -0.4545 (-0.4543) time: 0.7524 data: 0.0003 [11-22 18:48:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1251/1669] eta: 0:05:13 tlr: 0.00024 tnm: 0.53 Lm: 7.272 (7.293) Lt: 6.709 (6.763) Accm: 1.65 (1.65) Acct: 2.53 (2.40) proj_loss: -0.4673 (-0.4668) time: 0.7524 data: 0.0002 [11-22 18:48:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1251/1669] eta: 0:05:13 tlr: 0.00024 tnm: 0.53 Lm: 7.302 (7.352) Lt: 6.747 (6.796) Accm: 1.58 (1.58) Acct: 2.41 (2.47) proj_loss: -0.4422 (-0.4512) time: 0.7524 data: 0.0003 [11-22 18:48:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1251/1669] eta: 0:05:13 tlr: 0.00024 tnm: 0.53 Lm: 7.265 (7.249) Lt: 6.770 (6.731) Accm: 1.79 (1.77) Acct: 2.43 (2.51) proj_loss: -0.4432 (-0.4480) time: 0.7524 data: 0.0003 [11-22 18:48:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1251/1669] eta: 0:05:13 tlr: 0.00024 tnm: 0.53 Lm: 7.114 (7.150) Lt: 6.554 (6.591) Accm: 1.94 (1.99) Acct: 2.81 (2.93) proj_loss: -0.4601 (-0.4620) time: 0.7524 data: 0.0002 [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.53 Lm: 7.265 (7.236) Lt: 6.777 (6.735) Accm: 1.69 (1.67) Acct: 2.41 (2.41) proj_loss: -0.4496 (-0.4521) time: 0.7528 data: 0.0022 [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.53 Lm: 7.254 (7.262) Lt: 6.789 (6.739) Accm: 1.75 (1.74) Acct: 2.48 (2.46) proj_loss: -0.4531 (-0.4555) time: 0.7528 data: 0.0020 [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.53 Lm: 7.149 (7.206) Lt: 6.603 (6.678) Accm: 1.69 (1.77) Acct: 2.62 (2.59) proj_loss: -0.4735 (-0.4698) time: 0.7528 data: 0.0015 [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.53 Lm: 7.129 (7.182) Lt: 6.555 (6.613) Accm: 1.76 (1.95) Acct: 2.82 (2.91) proj_loss: -0.4577 (-0.4592) time: 0.7528 data: 0.0015 [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.53 Lm: 7.384 (7.345) Lt: 6.873 (6.808) Accm: 1.46 (1.58) Acct: 2.17 (2.36) proj_loss: -0.4571 (-0.4549) time: 0.7528 data: 0.0020 [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.53 Lm: 7.235 (7.246) Lt: 6.718 (6.715) Accm: 1.75 (1.77) Acct: 2.44 (2.56) proj_loss: -0.4484 (-0.4480) time: 0.7528 data: 0.0018 [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.53 Lm: 7.235 (7.270) Lt: 6.676 (6.737) Accm: 1.68 (1.67) Acct: 2.58 (2.45) proj_loss: -0.4707 (-0.4737) time: 0.7528 data: 0.0016 [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 8/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.53 Lm: 7.306 (7.364) Lt: 6.756 (6.807) Accm: 1.56 (1.58) Acct: 2.41 (2.45) proj_loss: -0.4397 (-0.4489) time: 0.7528 data: 0.0019 [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 8/350] Total time: 0:21:58 (0.790 s / it) [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 8/350] Total time: 0:21:58 (0.790 s / it) [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 8/350] Total time: 0:21:58 (0.790 s / it) [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 8/350] Total time: 0:21:58 (0.790 s / it) [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 8/350] Total time: 0:21:58 (0.790 s / it) [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 8/350] Total time: 0:21:58 (0.790 s / it) [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 8/350] Total time: 0:21:58 (0.790 s / it) [11-22 18:54:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 8/350] Total time: 0:21:58 (0.790 s / it) [11-22 18:54:56] (/home/user/VAR/train.py , line 276)=> [ep8] (training ) Lm: 7.273 (7.273), Lt: 6.728 (6.728), Acc m&t: 1.64 2.46, Remain: 4 days, 23:56:52, Finish: 2024-11-27 02:51 [11-22 18:54:56] (/home/user/VAR/train.py , line 276)=> [ep8] (training ) Lm: 7.273 (7.273), Lt: 6.728 (6.728), Acc m&t: 1.64 2.46, Remain: 4 days, 23:54:00, Finish: 2024-11-27 02:48 [11-22 18:54:56] (/home/user/VAR/train.py , line 276)=> [ep8] (training ) Lm: 7.273 (7.273), Lt: 6.728 (6.728), Acc m&t: 1.64 2.46, Remain: 4 days, 23:54:29, Finish: 2024-11-27 02:49 [11-22 18:54:56] (/home/user/VAR/train.py , line 276)=> [ep8] (training ) Lm: 7.273 (7.273), Lt: 6.728 (6.728), Acc m&t: 1.64 2.46, Remain: 4 days, 23:55:09, Finish: 2024-11-27 02:50 [11-22 18:54:56] (/home/user/VAR/train.py , line 276)=> [ep8] (training ) Lm: 7.273 (7.273), Lt: 6.728 (6.728), Acc m&t: 1.64 2.46, Remain: 4 days, 23:53:59, Finish: 2024-11-27 02:48 [11-22 18:54:56] (/home/user/VAR/train.py , line 276)=> [ep8] (training ) Lm: 7.273 (7.273), Lt: 6.728 (6.728), Acc m&t: 1.64 2.46, Remain: 4 days, 23:54:16, Finish: 2024-11-27 02:49 [11-22 18:54:56] (/home/user/VAR/train.py , line 276)=> [ep8] (training ) Lm: 7.273 (7.273), Lt: 6.728 (6.728), Acc m&t: 1.64 2.46, Remain: 4 days, 23:54:18, Finish: 2024-11-27 02:49 [11-22 18:54:56] (/home/user/VAR/train.py , line 276)=> [ep8] (training ) Lm: 7.273 (7.273), Lt: 6.728 (6.728), Acc m&t: 1.64 2.46, Remain: 4 days, 23:54:09, Finish: 2024-11-27 02:49 [11-22 18:54:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 0/1669] eta: 0:20:59 tlr: 0.00024 tnm: 0.63 Lm: 7.200 (7.200) Lt: 6.673 (6.673) Accm: 1.85 (1.85) Acct: 2.79 (2.79) proj_loss: -0.4513 (-0.4513) time: 0.7549 data: 0.0002 [11-22 18:54:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 0/1669] eta: 0:21:00 tlr: 0.00024 tnm: 0.63 Lm: 7.233 (7.233) Lt: 6.666 (6.666) Accm: 1.57 (1.57) Acct: 2.41 (2.41) proj_loss: -0.4538 (-0.4538) time: 0.7553 data: 0.0004 [11-22 18:54:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 0/1669] eta: 0:21:01 tlr: 0.00024 tnm: 0.63 Lm: 7.197 (7.197) Lt: 6.659 (6.659) Accm: 2.00 (2.00) Acct: 3.10 (3.10) proj_loss: -0.4670 (-0.4670) time: 0.7561 data: 0.0004 [11-22 18:54:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 0/1669] eta: 0:21:00 tlr: 0.00024 tnm: 0.63 Lm: 7.278 (7.278) Lt: 6.727 (6.727) Accm: 1.65 (1.65) Acct: 2.41 (2.41) proj_loss: -0.4652 (-0.4652) time: 0.7552 data: 0.0003 [11-22 18:54:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 0/1669] eta: 0:21:01 tlr: 0.00024 tnm: 0.63 Lm: 7.229 (7.229) Lt: 6.709 (6.709) Accm: 1.81 (1.81) Acct: 2.31 (2.31) proj_loss: -0.4618 (-0.4618) time: 0.7556 data: 0.0004 [11-22 18:54:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 0/1669] eta: 0:21:27 tlr: 0.00024 tnm: 0.63 Lm: 7.310 (7.310) Lt: 6.749 (6.749) Accm: 1.59 (1.59) Acct: 2.31 (2.31) proj_loss: -0.4511 (-0.4511) time: 0.7715 data: 0.0004 [11-22 18:54:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 0/1669] eta: 0:21:01 tlr: 0.00024 tnm: 0.63 Lm: 7.149 (7.149) Lt: 6.469 (6.469) Accm: 1.73 (1.73) Acct: 2.72 (2.72) proj_loss: -0.4538 (-0.4538) time: 0.7557 data: 0.0004 [11-22 18:54:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 0/1669] eta: 0:21:03 tlr: 0.00024 tnm: 0.63 Lm: 7.241 (7.241) Lt: 6.654 (6.654) Accm: 1.56 (1.56) Acct: 2.51 (2.51) proj_loss: -0.4655 (-0.4655) time: 0.7571 data: 0.0004 [11-22 19:00:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.54 Lm: 7.134 (7.134) Lt: 6.549 (6.549) Accm: 1.84 (1.84) Acct: 2.82 (2.82) proj_loss: -0.4642 (-0.4642) time: 0.7490 data: 0.0003 [11-22 19:00:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.54 Lm: 7.297 (7.297) Lt: 6.742 (6.742) Accm: 1.50 (1.50) Acct: 2.29 (2.29) proj_loss: -0.4720 (-0.4720) time: 0.7490 data: 0.0002 [11-22 19:00:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.54 Lm: 7.311 (7.311) Lt: 6.759 (6.759) Accm: 1.57 (1.57) Acct: 2.50 (2.50) proj_loss: -0.4619 (-0.4619) time: 0.7490 data: 0.0002 [11-22 19:00:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.54 Lm: 7.260 (7.260) Lt: 6.686 (6.686) Accm: 1.76 (1.76) Acct: 2.75 (2.75) proj_loss: -0.4548 (-0.4548) time: 0.7490 data: 0.0002 [11-22 19:00:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.54 Lm: 7.087 (7.087) Lt: 6.556 (6.556) Accm: 1.96 (1.96) Acct: 2.94 (2.94) proj_loss: -0.4549 (-0.4549) time: 0.7490 data: 0.0003 [11-22 19:00:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.54 Lm: 7.181 (7.181) Lt: 6.633 (6.633) Accm: 1.71 (1.71) Acct: 2.65 (2.65) proj_loss: -0.4701 (-0.4701) time: 0.7490 data: 0.0003 [11-22 19:00:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.54 Lm: 7.192 (7.192) Lt: 6.576 (6.576) Accm: 1.81 (1.81) Acct: 2.82 (2.82) proj_loss: -0.4616 (-0.4616) time: 0.7490 data: 0.0002 [11-22 19:00:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 417/1669] eta: 0:15:39 tlr: 0.00024 tnm: 0.54 Lm: 7.220 (7.220) Lt: 6.650 (6.650) Accm: 1.86 (1.86) Acct: 2.57 (2.57) proj_loss: -0.4603 (-0.4603) time: 0.7490 data: 0.0003 [11-22 19:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 834/1669] eta: 0:10:27 tlr: 0.00024 tnm: 0.57 Lm: 7.229 (7.251) Lt: 6.709 (6.696) Accm: 1.81 (1.76) Acct: 2.31 (2.46) proj_loss: -0.4618 (-0.4629) time: 0.7516 data: 0.0003 [11-22 19:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 834/1669] eta: 0:10:27 tlr: 0.00024 tnm: 0.57 Lm: 7.223 (7.282) Lt: 6.689 (6.736) Accm: 1.65 (1.59) Acct: 2.20 (2.38) proj_loss: -0.4513 (-0.4551) time: 0.7516 data: 0.0002 [11-22 19:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 834/1669] eta: 0:10:27 tlr: 0.00024 tnm: 0.57 Lm: 7.197 (7.183) Lt: 6.659 (6.642) Accm: 1.92 (1.77) Acct: 2.79 (2.71) proj_loss: -0.4516 (-0.4538) time: 0.7516 data: 0.0003 [11-22 19:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 834/1669] eta: 0:10:27 tlr: 0.00024 tnm: 0.57 Lm: 7.243 (7.234) Lt: 6.646 (6.650) Accm: 1.79 (1.77) Acct: 2.86 (2.79) proj_loss: -0.4521 (-0.4539) time: 0.7516 data: 0.0003 [11-22 19:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 834/1669] eta: 0:10:27 tlr: 0.00024 tnm: 0.57 Lm: 7.233 (7.249) Lt: 6.666 (6.669) Accm: 1.57 (1.69) Acct: 2.41 (2.63) proj_loss: -0.4627 (-0.4689) time: 0.7516 data: 0.0002 [11-22 19:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 834/1669] eta: 0:10:27 tlr: 0.00024 tnm: 0.57 Lm: 7.235 (7.208) Lt: 6.661 (6.604) Accm: 1.73 (1.71) Acct: 2.72 (2.64) proj_loss: -0.4694 (-0.4650) time: 0.7516 data: 0.0003 [11-22 19:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 834/1669] eta: 0:10:27 tlr: 0.00024 tnm: 0.57 Lm: 7.241 (7.175) Lt: 6.653 (6.584) Accm: 1.56 (1.74) Acct: 2.51 (2.72) proj_loss: -0.4628 (-0.4609) time: 0.7516 data: 0.0003 [11-22 19:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [ 834/1669] eta: 0:10:27 tlr: 0.00024 tnm: 0.57 Lm: 7.211 (7.191) Lt: 6.688 (6.651) Accm: 1.59 (1.66) Acct: 2.31 (2.40) proj_loss: -0.4576 (-0.4660) time: 0.7516 data: 0.0003 [11-22 19:10:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.51 Lm: 7.260 (7.224) Lt: 6.718 (6.689) Accm: 1.57 (1.58) Acct: 2.10 (2.26) proj_loss: -0.4544 (-0.4571) time: 0.7536 data: 0.0003 [11-22 19:10:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.51 Lm: 7.239 (7.275) Lt: 6.681 (6.714) Accm: 1.71 (1.64) Acct: 2.41 (2.44) proj_loss: -0.4464 (-0.4512) time: 0.7536 data: 0.0002 [11-22 19:10:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.51 Lm: 7.233 (7.247) Lt: 6.686 (6.688) Accm: 1.77 (1.76) Acct: 2.57 (2.55) proj_loss: -0.4649 (-0.4682) time: 0.7536 data: 0.0002 [11-22 19:10:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.51 Lm: 7.206 (7.191) Lt: 6.643 (6.638) Accm: 1.82 (1.76) Acct: 2.86 (2.76) proj_loss: -0.4476 (-0.4513) time: 0.7536 data: 0.0003 [11-22 19:10:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.51 Lm: 7.192 (7.201) Lt: 6.594 (6.610) Accm: 1.82 (1.81) Acct: 2.86 (2.80) proj_loss: -0.4764 (-0.4779) time: 0.7536 data: 0.0003 [11-22 19:10:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.51 Lm: 7.192 (7.183) Lt: 6.574 (6.575) Accm: 1.75 (1.73) Acct: 2.63 (2.62) proj_loss: -0.4707 (-0.4692) time: 0.7536 data: 0.0002 [11-22 19:10:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.51 Lm: 7.211 (7.192) Lt: 6.611 (6.589) Accm: 1.84 (1.83) Acct: 2.96 (2.86) proj_loss: -0.4516 (-0.4532) time: 0.7536 data: 0.0002 [11-22 19:10:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.51 Lm: 7.170 (7.156) Lt: 6.568 (6.559) Accm: 1.79 (1.81) Acct: 2.82 (2.82) proj_loss: -0.4642 (-0.4661) time: 0.7536 data: 0.0003 [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.50 Lm: 7.152 (7.188) Lt: 6.522 (6.592) Accm: 1.73 (1.79) Acct: 3.03 (2.84) proj_loss: -0.4689 (-0.4761) time: 0.7511 data: 0.0019 [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.50 Lm: 7.223 (7.249) Lt: 6.673 (6.669) Accm: 1.78 (1.68) Acct: 2.62 (2.54) proj_loss: -0.4415 (-0.4472) time: 0.7511 data: 0.0016 [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.50 Lm: 7.180 (7.156) Lt: 6.577 (6.548) Accm: 1.88 (1.87) Acct: 3.06 (2.91) proj_loss: -0.4510 (-0.4525) time: 0.7511 data: 0.0016 [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.50 Lm: 7.215 (7.241) Lt: 6.659 (6.691) Accm: 1.72 (1.67) Acct: 2.79 (2.62) proj_loss: -0.4516 (-0.4548) time: 0.7511 data: 0.0014 [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.50 Lm: 7.241 (7.175) Lt: 6.653 (6.580) Accm: 1.56 (1.76) Acct: 2.51 (2.73) proj_loss: -0.4628 (-0.4608) time: 0.7511 data: 0.0016 [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.50 Lm: 7.229 (7.181) Lt: 6.663 (6.605) Accm: 1.81 (1.86) Acct: 2.82 (2.78) proj_loss: -0.4680 (-0.4764) time: 0.7511 data: 0.0015 [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.50 Lm: 7.200 (7.186) Lt: 6.538 (6.567) Accm: 1.73 (1.72) Acct: 2.72 (2.66) proj_loss: -0.4694 (-0.4676) time: 0.7511 data: 0.0014 [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 9/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.50 Lm: 7.211 (7.196) Lt: 6.688 (6.647) Accm: 1.59 (1.65) Acct: 2.31 (2.44) proj_loss: -0.4576 (-0.4607) time: 0.7511 data: 0.0015 [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 9/350] Total time: 0:20:54 (0.752 s / it) [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 9/350] Total time: 0:20:54 (0.752 s / it) [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 9/350] Total time: 0:20:54 (0.752 s / it) [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 9/350] Total time: 0:20:54 (0.752 s / it) [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 9/350] Total time: 0:20:54 (0.752 s / it) [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 9/350] Total time: 0:20:54 (0.752 s / it) [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 9/350] Total time: 0:20:54 (0.752 s / it) [11-22 19:15:51] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 9/350] Total time: 0:20:54 (0.752 s / it) [11-22 19:20:06] (me/user/VAR/evaluator.py, line 590)=> downloading InceptionV3 model... [11-22 19:21:19] (home/user/VAR/trainer.py, line 114)=> FID: 13.107022376480984 [11-22 19:21:20] (/home/user/VAR/train.py , line 259)=> [*] [ep9] (val 50000) Lm: 7.1798, Lt: 6.5999, Acc m&t: 1.79 2.73, Val cost: 328.91s [11-22 19:21:20] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-22 19:21:55] (/home/user/VAR/train.py , line 276)=> [ep9] (training ) Lm: 7.180 (7.180), Lt: 6.600 (6.600), Acc m&t: 1.79 2.73, Remain: 4 days, 23:15:28, Finish: 2024-11-27 02:31 [11-22 19:21:55] (/home/user/VAR/train.py , line 276)=> [ep9] (training ) Lm: 7.180 (7.180), Lt: 6.600 (6.600), Acc m&t: 1.79 2.73, Remain: 4 days, 23:15:58, Finish: 2024-11-27 02:31 [11-22 19:21:55] (/home/user/VAR/train.py , line 276)=> [ep9] (training ) Lm: 7.180 (7.180), Lt: 6.600 (6.600), Acc m&t: 1.79 2.73, Remain: 4 days, 23:15:44, Finish: 2024-11-27 02:31 [11-22 19:21:55] (/home/user/VAR/train.py , line 276)=> [ep9] (training ) Lm: 7.180 (7.180), Lt: 6.600 (6.600), Acc m&t: 1.79 2.73, Remain: 4 days, 23:15:52, Finish: 2024-11-27 02:31 [11-22 19:21:55] (/home/user/VAR/train.py , line 276)=> [ep9] (training ) Lm: 7.180 (7.180), Lt: 6.600 (6.600), Acc m&t: 1.79 2.73, Remain: 4 days, 23:15:45, Finish: 2024-11-27 02:31 [11-22 19:21:55] (/home/user/VAR/train.py , line 276)=> [ep9] (training ) Lm: 7.180 (7.180), Lt: 6.600 (6.600), Acc m&t: 1.79 2.73, Remain: 4 days, 23:13:22, Finish: 2024-11-27 02:29 [11-22 19:21:55] (/home/user/VAR/train.py , line 276)=> [ep9] (training ) Lm: 7.180 (7.180), Lt: 6.600 (6.600), Acc m&t: 1.79 2.73, Remain: 4 days, 23:15:51, Finish: 2024-11-27 02:31 [11-22 19:21:55] (/home/user/VAR/train.py , line 276)=> [ep9] (training ) Lm: 7.180 (7.180), Lt: 6.600 (6.600), Acc m&t: 1.79 2.73, Remain: 4 days, 23:15:19, Finish: 2024-11-27 02:31 [11-22 19:21:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 0/1669] eta: 0:22:39 tlr: 0.00024 tnm: 0.49 Lm: 7.071 (7.071) Lt: 6.475 (6.475) Accm: 1.73 (1.73) Acct: 2.48 (2.48) proj_loss: -0.4353 (-0.4353) time: 0.8147 data: 0.0004 [11-22 19:21:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 0/1669] eta: 0:22:37 tlr: 0.00024 tnm: 0.49 Lm: 7.008 (7.008) Lt: 6.402 (6.402) Accm: 2.20 (2.20) Acct: 3.20 (3.20) proj_loss: -0.4533 (-0.4533) time: 0.8136 data: 0.0004 [11-22 19:21:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 0/1669] eta: 0:23:00 tlr: 0.00024 tnm: 0.49 Lm: 7.198 (7.198) Lt: 6.605 (6.605) Accm: 1.68 (1.68) Acct: 2.24 (2.24) proj_loss: -0.4592 (-0.4592) time: 0.8272 data: 0.0004 [11-22 19:21:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 0/1669] eta: 0:22:38 tlr: 0.00024 tnm: 0.49 Lm: 7.168 (7.168) Lt: 6.523 (6.523) Accm: 2.10 (2.10) Acct: 3.06 (3.06) proj_loss: -0.4583 (-0.4583) time: 0.8138 data: 0.0004 [11-22 19:21:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 0/1669] eta: 0:22:39 tlr: 0.00024 tnm: 0.49 Lm: 6.999 (6.999) Lt: 6.405 (6.405) Accm: 2.32 (2.32) Acct: 3.65 (3.65) proj_loss: -0.4904 (-0.4904) time: 0.8144 data: 0.0004 [11-22 19:21:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 0/1669] eta: 0:22:39 tlr: 0.00024 tnm: 0.49 Lm: 7.309 (7.309) Lt: 6.725 (6.725) Accm: 1.34 (1.34) Acct: 2.07 (2.07) proj_loss: -0.4690 (-0.4690) time: 0.8148 data: 0.0004 [11-22 19:21:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 0/1669] eta: 0:22:40 tlr: 0.00024 tnm: 0.49 Lm: 7.226 (7.226) Lt: 6.674 (6.674) Accm: 1.57 (1.57) Acct: 2.38 (2.38) proj_loss: -0.4516 (-0.4516) time: 0.8151 data: 0.0004 [11-22 19:21:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 0/1669] eta: 0:22:41 tlr: 0.00024 tnm: 0.49 Lm: 7.152 (7.152) Lt: 6.637 (6.637) Accm: 1.52 (1.52) Acct: 2.31 (2.31) proj_loss: -0.4567 (-0.4567) time: 0.8157 data: 0.0004 [11-22 19:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.52 Lm: 7.084 (7.084) Lt: 6.468 (6.468) Accm: 1.94 (1.94) Acct: 2.93 (2.93) proj_loss: -0.4517 (-0.4517) time: 0.7540 data: 0.0003 [11-22 19:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.52 Lm: 7.179 (7.179) Lt: 6.584 (6.584) Accm: 1.64 (1.64) Acct: 2.17 (2.17) proj_loss: -0.4575 (-0.4575) time: 0.7540 data: 0.0002 [11-22 19:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.52 Lm: 7.078 (7.078) Lt: 6.471 (6.471) Accm: 1.93 (1.93) Acct: 2.93 (2.93) proj_loss: -0.4752 (-0.4752) time: 0.7540 data: 0.0003 [11-22 19:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.52 Lm: 7.150 (7.150) Lt: 6.573 (6.573) Accm: 1.66 (1.66) Acct: 2.34 (2.34) proj_loss: -0.4466 (-0.4466) time: 0.7540 data: 0.0003 [11-22 19:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.52 Lm: 7.328 (7.328) Lt: 6.768 (6.768) Accm: 1.44 (1.44) Acct: 2.10 (2.10) proj_loss: -0.4661 (-0.4661) time: 0.7541 data: 0.0002 [11-22 19:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.52 Lm: 7.110 (7.110) Lt: 6.556 (6.556) Accm: 1.76 (1.76) Acct: 2.51 (2.51) proj_loss: -0.4679 (-0.4679) time: 0.7541 data: 0.0003 [11-22 19:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.52 Lm: 7.197 (7.197) Lt: 6.641 (6.641) Accm: 1.86 (1.86) Acct: 2.65 (2.65) proj_loss: -0.4605 (-0.4605) time: 0.7540 data: 0.0002 [11-22 19:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.52 Lm: 7.082 (7.082) Lt: 6.441 (6.441) Accm: 1.98 (1.98) Acct: 2.84 (2.84) proj_loss: -0.4664 (-0.4664) time: 0.7541 data: 0.0003 [11-22 19:32:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.49 Lm: 7.168 (7.134) Lt: 6.523 (6.525) Accm: 1.98 (1.98) Acct: 3.06 (2.92) proj_loss: -0.4746 (-0.4739) time: 0.7538 data: 0.0002 [11-22 19:32:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.49 Lm: 7.198 (7.222) Lt: 6.605 (6.641) Accm: 1.60 (1.58) Acct: 2.10 (2.15) proj_loss: -0.4558 (-0.4557) time: 0.7538 data: 0.0003 [11-22 19:32:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.49 Lm: 7.087 (7.081) Lt: 6.476 (6.473) Accm: 1.98 (1.95) Acct: 3.03 (2.96) proj_loss: -0.4600 (-0.4687) time: 0.7538 data: 0.0003 [11-22 19:32:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.49 Lm: 7.159 (7.124) Lt: 6.532 (6.489) Accm: 1.84 (1.91) Acct: 2.65 (2.80) proj_loss: -0.4500 (-0.4476) time: 0.7539 data: 0.0003 [11-22 19:32:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.49 Lm: 7.229 (7.206) Lt: 6.671 (6.636) Accm: 1.59 (1.62) Acct: 2.44 (2.38) proj_loss: -0.4580 (-0.4531) time: 0.7538 data: 0.0003 [11-22 19:32:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.49 Lm: 7.213 (7.203) Lt: 6.645 (6.643) Accm: 1.76 (1.83) Acct: 2.69 (2.66) proj_loss: -0.4693 (-0.4636) time: 0.7539 data: 0.0002 [11-22 19:32:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.49 Lm: 7.309 (7.279) Lt: 6.725 (6.721) Accm: 1.53 (1.64) Acct: 2.13 (2.33) proj_loss: -0.4690 (-0.4781) time: 0.7539 data: 0.0003 [11-22 19:32:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.49 Lm: 7.069 (7.096) Lt: 6.475 (6.521) Accm: 2.00 (1.84) Acct: 2.72 (2.69) proj_loss: -0.4792 (-0.4730) time: 0.7540 data: 0.0003 [11-22 19:37:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.49 Lm: 7.111 (7.116) Lt: 6.556 (6.554) Accm: 1.93 (1.85) Acct: 2.62 (2.64) proj_loss: -0.4812 (-0.4794) time: 0.7515 data: 0.0003 [11-22 19:37:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.49 Lm: 7.202 (7.218) Lt: 6.607 (6.633) Accm: 1.62 (1.59) Acct: 2.17 (2.20) proj_loss: -0.4573 (-0.4565) time: 0.7515 data: 0.0002 [11-22 19:37:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.49 Lm: 7.130 (7.118) Lt: 6.525 (6.497) Accm: 1.91 (1.93) Acct: 2.84 (2.86) proj_loss: -0.4517 (-0.4587) time: 0.7515 data: 0.0002 [11-22 19:37:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.49 Lm: 7.119 (7.098) Lt: 6.506 (6.496) Accm: 1.96 (1.94) Acct: 2.93 (2.93) proj_loss: -0.4746 (-0.4738) time: 0.7515 data: 0.0003 [11-22 19:37:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.49 Lm: 7.297 (7.281) Lt: 6.721 (6.720) Accm: 1.50 (1.60) Acct: 2.26 (2.34) proj_loss: -0.4770 (-0.4798) time: 0.7515 data: 0.0002 [11-22 19:37:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.49 Lm: 7.139 (7.128) Lt: 6.491 (6.508) Accm: 1.92 (1.94) Acct: 2.93 (2.88) proj_loss: -0.4664 (-0.4691) time: 0.7515 data: 0.0003 [11-22 19:37:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.49 Lm: 7.150 (7.169) Lt: 6.573 (6.596) Accm: 1.66 (1.70) Acct: 2.46 (2.53) proj_loss: -0.4527 (-0.4517) time: 0.7515 data: 0.0003 [11-22 19:37:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.49 Lm: 7.191 (7.182) Lt: 6.627 (6.633) Accm: 1.82 (1.84) Acct: 2.67 (2.66) proj_loss: -0.4696 (-0.4678) time: 0.7515 data: 0.0003 [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.46 Lm: 7.168 (7.165) Lt: 6.609 (6.612) Accm: 1.88 (1.93) Acct: 2.69 (2.79) proj_loss: -0.4693 (-0.4646) time: 0.7533 data: 0.0016 [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 10/350] Total time: 0:20:56 (0.753 s / it) [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.46 Lm: 7.150 (7.135) Lt: 6.537 (6.538) Accm: 1.94 (1.89) Acct: 2.82 (2.81) proj_loss: -0.4694 (-0.4729) time: 0.7533 data: 0.0015 [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.46 Lm: 7.198 (7.194) Lt: 6.605 (6.608) Accm: 1.63 (1.65) Acct: 2.24 (2.33) proj_loss: -0.4588 (-0.4621) time: 0.7533 data: 0.0017 [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.46 Lm: 7.110 (7.120) Lt: 6.500 (6.507) Accm: 1.98 (2.00) Acct: 3.06 (3.02) proj_loss: -0.4746 (-0.4749) time: 0.7533 data: 0.0016 [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.46 Lm: 7.138 (7.122) Lt: 6.524 (6.502) Accm: 1.84 (1.86) Acct: 2.65 (2.80) proj_loss: -0.4533 (-0.4643) time: 0.7533 data: 0.0018 [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.46 Lm: 7.284 (7.253) Lt: 6.717 (6.681) Accm: 1.53 (1.71) Acct: 2.38 (2.56) proj_loss: -0.4690 (-0.4736) time: 0.7533 data: 0.0018 [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.46 Lm: 7.071 (7.137) Lt: 6.476 (6.560) Accm: 1.73 (1.79) Acct: 2.48 (2.72) proj_loss: -0.4580 (-0.4552) time: 0.7533 data: 0.0017 [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 10/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.46 Lm: 7.152 (7.146) Lt: 6.637 (6.585) Accm: 1.86 (1.79) Acct: 2.51 (2.58) proj_loss: -0.4792 (-0.4783) time: 0.7533 data: 0.0016 [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 10/350] Total time: 0:20:56 (0.753 s / it) [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 10/350] Total time: 0:20:56 (0.753 s / it) [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 10/350] Total time: 0:20:56 (0.753 s / it) [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 10/350] Total time: 0:20:56 (0.753 s / it) [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 10/350] Total time: 0:20:56 (0.753 s / it) [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 10/350] Total time: 0:20:56 (0.753 s / it) [11-22 19:42:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 10/350] Total time: 0:20:56 (0.753 s / it) [11-22 19:42:52] (/home/user/VAR/train.py , line 276)=> [ep10] (training ) Lm: 7.128 (7.128), Lt: 6.531 (6.531), Acc m&t: 1.91 2.92, Remain: 4 days, 23:18:21, Finish: 2024-11-27 03:01 [11-22 19:42:52] (/home/user/VAR/train.py , line 276)=> [ep10] (training ) Lm: 7.128 (7.128), Lt: 6.531 (6.531), Acc m&t: 1.91 2.92, Remain: 4 days, 23:18:24, Finish: 2024-11-27 03:01 [11-22 19:42:52] (/home/user/VAR/train.py , line 276)=> [ep10] (training ) Lm: 7.128 (7.128), Lt: 6.531 (6.531), Acc m&t: 1.91 2.92, Remain: 4 days, 23:17:44, Finish: 2024-11-27 03:00 [11-22 19:42:52] (/home/user/VAR/train.py , line 276)=> [ep10] (training ) Lm: 7.128 (7.128), Lt: 6.531 (6.531), Acc m&t: 1.91 2.92, Remain: 4 days, 23:18:55, Finish: 2024-11-27 03:01 [11-22 19:42:52] (/home/user/VAR/train.py , line 276)=> [ep10] (training ) Lm: 7.128 (7.128), Lt: 6.531 (6.531), Acc m&t: 1.91 2.92, Remain: 4 days, 23:18:31, Finish: 2024-11-27 03:01 [11-22 19:42:52] (/home/user/VAR/train.py , line 276)=> [ep10] (training ) Lm: 7.128 (7.128), Lt: 6.531 (6.531), Acc m&t: 1.91 2.92, Remain: 4 days, 23:18:43, Finish: 2024-11-27 03:01 [11-22 19:42:52] (/home/user/VAR/train.py , line 276)=> [ep10] (training ) Lm: 7.128 (7.128), Lt: 6.531 (6.531), Acc m&t: 1.91 2.92, Remain: 4 days, 23:18:42, Finish: 2024-11-27 03:01 [11-22 19:42:52] (/home/user/VAR/train.py , line 276)=> [ep10] (training ) Lm: 7.128 (7.128), Lt: 6.531 (6.531), Acc m&t: 1.91 2.92, Remain: 4 days, 23:16:43, Finish: 2024-11-27 02:59 [11-22 19:42:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 0/1669] eta: 1:00:20 tlr: 0.00024 tnm: 0.45 Lm: 7.258 (7.258) Lt: 6.713 (6.713) Accm: 1.46 (1.46) Acct: 2.27 (2.27) proj_loss: -0.4710 (-0.4710) time: 2.1690 data: 0.0004 [11-22 19:42:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 0/1669] eta: 1:00:21 tlr: 0.00024 tnm: 0.45 Lm: 7.108 (7.108) Lt: 6.474 (6.474) Accm: 1.50 (1.50) Acct: 2.41 (2.41) proj_loss: -0.4368 (-0.4368) time: 2.1696 data: 0.0004 [11-22 19:42:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 0/1669] eta: 1:00:20 tlr: 0.00024 tnm: 0.45 Lm: 7.213 (7.213) Lt: 6.598 (6.598) Accm: 1.65 (1.65) Acct: 2.65 (2.65) proj_loss: -0.4782 (-0.4782) time: 2.1695 data: 0.0003 [11-22 19:42:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 0/1669] eta: 1:00:58 tlr: 0.00024 tnm: 0.45 Lm: 7.202 (7.202) Lt: 6.656 (6.656) Accm: 1.70 (1.70) Acct: 2.69 (2.69) proj_loss: -0.4761 (-0.4761) time: 2.1921 data: 0.0003 [11-22 19:42:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 0/1669] eta: 1:00:21 tlr: 0.00024 tnm: 0.45 Lm: 7.027 (7.027) Lt: 6.460 (6.460) Accm: 2.20 (2.20) Acct: 3.41 (3.41) proj_loss: -0.4691 (-0.4691) time: 2.1699 data: 0.0003 [11-22 19:42:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 0/1669] eta: 1:00:24 tlr: 0.00024 tnm: 0.45 Lm: 7.027 (7.027) Lt: 6.321 (6.321) Accm: 1.98 (1.98) Acct: 2.96 (2.96) proj_loss: -0.4606 (-0.4606) time: 2.1717 data: 0.0003 [11-22 19:42:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 0/1669] eta: 1:00:22 tlr: 0.00024 tnm: 0.45 Lm: 7.089 (7.089) Lt: 6.474 (6.474) Accm: 2.23 (2.23) Acct: 3.27 (3.27) proj_loss: -0.4437 (-0.4437) time: 2.1704 data: 0.0003 [11-22 19:42:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 0/1669] eta: 0:20:11 tlr: 0.00024 tnm: 0.45 Lm: 7.084 (7.084) Lt: 6.535 (6.535) Accm: 2.16 (2.16) Acct: 2.89 (2.89) proj_loss: -0.4891 (-0.4891) time: 0.7257 data: 0.0004 [11-22 19:48:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 417/1669] eta: 0:15:45 tlr: 0.00024 tnm: 0.43 Lm: 7.057 (7.057) Lt: 6.446 (6.446) Accm: 1.97 (1.97) Acct: 3.22 (3.22) proj_loss: -0.4626 (-0.4626) time: 0.7513 data: 0.0003 [11-22 19:48:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 417/1669] eta: 0:15:45 tlr: 0.00024 tnm: 0.43 Lm: 7.241 (7.241) Lt: 6.679 (6.679) Accm: 1.54 (1.54) Acct: 2.24 (2.24) proj_loss: -0.4655 (-0.4655) time: 0.7513 data: 0.0003 [11-22 19:48:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 417/1669] eta: 0:15:45 tlr: 0.00024 tnm: 0.43 Lm: 7.062 (7.062) Lt: 6.386 (6.386) Accm: 2.01 (2.01) Acct: 3.08 (3.08) proj_loss: -0.4603 (-0.4603) time: 0.7513 data: 0.0002 [11-22 19:48:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 417/1669] eta: 0:15:45 tlr: 0.00024 tnm: 0.43 Lm: 7.169 (7.169) Lt: 6.570 (6.570) Accm: 1.73 (1.73) Acct: 2.74 (2.74) proj_loss: -0.4785 (-0.4785) time: 0.7513 data: 0.0003 [11-22 19:48:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 417/1669] eta: 0:15:45 tlr: 0.00024 tnm: 0.43 Lm: 7.075 (7.075) Lt: 6.436 (6.436) Accm: 1.73 (1.73) Acct: 2.70 (2.70) proj_loss: -0.4621 (-0.4621) time: 0.7513 data: 0.0003 [11-22 19:48:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.43 Lm: 7.093 (7.093) Lt: 6.487 (6.487) Accm: 2.00 (2.00) Acct: 2.81 (2.81) proj_loss: -0.4658 (-0.4658) time: 0.7513 data: 0.0003 [11-22 19:48:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 417/1669] eta: 0:15:45 tlr: 0.00024 tnm: 0.43 Lm: 7.098 (7.098) Lt: 6.500 (6.500) Accm: 2.05 (2.05) Acct: 3.08 (3.08) proj_loss: -0.4640 (-0.4640) time: 0.7513 data: 0.0002 [11-22 19:48:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 417/1669] eta: 0:15:45 tlr: 0.00024 tnm: 0.43 Lm: 7.164 (7.164) Lt: 6.570 (6.570) Accm: 1.82 (1.82) Acct: 2.88 (2.88) proj_loss: -0.4784 (-0.4784) time: 0.7513 data: 0.0003 [11-22 19:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 834/1669] eta: 0:11:33 tlr: 0.00024 tnm: 0.48 Lm: 7.202 (7.221) Lt: 6.656 (6.647) Accm: 1.70 (1.77) Acct: 2.69 (2.77) proj_loss: -0.4808 (-0.4830) time: 1.0097 data: 0.0003 [11-22 19:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 834/1669] eta: 0:11:33 tlr: 0.00024 tnm: 0.48 Lm: 7.225 (7.147) Lt: 6.646 (6.546) Accm: 1.63 (1.81) Acct: 2.27 (2.82) proj_loss: -0.4710 (-0.4760) time: 1.0097 data: 0.0003 [11-22 19:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 834/1669] eta: 0:11:33 tlr: 0.00024 tnm: 0.48 Lm: 7.087 (7.069) Lt: 6.431 (6.437) Accm: 1.97 (1.97) Acct: 3.20 (3.21) proj_loss: -0.4691 (-0.4675) time: 1.0096 data: 0.0003 [11-22 19:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 834/1669] eta: 0:11:33 tlr: 0.00024 tnm: 0.48 Lm: 7.027 (7.036) Lt: 6.379 (6.384) Accm: 2.04 (2.10) Acct: 3.20 (3.18) proj_loss: -0.4606 (-0.4767) time: 1.0097 data: 0.0003 [11-22 19:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 834/1669] eta: 0:11:33 tlr: 0.00024 tnm: 0.48 Lm: 7.124 (7.151) Lt: 6.543 (6.548) Accm: 1.81 (1.76) Acct: 2.82 (2.81) proj_loss: -0.4782 (-0.4729) time: 1.0096 data: 0.0003 [11-22 19:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 834/1669] eta: 0:11:33 tlr: 0.00024 tnm: 0.48 Lm: 7.108 (7.103) Lt: 6.474 (6.488) Accm: 1.72 (1.72) Acct: 2.44 (2.62) proj_loss: -0.4875 (-0.4713) time: 1.0097 data: 0.0003 [11-22 19:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 834/1669] eta: 0:11:33 tlr: 0.00024 tnm: 0.48 Lm: 7.089 (7.008) Lt: 6.474 (6.394) Accm: 2.23 (2.27) Acct: 3.27 (3.46) proj_loss: -0.4819 (-0.4700) time: 1.0096 data: 0.0002 [11-22 19:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [ 834/1669] eta: 0:11:31 tlr: 0.00024 tnm: 0.48 Lm: 7.102 (7.098) Lt: 6.483 (6.486) Accm: 1.86 (1.96) Acct: 2.89 (2.87) proj_loss: -0.4568 (-0.4628) time: 1.0097 data: 0.0003 [11-22 19:59:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1251/1669] eta: 0:05:36 tlr: 0.00024 tnm: 0.46 Lm: 7.104 (7.126) Lt: 6.509 (6.516) Accm: 1.86 (1.90) Acct: 2.81 (2.83) proj_loss: -0.4723 (-0.4690) time: 0.7526 data: 0.0003 [11-22 19:59:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1251/1669] eta: 0:05:37 tlr: 0.00024 tnm: 0.46 Lm: 7.090 (7.090) Lt: 6.446 (6.463) Accm: 1.85 (1.89) Acct: 3.12 (3.00) proj_loss: -0.4732 (-0.4715) time: 0.7525 data: 0.0002 [11-22 19:59:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1251/1669] eta: 0:05:37 tlr: 0.00024 tnm: 0.46 Lm: 7.092 (7.086) Lt: 6.477 (6.486) Accm: 1.99 (1.97) Acct: 3.08 (3.09) proj_loss: -0.4809 (-0.4797) time: 0.7525 data: 0.0002 [11-22 19:59:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1251/1669] eta: 0:05:37 tlr: 0.00024 tnm: 0.46 Lm: 7.145 (7.154) Lt: 6.532 (6.541) Accm: 1.81 (1.78) Acct: 2.82 (2.82) proj_loss: -0.4700 (-0.4697) time: 0.7526 data: 0.0008 [11-22 19:59:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1251/1669] eta: 0:05:37 tlr: 0.00024 tnm: 0.46 Lm: 7.081 (7.091) Lt: 6.436 (6.462) Accm: 1.83 (1.78) Acct: 2.72 (2.71) proj_loss: -0.4863 (-0.4748) time: 0.7525 data: 0.0003 [11-22 19:59:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1251/1669] eta: 0:05:37 tlr: 0.00024 tnm: 0.46 Lm: 7.006 (7.014) Lt: 6.350 (6.355) Accm: 2.11 (2.12) Acct: 3.29 (3.27) proj_loss: -0.4750 (-0.4799) time: 0.7526 data: 0.0003 [11-22 19:59:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1251/1669] eta: 0:05:37 tlr: 0.00024 tnm: 0.46 Lm: 7.195 (7.213) Lt: 6.656 (6.649) Accm: 1.68 (1.68) Acct: 2.62 (2.66) proj_loss: -0.4814 (-0.4828) time: 0.7526 data: 0.0003 [11-22 19:59:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1251/1669] eta: 0:05:37 tlr: 0.00024 tnm: 0.46 Lm: 7.098 (7.048) Lt: 6.500 (6.441) Accm: 2.05 (2.17) Acct: 3.08 (3.25) proj_loss: -0.4808 (-0.4724) time: 0.7526 data: 0.0003 [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.44 Lm: 7.089 (7.044) Lt: 6.474 (6.436) Accm: 1.88 (2.11) Acct: 2.89 (3.13) proj_loss: -0.4797 (-0.4715) time: 0.7585 data: 0.0021 [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.44 Lm: 7.089 (7.090) Lt: 6.460 (6.472) Accm: 1.97 (1.94) Acct: 3.20 (3.04) proj_loss: -0.4691 (-0.4708) time: 0.7585 data: 0.0019 [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.44 Lm: 7.026 (7.017) Lt: 6.379 (6.365) Accm: 2.11 (2.12) Acct: 3.37 (3.31) proj_loss: -0.4868 (-0.4813) time: 0.7585 data: 0.0017 [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.44 Lm: 7.083 (7.086) Lt: 6.433 (6.476) Accm: 1.84 (1.94) Acct: 2.69 (3.01) proj_loss: -0.4723 (-0.4782) time: 0.7585 data: 0.0016 [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.44 Lm: 7.138 (7.151) Lt: 6.540 (6.541) Accm: 1.82 (1.86) Acct: 2.82 (2.95) proj_loss: -0.4675 (-0.4692) time: 0.7585 data: 0.0017 [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.44 Lm: 7.108 (7.108) Lt: 6.474 (6.495) Accm: 1.85 (1.79) Acct: 2.89 (2.75) proj_loss: -0.4851 (-0.4762) time: 0.7585 data: 0.0019 [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.44 Lm: 7.202 (7.227) Lt: 6.657 (6.671) Accm: 1.66 (1.61) Acct: 2.55 (2.51) proj_loss: -0.4820 (-0.4834) time: 0.7585 data: 0.0015 [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 11/350] Total time: 0:22:05 (0.794 s / it) [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 11/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.44 Lm: 7.102 (7.104) Lt: 6.483 (6.485) Accm: 1.86 (1.92) Acct: 2.89 (2.89) proj_loss: -0.4568 (-0.4636) time: 0.7585 data: 0.0017 [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 11/350] Total time: 0:22:05 (0.794 s / it) [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 11/350] Total time: 0:22:05 (0.794 s / it) [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 11/350] Total time: 0:22:05 (0.794 s / it) [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 11/350] Total time: 0:22:05 (0.794 s / it) [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 11/350] Total time: 0:22:05 (0.794 s / it) [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 11/350] Total time: 0:22:05 (0.794 s / it) [11-22 20:04:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 11/350] Total time: 0:22:03 (0.793 s / it) [11-22 20:04:57] (/home/user/VAR/train.py , line 276)=> [ep11] (training ) Lm: 7.097 (7.097), Lt: 6.487 (6.487), Acc m&t: 1.96 3.01, Remain: 4 days, 23:35:33, Finish: 2024-11-27 03:40 [11-22 20:04:57] (/home/user/VAR/train.py , line 276)=> [ep11] (training ) Lm: 7.097 (7.097), Lt: 6.487 (6.487), Acc m&t: 1.96 3.01, Remain: 4 days, 23:38:57, Finish: 2024-11-27 03:43 [11-22 20:04:57] (/home/user/VAR/train.py , line 276)=> [ep11] (training ) Lm: 7.097 (7.097), Lt: 6.487 (6.487), Acc m&t: 1.96 3.01, Remain: 4 days, 23:38:39, Finish: 2024-11-27 03:43 [11-22 20:04:57] (/home/user/VAR/train.py , line 276)=> [ep11] (training ) Lm: 7.097 (7.097), Lt: 6.487 (6.487), Acc m&t: 1.96 3.01, Remain: 4 days, 23:39:19, Finish: 2024-11-27 03:44 [11-22 20:04:57] (/home/user/VAR/train.py , line 276)=> [ep11] (training ) Lm: 7.097 (7.097), Lt: 6.487 (6.487), Acc m&t: 1.96 3.01, Remain: 4 days, 23:39:18, Finish: 2024-11-27 03:44 [11-22 20:04:57] (/home/user/VAR/train.py , line 276)=> [ep11] (training ) Lm: 7.097 (7.097), Lt: 6.487 (6.487), Acc m&t: 1.96 3.01, Remain: 4 days, 23:40:11, Finish: 2024-11-27 03:45 [11-22 20:04:57] (/home/user/VAR/train.py , line 276)=> [ep11] (training ) Lm: 7.097 (7.097), Lt: 6.487 (6.487), Acc m&t: 1.96 3.01, Remain: 4 days, 23:39:41, Finish: 2024-11-27 03:44 [11-22 20:04:57] (/home/user/VAR/train.py , line 276)=> [ep11] (training ) Lm: 7.097 (7.097), Lt: 6.487 (6.487), Acc m&t: 1.96 3.01, Remain: 4 days, 23:39:39, Finish: 2024-11-27 03:44 [11-22 20:04:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 0/1669] eta: 0:20:40 tlr: 0.00024 tnm: 0.46 Lm: 7.040 (7.040) Lt: 6.466 (6.466) Accm: 2.11 (2.11) Acct: 3.34 (3.34) proj_loss: -0.4809 (-0.4809) time: 0.7430 data: 0.0003 [11-22 20:04:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 0/1669] eta: 0:20:42 tlr: 0.00024 tnm: 0.46 Lm: 7.096 (7.096) Lt: 6.459 (6.459) Accm: 2.24 (2.24) Acct: 3.65 (3.65) proj_loss: -0.4564 (-0.4564) time: 0.7445 data: 0.0004 [11-22 20:04:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 0/1669] eta: 0:20:43 tlr: 0.00024 tnm: 0.46 Lm: 7.243 (7.243) Lt: 6.614 (6.614) Accm: 1.70 (1.70) Acct: 2.69 (2.69) proj_loss: -0.4781 (-0.4781) time: 0.7450 data: 0.0003 [11-22 20:04:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 0/1669] eta: 0:20:42 tlr: 0.00024 tnm: 0.46 Lm: 6.988 (6.988) Lt: 6.321 (6.321) Accm: 2.40 (2.40) Acct: 3.86 (3.86) proj_loss: -0.4688 (-0.4688) time: 0.7445 data: 0.0004 [11-22 20:04:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 0/1669] eta: 0:20:43 tlr: 0.00024 tnm: 0.46 Lm: 7.232 (7.232) Lt: 6.688 (6.688) Accm: 1.86 (1.86) Acct: 3.20 (3.20) proj_loss: -0.4747 (-0.4747) time: 0.7448 data: 0.0004 [11-22 20:04:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 0/1669] eta: 0:20:42 tlr: 0.00024 tnm: 0.46 Lm: 7.154 (7.154) Lt: 6.562 (6.562) Accm: 2.27 (2.27) Acct: 3.17 (3.17) proj_loss: -0.5236 (-0.5236) time: 0.7444 data: 0.0004 [11-22 20:04:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 0/1669] eta: 0:20:48 tlr: 0.00024 tnm: 0.46 Lm: 7.252 (7.252) Lt: 6.688 (6.688) Accm: 1.65 (1.65) Acct: 2.48 (2.48) proj_loss: -0.4574 (-0.4574) time: 0.7482 data: 0.0004 [11-22 20:04:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 0/1669] eta: 0:20:44 tlr: 0.00024 tnm: 0.46 Lm: 7.010 (7.010) Lt: 6.360 (6.360) Accm: 2.11 (2.11) Acct: 3.13 (3.13) proj_loss: -0.5113 (-0.5113) time: 0.7458 data: 0.0003 [11-22 20:10:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 417/1669] eta: 0:15:45 tlr: 0.00024 tnm: 0.50 Lm: 7.036 (7.036) Lt: 6.413 (6.413) Accm: 2.10 (2.10) Acct: 3.32 (3.32) proj_loss: -0.4823 (-0.4823) time: 0.7558 data: 0.0003 [11-22 20:10:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 417/1669] eta: 0:15:45 tlr: 0.00024 tnm: 0.50 Lm: 7.149 (7.149) Lt: 6.586 (6.586) Accm: 1.99 (1.99) Acct: 2.98 (2.98) proj_loss: -0.4774 (-0.4774) time: 0.7558 data: 0.0003 [11-22 20:10:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 417/1669] eta: 0:15:45 tlr: 0.00024 tnm: 0.50 Lm: 7.163 (7.163) Lt: 6.573 (6.573) Accm: 1.87 (1.87) Acct: 2.58 (2.58) proj_loss: -0.4965 (-0.4965) time: 0.7558 data: 0.0003 [11-22 20:10:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 417/1669] eta: 0:15:45 tlr: 0.00024 tnm: 0.50 Lm: 7.004 (7.004) Lt: 6.417 (6.417) Accm: 2.42 (2.42) Acct: 3.68 (3.68) proj_loss: -0.4835 (-0.4835) time: 0.7558 data: 0.0003 [11-22 20:10:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 417/1669] eta: 0:15:45 tlr: 0.00024 tnm: 0.50 Lm: 7.066 (7.066) Lt: 6.442 (6.442) Accm: 2.02 (2.02) Acct: 3.27 (3.27) proj_loss: -0.4695 (-0.4695) time: 0.7558 data: 0.0003 [11-22 20:10:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 417/1669] eta: 0:15:45 tlr: 0.00024 tnm: 0.50 Lm: 7.147 (7.147) Lt: 6.528 (6.528) Accm: 2.16 (2.16) Acct: 3.29 (3.29) proj_loss: -0.4554 (-0.4554) time: 0.7558 data: 0.0003 [11-22 20:10:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 417/1669] eta: 0:15:45 tlr: 0.00024 tnm: 0.50 Lm: 7.083 (7.083) Lt: 6.457 (6.457) Accm: 1.97 (1.97) Acct: 2.91 (2.91) proj_loss: -0.4964 (-0.4964) time: 0.7558 data: 0.0003 [11-22 20:10:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 417/1669] eta: 0:15:45 tlr: 0.00024 tnm: 0.50 Lm: 7.117 (7.117) Lt: 6.501 (6.501) Accm: 1.89 (1.89) Acct: 2.74 (2.74) proj_loss: -0.4720 (-0.4720) time: 0.7558 data: 0.0003 [11-22 20:15:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 834/1669] eta: 0:10:30 tlr: 0.00024 tnm: 0.43 Lm: 7.040 (7.111) Lt: 6.466 (6.513) Accm: 2.11 (2.05) Acct: 3.24 (3.06) proj_loss: -0.4809 (-0.4822) time: 0.7522 data: 0.0002 [11-22 20:15:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 834/1669] eta: 0:10:30 tlr: 0.00024 tnm: 0.43 Lm: 7.113 (7.082) Lt: 6.483 (6.456) Accm: 1.70 (1.91) Acct: 2.69 (3.05) proj_loss: -0.4610 (-0.4624) time: 0.7522 data: 0.0003 [11-22 20:15:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 834/1669] eta: 0:10:30 tlr: 0.00024 tnm: 0.43 Lm: 7.084 (7.055) Lt: 6.375 (6.400) Accm: 2.11 (2.10) Acct: 3.37 (3.34) proj_loss: -0.4688 (-0.4682) time: 0.7522 data: 0.0003 [11-22 20:15:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 834/1669] eta: 0:10:30 tlr: 0.00024 tnm: 0.43 Lm: 7.102 (7.037) Lt: 6.434 (6.423) Accm: 2.04 (2.29) Acct: 3.27 (3.55) proj_loss: -0.4747 (-0.4767) time: 0.7522 data: 0.0003 [11-22 20:15:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 834/1669] eta: 0:10:30 tlr: 0.00024 tnm: 0.43 Lm: 7.198 (7.165) Lt: 6.598 (6.558) Accm: 2.07 (2.04) Acct: 2.93 (3.15) proj_loss: -0.4564 (-0.4650) time: 0.7522 data: 0.0003 [11-22 20:15:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 834/1669] eta: 0:10:30 tlr: 0.00024 tnm: 0.43 Lm: 7.252 (7.182) Lt: 6.688 (6.574) Accm: 1.89 (1.89) Acct: 2.96 (2.81) proj_loss: -0.4740 (-0.4727) time: 0.7522 data: 0.0003 [11-22 20:15:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 834/1669] eta: 0:10:30 tlr: 0.00024 tnm: 0.43 Lm: 7.154 (7.103) Lt: 6.562 (6.507) Accm: 2.23 (1.99) Acct: 3.13 (2.77) proj_loss: -0.4904 (-0.4945) time: 0.7523 data: 0.0003 [11-22 20:15:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [ 834/1669] eta: 0:10:30 tlr: 0.00024 tnm: 0.43 Lm: 7.155 (7.144) Lt: 6.554 (6.541) Accm: 1.84 (1.85) Acct: 2.82 (2.88) proj_loss: -0.4877 (-0.4935) time: 0.7522 data: 0.0003 [11-22 20:20:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.39 Lm: 7.089 (7.082) Lt: 6.440 (6.434) Accm: 2.13 (2.11) Acct: 3.37 (3.35) proj_loss: -0.4823 (-0.4762) time: 0.7536 data: 0.0002 [11-22 20:20:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.39 Lm: 7.149 (7.148) Lt: 6.586 (6.575) Accm: 1.99 (1.98) Acct: 2.93 (2.94) proj_loss: -0.4819 (-0.4824) time: 0.7536 data: 0.0003 [11-22 20:20:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.39 Lm: 7.147 (7.122) Lt: 6.528 (6.507) Accm: 2.08 (2.05) Acct: 3.13 (3.19) proj_loss: -0.4704 (-0.4703) time: 0.7536 data: 0.0002 [11-22 20:20:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.39 Lm: 7.125 (7.095) Lt: 6.544 (6.493) Accm: 1.83 (1.92) Acct: 2.89 (3.06) proj_loss: -0.4695 (-0.4752) time: 0.7536 data: 0.0003 [11-22 20:20:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.39 Lm: 7.083 (7.098) Lt: 6.457 (6.476) Accm: 1.97 (1.97) Acct: 2.98 (3.15) proj_loss: -0.4846 (-0.4854) time: 0.7536 data: 0.0003 [11-22 20:20:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.39 Lm: 7.081 (7.080) Lt: 6.468 (6.471) Accm: 2.25 (2.07) Acct: 3.15 (2.91) proj_loss: -0.4913 (-0.4939) time: 0.7536 data: 0.0003 [11-22 20:20:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.39 Lm: 7.076 (7.040) Lt: 6.473 (6.445) Accm: 2.05 (2.23) Acct: 3.32 (3.50) proj_loss: -0.4720 (-0.4748) time: 0.7536 data: 0.0003 [11-22 20:20:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.39 Lm: 7.127 (7.137) Lt: 6.507 (6.512) Accm: 1.93 (1.91) Acct: 2.98 (2.91) proj_loss: -0.4720 (-0.4720) time: 0.7536 data: 0.0002 [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.39 Lm: 7.097 (7.129) Lt: 6.462 (6.502) Accm: 1.89 (1.86) Acct: 2.96 (2.82) proj_loss: -0.4740 (-0.4782) time: 1.1468 data: 0.0016 [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.39 Lm: 7.134 (7.145) Lt: 6.574 (6.575) Accm: 1.86 (1.91) Acct: 2.82 (2.91) proj_loss: -0.4829 (-0.4841) time: 1.1468 data: 0.0015 [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.39 Lm: 7.096 (7.101) Lt: 6.459 (6.479) Accm: 2.10 (2.08) Acct: 3.34 (3.25) proj_loss: -0.4797 (-0.4722) time: 1.1468 data: 0.0016 [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.39 Lm: 7.084 (7.081) Lt: 6.423 (6.432) Accm: 2.11 (2.08) Acct: 3.37 (3.29) proj_loss: -0.4957 (-0.4807) time: 1.1468 data: 0.0019 [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 12/350] Total time: 0:22:13 (0.799 s / it) [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.39 Lm: 7.113 (7.055) Lt: 6.483 (6.461) Accm: 1.95 (1.98) Acct: 3.10 (3.11) proj_loss: -0.4781 (-0.4760) time: 1.1468 data: 0.0019 [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.39 Lm: 7.023 (7.068) Lt: 6.377 (6.452) Accm: 2.23 (2.03) Acct: 3.17 (2.98) proj_loss: -0.4904 (-0.4902) time: 1.1468 data: 0.0017 [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.39 Lm: 7.041 (7.087) Lt: 6.425 (6.466) Accm: 2.11 (2.01) Acct: 3.13 (3.22) proj_loss: -0.4814 (-0.4832) time: 1.1468 data: 0.0016 [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 12/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.39 Lm: 7.102 (7.058) Lt: 6.434 (6.440) Accm: 2.05 (2.23) Acct: 3.37 (3.55) proj_loss: -0.4693 (-0.4732) time: 1.1468 data: 0.0018 [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 12/350] Total time: 0:22:13 (0.799 s / it) [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 12/350] Total time: 0:22:13 (0.799 s / it) [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 12/350] Total time: 0:22:13 (0.799 s / it) [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 12/350] Total time: 0:22:13 (0.799 s / it) [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 12/350] Total time: 0:22:13 (0.799 s / it) [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 12/350] Total time: 0:22:13 (0.799 s / it) [11-22 20:27:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 12/350] Total time: 0:22:13 (0.799 s / it) [11-22 20:27:10] (/home/user/VAR/train.py , line 276)=> [ep12] (training ) Lm: 7.065 (7.065), Lt: 6.442 (6.442), Acc m&t: 2.04 3.16, Remain: 4 days, 23:29:38, Finish: 2024-11-27 03:56 [11-22 20:27:10] (/home/user/VAR/train.py , line 276)=> [ep12] (training ) Lm: 7.065 (7.065), Lt: 6.442 (6.442), Acc m&t: 2.04 3.16, Remain: 4 days, 23:29:49, Finish: 2024-11-27 03:56 [11-22 20:27:10] (/home/user/VAR/train.py , line 276)=> [ep12] (training ) Lm: 7.065 (7.065), Lt: 6.442 (6.442), Acc m&t: 2.04 3.16, Remain: 4 days, 23:28:12, Finish: 2024-11-27 03:55 [11-22 20:27:10] (/home/user/VAR/train.py , line 276)=> [ep12] (training ) Lm: 7.065 (7.065), Lt: 6.442 (6.442), Acc m&t: 2.04 3.16, Remain: 4 days, 23:30:23, Finish: 2024-11-27 03:57 [11-22 20:27:10] (/home/user/VAR/train.py , line 276)=> [ep12] (training ) Lm: 7.065 (7.065), Lt: 6.442 (6.442), Acc m&t: 2.04 3.16, Remain: 4 days, 23:30:18, Finish: 2024-11-27 03:57 [11-22 20:27:10] (/home/user/VAR/train.py , line 276)=> [ep12] (training ) Lm: 7.065 (7.065), Lt: 6.442 (6.442), Acc m&t: 2.04 3.16, Remain: 4 days, 23:30:53, Finish: 2024-11-27 03:58 [11-22 20:27:10] (/home/user/VAR/train.py , line 276)=> [ep12] (training ) Lm: 7.065 (7.065), Lt: 6.442 (6.442), Acc m&t: 2.04 3.16, Remain: 4 days, 23:30:50, Finish: 2024-11-27 03:58 [11-22 20:27:10] (/home/user/VAR/train.py , line 276)=> [ep12] (training ) Lm: 7.065 (7.065), Lt: 6.442 (6.442), Acc m&t: 2.04 3.16, Remain: 4 days, 23:29:12, Finish: 2024-11-27 03:56 [11-22 20:27:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 0/1669] eta: 0:19:49 tlr: 0.00024 tnm: 0.45 Lm: 7.128 (7.128) Lt: 6.526 (6.526) Accm: 1.86 (1.86) Acct: 2.93 (2.93) proj_loss: -0.5006 (-0.5006) time: 0.7127 data: 0.0004 [11-22 20:27:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 0/1669] eta: 0:19:50 tlr: 0.00024 tnm: 0.45 Lm: 7.006 (7.006) Lt: 6.360 (6.360) Accm: 2.16 (2.16) Acct: 3.41 (3.41) proj_loss: -0.4923 (-0.4923) time: 0.7133 data: 0.0004 [11-22 20:27:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 0/1669] eta: 0:19:50 tlr: 0.00024 tnm: 0.45 Lm: 7.054 (7.054) Lt: 6.395 (6.395) Accm: 1.65 (1.65) Acct: 2.72 (2.72) proj_loss: -0.4845 (-0.4845) time: 0.7136 data: 0.0004 [11-22 20:27:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 0/1669] eta: 0:19:50 tlr: 0.00024 tnm: 0.45 Lm: 6.824 (6.824) Lt: 6.131 (6.131) Accm: 2.53 (2.53) Acct: 3.96 (3.96) proj_loss: -0.4828 (-0.4828) time: 0.7133 data: 0.0004 [11-22 20:27:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 0/1669] eta: 0:19:50 tlr: 0.00024 tnm: 0.45 Lm: 7.120 (7.120) Lt: 6.494 (6.494) Accm: 1.78 (1.78) Acct: 2.69 (2.69) proj_loss: -0.4593 (-0.4593) time: 0.7135 data: 0.0004 [11-22 20:27:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 0/1669] eta: 0:19:51 tlr: 0.00024 tnm: 0.45 Lm: 6.972 (6.972) Lt: 6.321 (6.321) Accm: 2.55 (2.55) Acct: 3.82 (3.82) proj_loss: -0.4701 (-0.4701) time: 0.7141 data: 0.0003 [11-22 20:27:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 0/1669] eta: 0:19:52 tlr: 0.00024 tnm: 0.45 Lm: 6.923 (6.923) Lt: 6.229 (6.229) Accm: 2.51 (2.51) Acct: 3.62 (3.62) proj_loss: -0.5014 (-0.5014) time: 0.7145 data: 0.0003 [11-22 20:27:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 0/1669] eta: 0:19:52 tlr: 0.00024 tnm: 0.45 Lm: 7.066 (7.066) Lt: 6.480 (6.480) Accm: 2.11 (2.11) Acct: 2.96 (2.96) proj_loss: -0.4418 (-0.4418) time: 0.7145 data: 0.0003 [11-22 20:32:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 417/1669] eta: 0:15:58 tlr: 0.00024 tnm: 0.40 Lm: 6.982 (6.982) Lt: 6.318 (6.318) Accm: 2.16 (2.16) Acct: 3.25 (3.25) proj_loss: -0.4635 (-0.4635) time: 0.7531 data: 0.0003 [11-22 20:32:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 417/1669] eta: 0:15:58 tlr: 0.00024 tnm: 0.40 Lm: 7.125 (7.125) Lt: 6.509 (6.509) Accm: 1.92 (1.92) Acct: 3.19 (3.19) proj_loss: -0.4823 (-0.4823) time: 0.7531 data: 0.0003 [11-22 20:32:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 417/1669] eta: 0:15:58 tlr: 0.00024 tnm: 0.40 Lm: 6.901 (6.901) Lt: 6.203 (6.203) Accm: 2.34 (2.34) Acct: 3.72 (3.72) proj_loss: -0.4839 (-0.4839) time: 0.7531 data: 0.0003 [11-22 20:32:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 417/1669] eta: 0:15:58 tlr: 0.00024 tnm: 0.40 Lm: 7.076 (7.076) Lt: 6.432 (6.432) Accm: 1.98 (1.98) Acct: 3.08 (3.08) proj_loss: -0.4976 (-0.4976) time: 0.7531 data: 0.0002 [11-22 20:32:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 417/1669] eta: 0:15:58 tlr: 0.00024 tnm: 0.40 Lm: 6.968 (6.968) Lt: 6.276 (6.276) Accm: 2.07 (2.07) Acct: 3.43 (3.43) proj_loss: -0.4792 (-0.4792) time: 0.7531 data: 0.0002 [11-22 20:32:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 417/1669] eta: 0:15:58 tlr: 0.00024 tnm: 0.40 Lm: 7.048 (7.048) Lt: 6.407 (6.407) Accm: 1.92 (1.92) Acct: 3.00 (3.00) proj_loss: -0.4572 (-0.4572) time: 0.7531 data: 0.0003 [11-22 20:32:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 417/1669] eta: 0:15:58 tlr: 0.00024 tnm: 0.40 Lm: 6.951 (6.951) Lt: 6.291 (6.291) Accm: 2.45 (2.45) Acct: 3.65 (3.65) proj_loss: -0.4773 (-0.4773) time: 0.7531 data: 0.0003 [11-22 20:32:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 417/1669] eta: 0:15:58 tlr: 0.00024 tnm: 0.40 Lm: 7.069 (7.069) Lt: 6.441 (6.441) Accm: 2.19 (2.19) Acct: 3.51 (3.51) proj_loss: -0.4698 (-0.4698) time: 0.7531 data: 0.0003 [11-22 20:37:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 834/1669] eta: 0:10:34 tlr: 0.00024 tnm: 0.45 Lm: 7.114 (7.084) Lt: 6.516 (6.466) Accm: 2.26 (2.21) Acct: 3.41 (3.42) proj_loss: -0.4812 (-0.4736) time: 0.7547 data: 0.0003 [11-22 20:37:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 834/1669] eta: 0:10:34 tlr: 0.00024 tnm: 0.45 Lm: 6.978 (6.938) Lt: 6.275 (6.263) Accm: 2.21 (2.30) Acct: 3.48 (3.58) proj_loss: -0.4850 (-0.4974) time: 0.7547 data: 0.0003 [11-22 20:37:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 834/1669] eta: 0:10:34 tlr: 0.00024 tnm: 0.45 Lm: 7.006 (6.995) Lt: 6.360 (6.329) Accm: 2.16 (2.23) Acct: 3.41 (3.52) proj_loss: -0.4923 (-0.4907) time: 0.7547 data: 0.0002 [11-22 20:37:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 834/1669] eta: 0:10:34 tlr: 0.00024 tnm: 0.45 Lm: 7.049 (6.995) Lt: 6.395 (6.322) Accm: 1.88 (2.01) Acct: 3.06 (3.31) proj_loss: -0.4739 (-0.4738) time: 0.7547 data: 0.0003 [11-22 20:37:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 834/1669] eta: 0:10:34 tlr: 0.00024 tnm: 0.45 Lm: 7.120 (7.095) Lt: 6.494 (6.485) Accm: 1.84 (1.89) Acct: 2.69 (2.85) proj_loss: -0.4593 (-0.4690) time: 0.7547 data: 0.0003 [11-22 20:37:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 834/1669] eta: 0:10:34 tlr: 0.00024 tnm: 0.45 Lm: 7.122 (7.051) Lt: 6.491 (6.408) Accm: 1.97 (2.10) Acct: 3.44 (3.55) proj_loss: -0.5006 (-0.4918) time: 0.7547 data: 0.0003 [11-22 20:37:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 834/1669] eta: 0:10:34 tlr: 0.00024 tnm: 0.45 Lm: 6.929 (6.914) Lt: 6.260 (6.261) Accm: 2.43 (2.45) Acct: 3.75 (3.68) proj_loss: -0.4770 (-0.4772) time: 0.7547 data: 0.0003 [11-22 20:37:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [ 834/1669] eta: 0:10:34 tlr: 0.00024 tnm: 0.45 Lm: 6.898 (6.932) Lt: 6.156 (6.254) Accm: 2.20 (2.34) Acct: 3.55 (3.58) proj_loss: -0.4853 (-0.4776) time: 0.7547 data: 0.0003 [11-22 20:42:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1251/1669] eta: 0:05:16 tlr: 0.00024 tnm: 0.44 Lm: 6.933 (6.941) Lt: 6.234 (6.269) Accm: 2.20 (2.31) Acct: 3.55 (3.57) proj_loss: -0.4743 (-0.4740) time: 0.7550 data: 0.0003 [11-22 20:42:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1251/1669] eta: 0:05:16 tlr: 0.00024 tnm: 0.44 Lm: 6.995 (6.987) Lt: 6.330 (6.306) Accm: 2.18 (2.20) Acct: 3.39 (3.48) proj_loss: -0.4839 (-0.4889) time: 0.7550 data: 0.0002 [11-22 20:42:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1251/1669] eta: 0:05:16 tlr: 0.00024 tnm: 0.44 Lm: 7.039 (7.014) Lt: 6.428 (6.371) Accm: 1.98 (2.11) Acct: 3.08 (3.30) proj_loss: -0.4910 (-0.4904) time: 0.7550 data: 0.0002 [11-22 20:42:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1251/1669] eta: 0:05:16 tlr: 0.00024 tnm: 0.44 Lm: 7.043 (7.029) Lt: 6.383 (6.375) Accm: 2.10 (2.13) Acct: 3.39 (3.50) proj_loss: -0.4877 (-0.4875) time: 0.7550 data: 0.0003 [11-22 20:42:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1251/1669] eta: 0:05:16 tlr: 0.00024 tnm: 0.44 Lm: 6.986 (6.977) Lt: 6.319 (6.303) Accm: 2.07 (2.07) Acct: 3.13 (3.28) proj_loss: -0.4792 (-0.4796) time: 0.7550 data: 0.0003 [11-22 20:42:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1251/1669] eta: 0:05:16 tlr: 0.00024 tnm: 0.44 Lm: 6.951 (6.963) Lt: 6.291 (6.314) Accm: 2.40 (2.32) Acct: 3.62 (3.54) proj_loss: -0.4736 (-0.4745) time: 0.7550 data: 0.0003 [11-22 20:42:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1251/1669] eta: 0:05:16 tlr: 0.00024 tnm: 0.44 Lm: 7.051 (7.067) Lt: 6.413 (6.447) Accm: 1.95 (1.94) Acct: 2.94 (2.94) proj_loss: -0.4760 (-0.4749) time: 0.7550 data: 0.0003 [11-22 20:42:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1251/1669] eta: 0:05:16 tlr: 0.00024 tnm: 0.44 Lm: 7.097 (7.083) Lt: 6.482 (6.462) Accm: 2.31 (2.25) Acct: 3.39 (3.41) proj_loss: -0.4751 (-0.4724) time: 0.7550 data: 0.0003 [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 6.998 (6.989) Lt: 6.384 (6.325) Accm: 2.14 (2.19) Acct: 3.31 (3.40) proj_loss: -0.4850 (-0.4887) time: 0.7550 data: 0.0015 [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 7.122 (7.066) Lt: 6.491 (6.425) Accm: 1.97 (2.09) Acct: 3.34 (3.40) proj_loss: -0.5006 (-0.4909) time: 0.7550 data: 0.0018 [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 6.924 (6.963) Lt: 6.267 (6.296) Accm: 2.26 (2.13) Acct: 3.20 (3.30) proj_loss: -0.4845 (-0.4901) time: 0.7550 data: 0.0019 [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 7.080 (7.041) Lt: 6.449 (6.414) Accm: 2.36 (2.33) Acct: 3.41 (3.55) proj_loss: -0.4812 (-0.4777) time: 0.7550 data: 0.0018 [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 7.120 (7.081) Lt: 6.494 (6.457) Accm: 2.01 (1.96) Acct: 3.20 (3.01) proj_loss: -0.4926 (-0.4790) time: 0.7550 data: 0.0019 [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 6.972 (6.985) Lt: 6.321 (6.326) Accm: 2.36 (2.24) Acct: 3.48 (3.46) proj_loss: -0.4770 (-0.4797) time: 0.7550 data: 0.0018 [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 6.968 (6.969) Lt: 6.312 (6.308) Accm: 2.20 (2.27) Acct: 3.55 (3.51) proj_loss: -0.4853 (-0.4825) time: 0.7550 data: 0.0015 [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 13/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 7.072 (7.029) Lt: 6.435 (6.383) Accm: 2.16 (2.15) Acct: 3.41 (3.38) proj_loss: -0.4923 (-0.4919) time: 0.7550 data: 0.0018 [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 13/350] Total time: 0:21:02 (0.756 s / it) [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 13/350] Total time: 0:21:02 (0.756 s / it) [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 13/350] Total time: 0:21:02 (0.756 s / it) [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 13/350] Total time: 0:21:02 (0.756 s / it) [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 13/350] Total time: 0:21:02 (0.756 s / it) [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 13/350] Total time: 0:21:02 (0.756 s / it) [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 13/350] Total time: 0:21:02 (0.756 s / it) [11-22 20:48:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 13/350] Total time: 0:21:02 (0.756 s / it) [11-22 20:48:13] (/home/user/VAR/train.py , line 276)=> [ep13] (training ) Lm: 7.014 (7.014), Lt: 6.374 (6.374), Acc m&t: 2.15 3.33, Remain: 4 days, 22:11:59, Finish: 2024-11-27 03:00 [11-22 20:48:13] (/home/user/VAR/train.py , line 276)=> [ep13] (training ) Lm: 7.014 (7.014), Lt: 6.374 (6.374), Acc m&t: 2.15 3.33, Remain: 4 days, 22:10:15, Finish: 2024-11-27 02:58 [11-22 20:48:13] (/home/user/VAR/train.py , line 276)=> [ep13] (training ) Lm: 7.014 (7.014), Lt: 6.374 (6.374), Acc m&t: 2.15 3.33, Remain: 4 days, 22:11:10, Finish: 2024-11-27 02:59 [11-22 20:48:13] (/home/user/VAR/train.py , line 276)=> [ep13] (training ) Lm: 7.014 (7.014), Lt: 6.374 (6.374), Acc m&t: 2.15 3.33, Remain: 4 days, 22:12:23, Finish: 2024-11-27 03:00 [11-22 20:48:13] (/home/user/VAR/train.py , line 276)=> [ep13] (training ) Lm: 7.014 (7.014), Lt: 6.374 (6.374), Acc m&t: 2.15 3.33, Remain: 4 days, 22:12:30, Finish: 2024-11-27 03:00 [11-22 20:48:13] (/home/user/VAR/train.py , line 276)=> [ep13] (training ) Lm: 7.014 (7.014), Lt: 6.374 (6.374), Acc m&t: 2.15 3.33, Remain: 4 days, 22:11:51, Finish: 2024-11-27 03:00 [11-22 20:48:13] (/home/user/VAR/train.py , line 276)=> [ep13] (training ) Lm: 7.014 (7.014), Lt: 6.374 (6.374), Acc m&t: 2.15 3.33, Remain: 4 days, 22:12:05, Finish: 2024-11-27 03:00 [11-22 20:48:13] (/home/user/VAR/train.py , line 276)=> [ep13] (training ) Lm: 7.014 (7.014), Lt: 6.374 (6.374), Acc m&t: 2.15 3.33, Remain: 4 days, 22:12:01, Finish: 2024-11-27 03:00 [11-22 20:48:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 0/1669] eta: 0:20:44 tlr: 0.00024 tnm: 0.49 Lm: 7.168 (7.168) Lt: 6.604 (6.604) Accm: 1.66 (1.66) Acct: 2.51 (2.51) proj_loss: -0.5193 (-0.5193) time: 0.7458 data: 0.0005 [11-22 20:48:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 0/1669] eta: 0:20:46 tlr: 0.00024 tnm: 0.49 Lm: 6.913 (6.913) Lt: 6.328 (6.328) Accm: 2.55 (2.55) Acct: 3.82 (3.82) proj_loss: -0.4915 (-0.4915) time: 0.7467 data: 0.0003 [11-22 20:48:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 0/1669] eta: 0:20:46 tlr: 0.00024 tnm: 0.49 Lm: 6.965 (6.965) Lt: 6.401 (6.401) Accm: 2.11 (2.11) Acct: 3.24 (3.24) proj_loss: -0.4814 (-0.4814) time: 0.7471 data: 0.0004 [11-22 20:48:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 0/1669] eta: 0:20:47 tlr: 0.00024 tnm: 0.49 Lm: 6.901 (6.901) Lt: 6.224 (6.224) Accm: 2.20 (2.20) Acct: 3.68 (3.68) proj_loss: -0.4236 (-0.4236) time: 0.7476 data: 0.0003 [11-22 20:48:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 0/1669] eta: 0:20:46 tlr: 0.00024 tnm: 0.49 Lm: 7.147 (7.147) Lt: 6.586 (6.586) Accm: 1.81 (1.81) Acct: 2.72 (2.72) proj_loss: -0.4968 (-0.4968) time: 0.7470 data: 0.0004 [11-22 20:48:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 0/1669] eta: 0:20:48 tlr: 0.00024 tnm: 0.49 Lm: 6.917 (6.917) Lt: 6.204 (6.204) Accm: 2.27 (2.27) Acct: 3.72 (3.72) proj_loss: -0.4821 (-0.4821) time: 0.7478 data: 0.0003 [11-22 20:48:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 0/1669] eta: 0:20:48 tlr: 0.00024 tnm: 0.49 Lm: 7.211 (7.211) Lt: 6.586 (6.586) Accm: 1.75 (1.75) Acct: 2.86 (2.86) proj_loss: -0.4855 (-0.4855) time: 0.7482 data: 0.0004 [11-22 20:48:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 0/1669] eta: 0:20:48 tlr: 0.00024 tnm: 0.49 Lm: 7.051 (7.051) Lt: 6.348 (6.348) Accm: 2.03 (2.03) Acct: 3.37 (3.37) proj_loss: -0.4932 (-0.4932) time: 0.7481 data: 0.0004 [11-22 20:53:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.41 Lm: 7.077 (7.077) Lt: 6.403 (6.403) Accm: 1.83 (1.83) Acct: 2.96 (2.96) proj_loss: -0.4831 (-0.4831) time: 0.7522 data: 0.0003 [11-22 20:53:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.41 Lm: 7.068 (7.068) Lt: 6.434 (6.434) Accm: 1.92 (1.92) Acct: 3.06 (3.06) proj_loss: -0.4953 (-0.4953) time: 0.7522 data: 0.0003 [11-22 20:53:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.41 Lm: 6.979 (6.979) Lt: 6.349 (6.349) Accm: 2.24 (2.24) Acct: 3.36 (3.36) proj_loss: -0.4798 (-0.4798) time: 0.7522 data: 0.0003 [11-22 20:53:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.41 Lm: 7.060 (7.060) Lt: 6.449 (6.449) Accm: 2.01 (2.01) Acct: 3.29 (3.29) proj_loss: -0.4914 (-0.4914) time: 0.7522 data: 0.0002 [11-22 20:53:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.41 Lm: 6.980 (6.980) Lt: 6.324 (6.324) Accm: 2.10 (2.10) Acct: 3.65 (3.65) proj_loss: -0.4491 (-0.4491) time: 0.7522 data: 0.0003 [11-22 20:53:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.41 Lm: 6.935 (6.935) Lt: 6.329 (6.329) Accm: 2.35 (2.35) Acct: 3.58 (3.58) proj_loss: -0.4825 (-0.4825) time: 0.7522 data: 0.0003 [11-22 20:53:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.41 Lm: 7.097 (7.097) Lt: 6.478 (6.478) Accm: 2.16 (2.16) Acct: 3.36 (3.36) proj_loss: -0.4836 (-0.4836) time: 0.7522 data: 0.0003 [11-22 20:53:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.41 Lm: 7.009 (7.009) Lt: 6.351 (6.351) Accm: 2.13 (2.13) Acct: 3.37 (3.37) proj_loss: -0.4846 (-0.4846) time: 0.7522 data: 0.0003 [11-22 20:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 834/1669] eta: 0:11:45 tlr: 0.00024 tnm: 0.40 Lm: 7.002 (7.046) Lt: 6.264 (6.377) Accm: 2.19 (2.07) Acct: 3.62 (3.27) proj_loss: -0.4938 (-0.4948) time: 0.9414 data: 0.0003 [11-22 20:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 834/1669] eta: 0:11:45 tlr: 0.00024 tnm: 0.40 Lm: 6.974 (6.973) Lt: 6.311 (6.327) Accm: 2.21 (2.26) Acct: 3.86 (3.59) proj_loss: -0.4968 (-0.4938) time: 0.9414 data: 0.0002 [11-22 20:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 834/1669] eta: 0:11:45 tlr: 0.00024 tnm: 0.40 Lm: 7.051 (7.016) Lt: 6.348 (6.337) Accm: 2.03 (2.00) Acct: 3.37 (3.31) proj_loss: -0.4811 (-0.4824) time: 0.9414 data: 0.0003 [11-22 20:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 834/1669] eta: 0:11:45 tlr: 0.00024 tnm: 0.40 Lm: 6.994 (6.989) Lt: 6.366 (6.355) Accm: 2.20 (2.22) Acct: 3.34 (3.35) proj_loss: -0.4783 (-0.4771) time: 0.9414 data: 0.0003 [11-22 20:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 834/1669] eta: 0:11:45 tlr: 0.00024 tnm: 0.40 Lm: 7.006 (7.008) Lt: 6.339 (6.347) Accm: 2.03 (2.09) Acct: 3.10 (3.28) proj_loss: -0.4856 (-0.4850) time: 0.9414 data: 0.0003 [11-22 20:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 834/1669] eta: 0:11:45 tlr: 0.00024 tnm: 0.40 Lm: 6.984 (7.010) Lt: 6.370 (6.384) Accm: 2.51 (2.28) Acct: 3.86 (3.65) proj_loss: -0.4817 (-0.4827) time: 0.9414 data: 0.0003 [11-22 20:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 834/1669] eta: 0:11:45 tlr: 0.00024 tnm: 0.40 Lm: 7.058 (7.046) Lt: 6.424 (6.418) Accm: 2.00 (2.02) Acct: 3.62 (3.35) proj_loss: -0.4745 (-0.4712) time: 0.9414 data: 0.0003 [11-22 20:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [ 834/1669] eta: 0:11:45 tlr: 0.00024 tnm: 0.40 Lm: 6.913 (6.886) Lt: 6.328 (6.266) Accm: 2.55 (2.48) Acct: 3.82 (3.90) proj_loss: -0.4735 (-0.4755) time: 0.9414 data: 0.0003 [11-22 21:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1251/1669] eta: 0:05:41 tlr: 0.00024 tnm: 0.40 Lm: 6.935 (6.935) Lt: 6.329 (6.321) Accm: 2.35 (2.33) Acct: 3.58 (3.59) proj_loss: -0.4825 (-0.4891) time: 0.7569 data: 0.0003 [11-22 21:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1251/1669] eta: 0:05:41 tlr: 0.00024 tnm: 0.40 Lm: 7.010 (7.025) Lt: 6.358 (6.387) Accm: 2.10 (2.17) Acct: 3.65 (3.48) proj_loss: -0.4789 (-0.4742) time: 0.7569 data: 0.0003 [11-22 21:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1251/1669] eta: 0:05:41 tlr: 0.00024 tnm: 0.40 Lm: 6.985 (7.013) Lt: 6.264 (6.325) Accm: 2.26 (2.13) Acct: 3.65 (3.37) proj_loss: -0.4826 (-0.4880) time: 0.7569 data: 0.0003 [11-22 21:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1251/1669] eta: 0:05:41 tlr: 0.00024 tnm: 0.40 Lm: 6.955 (6.964) Lt: 6.305 (6.320) Accm: 2.35 (2.32) Acct: 3.70 (3.58) proj_loss: -0.4928 (-0.4926) time: 0.7569 data: 0.0002 [11-22 21:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1251/1669] eta: 0:05:41 tlr: 0.00024 tnm: 0.40 Lm: 7.009 (7.009) Lt: 6.348 (6.349) Accm: 2.00 (2.05) Acct: 3.06 (3.15) proj_loss: -0.4864 (-0.4861) time: 0.7569 data: 0.0003 [11-22 21:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1251/1669] eta: 0:05:41 tlr: 0.00024 tnm: 0.40 Lm: 7.077 (7.074) Lt: 6.403 (6.407) Accm: 1.83 (1.86) Acct: 2.96 (3.01) proj_loss: -0.4871 (-0.4900) time: 0.7569 data: 0.0003 [11-22 21:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1251/1669] eta: 0:05:41 tlr: 0.00024 tnm: 0.40 Lm: 6.985 (6.986) Lt: 6.332 (6.334) Accm: 2.16 (2.20) Acct: 3.29 (3.32) proj_loss: -0.4798 (-0.4840) time: 0.7569 data: 0.0002 [11-22 21:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1251/1669] eta: 0:05:41 tlr: 0.00024 tnm: 0.40 Lm: 7.080 (7.052) Lt: 6.467 (6.429) Accm: 2.13 (2.14) Acct: 3.36 (3.43) proj_loss: -0.4836 (-0.4887) time: 0.7569 data: 0.0003 [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 6.984 (7.037) Lt: 6.381 (6.419) Accm: 1.97 (2.11) Acct: 3.20 (3.38) proj_loss: -0.4855 (-0.4904) time: 0.7600 data: 0.0018 [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 14/350] Total time: 0:22:17 (0.801 s / it) [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 6.962 (7.011) Lt: 6.341 (6.378) Accm: 2.20 (2.18) Acct: 3.62 (3.44) proj_loss: -0.4833 (-0.4765) time: 0.7601 data: 0.0018 [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 6.913 (6.903) Lt: 6.328 (6.259) Accm: 2.55 (2.51) Acct: 3.82 (3.80) proj_loss: -0.4817 (-0.4876) time: 0.7601 data: 0.0019 [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 6.935 (6.951) Lt: 6.298 (6.292) Accm: 2.21 (2.28) Acct: 3.55 (3.48) proj_loss: -0.4889 (-0.4870) time: 0.7601 data: 0.0016 [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 7.002 (7.015) Lt: 6.264 (6.328) Accm: 2.19 (2.11) Acct: 3.62 (3.34) proj_loss: -0.4714 (-0.4828) time: 0.7601 data: 0.0016 [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 7.051 (7.054) Lt: 6.348 (6.395) Accm: 1.98 (1.89) Acct: 2.86 (2.98) proj_loss: -0.4932 (-0.4956) time: 0.7601 data: 0.0020 [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 6.994 (7.002) Lt: 6.366 (6.352) Accm: 2.11 (2.17) Acct: 3.27 (3.31) proj_loss: -0.4814 (-0.4856) time: 0.7600 data: 0.0019 [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 14/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.42 Lm: 7.006 (6.987) Lt: 6.339 (6.329) Accm: 2.03 (2.14) Acct: 3.10 (3.27) proj_loss: -0.4856 (-0.4818) time: 0.7601 data: 0.0016 [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 14/350] Total time: 0:22:17 (0.801 s / it) [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 14/350] Total time: 0:22:17 (0.801 s / it) [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 14/350] Total time: 0:22:17 (0.801 s / it) [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 14/350] Total time: 0:22:17 (0.801 s / it) [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 14/350] Total time: 0:22:17 (0.801 s / it) [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 14/350] Total time: 0:22:17 (0.801 s / it) [11-22 21:10:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 14/350] Total time: 0:22:17 (0.801 s / it) [11-22 21:10:30] (/home/user/VAR/train.py , line 276)=> [ep14] (training ) Lm: 7.007 (7.007), Lt: 6.362 (6.362), Acc m&t: 2.15 3.33, Remain: 4 days, 22:38:58, Finish: 2024-11-27 03:49 [11-22 21:10:30] (/home/user/VAR/train.py , line 276)=> [ep14] (training ) Lm: 7.007 (7.007), Lt: 6.362 (6.362), Acc m&t: 2.15 3.33, Remain: 4 days, 22:39:45, Finish: 2024-11-27 03:50 [11-22 21:10:30] (/home/user/VAR/train.py , line 276)=> [ep14] (training ) Lm: 7.007 (7.007), Lt: 6.362 (6.362), Acc m&t: 2.15 3.33, Remain: 4 days, 22:40:21, Finish: 2024-11-27 03:50 [11-22 21:10:30] (/home/user/VAR/train.py , line 276)=> [ep14] (training ) Lm: 7.007 (7.007), Lt: 6.362 (6.362), Acc m&t: 2.15 3.33, Remain: 4 days, 22:39:36, Finish: 2024-11-27 03:50 [11-22 21:10:30] (/home/user/VAR/train.py , line 276)=> [ep14] (training ) Lm: 7.007 (7.007), Lt: 6.362 (6.362), Acc m&t: 2.15 3.33, Remain: 4 days, 22:39:53, Finish: 2024-11-27 03:50 [11-22 21:10:30] (/home/user/VAR/train.py , line 276)=> [ep14] (training ) Lm: 7.007 (7.007), Lt: 6.362 (6.362), Acc m&t: 2.15 3.33, Remain: 4 days, 22:39:49, Finish: 2024-11-27 03:50 [11-22 21:10:30] (/home/user/VAR/train.py , line 276)=> [ep14] (training ) Lm: 7.007 (7.007), Lt: 6.362 (6.362), Acc m&t: 2.15 3.33, Remain: 4 days, 22:40:11, Finish: 2024-11-27 03:50 [11-22 21:10:30] (/home/user/VAR/train.py , line 276)=> [ep14] (training ) Lm: 7.007 (7.007), Lt: 6.362 (6.362), Acc m&t: 2.15 3.33, Remain: 4 days, 22:42:16, Finish: 2024-11-27 03:52 [11-22 21:10:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 0/1669] eta: 0:20:27 tlr: 0.00024 tnm: 0.45 Lm: 6.773 (6.773) Lt: 6.104 (6.104) Accm: 2.61 (2.61) Acct: 3.93 (3.93) proj_loss: -0.4927 (-0.4927) time: 0.7356 data: 0.0004 [11-22 21:10:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 0/1669] eta: 0:20:28 tlr: 0.00024 tnm: 0.45 Lm: 6.993 (6.993) Lt: 6.376 (6.376) Accm: 2.42 (2.42) Acct: 3.31 (3.31) proj_loss: -0.4922 (-0.4922) time: 0.7360 data: 0.0003 [11-22 21:10:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 0/1669] eta: 0:20:27 tlr: 0.00024 tnm: 0.45 Lm: 6.903 (6.903) Lt: 6.181 (6.181) Accm: 2.78 (2.78) Acct: 4.10 (4.10) proj_loss: -0.4900 (-0.4900) time: 0.7354 data: 0.0004 [11-22 21:10:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 0/1669] eta: 0:20:28 tlr: 0.00024 tnm: 0.45 Lm: 7.118 (7.118) Lt: 6.510 (6.510) Accm: 1.92 (1.92) Acct: 3.00 (3.00) proj_loss: -0.5062 (-0.5062) time: 0.7363 data: 0.0003 [11-22 21:10:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 0/1669] eta: 0:20:28 tlr: 0.00024 tnm: 0.45 Lm: 6.888 (6.888) Lt: 6.272 (6.272) Accm: 2.43 (2.43) Acct: 3.41 (3.41) proj_loss: -0.5115 (-0.5115) time: 0.7361 data: 0.0004 [11-22 21:10:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 0/1669] eta: 0:20:27 tlr: 0.00024 tnm: 0.45 Lm: 7.090 (7.090) Lt: 6.428 (6.428) Accm: 2.04 (2.04) Acct: 2.86 (2.86) proj_loss: -0.5011 (-0.5011) time: 0.7356 data: 0.0004 [11-22 21:10:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 0/1669] eta: 0:20:30 tlr: 0.00024 tnm: 0.45 Lm: 6.888 (6.888) Lt: 6.203 (6.203) Accm: 2.29 (2.29) Acct: 3.72 (3.72) proj_loss: -0.4932 (-0.4932) time: 0.7371 data: 0.0004 [11-22 21:10:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 0/1669] eta: 0:20:27 tlr: 0.00024 tnm: 0.45 Lm: 6.903 (6.903) Lt: 6.243 (6.243) Accm: 2.49 (2.49) Acct: 3.75 (3.75) proj_loss: -0.4985 (-0.4985) time: 0.7354 data: 0.0004 [11-22 21:15:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 417/1669] eta: 0:15:44 tlr: 0.00024 tnm: 0.39 Lm: 6.884 (6.884) Lt: 6.228 (6.228) Accm: 2.37 (2.37) Acct: 3.62 (3.62) proj_loss: -0.4932 (-0.4932) time: 0.7547 data: 0.0003 [11-22 21:15:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 417/1669] eta: 0:15:44 tlr: 0.00024 tnm: 0.39 Lm: 6.971 (6.971) Lt: 6.280 (6.280) Accm: 2.26 (2.26) Acct: 3.44 (3.44) proj_loss: -0.4751 (-0.4751) time: 0.7547 data: 0.0003 [11-22 21:15:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 417/1669] eta: 0:15:44 tlr: 0.00024 tnm: 0.39 Lm: 7.051 (7.051) Lt: 6.418 (6.418) Accm: 2.03 (2.03) Acct: 3.00 (3.00) proj_loss: -0.4975 (-0.4975) time: 0.7547 data: 0.0003 [11-22 21:15:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 417/1669] eta: 0:15:44 tlr: 0.00024 tnm: 0.39 Lm: 6.961 (6.961) Lt: 6.330 (6.330) Accm: 2.38 (2.38) Acct: 3.56 (3.56) proj_loss: -0.4991 (-0.4991) time: 0.7547 data: 0.0003 [11-22 21:15:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 417/1669] eta: 0:15:44 tlr: 0.00024 tnm: 0.39 Lm: 7.057 (7.057) Lt: 6.408 (6.408) Accm: 1.89 (1.89) Acct: 2.84 (2.84) proj_loss: -0.4912 (-0.4912) time: 0.7547 data: 0.0002 [11-22 21:15:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 417/1669] eta: 0:15:44 tlr: 0.00024 tnm: 0.39 Lm: 6.918 (6.918) Lt: 6.258 (6.258) Accm: 2.48 (2.48) Acct: 3.63 (3.63) proj_loss: -0.4879 (-0.4879) time: 0.7547 data: 0.0003 [11-22 21:15:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 417/1669] eta: 0:15:44 tlr: 0.00024 tnm: 0.39 Lm: 6.997 (6.997) Lt: 6.336 (6.336) Accm: 2.20 (2.20) Acct: 3.43 (3.43) proj_loss: -0.4776 (-0.4776) time: 0.7547 data: 0.0003 [11-22 21:15:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 417/1669] eta: 0:15:44 tlr: 0.00024 tnm: 0.39 Lm: 6.903 (6.903) Lt: 6.242 (6.242) Accm: 2.41 (2.41) Acct: 3.25 (3.25) proj_loss: -0.4933 (-0.4933) time: 0.7547 data: 0.0003 [11-22 21:21:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 834/1669] eta: 0:10:29 tlr: 0.00024 tnm: 0.43 Lm: 7.034 (6.977) Lt: 6.380 (6.320) Accm: 2.21 (2.30) Acct: 3.24 (3.25) proj_loss: -0.4927 (-0.4931) time: 0.7550 data: 0.0003 [11-22 21:21:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 834/1669] eta: 0:10:29 tlr: 0.00024 tnm: 0.43 Lm: 6.973 (6.937) Lt: 6.359 (6.292) Accm: 2.42 (2.33) Acct: 3.31 (3.50) proj_loss: -0.4922 (-0.4969) time: 0.7550 data: 0.0003 [11-22 21:21:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 834/1669] eta: 0:10:29 tlr: 0.00024 tnm: 0.43 Lm: 6.903 (6.899) Lt: 6.243 (6.259) Accm: 2.39 (2.37) Acct: 3.58 (3.60) proj_loss: -0.4985 (-0.5082) time: 0.7550 data: 0.0002 [11-22 21:21:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 834/1669] eta: 0:10:29 tlr: 0.00024 tnm: 0.43 Lm: 7.069 (7.061) Lt: 6.423 (6.413) Accm: 2.01 (1.93) Acct: 2.86 (2.97) proj_loss: -0.5011 (-0.5008) time: 0.7550 data: 0.0002 [11-22 21:21:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 834/1669] eta: 0:10:29 tlr: 0.00024 tnm: 0.43 Lm: 6.903 (6.899) Lt: 6.181 (6.196) Accm: 2.78 (2.43) Acct: 4.10 (3.72) proj_loss: -0.4900 (-0.4852) time: 0.7550 data: 0.0003 [11-22 21:21:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 834/1669] eta: 0:10:29 tlr: 0.00024 tnm: 0.43 Lm: 6.985 (7.026) Lt: 6.327 (6.387) Accm: 2.14 (2.17) Acct: 3.00 (3.24) proj_loss: -0.4979 (-0.4976) time: 0.7550 data: 0.0003 [11-22 21:21:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 834/1669] eta: 0:10:29 tlr: 0.00024 tnm: 0.43 Lm: 7.070 (7.021) Lt: 6.469 (6.412) Accm: 2.11 (2.06) Acct: 3.13 (3.17) proj_loss: -0.4619 (-0.4690) time: 0.7550 data: 0.0003 [11-22 21:21:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [ 834/1669] eta: 0:10:29 tlr: 0.00024 tnm: 0.43 Lm: 6.992 (6.971) Lt: 6.341 (6.334) Accm: 2.33 (2.36) Acct: 3.41 (3.48) proj_loss: -0.4867 (-0.4903) time: 0.7550 data: 0.0002 [11-22 21:26:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.44 Lm: 6.951 (6.956) Lt: 6.308 (6.319) Accm: 2.38 (2.39) Acct: 3.51 (3.51) proj_loss: -0.4883 (-0.4902) time: 0.7557 data: 0.0002 [11-22 21:26:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.44 Lm: 6.916 (6.921) Lt: 6.283 (6.283) Accm: 2.36 (2.36) Acct: 3.55 (3.58) proj_loss: -0.5144 (-0.5137) time: 0.7557 data: 0.0002 [11-22 21:26:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.44 Lm: 7.047 (7.045) Lt: 6.410 (6.409) Accm: 2.03 (2.04) Acct: 3.05 (3.13) proj_loss: -0.5073 (-0.5040) time: 0.7557 data: 0.0002 [11-22 21:26:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.44 Lm: 6.971 (6.938) Lt: 6.280 (6.270) Accm: 2.47 (2.36) Acct: 3.68 (3.61) proj_loss: -0.4978 (-0.4935) time: 0.7558 data: 0.0003 [11-22 21:26:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.44 Lm: 6.983 (6.981) Lt: 6.368 (6.344) Accm: 2.22 (2.24) Acct: 3.27 (3.41) proj_loss: -0.4959 (-0.4976) time: 0.7557 data: 0.0002 [11-22 21:26:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.44 Lm: 7.072 (7.010) Lt: 6.428 (6.362) Accm: 2.15 (2.18) Acct: 3.00 (3.13) proj_loss: -0.4927 (-0.4919) time: 0.7557 data: 0.0003 [11-22 21:26:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.44 Lm: 6.979 (6.986) Lt: 6.341 (6.362) Accm: 2.20 (2.14) Acct: 3.43 (3.32) proj_loss: -0.4711 (-0.4718) time: 0.7557 data: 0.0003 [11-22 21:26:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.44 Lm: 7.051 (7.052) Lt: 6.416 (6.416) Accm: 2.08 (2.13) Acct: 3.01 (3.19) proj_loss: -0.4934 (-0.4900) time: 0.7558 data: 0.0003 [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 6.973 (6.940) Lt: 6.359 (6.283) Accm: 2.42 (2.27) Acct: 3.31 (3.46) proj_loss: -0.4922 (-0.4947) time: 0.8613 data: 0.0016 [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 6.929 (6.952) Lt: 6.322 (6.310) Accm: 2.33 (2.27) Acct: 3.51 (3.43) proj_loss: -0.4985 (-0.5046) time: 0.8614 data: 0.0016 [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 7.085 (7.025) Lt: 6.428 (6.375) Accm: 2.08 (2.16) Acct: 2.96 (3.09) proj_loss: -0.4927 (-0.4887) time: 0.8613 data: 0.0018 [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 7.024 (6.955) Lt: 6.371 (6.290) Accm: 2.16 (2.30) Acct: 3.27 (3.53) proj_loss: -0.4956 (-0.4939) time: 0.8614 data: 0.0019 [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 7.118 (7.068) Lt: 6.505 (6.446) Accm: 2.03 (2.06) Acct: 3.00 (3.09) proj_loss: -0.4891 (-0.4898) time: 0.8614 data: 0.0019 [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 6.992 (6.968) Lt: 6.341 (6.332) Accm: 2.33 (2.28) Acct: 3.41 (3.40) proj_loss: -0.4898 (-0.4925) time: 0.8613 data: 0.0015 [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 7.025 (7.037) Lt: 6.396 (6.387) Accm: 2.04 (2.07) Acct: 3.24 (3.24) proj_loss: -0.5011 (-0.5018) time: 0.8614 data: 0.0019 [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 15/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 6.888 (6.957) Lt: 6.214 (6.321) Accm: 2.29 (2.23) Acct: 3.72 (3.43) proj_loss: -0.4803 (-0.4740) time: 0.8613 data: 0.0016 [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 15/350] Total time: 0:22:18 (0.802 s / it) [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 15/350] Total time: 0:22:18 (0.802 s / it) [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 15/350] Total time: 0:22:18 (0.802 s / it) [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 15/350] Total time: 0:22:18 (0.802 s / it) [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 15/350] Total time: 0:22:18 (0.802 s / it) [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 15/350] Total time: 0:22:18 (0.802 s / it) [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 15/350] Total time: 0:22:18 (0.802 s / it) [11-22 21:32:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 15/350] Total time: 0:22:18 (0.802 s / it) [11-22 21:32:49] (/home/user/VAR/train.py , line 276)=> [ep15] (training ) Lm: 6.980 (6.980), Lt: 6.326 (6.326), Acc m&t: 2.22 3.45, Remain: 4 days, 22:08:22, Finish: 2024-11-27 03:41 [11-22 21:32:49] (/home/user/VAR/train.py , line 276)=> [ep15] (training ) Lm: 6.980 (6.980), Lt: 6.326 (6.326), Acc m&t: 2.22 3.45, Remain: 4 days, 22:05:40, Finish: 2024-11-27 03:38 [11-22 21:32:49] (/home/user/VAR/train.py , line 276)=> [ep15] (training ) Lm: 6.980 (6.980), Lt: 6.326 (6.326), Acc m&t: 2.22 3.45, Remain: 4 days, 22:06:02, Finish: 2024-11-27 03:38 [11-22 21:32:49] (/home/user/VAR/train.py , line 276)=> [ep15] (training ) Lm: 6.980 (6.980), Lt: 6.326 (6.326), Acc m&t: 2.22 3.45, Remain: 4 days, 22:07:34, Finish: 2024-11-27 03:40 [11-22 21:32:49] (/home/user/VAR/train.py , line 276)=> [ep15] (training ) Lm: 6.980 (6.980), Lt: 6.326 (6.326), Acc m&t: 2.22 3.45, Remain: 4 days, 22:07:40, Finish: 2024-11-27 03:40 [11-22 21:32:49] (/home/user/VAR/train.py , line 276)=> [ep15] (training ) Lm: 6.980 (6.980), Lt: 6.326 (6.326), Acc m&t: 2.22 3.45, Remain: 4 days, 22:09:30, Finish: 2024-11-27 03:42 [11-22 21:32:49] (/home/user/VAR/train.py , line 276)=> [ep15] (training ) Lm: 6.980 (6.980), Lt: 6.326 (6.326), Acc m&t: 2.22 3.45, Remain: 4 days, 22:07:35, Finish: 2024-11-27 03:40 [11-22 21:32:49] (/home/user/VAR/train.py , line 276)=> [ep15] (training ) Lm: 6.980 (6.980), Lt: 6.326 (6.326), Acc m&t: 2.22 3.45, Remain: 4 days, 22:06:47, Finish: 2024-11-27 03:39 [11-22 21:32:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 0/1669] eta: 0:21:27 tlr: 0.00024 tnm: 0.41 Lm: 7.123 (7.123) Lt: 6.503 (6.503) Accm: 2.00 (2.00) Acct: 3.06 (3.06) proj_loss: -0.4844 (-0.4844) time: 0.7714 data: 0.0003 [11-22 21:32:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 0/1669] eta: 0:21:24 tlr: 0.00024 tnm: 0.41 Lm: 6.685 (6.685) Lt: 5.956 (5.956) Accm: 3.12 (3.12) Acct: 4.34 (4.34) proj_loss: -0.4661 (-0.4661) time: 0.7699 data: 0.0004 [11-22 21:32:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 0/1669] eta: 0:21:26 tlr: 0.00024 tnm: 0.41 Lm: 7.047 (7.047) Lt: 6.456 (6.456) Accm: 2.13 (2.13) Acct: 3.03 (3.03) proj_loss: -0.4956 (-0.4956) time: 0.7707 data: 0.0004 [11-22 21:32:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 0/1669] eta: 0:21:26 tlr: 0.00024 tnm: 0.41 Lm: 6.744 (6.744) Lt: 6.053 (6.053) Accm: 2.71 (2.71) Acct: 4.10 (4.10) proj_loss: -0.4756 (-0.4756) time: 0.7706 data: 0.0004 [11-22 21:32:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 0/1669] eta: 0:21:27 tlr: 0.00024 tnm: 0.41 Lm: 6.628 (6.628) Lt: 5.867 (5.867) Accm: 3.07 (3.07) Acct: 4.86 (4.86) proj_loss: -0.4753 (-0.4753) time: 0.7712 data: 0.0003 [11-22 21:32:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 0/1669] eta: 0:21:26 tlr: 0.00024 tnm: 0.41 Lm: 7.048 (7.048) Lt: 6.426 (6.426) Accm: 2.07 (2.07) Acct: 3.27 (3.27) proj_loss: -0.4719 (-0.4719) time: 0.7710 data: 0.0004 [11-22 21:32:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 0/1669] eta: 0:21:27 tlr: 0.00024 tnm: 0.41 Lm: 6.964 (6.964) Lt: 6.266 (6.266) Accm: 2.10 (2.10) Acct: 3.27 (3.27) proj_loss: -0.4972 (-0.4972) time: 0.7714 data: 0.0004 [11-22 21:32:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 0/1669] eta: 0:21:28 tlr: 0.00024 tnm: 0.41 Lm: 6.956 (6.956) Lt: 6.283 (6.283) Accm: 2.14 (2.14) Acct: 3.24 (3.24) proj_loss: -0.4722 (-0.4722) time: 0.7721 data: 0.0004 [11-22 21:38:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 417/1669] eta: 0:15:50 tlr: 0.00024 tnm: 0.39 Lm: 6.834 (6.834) Lt: 6.151 (6.151) Accm: 2.47 (2.47) Acct: 3.84 (3.84) proj_loss: -0.4872 (-0.4872) time: 0.7549 data: 0.0002 [11-22 21:38:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 417/1669] eta: 0:15:50 tlr: 0.00024 tnm: 0.39 Lm: 6.720 (6.720) Lt: 6.010 (6.010) Accm: 2.96 (2.96) Acct: 4.63 (4.63) proj_loss: -0.4853 (-0.4853) time: 0.7549 data: 0.0002 [11-22 21:38:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 417/1669] eta: 0:15:50 tlr: 0.00024 tnm: 0.39 Lm: 6.856 (6.856) Lt: 6.181 (6.181) Accm: 2.57 (2.57) Acct: 3.65 (3.65) proj_loss: -0.4749 (-0.4749) time: 0.7550 data: 0.0002 [11-22 21:38:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 417/1669] eta: 0:15:50 tlr: 0.00024 tnm: 0.39 Lm: 6.950 (6.950) Lt: 6.347 (6.347) Accm: 2.28 (2.28) Acct: 3.25 (3.25) proj_loss: -0.5002 (-0.5002) time: 0.7550 data: 0.0003 [11-22 21:38:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 417/1669] eta: 0:15:50 tlr: 0.00024 tnm: 0.39 Lm: 6.976 (6.976) Lt: 6.330 (6.330) Accm: 2.20 (2.20) Acct: 3.37 (3.37) proj_loss: -0.4980 (-0.4980) time: 0.7549 data: 0.0002 [11-22 21:38:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 417/1669] eta: 0:15:50 tlr: 0.00024 tnm: 0.39 Lm: 6.986 (6.986) Lt: 6.316 (6.316) Accm: 2.18 (2.18) Acct: 3.44 (3.44) proj_loss: -0.4728 (-0.4728) time: 0.7549 data: 0.0003 [11-22 21:38:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 417/1669] eta: 0:15:50 tlr: 0.00024 tnm: 0.39 Lm: 7.043 (7.043) Lt: 6.406 (6.406) Accm: 2.10 (2.10) Acct: 3.15 (3.15) proj_loss: -0.4933 (-0.4933) time: 0.7549 data: 0.0003 [11-22 21:38:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 417/1669] eta: 0:15:50 tlr: 0.00024 tnm: 0.39 Lm: 6.963 (6.963) Lt: 6.282 (6.282) Accm: 2.11 (2.11) Acct: 3.17 (3.17) proj_loss: -0.4985 (-0.4985) time: 0.7549 data: 0.0003 [11-22 21:43:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 834/1669] eta: 0:10:32 tlr: 0.00024 tnm: 0.41 Lm: 6.962 (6.942) Lt: 6.266 (6.229) Accm: 2.11 (2.33) Acct: 3.27 (3.47) proj_loss: -0.4972 (-0.4842) time: 0.7541 data: 0.0002 [11-22 21:43:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 834/1669] eta: 0:10:32 tlr: 0.00024 tnm: 0.41 Lm: 6.993 (6.965) Lt: 6.310 (6.335) Accm: 2.13 (2.22) Acct: 3.37 (3.29) proj_loss: -0.4956 (-0.4922) time: 0.7541 data: 0.0002 [11-22 21:43:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 834/1669] eta: 0:10:32 tlr: 0.00024 tnm: 0.41 Lm: 6.812 (6.776) Lt: 6.153 (6.096) Accm: 2.86 (2.75) Acct: 4.41 (4.11) proj_loss: -0.4753 (-0.4815) time: 0.7541 data: 0.0002 [11-22 21:43:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 834/1669] eta: 0:10:32 tlr: 0.00024 tnm: 0.41 Lm: 7.048 (7.023) Lt: 6.426 (6.368) Accm: 2.07 (2.16) Acct: 3.27 (3.23) proj_loss: -0.4821 (-0.4927) time: 0.7541 data: 0.0002 [11-22 21:43:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 834/1669] eta: 0:10:32 tlr: 0.00024 tnm: 0.41 Lm: 7.027 (6.936) Lt: 6.405 (6.267) Accm: 2.03 (2.37) Acct: 3.41 (3.57) proj_loss: -0.4789 (-0.4762) time: 0.7541 data: 0.0002 [11-22 21:43:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 834/1669] eta: 0:10:32 tlr: 0.00024 tnm: 0.41 Lm: 6.880 (6.849) Lt: 6.129 (6.144) Accm: 2.30 (2.41) Acct: 3.79 (3.82) proj_loss: -0.4756 (-0.4822) time: 0.7541 data: 0.0003 [11-22 21:43:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 834/1669] eta: 0:10:32 tlr: 0.00024 tnm: 0.41 Lm: 7.025 (7.037) Lt: 6.333 (6.381) Accm: 2.00 (2.06) Acct: 3.10 (3.13) proj_loss: -0.4953 (-0.4940) time: 0.7541 data: 0.0003 [11-22 21:43:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [ 834/1669] eta: 0:10:32 tlr: 0.00024 tnm: 0.41 Lm: 6.956 (6.936) Lt: 6.283 (6.280) Accm: 2.21 (2.22) Acct: 3.41 (3.43) proj_loss: -0.4735 (-0.4732) time: 0.7541 data: 0.0003 [11-22 21:48:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.37 Lm: 6.964 (6.957) Lt: 6.282 (6.315) Accm: 2.16 (2.22) Acct: 3.43 (3.35) proj_loss: -0.4960 (-0.4933) time: 0.7544 data: 0.0002 [11-22 21:48:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.37 Lm: 7.019 (6.955) Lt: 6.346 (6.272) Accm: 2.15 (2.35) Acct: 3.46 (3.56) proj_loss: -0.4740 (-0.4745) time: 0.7544 data: 0.0003 [11-22 21:48:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.37 Lm: 6.976 (6.934) Lt: 6.330 (6.263) Accm: 2.20 (2.37) Acct: 3.37 (3.56) proj_loss: -0.4881 (-0.4931) time: 0.7544 data: 0.0002 [11-22 21:48:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.37 Lm: 7.042 (7.043) Lt: 6.379 (6.392) Accm: 2.09 (2.09) Acct: 3.17 (3.19) proj_loss: -0.4899 (-0.4894) time: 0.7544 data: 0.0003 [11-22 21:48:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.37 Lm: 6.849 (6.846) Lt: 6.210 (6.166) Accm: 2.59 (2.56) Acct: 3.77 (3.87) proj_loss: -0.4852 (-0.4849) time: 0.7544 data: 0.0003 [11-22 21:48:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.37 Lm: 6.896 (6.909) Lt: 6.245 (6.228) Accm: 2.27 (2.38) Acct: 3.53 (3.71) proj_loss: -0.4729 (-0.4729) time: 0.7544 data: 0.0003 [11-22 21:48:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.37 Lm: 6.902 (6.880) Lt: 6.190 (6.195) Accm: 2.27 (2.33) Acct: 3.68 (3.61) proj_loss: -0.4740 (-0.4789) time: 0.7544 data: 0.0003 [11-22 21:48:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1251/1669] eta: 0:05:15 tlr: 0.00024 tnm: 0.37 Lm: 6.963 (6.969) Lt: 6.282 (6.273) Accm: 2.11 (2.23) Acct: 3.19 (3.37) proj_loss: -0.4875 (-0.4826) time: 0.7544 data: 0.0003 [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 6.962 (6.938) Lt: 6.266 (6.241) Accm: 2.11 (2.23) Acct: 3.27 (3.38) proj_loss: -0.4911 (-0.4843) time: 0.7549 data: 0.0016 [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 16/350] Total time: 0:21:00 (0.755 s / it) [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 7.011 (6.955) Lt: 6.287 (6.259) Accm: 2.24 (2.33) Acct: 3.51 (3.55) proj_loss: -0.4789 (-0.4788) time: 0.7549 data: 0.0020 [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 6.993 (6.979) Lt: 6.310 (6.330) Accm: 2.13 (2.20) Acct: 3.37 (3.34) proj_loss: -0.4956 (-0.4870) time: 0.7549 data: 0.0017 [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 6.924 (6.892) Lt: 6.250 (6.217) Accm: 2.23 (2.27) Acct: 3.58 (3.57) proj_loss: -0.4756 (-0.4849) time: 0.7549 data: 0.0018 [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 6.934 (6.934) Lt: 6.245 (6.259) Accm: 2.23 (2.35) Acct: 3.37 (3.52) proj_loss: -0.4942 (-0.4935) time: 0.7549 data: 0.0017 [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 6.863 (6.849) Lt: 6.203 (6.173) Accm: 2.64 (2.57) Acct: 4.17 (3.93) proj_loss: -0.4951 (-0.4924) time: 0.7549 data: 0.0016 [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 7.025 (7.007) Lt: 6.333 (6.350) Accm: 2.19 (2.12) Acct: 3.24 (3.28) proj_loss: -0.4953 (-0.4986) time: 0.7549 data: 0.0020 [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 16/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.41 Lm: 6.835 (6.892) Lt: 6.207 (6.205) Accm: 2.32 (2.42) Acct: 3.65 (3.75) proj_loss: -0.4723 (-0.4718) time: 0.7549 data: 0.0019 [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 16/350] Total time: 0:21:00 (0.755 s / it) [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 16/350] Total time: 0:21:00 (0.755 s / it) [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 16/350] Total time: 0:21:00 (0.755 s / it) [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 16/350] Total time: 0:21:00 (0.755 s / it) [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 16/350] Total time: 0:21:00 (0.755 s / it) [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 16/350] Total time: 0:21:00 (0.755 s / it) [11-22 21:53:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 16/350] Total time: 0:21:00 (0.755 s / it) [11-22 21:53:49] (/home/user/VAR/train.py , line 276)=> [ep16] (training ) Lm: 6.959 (6.959), Lt: 6.298 (6.298), Acc m&t: 2.27 3.54, Remain: 4 days, 21:17:08, Finish: 2024-11-27 03:10 [11-22 21:53:49] (/home/user/VAR/train.py , line 276)=> [ep16] (training ) Lm: 6.959 (6.959), Lt: 6.298 (6.298), Acc m&t: 2.27 3.54, Remain: 4 days, 21:16:51, Finish: 2024-11-27 03:10 [11-22 21:53:49] (/home/user/VAR/train.py , line 276)=> [ep16] (training ) Lm: 6.959 (6.959), Lt: 6.298 (6.298), Acc m&t: 2.27 3.54, Remain: 4 days, 21:18:30, Finish: 2024-11-27 03:12 [11-22 21:53:49] (/home/user/VAR/train.py , line 276)=> [ep16] (training ) Lm: 6.959 (6.959), Lt: 6.298 (6.298), Acc m&t: 2.27 3.54, Remain: 4 days, 21:16:24, Finish: 2024-11-27 03:10 [11-22 21:53:49] (/home/user/VAR/train.py , line 276)=> [ep16] (training ) Lm: 6.959 (6.959), Lt: 6.298 (6.298), Acc m&t: 2.27 3.54, Remain: 4 days, 21:16:56, Finish: 2024-11-27 03:10 [11-22 21:53:49] (/home/user/VAR/train.py , line 276)=> [ep16] (training ) Lm: 6.959 (6.959), Lt: 6.298 (6.298), Acc m&t: 2.27 3.54, Remain: 4 days, 21:16:59, Finish: 2024-11-27 03:10 [11-22 21:53:49] (/home/user/VAR/train.py , line 276)=> [ep16] (training ) Lm: 6.959 (6.959), Lt: 6.298 (6.298), Acc m&t: 2.27 3.54, Remain: 4 days, 21:16:19, Finish: 2024-11-27 03:10 [11-22 21:53:49] (/home/user/VAR/train.py , line 276)=> [ep16] (training ) Lm: 6.959 (6.959), Lt: 6.298 (6.298), Acc m&t: 2.27 3.54, Remain: 4 days, 21:16:11, Finish: 2024-11-27 03:10 [11-22 21:53:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 0/1669] eta: 0:20:13 tlr: 0.00024 tnm: 0.37 Lm: 6.911 (6.911) Lt: 6.197 (6.197) Accm: 2.16 (2.16) Acct: 3.65 (3.65) proj_loss: -0.4691 (-0.4691) time: 0.7271 data: 0.0004 [11-22 21:53:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 0/1669] eta: 0:20:14 tlr: 0.00024 tnm: 0.37 Lm: 6.742 (6.742) Lt: 6.071 (6.071) Accm: 2.78 (2.78) Acct: 4.55 (4.55) proj_loss: -0.5096 (-0.5096) time: 0.7279 data: 0.0004 [11-22 21:53:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 0/1669] eta: 0:20:14 tlr: 0.00024 tnm: 0.37 Lm: 6.782 (6.782) Lt: 6.045 (6.045) Accm: 2.52 (2.52) Acct: 3.68 (3.68) proj_loss: -0.4988 (-0.4988) time: 0.7275 data: 0.0004 [11-22 21:53:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 0/1669] eta: 0:20:16 tlr: 0.00024 tnm: 0.37 Lm: 6.942 (6.942) Lt: 6.313 (6.313) Accm: 2.33 (2.33) Acct: 3.55 (3.55) proj_loss: -0.4876 (-0.4876) time: 0.7288 data: 0.0004 [11-22 21:53:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 0/1669] eta: 0:20:15 tlr: 0.00024 tnm: 0.37 Lm: 6.851 (6.851) Lt: 6.193 (6.193) Accm: 2.56 (2.56) Acct: 4.03 (4.03) proj_loss: -0.5167 (-0.5167) time: 0.7281 data: 0.0004 [11-22 21:53:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 0/1669] eta: 0:20:08 tlr: 0.00024 tnm: 0.37 Lm: 7.028 (7.028) Lt: 6.304 (6.304) Accm: 2.29 (2.29) Acct: 3.55 (3.55) proj_loss: -0.4874 (-0.4874) time: 0.7239 data: 0.0003 [11-22 21:53:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 0/1669] eta: 0:20:16 tlr: 0.00024 tnm: 0.37 Lm: 6.783 (6.783) Lt: 6.057 (6.057) Accm: 2.64 (2.64) Acct: 4.10 (4.10) proj_loss: -0.4433 (-0.4433) time: 0.7288 data: 0.0003 [11-22 21:53:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 0/1669] eta: 0:20:15 tlr: 0.00024 tnm: 0.37 Lm: 7.167 (7.167) Lt: 6.495 (6.495) Accm: 1.85 (1.85) Acct: 2.89 (2.89) proj_loss: -0.4872 (-0.4872) time: 0.7285 data: 0.0004 [11-22 21:59:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.39 Lm: 6.803 (6.803) Lt: 6.137 (6.137) Accm: 2.67 (2.67) Acct: 4.18 (4.18) proj_loss: -0.4981 (-0.4981) time: 0.7538 data: 0.0002 [11-22 21:59:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.39 Lm: 6.789 (6.789) Lt: 6.047 (6.047) Accm: 2.51 (2.51) Acct: 4.20 (4.20) proj_loss: -0.5026 (-0.5026) time: 0.7538 data: 0.0002 [11-22 21:59:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.39 Lm: 6.916 (6.916) Lt: 6.240 (6.240) Accm: 2.21 (2.21) Acct: 3.29 (3.29) proj_loss: -0.5051 (-0.5051) time: 0.7538 data: 0.0003 [11-22 21:59:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.39 Lm: 6.901 (6.901) Lt: 6.283 (6.283) Accm: 2.33 (2.33) Acct: 3.51 (3.51) proj_loss: -0.4923 (-0.4923) time: 0.7538 data: 0.0003 [11-22 21:59:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.39 Lm: 6.994 (6.994) Lt: 6.265 (6.265) Accm: 2.23 (2.23) Acct: 3.55 (3.55) proj_loss: -0.4946 (-0.4946) time: 0.7538 data: 0.0003 [11-22 21:59:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.39 Lm: 6.832 (6.832) Lt: 6.169 (6.169) Accm: 2.71 (2.71) Acct: 4.12 (4.12) proj_loss: -0.5013 (-0.5013) time: 0.7538 data: 0.0002 [11-22 21:59:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.39 Lm: 6.860 (6.860) Lt: 6.159 (6.159) Accm: 2.48 (2.48) Acct: 3.79 (3.79) proj_loss: -0.4744 (-0.4744) time: 0.7538 data: 0.0003 [11-22 21:59:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.39 Lm: 7.038 (7.038) Lt: 6.330 (6.330) Accm: 2.06 (2.06) Acct: 3.27 (3.27) proj_loss: -0.4739 (-0.4739) time: 0.7539 data: 0.0003 [11-22 22:05:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 834/1669] eta: 0:11:48 tlr: 0.00024 tnm: 0.43 Lm: 6.911 (6.864) Lt: 6.197 (6.140) Accm: 2.16 (2.29) Acct: 3.65 (3.85) proj_loss: -0.4986 (-0.5012) time: 0.7601 data: 0.0002 [11-22 22:05:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 834/1669] eta: 0:11:48 tlr: 0.00024 tnm: 0.43 Lm: 6.942 (6.934) Lt: 6.313 (6.303) Accm: 2.33 (2.34) Acct: 3.55 (3.52) proj_loss: -0.4970 (-0.4972) time: 0.7601 data: 0.0002 [11-22 22:05:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 834/1669] eta: 0:11:48 tlr: 0.00024 tnm: 0.43 Lm: 6.864 (6.838) Lt: 6.204 (6.193) Accm: 2.55 (2.54) Acct: 3.82 (3.94) proj_loss: -0.4932 (-0.4965) time: 0.7601 data: 0.0002 [11-22 22:05:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 834/1669] eta: 0:11:48 tlr: 0.00024 tnm: 0.43 Lm: 6.822 (6.829) Lt: 6.150 (6.162) Accm: 2.56 (2.65) Acct: 4.03 (3.94) proj_loss: -0.5106 (-0.5044) time: 0.7601 data: 0.0002 [11-22 22:05:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 834/1669] eta: 0:11:48 tlr: 0.00024 tnm: 0.43 Lm: 6.934 (6.922) Lt: 6.300 (6.260) Accm: 2.36 (2.26) Acct: 3.44 (3.34) proj_loss: -0.5113 (-0.5078) time: 0.7601 data: 0.0003 [11-22 22:05:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 834/1669] eta: 0:11:48 tlr: 0.00024 tnm: 0.43 Lm: 6.980 (6.989) Lt: 6.304 (6.298) Accm: 2.17 (2.21) Acct: 3.55 (3.42) proj_loss: -0.5017 (-0.5006) time: 0.7601 data: 0.0003 [11-22 22:05:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 834/1669] eta: 0:11:48 tlr: 0.00024 tnm: 0.43 Lm: 6.910 (6.987) Lt: 6.213 (6.291) Accm: 2.27 (2.20) Acct: 3.65 (3.52) proj_loss: -0.4872 (-0.4818) time: 0.7601 data: 0.0003 [11-22 22:05:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [ 834/1669] eta: 0:11:48 tlr: 0.00024 tnm: 0.43 Lm: 6.937 (6.911) Lt: 6.262 (6.221) Accm: 2.32 (2.38) Acct: 3.82 (3.80) proj_loss: -0.5055 (-0.4907) time: 0.7601 data: 0.0007 [11-22 22:10:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.42 Lm: 6.947 (6.894) Lt: 6.261 (6.189) Accm: 2.19 (2.27) Acct: 3.60 (3.77) proj_loss: -0.4988 (-0.5007) time: 0.7547 data: 0.0002 [11-22 22:10:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.42 Lm: 6.901 (6.916) Lt: 6.283 (6.268) Accm: 2.34 (2.39) Acct: 3.55 (3.63) proj_loss: -0.4923 (-0.4830) time: 0.7547 data: 0.0002 [11-22 22:10:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.42 Lm: 6.886 (6.869) Lt: 6.253 (6.232) Accm: 2.42 (2.41) Acct: 3.63 (3.75) proj_loss: -0.5014 (-0.5059) time: 0.7547 data: 0.0003 [11-22 22:10:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.42 Lm: 6.836 (6.843) Lt: 6.172 (6.172) Accm: 2.54 (2.60) Acct: 3.89 (3.89) proj_loss: -0.4982 (-0.4932) time: 0.7547 data: 0.0002 [11-22 22:10:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.42 Lm: 6.962 (6.930) Lt: 6.283 (6.242) Accm: 2.36 (2.39) Acct: 3.94 (3.87) proj_loss: -0.5017 (-0.4925) time: 0.7547 data: 0.0003 [11-22 22:10:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.42 Lm: 6.970 (6.974) Lt: 6.275 (6.285) Accm: 2.23 (2.26) Acct: 3.55 (3.54) proj_loss: -0.4946 (-0.4964) time: 0.7547 data: 0.0003 [11-22 22:10:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.42 Lm: 6.904 (6.910) Lt: 6.248 (6.244) Accm: 2.34 (2.28) Acct: 3.56 (3.43) proj_loss: -0.5051 (-0.5037) time: 0.7547 data: 0.0003 [11-22 22:10:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.42 Lm: 6.936 (6.981) Lt: 6.291 (6.310) Accm: 2.35 (2.26) Acct: 3.68 (3.57) proj_loss: -0.4924 (-0.4887) time: 0.7547 data: 0.0003 [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.38 Lm: 6.962 (7.013) Lt: 6.368 (6.362) Accm: 2.27 (2.20) Acct: 3.65 (3.44) proj_loss: -0.4976 (-0.4959) time: 0.7544 data: 0.0029 [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 17/350] Total time: 0:22:20 (0.803 s / it) [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.38 Lm: 6.908 (6.897) Lt: 6.270 (6.239) Accm: 2.29 (2.38) Acct: 3.72 (3.75) proj_loss: -0.4932 (-0.5006) time: 0.7544 data: 0.0016 [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.38 Lm: 6.942 (6.924) Lt: 6.312 (6.277) Accm: 2.33 (2.35) Acct: 3.55 (3.55) proj_loss: -0.4970 (-0.4882) time: 0.7544 data: 0.0016 [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.38 Lm: 6.934 (6.916) Lt: 6.300 (6.256) Accm: 2.32 (2.25) Acct: 3.44 (3.41) proj_loss: -0.5077 (-0.5045) time: 0.7544 data: 0.0019 [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.38 Lm: 6.980 (6.989) Lt: 6.304 (6.295) Accm: 2.17 (2.17) Acct: 3.55 (3.43) proj_loss: -0.4883 (-0.4947) time: 0.7544 data: 0.0016 [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.38 Lm: 6.943 (6.904) Lt: 6.318 (6.215) Accm: 2.16 (2.23) Acct: 3.55 (3.62) proj_loss: -0.4990 (-0.5007) time: 0.7544 data: 0.0015 [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.38 Lm: 6.937 (6.919) Lt: 6.262 (6.233) Accm: 2.32 (2.35) Acct: 3.82 (3.82) proj_loss: -0.4978 (-0.4901) time: 0.7544 data: 0.0016 [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 17/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.38 Lm: 6.851 (6.880) Lt: 6.193 (6.220) Accm: 2.52 (2.50) Acct: 3.75 (3.74) proj_loss: -0.4858 (-0.4897) time: 0.7545 data: 0.0017 [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 17/350] Total time: 0:22:20 (0.803 s / it) [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 17/350] Total time: 0:22:20 (0.803 s / it) [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 17/350] Total time: 0:22:20 (0.803 s / it) [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 17/350] Total time: 0:22:20 (0.803 s / it) [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 17/350] Total time: 0:22:20 (0.803 s / it) [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 17/350] Total time: 0:22:20 (0.803 s / it) [11-22 22:16:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 17/350] Total time: 0:22:20 (0.803 s / it) [11-22 22:16:10] (/home/user/VAR/train.py , line 276)=> [ep17] (training ) Lm: 6.932 (6.932), Lt: 6.268 (6.268), Acc m&t: 2.30 3.59, Remain: 4 days, 20:54:35, Finish: 2024-11-27 03:10 [11-22 22:16:10] (/home/user/VAR/train.py , line 276)=> [ep17] (training ) Lm: 6.932 (6.932), Lt: 6.268 (6.268), Acc m&t: 2.30 3.59, Remain: 4 days, 20:52:18, Finish: 2024-11-27 03:08 [11-22 22:16:10] (/home/user/VAR/train.py , line 276)=> [ep17] (training ) Lm: 6.932 (6.932), Lt: 6.268 (6.268), Acc m&t: 2.30 3.59, Remain: 4 days, 20:52:19, Finish: 2024-11-27 03:08 [11-22 22:16:10] (/home/user/VAR/train.py , line 276)=> [ep17] (training ) Lm: 6.932 (6.932), Lt: 6.268 (6.268), Acc m&t: 2.30 3.59, Remain: 4 days, 20:52:40, Finish: 2024-11-27 03:08 [11-22 22:16:10] (/home/user/VAR/train.py , line 276)=> [ep17] (training ) Lm: 6.932 (6.932), Lt: 6.268 (6.268), Acc m&t: 2.30 3.59, Remain: 4 days, 20:52:17, Finish: 2024-11-27 03:08 [11-22 22:16:10] (/home/user/VAR/train.py , line 276)=> [ep17] (training ) Lm: 6.932 (6.932), Lt: 6.268 (6.268), Acc m&t: 2.30 3.59, Remain: 4 days, 20:52:16, Finish: 2024-11-27 03:08 [11-22 22:16:10] (/home/user/VAR/train.py , line 276)=> [ep17] (training ) Lm: 6.932 (6.932), Lt: 6.268 (6.268), Acc m&t: 2.30 3.59, Remain: 4 days, 20:52:11, Finish: 2024-11-27 03:08 [11-22 22:16:10] (/home/user/VAR/train.py , line 276)=> [ep17] (training ) Lm: 6.932 (6.932), Lt: 6.268 (6.268), Acc m&t: 2.30 3.59, Remain: 4 days, 20:52:27, Finish: 2024-11-27 03:08 [11-22 22:16:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 0/1669] eta: 0:19:39 tlr: 0.00024 tnm: 0.33 Lm: 6.943 (6.943) Lt: 6.271 (6.271) Accm: 2.19 (2.19) Acct: 3.51 (3.51) proj_loss: -0.4957 (-0.4957) time: 0.7067 data: 0.0004 [11-22 22:16:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 0/1669] eta: 0:19:42 tlr: 0.00024 tnm: 0.33 Lm: 6.895 (6.895) Lt: 6.175 (6.175) Accm: 2.45 (2.45) Acct: 4.30 (4.30) proj_loss: -0.5080 (-0.5080) time: 0.7083 data: 0.0004 [11-22 22:16:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 0/1669] eta: 0:19:43 tlr: 0.00024 tnm: 0.33 Lm: 6.907 (6.907) Lt: 6.277 (6.277) Accm: 1.98 (1.98) Acct: 3.20 (3.20) proj_loss: -0.5090 (-0.5090) time: 0.7090 data: 0.0004 [11-22 22:16:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 0/1669] eta: 0:19:43 tlr: 0.00024 tnm: 0.33 Lm: 7.064 (7.064) Lt: 6.453 (6.453) Accm: 1.72 (1.72) Acct: 2.55 (2.55) proj_loss: -0.5161 (-0.5161) time: 0.7089 data: 0.0003 [11-22 22:16:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 0/1669] eta: 0:19:42 tlr: 0.00024 tnm: 0.33 Lm: 6.945 (6.945) Lt: 6.287 (6.287) Accm: 2.00 (2.00) Acct: 2.89 (2.89) proj_loss: -0.4920 (-0.4920) time: 0.7086 data: 0.0004 [11-22 22:16:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 0/1669] eta: 0:19:42 tlr: 0.00024 tnm: 0.33 Lm: 6.777 (6.777) Lt: 6.069 (6.069) Accm: 2.59 (2.59) Acct: 4.10 (4.10) proj_loss: -0.4907 (-0.4907) time: 0.7085 data: 0.0004 [11-22 22:16:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 0/1669] eta: 0:19:43 tlr: 0.00024 tnm: 0.33 Lm: 6.809 (6.809) Lt: 6.175 (6.175) Accm: 2.81 (2.81) Acct: 4.34 (4.34) proj_loss: -0.4937 (-0.4937) time: 0.7094 data: 0.0004 [11-22 22:16:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 0/1669] eta: 0:19:45 tlr: 0.00024 tnm: 0.33 Lm: 6.779 (6.779) Lt: 6.103 (6.103) Accm: 2.43 (2.43) Acct: 3.82 (3.82) proj_loss: -0.5131 (-0.5131) time: 0.7101 data: 0.0004 [11-22 22:21:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.41 Lm: 6.800 (6.800) Lt: 6.085 (6.085) Accm: 2.63 (2.63) Acct: 4.10 (4.10) proj_loss: -0.4901 (-0.4901) time: 0.7524 data: 0.0003 [11-22 22:21:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.41 Lm: 6.927 (6.927) Lt: 6.228 (6.228) Accm: 2.70 (2.70) Acct: 4.48 (4.48) proj_loss: -0.5086 (-0.5086) time: 0.7524 data: 0.0003 [11-22 22:21:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.41 Lm: 6.949 (6.949) Lt: 6.273 (6.273) Accm: 2.28 (2.28) Acct: 3.70 (3.70) proj_loss: -0.4959 (-0.4959) time: 0.7524 data: 0.0002 [11-22 22:21:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.41 Lm: 7.003 (7.003) Lt: 6.359 (6.359) Accm: 1.84 (1.84) Acct: 2.93 (2.93) proj_loss: -0.4984 (-0.4984) time: 0.7524 data: 0.0002 [11-22 22:21:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.41 Lm: 6.897 (6.897) Lt: 6.170 (6.170) Accm: 2.29 (2.29) Acct: 3.41 (3.41) proj_loss: -0.4880 (-0.4880) time: 0.7524 data: 0.0002 [11-22 22:21:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.41 Lm: 6.877 (6.877) Lt: 6.202 (6.202) Accm: 2.52 (2.52) Acct: 3.94 (3.94) proj_loss: -0.4959 (-0.4959) time: 0.7524 data: 0.0003 [11-22 22:21:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.41 Lm: 6.718 (6.718) Lt: 6.010 (6.010) Accm: 2.78 (2.78) Acct: 4.27 (4.27) proj_loss: -0.4950 (-0.4950) time: 0.7524 data: 0.0003 [11-22 22:21:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.41 Lm: 6.918 (6.918) Lt: 6.271 (6.271) Accm: 2.04 (2.04) Acct: 3.05 (3.05) proj_loss: -0.5156 (-0.5156) time: 0.7524 data: 0.0003 [11-22 22:26:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.876 (6.904) Lt: 6.146 (6.229) Accm: 2.27 (2.12) Acct: 3.55 (3.26) proj_loss: -0.5151 (-0.5113) time: 0.7525 data: 0.0003 [11-22 22:26:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 7.088 (7.031) Lt: 6.441 (6.413) Accm: 1.91 (1.86) Acct: 3.03 (2.96) proj_loss: -0.5090 (-0.5023) time: 0.7525 data: 0.0003 [11-22 22:26:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.895 (6.878) Lt: 6.175 (6.195) Accm: 2.70 (2.70) Acct: 4.55 (4.50) proj_loss: -0.5092 (-0.5132) time: 0.7525 data: 0.0003 [11-22 22:26:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.824 (6.909) Lt: 6.102 (6.223) Accm: 2.59 (2.43) Acct: 4.10 (3.75) proj_loss: -0.4907 (-0.5054) time: 0.7525 data: 0.0002 [11-22 22:26:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.943 (6.883) Lt: 6.271 (6.188) Accm: 2.37 (2.50) Acct: 3.89 (4.02) proj_loss: -0.4957 (-0.4936) time: 0.7525 data: 0.0002 [11-22 22:26:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.863 (6.886) Lt: 6.192 (6.177) Accm: 2.11 (2.23) Acct: 3.31 (3.37) proj_loss: -0.4920 (-0.4915) time: 0.7525 data: 0.0002 [11-22 22:26:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.809 (6.850) Lt: 6.175 (6.177) Accm: 2.75 (2.45) Acct: 4.20 (3.75) proj_loss: -0.4963 (-0.4976) time: 0.7525 data: 0.0003 [11-22 22:26:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.860 (6.872) Lt: 6.234 (6.212) Accm: 2.43 (2.47) Acct: 3.82 (3.72) proj_loss: -0.4952 (-0.4956) time: 0.7525 data: 0.0003 [11-22 22:31:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.36 Lm: 6.949 (6.923) Lt: 6.273 (6.220) Accm: 2.30 (2.43) Acct: 3.84 (3.96) proj_loss: -0.4959 (-0.4944) time: 0.7526 data: 0.0002 [11-22 22:31:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.36 Lm: 6.872 (6.871) Lt: 6.151 (6.173) Accm: 2.72 (2.71) Acct: 4.42 (4.44) proj_loss: -0.5128 (-0.5140) time: 0.7526 data: 0.0002 [11-22 22:31:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.36 Lm: 6.839 (6.895) Lt: 6.148 (6.216) Accm: 2.45 (2.40) Acct: 3.72 (3.65) proj_loss: -0.4997 (-0.5062) time: 0.7526 data: 0.0003 [11-22 22:31:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.36 Lm: 6.998 (6.966) Lt: 6.359 (6.344) Accm: 1.94 (2.01) Acct: 3.12 (3.20) proj_loss: -0.5033 (-0.5011) time: 0.7526 data: 0.0003 [11-22 22:31:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.36 Lm: 6.856 (6.870) Lt: 6.183 (6.176) Accm: 2.23 (2.26) Acct: 3.50 (3.45) proj_loss: -0.4953 (-0.4937) time: 0.7526 data: 0.0003 [11-22 22:31:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.36 Lm: 6.822 (6.846) Lt: 6.209 (6.193) Accm: 2.73 (2.51) Acct: 4.17 (3.85) proj_loss: -0.4995 (-0.4989) time: 0.7526 data: 0.0003 [11-22 22:31:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.36 Lm: 6.918 (6.918) Lt: 6.267 (6.265) Accm: 2.40 (2.44) Acct: 3.72 (3.69) proj_loss: -0.4869 (-0.4901) time: 0.7526 data: 0.0003 [11-22 22:31:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.36 Lm: 6.923 (6.921) Lt: 6.224 (6.247) Accm: 2.07 (2.05) Acct: 3.19 (3.15) proj_loss: -0.5088 (-0.5057) time: 0.7526 data: 0.0003 [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.36 Lm: 6.876 (6.910) Lt: 6.167 (6.231) Accm: 2.27 (2.11) Acct: 3.55 (3.31) proj_loss: -0.5025 (-0.5003) time: 0.8813 data: 0.0021 [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 18/350] Total time: 0:22:17 (0.801 s / it) [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.36 Lm: 6.933 (6.959) Lt: 6.277 (6.330) Accm: 1.98 (2.07) Acct: 3.20 (3.26) proj_loss: -0.5090 (-0.5092) time: 0.8813 data: 0.0018 [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.36 Lm: 6.895 (6.923) Lt: 6.175 (6.249) Accm: 2.70 (2.56) Acct: 4.30 (4.15) proj_loss: -0.5112 (-0.5134) time: 0.8813 data: 0.0016 [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.36 Lm: 6.943 (6.886) Lt: 6.271 (6.175) Accm: 2.29 (2.40) Acct: 3.79 (3.91) proj_loss: -0.4961 (-0.4948) time: 0.8813 data: 0.0021 [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.36 Lm: 6.854 (6.906) Lt: 6.194 (6.237) Accm: 2.32 (2.36) Acct: 3.34 (3.53) proj_loss: -0.4907 (-0.4998) time: 0.8813 data: 0.0020 [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.36 Lm: 6.835 (6.887) Lt: 6.243 (6.234) Accm: 2.71 (2.42) Acct: 4.13 (3.79) proj_loss: -0.4963 (-0.4954) time: 0.8813 data: 0.0021 [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.36 Lm: 6.863 (6.879) Lt: 6.192 (6.189) Accm: 2.35 (2.31) Acct: 3.68 (3.56) proj_loss: -0.4985 (-0.4977) time: 0.8813 data: 0.0017 [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 18/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.36 Lm: 6.860 (6.887) Lt: 6.234 (6.238) Accm: 2.43 (2.44) Acct: 3.72 (3.70) proj_loss: -0.4814 (-0.4884) time: 0.8813 data: 0.0022 [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 18/350] Total time: 0:22:17 (0.801 s / it) [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 18/350] Total time: 0:22:17 (0.801 s / it) [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 18/350] Total time: 0:22:17 (0.801 s / it) [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 18/350] Total time: 0:22:17 (0.801 s / it) [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 18/350] Total time: 0:22:17 (0.801 s / it) [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 18/350] Total time: 0:22:17 (0.801 s / it) [11-22 22:38:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 18/350] Total time: 0:22:17 (0.801 s / it) [11-22 22:38:27] (/home/user/VAR/train.py , line 276)=> [ep18] (training ) Lm: 6.917 (6.917), Lt: 6.246 (6.246), Acc m&t: 2.35 3.67, Remain: 4 days, 20:48:49, Finish: 2024-11-27 03:27 [11-22 22:38:27] (/home/user/VAR/train.py , line 276)=> [ep18] (training ) Lm: 6.917 (6.917), Lt: 6.246 (6.246), Acc m&t: 2.35 3.67, Remain: 4 days, 20:49:39, Finish: 2024-11-27 03:28 [11-22 22:38:27] (/home/user/VAR/train.py , line 276)=> [ep18] (training ) Lm: 6.917 (6.917), Lt: 6.246 (6.246), Acc m&t: 2.35 3.67, Remain: 4 days, 20:48:46, Finish: 2024-11-27 03:27 [11-22 22:38:27] (/home/user/VAR/train.py , line 276)=> [ep18] (training ) Lm: 6.917 (6.917), Lt: 6.246 (6.246), Acc m&t: 2.35 3.67, Remain: 4 days, 20:47:12, Finish: 2024-11-27 03:25 [11-22 22:38:27] (/home/user/VAR/train.py , line 276)=> [ep18] (training ) Lm: 6.917 (6.917), Lt: 6.246 (6.246), Acc m&t: 2.35 3.67, Remain: 4 days, 20:47:38, Finish: 2024-11-27 03:26 [11-22 22:38:27] (/home/user/VAR/train.py , line 276)=> [ep18] (training ) Lm: 6.917 (6.917), Lt: 6.246 (6.246), Acc m&t: 2.35 3.67, Remain: 4 days, 20:49:17, Finish: 2024-11-27 03:27 [11-22 22:38:27] (/home/user/VAR/train.py , line 276)=> [ep18] (training ) Lm: 6.917 (6.917), Lt: 6.246 (6.246), Acc m&t: 2.35 3.67, Remain: 4 days, 20:49:09, Finish: 2024-11-27 03:27 [11-22 22:38:27] (/home/user/VAR/train.py , line 276)=> [ep18] (training ) Lm: 6.917 (6.917), Lt: 6.246 (6.246), Acc m&t: 2.35 3.67, Remain: 4 days, 20:48:52, Finish: 2024-11-27 03:27 [11-22 22:38:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 0/1669] eta: 0:20:35 tlr: 0.00024 tnm: 0.36 Lm: 7.095 (7.095) Lt: 6.437 (6.437) Accm: 2.05 (2.05) Acct: 3.27 (3.27) proj_loss: -0.5279 (-0.5279) time: 0.7402 data: 0.0003 [11-22 22:38:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 0/1669] eta: 0:20:37 tlr: 0.00024 tnm: 0.36 Lm: 7.002 (7.002) Lt: 6.366 (6.366) Accm: 2.29 (2.29) Acct: 3.10 (3.10) proj_loss: -0.5155 (-0.5155) time: 0.7416 data: 0.0004 [11-22 22:38:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 0/1669] eta: 0:20:37 tlr: 0.00024 tnm: 0.36 Lm: 6.885 (6.885) Lt: 6.226 (6.226) Accm: 2.26 (2.26) Acct: 3.44 (3.44) proj_loss: -0.4990 (-0.4990) time: 0.7412 data: 0.0004 [11-22 22:38:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 0/1669] eta: 0:20:38 tlr: 0.00024 tnm: 0.36 Lm: 6.830 (6.830) Lt: 6.200 (6.200) Accm: 2.70 (2.70) Acct: 4.27 (4.27) proj_loss: -0.5150 (-0.5150) time: 0.7420 data: 0.0004 [11-22 22:38:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 0/1669] eta: 0:20:37 tlr: 0.00024 tnm: 0.36 Lm: 7.006 (7.006) Lt: 6.369 (6.369) Accm: 2.16 (2.16) Acct: 3.37 (3.37) proj_loss: -0.5030 (-0.5030) time: 0.7416 data: 0.0004 [11-22 22:38:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 0/1669] eta: 0:20:38 tlr: 0.00024 tnm: 0.36 Lm: 6.728 (6.728) Lt: 5.993 (5.993) Accm: 3.03 (3.03) Acct: 4.86 (4.86) proj_loss: -0.4972 (-0.4972) time: 0.7421 data: 0.0004 [11-22 22:38:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 0/1669] eta: 0:20:39 tlr: 0.00024 tnm: 0.36 Lm: 6.856 (6.856) Lt: 6.222 (6.222) Accm: 2.27 (2.27) Acct: 3.68 (3.68) proj_loss: -0.5193 (-0.5193) time: 0.7424 data: 0.0003 [11-22 22:38:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 0/1669] eta: 0:20:39 tlr: 0.00024 tnm: 0.36 Lm: 6.998 (6.998) Lt: 6.347 (6.347) Accm: 2.27 (2.27) Acct: 3.34 (3.34) proj_loss: -0.4486 (-0.4486) time: 0.7426 data: 0.0004 [11-22 22:43:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.40 Lm: 6.935 (6.935) Lt: 6.267 (6.267) Accm: 2.31 (2.31) Acct: 3.37 (3.37) proj_loss: -0.4694 (-0.4694) time: 0.7518 data: 0.0002 [11-22 22:43:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.40 Lm: 7.054 (7.054) Lt: 6.405 (6.405) Accm: 2.05 (2.05) Acct: 3.36 (3.36) proj_loss: -0.5061 (-0.5061) time: 0.7518 data: 0.0002 [11-22 22:43:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.40 Lm: 6.877 (6.877) Lt: 6.233 (6.233) Accm: 2.24 (2.24) Acct: 3.32 (3.32) proj_loss: -0.4958 (-0.4958) time: 0.7518 data: 0.0002 [11-22 22:43:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.40 Lm: 6.979 (6.979) Lt: 6.314 (6.314) Accm: 2.15 (2.15) Acct: 3.25 (3.25) proj_loss: -0.4922 (-0.4922) time: 0.7518 data: 0.0003 [11-22 22:43:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.40 Lm: 6.786 (6.786) Lt: 6.075 (6.075) Accm: 2.83 (2.83) Acct: 4.56 (4.56) proj_loss: -0.4945 (-0.4945) time: 0.7518 data: 0.0003 [11-22 22:43:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.40 Lm: 6.959 (6.959) Lt: 6.312 (6.312) Accm: 2.32 (2.32) Acct: 3.51 (3.51) proj_loss: -0.4980 (-0.4980) time: 0.7518 data: 0.0003 [11-22 22:43:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.40 Lm: 6.908 (6.908) Lt: 6.272 (6.272) Accm: 2.13 (2.13) Acct: 3.37 (3.37) proj_loss: -0.5289 (-0.5289) time: 0.7518 data: 0.0003 [11-22 22:43:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.40 Lm: 6.874 (6.874) Lt: 6.239 (6.239) Accm: 2.53 (2.53) Acct: 4.12 (4.12) proj_loss: -0.5063 (-0.5063) time: 0.7518 data: 0.0003 [11-22 22:48:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.39 Lm: 7.013 (7.000) Lt: 6.373 (6.321) Accm: 2.05 (2.21) Acct: 3.44 (3.67) proj_loss: -0.4843 (-0.4955) time: 0.7547 data: 0.0002 [11-22 22:48:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.39 Lm: 7.002 (6.992) Lt: 6.366 (6.337) Accm: 2.08 (2.13) Acct: 3.31 (3.27) proj_loss: -0.5155 (-0.5006) time: 0.7547 data: 0.0002 [11-22 22:48:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.39 Lm: 6.954 (6.941) Lt: 6.278 (6.271) Accm: 2.27 (2.30) Acct: 3.41 (3.48) proj_loss: -0.4903 (-0.4766) time: 0.7547 data: 0.0002 [11-22 22:48:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.39 Lm: 6.885 (6.884) Lt: 6.226 (6.210) Accm: 2.26 (2.34) Acct: 3.44 (3.52) proj_loss: -0.4926 (-0.4945) time: 0.7547 data: 0.0003 [11-22 22:48:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.39 Lm: 6.830 (6.808) Lt: 6.200 (6.151) Accm: 2.70 (2.63) Acct: 4.27 (4.30) proj_loss: -0.4975 (-0.4991) time: 0.7547 data: 0.0003 [11-22 22:48:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.39 Lm: 6.840 (6.804) Lt: 6.157 (6.129) Accm: 2.62 (2.68) Acct: 4.27 (4.22) proj_loss: -0.4972 (-0.5055) time: 0.7547 data: 0.0003 [11-22 22:48:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.39 Lm: 6.925 (6.948) Lt: 6.255 (6.283) Accm: 2.39 (2.34) Acct: 3.65 (3.63) proj_loss: -0.4930 (-0.4960) time: 0.7547 data: 0.0003 [11-22 22:48:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.39 Lm: 6.961 (6.961) Lt: 6.323 (6.324) Accm: 1.98 (2.02) Acct: 3.06 (3.26) proj_loss: -0.5370 (-0.5316) time: 0.7548 data: 0.0003 [11-22 22:54:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.33 Lm: 7.010 (7.002) Lt: 6.344 (6.320) Accm: 2.19 (2.24) Acct: 3.44 (3.62) proj_loss: -0.5027 (-0.5019) time: 0.7529 data: 0.0002 [11-22 22:54:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.33 Lm: 6.877 (6.878) Lt: 6.233 (6.221) Accm: 2.32 (2.35) Acct: 3.50 (3.53) proj_loss: -0.4952 (-0.4953) time: 0.7529 data: 0.0003 [11-22 22:54:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.33 Lm: 7.011 (7.015) Lt: 6.374 (6.353) Accm: 2.08 (2.11) Acct: 3.20 (3.21) proj_loss: -0.4922 (-0.4922) time: 0.7528 data: 0.0003 [11-22 22:54:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.33 Lm: 6.913 (6.858) Lt: 6.233 (6.157) Accm: 2.31 (2.61) Acct: 3.55 (4.06) proj_loss: -0.4906 (-0.4832) time: 0.7528 data: 0.0003 [11-22 22:54:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.33 Lm: 6.918 (6.927) Lt: 6.239 (6.249) Accm: 2.43 (2.41) Acct: 3.72 (3.67) proj_loss: -0.4932 (-0.4953) time: 0.7529 data: 0.0003 [11-22 22:54:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.33 Lm: 6.841 (6.815) Lt: 6.148 (6.131) Accm: 2.54 (2.62) Acct: 3.99 (4.10) proj_loss: -0.4976 (-0.5036) time: 0.7529 data: 0.0003 [11-22 22:54:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.33 Lm: 6.908 (6.932) Lt: 6.272 (6.274) Accm: 2.13 (2.17) Acct: 3.37 (3.44) proj_loss: -0.5282 (-0.5240) time: 0.7529 data: 0.0003 [11-22 22:54:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.33 Lm: 6.874 (6.838) Lt: 6.175 (6.151) Accm: 2.55 (2.57) Acct: 4.12 (4.15) proj_loss: -0.4912 (-0.4926) time: 0.7529 data: 0.0003 [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 6.830 (6.821) Lt: 6.151 (6.111) Accm: 2.70 (2.61) Acct: 4.27 (4.17) proj_loss: -0.4848 (-0.4880) time: 0.7520 data: 0.0018 [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 19/350] Total time: 0:20:55 (0.752 s / it) [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 6.885 (6.917) Lt: 6.240 (6.263) Accm: 2.26 (2.24) Acct: 3.44 (3.42) proj_loss: -0.4978 (-0.5000) time: 0.7520 data: 0.0016 [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 6.954 (6.880) Lt: 6.243 (6.174) Accm: 2.27 (2.52) Acct: 3.48 (3.95) proj_loss: -0.4909 (-0.4875) time: 0.7520 data: 0.0018 [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 7.002 (6.976) Lt: 6.366 (6.296) Accm: 2.08 (2.23) Acct: 3.31 (3.42) proj_loss: -0.5125 (-0.4962) time: 0.7520 data: 0.0015 [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 7.007 (6.962) Lt: 6.315 (6.275) Accm: 2.33 (2.30) Acct: 3.44 (3.69) proj_loss: -0.5212 (-0.5060) time: 0.7520 data: 0.0016 [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 6.911 (6.885) Lt: 6.224 (6.200) Accm: 2.48 (2.48) Acct: 3.79 (3.81) proj_loss: -0.4933 (-0.4977) time: 0.7520 data: 0.0017 [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 6.856 (6.876) Lt: 6.222 (6.197) Accm: 2.27 (2.34) Acct: 3.68 (3.74) proj_loss: -0.5193 (-0.5183) time: 0.7520 data: 0.0018 [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 19/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 6.840 (6.816) Lt: 6.147 (6.134) Accm: 2.62 (2.63) Acct: 4.06 (4.09) proj_loss: -0.4980 (-0.5051) time: 0.7520 data: 0.0022 [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 19/350] Total time: 0:20:55 (0.752 s / it) [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 19/350] Total time: 0:20:55 (0.752 s / it) [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 19/350] Total time: 0:20:55 (0.752 s / it) [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 19/350] Total time: 0:20:55 (0.752 s / it) [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 19/350] Total time: 0:20:55 (0.752 s / it) [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 19/350] Total time: 0:20:55 (0.752 s / it) [11-22 22:59:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 19/350] Total time: 0:20:55 (0.752 s / it) [11-22 23:01:26] (home/user/VAR/trainer.py, line 114)=> FID: 7.088123024520712 [11-22 23:01:28] (/home/user/VAR/train.py , line 259)=> [*] [ep19] (val 50000) Lm: 6.8931, Lt: 6.2119, Acc m&t: 2.40 3.74, Val cost: 124.53s [11-22 23:01:28] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-22 23:02:07] (/home/user/VAR/train.py , line 276)=> [ep19] (training ) Lm: 6.893 (6.893), Lt: 6.212 (6.212), Acc m&t: 2.40 3.74, Remain: 4 days, 20:02:09, Finish: 2024-11-27 03:01 [11-22 23:02:07] (/home/user/VAR/train.py , line 276)=> [ep19] (training ) Lm: 6.893 (6.893), Lt: 6.212 (6.212), Acc m&t: 2.40 3.74, Remain: 4 days, 20:02:22, Finish: 2024-11-27 03:01 [11-22 23:02:07] (/home/user/VAR/train.py , line 276)=> [ep19] (training ) Lm: 6.893 (6.893), Lt: 6.212 (6.212), Acc m&t: 2.40 3.74, Remain: 4 days, 20:02:22, Finish: 2024-11-27 03:01 [11-22 23:02:07] (/home/user/VAR/train.py , line 276)=> [ep19] (training ) Lm: 6.893 (6.893), Lt: 6.212 (6.212), Acc m&t: 2.40 3.74, Remain: 4 days, 20:02:07, Finish: 2024-11-27 03:01 [11-22 23:02:07] (/home/user/VAR/train.py , line 276)=> [ep19] (training ) Lm: 6.893 (6.893), Lt: 6.212 (6.212), Acc m&t: 2.40 3.74, Remain: 4 days, 20:02:36, Finish: 2024-11-27 03:01 [11-22 23:02:07] (/home/user/VAR/train.py , line 276)=> [ep19] (training ) Lm: 6.893 (6.893), Lt: 6.212 (6.212), Acc m&t: 2.40 3.74, Remain: 4 days, 20:02:28, Finish: 2024-11-27 03:01 [11-22 23:02:07] (/home/user/VAR/train.py , line 276)=> [ep19] (training ) Lm: 6.893 (6.893), Lt: 6.212 (6.212), Acc m&t: 2.40 3.74, Remain: 4 days, 20:01:43, Finish: 2024-11-27 03:01 [11-22 23:02:07] (/home/user/VAR/train.py , line 276)=> [ep19] (training ) Lm: 6.893 (6.893), Lt: 6.212 (6.212), Acc m&t: 2.40 3.74, Remain: 4 days, 20:03:08, Finish: 2024-11-27 03:02 [11-22 23:02:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 0/1669] eta: 0:20:28 tlr: 0.00024 tnm: 0.36 Lm: 6.724 (6.724) Lt: 6.023 (6.023) Accm: 2.51 (2.51) Acct: 3.82 (3.82) proj_loss: -0.5043 (-0.5043) time: 0.7363 data: 0.0003 [11-22 23:02:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 0/1669] eta: 0:20:36 tlr: 0.00024 tnm: 0.36 Lm: 7.015 (7.015) Lt: 6.287 (6.287) Accm: 2.19 (2.19) Acct: 3.20 (3.20) proj_loss: -0.5222 (-0.5222) time: 0.7410 data: 0.0004 [11-22 23:02:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 0/1669] eta: 0:20:29 tlr: 0.00024 tnm: 0.36 Lm: 6.880 (6.880) Lt: 6.265 (6.265) Accm: 2.58 (2.58) Acct: 4.03 (4.03) proj_loss: -0.5090 (-0.5090) time: 0.7370 data: 0.0003 [11-22 23:02:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 0/1669] eta: 0:20:34 tlr: 0.00024 tnm: 0.36 Lm: 6.852 (6.852) Lt: 6.103 (6.103) Accm: 2.14 (2.14) Acct: 3.44 (3.44) proj_loss: -0.5010 (-0.5010) time: 0.7396 data: 0.0003 [11-22 23:02:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 0/1669] eta: 0:20:29 tlr: 0.00024 tnm: 0.36 Lm: 6.807 (6.807) Lt: 6.065 (6.065) Accm: 2.19 (2.19) Acct: 3.34 (3.34) proj_loss: -0.5035 (-0.5035) time: 0.7367 data: 0.0004 [11-22 23:02:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 0/1669] eta: 0:20:34 tlr: 0.00024 tnm: 0.36 Lm: 6.949 (6.949) Lt: 6.318 (6.318) Accm: 2.08 (2.08) Acct: 3.37 (3.37) proj_loss: -0.5119 (-0.5119) time: 0.7398 data: 0.0004 [11-22 23:02:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 0/1669] eta: 0:20:30 tlr: 0.00024 tnm: 0.36 Lm: 6.804 (6.804) Lt: 6.197 (6.197) Accm: 2.68 (2.68) Acct: 3.93 (3.93) proj_loss: -0.5140 (-0.5140) time: 0.7373 data: 0.0004 [11-22 23:02:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 0/1669] eta: 0:20:30 tlr: 0.00024 tnm: 0.36 Lm: 6.949 (6.949) Lt: 6.338 (6.338) Accm: 2.49 (2.49) Acct: 3.75 (3.75) proj_loss: -0.5294 (-0.5294) time: 0.7375 data: 0.0004 [11-22 23:07:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.910 (6.910) Lt: 6.190 (6.190) Accm: 2.05 (2.05) Acct: 3.32 (3.32) proj_loss: -0.5005 (-0.5005) time: 0.7519 data: 0.0002 [11-22 23:07:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.953 (6.953) Lt: 6.255 (6.255) Accm: 2.27 (2.27) Acct: 3.44 (3.44) proj_loss: -0.5078 (-0.5078) time: 0.7519 data: 0.0002 [11-22 23:07:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.827 (6.827) Lt: 6.149 (6.149) Accm: 2.28 (2.28) Acct: 3.48 (3.48) proj_loss: -0.5026 (-0.5026) time: 0.7519 data: 0.0002 [11-22 23:07:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.808 (6.808) Lt: 6.172 (6.172) Accm: 2.59 (2.59) Acct: 4.01 (4.01) proj_loss: -0.5008 (-0.5008) time: 0.7519 data: 0.0002 [11-22 23:07:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.913 (6.913) Lt: 6.251 (6.251) Accm: 2.29 (2.29) Acct: 3.70 (3.70) proj_loss: -0.5200 (-0.5200) time: 0.7519 data: 0.0003 [11-22 23:07:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.932 (6.932) Lt: 6.251 (6.251) Accm: 2.21 (2.21) Acct: 3.31 (3.31) proj_loss: -0.4958 (-0.4958) time: 0.7519 data: 0.0002 [11-22 23:07:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.841 (6.841) Lt: 6.186 (6.186) Accm: 2.64 (2.64) Acct: 4.15 (4.15) proj_loss: -0.5374 (-0.5374) time: 0.7520 data: 0.0003 [11-22 23:07:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.912 (6.912) Lt: 6.221 (6.221) Accm: 2.16 (2.16) Acct: 3.29 (3.29) proj_loss: -0.4997 (-0.4997) time: 0.7519 data: 0.0003 [11-22 23:12:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.852 (6.885) Lt: 6.116 (6.186) Accm: 2.19 (2.33) Acct: 3.44 (3.58) proj_loss: -0.4984 (-0.4968) time: 0.7525 data: 0.0003 [11-22 23:12:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.810 (6.821) Lt: 6.079 (6.125) Accm: 2.51 (2.42) Acct: 3.82 (3.64) proj_loss: -0.5043 (-0.5126) time: 0.7525 data: 0.0002 [11-22 23:12:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.858 (6.825) Lt: 6.179 (6.174) Accm: 2.61 (2.62) Acct: 4.03 (4.13) proj_loss: -0.4995 (-0.5004) time: 0.7525 data: 0.0002 [11-22 23:12:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.902 (6.907) Lt: 6.208 (6.196) Accm: 2.19 (2.19) Acct: 3.34 (3.42) proj_loss: -0.5015 (-0.5008) time: 0.7525 data: 0.0003 [11-22 23:12:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.910 (6.939) Lt: 6.223 (6.227) Accm: 2.27 (2.27) Acct: 3.68 (3.57) proj_loss: -0.5076 (-0.5077) time: 0.7525 data: 0.0003 [11-22 23:12:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.877 (6.893) Lt: 6.184 (6.220) Accm: 2.49 (2.49) Acct: 4.03 (3.81) proj_loss: -0.5130 (-0.5177) time: 0.7525 data: 0.0003 [11-22 23:12:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.944 (6.936) Lt: 6.263 (6.255) Accm: 1.88 (2.10) Acct: 3.10 (3.24) proj_loss: -0.5009 (-0.4975) time: 0.7525 data: 0.0003 [11-22 23:12:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.937 (6.873) Lt: 6.338 (6.237) Accm: 2.49 (2.51) Acct: 3.75 (3.88) proj_loss: -0.5294 (-0.5291) time: 0.7525 data: 0.0003 [11-22 23:17:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.42 Lm: 6.901 (6.911) Lt: 6.197 (6.205) Accm: 2.31 (2.36) Acct: 3.75 (3.66) proj_loss: -0.5149 (-0.5149) time: 0.7523 data: 0.0002 [11-22 23:17:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.42 Lm: 6.799 (6.813) Lt: 6.051 (6.094) Accm: 2.60 (2.56) Acct: 3.89 (3.92) proj_loss: -0.5026 (-0.5073) time: 0.7523 data: 0.0002 [11-22 23:17:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.42 Lm: 6.909 (6.909) Lt: 6.211 (6.201) Accm: 2.16 (2.18) Acct: 3.32 (3.36) proj_loss: -0.5025 (-0.5021) time: 0.7523 data: 0.0002 [11-22 23:17:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.42 Lm: 6.913 (6.923) Lt: 6.251 (6.247) Accm: 2.29 (2.38) Acct: 3.77 (3.74) proj_loss: -0.5125 (-0.5119) time: 0.7523 data: 0.0003 [11-22 23:17:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.42 Lm: 6.869 (6.863) Lt: 6.222 (6.207) Accm: 2.59 (2.49) Acct: 4.01 (3.89) proj_loss: -0.5012 (-0.5010) time: 0.7522 data: 0.0003 [11-22 23:17:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.42 Lm: 6.842 (6.870) Lt: 6.117 (6.169) Accm: 2.27 (2.33) Acct: 3.67 (3.66) proj_loss: -0.4997 (-0.5024) time: 0.7523 data: 0.0002 [11-22 23:17:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.42 Lm: 6.914 (6.878) Lt: 6.294 (6.240) Accm: 2.37 (2.41) Acct: 3.55 (3.67) proj_loss: -0.5374 (-0.5333) time: 0.7523 data: 0.0003 [11-22 23:17:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.42 Lm: 6.977 (6.954) Lt: 6.284 (6.287) Accm: 1.96 (2.09) Acct: 3.13 (3.22) proj_loss: -0.5074 (-0.5075) time: 0.7523 data: 0.0002 [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 6.944 (6.934) Lt: 6.263 (6.254) Accm: 2.04 (2.21) Acct: 3.17 (3.46) proj_loss: -0.5140 (-0.5098) time: 0.7553 data: 0.0015 [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 6.910 (6.919) Lt: 6.223 (6.216) Accm: 2.35 (2.40) Acct: 3.82 (3.71) proj_loss: -0.5094 (-0.5138) time: 0.7553 data: 0.0015 [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 6.810 (6.823) Lt: 6.079 (6.107) Accm: 2.67 (2.58) Acct: 3.96 (3.95) proj_loss: -0.5033 (-0.5065) time: 0.7553 data: 0.0018 [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 6.902 (6.889) Lt: 6.208 (6.198) Accm: 2.19 (2.24) Acct: 3.34 (3.35) proj_loss: -0.5015 (-0.4990) time: 0.7553 data: 0.0019 [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 20/350] Total time: 0:20:56 (0.753 s / it) [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 6.858 (6.858) Lt: 6.179 (6.198) Accm: 2.58 (2.48) Acct: 3.99 (3.82) proj_loss: -0.4995 (-0.4993) time: 0.7553 data: 0.0016 [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 6.877 (6.888) Lt: 6.184 (6.195) Accm: 2.49 (2.53) Acct: 4.03 (4.02) proj_loss: -0.5119 (-0.5095) time: 0.7553 data: 0.0016 [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 6.832 (6.829) Lt: 6.116 (6.127) Accm: 2.35 (2.42) Acct: 3.89 (3.80) proj_loss: -0.4984 (-0.5008) time: 0.7553 data: 0.0016 [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 20/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.37 Lm: 6.891 (6.864) Lt: 6.250 (6.207) Accm: 2.49 (2.48) Acct: 3.75 (3.78) proj_loss: -0.5294 (-0.5253) time: 0.7553 data: 0.0021 [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 20/350] Total time: 0:20:56 (0.753 s / it) [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 20/350] Total time: 0:20:56 (0.753 s / it) [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 20/350] Total time: 0:20:56 (0.753 s / it) [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 20/350] Total time: 0:20:56 (0.753 s / it) [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 20/350] Total time: 0:20:56 (0.753 s / it) [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 20/350] Total time: 0:20:56 (0.753 s / it) [11-22 23:23:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 20/350] Total time: 0:20:56 (0.753 s / it) [11-22 23:23:03] (/home/user/VAR/train.py , line 276)=> [ep20] (training ) Lm: 6.893 (6.894), Lt: 6.212 (6.214), Acc m&t: 2.40 3.74, Remain: 4 days, 20:03:05, Finish: 2024-11-27 03:26 [11-22 23:23:03] (/home/user/VAR/train.py , line 276)=> [ep20] (training ) Lm: 6.893 (6.894), Lt: 6.212 (6.214), Acc m&t: 2.40 3.74, Remain: 4 days, 20:03:00, Finish: 2024-11-27 03:26 [11-22 23:23:03] (/home/user/VAR/train.py , line 276)=> [ep20] (training ) Lm: 6.893 (6.894), Lt: 6.212 (6.214), Acc m&t: 2.40 3.74, Remain: 4 days, 20:02:50, Finish: 2024-11-27 03:25 [11-22 23:23:03] (/home/user/VAR/train.py , line 276)=> [ep20] (training ) Lm: 6.893 (6.894), Lt: 6.212 (6.214), Acc m&t: 2.40 3.74, Remain: 4 days, 20:03:54, Finish: 2024-11-27 03:26 [11-22 23:23:03] (/home/user/VAR/train.py , line 276)=> [ep20] (training ) Lm: 6.893 (6.894), Lt: 6.212 (6.214), Acc m&t: 2.40 3.74, Remain: 4 days, 20:06:50, Finish: 2024-11-27 03:29 [11-22 23:23:03] (/home/user/VAR/train.py , line 276)=> [ep20] (training ) Lm: 6.893 (6.894), Lt: 6.212 (6.214), Acc m&t: 2.40 3.74, Remain: 4 days, 20:03:23, Finish: 2024-11-27 03:26 [11-22 23:23:03] (/home/user/VAR/train.py , line 276)=> [ep20] (training ) Lm: 6.893 (6.894), Lt: 6.212 (6.214), Acc m&t: 2.40 3.74, Remain: 4 days, 20:03:01, Finish: 2024-11-27 03:26 [11-22 23:23:03] (/home/user/VAR/train.py , line 276)=> [ep20] (training ) Lm: 6.893 (6.894), Lt: 6.212 (6.214), Acc m&t: 2.40 3.74, Remain: 4 days, 20:03:14, Finish: 2024-11-27 03:26 [11-22 23:23:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 0/1669] eta: 0:20:47 tlr: 0.00024 tnm: 0.37 Lm: 6.998 (6.998) Lt: 6.389 (6.389) Accm: 2.19 (2.19) Acct: 3.03 (3.03) proj_loss: -0.5104 (-0.5104) time: 0.7472 data: 0.0003 [11-22 23:23:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 0/1669] eta: 0:20:48 tlr: 0.00024 tnm: 0.37 Lm: 7.045 (7.045) Lt: 6.417 (6.417) Accm: 2.07 (2.07) Acct: 3.03 (3.03) proj_loss: -0.5253 (-0.5253) time: 0.7481 data: 0.0004 [11-22 23:23:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 0/1669] eta: 0:21:13 tlr: 0.00024 tnm: 0.37 Lm: 6.906 (6.906) Lt: 6.214 (6.214) Accm: 2.43 (2.43) Acct: 3.99 (3.99) proj_loss: -0.5125 (-0.5125) time: 0.7630 data: 0.0003 [11-22 23:23:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 0/1669] eta: 0:20:49 tlr: 0.00024 tnm: 0.37 Lm: 7.028 (7.028) Lt: 6.395 (6.395) Accm: 2.00 (2.00) Acct: 3.17 (3.17) proj_loss: -0.4930 (-0.4930) time: 0.7486 data: 0.0004 [11-22 23:23:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 0/1669] eta: 0:20:48 tlr: 0.00024 tnm: 0.37 Lm: 7.000 (7.000) Lt: 6.297 (6.297) Accm: 2.16 (2.16) Acct: 3.82 (3.82) proj_loss: -0.5091 (-0.5091) time: 0.7482 data: 0.0004 [11-22 23:23:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 0/1669] eta: 0:20:53 tlr: 0.00024 tnm: 0.37 Lm: 6.918 (6.918) Lt: 6.294 (6.294) Accm: 2.26 (2.26) Acct: 3.27 (3.27) proj_loss: -0.5126 (-0.5126) time: 0.7512 data: 0.0003 [11-22 23:23:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 0/1669] eta: 0:20:48 tlr: 0.00024 tnm: 0.37 Lm: 6.962 (6.962) Lt: 6.323 (6.323) Accm: 2.32 (2.32) Acct: 3.86 (3.86) proj_loss: -0.5118 (-0.5118) time: 0.7479 data: 0.0004 [11-22 23:23:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 0/1669] eta: 0:21:42 tlr: 0.00024 tnm: 0.37 Lm: 6.923 (6.923) Lt: 6.290 (6.290) Accm: 2.14 (2.14) Acct: 3.48 (3.48) proj_loss: -0.4924 (-0.4924) time: 0.7806 data: 0.0003 [11-22 23:28:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.36 Lm: 6.996 (6.996) Lt: 6.352 (6.352) Accm: 2.00 (2.00) Acct: 3.25 (3.25) proj_loss: -0.4789 (-0.4789) time: 0.7541 data: 0.0003 [11-22 23:28:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.36 Lm: 6.988 (6.988) Lt: 6.360 (6.360) Accm: 2.08 (2.08) Acct: 3.15 (3.15) proj_loss: -0.5130 (-0.5130) time: 0.7541 data: 0.0003 [11-22 23:28:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.36 Lm: 7.020 (7.020) Lt: 6.426 (6.426) Accm: 2.21 (2.21) Acct: 3.39 (3.39) proj_loss: -0.5121 (-0.5121) time: 0.7541 data: 0.0002 [11-22 23:28:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.36 Lm: 6.865 (6.865) Lt: 6.179 (6.179) Accm: 2.47 (2.47) Acct: 3.68 (3.68) proj_loss: -0.5205 (-0.5205) time: 0.7541 data: 0.0003 [11-22 23:28:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.36 Lm: 6.948 (6.948) Lt: 6.270 (6.270) Accm: 2.31 (2.31) Acct: 3.81 (3.81) proj_loss: -0.5077 (-0.5077) time: 0.7541 data: 0.0002 [11-22 23:28:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.36 Lm: 6.889 (6.889) Lt: 6.176 (6.176) Accm: 2.40 (2.40) Acct: 3.84 (3.84) proj_loss: -0.5086 (-0.5086) time: 0.7541 data: 0.0003 [11-22 23:28:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.36 Lm: 6.875 (6.875) Lt: 6.252 (6.252) Accm: 2.51 (2.51) Acct: 4.03 (4.03) proj_loss: -0.5196 (-0.5196) time: 0.7541 data: 0.0003 [11-22 23:28:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.36 Lm: 6.957 (6.957) Lt: 6.278 (6.278) Accm: 2.18 (2.18) Acct: 3.19 (3.19) proj_loss: -0.5080 (-0.5080) time: 0.7541 data: 0.0002 [11-22 23:34:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 834/1669] eta: 0:11:26 tlr: 0.00024 tnm: 0.36 Lm: 6.918 (6.914) Lt: 6.262 (6.209) Accm: 2.26 (2.36) Acct: 3.27 (3.55) proj_loss: -0.5033 (-0.5018) time: 1.6128 data: 0.0003 [11-22 23:34:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 834/1669] eta: 0:11:26 tlr: 0.00024 tnm: 0.36 Lm: 7.012 (6.995) Lt: 6.395 (6.369) Accm: 2.04 (2.15) Acct: 3.41 (3.40) proj_loss: -0.5244 (-0.5162) time: 1.6128 data: 0.0003 [11-22 23:34:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 834/1669] eta: 0:11:26 tlr: 0.00024 tnm: 0.36 Lm: 7.045 (6.925) Lt: 6.368 (6.242) Accm: 2.07 (2.25) Acct: 3.03 (3.41) proj_loss: -0.5156 (-0.5105) time: 1.6128 data: 0.0003 [11-22 23:34:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 834/1669] eta: 0:11:26 tlr: 0.00024 tnm: 0.36 Lm: 6.978 (6.960) Lt: 6.331 (6.328) Accm: 2.19 (2.14) Acct: 3.27 (3.32) proj_loss: -0.5104 (-0.5000) time: 1.6128 data: 0.0003 [11-22 23:34:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 834/1669] eta: 0:11:26 tlr: 0.00024 tnm: 0.36 Lm: 6.895 (6.926) Lt: 6.242 (6.229) Accm: 2.19 (2.27) Acct: 3.79 (3.72) proj_loss: -0.5091 (-0.5121) time: 1.6128 data: 0.0003 [11-22 23:34:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 834/1669] eta: 0:11:26 tlr: 0.00024 tnm: 0.36 Lm: 6.962 (6.906) Lt: 6.239 (6.247) Accm: 2.32 (2.44) Acct: 3.86 (3.95) proj_loss: -0.5118 (-0.5057) time: 1.6128 data: 0.0003 [11-22 23:34:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 834/1669] eta: 0:11:26 tlr: 0.00024 tnm: 0.36 Lm: 6.906 (6.902) Lt: 6.214 (6.207) Accm: 2.36 (2.29) Acct: 3.68 (3.65) proj_loss: -0.5125 (-0.5106) time: 1.6128 data: 0.0003 [11-22 23:34:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [ 834/1669] eta: 0:11:26 tlr: 0.00024 tnm: 0.36 Lm: 6.923 (6.931) Lt: 6.290 (6.276) Accm: 2.14 (2.14) Acct: 3.48 (3.47) proj_loss: -0.4924 (-0.5019) time: 1.6128 data: 0.0003 [11-22 23:40:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1251/1669] eta: 0:05:40 tlr: 0.00024 tnm: 0.36 Lm: 6.941 (6.938) Lt: 6.285 (6.277) Accm: 2.27 (2.23) Acct: 3.68 (3.57) proj_loss: -0.4991 (-0.5029) time: 0.7524 data: 0.0003 [11-22 23:40:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1251/1669] eta: 0:05:40 tlr: 0.00024 tnm: 0.36 Lm: 6.979 (6.941) Lt: 6.324 (6.312) Accm: 2.23 (2.28) Acct: 3.51 (3.57) proj_loss: -0.5259 (-0.5190) time: 0.7524 data: 0.0003 [11-22 23:40:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1251/1669] eta: 0:05:40 tlr: 0.00024 tnm: 0.36 Lm: 6.942 (6.943) Lt: 6.326 (6.326) Accm: 2.23 (2.25) Acct: 3.46 (3.46) proj_loss: -0.5018 (-0.4983) time: 0.7524 data: 0.0003 [11-22 23:40:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1251/1669] eta: 0:05:40 tlr: 0.00024 tnm: 0.36 Lm: 6.917 (6.914) Lt: 6.242 (6.228) Accm: 2.31 (2.28) Acct: 3.53 (3.58) proj_loss: -0.5086 (-0.5042) time: 0.7524 data: 0.0003 [11-22 23:40:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1251/1669] eta: 0:05:40 tlr: 0.00024 tnm: 0.36 Lm: 6.889 (6.893) Lt: 6.195 (6.196) Accm: 2.32 (2.37) Acct: 3.81 (3.92) proj_loss: -0.5077 (-0.5054) time: 0.7524 data: 0.0002 [11-22 23:40:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1251/1669] eta: 0:05:40 tlr: 0.00024 tnm: 0.36 Lm: 6.947 (6.906) Lt: 6.275 (6.227) Accm: 2.26 (2.30) Acct: 3.25 (3.43) proj_loss: -0.5205 (-0.5158) time: 0.7524 data: 0.0003 [11-22 23:40:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1251/1669] eta: 0:05:40 tlr: 0.00024 tnm: 0.36 Lm: 6.888 (6.883) Lt: 6.210 (6.201) Accm: 2.51 (2.60) Acct: 4.03 (4.27) proj_loss: -0.5108 (-0.5067) time: 0.7524 data: 0.0003 [11-22 23:40:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1251/1669] eta: 0:05:40 tlr: 0.00024 tnm: 0.36 Lm: 6.874 (6.887) Lt: 6.202 (6.192) Accm: 2.34 (2.37) Acct: 3.43 (3.56) proj_loss: -0.4964 (-0.4984) time: 0.7524 data: 0.0003 [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.829 (6.868) Lt: 6.141 (6.173) Accm: 2.42 (2.43) Acct: 3.58 (3.71) proj_loss: -0.4979 (-0.4983) time: 0.7589 data: 0.0016 [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 21/350] Total time: 0:22:13 (0.799 s / it) [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.919 (6.909) Lt: 6.270 (6.236) Accm: 2.26 (2.29) Acct: 3.48 (3.48) proj_loss: -0.5253 (-0.5184) time: 0.7589 data: 0.0016 [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.945 (6.933) Lt: 6.254 (6.284) Accm: 2.37 (2.30) Acct: 3.62 (3.62) proj_loss: -0.5244 (-0.5152) time: 0.7589 data: 0.0015 [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.884 (6.836) Lt: 6.147 (6.128) Accm: 2.46 (2.55) Acct: 3.82 (4.16) proj_loss: -0.5091 (-0.5084) time: 0.7589 data: 0.0015 [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.866 (6.880) Lt: 6.204 (6.202) Accm: 2.32 (2.52) Acct: 3.86 (4.08) proj_loss: -0.5118 (-0.5081) time: 0.7589 data: 0.0019 [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.949 (6.944) Lt: 6.322 (6.323) Accm: 2.19 (2.21) Acct: 3.27 (3.35) proj_loss: -0.5104 (-0.5032) time: 0.7589 data: 0.0015 [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.960 (6.950) Lt: 6.290 (6.284) Accm: 2.39 (2.26) Acct: 3.89 (3.64) proj_loss: -0.5058 (-0.5067) time: 0.7589 data: 0.0017 [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 21/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.928 (6.937) Lt: 6.271 (6.256) Accm: 2.26 (2.25) Acct: 3.41 (3.55) proj_loss: -0.5047 (-0.5004) time: 0.7589 data: 0.0018 [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 21/350] Total time: 0:22:13 (0.799 s / it) [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 21/350] Total time: 0:22:13 (0.799 s / it) [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 21/350] Total time: 0:22:13 (0.799 s / it) [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 21/350] Total time: 0:22:13 (0.799 s / it) [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 21/350] Total time: 0:22:13 (0.799 s / it) [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 21/350] Total time: 0:22:13 (0.799 s / it) [11-22 23:45:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 21/350] Total time: 0:22:13 (0.799 s / it) [11-22 23:45:17] (/home/user/VAR/train.py , line 276)=> [ep21] (training ) Lm: 6.879 (6.879), Lt: 6.191 (6.191), Acc m&t: 2.44 3.84, Remain: 4 days, 20:09:53, Finish: 2024-11-27 03:55 [11-22 23:45:17] (/home/user/VAR/train.py , line 276)=> [ep21] (training ) Lm: 6.879 (6.879), Lt: 6.191 (6.191), Acc m&t: 2.44 3.84, Remain: 4 days, 20:08:28, Finish: 2024-11-27 03:53 [11-22 23:45:17] (/home/user/VAR/train.py , line 276)=> [ep21] (training ) Lm: 6.879 (6.879), Lt: 6.191 (6.191), Acc m&t: 2.44 3.84, Remain: 4 days, 20:09:29, Finish: 2024-11-27 03:54 [11-22 23:45:17] (/home/user/VAR/train.py , line 276)=> [ep21] (training ) Lm: 6.879 (6.879), Lt: 6.191 (6.191), Acc m&t: 2.44 3.84, Remain: 4 days, 20:07:44, Finish: 2024-11-27 03:53 [11-22 23:45:17] (/home/user/VAR/train.py , line 276)=> [ep21] (training ) Lm: 6.879 (6.879), Lt: 6.191 (6.191), Acc m&t: 2.44 3.84, Remain: 4 days, 20:08:49, Finish: 2024-11-27 03:54 [11-22 23:45:17] (/home/user/VAR/train.py , line 276)=> [ep21] (training ) Lm: 6.879 (6.879), Lt: 6.191 (6.191), Acc m&t: 2.44 3.84, Remain: 4 days, 20:08:40, Finish: 2024-11-27 03:53 [11-22 23:45:17] (/home/user/VAR/train.py , line 276)=> [ep21] (training ) Lm: 6.879 (6.879), Lt: 6.191 (6.191), Acc m&t: 2.44 3.84, Remain: 4 days, 20:07:40, Finish: 2024-11-27 03:52 [11-22 23:45:17] (/home/user/VAR/train.py , line 276)=> [ep21] (training ) Lm: 6.879 (6.879), Lt: 6.191 (6.191), Acc m&t: 2.44 3.84, Remain: 4 days, 20:09:45, Finish: 2024-11-27 03:55 [11-22 23:45:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 0/1669] eta: 0:20:12 tlr: 0.00024 tnm: 0.32 Lm: 6.733 (6.733) Lt: 6.162 (6.162) Accm: 2.84 (2.84) Acct: 4.20 (4.20) proj_loss: -0.5087 (-0.5087) time: 0.7265 data: 0.0003 [11-22 23:45:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 0/1669] eta: 0:20:10 tlr: 0.00024 tnm: 0.32 Lm: 6.636 (6.636) Lt: 5.957 (5.957) Accm: 3.21 (3.21) Acct: 5.41 (5.41) proj_loss: -0.5035 (-0.5035) time: 0.7253 data: 0.0004 [11-22 23:45:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 0/1669] eta: 0:20:13 tlr: 0.00024 tnm: 0.32 Lm: 7.018 (7.018) Lt: 6.380 (6.380) Accm: 2.20 (2.20) Acct: 3.55 (3.55) proj_loss: -0.5027 (-0.5027) time: 0.7268 data: 0.0004 [11-22 23:45:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 0/1669] eta: 0:20:13 tlr: 0.00024 tnm: 0.32 Lm: 6.734 (6.734) Lt: 5.992 (5.992) Accm: 2.77 (2.77) Acct: 4.75 (4.75) proj_loss: -0.4890 (-0.4890) time: 0.7269 data: 0.0004 [11-22 23:45:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 0/1669] eta: 0:20:13 tlr: 0.00024 tnm: 0.32 Lm: 6.916 (6.916) Lt: 6.304 (6.304) Accm: 2.20 (2.20) Acct: 3.41 (3.41) proj_loss: -0.5344 (-0.5344) time: 0.7269 data: 0.0003 [11-22 23:45:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 0/1669] eta: 0:20:13 tlr: 0.00024 tnm: 0.32 Lm: 6.908 (6.908) Lt: 6.230 (6.230) Accm: 2.17 (2.17) Acct: 3.10 (3.10) proj_loss: -0.5263 (-0.5263) time: 0.7271 data: 0.0004 [11-22 23:45:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 0/1669] eta: 0:20:14 tlr: 0.00024 tnm: 0.32 Lm: 6.876 (6.876) Lt: 6.246 (6.246) Accm: 2.78 (2.78) Acct: 4.27 (4.27) proj_loss: -0.5163 (-0.5163) time: 0.7279 data: 0.0004 [11-22 23:45:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 0/1669] eta: 0:20:15 tlr: 0.00024 tnm: 0.32 Lm: 6.874 (6.874) Lt: 6.230 (6.230) Accm: 2.52 (2.52) Acct: 3.55 (3.55) proj_loss: -0.5260 (-0.5260) time: 0.7284 data: 0.0004 [11-22 23:50:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.829 (6.829) Lt: 6.144 (6.144) Accm: 2.44 (2.44) Acct: 3.65 (3.65) proj_loss: -0.5228 (-0.5228) time: 0.7524 data: 0.0003 [11-22 23:50:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.984 (6.984) Lt: 6.323 (6.323) Accm: 2.03 (2.03) Acct: 3.20 (3.20) proj_loss: -0.5049 (-0.5049) time: 0.7524 data: 0.0003 [11-22 23:50:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.982 (6.982) Lt: 6.372 (6.372) Accm: 2.13 (2.13) Acct: 3.25 (3.25) proj_loss: -0.5197 (-0.5197) time: 0.7524 data: 0.0002 [11-22 23:50:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.775 (6.775) Lt: 6.075 (6.075) Accm: 2.82 (2.82) Acct: 4.44 (4.44) proj_loss: -0.5095 (-0.5095) time: 0.7524 data: 0.0002 [11-22 23:50:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.672 (6.672) Lt: 5.968 (5.968) Accm: 2.98 (2.98) Acct: 4.73 (4.73) proj_loss: -0.5257 (-0.5257) time: 0.7524 data: 0.0002 [11-22 23:50:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.706 (6.706) Lt: 5.979 (5.979) Accm: 2.86 (2.86) Acct: 4.72 (4.72) proj_loss: -0.5019 (-0.5019) time: 0.7524 data: 0.0003 [11-22 23:50:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.921 (6.921) Lt: 6.299 (6.299) Accm: 2.46 (2.46) Acct: 3.72 (3.72) proj_loss: -0.5326 (-0.5326) time: 0.7524 data: 0.0003 [11-22 23:50:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.35 Lm: 6.901 (6.901) Lt: 6.199 (6.199) Accm: 2.29 (2.29) Acct: 3.41 (3.41) proj_loss: -0.5021 (-0.5021) time: 0.7524 data: 0.0003 [11-22 23:55:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 6.893 (6.851) Lt: 6.168 (6.134) Accm: 2.40 (2.36) Acct: 3.72 (3.64) proj_loss: -0.4860 (-0.4967) time: 0.7545 data: 0.0002 [11-22 23:55:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 6.914 (6.838) Lt: 6.192 (6.150) Accm: 2.43 (2.47) Acct: 3.48 (3.79) proj_loss: -0.5155 (-0.5174) time: 0.7545 data: 0.0002 [11-22 23:55:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 6.893 (6.912) Lt: 6.341 (6.313) Accm: 2.71 (2.54) Acct: 3.89 (3.78) proj_loss: -0.5163 (-0.5264) time: 0.7545 data: 0.0003 [11-22 23:55:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 6.941 (6.968) Lt: 6.304 (6.337) Accm: 2.16 (2.14) Acct: 3.41 (3.40) proj_loss: -0.5051 (-0.5097) time: 0.7545 data: 0.0003 [11-22 23:55:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 6.733 (6.783) Lt: 6.162 (6.078) Accm: 2.84 (2.70) Acct: 4.20 (4.37) proj_loss: -0.5149 (-0.5221) time: 0.7545 data: 0.0003 [11-22 23:55:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 6.972 (6.980) Lt: 6.265 (6.295) Accm: 2.16 (2.07) Acct: 3.20 (3.20) proj_loss: -0.5071 (-0.5136) time: 0.7545 data: 0.0002 [11-22 23:55:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 6.734 (6.791) Lt: 5.992 (6.086) Accm: 2.77 (2.63) Acct: 4.68 (4.35) proj_loss: -0.4890 (-0.4962) time: 0.7545 data: 0.0003 [11-22 23:55:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 6.784 (6.811) Lt: 6.098 (6.128) Accm: 2.52 (2.51) Acct: 3.75 (3.80) proj_loss: -0.5196 (-0.5163) time: 0.7545 data: 0.0003 [11-23 00:00:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.34 Lm: 6.961 (6.961) Lt: 6.253 (6.281) Accm: 2.18 (2.15) Acct: 3.37 (3.32) proj_loss: -0.5049 (-0.5099) time: 0.7520 data: 0.0002 [11-23 00:00:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.34 Lm: 6.876 (6.838) Lt: 6.176 (6.153) Accm: 2.40 (2.45) Acct: 3.50 (3.72) proj_loss: -0.5095 (-0.5075) time: 0.7520 data: 0.0002 [11-23 00:00:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.34 Lm: 6.772 (6.796) Lt: 6.042 (6.087) Accm: 2.66 (2.61) Acct: 4.39 (4.29) proj_loss: -0.5019 (-0.5035) time: 0.7520 data: 0.0002 [11-23 00:00:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.34 Lm: 6.797 (6.811) Lt: 6.133 (6.138) Accm: 2.58 (2.56) Acct: 3.93 (3.95) proj_loss: -0.5158 (-0.5152) time: 0.7520 data: 0.0003 [11-23 00:00:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.34 Lm: 6.928 (6.921) Lt: 6.286 (6.268) Accm: 2.18 (2.20) Acct: 3.55 (3.47) proj_loss: -0.4998 (-0.5059) time: 0.7520 data: 0.0003 [11-23 00:00:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.34 Lm: 6.834 (6.832) Lt: 6.129 (6.123) Accm: 2.46 (2.46) Acct: 3.91 (3.80) proj_loss: -0.5025 (-0.5023) time: 0.7520 data: 0.0003 [11-23 00:00:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.34 Lm: 6.752 (6.780) Lt: 6.141 (6.088) Accm: 2.75 (2.68) Acct: 4.03 (4.24) proj_loss: -0.5236 (-0.5247) time: 0.7520 data: 0.0002 [11-23 00:00:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.34 Lm: 6.884 (6.901) Lt: 6.294 (6.276) Accm: 2.43 (2.45) Acct: 3.65 (3.68) proj_loss: -0.5260 (-0.5287) time: 0.7520 data: 0.0003 [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.33 Lm: 6.876 (6.886) Lt: 6.246 (6.258) Accm: 2.65 (2.49) Acct: 3.89 (3.75) proj_loss: -0.5163 (-0.5239) time: 1.3856 data: 0.0019 [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 22/350] Total time: 0:22:06 (0.795 s / it) [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.33 Lm: 6.950 (6.940) Lt: 6.241 (6.262) Accm: 2.20 (2.19) Acct: 3.55 (3.37) proj_loss: -0.5071 (-0.5119) time: 1.3856 data: 0.0016 [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.33 Lm: 6.897 (6.850) Lt: 6.192 (6.170) Accm: 2.37 (2.41) Acct: 3.51 (3.68) proj_loss: -0.5155 (-0.5093) time: 1.3856 data: 0.0015 [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.33 Lm: 6.771 (6.840) Lt: 6.162 (6.163) Accm: 2.65 (2.53) Acct: 3.86 (4.06) proj_loss: -0.5311 (-0.5260) time: 1.3856 data: 0.0017 [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.33 Lm: 6.810 (6.821) Lt: 6.092 (6.141) Accm: 2.55 (2.53) Acct: 4.10 (4.06) proj_loss: -0.5148 (-0.5090) time: 1.3856 data: 0.0021 [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.33 Lm: 6.916 (6.886) Lt: 6.268 (6.216) Accm: 2.20 (2.26) Acct: 3.68 (3.55) proj_loss: -0.5051 (-0.5097) time: 1.3856 data: 0.0020 [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.33 Lm: 6.784 (6.786) Lt: 6.098 (6.097) Accm: 2.64 (2.70) Acct: 4.10 (4.30) proj_loss: -0.5119 (-0.5042) time: 1.3856 data: 0.0018 [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 22/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.33 Lm: 6.893 (6.853) Lt: 6.168 (6.137) Accm: 2.52 (2.48) Acct: 3.79 (3.79) proj_loss: -0.4860 (-0.4976) time: 1.3856 data: 0.0018 [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 22/350] Total time: 0:22:06 (0.795 s / it) [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 22/350] Total time: 0:22:06 (0.795 s / it) [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 22/350] Total time: 0:22:06 (0.795 s / it) [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 22/350] Total time: 0:22:06 (0.795 s / it) [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 22/350] Total time: 0:22:06 (0.795 s / it) [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 22/350] Total time: 0:22:06 (0.795 s / it) [11-23 00:07:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 22/350] Total time: 0:22:06 (0.795 s / it) [11-23 00:07:23] (/home/user/VAR/train.py , line 276)=> [ep22] (training ) Lm: 6.876 (6.876), Lt: 6.191 (6.192), Acc m&t: 2.44 3.84, Remain: 4 days, 21:30:39, Finish: 2024-11-27 05:38 [11-23 00:07:23] (/home/user/VAR/train.py , line 276)=> [ep22] (training ) Lm: 6.876 (6.876), Lt: 6.191 (6.192), Acc m&t: 2.44 3.84, Remain: 4 days, 21:30:50, Finish: 2024-11-27 05:38 [11-23 00:07:23] (/home/user/VAR/train.py , line 276)=> [ep22] (training ) Lm: 6.876 (6.876), Lt: 6.191 (6.192), Acc m&t: 2.44 3.84, Remain: 4 days, 21:32:30, Finish: 2024-11-27 05:39 [11-23 00:07:23] (/home/user/VAR/train.py , line 276)=> [ep22] (training ) Lm: 6.876 (6.876), Lt: 6.191 (6.192), Acc m&t: 2.44 3.84, Remain: 4 days, 21:32:28, Finish: 2024-11-27 05:39 [11-23 00:07:23] (/home/user/VAR/train.py , line 276)=> [ep22] (training ) Lm: 6.876 (6.876), Lt: 6.191 (6.192), Acc m&t: 2.44 3.84, Remain: 4 days, 21:30:55, Finish: 2024-11-27 05:38 [11-23 00:07:23] (/home/user/VAR/train.py , line 276)=> [ep22] (training ) Lm: 6.876 (6.876), Lt: 6.191 (6.192), Acc m&t: 2.44 3.84, Remain: 4 days, 21:31:59, Finish: 2024-11-27 05:39 [11-23 00:07:23] (/home/user/VAR/train.py , line 276)=> [ep22] (training ) Lm: 6.876 (6.876), Lt: 6.191 (6.192), Acc m&t: 2.44 3.84, Remain: 4 days, 21:30:14, Finish: 2024-11-27 05:37 [11-23 00:07:23] (/home/user/VAR/train.py , line 276)=> [ep22] (training ) Lm: 6.876 (6.876), Lt: 6.191 (6.192), Acc m&t: 2.44 3.84, Remain: 4 days, 21:31:41, Finish: 2024-11-27 05:39 [11-23 00:07:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 0/1669] eta: 0:20:14 tlr: 0.00024 tnm: 0.37 Lm: 6.924 (6.924) Lt: 6.211 (6.211) Accm: 2.16 (2.16) Acct: 3.48 (3.48) proj_loss: -0.5019 (-0.5019) time: 0.7279 data: 0.0004 [11-23 00:07:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 0/1669] eta: 0:20:16 tlr: 0.00024 tnm: 0.37 Lm: 6.682 (6.682) Lt: 5.989 (5.989) Accm: 3.06 (3.06) Acct: 4.86 (4.86) proj_loss: -0.5251 (-0.5251) time: 0.7286 data: 0.0004 [11-23 00:07:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 0/1669] eta: 0:20:16 tlr: 0.00024 tnm: 0.37 Lm: 6.897 (6.897) Lt: 6.212 (6.212) Accm: 2.23 (2.23) Acct: 3.75 (3.75) proj_loss: -0.5215 (-0.5215) time: 0.7289 data: 0.0004 [11-23 00:07:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 0/1669] eta: 0:20:16 tlr: 0.00024 tnm: 0.37 Lm: 6.793 (6.793) Lt: 6.144 (6.144) Accm: 2.32 (2.32) Acct: 3.75 (3.75) proj_loss: -0.5267 (-0.5267) time: 0.7292 data: 0.0003 [11-23 00:07:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 0/1669] eta: 0:20:17 tlr: 0.00024 tnm: 0.37 Lm: 6.898 (6.898) Lt: 6.259 (6.259) Accm: 2.62 (2.62) Acct: 3.99 (3.99) proj_loss: -0.5540 (-0.5540) time: 0.7292 data: 0.0003 [11-23 00:07:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 0/1669] eta: 0:20:17 tlr: 0.00024 tnm: 0.37 Lm: 6.583 (6.583) Lt: 5.841 (5.841) Accm: 3.21 (3.21) Acct: 5.10 (5.10) proj_loss: -0.4757 (-0.4757) time: 0.7295 data: 0.0004 [11-23 00:07:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 0/1669] eta: 0:20:17 tlr: 0.00024 tnm: 0.37 Lm: 6.929 (6.929) Lt: 6.266 (6.266) Accm: 1.94 (1.94) Acct: 2.89 (2.89) proj_loss: -0.5045 (-0.5045) time: 0.7292 data: 0.0004 [11-23 00:07:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 0/1669] eta: 0:20:18 tlr: 0.00024 tnm: 0.37 Lm: 6.822 (6.822) Lt: 6.166 (6.166) Accm: 2.42 (2.42) Acct: 3.55 (3.55) proj_loss: -0.5059 (-0.5059) time: 0.7301 data: 0.0004 [11-23 00:12:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 417/1669] eta: 0:16:22 tlr: 0.00024 tnm: 0.33 Lm: 6.840 (6.840) Lt: 6.155 (6.155) Accm: 2.27 (2.27) Acct: 3.60 (3.60) proj_loss: -0.4971 (-0.4971) time: 0.7562 data: 0.0002 [11-23 00:12:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 417/1669] eta: 0:16:22 tlr: 0.00024 tnm: 0.33 Lm: 6.735 (6.735) Lt: 6.017 (6.017) Accm: 3.19 (3.19) Acct: 5.23 (5.23) proj_loss: -0.5223 (-0.5223) time: 0.7562 data: 0.0002 [11-23 00:12:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 417/1669] eta: 0:16:22 tlr: 0.00024 tnm: 0.33 Lm: 6.946 (6.946) Lt: 6.270 (6.270) Accm: 2.11 (2.11) Acct: 3.53 (3.53) proj_loss: -0.5154 (-0.5154) time: 0.7562 data: 0.0002 [11-23 00:12:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 417/1669] eta: 0:16:22 tlr: 0.00024 tnm: 0.33 Lm: 6.637 (6.637) Lt: 5.944 (5.944) Accm: 2.80 (2.80) Acct: 4.56 (4.56) proj_loss: -0.5157 (-0.5157) time: 0.7562 data: 0.0002 [11-23 00:12:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 417/1669] eta: 0:16:22 tlr: 0.00024 tnm: 0.33 Lm: 6.894 (6.894) Lt: 6.250 (6.250) Accm: 2.59 (2.59) Acct: 3.89 (3.89) proj_loss: -0.5517 (-0.5517) time: 0.7562 data: 0.0003 [11-23 00:12:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 417/1669] eta: 0:16:22 tlr: 0.00024 tnm: 0.33 Lm: 6.846 (6.846) Lt: 6.176 (6.176) Accm: 2.51 (2.51) Acct: 3.77 (3.77) proj_loss: -0.5145 (-0.5145) time: 0.7562 data: 0.0003 [11-23 00:12:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 417/1669] eta: 0:16:22 tlr: 0.00024 tnm: 0.33 Lm: 6.733 (6.733) Lt: 6.083 (6.083) Accm: 2.89 (2.89) Acct: 4.67 (4.67) proj_loss: -0.4960 (-0.4960) time: 0.7562 data: 0.0003 [11-23 00:12:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 417/1669] eta: 0:16:22 tlr: 0.00024 tnm: 0.33 Lm: 6.843 (6.843) Lt: 6.168 (6.168) Accm: 2.53 (2.53) Acct: 4.03 (4.03) proj_loss: -0.5127 (-0.5127) time: 0.7562 data: 0.0002 [11-23 00:18:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 834/1669] eta: 0:10:42 tlr: 0.00024 tnm: 0.34 Lm: 6.788 (6.754) Lt: 6.123 (6.079) Accm: 2.83 (2.71) Acct: 4.30 (4.33) proj_loss: -0.5215 (-0.5193) time: 0.7529 data: 0.0002 [11-23 00:18:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 834/1669] eta: 0:10:42 tlr: 0.00024 tnm: 0.34 Lm: 6.924 (6.916) Lt: 6.211 (6.217) Accm: 2.16 (2.22) Acct: 3.58 (3.75) proj_loss: -0.5019 (-0.5034) time: 0.7529 data: 0.0003 [11-23 00:18:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 834/1669] eta: 0:10:42 tlr: 0.00024 tnm: 0.34 Lm: 6.925 (6.868) Lt: 6.266 (6.224) Accm: 2.26 (2.27) Acct: 3.27 (3.49) proj_loss: -0.5045 (-0.5184) time: 0.7529 data: 0.0003 [11-23 00:18:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 834/1669] eta: 0:10:42 tlr: 0.00024 tnm: 0.34 Lm: 6.853 (6.848) Lt: 6.186 (6.189) Accm: 2.42 (2.40) Acct: 3.55 (3.59) proj_loss: -0.5231 (-0.5246) time: 0.7529 data: 0.0003 [11-23 00:18:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 834/1669] eta: 0:10:42 tlr: 0.00024 tnm: 0.34 Lm: 6.890 (6.866) Lt: 6.240 (6.193) Accm: 2.62 (2.62) Acct: 3.99 (3.99) proj_loss: -0.5495 (-0.5330) time: 0.7529 data: 0.0003 [11-23 00:18:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 834/1669] eta: 0:10:42 tlr: 0.00024 tnm: 0.34 Lm: 6.788 (6.753) Lt: 6.045 (6.049) Accm: 3.06 (2.99) Acct: 4.86 (4.96) proj_loss: -0.5251 (-0.5277) time: 0.7529 data: 0.0003 [11-23 00:18:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 834/1669] eta: 0:10:42 tlr: 0.00024 tnm: 0.34 Lm: 6.874 (6.780) Lt: 6.100 (6.089) Accm: 2.58 (2.78) Acct: 4.41 (4.58) proj_loss: -0.5083 (-0.5001) time: 0.7528 data: 0.0002 [11-23 00:18:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [ 834/1669] eta: 0:10:42 tlr: 0.00024 tnm: 0.34 Lm: 6.793 (6.716) Lt: 6.081 (5.990) Accm: 2.32 (2.58) Acct: 3.75 (4.25) proj_loss: -0.5047 (-0.5119) time: 0.7529 data: 0.0002 [11-23 00:23:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1251/1669] eta: 0:05:19 tlr: 0.00024 tnm: 0.35 Lm: 6.788 (6.732) Lt: 6.072 (6.008) Accm: 2.35 (2.53) Acct: 3.68 (4.06) proj_loss: -0.5045 (-0.5067) time: 0.7541 data: 0.0003 [11-23 00:23:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1251/1669] eta: 0:05:19 tlr: 0.00024 tnm: 0.35 Lm: 6.735 (6.696) Lt: 6.017 (5.967) Accm: 3.19 (3.22) Acct: 5.23 (5.25) proj_loss: -0.5223 (-0.5242) time: 0.7541 data: 0.0003 [11-23 00:23:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1251/1669] eta: 0:05:19 tlr: 0.00024 tnm: 0.35 Lm: 6.819 (6.778) Lt: 6.131 (6.094) Accm: 2.66 (2.66) Acct: 4.24 (4.29) proj_loss: -0.5127 (-0.5136) time: 0.7541 data: 0.0003 [11-23 00:23:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1251/1669] eta: 0:05:19 tlr: 0.00024 tnm: 0.35 Lm: 6.910 (6.911) Lt: 6.190 (6.205) Accm: 2.20 (2.23) Acct: 3.53 (3.65) proj_loss: -0.5154 (-0.5115) time: 0.7541 data: 0.0003 [11-23 00:23:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1251/1669] eta: 0:05:19 tlr: 0.00024 tnm: 0.35 Lm: 6.850 (6.835) Lt: 6.160 (6.139) Accm: 2.59 (2.57) Acct: 3.96 (3.98) proj_loss: -0.5303 (-0.5275) time: 0.7541 data: 0.0003 [11-23 00:23:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1251/1669] eta: 0:05:19 tlr: 0.00024 tnm: 0.35 Lm: 6.920 (6.880) Lt: 6.246 (6.225) Accm: 2.33 (2.30) Acct: 3.58 (3.59) proj_loss: -0.5060 (-0.5157) time: 0.7541 data: 0.0003 [11-23 00:23:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1251/1669] eta: 0:05:19 tlr: 0.00024 tnm: 0.35 Lm: 6.879 (6.808) Lt: 6.171 (6.127) Accm: 2.57 (2.72) Acct: 4.32 (4.41) proj_loss: -0.5031 (-0.4995) time: 0.7541 data: 0.0002 [11-23 00:23:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1251/1669] eta: 0:05:19 tlr: 0.00024 tnm: 0.35 Lm: 6.837 (6.834) Lt: 6.176 (6.167) Accm: 2.51 (2.45) Acct: 3.67 (3.64) proj_loss: -0.5235 (-0.5245) time: 0.7541 data: 0.0003 [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.924 (6.915) Lt: 6.211 (6.216) Accm: 2.24 (2.23) Acct: 3.58 (3.68) proj_loss: -0.5019 (-0.5087) time: 0.7561 data: 0.0014 [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.915 (6.878) Lt: 6.226 (6.218) Accm: 2.40 (2.34) Acct: 3.75 (3.62) proj_loss: -0.5075 (-0.5222) time: 0.7562 data: 0.0017 [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.782 (6.710) Lt: 6.062 (5.984) Accm: 2.37 (2.65) Acct: 3.75 (4.23) proj_loss: -0.5047 (-0.5075) time: 0.7562 data: 0.0017 [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.788 (6.717) Lt: 6.045 (5.994) Accm: 3.06 (3.07) Acct: 4.86 (5.01) proj_loss: -0.5251 (-0.5314) time: 0.7562 data: 0.0015 [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.883 (6.844) Lt: 6.241 (6.176) Accm: 2.56 (2.54) Acct: 4.24 (4.07) proj_loss: -0.5083 (-0.5097) time: 0.7562 data: 0.0015 [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.849 (6.810) Lt: 6.138 (6.113) Accm: 2.49 (2.56) Acct: 4.17 (4.12) proj_loss: -0.5039 (-0.5051) time: 0.7562 data: 0.0016 [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.822 (6.821) Lt: 6.166 (6.135) Accm: 2.61 (2.56) Acct: 3.79 (3.84) proj_loss: -0.5231 (-0.5226) time: 0.7562 data: 0.0018 [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 23/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.890 (6.865) Lt: 6.240 (6.169) Accm: 2.55 (2.49) Acct: 3.93 (3.79) proj_loss: -0.5232 (-0.5266) time: 0.7562 data: 0.0018 [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 23/350] Total time: 0:21:10 (0.761 s / it) [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 23/350] Total time: 0:21:10 (0.761 s / it) [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 23/350] Total time: 0:21:10 (0.761 s / it) [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 23/350] Total time: 0:21:10 (0.761 s / it) [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 23/350] Total time: 0:21:10 (0.761 s / it) [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 23/350] Total time: 0:21:10 (0.761 s / it) [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 23/350] Total time: 0:21:10 (0.761 s / it) [11-23 00:28:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 23/350] Total time: 0:21:10 (0.761 s / it) [11-23 00:28:34] (/home/user/VAR/train.py , line 276)=> [ep23] (training ) Lm: 6.848 (6.848), Lt: 6.158 (6.158), Acc m&t: 2.50 3.95, Remain: 4 days, 19:03:37, Finish: 2024-11-27 03:32 [11-23 00:28:34] (/home/user/VAR/train.py , line 276)=> [ep23] (training ) Lm: 6.848 (6.848), Lt: 6.158 (6.158), Acc m&t: 2.50 3.95, Remain: 4 days, 19:04:04, Finish: 2024-11-27 03:32 [11-23 00:28:34] (/home/user/VAR/train.py , line 276)=> [ep23] (training ) Lm: 6.848 (6.848), Lt: 6.158 (6.158), Acc m&t: 2.50 3.95, Remain: 4 days, 19:04:19, Finish: 2024-11-27 03:32 [11-23 00:28:34] (/home/user/VAR/train.py , line 276)=> [ep23] (training ) Lm: 6.848 (6.848), Lt: 6.158 (6.158), Acc m&t: 2.50 3.95, Remain: 4 days, 19:03:43, Finish: 2024-11-27 03:32 [11-23 00:28:34] (/home/user/VAR/train.py , line 276)=> [ep23] (training ) Lm: 6.848 (6.848), Lt: 6.158 (6.158), Acc m&t: 2.50 3.95, Remain: 4 days, 19:03:28, Finish: 2024-11-27 03:32 [11-23 00:28:34] (/home/user/VAR/train.py , line 276)=> [ep23] (training ) Lm: 6.848 (6.848), Lt: 6.158 (6.158), Acc m&t: 2.50 3.95, Remain: 4 days, 19:03:50, Finish: 2024-11-27 03:32 [11-23 00:28:34] (/home/user/VAR/train.py , line 276)=> [ep23] (training ) Lm: 6.848 (6.848), Lt: 6.158 (6.158), Acc m&t: 2.50 3.95, Remain: 4 days, 19:03:50, Finish: 2024-11-27 03:32 [11-23 00:28:34] (/home/user/VAR/train.py , line 276)=> [ep23] (training ) Lm: 6.848 (6.848), Lt: 6.158 (6.158), Acc m&t: 2.50 3.95, Remain: 4 days, 19:03:37, Finish: 2024-11-27 03:32 [11-23 00:28:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 0/1669] eta: 0:20:43 tlr: 0.00024 tnm: 0.31 Lm: 6.956 (6.956) Lt: 6.295 (6.295) Accm: 2.29 (2.29) Acct: 3.58 (3.58) proj_loss: -0.5074 (-0.5074) time: 0.7453 data: 0.0003 [11-23 00:28:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 0/1669] eta: 0:20:45 tlr: 0.00024 tnm: 0.31 Lm: 6.728 (6.728) Lt: 6.009 (6.009) Accm: 2.91 (2.91) Acct: 4.79 (4.79) proj_loss: -0.5028 (-0.5028) time: 0.7462 data: 0.0003 [11-23 00:28:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 0/1669] eta: 0:20:45 tlr: 0.00024 tnm: 0.31 Lm: 6.930 (6.930) Lt: 6.315 (6.315) Accm: 2.59 (2.59) Acct: 4.13 (4.13) proj_loss: -0.5298 (-0.5298) time: 0.7463 data: 0.0004 [11-23 00:28:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 0/1669] eta: 0:20:46 tlr: 0.00024 tnm: 0.31 Lm: 6.572 (6.572) Lt: 5.760 (5.760) Accm: 3.34 (3.34) Acct: 5.85 (5.85) proj_loss: -0.5186 (-0.5186) time: 0.7467 data: 0.0003 [11-23 00:28:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 0/1669] eta: 0:20:45 tlr: 0.00024 tnm: 0.31 Lm: 6.792 (6.792) Lt: 6.016 (6.016) Accm: 2.39 (2.39) Acct: 4.34 (4.34) proj_loss: -0.4800 (-0.4800) time: 0.7460 data: 0.0004 [11-23 00:28:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 0/1669] eta: 0:20:46 tlr: 0.00024 tnm: 0.31 Lm: 6.857 (6.857) Lt: 6.134 (6.134) Accm: 2.61 (2.61) Acct: 4.06 (4.06) proj_loss: -0.5491 (-0.5491) time: 0.7468 data: 0.0004 [11-23 00:28:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 0/1669] eta: 0:20:47 tlr: 0.00024 tnm: 0.31 Lm: 6.843 (6.843) Lt: 6.077 (6.077) Accm: 2.72 (2.72) Acct: 4.92 (4.92) proj_loss: -0.4933 (-0.4933) time: 0.7472 data: 0.0004 [11-23 00:28:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 0/1669] eta: 0:20:46 tlr: 0.00024 tnm: 0.31 Lm: 6.824 (6.824) Lt: 6.109 (6.109) Accm: 2.32 (2.32) Acct: 3.65 (3.65) proj_loss: -0.5116 (-0.5116) time: 0.7470 data: 0.0003 [11-23 00:33:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.31 Lm: 6.783 (6.783) Lt: 6.076 (6.076) Accm: 2.48 (2.48) Acct: 3.75 (3.75) proj_loss: -0.5452 (-0.5452) time: 0.7544 data: 0.0002 [11-23 00:33:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.31 Lm: 6.977 (6.977) Lt: 6.338 (6.338) Accm: 2.43 (2.43) Acct: 3.79 (3.79) proj_loss: -0.5131 (-0.5131) time: 0.7544 data: 0.0002 [11-23 00:33:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.31 Lm: 6.721 (6.721) Lt: 5.963 (5.963) Accm: 2.91 (2.91) Acct: 4.84 (4.84) proj_loss: -0.5057 (-0.5057) time: 0.7544 data: 0.0003 [11-23 00:33:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.31 Lm: 6.716 (6.716) Lt: 6.015 (6.015) Accm: 2.95 (2.95) Acct: 4.58 (4.58) proj_loss: -0.5137 (-0.5137) time: 0.7544 data: 0.0002 [11-23 00:33:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.31 Lm: 6.772 (6.772) Lt: 6.037 (6.037) Accm: 2.94 (2.94) Acct: 4.44 (4.44) proj_loss: -0.5314 (-0.5314) time: 0.7544 data: 0.0003 [11-23 00:33:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.31 Lm: 6.753 (6.753) Lt: 6.011 (6.011) Accm: 2.59 (2.59) Acct: 4.39 (4.39) proj_loss: -0.5006 (-0.5006) time: 0.7544 data: 0.0002 [11-23 00:33:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.31 Lm: 6.758 (6.758) Lt: 6.055 (6.055) Accm: 2.68 (2.68) Acct: 4.46 (4.46) proj_loss: -0.5204 (-0.5204) time: 0.7544 data: 0.0003 [11-23 00:33:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.31 Lm: 6.866 (6.866) Lt: 6.178 (6.178) Accm: 2.45 (2.45) Acct: 3.75 (3.75) proj_loss: -0.5035 (-0.5035) time: 0.7544 data: 0.0003 [11-23 00:40:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 834/1669] eta: 0:11:43 tlr: 0.00024 tnm: 0.33 Lm: 6.775 (6.815) Lt: 6.060 (6.111) Accm: 2.61 (2.52) Acct: 3.93 (3.89) proj_loss: -0.5053 (-0.5041) time: 1.1946 data: 0.0003 [11-23 00:40:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 834/1669] eta: 0:11:43 tlr: 0.00024 tnm: 0.33 Lm: 6.792 (6.858) Lt: 6.016 (6.165) Accm: 2.39 (2.44) Acct: 4.34 (4.05) proj_loss: -0.5030 (-0.5014) time: 1.1946 data: 0.0003 [11-23 00:40:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 834/1669] eta: 0:11:43 tlr: 0.00024 tnm: 0.33 Lm: 6.857 (6.838) Lt: 6.134 (6.110) Accm: 2.61 (2.65) Acct: 4.06 (4.06) proj_loss: -0.5247 (-0.5291) time: 1.1946 data: 0.0003 [11-23 00:40:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 834/1669] eta: 0:11:43 tlr: 0.00024 tnm: 0.33 Lm: 6.728 (6.752) Lt: 6.020 (6.073) Accm: 2.91 (2.76) Acct: 4.37 (4.24) proj_loss: -0.5246 (-0.5220) time: 1.1946 data: 0.0003 [11-23 00:40:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 834/1669] eta: 0:11:43 tlr: 0.00024 tnm: 0.33 Lm: 6.742 (6.722) Lt: 6.043 (6.017) Accm: 2.65 (2.72) Acct: 3.86 (4.19) proj_loss: -0.5243 (-0.5382) time: 1.1946 data: 0.0002 [11-23 00:40:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 834/1669] eta: 0:11:43 tlr: 0.00024 tnm: 0.33 Lm: 6.773 (6.738) Lt: 6.106 (6.011) Accm: 2.78 (2.87) Acct: 3.96 (4.55) proj_loss: -0.5181 (-0.5099) time: 1.1946 data: 0.0003 [11-23 00:40:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 834/1669] eta: 0:11:43 tlr: 0.00024 tnm: 0.33 Lm: 6.930 (6.918) Lt: 6.315 (6.228) Accm: 2.59 (2.52) Acct: 4.13 (4.01) proj_loss: -0.5012 (-0.5091) time: 1.1946 data: 0.0002 [11-23 00:40:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [ 834/1669] eta: 0:11:43 tlr: 0.00024 tnm: 0.33 Lm: 6.843 (6.804) Lt: 6.077 (6.108) Accm: 2.64 (2.52) Acct: 3.99 (4.09) proj_loss: -0.5350 (-0.5252) time: 1.1946 data: 0.0003 [11-23 00:45:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.34 Lm: 6.866 (6.882) Lt: 6.178 (6.188) Accm: 2.45 (2.28) Acct: 3.75 (3.55) proj_loss: -0.5024 (-0.5027) time: 0.7501 data: 0.0002 [11-23 00:45:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.34 Lm: 6.776 (6.784) Lt: 6.105 (6.108) Accm: 2.72 (2.70) Acct: 3.96 (4.03) proj_loss: -0.5312 (-0.5259) time: 0.7501 data: 0.0002 [11-23 00:45:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.34 Lm: 6.967 (6.940) Lt: 6.293 (6.239) Accm: 2.43 (2.38) Acct: 3.79 (3.86) proj_loss: -0.4988 (-0.5055) time: 0.7501 data: 0.0003 [11-23 00:45:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.34 Lm: 6.914 (6.902) Lt: 6.192 (6.216) Accm: 2.37 (2.41) Acct: 3.93 (3.92) proj_loss: -0.4978 (-0.4992) time: 0.7501 data: 0.0002 [11-23 00:45:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.34 Lm: 6.769 (6.745) Lt: 6.096 (6.029) Accm: 2.79 (2.85) Acct: 4.20 (4.52) proj_loss: -0.5182 (-0.5120) time: 0.7501 data: 0.0002 [11-23 00:45:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.34 Lm: 6.914 (6.884) Lt: 6.194 (6.171) Accm: 2.41 (2.54) Acct: 3.68 (3.87) proj_loss: -0.5369 (-0.5343) time: 0.7501 data: 0.0003 [11-23 00:45:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.34 Lm: 6.869 (6.843) Lt: 6.146 (6.145) Accm: 2.47 (2.46) Acct: 3.93 (4.03) proj_loss: -0.5240 (-0.5222) time: 0.7501 data: 0.0003 [11-23 00:45:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1251/1669] eta: 0:05:42 tlr: 0.00024 tnm: 0.34 Lm: 6.783 (6.756) Lt: 6.076 (6.059) Accm: 2.48 (2.58) Acct: 3.77 (4.06) proj_loss: -0.5187 (-0.5319) time: 0.7501 data: 0.0002 [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.824 (6.792) Lt: 6.109 (6.101) Accm: 2.65 (2.61) Acct: 3.86 (4.14) proj_loss: -0.5243 (-0.5312) time: 0.7548 data: 0.0015 [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 24/350] Total time: 0:22:20 (0.803 s / it) [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.775 (6.845) Lt: 6.060 (6.156) Accm: 2.58 (2.34) Acct: 3.93 (3.64) proj_loss: -0.4995 (-0.5009) time: 0.7548 data: 0.0015 [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.989 (6.950) Lt: 6.315 (6.258) Accm: 2.26 (2.29) Acct: 3.44 (3.71) proj_loss: -0.5012 (-0.5051) time: 0.7548 data: 0.0018 [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.766 (6.747) Lt: 6.105 (6.044) Accm: 2.78 (2.82) Acct: 4.24 (4.46) proj_loss: -0.5181 (-0.5061) time: 0.7548 data: 0.0016 [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.784 (6.784) Lt: 6.078 (6.102) Accm: 2.59 (2.68) Acct: 3.99 (4.02) proj_loss: -0.5246 (-0.5180) time: 0.7548 data: 0.0019 [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.857 (6.876) Lt: 6.134 (6.162) Accm: 2.61 (2.55) Acct: 3.86 (3.87) proj_loss: -0.5247 (-0.5281) time: 0.7548 data: 0.0019 [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.896 (6.854) Lt: 6.201 (6.156) Accm: 2.43 (2.46) Acct: 3.99 (4.04) proj_loss: -0.5131 (-0.5196) time: 0.7548 data: 0.0017 [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 24/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.35 Lm: 6.792 (6.867) Lt: 6.047 (6.182) Accm: 2.39 (2.46) Acct: 4.06 (3.95) proj_loss: -0.5030 (-0.5068) time: 0.7548 data: 0.0017 [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 24/350] Total time: 0:22:20 (0.803 s / it) [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 24/350] Total time: 0:22:20 (0.803 s / it) [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 24/350] Total time: 0:22:20 (0.803 s / it) [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 24/350] Total time: 0:22:20 (0.803 s / it) [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 24/350] Total time: 0:22:20 (0.803 s / it) [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 24/350] Total time: 0:22:20 (0.803 s / it) [11-23 00:50:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 24/350] Total time: 0:22:20 (0.803 s / it) [11-23 00:50:55] (/home/user/VAR/train.py , line 276)=> [ep24] (training ) Lm: 6.846 (6.846), Lt: 6.154 (6.154), Acc m&t: 2.50 3.95, Remain: 4 days, 18:30:24, Finish: 2024-11-27 03:21 [11-23 00:50:55] (/home/user/VAR/train.py , line 276)=> [ep24] (training ) Lm: 6.846 (6.846), Lt: 6.154 (6.154), Acc m&t: 2.50 3.95, Remain: 4 days, 18:29:18, Finish: 2024-11-27 03:20 [11-23 00:50:55] (/home/user/VAR/train.py , line 276)=> [ep24] (training ) Lm: 6.846 (6.846), Lt: 6.154 (6.154), Acc m&t: 2.50 3.95, Remain: 4 days, 18:28:54, Finish: 2024-11-27 03:19 [11-23 00:50:55] (/home/user/VAR/train.py , line 276)=> [ep24] (training ) Lm: 6.846 (6.846), Lt: 6.154 (6.154), Acc m&t: 2.50 3.95, Remain: 4 days, 18:30:34, Finish: 2024-11-27 03:21 [11-23 00:50:55] (/home/user/VAR/train.py , line 276)=> [ep24] (training ) Lm: 6.846 (6.846), Lt: 6.154 (6.154), Acc m&t: 2.50 3.95, Remain: 4 days, 18:28:55, Finish: 2024-11-27 03:19 [11-23 00:50:55] (/home/user/VAR/train.py , line 276)=> [ep24] (training ) Lm: 6.846 (6.846), Lt: 6.154 (6.154), Acc m&t: 2.50 3.95, Remain: 4 days, 18:31:31, Finish: 2024-11-27 03:22 [11-23 00:50:55] (/home/user/VAR/train.py , line 276)=> [ep24] (training ) Lm: 6.846 (6.846), Lt: 6.154 (6.154), Acc m&t: 2.50 3.95, Remain: 4 days, 18:28:35, Finish: 2024-11-27 03:19 [11-23 00:50:55] (/home/user/VAR/train.py , line 276)=> [ep24] (training ) Lm: 6.846 (6.846), Lt: 6.154 (6.154), Acc m&t: 2.50 3.95, Remain: 4 days, 18:29:25, Finish: 2024-11-27 03:20 [11-23 00:50:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 0/1669] eta: 0:20:40 tlr: 0.00024 tnm: 0.32 Lm: 6.698 (6.698) Lt: 5.952 (5.952) Accm: 2.99 (2.99) Acct: 4.68 (4.68) proj_loss: -0.4991 (-0.4991) time: 0.7435 data: 0.0003 [11-23 00:50:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 0/1669] eta: 0:20:42 tlr: 0.00024 tnm: 0.32 Lm: 6.939 (6.939) Lt: 6.290 (6.290) Accm: 2.21 (2.21) Acct: 3.34 (3.34) proj_loss: -0.4898 (-0.4898) time: 0.7442 data: 0.0004 [11-23 00:50:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 0/1669] eta: 0:20:42 tlr: 0.00024 tnm: 0.32 Lm: 6.805 (6.805) Lt: 6.085 (6.085) Accm: 2.53 (2.53) Acct: 3.58 (3.58) proj_loss: -0.5081 (-0.5081) time: 0.7444 data: 0.0004 [11-23 00:50:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 0/1669] eta: 0:20:43 tlr: 0.00024 tnm: 0.32 Lm: 6.967 (6.967) Lt: 6.351 (6.351) Accm: 2.48 (2.48) Acct: 3.86 (3.86) proj_loss: -0.5003 (-0.5003) time: 0.7449 data: 0.0004 [11-23 00:50:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 0/1669] eta: 0:20:42 tlr: 0.00024 tnm: 0.32 Lm: 6.685 (6.685) Lt: 5.961 (5.961) Accm: 2.83 (2.83) Acct: 4.37 (4.37) proj_loss: -0.5063 (-0.5063) time: 0.7447 data: 0.0003 [11-23 00:50:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 0/1669] eta: 0:20:43 tlr: 0.00024 tnm: 0.32 Lm: 6.544 (6.544) Lt: 5.824 (5.824) Accm: 3.38 (3.38) Acct: 5.27 (5.27) proj_loss: -0.5569 (-0.5569) time: 0.7448 data: 0.0004 [11-23 00:50:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 0/1669] eta: 0:20:43 tlr: 0.00024 tnm: 0.32 Lm: 6.837 (6.837) Lt: 6.204 (6.204) Accm: 2.58 (2.58) Acct: 4.17 (4.17) proj_loss: -0.5075 (-0.5075) time: 0.7451 data: 0.0003 [11-23 00:50:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 0/1669] eta: 0:20:45 tlr: 0.00024 tnm: 0.32 Lm: 6.914 (6.914) Lt: 6.229 (6.229) Accm: 2.75 (2.75) Acct: 4.27 (4.27) proj_loss: -0.5192 (-0.5192) time: 0.7461 data: 0.0004 [11-23 00:56:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.34 Lm: 6.797 (6.797) Lt: 6.089 (6.089) Accm: 2.52 (2.52) Acct: 3.99 (3.99) proj_loss: -0.5111 (-0.5111) time: 0.7513 data: 0.0003 [11-23 00:56:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.34 Lm: 6.938 (6.938) Lt: 6.261 (6.261) Accm: 2.27 (2.27) Acct: 3.48 (3.48) proj_loss: -0.4976 (-0.4976) time: 0.7513 data: 0.0002 [11-23 00:56:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.34 Lm: 6.972 (6.972) Lt: 6.319 (6.319) Accm: 2.21 (2.21) Acct: 3.37 (3.37) proj_loss: -0.5018 (-0.5018) time: 0.7513 data: 0.0003 [11-23 00:56:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.34 Lm: 6.908 (6.908) Lt: 6.153 (6.153) Accm: 2.47 (2.47) Acct: 3.67 (3.67) proj_loss: -0.5145 (-0.5145) time: 0.7513 data: 0.0003 [11-23 00:56:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.34 Lm: 6.865 (6.865) Lt: 6.149 (6.149) Accm: 2.56 (2.56) Acct: 3.93 (3.93) proj_loss: -0.5069 (-0.5069) time: 0.7513 data: 0.0003 [11-23 00:56:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.34 Lm: 6.896 (6.896) Lt: 6.239 (6.239) Accm: 2.37 (2.37) Acct: 3.79 (3.79) proj_loss: -0.5237 (-0.5237) time: 0.7513 data: 0.0002 [11-23 00:56:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.34 Lm: 6.755 (6.755) Lt: 6.047 (6.047) Accm: 2.69 (2.69) Acct: 4.24 (4.24) proj_loss: -0.5196 (-0.5196) time: 0.7513 data: 0.0003 [11-23 00:56:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 417/1669] eta: 0:15:43 tlr: 0.00024 tnm: 0.34 Lm: 6.765 (6.765) Lt: 6.069 (6.069) Accm: 2.86 (2.86) Acct: 4.67 (4.67) proj_loss: -0.5460 (-0.5460) time: 0.7513 data: 0.0003 [11-23 01:01:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.940 (6.823) Lt: 6.258 (6.132) Accm: 2.33 (2.55) Acct: 4.06 (4.29) proj_loss: -0.5351 (-0.5321) time: 0.7534 data: 0.0003 [11-23 01:01:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.967 (6.943) Lt: 6.286 (6.263) Accm: 2.48 (2.33) Acct: 3.86 (3.78) proj_loss: -0.5034 (-0.5048) time: 0.7534 data: 0.0003 [11-23 01:01:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.828 (6.882) Lt: 6.141 (6.149) Accm: 2.52 (2.49) Acct: 3.75 (3.80) proj_loss: -0.5209 (-0.5196) time: 0.7534 data: 0.0003 [11-23 01:01:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.698 (6.758) Lt: 6.021 (6.066) Accm: 2.86 (2.63) Acct: 4.48 (4.16) proj_loss: -0.5145 (-0.5122) time: 0.7534 data: 0.0003 [11-23 01:01:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.685 (6.729) Lt: 5.961 (6.012) Accm: 2.83 (2.78) Acct: 4.37 (4.34) proj_loss: -0.5176 (-0.5189) time: 0.7534 data: 0.0002 [11-23 01:01:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.937 (6.929) Lt: 6.240 (6.254) Accm: 2.27 (2.27) Acct: 3.62 (3.55) proj_loss: -0.5054 (-0.5041) time: 0.7534 data: 0.0003 [11-23 01:01:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.914 (6.886) Lt: 6.229 (6.179) Accm: 2.36 (2.40) Acct: 3.58 (3.80) proj_loss: -0.5112 (-0.5083) time: 0.7534 data: 0.0003 [11-23 01:01:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.35 Lm: 6.837 (6.826) Lt: 6.204 (6.139) Accm: 2.58 (2.53) Acct: 4.17 (4.04) proj_loss: -0.5294 (-0.5256) time: 0.7534 data: 0.0003 [11-23 01:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.32 Lm: 6.838 (6.829) Lt: 6.171 (6.139) Accm: 2.64 (2.58) Acct: 4.34 (4.16) proj_loss: -0.5347 (-0.5298) time: 0.7532 data: 0.0002 [11-23 01:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.32 Lm: 6.716 (6.752) Lt: 6.006 (6.048) Accm: 2.75 (2.64) Acct: 4.27 (4.13) proj_loss: -0.5093 (-0.5102) time: 0.7532 data: 0.0002 [11-23 01:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.32 Lm: 6.926 (6.905) Lt: 6.219 (6.210) Accm: 2.52 (2.47) Acct: 4.15 (3.94) proj_loss: -0.5071 (-0.5077) time: 0.7532 data: 0.0003 [11-23 01:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.32 Lm: 6.854 (6.881) Lt: 6.181 (6.177) Accm: 2.46 (2.41) Acct: 3.67 (3.65) proj_loss: -0.5253 (-0.5238) time: 0.7532 data: 0.0002 [11-23 01:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.32 Lm: 6.925 (6.867) Lt: 6.236 (6.181) Accm: 2.30 (2.39) Acct: 3.65 (3.68) proj_loss: -0.5030 (-0.5032) time: 0.7532 data: 0.0003 [11-23 01:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.32 Lm: 6.719 (6.735) Lt: 6.007 (6.022) Accm: 2.69 (2.71) Acct: 4.24 (4.15) proj_loss: -0.5134 (-0.5165) time: 0.7532 data: 0.0002 [11-23 01:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.32 Lm: 6.793 (6.779) Lt: 6.115 (6.092) Accm: 2.71 (2.68) Acct: 4.37 (4.39) proj_loss: -0.5385 (-0.5345) time: 0.7532 data: 0.0003 [11-23 01:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.32 Lm: 6.922 (6.909) Lt: 6.235 (6.215) Accm: 2.23 (2.29) Acct: 3.56 (3.60) proj_loss: -0.5152 (-0.5198) time: 0.7532 data: 0.0003 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.733 (6.774) Lt: 6.021 (6.078) Accm: 2.65 (2.54) Acct: 4.06 (3.98) proj_loss: -0.5145 (-0.5196) time: 0.9707 data: 0.0014 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.685 (6.684) Lt: 5.961 (5.967) Accm: 2.83 (2.88) Acct: 4.37 (4.32) proj_loss: -0.5160 (-0.5164) time: 0.9707 data: 0.0017 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.885 (6.855) Lt: 6.152 (6.159) Accm: 2.56 (2.59) Acct: 4.44 (4.13) proj_loss: -0.5109 (-0.5121) time: 0.9707 data: 0.0016 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.937 (6.884) Lt: 6.240 (6.201) Accm: 2.33 (2.38) Acct: 3.62 (3.65) proj_loss: -0.5054 (-0.5070) time: 0.9707 data: 0.0016 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.914 (6.887) Lt: 6.229 (6.196) Accm: 2.36 (2.35) Acct: 3.58 (3.65) proj_loss: -0.5192 (-0.5210) time: 0.9707 data: 0.0022 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.819 (6.787) Lt: 6.111 (6.096) Accm: 2.40 (2.63) Acct: 4.06 (4.33) proj_loss: -0.5351 (-0.5262) time: 0.9707 data: 0.0022 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.828 (6.851) Lt: 6.141 (6.139) Accm: 2.52 (2.50) Acct: 3.75 (3.84) proj_loss: -0.5297 (-0.5266) time: 0.9707 data: 0.0020 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 25/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.839 (6.876) Lt: 6.204 (6.195) Accm: 2.58 (2.41) Acct: 4.17 (3.90) proj_loss: -0.5294 (-0.5283) time: 0.9707 data: 0.0019 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 25/350] Total time: 0:22:06 (0.795 s / it) [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 25/350] Total time: 0:22:06 (0.795 s / it) [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 25/350] Total time: 0:22:06 (0.795 s / it) [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 25/350] Total time: 0:22:06 (0.795 s / it) [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 25/350] Total time: 0:22:06 (0.795 s / it) [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 25/350] Total time: 0:22:06 (0.795 s / it) [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 25/350] Total time: 0:22:06 (0.795 s / it) [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 25/350] Total time: 0:22:06 (0.795 s / it) [11-23 01:13:02] (/home/user/VAR/train.py , line 276)=> [ep25] (training ) Lm: 6.842 (6.842), Lt: 6.142 (6.142), Acc m&t: 2.53 3.99, Remain: 4 days, 18:46:15, Finish: 2024-11-27 03:59 [11-23 01:13:02] (/home/user/VAR/train.py , line 276)=> [ep25] (training ) Lm: 6.842 (6.842), Lt: 6.142 (6.142), Acc m&t: 2.53 3.99, Remain: 4 days, 18:45:46, Finish: 2024-11-27 03:58 [11-23 01:13:02] (/home/user/VAR/train.py , line 276)=> [ep25] (training ) Lm: 6.842 (6.842), Lt: 6.142 (6.142), Acc m&t: 2.53 3.99, Remain: 4 days, 18:45:08, Finish: 2024-11-27 03:58 [11-23 01:13:02] (/home/user/VAR/train.py , line 276)=> [ep25] (training ) Lm: 6.842 (6.842), Lt: 6.142 (6.142), Acc m&t: 2.53 3.99, Remain: 4 days, 18:47:13, Finish: 2024-11-27 04:00 [11-23 01:13:02] (/home/user/VAR/train.py , line 276)=> [ep25] (training ) Lm: 6.842 (6.842), Lt: 6.142 (6.142), Acc m&t: 2.53 3.99, Remain: 4 days, 18:46:34, Finish: 2024-11-27 03:59 [11-23 01:13:02] (/home/user/VAR/train.py , line 276)=> [ep25] (training ) Lm: 6.842 (6.842), Lt: 6.142 (6.142), Acc m&t: 2.53 3.99, Remain: 4 days, 18:45:24, Finish: 2024-11-27 03:58 [11-23 01:13:02] (/home/user/VAR/train.py , line 276)=> [ep25] (training ) Lm: 6.842 (6.842), Lt: 6.142 (6.142), Acc m&t: 2.53 3.99, Remain: 4 days, 18:48:20, Finish: 2024-11-27 04:01 [11-23 01:13:02] (/home/user/VAR/train.py , line 276)=> [ep25] (training ) Lm: 6.842 (6.842), Lt: 6.142 (6.142), Acc m&t: 2.53 3.99, Remain: 4 days, 18:36:37, Finish: 2024-11-27 03:49 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 0/1669] eta: 0:20:36 tlr: 0.00024 tnm: 0.33 Lm: 6.931 (6.931) Lt: 6.231 (6.231) Accm: 2.42 (2.42) Acct: 3.65 (3.65) proj_loss: -0.5059 (-0.5059) time: 0.7409 data: 0.0004 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 0/1669] eta: 0:20:34 tlr: 0.00024 tnm: 0.33 Lm: 6.856 (6.856) Lt: 6.223 (6.223) Accm: 2.07 (2.07) Acct: 3.06 (3.06) proj_loss: -0.5225 (-0.5225) time: 0.7396 data: 0.0004 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 0/1669] eta: 0:20:38 tlr: 0.00024 tnm: 0.33 Lm: 6.839 (6.839) Lt: 6.129 (6.129) Accm: 2.62 (2.62) Acct: 4.20 (4.20) proj_loss: -0.5136 (-0.5136) time: 0.7418 data: 0.0004 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 0/1669] eta: 0:20:36 tlr: 0.00024 tnm: 0.33 Lm: 6.681 (6.681) Lt: 5.926 (5.926) Accm: 3.21 (3.21) Acct: 4.99 (4.99) proj_loss: -0.5275 (-0.5275) time: 0.7409 data: 0.0003 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 0/1669] eta: 0:20:39 tlr: 0.00024 tnm: 0.33 Lm: 6.625 (6.625) Lt: 5.932 (5.932) Accm: 3.12 (3.12) Acct: 4.51 (4.51) proj_loss: -0.5336 (-0.5336) time: 0.7426 data: 0.0004 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 0/1669] eta: 0:20:41 tlr: 0.00024 tnm: 0.33 Lm: 6.939 (6.939) Lt: 6.281 (6.281) Accm: 2.05 (2.05) Acct: 3.44 (3.44) proj_loss: -0.5458 (-0.5458) time: 0.7436 data: 0.0004 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 0/1669] eta: 0:20:40 tlr: 0.00024 tnm: 0.33 Lm: 6.812 (6.812) Lt: 6.095 (6.095) Accm: 2.83 (2.83) Acct: 4.51 (4.51) proj_loss: -0.5230 (-0.5230) time: 0.7432 data: 0.0004 [11-23 01:13:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 0/1669] eta: 0:20:41 tlr: 0.00024 tnm: 0.33 Lm: 6.765 (6.765) Lt: 6.035 (6.035) Accm: 2.75 (2.75) Acct: 4.37 (4.37) proj_loss: -0.5099 (-0.5099) time: 0.7441 data: 0.0004 [11-23 01:18:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 417/1669] eta: 0:16:16 tlr: 0.00024 tnm: 0.34 Lm: 6.839 (6.839) Lt: 6.138 (6.138) Accm: 2.43 (2.43) Acct: 3.81 (3.81) proj_loss: -0.5124 (-0.5124) time: 0.7531 data: 0.0003 [11-23 01:18:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 417/1669] eta: 0:16:16 tlr: 0.00024 tnm: 0.34 Lm: 7.005 (7.005) Lt: 6.315 (6.315) Accm: 2.19 (2.19) Acct: 3.39 (3.39) proj_loss: -0.5136 (-0.5136) time: 0.7531 data: 0.0003 [11-23 01:18:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 417/1669] eta: 0:16:16 tlr: 0.00024 tnm: 0.34 Lm: 6.869 (6.869) Lt: 6.200 (6.200) Accm: 2.15 (2.15) Acct: 3.24 (3.24) proj_loss: -0.5347 (-0.5347) time: 0.7531 data: 0.0003 [11-23 01:18:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 417/1669] eta: 0:16:16 tlr: 0.00024 tnm: 0.34 Lm: 6.842 (6.842) Lt: 6.106 (6.106) Accm: 2.75 (2.75) Acct: 4.39 (4.39) proj_loss: -0.5314 (-0.5314) time: 0.7531 data: 0.0003 [11-23 01:18:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 417/1669] eta: 0:16:16 tlr: 0.00024 tnm: 0.34 Lm: 6.722 (6.722) Lt: 6.031 (6.031) Accm: 2.91 (2.91) Acct: 4.39 (4.39) proj_loss: -0.5188 (-0.5188) time: 0.7531 data: 0.0003 [11-23 01:18:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 417/1669] eta: 0:16:16 tlr: 0.00024 tnm: 0.34 Lm: 6.809 (6.809) Lt: 6.086 (6.086) Accm: 2.53 (2.53) Acct: 4.13 (4.13) proj_loss: -0.5117 (-0.5117) time: 0.7531 data: 0.0002 [11-23 01:18:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 417/1669] eta: 0:16:16 tlr: 0.00024 tnm: 0.34 Lm: 6.835 (6.835) Lt: 6.157 (6.157) Accm: 2.69 (2.69) Acct: 4.20 (4.20) proj_loss: -0.5395 (-0.5395) time: 0.7531 data: 0.0003 [11-23 01:18:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 417/1669] eta: 0:16:16 tlr: 0.00024 tnm: 0.34 Lm: 6.919 (6.919) Lt: 6.190 (6.190) Accm: 2.24 (2.24) Acct: 3.81 (3.81) proj_loss: -0.5155 (-0.5155) time: 0.7531 data: 0.0003 [11-23 01:23:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 834/1669] eta: 0:10:39 tlr: 0.00024 tnm: 0.34 Lm: 6.931 (6.936) Lt: 6.231 (6.241) Accm: 2.42 (2.39) Acct: 3.65 (3.81) proj_loss: -0.5059 (-0.5106) time: 0.7547 data: 0.0002 [11-23 01:23:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 834/1669] eta: 0:10:39 tlr: 0.00024 tnm: 0.34 Lm: 6.881 (6.897) Lt: 6.177 (6.190) Accm: 2.23 (2.22) Acct: 3.41 (3.51) proj_loss: -0.5225 (-0.5294) time: 0.7547 data: 0.0003 [11-23 01:23:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 834/1669] eta: 0:10:39 tlr: 0.00024 tnm: 0.34 Lm: 6.899 (6.875) Lt: 6.099 (6.149) Accm: 2.43 (2.36) Acct: 4.13 (3.91) proj_loss: -0.5039 (-0.5116) time: 0.7547 data: 0.0003 [11-23 01:23:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 834/1669] eta: 0:10:39 tlr: 0.00024 tnm: 0.34 Lm: 6.820 (6.802) Lt: 6.130 (6.114) Accm: 2.70 (2.70) Acct: 4.27 (4.20) proj_loss: -0.5039 (-0.5116) time: 0.7547 data: 0.0003 [11-23 01:23:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 834/1669] eta: 0:10:39 tlr: 0.00024 tnm: 0.34 Lm: 6.780 (6.733) Lt: 6.043 (6.005) Accm: 2.62 (2.67) Acct: 4.20 (4.37) proj_loss: -0.5098 (-0.5098) time: 0.7547 data: 0.0003 [11-23 01:23:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 834/1669] eta: 0:10:39 tlr: 0.00024 tnm: 0.34 Lm: 6.913 (6.876) Lt: 6.242 (6.193) Accm: 2.19 (2.35) Acct: 3.41 (3.67) proj_loss: -0.5099 (-0.5108) time: 0.7547 data: 0.0003 [11-23 01:23:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 834/1669] eta: 0:10:39 tlr: 0.00024 tnm: 0.34 Lm: 6.866 (6.850) Lt: 6.098 (6.103) Accm: 2.42 (2.64) Acct: 3.96 (4.25) proj_loss: -0.5275 (-0.5123) time: 0.7548 data: 0.0002 [11-23 01:23:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [ 834/1669] eta: 0:10:39 tlr: 0.00024 tnm: 0.34 Lm: 6.857 (6.871) Lt: 6.219 (6.201) Accm: 2.55 (2.58) Acct: 3.89 (3.99) proj_loss: -0.5400 (-0.5397) time: 0.7548 data: 0.0003 [11-23 01:28:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1251/1669] eta: 0:05:18 tlr: 0.00024 tnm: 0.33 Lm: 6.835 (6.834) Lt: 6.157 (6.162) Accm: 2.57 (2.59) Acct: 3.87 (3.96) proj_loss: -0.5405 (-0.5400) time: 0.7515 data: 0.0003 [11-23 01:28:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1251/1669] eta: 0:05:18 tlr: 0.00024 tnm: 0.33 Lm: 6.866 (6.903) Lt: 6.162 (6.193) Accm: 2.51 (2.44) Acct: 3.93 (3.91) proj_loss: -0.5120 (-0.5125) time: 0.7515 data: 0.0003 [11-23 01:28:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1251/1669] eta: 0:05:18 tlr: 0.00024 tnm: 0.33 Lm: 6.761 (6.778) Lt: 6.031 (6.066) Accm: 2.91 (2.81) Acct: 4.39 (4.39) proj_loss: -0.5059 (-0.5106) time: 0.7515 data: 0.0003 [11-23 01:28:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1251/1669] eta: 0:05:18 tlr: 0.00024 tnm: 0.33 Lm: 6.934 (6.888) Lt: 6.192 (6.159) Accm: 2.35 (2.49) Acct: 3.87 (3.97) proj_loss: -0.5298 (-0.5173) time: 0.7515 data: 0.0003 [11-23 01:28:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1251/1669] eta: 0:05:18 tlr: 0.00024 tnm: 0.33 Lm: 6.918 (6.923) Lt: 6.200 (6.224) Accm: 2.29 (2.26) Acct: 3.58 (3.57) proj_loss: -0.5274 (-0.5301) time: 0.7515 data: 0.0002 [11-23 01:28:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1251/1669] eta: 0:05:18 tlr: 0.00024 tnm: 0.33 Lm: 6.734 (6.722) Lt: 5.975 (5.980) Accm: 2.78 (2.78) Acct: 4.53 (4.55) proj_loss: -0.5078 (-0.5040) time: 0.7515 data: 0.0002 [11-23 01:28:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1251/1669] eta: 0:05:18 tlr: 0.00024 tnm: 0.33 Lm: 6.875 (6.866) Lt: 6.165 (6.167) Accm: 2.25 (2.34) Acct: 3.46 (3.63) proj_loss: -0.5088 (-0.5096) time: 0.7515 data: 0.0002 [11-23 01:28:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1251/1669] eta: 0:05:18 tlr: 0.00024 tnm: 0.33 Lm: 6.843 (6.823) Lt: 6.083 (6.109) Accm: 2.52 (2.61) Acct: 4.15 (4.24) proj_loss: -0.4989 (-0.5072) time: 0.7515 data: 0.0003 [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.866 (6.827) Lt: 6.098 (6.109) Accm: 2.42 (2.62) Acct: 3.96 (4.15) proj_loss: -0.5294 (-0.5197) time: 0.7531 data: 0.0017 [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.893 (6.917) Lt: 6.177 (6.209) Accm: 2.36 (2.35) Acct: 3.75 (3.78) proj_loss: -0.5225 (-0.5283) time: 0.7531 data: 0.0017 [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.808 (6.884) Lt: 6.096 (6.174) Accm: 2.59 (2.53) Acct: 4.20 (3.97) proj_loss: -0.5110 (-0.5122) time: 0.7531 data: 0.0015 [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.746 (6.771) Lt: 6.058 (6.064) Accm: 2.81 (2.81) Acct: 4.34 (4.38) proj_loss: -0.5078 (-0.5146) time: 0.7531 data: 0.0018 [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.848 (6.828) Lt: 6.099 (6.110) Accm: 2.46 (2.58) Acct: 4.13 (4.14) proj_loss: -0.5039 (-0.5115) time: 0.7531 data: 0.0017 [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.780 (6.764) Lt: 6.043 (6.035) Accm: 2.62 (2.65) Acct: 4.20 (4.32) proj_loss: -0.5098 (-0.5061) time: 0.7531 data: 0.0015 [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.837 (6.804) Lt: 6.088 (6.093) Accm: 2.32 (2.47) Acct: 3.51 (3.90) proj_loss: -0.5099 (-0.5145) time: 0.7531 data: 0.0018 [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 26/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.34 Lm: 6.812 (6.826) Lt: 6.105 (6.150) Accm: 2.59 (2.62) Acct: 3.89 (4.06) proj_loss: -0.5400 (-0.5360) time: 0.7531 data: 0.0018 [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 26/350] Total time: 0:21:08 (0.760 s / it) [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 26/350] Total time: 0:21:08 (0.760 s / it) [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 26/350] Total time: 0:21:08 (0.760 s / it) [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 26/350] Total time: 0:21:08 (0.760 s / it) [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 26/350] Total time: 0:21:08 (0.760 s / it) [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 26/350] Total time: 0:21:08 (0.760 s / it) [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 26/350] Total time: 0:21:08 (0.760 s / it) [11-23 01:34:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 26/350] Total time: 0:21:08 (0.760 s / it) [11-23 01:34:10] (/home/user/VAR/train.py , line 276)=> [ep26] (training ) Lm: 6.838 (6.838), Lt: 6.140 (6.140), Acc m&t: 2.53 3.99, Remain: 4 days, 17:21:32, Finish: 2024-11-27 02:55 [11-23 01:34:10] (/home/user/VAR/train.py , line 276)=> [ep26] (training ) Lm: 6.838 (6.838), Lt: 6.140 (6.140), Acc m&t: 2.53 3.99, Remain: 4 days, 17:21:27, Finish: 2024-11-27 02:55 [11-23 01:34:10] (/home/user/VAR/train.py , line 276)=> [ep26] (training ) Lm: 6.838 (6.838), Lt: 6.140 (6.140), Acc m&t: 2.53 3.99, Remain: 4 days, 17:21:25, Finish: 2024-11-27 02:55 [11-23 01:34:10] (/home/user/VAR/train.py , line 276)=> [ep26] (training ) Lm: 6.838 (6.838), Lt: 6.140 (6.140), Acc m&t: 2.53 3.99, Remain: 4 days, 17:22:15, Finish: 2024-11-27 02:56 [11-23 01:34:10] (/home/user/VAR/train.py , line 276)=> [ep26] (training ) Lm: 6.838 (6.838), Lt: 6.140 (6.140), Acc m&t: 2.53 3.99, Remain: 4 days, 17:21:41, Finish: 2024-11-27 02:55 [11-23 01:34:10] (/home/user/VAR/train.py , line 276)=> [ep26] (training ) Lm: 6.838 (6.838), Lt: 6.140 (6.140), Acc m&t: 2.53 3.99, Remain: 4 days, 17:21:52, Finish: 2024-11-27 02:56 [11-23 01:34:10] (/home/user/VAR/train.py , line 276)=> [ep26] (training ) Lm: 6.838 (6.838), Lt: 6.140 (6.140), Acc m&t: 2.53 3.99, Remain: 4 days, 17:20:49, Finish: 2024-11-27 02:55 [11-23 01:34:10] (/home/user/VAR/train.py , line 276)=> [ep26] (training ) Lm: 6.838 (6.838), Lt: 6.140 (6.140), Acc m&t: 2.53 3.99, Remain: 4 days, 17:20:58, Finish: 2024-11-27 02:55 [11-23 01:34:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 0/1669] eta: 0:21:07 tlr: 0.00024 tnm: 0.35 Lm: 6.872 (6.872) Lt: 6.155 (6.155) Accm: 2.81 (2.81) Acct: 4.65 (4.65) proj_loss: -0.5347 (-0.5347) time: 0.7596 data: 0.0003 [11-23 01:34:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 0/1669] eta: 0:21:05 tlr: 0.00024 tnm: 0.35 Lm: 6.710 (6.710) Lt: 5.982 (5.982) Accm: 2.75 (2.75) Acct: 4.55 (4.55) proj_loss: -0.4801 (-0.4801) time: 0.7585 data: 0.0003 [11-23 01:34:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 0/1669] eta: 0:21:07 tlr: 0.00024 tnm: 0.35 Lm: 6.852 (6.852) Lt: 6.182 (6.182) Accm: 2.62 (2.62) Acct: 4.48 (4.48) proj_loss: -0.5481 (-0.5481) time: 0.7592 data: 0.0004 [11-23 01:34:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 0/1669] eta: 0:21:08 tlr: 0.00024 tnm: 0.35 Lm: 6.923 (6.923) Lt: 6.200 (6.200) Accm: 2.29 (2.29) Acct: 3.55 (3.55) proj_loss: -0.4958 (-0.4958) time: 0.7602 data: 0.0003 [11-23 01:34:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 0/1669] eta: 0:21:08 tlr: 0.00024 tnm: 0.35 Lm: 6.731 (6.731) Lt: 6.077 (6.077) Accm: 2.96 (2.96) Acct: 4.92 (4.92) proj_loss: -0.5134 (-0.5134) time: 0.7598 data: 0.0004 [11-23 01:34:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 0/1669] eta: 0:21:08 tlr: 0.00024 tnm: 0.35 Lm: 6.735 (6.735) Lt: 6.100 (6.100) Accm: 2.62 (2.62) Acct: 3.27 (3.27) proj_loss: -0.5236 (-0.5236) time: 0.7602 data: 0.0004 [11-23 01:34:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 0/1669] eta: 0:21:07 tlr: 0.00024 tnm: 0.35 Lm: 6.956 (6.956) Lt: 6.311 (6.311) Accm: 2.43 (2.43) Acct: 3.82 (3.82) proj_loss: -0.5036 (-0.5036) time: 0.7596 data: 0.0004 [11-23 01:34:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 0/1669] eta: 0:21:09 tlr: 0.00024 tnm: 0.35 Lm: 6.568 (6.568) Lt: 5.818 (5.818) Accm: 3.39 (3.39) Acct: 5.44 (5.44) proj_loss: -0.5315 (-0.5315) time: 0.7608 data: 0.0004 [11-23 01:39:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.31 Lm: 6.802 (6.802) Lt: 6.109 (6.109) Accm: 2.77 (2.77) Acct: 4.34 (4.34) proj_loss: -0.5301 (-0.5301) time: 0.7514 data: 0.0003 [11-23 01:39:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.31 Lm: 6.817 (6.817) Lt: 6.140 (6.140) Accm: 2.56 (2.56) Acct: 4.29 (4.29) proj_loss: -0.5392 (-0.5392) time: 0.7514 data: 0.0003 [11-23 01:39:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.31 Lm: 6.827 (6.827) Lt: 6.117 (6.117) Accm: 2.46 (2.46) Acct: 3.91 (3.91) proj_loss: -0.5088 (-0.5088) time: 0.7514 data: 0.0003 [11-23 01:39:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.31 Lm: 6.840 (6.840) Lt: 6.111 (6.111) Accm: 2.59 (2.59) Acct: 4.10 (4.10) proj_loss: -0.4950 (-0.4950) time: 0.7514 data: 0.0003 [11-23 01:39:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.31 Lm: 6.715 (6.715) Lt: 6.036 (6.036) Accm: 2.83 (2.83) Acct: 4.49 (4.49) proj_loss: -0.5256 (-0.5256) time: 0.7514 data: 0.0003 [11-23 01:39:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.31 Lm: 6.690 (6.690) Lt: 5.977 (5.977) Accm: 2.65 (2.65) Acct: 3.70 (3.70) proj_loss: -0.5294 (-0.5294) time: 0.7514 data: 0.0003 [11-23 01:39:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.31 Lm: 6.742 (6.742) Lt: 6.011 (6.011) Accm: 2.87 (2.87) Acct: 4.65 (4.65) proj_loss: -0.4963 (-0.4963) time: 0.7514 data: 0.0003 [11-23 01:39:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.31 Lm: 6.648 (6.648) Lt: 5.931 (5.931) Accm: 2.98 (2.98) Acct: 4.68 (4.68) proj_loss: -0.5349 (-0.5349) time: 0.7514 data: 0.0003 [11-23 01:46:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 834/1669] eta: 0:11:51 tlr: 0.00024 tnm: 0.36 Lm: 6.732 (6.733) Lt: 6.063 (6.017) Accm: 2.81 (2.85) Acct: 4.65 (4.47) proj_loss: -0.5347 (-0.5328) time: 0.9038 data: 0.0002 [11-23 01:46:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 834/1669] eta: 0:11:51 tlr: 0.00024 tnm: 0.36 Lm: 6.842 (6.841) Lt: 6.145 (6.122) Accm: 2.46 (2.54) Acct: 3.79 (3.99) proj_loss: -0.4958 (-0.5087) time: 0.9038 data: 0.0002 [11-23 01:46:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 834/1669] eta: 0:11:51 tlr: 0.00024 tnm: 0.36 Lm: 6.782 (6.794) Lt: 6.098 (6.121) Accm: 2.62 (2.59) Acct: 4.27 (4.28) proj_loss: -0.5383 (-0.5389) time: 0.9038 data: 0.0003 [11-23 01:46:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 834/1669] eta: 0:11:51 tlr: 0.00024 tnm: 0.36 Lm: 6.892 (6.849) Lt: 6.170 (6.134) Accm: 2.72 (2.55) Acct: 4.48 (4.10) proj_loss: -0.5356 (-0.5178) time: 0.9038 data: 0.0003 [11-23 01:46:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 834/1669] eta: 0:11:51 tlr: 0.00024 tnm: 0.36 Lm: 6.731 (6.805) Lt: 6.077 (6.115) Accm: 2.71 (2.57) Acct: 4.06 (4.16) proj_loss: -0.5134 (-0.5183) time: 0.9038 data: 0.0003 [11-23 01:46:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 834/1669] eta: 0:11:51 tlr: 0.00024 tnm: 0.36 Lm: 6.728 (6.733) Lt: 6.044 (6.030) Accm: 2.56 (2.76) Acct: 3.93 (4.29) proj_loss: -0.5315 (-0.5233) time: 0.9038 data: 0.0003 [11-23 01:46:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 834/1669] eta: 0:11:51 tlr: 0.00024 tnm: 0.36 Lm: 6.735 (6.740) Lt: 6.100 (6.053) Accm: 2.62 (2.52) Acct: 3.27 (3.37) proj_loss: -0.5300 (-0.5296) time: 0.9039 data: 0.0003 [11-23 01:46:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [ 834/1669] eta: 0:11:51 tlr: 0.00024 tnm: 0.36 Lm: 6.838 (6.774) Lt: 6.111 (6.044) Accm: 2.55 (2.76) Acct: 4.06 (4.45) proj_loss: -0.5036 (-0.5104) time: 0.9038 data: 0.0002 [11-23 01:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1251/1669] eta: 0:05:45 tlr: 0.00024 tnm: 0.34 Lm: 6.703 (6.719) Lt: 5.994 (5.994) Accm: 2.86 (2.86) Acct: 4.56 (4.47) proj_loss: -0.5355 (-0.5337) time: 0.7529 data: 0.0002 [11-23 01:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1251/1669] eta: 0:05:45 tlr: 0.00024 tnm: 0.34 Lm: 6.834 (6.831) Lt: 6.098 (6.107) Accm: 2.62 (2.54) Acct: 4.30 (4.11) proj_loss: -0.5339 (-0.5214) time: 0.7529 data: 0.0003 [11-23 01:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1251/1669] eta: 0:05:45 tlr: 0.00024 tnm: 0.34 Lm: 6.791 (6.795) Lt: 6.091 (6.104) Accm: 2.64 (2.62) Acct: 4.37 (4.35) proj_loss: -0.5343 (-0.5314) time: 0.7530 data: 0.0002 [11-23 01:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1251/1669] eta: 0:05:45 tlr: 0.00024 tnm: 0.34 Lm: 6.814 (6.827) Lt: 6.108 (6.110) Accm: 2.45 (2.52) Acct: 3.74 (3.92) proj_loss: -0.5137 (-0.5144) time: 0.7529 data: 0.0002 [11-23 01:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1251/1669] eta: 0:05:45 tlr: 0.00024 tnm: 0.34 Lm: 6.768 (6.805) Lt: 6.113 (6.123) Accm: 2.83 (2.68) Acct: 4.32 (4.26) proj_loss: -0.5220 (-0.5213) time: 0.7529 data: 0.0003 [11-23 01:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1251/1669] eta: 0:05:45 tlr: 0.00024 tnm: 0.34 Lm: 6.897 (6.840) Lt: 6.211 (6.128) Accm: 2.49 (2.62) Acct: 3.94 (4.20) proj_loss: -0.5052 (-0.5095) time: 0.7529 data: 0.0002 [11-23 01:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1251/1669] eta: 0:05:45 tlr: 0.00024 tnm: 0.34 Lm: 6.788 (6.788) Lt: 6.153 (6.114) Accm: 2.43 (2.39) Acct: 3.19 (3.31) proj_loss: -0.5268 (-0.5261) time: 0.7530 data: 0.0003 [11-23 01:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1251/1669] eta: 0:05:45 tlr: 0.00024 tnm: 0.34 Lm: 6.806 (6.770) Lt: 6.104 (6.063) Accm: 2.50 (2.68) Acct: 3.72 (4.10) proj_loss: -0.5331 (-0.5261) time: 0.7529 data: 0.0003 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.675 (6.705) Lt: 5.926 (5.973) Accm: 2.86 (2.86) Acct: 4.65 (4.50) proj_loss: -0.5363 (-0.5345) time: 0.7556 data: 0.0014 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.821 (6.826) Lt: 6.072 (6.096) Accm: 2.46 (2.52) Acct: 3.79 (3.89) proj_loss: -0.5259 (-0.5167) time: 0.7556 data: 0.0016 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.782 (6.774) Lt: 6.084 (6.066) Accm: 2.67 (2.70) Acct: 4.48 (4.47) proj_loss: -0.5304 (-0.5203) time: 0.7556 data: 0.0017 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.892 (6.866) Lt: 6.170 (6.151) Accm: 2.51 (2.46) Acct: 4.13 (3.99) proj_loss: -0.5322 (-0.5145) time: 0.7556 data: 0.0015 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.876 (6.791) Lt: 6.145 (6.080) Accm: 2.51 (2.64) Acct: 3.93 (4.08) proj_loss: -0.5315 (-0.5200) time: 0.7556 data: 0.0020 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.806 (6.816) Lt: 6.149 (6.149) Accm: 2.71 (2.64) Acct: 4.06 (4.12) proj_loss: -0.5224 (-0.5216) time: 0.7556 data: 0.0016 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.735 (6.750) Lt: 6.100 (6.056) Accm: 2.62 (2.57) Acct: 3.27 (3.69) proj_loss: -0.5236 (-0.5164) time: 0.7556 data: 0.0019 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 27/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.838 (6.830) Lt: 6.111 (6.123) Accm: 2.43 (2.57) Acct: 3.82 (4.06) proj_loss: -0.5068 (-0.5154) time: 0.7556 data: 0.0017 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 27/350] Total time: 0:22:28 (0.808 s / it) [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 27/350] Total time: 0:22:28 (0.808 s / it) [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 27/350] Total time: 0:22:28 (0.808 s / it) [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 27/350] Total time: 0:22:28 (0.808 s / it) [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 27/350] Total time: 0:22:28 (0.808 s / it) [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 27/350] Total time: 0:22:28 (0.808 s / it) [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 27/350] Total time: 0:22:28 (0.808 s / it) [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 27/350] Total time: 0:22:28 (0.808 s / it) [11-23 01:56:39] (/home/user/VAR/train.py , line 276)=> [ep27] (training ) Lm: 6.822 (6.822), Lt: 6.118 (6.118), Acc m&t: 2.58 4.08, Remain: 4 days, 17:23:29, Finish: 2024-11-27 03:20 [11-23 01:56:39] (/home/user/VAR/train.py , line 276)=> [ep27] (training ) Lm: 6.822 (6.822), Lt: 6.118 (6.118), Acc m&t: 2.58 4.08, Remain: 4 days, 17:23:20, Finish: 2024-11-27 03:19 [11-23 01:56:39] (/home/user/VAR/train.py , line 276)=> [ep27] (training ) Lm: 6.822 (6.822), Lt: 6.118 (6.118), Acc m&t: 2.58 4.08, Remain: 4 days, 17:21:30, Finish: 2024-11-27 03:18 [11-23 01:56:39] (/home/user/VAR/train.py , line 276)=> [ep27] (training ) Lm: 6.822 (6.822), Lt: 6.118 (6.118), Acc m&t: 2.58 4.08, Remain: 4 days, 17:22:51, Finish: 2024-11-27 03:19 [11-23 01:56:39] (/home/user/VAR/train.py , line 276)=> [ep27] (training ) Lm: 6.822 (6.822), Lt: 6.118 (6.118), Acc m&t: 2.58 4.08, Remain: 4 days, 17:24:49, Finish: 2024-11-27 03:21 [11-23 01:56:39] (/home/user/VAR/train.py , line 276)=> [ep27] (training ) Lm: 6.822 (6.822), Lt: 6.118 (6.118), Acc m&t: 2.58 4.08, Remain: 4 days, 17:22:34, Finish: 2024-11-27 03:19 [11-23 01:56:39] (/home/user/VAR/train.py , line 276)=> [ep27] (training ) Lm: 6.822 (6.822), Lt: 6.118 (6.118), Acc m&t: 2.58 4.08, Remain: 4 days, 17:23:27, Finish: 2024-11-27 03:20 [11-23 01:56:39] (/home/user/VAR/train.py , line 276)=> [ep27] (training ) Lm: 6.822 (6.822), Lt: 6.118 (6.118), Acc m&t: 2.58 4.08, Remain: 4 days, 17:23:09, Finish: 2024-11-27 03:19 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 0/1669] eta: 0:20:36 tlr: 0.00024 tnm: 0.32 Lm: 6.934 (6.934) Lt: 6.243 (6.243) Accm: 2.16 (2.16) Acct: 3.24 (3.24) proj_loss: -0.5236 (-0.5236) time: 0.7409 data: 0.0003 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 0/1669] eta: 0:20:35 tlr: 0.00024 tnm: 0.32 Lm: 7.022 (7.022) Lt: 6.284 (6.284) Accm: 1.79 (1.79) Acct: 3.13 (3.13) proj_loss: -0.5390 (-0.5390) time: 0.7402 data: 0.0004 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 0/1669] eta: 0:20:36 tlr: 0.00024 tnm: 0.32 Lm: 7.043 (7.043) Lt: 6.364 (6.364) Accm: 2.14 (2.14) Acct: 3.41 (3.41) proj_loss: -0.5258 (-0.5258) time: 0.7410 data: 0.0004 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 0/1669] eta: 0:20:41 tlr: 0.00024 tnm: 0.32 Lm: 6.823 (6.823) Lt: 6.112 (6.112) Accm: 2.32 (2.32) Acct: 3.62 (3.62) proj_loss: -0.4726 (-0.4726) time: 0.7441 data: 0.0003 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 0/1669] eta: 0:20:37 tlr: 0.00024 tnm: 0.32 Lm: 6.912 (6.912) Lt: 6.265 (6.265) Accm: 2.23 (2.23) Acct: 3.75 (3.75) proj_loss: -0.4868 (-0.4868) time: 0.7416 data: 0.0004 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 0/1669] eta: 0:20:38 tlr: 0.00024 tnm: 0.32 Lm: 6.935 (6.935) Lt: 6.292 (6.292) Accm: 2.23 (2.23) Acct: 3.55 (3.55) proj_loss: -0.5153 (-0.5153) time: 0.7418 data: 0.0004 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 0/1669] eta: 0:20:38 tlr: 0.00024 tnm: 0.32 Lm: 6.858 (6.858) Lt: 6.081 (6.081) Accm: 2.33 (2.33) Acct: 3.68 (3.68) proj_loss: -0.4871 (-0.4871) time: 0.7418 data: 0.0004 [11-23 01:56:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 0/1669] eta: 0:20:38 tlr: 0.00024 tnm: 0.32 Lm: 6.605 (6.605) Lt: 5.875 (5.875) Accm: 3.15 (3.15) Acct: 4.86 (4.86) proj_loss: -0.5367 (-0.5367) time: 0.7421 data: 0.0004 [11-23 02:01:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.32 Lm: 6.770 (6.770) Lt: 6.055 (6.055) Accm: 2.74 (2.74) Acct: 4.22 (4.22) proj_loss: -0.5251 (-0.5251) time: 0.7542 data: 0.0003 [11-23 02:01:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.32 Lm: 6.854 (6.854) Lt: 6.169 (6.169) Accm: 2.51 (2.51) Acct: 3.93 (3.93) proj_loss: -0.5178 (-0.5178) time: 0.7542 data: 0.0002 [11-23 02:01:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.32 Lm: 6.937 (6.937) Lt: 6.258 (6.258) Accm: 2.25 (2.25) Acct: 3.56 (3.56) proj_loss: -0.5229 (-0.5229) time: 0.7542 data: 0.0003 [11-23 02:01:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.32 Lm: 6.986 (6.986) Lt: 6.293 (6.293) Accm: 2.00 (2.00) Acct: 3.36 (3.36) proj_loss: -0.5214 (-0.5214) time: 0.7542 data: 0.0003 [11-23 02:01:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.32 Lm: 6.883 (6.883) Lt: 6.154 (6.154) Accm: 2.46 (2.46) Acct: 3.82 (3.82) proj_loss: -0.4961 (-0.4961) time: 0.7542 data: 0.0003 [11-23 02:01:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.32 Lm: 6.819 (6.819) Lt: 6.159 (6.159) Accm: 2.43 (2.43) Acct: 4.06 (4.06) proj_loss: -0.4958 (-0.4958) time: 0.7542 data: 0.0003 [11-23 02:01:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.32 Lm: 6.921 (6.921) Lt: 6.202 (6.202) Accm: 2.11 (2.11) Acct: 3.34 (3.34) proj_loss: -0.4867 (-0.4867) time: 0.7542 data: 0.0002 [11-23 02:01:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 417/1669] eta: 0:15:41 tlr: 0.00024 tnm: 0.32 Lm: 6.824 (6.824) Lt: 6.133 (6.133) Accm: 2.42 (2.42) Acct: 3.65 (3.65) proj_loss: -0.5046 (-0.5046) time: 0.7542 data: 0.0003 [11-23 02:07:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 6.903 (6.850) Lt: 6.188 (6.151) Accm: 2.45 (2.43) Acct: 3.75 (3.72) proj_loss: -0.5153 (-0.5084) time: 0.7511 data: 0.0003 [11-23 02:07:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 7.015 (6.963) Lt: 6.349 (6.289) Accm: 2.14 (2.19) Acct: 3.41 (3.46) proj_loss: -0.5199 (-0.5184) time: 0.7511 data: 0.0002 [11-23 02:07:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 6.823 (6.855) Lt: 6.112 (6.147) Accm: 2.32 (2.50) Acct: 3.62 (3.81) proj_loss: -0.4978 (-0.4904) time: 0.7511 data: 0.0003 [11-23 02:07:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 6.950 (6.950) Lt: 6.284 (6.261) Accm: 2.20 (2.07) Acct: 3.58 (3.46) proj_loss: -0.5235 (-0.5221) time: 0.7511 data: 0.0003 [11-23 02:07:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 6.858 (6.861) Lt: 6.109 (6.139) Accm: 2.46 (2.46) Acct: 3.86 (3.83) proj_loss: -0.5051 (-0.5025) time: 0.7511 data: 0.0003 [11-23 02:07:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 6.726 (6.770) Lt: 6.052 (6.097) Accm: 2.62 (2.70) Acct: 4.37 (4.37) proj_loss: -0.5048 (-0.5150) time: 0.7511 data: 0.0003 [11-23 02:07:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 6.773 (6.821) Lt: 6.095 (6.123) Accm: 2.58 (2.53) Acct: 3.79 (3.88) proj_loss: -0.5122 (-0.5159) time: 0.7511 data: 0.0002 [11-23 02:07:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [ 834/1669] eta: 0:10:28 tlr: 0.00024 tnm: 0.32 Lm: 6.626 (6.722) Lt: 5.877 (5.996) Accm: 3.15 (2.89) Acct: 4.86 (4.51) proj_loss: -0.5135 (-0.5181) time: 0.7511 data: 0.0003 [11-23 02:12:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.31 Lm: 6.781 (6.823) Lt: 6.056 (6.117) Accm: 2.74 (2.66) Acct: 4.22 (4.14) proj_loss: -0.5229 (-0.5217) time: 0.7526 data: 0.0003 [11-23 02:12:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.31 Lm: 6.923 (6.923) Lt: 6.251 (6.247) Accm: 2.25 (2.34) Acct: 3.56 (3.76) proj_loss: -0.5229 (-0.5222) time: 0.7526 data: 0.0002 [11-23 02:12:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.31 Lm: 6.775 (6.810) Lt: 6.064 (6.100) Accm: 2.65 (2.58) Acct: 4.06 (3.99) proj_loss: -0.5158 (-0.5168) time: 0.7526 data: 0.0003 [11-23 02:12:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.31 Lm: 6.874 (6.872) Lt: 6.165 (6.165) Accm: 2.30 (2.44) Acct: 3.50 (3.70) proj_loss: -0.4959 (-0.4913) time: 0.7526 data: 0.0002 [11-23 02:12:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.31 Lm: 6.838 (6.845) Lt: 6.095 (6.112) Accm: 2.53 (2.50) Acct: 3.91 (3.96) proj_loss: -0.5102 (-0.5091) time: 0.7526 data: 0.0002 [11-23 02:12:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.31 Lm: 6.914 (6.918) Lt: 6.241 (6.218) Accm: 2.21 (2.23) Acct: 3.62 (3.77) proj_loss: -0.5312 (-0.5283) time: 0.7526 data: 0.0003 [11-23 02:12:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.31 Lm: 6.730 (6.761) Lt: 6.048 (6.084) Accm: 2.56 (2.65) Acct: 4.15 (4.26) proj_loss: -0.5193 (-0.5197) time: 0.7526 data: 0.0003 [11-23 02:12:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1251/1669] eta: 0:05:14 tlr: 0.00024 tnm: 0.31 Lm: 6.919 (6.884) Lt: 6.233 (6.183) Accm: 2.45 (2.43) Acct: 3.72 (3.71) proj_loss: -0.5132 (-0.5091) time: 0.7526 data: 0.0003 [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.903 (6.887) Lt: 6.231 (6.192) Accm: 2.45 (2.43) Acct: 3.75 (3.77) proj_loss: -0.5153 (-0.5106) time: 1.0804 data: 0.0021 [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 28/350] Total time: 0:22:20 (0.803 s / it) [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.773 (6.766) Lt: 6.033 (6.062) Accm: 2.72 (2.71) Acct: 4.34 (4.28) proj_loss: -0.5194 (-0.5194) time: 1.0804 data: 0.0019 [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.918 (6.881) Lt: 6.218 (6.195) Accm: 2.29 (2.38) Acct: 3.44 (3.65) proj_loss: -0.4978 (-0.5038) time: 1.0804 data: 0.0018 [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.735 (6.757) Lt: 6.052 (6.082) Accm: 2.62 (2.66) Acct: 3.93 (4.19) proj_loss: -0.5171 (-0.5191) time: 1.0804 data: 0.0017 [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.830 (6.855) Lt: 6.153 (6.156) Accm: 2.36 (2.55) Acct: 3.72 (4.10) proj_loss: -0.5258 (-0.5266) time: 1.0804 data: 0.0018 [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.924 (6.919) Lt: 6.236 (6.221) Accm: 2.21 (2.23) Acct: 3.65 (3.75) proj_loss: -0.5390 (-0.5332) time: 1.0804 data: 0.0016 [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.818 (6.838) Lt: 6.109 (6.112) Accm: 2.59 (2.52) Acct: 3.96 (3.99) proj_loss: -0.5051 (-0.5065) time: 1.0804 data: 0.0019 [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 28/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.32 Lm: 6.767 (6.811) Lt: 6.146 (6.123) Accm: 2.52 (2.63) Acct: 3.72 (4.06) proj_loss: -0.5135 (-0.5194) time: 1.0804 data: 0.0020 [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 28/350] Total time: 0:22:20 (0.803 s / it) [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 28/350] Total time: 0:22:20 (0.803 s / it) [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 28/350] Total time: 0:22:20 (0.803 s / it) [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 28/350] Total time: 0:22:20 (0.803 s / it) [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 28/350] Total time: 0:22:20 (0.803 s / it) [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 28/350] Total time: 0:22:20 (0.803 s / it) [11-23 02:18:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 28/350] Total time: 0:22:20 (0.803 s / it) [11-23 02:19:00] (/home/user/VAR/train.py , line 276)=> [ep28] (training ) Lm: 6.815 (6.815), Lt: 6.109 (6.109), Acc m&t: 2.58 4.08, Remain: 4 days, 17:27:08, Finish: 2024-11-27 03:46 [11-23 02:19:00] (/home/user/VAR/train.py , line 276)=> [ep28] (training ) Lm: 6.815 (6.815), Lt: 6.109 (6.109), Acc m&t: 2.58 4.08, Remain: 4 days, 17:30:43, Finish: 2024-11-27 03:49 [11-23 02:19:00] (/home/user/VAR/train.py , line 276)=> [ep28] (training ) Lm: 6.815 (6.815), Lt: 6.109 (6.109), Acc m&t: 2.58 4.08, Remain: 4 days, 17:30:41, Finish: 2024-11-27 03:49 [11-23 02:19:00] (/home/user/VAR/train.py , line 276)=> [ep28] (training ) Lm: 6.815 (6.815), Lt: 6.109 (6.109), Acc m&t: 2.58 4.08, Remain: 4 days, 17:28:59, Finish: 2024-11-27 03:47 [11-23 02:19:00] (/home/user/VAR/train.py , line 276)=> [ep28] (training ) Lm: 6.815 (6.815), Lt: 6.109 (6.109), Acc m&t: 2.58 4.08, Remain: 4 days, 17:30:06, Finish: 2024-11-27 03:49 [11-23 02:19:00] (/home/user/VAR/train.py , line 276)=> [ep28] (training ) Lm: 6.815 (6.815), Lt: 6.109 (6.109), Acc m&t: 2.58 4.08, Remain: 4 days, 17:29:49, Finish: 2024-11-27 03:48 [11-23 02:19:00] (/home/user/VAR/train.py , line 276)=> [ep28] (training ) Lm: 6.815 (6.815), Lt: 6.109 (6.109), Acc m&t: 2.58 4.08, Remain: 4 days, 17:29:56, Finish: 2024-11-27 03:48 [11-23 02:19:00] (/home/user/VAR/train.py , line 276)=> [ep28] (training ) Lm: 6.815 (6.815), Lt: 6.109 (6.109), Acc m&t: 2.58 4.08, Remain: 4 days, 17:28:51, Finish: 2024-11-27 03:47 [11-23 02:19:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 0/1669] eta: 0:20:43 tlr: 0.00024 tnm: 0.32 Lm: 6.935 (6.935) Lt: 6.266 (6.266) Accm: 2.16 (2.16) Acct: 3.06 (3.06) proj_loss: -0.5300 (-0.5300) time: 0.7453 data: 0.0003 [11-23 02:19:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 0/1669] eta: 0:20:41 tlr: 0.00024 tnm: 0.32 Lm: 6.569 (6.569) Lt: 5.869 (5.869) Accm: 3.16 (3.16) Acct: 4.79 (4.79) proj_loss: -0.5203 (-0.5203) time: 0.7441 data: 0.0003 [11-23 02:19:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 0/1669] eta: 0:20:43 tlr: 0.00024 tnm: 0.32 Lm: 6.854 (6.854) Lt: 6.165 (6.165) Accm: 2.37 (2.37) Acct: 4.10 (4.10) proj_loss: -0.4981 (-0.4981) time: 0.7448 data: 0.0004 [11-23 02:19:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 0/1669] eta: 0:20:42 tlr: 0.00024 tnm: 0.32 Lm: 6.996 (6.996) Lt: 6.322 (6.322) Accm: 2.05 (2.05) Acct: 3.44 (3.44) proj_loss: -0.5379 (-0.5379) time: 0.7448 data: 0.0004 [11-23 02:19:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 0/1669] eta: 0:20:44 tlr: 0.00024 tnm: 0.32 Lm: 6.846 (6.846) Lt: 6.161 (6.161) Accm: 2.24 (2.24) Acct: 3.65 (3.65) proj_loss: -0.5285 (-0.5285) time: 0.7457 data: 0.0004 [11-23 02:19:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 0/1669] eta: 0:20:43 tlr: 0.00024 tnm: 0.32 Lm: 6.595 (6.595) Lt: 5.846 (5.846) Accm: 3.04 (3.04) Acct: 4.82 (4.82) proj_loss: -0.5197 (-0.5197) time: 0.7448 data: 0.0004 [11-23 02:19:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 0/1669] eta: 0:20:43 tlr: 0.00024 tnm: 0.32 Lm: 6.738 (6.738) Lt: 5.946 (5.946) Accm: 3.12 (3.12) Acct: 5.44 (5.44) proj_loss: -0.5085 (-0.5085) time: 0.7452 data: 0.0004 [11-23 02:19:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 0/1669] eta: 0:20:45 tlr: 0.00024 tnm: 0.32 Lm: 6.572 (6.572) Lt: 5.778 (5.778) Accm: 3.04 (3.04) Acct: 4.96 (4.96) proj_loss: -0.5104 (-0.5104) time: 0.7460 data: 0.0004 [11-23 02:24:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 417/1669] eta: 0:16:01 tlr: 0.00024 tnm: 0.32 Lm: 6.738 (6.738) Lt: 6.078 (6.078) Accm: 2.77 (2.77) Acct: 4.42 (4.42) proj_loss: -0.5144 (-0.5144) time: 0.7540 data: 0.0003 [11-23 02:24:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 417/1669] eta: 0:16:01 tlr: 0.00024 tnm: 0.32 Lm: 6.642 (6.642) Lt: 5.965 (5.965) Accm: 2.94 (2.94) Acct: 4.49 (4.49) proj_loss: -0.5535 (-0.5535) time: 0.7541 data: 0.0002 [11-23 02:24:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 417/1669] eta: 0:16:01 tlr: 0.00024 tnm: 0.32 Lm: 6.938 (6.938) Lt: 6.259 (6.259) Accm: 2.19 (2.19) Acct: 3.51 (3.51) proj_loss: -0.5208 (-0.5208) time: 0.7540 data: 0.0003 [11-23 02:24:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 417/1669] eta: 0:16:01 tlr: 0.00024 tnm: 0.32 Lm: 6.858 (6.858) Lt: 6.153 (6.153) Accm: 2.23 (2.23) Acct: 3.44 (3.44) proj_loss: -0.5075 (-0.5075) time: 0.7540 data: 0.0002 [11-23 02:24:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 417/1669] eta: 0:16:01 tlr: 0.00024 tnm: 0.32 Lm: 6.866 (6.866) Lt: 6.174 (6.174) Accm: 2.43 (2.43) Acct: 3.68 (3.68) proj_loss: -0.5104 (-0.5104) time: 0.7541 data: 0.0003 [11-23 02:24:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 417/1669] eta: 0:16:01 tlr: 0.00024 tnm: 0.32 Lm: 6.617 (6.617) Lt: 5.869 (5.869) Accm: 3.15 (3.15) Acct: 5.10 (5.10) proj_loss: -0.5229 (-0.5229) time: 0.7540 data: 0.0003 [11-23 02:24:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 417/1669] eta: 0:16:01 tlr: 0.00024 tnm: 0.32 Lm: 6.696 (6.696) Lt: 5.939 (5.939) Accm: 2.77 (2.77) Acct: 4.58 (4.58) proj_loss: -0.5068 (-0.5068) time: 0.7540 data: 0.0003 [11-23 02:24:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 417/1669] eta: 0:16:01 tlr: 0.00024 tnm: 0.32 Lm: 6.823 (6.823) Lt: 6.090 (6.090) Accm: 2.75 (2.75) Acct: 4.56 (4.56) proj_loss: -0.5120 (-0.5120) time: 0.7540 data: 0.0003 [11-23 02:29:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 834/1669] eta: 0:10:35 tlr: 0.00024 tnm: 0.30 Lm: 6.797 (6.814) Lt: 6.060 (6.080) Accm: 2.37 (2.59) Acct: 3.68 (4.17) proj_loss: -0.5154 (-0.5153) time: 0.7540 data: 0.0003 [11-23 02:29:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 834/1669] eta: 0:10:35 tlr: 0.00024 tnm: 0.30 Lm: 6.665 (6.650) Lt: 5.911 (5.947) Accm: 2.97 (2.95) Acct: 4.79 (4.69) proj_loss: -0.5273 (-0.5448) time: 0.7540 data: 0.0002 [11-23 02:29:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 834/1669] eta: 0:10:35 tlr: 0.00024 tnm: 0.30 Lm: 6.846 (6.813) Lt: 6.145 (6.121) Accm: 2.24 (2.31) Acct: 3.58 (3.49) proj_loss: -0.5213 (-0.5121) time: 0.7540 data: 0.0003 [11-23 02:29:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 834/1669] eta: 0:10:35 tlr: 0.00024 tnm: 0.30 Lm: 6.639 (6.637) Lt: 5.891 (5.903) Accm: 3.12 (3.14) Acct: 4.82 (4.95) proj_loss: -0.5262 (-0.5252) time: 0.7540 data: 0.0002 [11-23 02:29:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 834/1669] eta: 0:10:35 tlr: 0.00024 tnm: 0.30 Lm: 6.801 (6.844) Lt: 6.135 (6.161) Accm: 2.70 (2.64) Acct: 4.30 (4.07) proj_loss: -0.5253 (-0.5154) time: 0.7540 data: 0.0003 [11-23 02:29:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 834/1669] eta: 0:10:35 tlr: 0.00024 tnm: 0.30 Lm: 6.718 (6.704) Lt: 5.992 (5.957) Accm: 2.75 (2.76) Acct: 4.20 (4.40) proj_loss: -0.5032 (-0.5047) time: 0.7540 data: 0.0003 [11-23 02:29:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 834/1669] eta: 0:10:35 tlr: 0.00024 tnm: 0.30 Lm: 6.880 (6.870) Lt: 6.196 (6.182) Accm: 2.33 (2.40) Acct: 3.58 (3.72) proj_loss: -0.5192 (-0.5203) time: 0.7540 data: 0.0003 [11-23 02:29:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [ 834/1669] eta: 0:10:35 tlr: 0.00024 tnm: 0.30 Lm: 6.854 (6.778) Lt: 6.165 (6.108) Accm: 2.37 (2.62) Acct: 4.10 (4.14) proj_loss: -0.5051 (-0.5113) time: 0.7540 data: 0.0003 [11-23 02:35:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1251/1669] eta: 0:05:16 tlr: 0.00024 tnm: 0.31 Lm: 6.804 (6.772) Lt: 6.097 (6.088) Accm: 2.68 (2.71) Acct: 4.41 (4.29) proj_loss: -0.5179 (-0.5207) time: 0.7534 data: 0.0002 [11-23 02:35:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1251/1669] eta: 0:05:21 tlr: 0.00024 tnm: 0.31 Lm: 6.690 (6.671) Lt: 5.972 (5.969) Accm: 2.88 (2.91) Acct: 4.61 (4.63) proj_loss: -0.5271 (-0.5403) time: 0.7534 data: 0.0003 [11-23 02:35:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1251/1669] eta: 0:05:21 tlr: 0.00024 tnm: 0.31 Lm: 6.785 (6.771) Lt: 6.100 (6.083) Accm: 2.35 (2.55) Acct: 3.62 (3.78) proj_loss: -0.5249 (-0.5267) time: 0.7534 data: 0.0002 [11-23 02:35:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1251/1669] eta: 0:05:21 tlr: 0.00024 tnm: 0.31 Lm: 6.851 (6.858) Lt: 6.160 (6.167) Accm: 2.51 (2.56) Acct: 4.01 (3.99) proj_loss: -0.5248 (-0.5176) time: 0.7535 data: 0.0003 [11-23 02:35:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1251/1669] eta: 0:05:21 tlr: 0.00024 tnm: 0.31 Lm: 6.657 (6.695) Lt: 5.931 (5.990) Accm: 3.08 (2.90) Acct: 4.73 (4.58) proj_loss: -0.5279 (-0.5275) time: 0.7534 data: 0.0003 [11-23 02:35:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1251/1669] eta: 0:05:21 tlr: 0.00024 tnm: 0.31 Lm: 6.807 (6.811) Lt: 6.112 (6.105) Accm: 2.58 (2.63) Acct: 3.86 (4.06) proj_loss: -0.5122 (-0.5165) time: 0.7534 data: 0.0003 [11-23 02:35:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1251/1669] eta: 0:05:21 tlr: 0.00024 tnm: 0.31 Lm: 6.769 (6.771) Lt: 6.046 (6.048) Accm: 2.62 (2.66) Acct: 4.12 (4.23) proj_loss: -0.5068 (-0.5113) time: 0.7534 data: 0.0003 [11-23 02:35:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1251/1669] eta: 0:05:21 tlr: 0.00024 tnm: 0.31 Lm: 6.838 (6.831) Lt: 6.093 (6.091) Accm: 2.44 (2.57) Acct: 3.82 (4.12) proj_loss: -0.5120 (-0.5070) time: 0.7534 data: 0.0002 [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.31 Lm: 6.880 (6.840) Lt: 6.101 (6.093) Accm: 2.43 (2.54) Acct: 3.96 (4.08) proj_loss: -0.5109 (-0.5077) time: 0.7542 data: 0.0015 [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.31 Lm: 6.854 (6.794) Lt: 6.165 (6.112) Accm: 2.51 (2.67) Acct: 4.10 (4.19) proj_loss: -0.5307 (-0.5253) time: 0.7542 data: 0.0016 [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.31 Lm: 6.716 (6.698) Lt: 6.034 (6.000) Accm: 2.78 (2.80) Acct: 4.44 (4.46) proj_loss: -0.5268 (-0.5359) time: 0.7542 data: 0.0018 [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.31 Lm: 6.724 (6.724) Lt: 6.055 (6.048) Accm: 2.46 (3.55) Acct: 3.65 (4.83) proj_loss: -0.5285 (-0.5304) time: 0.7542 data: 0.0016 [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 29/350] Total time: 0:21:17 (0.765 s / it) [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.31 Lm: 6.801 (6.844) Lt: 6.135 (6.149) Accm: 2.70 (2.64) Acct: 4.30 (4.20) proj_loss: -0.5253 (-0.5224) time: 0.7542 data: 0.0017 [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.31 Lm: 6.675 (6.692) Lt: 5.966 (5.985) Accm: 3.12 (2.96) Acct: 4.82 (4.75) proj_loss: -0.5296 (-0.5290) time: 0.7542 data: 0.0018 [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.31 Lm: 6.735 (6.790) Lt: 6.029 (6.084) Accm: 2.75 (2.65) Acct: 3.99 (4.05) proj_loss: -0.5192 (-0.5174) time: 0.7542 data: 0.0019 [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 29/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.31 Lm: 6.718 (6.757) Lt: 5.992 (6.024) Accm: 2.49 (2.63) Acct: 4.03 (4.18) proj_loss: -0.5104 (-0.5151) time: 0.7542 data: 0.0016 [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 29/350] Total time: 0:21:17 (0.765 s / it) [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 29/350] Total time: 0:21:17 (0.765 s / it) [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 29/350] Total time: 0:21:17 (0.765 s / it) [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 29/350] Total time: 0:21:17 (0.765 s / it) [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 29/350] Total time: 0:21:17 (0.765 s / it) [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 29/350] Total time: 0:21:17 (0.765 s / it) [11-23 02:40:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 29/350] Total time: 0:21:17 (0.765 s / it) [11-23 02:42:14] (home/user/VAR/trainer.py, line 114)=> FID: 5.622467272379993 [11-23 02:42:14] (/home/user/VAR/train.py , line 259)=> [*] [ep29] (val 50000) Lm: 6.8041, Lt: 6.1028, Acc m&t: 2.62 4.09, Val cost: 116.64s [11-23 02:42:14] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-23 02:42:50] (/home/user/VAR/train.py , line 276)=> [ep29] (training ) Lm: 6.804 (6.804), Lt: 6.103 (6.103), Acc m&t: 2.62 4.09, Remain: 4 days, 16:35:30, Finish: 2024-11-27 03:15 [11-23 02:42:50] (/home/user/VAR/train.py , line 276)=> [ep29] (training ) Lm: 6.804 (6.804), Lt: 6.103 (6.103), Acc m&t: 2.62 4.09, Remain: 4 days, 16:32:14, Finish: 2024-11-27 03:12 [11-23 02:42:50] (/home/user/VAR/train.py , line 276)=> [ep29] (training ) Lm: 6.804 (6.804), Lt: 6.103 (6.103), Acc m&t: 2.62 4.09, Remain: 4 days, 16:32:33, Finish: 2024-11-27 03:12 [11-23 02:42:50] (/home/user/VAR/train.py , line 276)=> [ep29] (training ) Lm: 6.804 (6.804), Lt: 6.103 (6.103), Acc m&t: 2.62 4.09, Remain: 4 days, 16:32:40, Finish: 2024-11-27 03:12 [11-23 02:42:50] (/home/user/VAR/train.py , line 276)=> [ep29] (training ) Lm: 6.804 (6.804), Lt: 6.103 (6.103), Acc m&t: 2.62 4.09, Remain: 4 days, 16:34:23, Finish: 2024-11-27 03:14 [11-23 02:42:50] (/home/user/VAR/train.py , line 276)=> [ep29] (training ) Lm: 6.804 (6.804), Lt: 6.103 (6.103), Acc m&t: 2.62 4.09, Remain: 4 days, 16:33:02, Finish: 2024-11-27 03:13 [11-23 02:42:50] (/home/user/VAR/train.py , line 276)=> [ep29] (training ) Lm: 6.804 (6.804), Lt: 6.103 (6.103), Acc m&t: 2.62 4.09, Remain: 4 days, 16:33:38, Finish: 2024-11-27 03:13 [11-23 02:42:50] (/home/user/VAR/train.py , line 276)=> [ep29] (training ) Lm: 6.804 (6.804), Lt: 6.103 (6.103), Acc m&t: 2.62 4.09, Remain: 4 days, 16:33:44, Finish: 2024-11-27 03:14 [11-23 02:42:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 0/1669] eta: 0:20:38 tlr: 0.00024 tnm: 0.32 Lm: 6.869 (6.869) Lt: 6.152 (6.152) Accm: 2.42 (2.42) Acct: 3.68 (3.68) proj_loss: -0.5171 (-0.5171) time: 0.7419 data: 0.0003 [11-23 02:42:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 0/1669] eta: 0:21:16 tlr: 0.00024 tnm: 0.32 Lm: 6.818 (6.818) Lt: 6.133 (6.133) Accm: 2.71 (2.71) Acct: 4.10 (4.10) proj_loss: -0.5054 (-0.5054) time: 0.7650 data: 0.0003 [11-23 02:42:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 0/1669] eta: 0:20:40 tlr: 0.00024 tnm: 0.32 Lm: 6.679 (6.679) Lt: 6.005 (6.005) Accm: 3.21 (3.21) Acct: 5.13 (5.13) proj_loss: -0.5161 (-0.5161) time: 0.7433 data: 0.0004 [11-23 02:42:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 0/1669] eta: 0:20:38 tlr: 0.00024 tnm: 0.32 Lm: 6.676 (6.676) Lt: 5.961 (5.961) Accm: 2.93 (2.93) Acct: 4.37 (4.37) proj_loss: -0.5177 (-0.5177) time: 0.7423 data: 0.0004 [11-23 02:42:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 0/1669] eta: 0:20:39 tlr: 0.00024 tnm: 0.32 Lm: 6.705 (6.705) Lt: 6.050 (6.050) Accm: 3.38 (3.38) Acct: 5.20 (5.20) proj_loss: -0.5520 (-0.5520) time: 0.7426 data: 0.0004 [11-23 02:42:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 0/1669] eta: 0:20:39 tlr: 0.00024 tnm: 0.32 Lm: 6.842 (6.842) Lt: 6.104 (6.104) Accm: 2.48 (2.48) Acct: 3.99 (3.99) proj_loss: -0.5137 (-0.5137) time: 0.7429 data: 0.0004 [11-23 02:42:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 0/1669] eta: 0:20:38 tlr: 0.00024 tnm: 0.32 Lm: 6.678 (6.678) Lt: 5.969 (5.969) Accm: 2.72 (2.72) Acct: 4.41 (4.41) proj_loss: -0.5366 (-0.5366) time: 0.7421 data: 0.0003 [11-23 02:42:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 0/1669] eta: 0:20:39 tlr: 0.00024 tnm: 0.32 Lm: 6.758 (6.758) Lt: 6.036 (6.036) Accm: 3.23 (3.23) Acct: 5.03 (5.03) proj_loss: -0.5149 (-0.5149) time: 0.7429 data: 0.0004 [11-23 02:48:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.30 Lm: 6.723 (6.723) Lt: 5.991 (5.991) Accm: 2.93 (2.93) Acct: 4.72 (4.72) proj_loss: -0.5175 (-0.5175) time: 0.7553 data: 0.0002 [11-23 02:48:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.30 Lm: 6.772 (6.772) Lt: 6.017 (6.017) Accm: 2.81 (2.81) Acct: 4.37 (4.37) proj_loss: -0.5133 (-0.5133) time: 0.7553 data: 0.0003 [11-23 02:48:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.30 Lm: 6.785 (6.785) Lt: 6.059 (6.059) Accm: 2.57 (2.57) Acct: 3.91 (3.91) proj_loss: -0.5242 (-0.5242) time: 0.7553 data: 0.0002 [11-23 02:48:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.30 Lm: 6.789 (6.789) Lt: 6.110 (6.110) Accm: 2.67 (2.67) Acct: 4.32 (4.32) proj_loss: -0.5371 (-0.5371) time: 0.7553 data: 0.0003 [11-23 02:48:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.30 Lm: 6.792 (6.792) Lt: 6.090 (6.090) Accm: 2.85 (2.85) Acct: 4.61 (4.61) proj_loss: -0.5211 (-0.5211) time: 0.7553 data: 0.0003 [11-23 02:48:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.30 Lm: 6.608 (6.608) Lt: 5.882 (5.882) Accm: 3.21 (3.21) Acct: 5.01 (5.01) proj_loss: -0.5171 (-0.5171) time: 0.7553 data: 0.0003 [11-23 02:48:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.30 Lm: 6.651 (6.651) Lt: 5.906 (5.906) Accm: 2.74 (2.74) Acct: 4.30 (4.30) proj_loss: -0.5223 (-0.5223) time: 0.7553 data: 0.0003 [11-23 02:48:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 417/1669] eta: 0:15:42 tlr: 0.00024 tnm: 0.30 Lm: 6.905 (6.905) Lt: 6.187 (6.187) Accm: 2.38 (2.38) Acct: 3.94 (3.94) proj_loss: -0.5296 (-0.5296) time: 0.7554 data: 0.0003 [11-23 02:54:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 834/1669] eta: 0:11:50 tlr: 0.00024 tnm: 0.30 Lm: 6.842 (6.794) Lt: 6.104 (6.059) Accm: 2.48 (2.62) Acct: 3.99 (4.19) proj_loss: -0.5196 (-0.5263) time: 0.9215 data: 0.0002 [11-23 02:54:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 834/1669] eta: 0:11:50 tlr: 0.00024 tnm: 0.30 Lm: 6.869 (6.845) Lt: 6.152 (6.115) Accm: 2.42 (2.62) Acct: 3.68 (4.01) proj_loss: -0.5171 (-0.5209) time: 0.9215 data: 0.0003 [11-23 02:54:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 834/1669] eta: 0:11:50 tlr: 0.00024 tnm: 0.30 Lm: 6.751 (6.734) Lt: 5.985 (5.983) Accm: 2.71 (2.70) Acct: 4.10 (4.06) proj_loss: -0.5165 (-0.5216) time: 0.9215 data: 0.0002 [11-23 02:54:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 834/1669] eta: 0:11:50 tlr: 0.00024 tnm: 0.30 Lm: 6.676 (6.655) Lt: 5.961 (5.917) Accm: 2.93 (3.03) Acct: 4.37 (4.72) proj_loss: -0.5177 (-0.5182) time: 0.9215 data: 0.0003 [11-23 02:54:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 834/1669] eta: 0:11:50 tlr: 0.00024 tnm: 0.30 Lm: 6.813 (6.799) Lt: 6.151 (6.111) Accm: 2.49 (2.71) Acct: 4.10 (4.24) proj_loss: -0.5260 (-0.5268) time: 0.9215 data: 0.0002 [11-23 02:54:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 834/1669] eta: 0:11:50 tlr: 0.00024 tnm: 0.30 Lm: 6.756 (6.778) Lt: 6.050 (6.068) Accm: 2.75 (2.70) Acct: 4.41 (4.35) proj_loss: -0.5222 (-0.5208) time: 0.9215 data: 0.0002 [11-23 02:54:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 834/1669] eta: 0:11:50 tlr: 0.00024 tnm: 0.30 Lm: 6.689 (6.707) Lt: 6.036 (6.015) Accm: 3.23 (3.03) Acct: 4.92 (4.79) proj_loss: -0.5201 (-0.5280) time: 0.9215 data: 0.0002 [11-23 02:54:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [ 834/1669] eta: 0:11:50 tlr: 0.00024 tnm: 0.30 Lm: 6.624 (6.554) Lt: 5.842 (5.825) Accm: 2.75 (2.98) Acct: 4.41 (4.57) proj_loss: -0.5366 (-0.5319) time: 0.9215 data: 0.0003 [11-23 03:00:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1251/1669] eta: 0:05:44 tlr: 0.00024 tnm: 0.29 Lm: 6.651 (6.602) Lt: 5.906 (5.869) Accm: 2.79 (2.94) Acct: 4.42 (4.54) proj_loss: -0.5405 (-0.5350) time: 0.7528 data: 0.0003 [11-23 03:00:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1251/1669] eta: 0:05:44 tlr: 0.00024 tnm: 0.29 Lm: 6.691 (6.706) Lt: 5.952 (5.967) Accm: 2.83 (2.84) Acct: 4.24 (4.26) proj_loss: -0.5128 (-0.5185) time: 0.7528 data: 0.0002 [11-23 03:00:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1251/1669] eta: 0:05:44 tlr: 0.00024 tnm: 0.29 Lm: 6.796 (6.815) Lt: 6.054 (6.075) Accm: 2.71 (2.71) Acct: 4.13 (4.15) proj_loss: -0.5133 (-0.5123) time: 0.7528 data: 0.0003 [11-23 03:00:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1251/1669] eta: 0:05:44 tlr: 0.00024 tnm: 0.29 Lm: 6.746 (6.764) Lt: 6.078 (6.041) Accm: 2.79 (2.80) Acct: 4.61 (4.51) proj_loss: -0.5321 (-0.5304) time: 0.7528 data: 0.0002 [11-23 03:00:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1251/1669] eta: 0:05:44 tlr: 0.00024 tnm: 0.29 Lm: 6.713 (6.705) Lt: 5.975 (5.987) Accm: 2.80 (2.83) Acct: 4.25 (4.39) proj_loss: -0.5190 (-0.5194) time: 0.7528 data: 0.0003 [11-23 03:00:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1251/1669] eta: 0:05:44 tlr: 0.00024 tnm: 0.29 Lm: 6.772 (6.771) Lt: 6.055 (6.045) Accm: 2.71 (2.70) Acct: 4.10 (4.19) proj_loss: -0.5185 (-0.5240) time: 0.7528 data: 0.0003 [11-23 03:00:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1251/1669] eta: 0:05:44 tlr: 0.00024 tnm: 0.29 Lm: 6.723 (6.726) Lt: 6.049 (6.026) Accm: 2.93 (2.85) Acct: 4.67 (4.46) proj_loss: -0.5321 (-0.5320) time: 0.7527 data: 0.0003 [11-23 03:00:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1251/1669] eta: 0:05:44 tlr: 0.00024 tnm: 0.29 Lm: 6.802 (6.796) Lt: 6.110 (6.094) Accm: 2.55 (2.61) Acct: 3.99 (4.16) proj_loss: -0.5212 (-0.5206) time: 0.7528 data: 0.0003 [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.30 Lm: 6.810 (6.798) Lt: 6.151 (6.106) Accm: 2.75 (2.66) Acct: 4.34 (4.19) proj_loss: -0.5222 (-0.5223) time: 0.7560 data: 0.0016 [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 30/350] Total time: 0:22:25 (0.806 s / it) [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.30 Lm: 6.751 (6.737) Lt: 5.985 (6.014) Accm: 2.71 (2.72) Acct: 4.10 (4.09) proj_loss: -0.5165 (-0.5241) time: 0.7560 data: 0.0013 [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.30 Lm: 6.725 (6.756) Lt: 6.055 (6.044) Accm: 3.09 (2.88) Acct: 4.89 (4.59) proj_loss: -0.5260 (-0.5247) time: 0.7560 data: 0.0017 [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.30 Lm: 6.752 (6.802) Lt: 6.028 (6.066) Accm: 2.71 (2.71) Acct: 3.96 (4.11) proj_loss: -0.5171 (-0.5181) time: 0.7560 data: 0.0017 [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.30 Lm: 6.736 (6.764) Lt: 6.006 (6.032) Accm: 2.78 (2.72) Acct: 4.20 (4.28) proj_loss: -0.5174 (-0.5225) time: 0.7560 data: 0.0018 [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.30 Lm: 6.749 (6.719) Lt: 5.989 (5.995) Accm: 2.93 (2.85) Acct: 4.37 (4.44) proj_loss: -0.5203 (-0.5227) time: 0.7560 data: 0.0020 [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.30 Lm: 6.758 (6.801) Lt: 6.061 (6.097) Accm: 2.62 (2.64) Acct: 4.41 (4.24) proj_loss: -0.5201 (-0.5275) time: 0.7560 data: 0.0015 [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 30/350] [1668/1669] eta: 0:00:00 tlr: 0.00024 tnm: 0.30 Lm: 6.678 (6.624) Lt: 5.969 (5.891) Accm: 2.83 (2.92) Acct: 4.44 (4.57) proj_loss: -0.5366 (-0.5341) time: 0.7560 data: 0.0017 [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 30/350] Total time: 0:22:25 (0.806 s / it) [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 30/350] Total time: 0:22:25 (0.806 s / it) [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 30/350] Total time: 0:22:25 (0.806 s / it) [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 30/350] Total time: 0:22:25 (0.806 s / it) [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 30/350] Total time: 0:22:25 (0.806 s / it) [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 30/350] Total time: 0:22:25 (0.806 s / it) [11-23 03:05:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 30/350] Total time: 0:22:25 (0.806 s / it) [11-23 03:05:15] (/home/user/VAR/train.py , line 276)=> [ep30] (training ) Lm: 6.803 (6.803), Lt: 6.096 (6.096), Acc m&t: 2.62 4.11, Remain: 4 days, 16:47:35, Finish: 2024-11-27 03:52 [11-23 03:05:15] (/home/user/VAR/train.py , line 276)=> [ep30] (training ) Lm: 6.803 (6.803), Lt: 6.096 (6.096), Acc m&t: 2.62 4.11, Remain: 4 days, 16:45:25, Finish: 2024-11-27 03:50 [11-23 03:05:15] (/home/user/VAR/train.py , line 276)=> [ep30] (training ) Lm: 6.803 (6.803), Lt: 6.096 (6.096), Acc m&t: 2.62 4.11, Remain: 4 days, 16:47:53, Finish: 2024-11-27 03:53 [11-23 03:05:15] (/home/user/VAR/train.py , line 276)=> [ep30] (training ) Lm: 6.803 (6.803), Lt: 6.096 (6.096), Acc m&t: 2.62 4.11, Remain: 4 days, 16:47:38, Finish: 2024-11-27 03:52 [11-23 03:05:15] (/home/user/VAR/train.py , line 276)=> [ep30] (training ) Lm: 6.803 (6.803), Lt: 6.096 (6.096), Acc m&t: 2.62 4.11, Remain: 4 days, 16:46:33, Finish: 2024-11-27 03:51 [11-23 03:05:15] (/home/user/VAR/train.py , line 276)=> [ep30] (training ) Lm: 6.803 (6.803), Lt: 6.096 (6.096), Acc m&t: 2.62 4.11, Remain: 4 days, 16:41:40, Finish: 2024-11-27 03:46 [11-23 03:05:15] (/home/user/VAR/train.py , line 276)=> [ep30] (training ) Lm: 6.803 (6.803), Lt: 6.096 (6.096), Acc m&t: 2.62 4.11, Remain: 4 days, 16:45:38, Finish: 2024-11-27 03:50 [11-23 03:05:15] (/home/user/VAR/train.py , line 276)=> [ep30] (training ) Lm: 6.803 (6.803), Lt: 6.096 (6.096), Acc m&t: 2.62 4.11, Remain: 4 days, 16:47:28, Finish: 2024-11-27 03:52 [11-23 03:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 0/1669] eta: 0:19:44 tlr: 0.00024 tnm: 0.31 Lm: 6.956 (6.956) Lt: 6.352 (6.352) Accm: 2.19 (2.19) Acct: 3.31 (3.31) proj_loss: -0.5578 (-0.5578) time: 0.7098 data: 0.0004 [11-23 03:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 0/1669] eta: 0:19:45 tlr: 0.00024 tnm: 0.31 Lm: 6.724 (6.724) Lt: 5.943 (5.943) Accm: 2.70 (2.70) Acct: 4.17 (4.17) proj_loss: -0.5546 (-0.5546) time: 0.7101 data: 0.0003 [11-23 03:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 0/1669] eta: 0:19:33 tlr: 0.00024 tnm: 0.31 Lm: 6.699 (6.699) Lt: 5.962 (5.962) Accm: 2.94 (2.94) Acct: 4.44 (4.44) proj_loss: -0.5247 (-0.5247) time: 0.7034 data: 0.0003 [11-23 03:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 0/1669] eta: 0:19:46 tlr: 0.00024 tnm: 0.31 Lm: 7.029 (7.029) Lt: 6.366 (6.366) Accm: 2.17 (2.17) Acct: 3.55 (3.55) proj_loss: -0.5182 (-0.5182) time: 0.7106 data: 0.0003 [11-23 03:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 0/1669] eta: 0:19:57 tlr: 0.00024 tnm: 0.31 Lm: 6.681 (6.681) Lt: 5.935 (5.935) Accm: 2.94 (2.94) Acct: 4.72 (4.72) proj_loss: -0.5620 (-0.5620) time: 0.7173 data: 0.0003 [11-23 03:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 0/1669] eta: 0:19:44 tlr: 0.00024 tnm: 0.31 Lm: 6.711 (6.711) Lt: 5.996 (5.996) Accm: 2.62 (2.62) Acct: 4.20 (4.20) proj_loss: -0.5652 (-0.5652) time: 0.7096 data: 0.0003 [11-23 03:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 0/1669] eta: 0:19:46 tlr: 0.00024 tnm: 0.31 Lm: 6.655 (6.655) Lt: 5.960 (5.960) Accm: 2.84 (2.84) Acct: 4.55 (4.55) proj_loss: -0.5685 (-0.5685) time: 0.7110 data: 0.0003 [11-23 03:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 0/1669] eta: 0:19:47 tlr: 0.00024 tnm: 0.31 Lm: 6.846 (6.846) Lt: 6.123 (6.123) Accm: 2.49 (2.49) Acct: 3.82 (3.82) proj_loss: -0.5348 (-0.5348) time: 0.7115 data: 0.0004 [11-23 03:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.31 Lm: 6.700 (6.700) Lt: 5.996 (5.996) Accm: 2.87 (2.87) Acct: 4.39 (4.39) proj_loss: -0.5375 (-0.5375) time: 0.7531 data: 0.0003 [11-23 03:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.31 Lm: 6.728 (6.728) Lt: 5.974 (5.974) Accm: 2.65 (2.65) Acct: 4.22 (4.22) proj_loss: -0.5390 (-0.5390) time: 0.7531 data: 0.0002 [11-23 03:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.31 Lm: 6.806 (6.806) Lt: 6.082 (6.082) Accm: 2.72 (2.72) Acct: 4.44 (4.44) proj_loss: -0.5335 (-0.5335) time: 0.7531 data: 0.0002 [11-23 03:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.31 Lm: 6.837 (6.837) Lt: 6.205 (6.205) Accm: 2.35 (2.35) Acct: 3.51 (3.51) proj_loss: -0.5517 (-0.5517) time: 0.7531 data: 0.0003 [11-23 03:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.31 Lm: 6.752 (6.752) Lt: 6.019 (6.019) Accm: 2.77 (2.77) Acct: 4.46 (4.46) proj_loss: -0.5156 (-0.5156) time: 0.7531 data: 0.0003 [11-23 03:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.31 Lm: 6.737 (6.737) Lt: 6.043 (6.043) Accm: 2.84 (2.84) Acct: 4.51 (4.51) proj_loss: -0.5452 (-0.5452) time: 0.7531 data: 0.0003 [11-23 03:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.31 Lm: 6.892 (6.892) Lt: 6.193 (6.193) Accm: 2.34 (2.34) Acct: 3.77 (3.77) proj_loss: -0.5276 (-0.5276) time: 0.7531 data: 0.0002 [11-23 03:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.31 Lm: 6.692 (6.692) Lt: 5.959 (5.959) Accm: 2.80 (2.80) Acct: 4.34 (4.34) proj_loss: -0.5348 (-0.5348) time: 0.7531 data: 0.0002 [11-23 03:15:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.34 Lm: 6.717 (6.701) Lt: 5.958 (5.957) Accm: 2.75 (2.76) Acct: 4.51 (4.40) proj_loss: -0.5064 (-0.5254) time: 0.7539 data: 0.0002 [11-23 03:15:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.34 Lm: 6.748 (6.787) Lt: 5.988 (6.051) Accm: 2.52 (2.65) Acct: 4.17 (4.33) proj_loss: -0.5050 (-0.5200) time: 0.7539 data: 0.0002 [11-23 03:15:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.34 Lm: 6.805 (6.834) Lt: 6.076 (6.117) Accm: 2.59 (2.58) Acct: 4.44 (4.22) proj_loss: -0.5247 (-0.5219) time: 0.7539 data: 0.0004 [11-23 03:15:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.34 Lm: 6.762 (6.810) Lt: 6.090 (6.128) Accm: 2.62 (2.53) Acct: 4.20 (4.04) proj_loss: -0.5537 (-0.5480) time: 0.7539 data: 0.0003 [11-23 03:15:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.34 Lm: 6.848 (6.878) Lt: 6.103 (6.163) Accm: 2.26 (2.31) Acct: 3.55 (3.64) proj_loss: -0.5294 (-0.5282) time: 0.7539 data: 0.0002 [11-23 03:15:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.34 Lm: 6.882 (6.852) Lt: 6.219 (6.210) Accm: 2.51 (2.49) Acct: 3.72 (3.86) proj_loss: -0.5456 (-0.5459) time: 0.7539 data: 0.0003 [11-23 03:15:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.34 Lm: 6.725 (6.727) Lt: 5.976 (5.975) Accm: 2.61 (2.59) Acct: 4.27 (4.24) proj_loss: -0.5239 (-0.5340) time: 0.7539 data: 0.0002 [11-23 03:15:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.34 Lm: 6.773 (6.724) Lt: 6.056 (6.016) Accm: 2.52 (2.75) Acct: 3.93 (4.24) proj_loss: -0.5348 (-0.5329) time: 0.7539 data: 0.0003 [11-23 03:20:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.884 (6.861) Lt: 6.240 (6.222) Accm: 2.45 (2.46) Acct: 3.63 (3.78) proj_loss: -0.5458 (-0.5460) time: 0.7556 data: 0.0003 [11-23 03:20:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.870 (6.881) Lt: 6.127 (6.160) Accm: 2.38 (2.40) Acct: 3.77 (3.74) proj_loss: -0.5292 (-0.5284) time: 0.7555 data: 0.0003 [11-23 03:20:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.729 (6.749) Lt: 5.991 (6.026) Accm: 2.53 (2.55) Acct: 4.22 (4.08) proj_loss: -0.5237 (-0.5277) time: 0.7556 data: 0.0002 [11-23 03:20:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.810 (6.772) Lt: 6.090 (6.070) Accm: 2.51 (2.64) Acct: 3.87 (4.08) proj_loss: -0.5303 (-0.5311) time: 0.7556 data: 0.0003 [11-23 03:20:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.752 (6.784) Lt: 6.019 (6.072) Accm: 2.77 (2.68) Acct: 4.46 (4.31) proj_loss: -0.5296 (-0.5283) time: 0.7556 data: 0.0003 [11-23 03:20:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.757 (6.795) Lt: 6.043 (6.094) Accm: 2.63 (2.56) Acct: 4.20 (4.08) proj_loss: -0.5395 (-0.5402) time: 0.7556 data: 0.0003 [11-23 03:20:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.724 (6.792) Lt: 5.959 (6.069) Accm: 2.72 (2.63) Acct: 4.32 (4.21) proj_loss: -0.5160 (-0.5254) time: 0.7556 data: 0.0003 [11-23 03:20:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.715 (6.747) Lt: 5.962 (6.002) Accm: 2.67 (2.70) Acct: 4.25 (4.33) proj_loss: -0.5245 (-0.5260) time: 0.7556 data: 0.0003 [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.699 (6.738) Lt: 5.935 (5.985) Accm: 2.83 (2.75) Acct: 4.34 (4.38) proj_loss: -0.5080 (-0.5224) time: 0.8134 data: 0.0015 [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 31/350] Total time: 0:22:20 (0.803 s / it) [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.733 (6.752) Lt: 6.005 (6.041) Accm: 2.61 (2.58) Acct: 4.27 (4.12) proj_loss: -0.5239 (-0.5289) time: 0.8134 data: 0.0015 [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.882 (6.846) Lt: 6.219 (6.182) Accm: 2.46 (2.46) Acct: 3.72 (3.80) proj_loss: -0.5456 (-0.5448) time: 0.8134 data: 0.0016 [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.805 (6.790) Lt: 6.076 (6.079) Accm: 2.83 (2.71) Acct: 4.44 (4.28) proj_loss: -0.5247 (-0.5265) time: 0.8134 data: 0.0017 [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.752 (6.761) Lt: 5.996 (6.050) Accm: 2.64 (2.70) Acct: 4.20 (4.33) proj_loss: -0.5313 (-0.5385) time: 0.8134 data: 0.0021 [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.730 (6.797) Lt: 5.960 (6.079) Accm: 2.70 (2.62) Acct: 4.30 (4.23) proj_loss: -0.5255 (-0.5300) time: 0.8134 data: 0.0014 [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.848 (6.856) Lt: 6.103 (6.141) Accm: 2.26 (2.36) Acct: 3.55 (3.67) proj_loss: -0.5294 (-0.5294) time: 0.8134 data: 0.0016 [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 31/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.846 (6.797) Lt: 6.123 (6.110) Accm: 2.49 (2.57) Acct: 3.82 (3.93) proj_loss: -0.5348 (-0.5318) time: 0.8134 data: 0.0019 [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 31/350] Total time: 0:22:20 (0.803 s / it) [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 31/350] Total time: 0:22:20 (0.803 s / it) [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 31/350] Total time: 0:22:20 (0.803 s / it) [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 31/350] Total time: 0:22:20 (0.803 s / it) [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 31/350] Total time: 0:22:20 (0.803 s / it) [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 31/350] Total time: 0:22:20 (0.803 s / it) [11-23 03:27:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 31/350] Total time: 0:22:20 (0.803 s / it) [11-23 03:27:36] (/home/user/VAR/train.py , line 276)=> [ep31] (training ) Lm: 6.789 (6.789), Lt: 6.077 (6.077), Acc m&t: 2.62 4.13, Remain: 4 days, 16:02:11, Finish: 2024-11-27 03:29 [11-23 03:27:36] (/home/user/VAR/train.py , line 276)=> [ep31] (training ) Lm: 6.789 (6.789), Lt: 6.077 (6.077), Acc m&t: 2.62 4.13, Remain: 4 days, 16:02:47, Finish: 2024-11-27 03:30 [11-23 03:27:36] (/home/user/VAR/train.py , line 276)=> [ep31] (training ) Lm: 6.789 (6.789), Lt: 6.077 (6.077), Acc m&t: 2.62 4.13, Remain: 4 days, 16:03:35, Finish: 2024-11-27 03:31 [11-23 03:27:36] (/home/user/VAR/train.py , line 276)=> [ep31] (training ) Lm: 6.789 (6.789), Lt: 6.077 (6.077), Acc m&t: 2.62 4.13, Remain: 4 days, 16:03:32, Finish: 2024-11-27 03:31 [11-23 03:27:36] (/home/user/VAR/train.py , line 276)=> [ep31] (training ) Lm: 6.789 (6.789), Lt: 6.077 (6.077), Acc m&t: 2.62 4.13, Remain: 4 days, 16:04:21, Finish: 2024-11-27 03:31 [11-23 03:27:36] (/home/user/VAR/train.py , line 276)=> [ep31] (training ) Lm: 6.789 (6.789), Lt: 6.077 (6.077), Acc m&t: 2.62 4.13, Remain: 4 days, 16:04:53, Finish: 2024-11-27 03:32 [11-23 03:27:36] (/home/user/VAR/train.py , line 276)=> [ep31] (training ) Lm: 6.789 (6.789), Lt: 6.077 (6.077), Acc m&t: 2.62 4.13, Remain: 4 days, 16:03:31, Finish: 2024-11-27 03:31 [11-23 03:27:36] (/home/user/VAR/train.py , line 276)=> [ep31] (training ) Lm: 6.789 (6.789), Lt: 6.077 (6.077), Acc m&t: 2.62 4.13, Remain: 4 days, 16:04:28, Finish: 2024-11-27 03:32 [11-23 03:27:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 0/1669] eta: 0:20:34 tlr: 0.00023 tnm: 0.32 Lm: 6.684 (6.684) Lt: 5.923 (5.923) Accm: 2.84 (2.84) Acct: 4.61 (4.61) proj_loss: -0.5249 (-0.5249) time: 0.7399 data: 0.0003 [11-23 03:27:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 0/1669] eta: 0:20:35 tlr: 0.00023 tnm: 0.32 Lm: 6.822 (6.822) Lt: 6.035 (6.035) Accm: 2.42 (2.42) Acct: 4.06 (4.06) proj_loss: -0.4992 (-0.4992) time: 0.7400 data: 0.0004 [11-23 03:27:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 0/1669] eta: 0:20:35 tlr: 0.00023 tnm: 0.32 Lm: 6.844 (6.844) Lt: 6.216 (6.216) Accm: 2.24 (2.24) Acct: 3.17 (3.17) proj_loss: -0.5401 (-0.5401) time: 0.7403 data: 0.0004 [11-23 03:27:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 0/1669] eta: 0:20:36 tlr: 0.00023 tnm: 0.32 Lm: 6.886 (6.886) Lt: 6.135 (6.135) Accm: 2.35 (2.35) Acct: 4.03 (4.03) proj_loss: -0.5086 (-0.5086) time: 0.7409 data: 0.0004 [11-23 03:27:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 0/1669] eta: 0:20:35 tlr: 0.00023 tnm: 0.32 Lm: 6.785 (6.785) Lt: 6.089 (6.089) Accm: 2.53 (2.53) Acct: 4.10 (4.10) proj_loss: -0.5411 (-0.5411) time: 0.7405 data: 0.0003 [11-23 03:27:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 0/1669] eta: 0:20:37 tlr: 0.00023 tnm: 0.32 Lm: 6.636 (6.636) Lt: 6.010 (6.010) Accm: 3.15 (3.15) Acct: 4.99 (4.99) proj_loss: -0.5211 (-0.5211) time: 0.7412 data: 0.0004 [11-23 03:27:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 0/1669] eta: 0:20:37 tlr: 0.00023 tnm: 0.32 Lm: 6.640 (6.640) Lt: 5.920 (5.920) Accm: 2.97 (2.97) Acct: 5.13 (5.13) proj_loss: -0.5309 (-0.5309) time: 0.7414 data: 0.0003 [11-23 03:27:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 0/1669] eta: 0:20:37 tlr: 0.00023 tnm: 0.32 Lm: 6.766 (6.766) Lt: 6.008 (6.008) Accm: 2.59 (2.59) Acct: 4.37 (4.37) proj_loss: -0.5093 (-0.5093) time: 0.7414 data: 0.0004 [11-23 03:32:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.29 Lm: 6.744 (6.744) Lt: 5.997 (5.997) Accm: 2.83 (2.83) Acct: 4.49 (4.49) proj_loss: -0.5283 (-0.5283) time: 0.7525 data: 0.0003 [11-23 03:32:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.29 Lm: 6.696 (6.696) Lt: 5.942 (5.942) Accm: 2.68 (2.68) Acct: 4.39 (4.39) proj_loss: -0.5402 (-0.5402) time: 0.7525 data: 0.0002 [11-23 03:32:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.29 Lm: 6.951 (6.951) Lt: 6.312 (6.312) Accm: 2.16 (2.16) Acct: 3.25 (3.25) proj_loss: -0.5317 (-0.5317) time: 0.7525 data: 0.0003 [11-23 03:32:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.29 Lm: 6.749 (6.749) Lt: 5.973 (5.973) Accm: 2.63 (2.63) Acct: 4.34 (4.34) proj_loss: -0.5149 (-0.5149) time: 0.7525 data: 0.0002 [11-23 03:32:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.29 Lm: 6.800 (6.800) Lt: 6.049 (6.049) Accm: 2.60 (2.60) Acct: 4.17 (4.17) proj_loss: -0.5200 (-0.5200) time: 0.7525 data: 0.0003 [11-23 03:32:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.29 Lm: 6.703 (6.703) Lt: 6.053 (6.053) Accm: 2.78 (2.78) Acct: 4.18 (4.18) proj_loss: -0.5226 (-0.5226) time: 0.7525 data: 0.0003 [11-23 03:32:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.29 Lm: 6.934 (6.934) Lt: 6.225 (6.225) Accm: 2.12 (2.12) Acct: 3.43 (3.43) proj_loss: -0.5165 (-0.5165) time: 0.7525 data: 0.0003 [11-23 03:32:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.29 Lm: 6.762 (6.762) Lt: 6.076 (6.076) Accm: 2.69 (2.69) Acct: 4.36 (4.36) proj_loss: -0.5084 (-0.5084) time: 0.7525 data: 0.0003 [11-23 03:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 834/1669] eta: 0:10:35 tlr: 0.00023 tnm: 0.32 Lm: 6.823 (6.783) Lt: 6.077 (6.077) Accm: 2.53 (2.64) Acct: 3.99 (4.24) proj_loss: -0.5170 (-0.5113) time: 0.7528 data: 0.0003 [11-23 03:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 834/1669] eta: 0:10:35 tlr: 0.00023 tnm: 0.32 Lm: 6.804 (6.809) Lt: 6.071 (6.069) Accm: 2.83 (2.62) Acct: 4.37 (4.19) proj_loss: -0.5249 (-0.5240) time: 0.7528 data: 0.0002 [11-23 03:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 834/1669] eta: 0:10:35 tlr: 0.00023 tnm: 0.32 Lm: 6.769 (6.721) Lt: 6.089 (6.006) Accm: 2.83 (2.75) Acct: 4.68 (4.55) proj_loss: -0.5392 (-0.5356) time: 0.7528 data: 0.0002 [11-23 03:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 834/1669] eta: 0:10:35 tlr: 0.00023 tnm: 0.32 Lm: 6.674 (6.693) Lt: 6.010 (6.013) Accm: 2.55 (2.70) Acct: 3.62 (3.99) proj_loss: -0.5211 (-0.5157) time: 0.7528 data: 0.0003 [11-23 03:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 834/1669] eta: 0:10:35 tlr: 0.00023 tnm: 0.32 Lm: 6.766 (6.839) Lt: 6.008 (6.096) Accm: 2.59 (2.38) Acct: 4.37 (3.88) proj_loss: -0.5093 (-0.5119) time: 0.7528 data: 0.0003 [11-23 03:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 834/1669] eta: 0:10:35 tlr: 0.00023 tnm: 0.32 Lm: 6.663 (6.720) Lt: 5.908 (5.951) Accm: 2.91 (2.73) Acct: 4.65 (4.55) proj_loss: -0.5211 (-0.5216) time: 0.7528 data: 0.0002 [11-23 03:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 834/1669] eta: 0:10:35 tlr: 0.00023 tnm: 0.32 Lm: 6.788 (6.796) Lt: 6.063 (6.093) Accm: 2.67 (2.62) Acct: 4.06 (4.05) proj_loss: -0.5248 (-0.5216) time: 0.7528 data: 0.0002 [11-23 03:38:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [ 834/1669] eta: 0:10:35 tlr: 0.00023 tnm: 0.32 Lm: 6.844 (6.866) Lt: 6.216 (6.185) Accm: 2.24 (2.38) Acct: 3.34 (3.70) proj_loss: -0.5233 (-0.5279) time: 0.7528 data: 0.0002 [11-23 03:43:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.28 Lm: 6.780 (6.828) Lt: 6.101 (6.135) Accm: 2.53 (2.55) Acct: 3.96 (3.99) proj_loss: -0.5317 (-0.5326) time: 0.7507 data: 0.0003 [11-23 03:43:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.28 Lm: 6.872 (6.847) Lt: 6.142 (6.130) Accm: 2.55 (2.53) Acct: 3.98 (3.92) proj_loss: -0.5252 (-0.5244) time: 0.7507 data: 0.0002 [11-23 03:43:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.28 Lm: 6.775 (6.770) Lt: 6.021 (6.046) Accm: 2.71 (2.67) Acct: 4.34 (4.33) proj_loss: -0.5264 (-0.5241) time: 0.7507 data: 0.0002 [11-23 03:43:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.28 Lm: 6.805 (6.834) Lt: 6.121 (6.141) Accm: 2.54 (2.52) Acct: 3.94 (3.89) proj_loss: -0.5328 (-0.5279) time: 0.7507 data: 0.0003 [11-23 03:43:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.28 Lm: 6.699 (6.697) Lt: 5.942 (5.948) Accm: 2.86 (2.81) Acct: 4.72 (4.60) proj_loss: -0.5328 (-0.5307) time: 0.7507 data: 0.0003 [11-23 03:43:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.28 Lm: 6.722 (6.735) Lt: 6.053 (6.049) Accm: 2.61 (2.69) Acct: 3.86 (4.02) proj_loss: -0.5174 (-0.5152) time: 0.7507 data: 0.0003 [11-23 03:43:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.28 Lm: 6.732 (6.742) Lt: 6.009 (6.042) Accm: 2.75 (2.75) Acct: 4.27 (4.31) proj_loss: -0.5240 (-0.5211) time: 0.7507 data: 0.0003 [11-23 03:43:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.28 Lm: 6.707 (6.762) Lt: 5.923 (6.003) Accm: 2.75 (2.60) Acct: 4.58 (4.13) proj_loss: -0.5165 (-0.5170) time: 0.7507 data: 0.0004 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.766 (6.801) Lt: 6.008 (6.064) Accm: 2.59 (2.49) Acct: 4.37 (3.88) proj_loss: -0.5236 (-0.5201) time: 0.7532 data: 0.0016 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 32/350] Total time: 0:21:02 (0.757 s / it) [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.844 (6.853) Lt: 6.216 (6.169) Accm: 2.24 (2.45) Acct: 3.34 (3.75) proj_loss: -0.5374 (-0.5336) time: 0.7532 data: 0.0015 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.940 (6.882) Lt: 6.214 (6.188) Accm: 2.36 (2.50) Acct: 3.65 (3.86) proj_loss: -0.5256 (-0.5298) time: 0.7532 data: 0.0014 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.786 (6.773) Lt: 6.019 (6.041) Accm: 2.75 (2.69) Acct: 4.48 (4.36) proj_loss: -0.5211 (-0.5229) time: 0.7532 data: 0.0016 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.822 (6.851) Lt: 6.175 (6.147) Accm: 2.42 (2.49) Acct: 3.86 (3.88) proj_loss: -0.5408 (-0.5310) time: 0.7532 data: 0.0016 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.672 (6.692) Lt: 5.924 (5.944) Accm: 2.88 (2.86) Acct: 4.75 (4.65) proj_loss: -0.5264 (-0.5268) time: 0.7532 data: 0.0017 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.720 (6.732) Lt: 6.055 (6.050) Accm: 2.67 (2.74) Acct: 4.10 (4.12) proj_loss: -0.5211 (-0.5202) time: 0.7532 data: 0.0019 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 32/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.823 (6.773) Lt: 6.077 (6.085) Accm: 2.53 (2.69) Acct: 3.99 (4.17) proj_loss: -0.5287 (-0.5227) time: 0.7532 data: 0.0018 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 32/350] Total time: 0:21:02 (0.757 s / it) [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 32/350] Total time: 0:21:02 (0.757 s / it) [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 32/350] Total time: 0:21:02 (0.757 s / it) [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 32/350] Total time: 0:21:02 (0.757 s / it) [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 32/350] Total time: 0:21:02 (0.757 s / it) [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 32/350] Total time: 0:21:02 (0.757 s / it) [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 32/350] Total time: 0:21:02 (0.757 s / it) [11-23 03:48:39] (/home/user/VAR/train.py , line 276)=> [ep32] (training ) Lm: 6.789 (6.792), Lt: 6.077 (6.080), Acc m&t: 2.63 4.14, Remain: 4 days, 15:17:51, Finish: 2024-11-27 03:06 [11-23 03:48:39] (/home/user/VAR/train.py , line 276)=> [ep32] (training ) Lm: 6.789 (6.792), Lt: 6.077 (6.080), Acc m&t: 2.63 4.14, Remain: 4 days, 15:18:54, Finish: 2024-11-27 03:07 [11-23 03:48:39] (/home/user/VAR/train.py , line 276)=> [ep32] (training ) Lm: 6.789 (6.792), Lt: 6.077 (6.080), Acc m&t: 2.63 4.14, Remain: 4 days, 15:17:37, Finish: 2024-11-27 03:06 [11-23 03:48:39] (/home/user/VAR/train.py , line 276)=> [ep32] (training ) Lm: 6.789 (6.792), Lt: 6.077 (6.080), Acc m&t: 2.63 4.14, Remain: 4 days, 15:18:34, Finish: 2024-11-27 03:07 [11-23 03:48:39] (/home/user/VAR/train.py , line 276)=> [ep32] (training ) Lm: 6.789 (6.792), Lt: 6.077 (6.080), Acc m&t: 2.63 4.14, Remain: 4 days, 15:19:58, Finish: 2024-11-27 03:08 [11-23 03:48:39] (/home/user/VAR/train.py , line 276)=> [ep32] (training ) Lm: 6.789 (6.792), Lt: 6.077 (6.080), Acc m&t: 2.63 4.14, Remain: 4 days, 15:18:38, Finish: 2024-11-27 03:07 [11-23 03:48:39] (/home/user/VAR/train.py , line 276)=> [ep32] (training ) Lm: 6.789 (6.792), Lt: 6.077 (6.080), Acc m&t: 2.63 4.14, Remain: 4 days, 15:18:13, Finish: 2024-11-27 03:06 [11-23 03:48:39] (/home/user/VAR/train.py , line 276)=> [ep32] (training ) Lm: 6.789 (6.792), Lt: 6.077 (6.080), Acc m&t: 2.63 4.14, Remain: 4 days, 15:19:47, Finish: 2024-11-27 03:08 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 0/1669] eta: 0:19:38 tlr: 0.00023 tnm: 0.35 Lm: 6.819 (6.819) Lt: 6.054 (6.054) Accm: 2.49 (2.49) Acct: 4.06 (4.06) proj_loss: -0.5502 (-0.5502) time: 0.7062 data: 0.0004 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 0/1669] eta: 0:19:39 tlr: 0.00023 tnm: 0.35 Lm: 6.675 (6.675) Lt: 6.006 (6.006) Accm: 3.04 (3.04) Acct: 4.34 (4.34) proj_loss: -0.4930 (-0.4930) time: 0.7069 data: 0.0004 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 0/1669] eta: 0:19:40 tlr: 0.00023 tnm: 0.35 Lm: 6.750 (6.750) Lt: 5.980 (5.980) Accm: 2.81 (2.81) Acct: 4.24 (4.24) proj_loss: -0.5559 (-0.5559) time: 0.7073 data: 0.0003 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 0/1669] eta: 0:19:42 tlr: 0.00023 tnm: 0.35 Lm: 6.662 (6.662) Lt: 5.887 (5.887) Accm: 3.26 (3.26) Acct: 5.03 (5.03) proj_loss: -0.5098 (-0.5098) time: 0.7086 data: 0.0003 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 0/1669] eta: 0:19:41 tlr: 0.00023 tnm: 0.35 Lm: 6.592 (6.592) Lt: 5.745 (5.745) Accm: 2.80 (2.80) Acct: 4.75 (4.75) proj_loss: -0.5203 (-0.5203) time: 0.7076 data: 0.0004 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 0/1669] eta: 0:19:42 tlr: 0.00023 tnm: 0.35 Lm: 6.951 (6.951) Lt: 6.307 (6.307) Accm: 2.17 (2.17) Acct: 3.20 (3.20) proj_loss: -0.5308 (-0.5308) time: 0.7085 data: 0.0004 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 0/1669] eta: 0:19:41 tlr: 0.00023 tnm: 0.35 Lm: 6.671 (6.671) Lt: 5.962 (5.962) Accm: 3.15 (3.15) Acct: 5.61 (5.61) proj_loss: -0.5533 (-0.5533) time: 0.7076 data: 0.0003 [11-23 03:48:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 0/1669] eta: 0:19:41 tlr: 0.00023 tnm: 0.35 Lm: 6.936 (6.936) Lt: 6.203 (6.203) Accm: 2.14 (2.14) Acct: 3.41 (3.41) proj_loss: -0.5009 (-0.5009) time: 0.7078 data: 0.0003 [11-23 03:53:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.30 Lm: 6.774 (6.774) Lt: 6.037 (6.037) Accm: 2.62 (2.62) Acct: 4.24 (4.24) proj_loss: -0.5285 (-0.5285) time: 0.7480 data: 0.0003 [11-23 03:53:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.30 Lm: 6.710 (6.710) Lt: 6.035 (6.035) Accm: 2.77 (2.77) Acct: 4.25 (4.25) proj_loss: -0.5226 (-0.5226) time: 0.7480 data: 0.0002 [11-23 03:53:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.30 Lm: 6.840 (6.840) Lt: 6.081 (6.081) Accm: 2.43 (2.43) Acct: 3.68 (3.68) proj_loss: -0.5402 (-0.5402) time: 0.7481 data: 0.0002 [11-23 03:53:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 417/1669] eta: 0:15:40 tlr: 0.00023 tnm: 0.30 Lm: 6.770 (6.770) Lt: 6.032 (6.032) Accm: 2.48 (2.48) Acct: 4.01 (4.01) proj_loss: -0.5236 (-0.5236) time: 0.7481 data: 0.0003 [11-23 03:53:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.30 Lm: 6.645 (6.645) Lt: 5.880 (5.880) Accm: 3.15 (3.15) Acct: 4.96 (4.96) proj_loss: -0.5148 (-0.5148) time: 0.7481 data: 0.0003 [11-23 03:53:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.30 Lm: 6.772 (6.772) Lt: 6.010 (6.010) Accm: 2.52 (2.52) Acct: 3.99 (3.99) proj_loss: -0.5116 (-0.5116) time: 0.7481 data: 0.0003 [11-23 03:53:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 417/1669] eta: 0:15:40 tlr: 0.00023 tnm: 0.30 Lm: 6.720 (6.720) Lt: 5.999 (5.999) Accm: 2.92 (2.92) Acct: 4.99 (4.99) proj_loss: -0.5359 (-0.5359) time: 0.7481 data: 0.0003 [11-23 03:53:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.30 Lm: 6.871 (6.871) Lt: 6.218 (6.218) Accm: 2.38 (2.38) Acct: 3.67 (3.67) proj_loss: -0.5294 (-0.5294) time: 0.7481 data: 0.0002 [11-23 04:00:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 834/1669] eta: 0:11:53 tlr: 0.00023 tnm: 0.33 Lm: 6.745 (6.723) Lt: 6.049 (6.040) Accm: 3.03 (2.86) Acct: 4.34 (4.51) proj_loss: -0.5414 (-0.5289) time: 0.8651 data: 0.0002 [11-23 04:00:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 834/1669] eta: 0:11:53 tlr: 0.00023 tnm: 0.33 Lm: 6.819 (6.792) Lt: 6.054 (6.086) Accm: 2.56 (2.60) Acct: 4.06 (4.11) proj_loss: -0.5385 (-0.5318) time: 0.8651 data: 0.0003 [11-23 04:00:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 834/1669] eta: 0:11:53 tlr: 0.00023 tnm: 0.33 Lm: 6.799 (6.781) Lt: 6.113 (6.045) Accm: 2.74 (2.59) Acct: 4.03 (4.01) proj_loss: -0.5203 (-0.5285) time: 0.8652 data: 0.0002 [11-23 04:00:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 834/1669] eta: 0:11:53 tlr: 0.00023 tnm: 0.33 Lm: 6.662 (6.659) Lt: 5.887 (5.891) Accm: 3.09 (3.13) Acct: 4.96 (4.96) proj_loss: -0.5131 (-0.5142) time: 0.8652 data: 0.0002 [11-23 04:00:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 834/1669] eta: 0:11:53 tlr: 0.00023 tnm: 0.33 Lm: 6.750 (6.776) Lt: 5.991 (6.051) Accm: 2.81 (2.63) Acct: 4.24 (3.98) proj_loss: -0.5478 (-0.5428) time: 0.8651 data: 0.0003 [11-23 04:00:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 834/1669] eta: 0:11:53 tlr: 0.00023 tnm: 0.33 Lm: 6.791 (6.828) Lt: 6.128 (6.171) Accm: 2.59 (2.49) Acct: 4.13 (3.88) proj_loss: -0.5308 (-0.5379) time: 0.8652 data: 0.0003 [11-23 04:00:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 834/1669] eta: 0:11:53 tlr: 0.00023 tnm: 0.33 Lm: 6.856 (6.799) Lt: 6.153 (6.072) Accm: 2.78 (2.58) Acct: 4.20 (4.07) proj_loss: -0.5249 (-0.5240) time: 0.8651 data: 0.0003 [11-23 04:00:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [ 834/1669] eta: 0:11:53 tlr: 0.00023 tnm: 0.33 Lm: 6.671 (6.700) Lt: 5.962 (5.959) Accm: 2.93 (2.92) Acct: 4.55 (4.84) proj_loss: -0.5533 (-0.5420) time: 0.8652 data: 0.0003 [11-23 04:05:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1251/1669] eta: 0:05:44 tlr: 0.00023 tnm: 0.31 Lm: 6.666 (6.662) Lt: 5.920 (5.897) Accm: 3.04 (3.06) Acct: 5.08 (5.04) proj_loss: -0.5359 (-0.5349) time: 0.7531 data: 0.0003 [11-23 04:05:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1251/1669] eta: 0:05:44 tlr: 0.00023 tnm: 0.31 Lm: 6.818 (6.798) Lt: 6.107 (6.104) Accm: 2.53 (2.54) Acct: 3.96 (3.96) proj_loss: -0.5317 (-0.5301) time: 0.7531 data: 0.0002 [11-23 04:05:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1251/1669] eta: 0:05:44 tlr: 0.00023 tnm: 0.31 Lm: 6.710 (6.708) Lt: 6.028 (6.016) Accm: 3.04 (2.96) Acct: 4.58 (4.59) proj_loss: -0.5420 (-0.5323) time: 0.7531 data: 0.0003 [11-23 04:05:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1251/1669] eta: 0:05:44 tlr: 0.00023 tnm: 0.31 Lm: 6.674 (6.693) Lt: 5.900 (5.950) Accm: 3.06 (3.03) Acct: 4.92 (4.76) proj_loss: -0.5164 (-0.5206) time: 0.7531 data: 0.0002 [11-23 04:05:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1251/1669] eta: 0:05:44 tlr: 0.00023 tnm: 0.31 Lm: 6.834 (6.803) Lt: 6.138 (6.074) Accm: 2.64 (2.58) Acct: 4.15 (4.07) proj_loss: -0.5218 (-0.5272) time: 0.7531 data: 0.0003 [11-23 04:05:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1251/1669] eta: 0:05:44 tlr: 0.00023 tnm: 0.31 Lm: 6.794 (6.820) Lt: 6.132 (6.162) Accm: 2.53 (2.48) Acct: 3.96 (3.86) proj_loss: -0.5379 (-0.5397) time: 0.7531 data: 0.0003 [11-23 04:05:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1251/1669] eta: 0:05:44 tlr: 0.00023 tnm: 0.31 Lm: 6.774 (6.772) Lt: 6.041 (6.036) Accm: 2.80 (2.68) Acct: 4.41 (4.26) proj_loss: -0.5356 (-0.5296) time: 0.7532 data: 0.0003 [11-23 04:05:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1251/1669] eta: 0:05:44 tlr: 0.00023 tnm: 0.31 Lm: 6.757 (6.773) Lt: 5.997 (6.039) Accm: 2.68 (2.61) Acct: 4.32 (4.09) proj_loss: -0.5466 (-0.5434) time: 0.7531 data: 0.0003 [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.750 (6.750) Lt: 5.991 (6.017) Accm: 2.81 (2.65) Acct: 4.27 (4.13) proj_loss: -0.5478 (-0.5474) time: 0.7546 data: 0.0020 [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 33/350] Total time: 0:22:25 (0.806 s / it) [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.745 (6.740) Lt: 6.049 (6.046) Accm: 3.03 (2.85) Acct: 4.34 (4.36) proj_loss: -0.5414 (-0.5332) time: 0.7546 data: 0.0015 [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.686 (6.695) Lt: 5.913 (5.946) Accm: 3.03 (2.96) Acct: 4.89 (4.63) proj_loss: -0.5197 (-0.5254) time: 0.7545 data: 0.0015 [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.817 (6.766) Lt: 6.054 (6.071) Accm: 2.56 (2.63) Acct: 4.06 (4.07) proj_loss: -0.5385 (-0.5327) time: 0.7546 data: 0.0016 [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.799 (6.797) Lt: 6.113 (6.074) Accm: 2.74 (2.62) Acct: 4.27 (4.17) proj_loss: -0.5233 (-0.5340) time: 0.7546 data: 0.0016 [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.671 (6.693) Lt: 5.962 (5.934) Accm: 2.93 (2.93) Acct: 4.55 (4.81) proj_loss: -0.5261 (-0.5332) time: 0.7546 data: 0.0016 [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.791 (6.796) Lt: 6.128 (6.129) Accm: 2.59 (2.56) Acct: 4.13 (3.98) proj_loss: -0.5308 (-0.5371) time: 0.7546 data: 0.0019 [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 33/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.801 (6.778) Lt: 6.107 (6.050) Accm: 2.78 (2.65) Acct: 4.30 (4.27) proj_loss: -0.5375 (-0.5312) time: 0.7546 data: 0.0020 [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 33/350] Total time: 0:22:25 (0.806 s / it) [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 33/350] Total time: 0:22:25 (0.806 s / it) [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 33/350] Total time: 0:22:25 (0.806 s / it) [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 33/350] Total time: 0:22:25 (0.806 s / it) [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 33/350] Total time: 0:22:25 (0.806 s / it) [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 33/350] Total time: 0:22:25 (0.806 s / it) [11-23 04:11:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 33/350] Total time: 0:22:25 (0.806 s / it) [11-23 04:11:05] (/home/user/VAR/train.py , line 276)=> [ep33] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.061), Acc m&t: 2.68 4.22, Remain: 4 days, 15:06:46, Finish: 2024-11-27 03:17 [11-23 04:11:05] (/home/user/VAR/train.py , line 276)=> [ep33] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.061), Acc m&t: 2.68 4.22, Remain: 4 days, 15:05:54, Finish: 2024-11-27 03:16 [11-23 04:11:05] (/home/user/VAR/train.py , line 276)=> [ep33] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.061), Acc m&t: 2.68 4.22, Remain: 4 days, 15:06:56, Finish: 2024-11-27 03:18 [11-23 04:11:05] (/home/user/VAR/train.py , line 276)=> [ep33] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.061), Acc m&t: 2.68 4.22, Remain: 4 days, 15:05:34, Finish: 2024-11-27 03:16 [11-23 04:11:05] (/home/user/VAR/train.py , line 276)=> [ep33] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.061), Acc m&t: 2.68 4.22, Remain: 4 days, 15:03:04, Finish: 2024-11-27 03:14 [11-23 04:11:05] (/home/user/VAR/train.py , line 276)=> [ep33] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.061), Acc m&t: 2.68 4.22, Remain: 4 days, 15:08:31, Finish: 2024-11-27 03:19 [11-23 04:11:05] (/home/user/VAR/train.py , line 276)=> [ep33] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.061), Acc m&t: 2.68 4.22, Remain: 4 days, 15:06:11, Finish: 2024-11-27 03:17 [11-23 04:11:05] (/home/user/VAR/train.py , line 276)=> [ep33] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.061), Acc m&t: 2.68 4.22, Remain: 4 days, 15:06:01, Finish: 2024-11-27 03:17 [11-23 04:11:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 0/1669] eta: 0:20:33 tlr: 0.00023 tnm: 0.28 Lm: 6.606 (6.606) Lt: 5.890 (5.890) Accm: 3.35 (3.35) Acct: 5.06 (5.06) proj_loss: -0.5361 (-0.5361) time: 0.7392 data: 0.0005 [11-23 04:11:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 0/1669] eta: 0:20:33 tlr: 0.00023 tnm: 0.28 Lm: 6.975 (6.975) Lt: 6.284 (6.284) Accm: 2.58 (2.58) Acct: 4.20 (4.20) proj_loss: -0.5227 (-0.5227) time: 0.7390 data: 0.0003 [11-23 04:11:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 0/1669] eta: 0:20:34 tlr: 0.00023 tnm: 0.28 Lm: 6.783 (6.783) Lt: 6.073 (6.073) Accm: 2.53 (2.53) Acct: 3.79 (3.79) proj_loss: -0.5324 (-0.5324) time: 0.7398 data: 0.0003 [11-23 04:11:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 0/1669] eta: 0:20:32 tlr: 0.00023 tnm: 0.28 Lm: 6.865 (6.865) Lt: 6.170 (6.170) Accm: 2.55 (2.55) Acct: 4.10 (4.10) proj_loss: -0.5613 (-0.5613) time: 0.7382 data: 0.0003 [11-23 04:11:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 0/1669] eta: 0:20:33 tlr: 0.00023 tnm: 0.28 Lm: 6.726 (6.726) Lt: 5.924 (5.924) Accm: 2.96 (2.96) Acct: 4.75 (4.75) proj_loss: -0.5199 (-0.5199) time: 0.7390 data: 0.0004 [11-23 04:11:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 0/1669] eta: 0:20:34 tlr: 0.00023 tnm: 0.28 Lm: 6.825 (6.825) Lt: 6.170 (6.170) Accm: 2.59 (2.59) Acct: 4.13 (4.13) proj_loss: -0.5280 (-0.5280) time: 0.7396 data: 0.0004 [11-23 04:11:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 0/1669] eta: 0:20:32 tlr: 0.00023 tnm: 0.28 Lm: 6.665 (6.665) Lt: 5.949 (5.949) Accm: 2.99 (2.99) Acct: 4.79 (4.79) proj_loss: -0.5506 (-0.5506) time: 0.7386 data: 0.0004 [11-23 04:11:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 0/1669] eta: 0:20:35 tlr: 0.00023 tnm: 0.28 Lm: 6.832 (6.832) Lt: 6.147 (6.147) Accm: 2.33 (2.33) Acct: 3.62 (3.62) proj_loss: -0.5643 (-0.5643) time: 0.7404 data: 0.0003 [11-23 04:16:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.30 Lm: 6.881 (6.881) Lt: 6.139 (6.139) Accm: 2.62 (2.62) Acct: 4.32 (4.32) proj_loss: -0.5249 (-0.5249) time: 0.7551 data: 0.0003 [11-23 04:16:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.30 Lm: 6.787 (6.787) Lt: 6.079 (6.079) Accm: 2.83 (2.83) Acct: 4.55 (4.55) proj_loss: -0.5485 (-0.5485) time: 0.7551 data: 0.0002 [11-23 04:16:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.30 Lm: 6.781 (6.781) Lt: 6.091 (6.091) Accm: 2.56 (2.56) Acct: 3.84 (3.84) proj_loss: -0.5314 (-0.5314) time: 0.7551 data: 0.0002 [11-23 04:16:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.30 Lm: 6.883 (6.883) Lt: 6.192 (6.192) Accm: 2.61 (2.61) Acct: 4.15 (4.15) proj_loss: -0.5343 (-0.5343) time: 0.7551 data: 0.0003 [11-23 04:16:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.30 Lm: 6.593 (6.593) Lt: 5.824 (5.824) Accm: 3.30 (3.30) Acct: 5.37 (5.37) proj_loss: -0.5303 (-0.5303) time: 0.7551 data: 0.0003 [11-23 04:16:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.30 Lm: 6.744 (6.744) Lt: 6.050 (6.050) Accm: 2.64 (2.64) Acct: 4.03 (4.03) proj_loss: -0.5487 (-0.5487) time: 0.7551 data: 0.0003 [11-23 04:16:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.30 Lm: 6.773 (6.773) Lt: 6.012 (6.012) Accm: 2.69 (2.69) Acct: 4.34 (4.34) proj_loss: -0.5341 (-0.5341) time: 0.7551 data: 0.0003 [11-23 04:16:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.30 Lm: 6.752 (6.752) Lt: 6.048 (6.048) Accm: 2.88 (2.88) Acct: 4.55 (4.55) proj_loss: -0.5272 (-0.5272) time: 0.7551 data: 0.0003 [11-23 04:21:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.31 Lm: 6.787 (6.759) Lt: 5.994 (6.030) Accm: 2.65 (2.87) Acct: 4.44 (4.63) proj_loss: -0.5271 (-0.5345) time: 0.7536 data: 0.0002 [11-23 04:21:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.31 Lm: 6.783 (6.856) Lt: 6.109 (6.189) Accm: 2.53 (2.48) Acct: 3.79 (3.75) proj_loss: -0.5324 (-0.5411) time: 0.7536 data: 0.0002 [11-23 04:21:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.31 Lm: 6.820 (6.862) Lt: 6.099 (6.143) Accm: 2.42 (2.51) Acct: 3.93 (3.96) proj_loss: -0.5290 (-0.5324) time: 0.7536 data: 0.0003 [11-23 04:21:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.31 Lm: 6.751 (6.775) Lt: 6.042 (6.067) Accm: 2.74 (2.80) Acct: 4.20 (4.43) proj_loss: -0.5578 (-0.5516) time: 0.7536 data: 0.0002 [11-23 04:21:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.31 Lm: 6.665 (6.699) Lt: 5.949 (5.976) Accm: 2.99 (3.04) Acct: 4.79 (4.83) proj_loss: -0.5176 (-0.5261) time: 0.7536 data: 0.0003 [11-23 04:21:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.31 Lm: 6.825 (6.788) Lt: 6.170 (6.067) Accm: 2.62 (2.79) Acct: 4.17 (4.47) proj_loss: -0.5285 (-0.5323) time: 0.7536 data: 0.0003 [11-23 04:21:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.31 Lm: 6.691 (6.732) Lt: 5.899 (5.998) Accm: 3.03 (2.93) Acct: 4.75 (4.61) proj_loss: -0.5361 (-0.5354) time: 0.7536 data: 0.0003 [11-23 04:21:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.31 Lm: 6.819 (6.769) Lt: 6.097 (6.066) Accm: 2.77 (2.68) Acct: 4.44 (4.26) proj_loss: -0.5330 (-0.5362) time: 0.7536 data: 0.0003 [11-23 04:26:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.31 Lm: 6.705 (6.725) Lt: 5.926 (5.987) Accm: 2.99 (2.98) Acct: 4.84 (4.83) proj_loss: -0.5249 (-0.5275) time: 0.8697 data: 0.0003 [11-23 04:26:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.31 Lm: 6.730 (6.742) Lt: 6.032 (6.056) Accm: 2.82 (2.83) Acct: 4.22 (4.38) proj_loss: -0.5531 (-0.5508) time: 0.8697 data: 0.0002 [11-23 04:26:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.31 Lm: 6.799 (6.841) Lt: 6.073 (6.119) Accm: 2.69 (2.63) Acct: 4.34 (4.20) proj_loss: -0.5386 (-0.5431) time: 0.8697 data: 0.0002 [11-23 04:26:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.31 Lm: 6.781 (6.827) Lt: 6.091 (6.135) Accm: 2.56 (2.56) Acct: 3.84 (3.97) proj_loss: -0.5314 (-0.5340) time: 0.8697 data: 0.0002 [11-23 04:26:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.31 Lm: 6.787 (6.752) Lt: 6.114 (6.053) Accm: 2.76 (2.85) Acct: 4.27 (4.45) proj_loss: -0.5341 (-0.5322) time: 0.8697 data: 0.0003 [11-23 04:26:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.31 Lm: 6.712 (6.736) Lt: 5.993 (5.999) Accm: 2.88 (2.91) Acct: 4.63 (4.71) proj_loss: -0.5327 (-0.5335) time: 0.8698 data: 0.0003 [11-23 04:26:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.31 Lm: 6.674 (6.713) Lt: 5.925 (5.986) Accm: 2.94 (2.91) Acct: 4.70 (4.62) proj_loss: -0.5440 (-0.5406) time: 0.8698 data: 0.0003 [11-23 04:26:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1251/1669] eta: 0:05:16 tlr: 0.00023 tnm: 0.31 Lm: 6.823 (6.783) Lt: 6.112 (6.081) Accm: 2.82 (2.73) Acct: 4.55 (4.36) proj_loss: -0.5299 (-0.5339) time: 0.8697 data: 0.0003 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.787 (6.738) Lt: 5.994 (6.009) Accm: 2.65 (2.90) Acct: 4.44 (4.66) proj_loss: -0.5267 (-0.5274) time: 0.8070 data: 0.0016 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.783 (6.865) Lt: 6.109 (6.165) Accm: 2.53 (2.45) Acct: 3.79 (3.85) proj_loss: -0.5305 (-0.5328) time: 0.8070 data: 0.0018 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.665 (6.732) Lt: 5.949 (6.023) Accm: 2.77 (2.83) Acct: 4.34 (4.43) proj_loss: -0.5324 (-0.5323) time: 0.8070 data: 0.0018 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.825 (6.822) Lt: 6.170 (6.115) Accm: 2.62 (2.73) Acct: 4.17 (4.37) proj_loss: -0.5370 (-0.5394) time: 0.8070 data: 0.0016 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.709 (6.733) Lt: 6.023 (6.039) Accm: 2.81 (2.82) Acct: 4.24 (4.47) proj_loss: -0.5485 (-0.5468) time: 0.8070 data: 0.0014 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.828 (6.796) Lt: 6.114 (6.087) Accm: 2.77 (2.69) Acct: 4.44 (4.37) proj_loss: -0.5269 (-0.5281) time: 0.8070 data: 0.0018 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.794 (6.831) Lt: 6.048 (6.105) Accm: 2.49 (2.60) Acct: 4.17 (4.19) proj_loss: -0.5371 (-0.5419) time: 0.8070 data: 0.0019 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 34/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.691 (6.720) Lt: 5.951 (6.001) Accm: 2.87 (2.90) Acct: 4.65 (4.55) proj_loss: -0.5519 (-0.5436) time: 0.8070 data: 0.0021 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 34/350] Total time: 0:22:29 (0.809 s / it) [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 34/350] Total time: 0:22:29 (0.809 s / it) [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 34/350] Total time: 0:22:29 (0.809 s / it) [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 34/350] Total time: 0:22:29 (0.809 s / it) [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 34/350] Total time: 0:22:29 (0.809 s / it) [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 34/350] Total time: 0:22:29 (0.809 s / it) [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 34/350] Total time: 0:22:29 (0.809 s / it) [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 34/350] Total time: 0:22:29 (0.809 s / it) [11-23 04:33:35] (/home/user/VAR/train.py , line 276)=> [ep34] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.062), Acc m&t: 2.68 4.23, Remain: 4 days, 14:57:15, Finish: 2024-11-27 03:30 [11-23 04:33:35] (/home/user/VAR/train.py , line 276)=> [ep34] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.062), Acc m&t: 2.68 4.23, Remain: 4 days, 14:58:00, Finish: 2024-11-27 03:31 [11-23 04:33:35] (/home/user/VAR/train.py , line 276)=> [ep34] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.062), Acc m&t: 2.68 4.23, Remain: 4 days, 14:57:10, Finish: 2024-11-27 03:30 [11-23 04:33:35] (/home/user/VAR/train.py , line 276)=> [ep34] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.062), Acc m&t: 2.68 4.23, Remain: 4 days, 14:57:40, Finish: 2024-11-27 03:31 [11-23 04:33:35] (/home/user/VAR/train.py , line 276)=> [ep34] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.062), Acc m&t: 2.68 4.23, Remain: 4 days, 14:57:11, Finish: 2024-11-27 03:30 [11-23 04:33:35] (/home/user/VAR/train.py , line 276)=> [ep34] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.062), Acc m&t: 2.68 4.23, Remain: 4 days, 14:57:22, Finish: 2024-11-27 03:30 [11-23 04:33:35] (/home/user/VAR/train.py , line 276)=> [ep34] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.062), Acc m&t: 2.68 4.23, Remain: 4 days, 14:57:08, Finish: 2024-11-27 03:30 [11-23 04:33:35] (/home/user/VAR/train.py , line 276)=> [ep34] (training ) Lm: 6.774 (6.774), Lt: 6.061 (6.062), Acc m&t: 2.68 4.23, Remain: 4 days, 14:57:06, Finish: 2024-11-27 03:30 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 0/1669] eta: 0:20:53 tlr: 0.00023 tnm: 0.30 Lm: 6.813 (6.813) Lt: 6.205 (6.205) Accm: 2.80 (2.80) Acct: 4.17 (4.17) proj_loss: -0.5537 (-0.5537) time: 0.7509 data: 0.0003 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 0/1669] eta: 0:20:53 tlr: 0.00023 tnm: 0.30 Lm: 6.935 (6.935) Lt: 6.176 (6.176) Accm: 2.26 (2.26) Acct: 3.86 (3.86) proj_loss: -0.5263 (-0.5263) time: 0.7511 data: 0.0003 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 0/1669] eta: 0:20:53 tlr: 0.00023 tnm: 0.30 Lm: 6.843 (6.843) Lt: 6.146 (6.146) Accm: 1.98 (1.98) Acct: 3.20 (3.20) proj_loss: -0.5168 (-0.5168) time: 0.7512 data: 0.0004 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 0/1669] eta: 0:20:54 tlr: 0.00023 tnm: 0.30 Lm: 6.796 (6.796) Lt: 6.065 (6.065) Accm: 2.77 (2.77) Acct: 4.34 (4.34) proj_loss: -0.5301 (-0.5301) time: 0.7518 data: 0.0004 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 0/1669] eta: 0:20:54 tlr: 0.00023 tnm: 0.30 Lm: 6.700 (6.700) Lt: 5.894 (5.894) Accm: 2.96 (2.96) Acct: 4.72 (4.72) proj_loss: -0.5234 (-0.5234) time: 0.7519 data: 0.0004 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 0/1669] eta: 0:20:55 tlr: 0.00023 tnm: 0.30 Lm: 6.698 (6.698) Lt: 5.953 (5.953) Accm: 2.64 (2.64) Acct: 4.68 (4.68) proj_loss: -0.5281 (-0.5281) time: 0.7521 data: 0.0004 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 0/1669] eta: 0:20:56 tlr: 0.00023 tnm: 0.30 Lm: 6.739 (6.739) Lt: 6.099 (6.099) Accm: 2.78 (2.78) Acct: 3.93 (3.93) proj_loss: -0.5345 (-0.5345) time: 0.7528 data: 0.0004 [11-23 04:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 0/1669] eta: 0:20:56 tlr: 0.00023 tnm: 0.30 Lm: 6.824 (6.824) Lt: 6.175 (6.175) Accm: 2.33 (2.33) Acct: 3.62 (3.62) proj_loss: -0.5585 (-0.5585) time: 0.7526 data: 0.0004 [11-23 04:38:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 417/1669] eta: 0:15:51 tlr: 0.00023 tnm: 0.28 Lm: 6.839 (6.839) Lt: 6.117 (6.117) Accm: 2.51 (2.51) Acct: 4.05 (4.05) proj_loss: -0.5314 (-0.5314) time: 0.7516 data: 0.0002 [11-23 04:38:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 417/1669] eta: 0:15:51 tlr: 0.00023 tnm: 0.28 Lm: 6.741 (6.741) Lt: 6.046 (6.046) Accm: 2.74 (2.74) Acct: 4.15 (4.15) proj_loss: -0.5428 (-0.5428) time: 0.7516 data: 0.0002 [11-23 04:38:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 417/1669] eta: 0:15:51 tlr: 0.00023 tnm: 0.28 Lm: 6.727 (6.727) Lt: 6.029 (6.029) Accm: 2.63 (2.63) Acct: 4.24 (4.24) proj_loss: -0.5342 (-0.5342) time: 0.7516 data: 0.0002 [11-23 04:38:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 417/1669] eta: 0:15:51 tlr: 0.00023 tnm: 0.28 Lm: 6.799 (6.799) Lt: 6.045 (6.045) Accm: 2.59 (2.59) Acct: 4.08 (4.08) proj_loss: -0.5179 (-0.5179) time: 0.7516 data: 0.0002 [11-23 04:38:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 417/1669] eta: 0:15:51 tlr: 0.00023 tnm: 0.28 Lm: 6.869 (6.869) Lt: 6.157 (6.157) Accm: 2.28 (2.28) Acct: 3.87 (3.87) proj_loss: -0.5280 (-0.5280) time: 0.7516 data: 0.0003 [11-23 04:38:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 417/1669] eta: 0:15:51 tlr: 0.00023 tnm: 0.28 Lm: 6.796 (6.796) Lt: 6.075 (6.075) Accm: 2.68 (2.68) Acct: 4.25 (4.25) proj_loss: -0.5395 (-0.5395) time: 0.7516 data: 0.0002 [11-23 04:38:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 417/1669] eta: 0:15:51 tlr: 0.00023 tnm: 0.28 Lm: 6.814 (6.814) Lt: 6.133 (6.133) Accm: 2.42 (2.42) Acct: 3.84 (3.84) proj_loss: -0.5454 (-0.5454) time: 0.7516 data: 0.0003 [11-23 04:38:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 417/1669] eta: 0:15:51 tlr: 0.00023 tnm: 0.28 Lm: 6.720 (6.720) Lt: 6.038 (6.038) Accm: 2.93 (2.93) Acct: 4.17 (4.17) proj_loss: -0.5335 (-0.5335) time: 0.7516 data: 0.0003 [11-23 04:44:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 834/1669] eta: 0:10:31 tlr: 0.00023 tnm: 0.29 Lm: 6.830 (6.836) Lt: 6.128 (6.120) Accm: 2.58 (2.53) Acct: 4.13 (4.07) proj_loss: -0.5301 (-0.5309) time: 0.7543 data: 0.0003 [11-23 04:44:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 834/1669] eta: 0:10:31 tlr: 0.00023 tnm: 0.29 Lm: 6.745 (6.743) Lt: 5.985 (6.026) Accm: 2.68 (2.68) Acct: 4.17 (4.20) proj_loss: -0.5461 (-0.5439) time: 0.7543 data: 0.0002 [11-23 04:44:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 834/1669] eta: 0:10:31 tlr: 0.00023 tnm: 0.29 Lm: 6.796 (6.763) Lt: 6.065 (6.023) Accm: 2.77 (2.71) Acct: 4.34 (4.35) proj_loss: -0.5422 (-0.5404) time: 0.7543 data: 0.0003 [11-23 04:44:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 834/1669] eta: 0:10:31 tlr: 0.00023 tnm: 0.29 Lm: 6.843 (6.813) Lt: 6.071 (6.053) Accm: 2.94 (2.71) Acct: 4.72 (4.38) proj_loss: -0.5123 (-0.5143) time: 0.7543 data: 0.0002 [11-23 04:44:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 834/1669] eta: 0:10:31 tlr: 0.00023 tnm: 0.29 Lm: 6.701 (6.654) Lt: 5.976 (5.920) Accm: 3.07 (3.16) Acct: 4.41 (4.72) proj_loss: -0.5345 (-0.5444) time: 0.7543 data: 0.0003 [11-23 04:44:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 834/1669] eta: 0:10:31 tlr: 0.00023 tnm: 0.29 Lm: 6.673 (6.709) Lt: 6.004 (6.020) Accm: 2.86 (2.70) Acct: 4.30 (4.26) proj_loss: -0.5370 (-0.5351) time: 0.7543 data: 0.0003 [11-23 04:44:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 834/1669] eta: 0:10:31 tlr: 0.00023 tnm: 0.29 Lm: 6.805 (6.671) Lt: 6.092 (5.968) Accm: 2.51 (2.96) Acct: 4.06 (4.74) proj_loss: -0.5433 (-0.5447) time: 0.7543 data: 0.0003 [11-23 04:44:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [ 834/1669] eta: 0:10:31 tlr: 0.00023 tnm: 0.29 Lm: 6.698 (6.797) Lt: 5.953 (6.075) Accm: 2.64 (2.45) Acct: 4.61 (4.12) proj_loss: -0.5281 (-0.5310) time: 0.7543 data: 0.0003 [11-23 04:49:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.27 Lm: 6.777 (6.812) Lt: 6.076 (6.106) Accm: 2.48 (2.41) Acct: 3.93 (3.90) proj_loss: -0.5280 (-0.5303) time: 0.7536 data: 0.0003 [11-23 04:49:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.27 Lm: 6.777 (6.759) Lt: 6.005 (6.025) Accm: 2.65 (2.67) Acct: 4.24 (4.23) proj_loss: -0.5440 (-0.5434) time: 0.7536 data: 0.0002 [11-23 04:49:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.27 Lm: 6.787 (6.808) Lt: 6.092 (6.089) Accm: 2.67 (2.64) Acct: 4.18 (4.17) proj_loss: -0.5312 (-0.5313) time: 0.7536 data: 0.0003 [11-23 04:49:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.27 Lm: 6.796 (6.809) Lt: 6.075 (6.077) Accm: 2.68 (2.62) Acct: 4.25 (4.18) proj_loss: -0.5362 (-0.5312) time: 0.7536 data: 0.0003 [11-23 04:49:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.27 Lm: 6.683 (6.705) Lt: 5.977 (6.003) Accm: 2.84 (2.74) Acct: 4.44 (4.34) proj_loss: -0.5335 (-0.5339) time: 0.7536 data: 0.0002 [11-23 04:49:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.27 Lm: 6.814 (6.806) Lt: 6.115 (6.080) Accm: 2.77 (2.68) Acct: 4.34 (4.28) proj_loss: -0.5179 (-0.5203) time: 0.7536 data: 0.0002 [11-23 04:49:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.27 Lm: 6.814 (6.718) Lt: 6.120 (6.013) Accm: 2.48 (2.83) Acct: 4.13 (4.61) proj_loss: -0.5379 (-0.5361) time: 0.7536 data: 0.0003 [11-23 04:49:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.27 Lm: 6.720 (6.692) Lt: 6.009 (5.951) Accm: 2.93 (3.01) Acct: 4.17 (4.51) proj_loss: -0.5335 (-0.5358) time: 0.7536 data: 0.0003 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.808 (6.777) Lt: 6.024 (6.063) Accm: 2.62 (2.65) Acct: 4.17 (4.16) proj_loss: -0.5420 (-0.5419) time: 0.7550 data: 0.0018 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.693 (6.710) Lt: 5.950 (5.989) Accm: 2.83 (2.72) Acct: 4.30 (4.30) proj_loss: -0.5301 (-0.5328) time: 0.7550 data: 0.0017 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.830 (6.819) Lt: 6.128 (6.097) Accm: 2.67 (2.64) Acct: 4.24 (4.23) proj_loss: -0.5322 (-0.5361) time: 0.7550 data: 0.0016 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.796 (6.809) Lt: 6.086 (6.085) Accm: 2.77 (2.66) Acct: 4.27 (4.20) proj_loss: -0.5422 (-0.5370) time: 0.7550 data: 0.0017 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.784 (6.769) Lt: 6.071 (6.037) Accm: 2.94 (2.78) Acct: 4.72 (4.43) proj_loss: -0.5234 (-0.5225) time: 0.7550 data: 0.0015 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.698 (6.770) Lt: 5.953 (6.058) Accm: 2.64 (2.49) Acct: 4.24 (3.97) proj_loss: -0.5280 (-0.5287) time: 0.7550 data: 0.0020 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.739 (6.719) Lt: 6.041 (5.985) Accm: 2.78 (2.89) Acct: 3.93 (4.33) proj_loss: -0.5325 (-0.5326) time: 0.7550 data: 0.0019 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 35/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.805 (6.712) Lt: 6.092 (5.981) Accm: 2.51 (2.92) Acct: 4.20 (4.77) proj_loss: -0.5433 (-0.5381) time: 0.7550 data: 0.0015 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 35/350] Total time: 0:20:59 (0.755 s / it) [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 35/350] Total time: 0:20:59 (0.755 s / it) [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 35/350] Total time: 0:20:59 (0.755 s / it) [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 35/350] Total time: 0:20:59 (0.755 s / it) [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 35/350] Total time: 0:20:59 (0.755 s / it) [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 35/350] Total time: 0:20:59 (0.755 s / it) [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 35/350] Total time: 0:20:59 (0.755 s / it) [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 35/350] Total time: 0:20:59 (0.755 s / it) [11-23 04:54:35] (/home/user/VAR/train.py , line 276)=> [ep35] (training ) Lm: 6.774 (6.781), Lt: 6.060 (6.060), Acc m&t: 2.68 4.23, Remain: 4 days, 14:20:24, Finish: 2024-11-27 03:14 [11-23 04:54:35] (/home/user/VAR/train.py , line 276)=> [ep35] (training ) Lm: 6.774 (6.781), Lt: 6.060 (6.060), Acc m&t: 2.68 4.23, Remain: 4 days, 14:20:09, Finish: 2024-11-27 03:14 [11-23 04:54:35] (/home/user/VAR/train.py , line 276)=> [ep35] (training ) Lm: 6.774 (6.781), Lt: 6.060 (6.060), Acc m&t: 2.68 4.23, Remain: 4 days, 14:20:49, Finish: 2024-11-27 03:15 [11-23 04:54:35] (/home/user/VAR/train.py , line 276)=> [ep35] (training ) Lm: 6.774 (6.781), Lt: 6.060 (6.060), Acc m&t: 2.68 4.23, Remain: 4 days, 14:19:14, Finish: 2024-11-27 03:13 [11-23 04:54:35] (/home/user/VAR/train.py , line 276)=> [ep35] (training ) Lm: 6.774 (6.781), Lt: 6.060 (6.060), Acc m&t: 2.68 4.23, Remain: 4 days, 14:19:30, Finish: 2024-11-27 03:14 [11-23 04:54:35] (/home/user/VAR/train.py , line 276)=> [ep35] (training ) Lm: 6.774 (6.781), Lt: 6.060 (6.060), Acc m&t: 2.68 4.23, Remain: 4 days, 14:19:31, Finish: 2024-11-27 03:14 [11-23 04:54:35] (/home/user/VAR/train.py , line 276)=> [ep35] (training ) Lm: 6.774 (6.781), Lt: 6.060 (6.060), Acc m&t: 2.68 4.23, Remain: 4 days, 14:20:14, Finish: 2024-11-27 03:14 [11-23 04:54:35] (/home/user/VAR/train.py , line 276)=> [ep35] (training ) Lm: 6.774 (6.781), Lt: 6.060 (6.060), Acc m&t: 2.68 4.23, Remain: 4 days, 14:20:10, Finish: 2024-11-27 03:14 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 0/1669] eta: 0:20:38 tlr: 0.00023 tnm: 0.29 Lm: 6.833 (6.833) Lt: 6.169 (6.169) Accm: 2.71 (2.71) Acct: 4.13 (4.13) proj_loss: -0.5459 (-0.5459) time: 0.7421 data: 0.0004 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 0/1669] eta: 0:20:36 tlr: 0.00023 tnm: 0.29 Lm: 6.643 (6.643) Lt: 5.914 (5.914) Accm: 3.25 (3.25) Acct: 5.23 (5.23) proj_loss: -0.5474 (-0.5474) time: 0.7407 data: 0.0003 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 0/1669] eta: 0:20:36 tlr: 0.00023 tnm: 0.29 Lm: 6.776 (6.776) Lt: 6.076 (6.076) Accm: 2.61 (2.61) Acct: 3.86 (3.86) proj_loss: -0.5298 (-0.5298) time: 0.7408 data: 0.0004 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 0/1669] eta: 0:20:39 tlr: 0.00023 tnm: 0.29 Lm: 6.690 (6.690) Lt: 5.952 (5.952) Accm: 2.62 (2.62) Acct: 4.17 (4.17) proj_loss: -0.5366 (-0.5366) time: 0.7426 data: 0.0004 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 0/1669] eta: 0:20:36 tlr: 0.00023 tnm: 0.29 Lm: 6.634 (6.634) Lt: 5.825 (5.825) Accm: 2.72 (2.72) Acct: 4.44 (4.44) proj_loss: -0.5629 (-0.5629) time: 0.7410 data: 0.0004 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 0/1669] eta: 0:20:36 tlr: 0.00023 tnm: 0.29 Lm: 6.861 (6.861) Lt: 6.111 (6.111) Accm: 2.24 (2.24) Acct: 3.55 (3.55) proj_loss: -0.5165 (-0.5165) time: 0.7410 data: 0.0004 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 0/1669] eta: 0:20:41 tlr: 0.00023 tnm: 0.29 Lm: 6.955 (6.955) Lt: 6.326 (6.326) Accm: 2.10 (2.10) Acct: 2.96 (2.96) proj_loss: -0.5438 (-0.5438) time: 0.7441 data: 0.0004 [11-23 04:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 0/1669] eta: 0:20:37 tlr: 0.00023 tnm: 0.29 Lm: 6.548 (6.548) Lt: 5.743 (5.743) Accm: 3.25 (3.25) Acct: 5.58 (5.58) proj_loss: -0.5370 (-0.5370) time: 0.7416 data: 0.0004 [11-23 04:59:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.30 Lm: 6.550 (6.550) Lt: 5.765 (5.765) Accm: 3.13 (3.13) Acct: 5.22 (5.22) proj_loss: -0.5307 (-0.5307) time: 0.8733 data: 0.0003 [11-23 04:59:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.30 Lm: 6.760 (6.760) Lt: 6.101 (6.101) Accm: 2.51 (2.51) Acct: 3.81 (3.81) proj_loss: -0.5385 (-0.5385) time: 0.8733 data: 0.0002 [11-23 04:59:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.30 Lm: 6.716 (6.716) Lt: 6.021 (6.021) Accm: 3.08 (3.08) Acct: 4.70 (4.70) proj_loss: -0.5374 (-0.5374) time: 0.8733 data: 0.0003 [11-23 04:59:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.30 Lm: 6.648 (6.648) Lt: 5.864 (5.864) Accm: 2.82 (2.82) Acct: 4.56 (4.56) proj_loss: -0.5539 (-0.5539) time: 0.8733 data: 0.0002 [11-23 04:59:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.30 Lm: 6.673 (6.673) Lt: 5.935 (5.935) Accm: 2.65 (2.65) Acct: 4.18 (4.18) proj_loss: -0.5184 (-0.5184) time: 0.8733 data: 0.0002 [11-23 04:59:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.30 Lm: 6.728 (6.728) Lt: 6.012 (6.012) Accm: 2.81 (2.81) Acct: 4.44 (4.44) proj_loss: -0.5269 (-0.5269) time: 0.8733 data: 0.0003 [11-23 04:59:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.30 Lm: 6.693 (6.693) Lt: 5.953 (5.953) Accm: 2.88 (2.88) Acct: 4.53 (4.53) proj_loss: -0.5298 (-0.5298) time: 0.8733 data: 0.0003 [11-23 04:59:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 417/1669] eta: 0:16:02 tlr: 0.00023 tnm: 0.30 Lm: 6.844 (6.844) Lt: 6.174 (6.174) Accm: 2.50 (2.50) Acct: 3.70 (3.70) proj_loss: -0.5465 (-0.5465) time: 0.8733 data: 0.0003 [11-23 05:06:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 834/1669] eta: 0:11:48 tlr: 0.00023 tnm: 0.28 Lm: 6.833 (6.793) Lt: 6.169 (6.084) Accm: 2.71 (2.74) Acct: 4.13 (4.34) proj_loss: -0.5416 (-0.5388) time: 0.7542 data: 0.0002 [11-23 05:06:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 834/1669] eta: 0:11:48 tlr: 0.00023 tnm: 0.28 Lm: 6.690 (6.692) Lt: 5.952 (5.941) Accm: 2.68 (2.80) Acct: 4.20 (4.51) proj_loss: -0.5366 (-0.5258) time: 0.7542 data: 0.0002 [11-23 05:06:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 834/1669] eta: 0:11:48 tlr: 0.00023 tnm: 0.28 Lm: 6.744 (6.745) Lt: 6.076 (6.079) Accm: 2.61 (2.68) Acct: 3.86 (4.05) proj_loss: -0.5473 (-0.5512) time: 0.7542 data: 0.0003 [11-23 05:06:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 834/1669] eta: 0:11:48 tlr: 0.00023 tnm: 0.28 Lm: 6.634 (6.627) Lt: 5.876 (5.868) Accm: 2.72 (2.79) Acct: 4.44 (4.34) proj_loss: -0.5449 (-0.5484) time: 0.7542 data: 0.0003 [11-23 05:06:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 834/1669] eta: 0:11:48 tlr: 0.00023 tnm: 0.28 Lm: 6.742 (6.763) Lt: 5.993 (6.036) Accm: 2.61 (2.79) Acct: 4.20 (4.42) proj_loss: -0.5474 (-0.5441) time: 0.7542 data: 0.0003 [11-23 05:06:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 834/1669] eta: 0:11:48 tlr: 0.00023 tnm: 0.28 Lm: 6.733 (6.759) Lt: 6.023 (6.055) Accm: 2.90 (2.71) Acct: 4.44 (4.03) proj_loss: -0.5438 (-0.5364) time: 0.7542 data: 0.0003 [11-23 05:06:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 834/1669] eta: 0:11:48 tlr: 0.00023 tnm: 0.28 Lm: 6.760 (6.739) Lt: 6.014 (6.012) Accm: 2.72 (2.78) Acct: 4.48 (4.45) proj_loss: -0.5165 (-0.5210) time: 0.7541 data: 0.0004 [11-23 05:06:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [ 834/1669] eta: 0:11:48 tlr: 0.00023 tnm: 0.28 Lm: 6.553 (6.675) Lt: 5.787 (5.909) Accm: 3.02 (2.77) Acct: 4.86 (4.60) proj_loss: -0.5370 (-0.5330) time: 0.7542 data: 0.0003 [11-23 05:11:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1251/1669] eta: 0:05:42 tlr: 0.00023 tnm: 0.33 Lm: 6.638 (6.688) Lt: 5.868 (5.919) Accm: 2.83 (2.74) Acct: 4.67 (4.57) proj_loss: -0.5373 (-0.5399) time: 0.7529 data: 0.0002 [11-23 05:11:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1251/1669] eta: 0:05:42 tlr: 0.00023 tnm: 0.33 Lm: 6.720 (6.746) Lt: 6.021 (6.017) Accm: 3.08 (2.98) Acct: 4.70 (4.68) proj_loss: -0.5438 (-0.5418) time: 0.7529 data: 0.0002 [11-23 05:11:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1251/1669] eta: 0:05:42 tlr: 0.00023 tnm: 0.33 Lm: 6.760 (6.776) Lt: 6.101 (6.097) Accm: 2.51 (2.61) Acct: 3.81 (3.97) proj_loss: -0.5385 (-0.5450) time: 0.7529 data: 0.0002 [11-23 05:11:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1251/1669] eta: 0:05:42 tlr: 0.00023 tnm: 0.33 Lm: 6.648 (6.657) Lt: 5.890 (5.899) Accm: 2.82 (2.86) Acct: 4.56 (4.53) proj_loss: -0.5411 (-0.5405) time: 0.7529 data: 0.0002 [11-23 05:11:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1251/1669] eta: 0:05:42 tlr: 0.00023 tnm: 0.33 Lm: 6.710 (6.750) Lt: 5.953 (6.007) Accm: 2.65 (2.74) Acct: 4.18 (4.42) proj_loss: -0.5385 (-0.5351) time: 0.7529 data: 0.0002 [11-23 05:11:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1251/1669] eta: 0:05:42 tlr: 0.00023 tnm: 0.33 Lm: 6.705 (6.716) Lt: 5.966 (5.989) Accm: 2.87 (2.84) Acct: 4.68 (4.56) proj_loss: -0.5269 (-0.5297) time: 0.7529 data: 0.0003 [11-23 05:11:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1251/1669] eta: 0:05:42 tlr: 0.00023 tnm: 0.33 Lm: 6.730 (6.752) Lt: 5.997 (6.027) Accm: 2.71 (2.79) Acct: 4.34 (4.43) proj_loss: -0.5530 (-0.5477) time: 0.7529 data: 0.0003 [11-23 05:11:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1251/1669] eta: 0:05:42 tlr: 0.00023 tnm: 0.33 Lm: 6.741 (6.757) Lt: 5.997 (6.034) Accm: 2.68 (2.65) Acct: 4.13 (3.98) proj_loss: -0.5442 (-0.5384) time: 0.7529 data: 0.0003 [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.744 (6.740) Lt: 6.076 (6.046) Accm: 2.61 (2.70) Acct: 3.86 (4.17) proj_loss: -0.5351 (-0.5430) time: 0.7548 data: 0.0020 [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.661 (6.693) Lt: 5.903 (5.968) Accm: 2.72 (2.76) Acct: 4.44 (4.36) proj_loss: -0.5449 (-0.5431) time: 0.7548 data: 0.0017 [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.649 (6.690) Lt: 5.918 (5.955) Accm: 2.81 (2.84) Acct: 4.48 (4.46) proj_loss: -0.5313 (-0.5300) time: 0.7548 data: 0.0018 [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.833 (6.770) Lt: 6.165 (6.047) Accm: 2.71 (2.83) Acct: 4.13 (4.46) proj_loss: -0.5416 (-0.5414) time: 0.7548 data: 0.0013 [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.730 (6.769) Lt: 5.953 (6.033) Accm: 2.62 (2.69) Acct: 4.17 (4.35) proj_loss: -0.5366 (-0.5348) time: 0.7548 data: 0.0015 [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.749 (6.759) Lt: 6.020 (6.031) Accm: 2.65 (2.65) Acct: 4.41 (4.06) proj_loss: -0.5438 (-0.5344) time: 0.7548 data: 0.0020 [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.721 (6.746) Lt: 5.993 (6.020) Accm: 2.61 (2.73) Acct: 4.20 (4.39) proj_loss: -0.5580 (-0.5498) time: 0.7548 data: 0.0018 [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 36/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.724 (6.697) Lt: 5.949 (5.937) Accm: 2.93 (2.77) Acct: 4.58 (4.57) proj_loss: -0.5376 (-0.5434) time: 0.7548 data: 0.0016 [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 36/350] Total time: 0:22:20 (0.803 s / it) [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 36/350] Total time: 0:22:20 (0.803 s / it) [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 36/350] Total time: 0:22:20 (0.803 s / it) [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 36/350] Total time: 0:22:20 (0.803 s / it) [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 36/350] Total time: 0:22:20 (0.803 s / it) [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 36/350] Total time: 0:22:20 (0.803 s / it) [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 36/350] Total time: 0:22:20 (0.803 s / it) [11-23 05:16:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 36/350] Total time: 0:22:20 (0.803 s / it) [11-23 05:16:55] (/home/user/VAR/train.py , line 276)=> [ep36] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.033), Acc m&t: 2.72 4.32, Remain: 4 days, 14:25:31, Finish: 2024-11-27 03:42 [11-23 05:16:55] (/home/user/VAR/train.py , line 276)=> [ep36] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.033), Acc m&t: 2.72 4.32, Remain: 4 days, 14:25:59, Finish: 2024-11-27 03:42 [11-23 05:16:55] (/home/user/VAR/train.py , line 276)=> [ep36] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.033), Acc m&t: 2.72 4.32, Remain: 4 days, 14:24:31, Finish: 2024-11-27 03:41 [11-23 05:16:55] (/home/user/VAR/train.py , line 276)=> [ep36] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.033), Acc m&t: 2.72 4.32, Remain: 4 days, 14:25:04, Finish: 2024-11-27 03:41 [11-23 05:16:55] (/home/user/VAR/train.py , line 276)=> [ep36] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.033), Acc m&t: 2.72 4.32, Remain: 4 days, 14:21:27, Finish: 2024-11-27 03:38 [11-23 05:16:55] (/home/user/VAR/train.py , line 276)=> [ep36] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.033), Acc m&t: 2.72 4.32, Remain: 4 days, 14:22:46, Finish: 2024-11-27 03:39 [11-23 05:16:55] (/home/user/VAR/train.py , line 276)=> [ep36] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.033), Acc m&t: 2.72 4.32, Remain: 4 days, 14:22:53, Finish: 2024-11-27 03:39 [11-23 05:16:55] (/home/user/VAR/train.py , line 276)=> [ep36] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.033), Acc m&t: 2.72 4.32, Remain: 4 days, 14:24:57, Finish: 2024-11-27 03:41 [11-23 05:16:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 0/1669] eta: 0:20:53 tlr: 0.00023 tnm: 0.29 Lm: 6.620 (6.620) Lt: 5.857 (5.857) Accm: 3.02 (3.02) Acct: 4.72 (4.72) proj_loss: -0.5408 (-0.5408) time: 0.7512 data: 0.0003 [11-23 05:16:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 0/1669] eta: 0:20:55 tlr: 0.00023 tnm: 0.29 Lm: 6.759 (6.759) Lt: 6.006 (6.006) Accm: 2.64 (2.64) Acct: 4.30 (4.30) proj_loss: -0.5249 (-0.5249) time: 0.7523 data: 0.0004 [11-23 05:16:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 0/1669] eta: 0:20:56 tlr: 0.00023 tnm: 0.29 Lm: 7.087 (7.087) Lt: 6.483 (6.483) Accm: 2.01 (2.01) Acct: 3.37 (3.37) proj_loss: -0.5472 (-0.5472) time: 0.7530 data: 0.0003 [11-23 05:16:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 0/1669] eta: 0:20:57 tlr: 0.00023 tnm: 0.29 Lm: 6.848 (6.848) Lt: 6.169 (6.169) Accm: 2.24 (2.24) Acct: 3.82 (3.82) proj_loss: -0.5569 (-0.5569) time: 0.7532 data: 0.0003 [11-23 05:16:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 0/1669] eta: 0:20:55 tlr: 0.00023 tnm: 0.29 Lm: 6.426 (6.426) Lt: 5.660 (5.660) Accm: 3.38 (3.38) Acct: 5.54 (5.54) proj_loss: -0.5355 (-0.5355) time: 0.7522 data: 0.0004 [11-23 05:16:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 0/1669] eta: 0:20:57 tlr: 0.00023 tnm: 0.29 Lm: 6.838 (6.838) Lt: 6.104 (6.104) Accm: 2.27 (2.27) Acct: 3.51 (3.51) proj_loss: -0.5468 (-0.5468) time: 0.7532 data: 0.0004 [11-23 05:16:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 0/1669] eta: 0:20:57 tlr: 0.00023 tnm: 0.29 Lm: 6.936 (6.936) Lt: 6.240 (6.240) Accm: 2.21 (2.21) Acct: 3.31 (3.31) proj_loss: -0.4938 (-0.4938) time: 0.7536 data: 0.0004 [11-23 05:16:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 0/1669] eta: 0:20:57 tlr: 0.00023 tnm: 0.29 Lm: 6.706 (6.706) Lt: 6.069 (6.069) Accm: 2.68 (2.68) Acct: 3.68 (3.68) proj_loss: -0.5673 (-0.5673) time: 0.7537 data: 0.0004 [11-23 05:22:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.31 Lm: 6.796 (6.796) Lt: 6.054 (6.054) Accm: 2.70 (2.70) Acct: 4.29 (4.29) proj_loss: -0.5254 (-0.5254) time: 0.7551 data: 0.0003 [11-23 05:22:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.31 Lm: 6.790 (6.790) Lt: 6.095 (6.095) Accm: 2.45 (2.45) Acct: 3.93 (3.93) proj_loss: -0.5290 (-0.5290) time: 0.7551 data: 0.0002 [11-23 05:22:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.31 Lm: 6.533 (6.533) Lt: 5.792 (5.792) Accm: 2.91 (2.91) Acct: 4.58 (4.58) proj_loss: -0.5323 (-0.5323) time: 0.7551 data: 0.0002 [11-23 05:22:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.31 Lm: 6.880 (6.880) Lt: 6.207 (6.207) Accm: 2.54 (2.54) Acct: 4.06 (4.06) proj_loss: -0.5058 (-0.5058) time: 0.7551 data: 0.0003 [11-23 05:22:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.31 Lm: 6.664 (6.664) Lt: 5.989 (5.989) Accm: 2.77 (2.77) Acct: 4.18 (4.18) proj_loss: -0.5467 (-0.5467) time: 0.7551 data: 0.0003 [11-23 05:22:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.31 Lm: 6.794 (6.794) Lt: 6.164 (6.164) Accm: 2.41 (2.41) Acct: 3.53 (3.53) proj_loss: -0.5748 (-0.5748) time: 0.7551 data: 0.0003 [11-23 05:22:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.31 Lm: 6.767 (6.767) Lt: 6.013 (6.013) Accm: 2.42 (2.42) Acct: 3.96 (3.96) proj_loss: -0.5319 (-0.5319) time: 0.7551 data: 0.0003 [11-23 05:22:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 417/1669] eta: 0:15:43 tlr: 0.00023 tnm: 0.31 Lm: 6.800 (6.800) Lt: 6.083 (6.083) Accm: 2.59 (2.59) Acct: 4.10 (4.10) proj_loss: -0.5108 (-0.5108) time: 0.7551 data: 0.0003 [11-23 05:27:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.28 Lm: 6.664 (6.718) Lt: 5.927 (5.973) Accm: 2.96 (2.85) Acct: 4.89 (4.44) proj_loss: -0.5278 (-0.5205) time: 0.7524 data: 0.0004 [11-23 05:27:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.28 Lm: 6.729 (6.774) Lt: 5.998 (6.035) Accm: 2.39 (2.59) Acct: 3.86 (4.11) proj_loss: -0.5312 (-0.5273) time: 0.7523 data: 0.0003 [11-23 05:27:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.28 Lm: 6.797 (6.777) Lt: 6.089 (6.038) Accm: 2.56 (2.52) Acct: 4.30 (4.07) proj_loss: -0.5169 (-0.5257) time: 0.7524 data: 0.0003 [11-23 05:27:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.28 Lm: 6.639 (6.659) Lt: 5.924 (5.946) Accm: 2.45 (2.71) Acct: 3.82 (4.33) proj_loss: -0.5329 (-0.5325) time: 0.7524 data: 0.0003 [11-23 05:27:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.28 Lm: 6.848 (6.732) Lt: 6.169 (6.076) Accm: 2.32 (2.62) Acct: 3.82 (3.86) proj_loss: -0.5366 (-0.5386) time: 0.7524 data: 0.0003 [11-23 05:27:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.28 Lm: 6.706 (6.751) Lt: 6.069 (6.050) Accm: 2.68 (2.69) Acct: 3.68 (4.05) proj_loss: -0.5673 (-0.5571) time: 0.7524 data: 0.0003 [11-23 05:27:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.28 Lm: 6.908 (6.889) Lt: 6.203 (6.205) Accm: 2.33 (2.47) Acct: 3.86 (3.99) proj_loss: -0.5327 (-0.5148) time: 0.7524 data: 0.0002 [11-23 05:27:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.28 Lm: 6.759 (6.704) Lt: 6.006 (6.001) Accm: 2.64 (2.66) Acct: 4.30 (4.19) proj_loss: -0.5331 (-0.5310) time: 0.7524 data: 0.0002 [11-23 05:32:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1251/1669] eta: 0:05:17 tlr: 0.00023 tnm: 0.30 Lm: 6.675 (6.676) Lt: 5.955 (5.977) Accm: 2.86 (2.80) Acct: 4.51 (4.33) proj_loss: -0.5318 (-0.5308) time: 0.8080 data: 0.0002 [11-23 05:32:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1251/1669] eta: 0:05:17 tlr: 0.00023 tnm: 0.30 Lm: 6.850 (6.865) Lt: 6.139 (6.173) Accm: 2.53 (2.53) Acct: 3.86 (3.96) proj_loss: -0.5335 (-0.5197) time: 0.8080 data: 0.0003 [11-23 05:32:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1251/1669] eta: 0:05:17 tlr: 0.00023 tnm: 0.30 Lm: 6.674 (6.671) Lt: 5.942 (5.949) Accm: 2.69 (2.77) Acct: 4.20 (4.39) proj_loss: -0.5342 (-0.5353) time: 0.8080 data: 0.0002 [11-23 05:32:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1251/1669] eta: 0:05:17 tlr: 0.00023 tnm: 0.30 Lm: 6.675 (6.727) Lt: 5.927 (5.990) Accm: 2.62 (2.66) Acct: 4.20 (4.22) proj_loss: -0.5360 (-0.5350) time: 0.8080 data: 0.0002 [11-23 05:32:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1251/1669] eta: 0:05:17 tlr: 0.00023 tnm: 0.30 Lm: 6.752 (6.760) Lt: 6.027 (6.020) Accm: 2.64 (2.66) Acct: 4.36 (4.23) proj_loss: -0.5319 (-0.5324) time: 0.8080 data: 0.0003 [11-23 05:32:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1251/1669] eta: 0:05:17 tlr: 0.00023 tnm: 0.30 Lm: 6.853 (6.763) Lt: 6.163 (6.097) Accm: 2.48 (2.63) Acct: 3.91 (3.89) proj_loss: -0.5414 (-0.5405) time: 0.8080 data: 0.0003 [11-23 05:32:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1251/1669] eta: 0:05:17 tlr: 0.00023 tnm: 0.30 Lm: 6.780 (6.762) Lt: 6.072 (6.034) Accm: 2.63 (2.71) Acct: 4.37 (4.30) proj_loss: -0.5338 (-0.5277) time: 0.8080 data: 0.0004 [11-23 05:32:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1251/1669] eta: 0:05:17 tlr: 0.00023 tnm: 0.30 Lm: 6.760 (6.767) Lt: 6.076 (6.058) Accm: 2.75 (2.72) Acct: 4.03 (4.13) proj_loss: -0.5483 (-0.5501) time: 0.8080 data: 0.0003 [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.706 (6.729) Lt: 6.069 (6.016) Accm: 2.81 (2.83) Acct: 4.37 (4.30) proj_loss: -0.5673 (-0.5542) time: 0.8708 data: 0.0018 [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 37/350] Total time: 0:22:21 (0.804 s / it) [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.787 (6.765) Lt: 6.089 (6.037) Accm: 2.56 (2.59) Acct: 4.30 (4.09) proj_loss: -0.5452 (-0.5350) time: 0.8708 data: 0.0019 [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.729 (6.744) Lt: 5.998 (6.009) Accm: 2.45 (2.62) Acct: 4.27 (4.23) proj_loss: -0.5408 (-0.5369) time: 0.8708 data: 0.0013 [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.709 (6.720) Lt: 5.959 (6.007) Accm: 2.45 (2.69) Acct: 3.99 (4.31) proj_loss: -0.5355 (-0.5406) time: 0.8708 data: 0.0019 [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.806 (6.853) Lt: 6.124 (6.163) Accm: 2.49 (2.53) Acct: 3.86 (3.96) proj_loss: -0.5343 (-0.5234) time: 0.8708 data: 0.0016 [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.761 (6.762) Lt: 6.037 (6.034) Accm: 2.58 (2.68) Acct: 4.13 (4.26) proj_loss: -0.5354 (-0.5293) time: 0.8708 data: 0.0018 [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.652 (6.671) Lt: 5.987 (5.979) Accm: 3.07 (2.88) Acct: 4.72 (4.42) proj_loss: -0.5331 (-0.5330) time: 0.8708 data: 0.0018 [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 37/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.848 (6.769) Lt: 6.158 (6.084) Accm: 2.65 (2.65) Acct: 3.99 (4.04) proj_loss: -0.5377 (-0.5399) time: 0.8708 data: 0.0018 [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 37/350] Total time: 0:22:21 (0.804 s / it) [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 37/350] Total time: 0:22:21 (0.804 s / it) [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 37/350] Total time: 0:22:21 (0.804 s / it) [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 37/350] Total time: 0:22:21 (0.804 s / it) [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 37/350] Total time: 0:22:21 (0.804 s / it) [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 37/350] Total time: 0:22:21 (0.804 s / it) [11-23 05:39:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 37/350] Total time: 0:22:21 (0.804 s / it) [11-23 05:39:17] (/home/user/VAR/train.py , line 276)=> [ep37] (training ) Lm: 6.756 (6.757), Lt: 6.033 (6.045), Acc m&t: 2.72 4.32, Remain: 4 days, 13:58:29, Finish: 2024-11-27 03:37 [11-23 05:39:17] (/home/user/VAR/train.py , line 276)=> [ep37] (training ) Lm: 6.756 (6.757), Lt: 6.033 (6.045), Acc m&t: 2.72 4.32, Remain: 4 days, 13:58:39, Finish: 2024-11-27 03:37 [11-23 05:39:17] (/home/user/VAR/train.py , line 276)=> [ep37] (training ) Lm: 6.756 (6.757), Lt: 6.033 (6.045), Acc m&t: 2.72 4.32, Remain: 4 days, 13:58:01, Finish: 2024-11-27 03:37 [11-23 05:39:17] (/home/user/VAR/train.py , line 276)=> [ep37] (training ) Lm: 6.756 (6.757), Lt: 6.033 (6.045), Acc m&t: 2.72 4.32, Remain: 4 days, 13:58:18, Finish: 2024-11-27 03:37 [11-23 05:39:17] (/home/user/VAR/train.py , line 276)=> [ep37] (training ) Lm: 6.756 (6.757), Lt: 6.033 (6.045), Acc m&t: 2.72 4.32, Remain: 4 days, 13:58:39, Finish: 2024-11-27 03:37 [11-23 05:39:17] (/home/user/VAR/train.py , line 276)=> [ep37] (training ) Lm: 6.756 (6.757), Lt: 6.033 (6.045), Acc m&t: 2.72 4.32, Remain: 4 days, 13:57:41, Finish: 2024-11-27 03:36 [11-23 05:39:17] (/home/user/VAR/train.py , line 276)=> [ep37] (training ) Lm: 6.756 (6.757), Lt: 6.033 (6.045), Acc m&t: 2.72 4.32, Remain: 4 days, 13:58:07, Finish: 2024-11-27 03:37 [11-23 05:39:17] (/home/user/VAR/train.py , line 276)=> [ep37] (training ) Lm: 6.756 (6.757), Lt: 6.033 (6.045), Acc m&t: 2.72 4.32, Remain: 4 days, 13:57:38, Finish: 2024-11-27 03:36 [11-23 05:39:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 0/1669] eta: 0:19:57 tlr: 0.00023 tnm: 0.32 Lm: 6.945 (6.945) Lt: 6.331 (6.331) Accm: 2.37 (2.37) Acct: 3.55 (3.55) proj_loss: -0.5535 (-0.5535) time: 0.7178 data: 0.0003 [11-23 05:39:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 0/1669] eta: 0:19:58 tlr: 0.00023 tnm: 0.32 Lm: 6.936 (6.936) Lt: 6.242 (6.242) Accm: 1.98 (1.98) Acct: 3.06 (3.06) proj_loss: -0.5383 (-0.5383) time: 0.7179 data: 0.0004 [11-23 05:39:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 0/1669] eta: 0:19:59 tlr: 0.00023 tnm: 0.32 Lm: 6.663 (6.663) Lt: 5.968 (5.968) Accm: 2.83 (2.83) Acct: 4.10 (4.10) proj_loss: -0.5437 (-0.5437) time: 0.7188 data: 0.0004 [11-23 05:39:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 0/1669] eta: 0:20:00 tlr: 0.00023 tnm: 0.32 Lm: 6.606 (6.606) Lt: 5.941 (5.941) Accm: 2.93 (2.93) Acct: 4.58 (4.58) proj_loss: -0.5010 (-0.5010) time: 0.7195 data: 0.0003 [11-23 05:39:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 0/1669] eta: 0:20:00 tlr: 0.00023 tnm: 0.32 Lm: 6.709 (6.709) Lt: 6.058 (6.058) Accm: 2.77 (2.77) Acct: 4.10 (4.10) proj_loss: -0.5273 (-0.5273) time: 0.7192 data: 0.0004 [11-23 05:39:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 0/1669] eta: 0:20:00 tlr: 0.00023 tnm: 0.32 Lm: 6.620 (6.620) Lt: 5.868 (5.868) Accm: 3.31 (3.31) Acct: 5.27 (5.27) proj_loss: -0.5323 (-0.5323) time: 0.7191 data: 0.0003 [11-23 05:39:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 0/1669] eta: 0:20:01 tlr: 0.00023 tnm: 0.32 Lm: 6.555 (6.555) Lt: 5.747 (5.747) Accm: 3.51 (3.51) Acct: 5.82 (5.82) proj_loss: -0.5016 (-0.5016) time: 0.7198 data: 0.0004 [11-23 05:39:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 0/1669] eta: 0:20:00 tlr: 0.00023 tnm: 0.32 Lm: 6.716 (6.716) Lt: 5.871 (5.871) Accm: 2.84 (2.84) Acct: 4.65 (4.65) proj_loss: -0.5190 (-0.5190) time: 0.7195 data: 0.0003 [11-23 05:44:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.29 Lm: 6.705 (6.705) Lt: 5.909 (5.909) Accm: 2.75 (2.75) Acct: 4.41 (4.41) proj_loss: -0.5315 (-0.5315) time: 0.7530 data: 0.0003 [11-23 05:44:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.29 Lm: 6.831 (6.831) Lt: 6.154 (6.154) Accm: 2.74 (2.74) Acct: 4.15 (4.15) proj_loss: -0.5412 (-0.5412) time: 0.7530 data: 0.0002 [11-23 05:44:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.29 Lm: 6.796 (6.796) Lt: 6.109 (6.109) Accm: 2.43 (2.43) Acct: 3.70 (3.70) proj_loss: -0.5440 (-0.5440) time: 0.7530 data: 0.0003 [11-23 05:44:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.29 Lm: 6.756 (6.756) Lt: 6.075 (6.075) Accm: 2.59 (2.59) Acct: 4.12 (4.12) proj_loss: -0.5326 (-0.5326) time: 0.7530 data: 0.0002 [11-23 05:44:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.29 Lm: 6.723 (6.723) Lt: 6.053 (6.053) Accm: 2.83 (2.83) Acct: 4.42 (4.42) proj_loss: -0.5305 (-0.5305) time: 0.7530 data: 0.0003 [11-23 05:44:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.29 Lm: 6.711 (6.711) Lt: 5.974 (5.974) Accm: 2.62 (2.62) Acct: 4.08 (4.08) proj_loss: -0.5399 (-0.5399) time: 0.7530 data: 0.0003 [11-23 05:44:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.29 Lm: 6.719 (6.719) Lt: 5.947 (5.947) Accm: 2.95 (2.95) Acct: 4.82 (4.82) proj_loss: -0.5283 (-0.5283) time: 0.7530 data: 0.0003 [11-23 05:44:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.29 Lm: 6.659 (6.659) Lt: 5.908 (5.908) Accm: 3.18 (3.18) Acct: 5.08 (5.08) proj_loss: -0.5223 (-0.5223) time: 0.7530 data: 0.0003 [11-23 05:49:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.719 (6.679) Lt: 5.940 (5.919) Accm: 2.84 (2.97) Acct: 4.34 (4.74) proj_loss: -0.5356 (-0.5267) time: 0.7529 data: 0.0003 [11-23 05:49:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.669 (6.705) Lt: 5.941 (5.990) Accm: 2.93 (2.93) Acct: 4.58 (4.65) proj_loss: -0.5564 (-0.5391) time: 0.7529 data: 0.0003 [11-23 05:49:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.719 (6.794) Lt: 5.978 (6.095) Accm: 2.99 (2.82) Acct: 4.41 (4.24) proj_loss: -0.5445 (-0.5423) time: 0.7529 data: 0.0002 [11-23 05:49:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.648 (6.690) Lt: 5.874 (5.941) Accm: 2.94 (2.72) Acct: 4.51 (4.22) proj_loss: -0.5383 (-0.5355) time: 0.7529 data: 0.0003 [11-23 05:49:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.712 (6.768) Lt: 6.038 (6.085) Accm: 2.70 (2.52) Acct: 4.10 (3.86) proj_loss: -0.5442 (-0.5513) time: 0.7529 data: 0.0003 [11-23 05:49:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.709 (6.729) Lt: 6.058 (6.038) Accm: 2.77 (2.78) Acct: 4.13 (4.52) proj_loss: -0.5380 (-0.5376) time: 0.7529 data: 0.0002 [11-23 05:49:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.716 (6.719) Lt: 5.947 (5.938) Accm: 2.67 (2.68) Acct: 4.27 (4.36) proj_loss: -0.5440 (-0.5389) time: 0.7529 data: 0.0003 [11-23 05:49:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.819 (6.780) Lt: 6.026 (6.052) Accm: 2.59 (2.74) Acct: 4.37 (4.41) proj_loss: -0.5323 (-0.5345) time: 0.7529 data: 0.0003 [11-23 05:54:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.769 (6.764) Lt: 6.018 (6.042) Accm: 2.56 (2.69) Acct: 3.99 (4.21) proj_loss: -0.5334 (-0.5345) time: 0.7508 data: 0.0003 [11-23 05:54:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.774 (6.802) Lt: 6.030 (6.092) Accm: 2.88 (2.81) Acct: 4.29 (4.22) proj_loss: -0.5367 (-0.5335) time: 0.7508 data: 0.0002 [11-23 05:54:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.820 (6.813) Lt: 6.143 (6.145) Accm: 2.53 (2.48) Acct: 3.96 (3.85) proj_loss: -0.5480 (-0.5514) time: 0.7508 data: 0.0003 [11-23 05:54:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.792 (6.758) Lt: 6.058 (6.026) Accm: 2.52 (2.57) Acct: 3.94 (4.01) proj_loss: -0.5326 (-0.5333) time: 0.7508 data: 0.0003 [11-23 05:54:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.756 (6.782) Lt: 6.075 (6.093) Accm: 2.59 (2.67) Acct: 4.12 (4.27) proj_loss: -0.5376 (-0.5375) time: 0.7508 data: 0.0002 [11-23 05:54:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.734 (6.728) Lt: 6.002 (6.008) Accm: 2.84 (2.89) Acct: 4.61 (4.65) proj_loss: -0.5352 (-0.5328) time: 0.7508 data: 0.0003 [11-23 05:54:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.732 (6.731) Lt: 5.972 (5.973) Accm: 2.69 (2.68) Acct: 4.29 (4.35) proj_loss: -0.5341 (-0.5353) time: 0.7508 data: 0.0003 [11-23 05:54:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1251/1669] eta: 0:05:14 tlr: 0.00023 tnm: 0.34 Lm: 6.732 (6.696) Lt: 6.004 (5.964) Accm: 2.70 (2.86) Acct: 4.20 (4.50) proj_loss: -0.5393 (-0.5396) time: 0.7508 data: 0.0003 [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.744 (6.718) Lt: 6.068 (6.003) Accm: 2.56 (2.77) Acct: 4.13 (4.43) proj_loss: -0.5423 (-0.5401) time: 0.7541 data: 0.0016 [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 38/350] Total time: 0:20:55 (0.752 s / it) [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.828 (6.828) Lt: 6.083 (6.132) Accm: 2.78 (2.74) Acct: 4.17 (4.09) proj_loss: -0.5445 (-0.5395) time: 0.7541 data: 0.0014 [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.803 (6.808) Lt: 6.092 (6.123) Accm: 2.42 (2.58) Acct: 4.10 (4.10) proj_loss: -0.5373 (-0.5334) time: 0.7541 data: 0.0017 [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.712 (6.765) Lt: 6.038 (6.078) Accm: 2.70 (2.64) Acct: 4.10 (4.12) proj_loss: -0.5442 (-0.5494) time: 0.7541 data: 0.0017 [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.648 (6.716) Lt: 5.874 (5.962) Accm: 2.94 (2.71) Acct: 4.51 (4.27) proj_loss: -0.5268 (-0.5276) time: 0.7541 data: 0.0018 [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.719 (6.742) Lt: 6.011 (6.029) Accm: 2.59 (2.73) Acct: 4.37 (4.24) proj_loss: -0.5346 (-0.5395) time: 0.7541 data: 0.0018 [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.758 (6.734) Lt: 6.040 (6.015) Accm: 2.75 (2.83) Acct: 4.58 (4.62) proj_loss: -0.5288 (-0.5320) time: 0.7541 data: 0.0018 [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 38/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.737 (6.732) Lt: 5.997 (5.979) Accm: 2.71 (2.73) Acct: 4.30 (4.35) proj_loss: -0.5440 (-0.5381) time: 0.7541 data: 0.0018 [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 38/350] Total time: 0:20:55 (0.752 s / it) [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 38/350] Total time: 0:20:55 (0.752 s / it) [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 38/350] Total time: 0:20:55 (0.752 s / it) [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 38/350] Total time: 0:20:55 (0.752 s / it) [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 38/350] Total time: 0:20:55 (0.752 s / it) [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 38/350] Total time: 0:20:55 (0.752 s / it) [11-23 06:00:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 38/350] Total time: 0:20:55 (0.752 s / it) [11-23 06:00:12] (/home/user/VAR/train.py , line 276)=> [ep38] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.036), Acc m&t: 2.73 4.32, Remain: 4 days, 13:19:13, Finish: 2024-11-27 03:19 [11-23 06:00:12] (/home/user/VAR/train.py , line 276)=> [ep38] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.036), Acc m&t: 2.73 4.32, Remain: 4 days, 13:20:14, Finish: 2024-11-27 03:20 [11-23 06:00:12] (/home/user/VAR/train.py , line 276)=> [ep38] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.036), Acc m&t: 2.73 4.32, Remain: 4 days, 13:19:41, Finish: 2024-11-27 03:19 [11-23 06:00:12] (/home/user/VAR/train.py , line 276)=> [ep38] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.036), Acc m&t: 2.73 4.32, Remain: 4 days, 13:19:08, Finish: 2024-11-27 03:19 [11-23 06:00:12] (/home/user/VAR/train.py , line 276)=> [ep38] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.036), Acc m&t: 2.73 4.32, Remain: 4 days, 13:19:47, Finish: 2024-11-27 03:19 [11-23 06:00:12] (/home/user/VAR/train.py , line 276)=> [ep38] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.036), Acc m&t: 2.73 4.32, Remain: 4 days, 13:19:37, Finish: 2024-11-27 03:19 [11-23 06:00:12] (/home/user/VAR/train.py , line 276)=> [ep38] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.036), Acc m&t: 2.73 4.32, Remain: 4 days, 13:19:26, Finish: 2024-11-27 03:19 [11-23 06:00:12] (/home/user/VAR/train.py , line 276)=> [ep38] (training ) Lm: 6.756 (6.756), Lt: 6.033 (6.036), Acc m&t: 2.73 4.32, Remain: 4 days, 13:18:59, Finish: 2024-11-27 03:19 [11-23 06:00:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 0/1669] eta: 0:19:59 tlr: 0.00023 tnm: 0.31 Lm: 6.700 (6.700) Lt: 6.030 (6.030) Accm: 3.16 (3.16) Acct: 5.06 (5.06) proj_loss: -0.5316 (-0.5316) time: 0.7186 data: 0.0004 [11-23 06:00:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 0/1669] eta: 0:19:58 tlr: 0.00023 tnm: 0.31 Lm: 6.874 (6.874) Lt: 6.234 (6.234) Accm: 2.32 (2.32) Acct: 3.51 (3.51) proj_loss: -0.5159 (-0.5159) time: 0.7178 data: 0.0004 [11-23 06:00:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 0/1669] eta: 0:20:01 tlr: 0.00023 tnm: 0.31 Lm: 6.889 (6.889) Lt: 6.204 (6.204) Accm: 2.32 (2.32) Acct: 3.17 (3.17) proj_loss: -0.5430 (-0.5430) time: 0.7197 data: 0.0003 [11-23 06:00:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 0/1669] eta: 0:19:53 tlr: 0.00023 tnm: 0.31 Lm: 6.741 (6.741) Lt: 5.990 (5.990) Accm: 2.55 (2.55) Acct: 4.24 (4.24) proj_loss: -0.5344 (-0.5344) time: 0.7148 data: 0.0004 [11-23 06:00:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 0/1669] eta: 0:20:02 tlr: 0.00023 tnm: 0.31 Lm: 6.544 (6.544) Lt: 5.788 (5.788) Accm: 3.31 (3.31) Acct: 5.68 (5.68) proj_loss: -0.5418 (-0.5418) time: 0.7203 data: 0.0004 [11-23 06:00:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 0/1669] eta: 0:20:03 tlr: 0.00023 tnm: 0.31 Lm: 6.774 (6.774) Lt: 5.976 (5.976) Accm: 2.51 (2.51) Acct: 4.37 (4.37) proj_loss: -0.5419 (-0.5419) time: 0.7208 data: 0.0004 [11-23 06:00:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 0/1669] eta: 0:20:03 tlr: 0.00023 tnm: 0.31 Lm: 6.362 (6.362) Lt: 5.579 (5.579) Accm: 4.05 (4.05) Acct: 6.27 (6.27) proj_loss: -0.5675 (-0.5675) time: 0.7210 data: 0.0003 [11-23 06:00:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 0/1669] eta: 0:20:02 tlr: 0.00023 tnm: 0.31 Lm: 6.848 (6.848) Lt: 6.110 (6.110) Accm: 2.10 (2.10) Acct: 3.27 (3.27) proj_loss: -0.5189 (-0.5189) time: 0.7206 data: 0.0004 [11-23 06:05:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 417/1669] eta: 0:16:25 tlr: 0.00023 tnm: 0.28 Lm: 6.650 (6.650) Lt: 5.943 (5.943) Accm: 3.23 (3.23) Acct: 5.32 (5.32) proj_loss: -0.5362 (-0.5362) time: 0.9138 data: 0.0003 [11-23 06:05:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 417/1669] eta: 0:16:25 tlr: 0.00023 tnm: 0.28 Lm: 6.858 (6.858) Lt: 6.182 (6.182) Accm: 2.33 (2.33) Acct: 3.60 (3.60) proj_loss: -0.5303 (-0.5303) time: 0.9138 data: 0.0003 [11-23 06:05:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 417/1669] eta: 0:16:25 tlr: 0.00023 tnm: 0.28 Lm: 6.522 (6.522) Lt: 5.737 (5.737) Accm: 3.63 (3.63) Acct: 5.91 (5.91) proj_loss: -0.5502 (-0.5502) time: 0.9138 data: 0.0003 [11-23 06:05:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 417/1669] eta: 0:16:25 tlr: 0.00023 tnm: 0.28 Lm: 6.837 (6.837) Lt: 6.116 (6.116) Accm: 2.62 (2.62) Acct: 3.91 (3.91) proj_loss: -0.5425 (-0.5425) time: 0.9138 data: 0.0003 [11-23 06:05:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 417/1669] eta: 0:16:25 tlr: 0.00023 tnm: 0.28 Lm: 6.715 (6.715) Lt: 5.978 (5.978) Accm: 2.86 (2.86) Acct: 4.58 (4.58) proj_loss: -0.5262 (-0.5262) time: 0.9138 data: 0.0003 [11-23 06:05:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 417/1669] eta: 0:16:25 tlr: 0.00023 tnm: 0.28 Lm: 6.802 (6.802) Lt: 6.067 (6.067) Accm: 2.32 (2.32) Acct: 3.84 (3.84) proj_loss: -0.5212 (-0.5212) time: 0.9138 data: 0.0003 [11-23 06:05:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 417/1669] eta: 0:16:25 tlr: 0.00023 tnm: 0.28 Lm: 6.656 (6.656) Lt: 5.939 (5.939) Accm: 2.97 (2.97) Acct: 4.73 (4.73) proj_loss: -0.5318 (-0.5318) time: 0.9138 data: 0.0003 [11-23 06:05:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 417/1669] eta: 0:16:25 tlr: 0.00023 tnm: 0.28 Lm: 6.590 (6.590) Lt: 5.879 (5.879) Accm: 3.39 (3.39) Acct: 5.10 (5.10) proj_loss: -0.5459 (-0.5459) time: 0.9138 data: 0.0003 [11-23 06:12:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 834/1669] eta: 0:12:02 tlr: 0.00023 tnm: 0.29 Lm: 6.621 (6.601) Lt: 5.945 (5.901) Accm: 3.48 (3.42) Acct: 4.99 (5.06) proj_loss: -0.5386 (-0.5435) time: 0.8736 data: 0.0003 [11-23 06:12:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 834/1669] eta: 0:12:02 tlr: 0.00023 tnm: 0.29 Lm: 6.627 (6.642) Lt: 5.880 (5.922) Accm: 3.19 (3.21) Acct: 5.37 (5.34) proj_loss: -0.5316 (-0.5336) time: 0.8736 data: 0.0003 [11-23 06:12:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 834/1669] eta: 0:12:02 tlr: 0.00023 tnm: 0.29 Lm: 6.544 (6.588) Lt: 5.788 (5.814) Accm: 3.31 (3.25) Acct: 5.68 (5.10) proj_loss: -0.5418 (-0.5362) time: 0.8736 data: 0.0003 [11-23 06:12:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 834/1669] eta: 0:12:02 tlr: 0.00023 tnm: 0.29 Lm: 6.784 (6.750) Lt: 6.029 (6.043) Accm: 2.91 (2.88) Acct: 4.65 (4.26) proj_loss: -0.5430 (-0.5514) time: 0.8736 data: 0.0003 [11-23 06:12:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 834/1669] eta: 0:12:02 tlr: 0.00023 tnm: 0.29 Lm: 6.842 (6.737) Lt: 6.130 (6.022) Accm: 2.35 (2.74) Acct: 3.68 (4.30) proj_loss: -0.5159 (-0.5249) time: 0.8736 data: 0.0003 [11-23 06:12:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 834/1669] eta: 0:12:02 tlr: 0.00023 tnm: 0.29 Lm: 6.571 (6.611) Lt: 5.888 (5.847) Accm: 3.39 (3.23) Acct: 5.23 (5.21) proj_loss: -0.5292 (-0.5271) time: 0.8736 data: 0.0003 [11-23 06:12:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 834/1669] eta: 0:12:02 tlr: 0.00023 tnm: 0.29 Lm: 6.774 (6.751) Lt: 5.979 (6.017) Accm: 2.51 (2.72) Acct: 4.37 (4.26) proj_loss: -0.5256 (-0.5260) time: 0.8736 data: 0.0003 [11-23 06:12:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [ 834/1669] eta: 0:12:02 tlr: 0.00023 tnm: 0.29 Lm: 6.848 (6.829) Lt: 6.110 (6.101) Accm: 2.53 (2.57) Acct: 4.41 (4.18) proj_loss: -0.5234 (-0.5284) time: 0.8736 data: 0.0003 [11-23 06:17:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1251/1669] eta: 0:05:46 tlr: 0.00023 tnm: 0.30 Lm: 6.865 (6.845) Lt: 6.139 (6.134) Accm: 2.40 (2.49) Acct: 3.98 (4.02) proj_loss: -0.5212 (-0.5233) time: 0.7541 data: 0.0003 [11-23 06:17:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1251/1669] eta: 0:05:46 tlr: 0.00023 tnm: 0.30 Lm: 6.858 (6.771) Lt: 6.140 (6.054) Accm: 2.33 (2.63) Acct: 3.84 (4.23) proj_loss: -0.5254 (-0.5274) time: 0.7540 data: 0.0002 [11-23 06:17:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1251/1669] eta: 0:05:46 tlr: 0.00023 tnm: 0.30 Lm: 6.664 (6.661) Lt: 5.928 (5.936) Accm: 3.18 (3.08) Acct: 5.22 (4.93) proj_loss: -0.5300 (-0.5284) time: 0.7541 data: 0.0003 [11-23 06:17:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1251/1669] eta: 0:05:46 tlr: 0.00023 tnm: 0.30 Lm: 6.633 (6.642) Lt: 5.879 (5.887) Accm: 2.89 (3.02) Acct: 4.73 (4.77) proj_loss: -0.5428 (-0.5381) time: 0.7541 data: 0.0002 [11-23 06:17:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1251/1669] eta: 0:05:46 tlr: 0.00023 tnm: 0.30 Lm: 6.656 (6.715) Lt: 5.939 (5.975) Accm: 2.97 (2.86) Acct: 4.73 (4.67) proj_loss: -0.5270 (-0.5266) time: 0.7541 data: 0.0003 [11-23 06:17:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1251/1669] eta: 0:05:46 tlr: 0.00023 tnm: 0.30 Lm: 6.725 (6.729) Lt: 5.984 (6.017) Accm: 2.92 (2.89) Acct: 4.58 (4.32) proj_loss: -0.5425 (-0.5451) time: 0.7541 data: 0.0002 [11-23 06:17:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1251/1669] eta: 0:05:46 tlr: 0.00023 tnm: 0.30 Lm: 6.798 (6.775) Lt: 6.035 (6.035) Accm: 2.64 (2.74) Acct: 4.37 (4.29) proj_loss: -0.5310 (-0.5286) time: 0.7541 data: 0.0003 [11-23 06:17:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1251/1669] eta: 0:05:46 tlr: 0.00023 tnm: 0.30 Lm: 6.720 (6.668) Lt: 6.044 (5.962) Accm: 3.10 (3.13) Acct: 4.46 (4.62) proj_loss: -0.5420 (-0.5439) time: 0.7541 data: 0.0003 [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.874 (6.807) Lt: 6.151 (6.109) Accm: 2.32 (2.54) Acct: 3.68 (3.99) proj_loss: -0.5349 (-0.5299) time: 0.7531 data: 0.0018 [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.667 (6.711) Lt: 5.939 (5.995) Accm: 2.93 (2.92) Acct: 4.51 (4.36) proj_loss: -0.5419 (-0.5415) time: 0.7531 data: 0.0018 [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.669 (6.648) Lt: 5.894 (5.888) Accm: 2.87 (2.99) Acct: 4.68 (4.75) proj_loss: -0.5418 (-0.5342) time: 0.7531 data: 0.0017 [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.659 (6.661) Lt: 5.891 (5.927) Accm: 3.16 (3.10) Acct: 5.20 (4.99) proj_loss: -0.5284 (-0.5282) time: 0.7531 data: 0.0018 [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.721 (6.678) Lt: 5.955 (5.960) Accm: 2.84 (3.07) Acct: 4.37 (4.57) proj_loss: -0.5386 (-0.5391) time: 0.7531 data: 0.0020 [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.823 (6.808) Lt: 6.091 (6.072) Accm: 2.51 (2.60) Acct: 4.37 (4.11) proj_loss: -0.5353 (-0.5300) time: 0.7531 data: 0.0020 [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.883 (6.868) Lt: 6.168 (6.154) Accm: 2.40 (2.47) Acct: 3.65 (3.95) proj_loss: -0.5234 (-0.5286) time: 0.7531 data: 0.0020 [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 39/350] Total time: 0:22:30 (0.809 s / it) [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 39/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.30 Lm: 6.741 (6.731) Lt: 5.990 (5.991) Accm: 3.16 (2.92) Acct: 4.99 (4.73) proj_loss: -0.5292 (-0.5290) time: 0.7531 data: 0.0017 [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 39/350] Total time: 0:22:30 (0.809 s / it) [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 39/350] Total time: 0:22:30 (0.809 s / it) [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 39/350] Total time: 0:22:30 (0.809 s / it) [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 39/350] Total time: 0:22:30 (0.809 s / it) [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 39/350] Total time: 0:22:30 (0.809 s / it) [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 39/350] Total time: 0:22:30 (0.809 s / it) [11-23 06:22:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 39/350] Total time: 0:22:30 (0.809 s / it) [11-23 06:24:39] (home/user/VAR/trainer.py, line 114)=> FID: 5.028736933822131 [11-23 06:24:40] (/home/user/VAR/train.py , line 259)=> [*] [ep39] (val 50000) Lm: 6.7470, Lt: 6.0209, Acc m&t: 2.75 4.34, Val cost: 116.62s [11-23 06:24:40] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-23 06:25:16] (/home/user/VAR/train.py , line 276)=> [ep39] (training ) Lm: 6.747 (6.747), Lt: 6.021 (6.021), Acc m&t: 2.75 4.34, Remain: 4 days, 13:00:15, Finish: 2024-11-27 03:22 [11-23 06:25:16] (/home/user/VAR/train.py , line 276)=> [ep39] (training ) Lm: 6.747 (6.747), Lt: 6.021 (6.021), Acc m&t: 2.75 4.34, Remain: 4 days, 13:01:17, Finish: 2024-11-27 03:23 [11-23 06:25:16] (/home/user/VAR/train.py , line 276)=> [ep39] (training ) Lm: 6.747 (6.747), Lt: 6.021 (6.021), Acc m&t: 2.75 4.34, Remain: 4 days, 12:59:27, Finish: 2024-11-27 03:22 [11-23 06:25:16] (/home/user/VAR/train.py , line 276)=> [ep39] (training ) Lm: 6.747 (6.747), Lt: 6.021 (6.021), Acc m&t: 2.75 4.34, Remain: 4 days, 13:00:32, Finish: 2024-11-27 03:23 [11-23 06:25:16] (/home/user/VAR/train.py , line 276)=> [ep39] (training ) Lm: 6.747 (6.747), Lt: 6.021 (6.021), Acc m&t: 2.75 4.34, Remain: 4 days, 12:59:58, Finish: 2024-11-27 03:22 [11-23 06:25:16] (/home/user/VAR/train.py , line 276)=> [ep39] (training ) Lm: 6.747 (6.747), Lt: 6.021 (6.021), Acc m&t: 2.75 4.34, Remain: 4 days, 13:00:03, Finish: 2024-11-27 03:22 [11-23 06:25:16] (/home/user/VAR/train.py , line 276)=> [ep39] (training ) Lm: 6.747 (6.747), Lt: 6.021 (6.021), Acc m&t: 2.75 4.34, Remain: 4 days, 12:59:46, Finish: 2024-11-27 03:22 [11-23 06:25:16] (/home/user/VAR/train.py , line 276)=> [ep39] (training ) Lm: 6.747 (6.747), Lt: 6.021 (6.021), Acc m&t: 2.75 4.34, Remain: 4 days, 13:00:22, Finish: 2024-11-27 03:23 [11-23 06:25:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 0/1669] eta: 0:19:52 tlr: 0.00023 tnm: 0.29 Lm: 6.866 (6.866) Lt: 6.151 (6.151) Accm: 2.40 (2.40) Acct: 3.72 (3.72) proj_loss: -0.5339 (-0.5339) time: 0.7146 data: 0.0004 [11-23 06:25:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 0/1669] eta: 0:19:49 tlr: 0.00023 tnm: 0.29 Lm: 6.852 (6.852) Lt: 6.186 (6.186) Accm: 2.49 (2.49) Acct: 3.99 (3.99) proj_loss: -0.5418 (-0.5418) time: 0.7128 data: 0.0004 [11-23 06:25:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 0/1669] eta: 0:19:51 tlr: 0.00023 tnm: 0.29 Lm: 6.732 (6.732) Lt: 6.062 (6.062) Accm: 2.80 (2.80) Acct: 4.03 (4.03) proj_loss: -0.5501 (-0.5501) time: 0.7139 data: 0.0004 [11-23 06:25:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 0/1669] eta: 0:19:52 tlr: 0.00023 tnm: 0.29 Lm: 6.988 (6.988) Lt: 6.344 (6.344) Accm: 2.14 (2.14) Acct: 3.62 (3.62) proj_loss: -0.5312 (-0.5312) time: 0.7145 data: 0.0003 [11-23 06:25:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 0/1669] eta: 0:19:51 tlr: 0.00023 tnm: 0.29 Lm: 6.818 (6.818) Lt: 6.043 (6.043) Accm: 2.45 (2.45) Acct: 4.03 (4.03) proj_loss: -0.5056 (-0.5056) time: 0.7140 data: 0.0004 [11-23 06:25:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 0/1669] eta: 0:20:00 tlr: 0.00023 tnm: 0.29 Lm: 6.684 (6.684) Lt: 5.941 (5.941) Accm: 2.75 (2.75) Acct: 4.55 (4.55) proj_loss: -0.5392 (-0.5392) time: 0.7192 data: 0.0003 [11-23 06:25:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 0/1669] eta: 0:19:53 tlr: 0.00023 tnm: 0.29 Lm: 6.653 (6.653) Lt: 5.932 (5.932) Accm: 2.87 (2.87) Acct: 4.51 (4.51) proj_loss: -0.5336 (-0.5336) time: 0.7152 data: 0.0004 [11-23 06:25:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 0/1669] eta: 0:19:55 tlr: 0.00023 tnm: 0.29 Lm: 6.803 (6.803) Lt: 6.050 (6.050) Accm: 2.43 (2.43) Acct: 3.65 (3.65) proj_loss: -0.5576 (-0.5576) time: 0.7164 data: 0.0003 [11-23 06:30:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.29 Lm: 6.754 (6.754) Lt: 6.002 (6.002) Accm: 2.66 (2.66) Acct: 3.99 (3.99) proj_loss: -0.5554 (-0.5554) time: 0.7506 data: 0.0003 [11-23 06:30:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.29 Lm: 6.742 (6.742) Lt: 6.045 (6.045) Accm: 2.64 (2.64) Acct: 3.87 (3.87) proj_loss: -0.5492 (-0.5492) time: 0.7506 data: 0.0003 [11-23 06:30:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.29 Lm: 6.839 (6.839) Lt: 6.148 (6.148) Accm: 2.53 (2.53) Acct: 4.22 (4.22) proj_loss: -0.5488 (-0.5488) time: 0.7506 data: 0.0002 [11-23 06:30:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.29 Lm: 6.744 (6.744) Lt: 6.040 (6.040) Accm: 2.51 (2.51) Acct: 3.96 (3.96) proj_loss: -0.5416 (-0.5416) time: 0.7506 data: 0.0003 [11-23 06:30:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.29 Lm: 6.744 (6.744) Lt: 5.992 (5.992) Accm: 2.67 (2.67) Acct: 4.34 (4.34) proj_loss: -0.5378 (-0.5378) time: 0.7506 data: 0.0003 [11-23 06:30:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.29 Lm: 6.795 (6.795) Lt: 6.067 (6.067) Accm: 2.51 (2.51) Acct: 3.91 (3.91) proj_loss: -0.5485 (-0.5485) time: 0.7506 data: 0.0003 [11-23 06:30:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.29 Lm: 6.767 (6.767) Lt: 6.009 (6.009) Accm: 2.54 (2.54) Acct: 4.15 (4.15) proj_loss: -0.5154 (-0.5154) time: 0.7506 data: 0.0003 [11-23 06:30:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 417/1669] eta: 0:15:42 tlr: 0.00023 tnm: 0.29 Lm: 6.849 (6.849) Lt: 6.156 (6.156) Accm: 2.32 (2.32) Acct: 3.81 (3.81) proj_loss: -0.5460 (-0.5460) time: 0.7506 data: 0.0003 [11-23 06:35:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.846 (6.824) Lt: 6.125 (6.137) Accm: 2.49 (2.42) Acct: 3.99 (3.99) proj_loss: -0.5418 (-0.5443) time: 0.7508 data: 0.0002 [11-23 06:35:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.691 (6.716) Lt: 5.953 (5.967) Accm: 2.91 (2.94) Acct: 4.82 (4.81) proj_loss: -0.5582 (-0.5519) time: 0.7508 data: 0.0003 [11-23 06:35:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.743 (6.743) Lt: 6.028 (6.018) Accm: 2.49 (2.59) Acct: 4.03 (3.99) proj_loss: -0.5483 (-0.5436) time: 0.7508 data: 0.0002 [11-23 06:35:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.723 (6.729) Lt: 5.984 (5.992) Accm: 2.62 (2.67) Acct: 4.10 (4.20) proj_loss: -0.5339 (-0.5412) time: 0.7508 data: 0.0003 [11-23 06:35:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.818 (6.833) Lt: 6.043 (6.105) Accm: 2.45 (2.40) Acct: 4.03 (3.83) proj_loss: -0.5252 (-0.5327) time: 0.7508 data: 0.0003 [11-23 06:35:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.803 (6.772) Lt: 6.022 (6.008) Accm: 2.58 (2.63) Acct: 4.10 (4.03) proj_loss: -0.5532 (-0.5422) time: 0.7508 data: 0.0003 [11-23 06:35:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.684 (6.724) Lt: 5.966 (5.984) Accm: 2.67 (2.67) Acct: 4.13 (4.22) proj_loss: -0.5392 (-0.5422) time: 0.7508 data: 0.0003 [11-23 06:35:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.764 (6.751) Lt: 6.015 (6.031) Accm: 2.87 (2.66) Acct: 4.51 (4.21) proj_loss: -0.5336 (-0.5357) time: 0.7508 data: 0.0003 [11-23 06:41:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1251/1669] eta: 0:05:20 tlr: 0.00023 tnm: 0.33 Lm: 6.708 (6.724) Lt: 5.974 (5.994) Accm: 2.91 (2.76) Acct: 4.61 (4.42) proj_loss: -0.5416 (-0.5454) time: 0.9098 data: 0.0003 [11-23 06:41:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1251/1669] eta: 0:05:20 tlr: 0.00023 tnm: 0.33 Lm: 6.849 (6.845) Lt: 6.156 (6.169) Accm: 2.51 (2.45) Acct: 3.98 (3.99) proj_loss: -0.5460 (-0.5496) time: 0.9098 data: 0.0003 [11-23 06:41:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1251/1669] eta: 0:05:20 tlr: 0.00023 tnm: 0.33 Lm: 6.731 (6.730) Lt: 6.022 (5.998) Accm: 2.78 (2.87) Acct: 4.53 (4.67) proj_loss: -0.5447 (-0.5460) time: 0.9098 data: 0.0003 [11-23 06:41:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1251/1669] eta: 0:05:20 tlr: 0.00023 tnm: 0.33 Lm: 6.748 (6.760) Lt: 6.045 (6.048) Accm: 2.51 (2.58) Acct: 4.05 (4.01) proj_loss: -0.5492 (-0.5469) time: 0.9098 data: 0.0003 [11-23 06:41:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1251/1669] eta: 0:05:20 tlr: 0.00023 tnm: 0.33 Lm: 6.772 (6.807) Lt: 6.009 (6.061) Accm: 2.54 (2.53) Acct: 4.15 (4.09) proj_loss: -0.5383 (-0.5374) time: 0.9098 data: 0.0003 [11-23 06:41:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1251/1669] eta: 0:05:20 tlr: 0.00023 tnm: 0.33 Lm: 6.754 (6.743) Lt: 6.024 (6.010) Accm: 2.74 (2.72) Acct: 4.41 (4.33) proj_loss: -0.5485 (-0.5494) time: 0.9098 data: 0.0003 [11-23 06:41:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1251/1669] eta: 0:05:20 tlr: 0.00023 tnm: 0.33 Lm: 6.709 (6.726) Lt: 5.981 (5.987) Accm: 2.71 (2.75) Acct: 4.34 (4.36) proj_loss: -0.5451 (-0.5486) time: 0.9098 data: 0.0003 [11-23 06:41:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1251/1669] eta: 0:05:20 tlr: 0.00023 tnm: 0.33 Lm: 6.754 (6.742) Lt: 5.988 (5.966) Accm: 2.73 (2.70) Acct: 4.22 (4.27) proj_loss: -0.5509 (-0.5438) time: 0.9098 data: 0.0003 [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.753 (6.812) Lt: 6.062 (6.114) Accm: 2.49 (2.48) Acct: 4.03 (3.89) proj_loss: -0.5483 (-0.5463) time: 0.7921 data: 0.0019 [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.771 (6.761) Lt: 6.091 (6.022) Accm: 2.64 (2.80) Acct: 4.24 (4.57) proj_loss: -0.5350 (-0.5438) time: 0.7921 data: 0.0017 [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.723 (6.715) Lt: 5.984 (5.987) Accm: 2.86 (2.83) Acct: 4.72 (4.46) proj_loss: -0.5339 (-0.5428) time: 0.7921 data: 0.0017 [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.744 (6.743) Lt: 6.022 (5.984) Accm: 2.61 (2.68) Acct: 4.10 (4.17) proj_loss: -0.5532 (-0.5475) time: 0.7921 data: 0.0021 [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.684 (6.698) Lt: 5.966 (5.960) Accm: 2.75 (2.91) Acct: 4.55 (4.61) proj_loss: -0.5392 (-0.5445) time: 0.7921 data: 0.0017 [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.818 (6.834) Lt: 6.043 (6.110) Accm: 2.56 (2.54) Acct: 4.03 (4.01) proj_loss: -0.5431 (-0.5385) time: 0.7921 data: 0.0020 [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.653 (6.709) Lt: 5.932 (5.966) Accm: 2.87 (2.77) Acct: 4.65 (4.46) proj_loss: -0.5427 (-0.5449) time: 0.7921 data: 0.0015 [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 40/350] Total time: 0:22:18 (0.802 s / it) [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 40/350] Total time: 0:22:18 (0.802 s / it) [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 40/350] Total time: 0:22:18 (0.802 s / it) [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 40/350] Total time: 0:22:18 (0.802 s / it) [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 40/350] Total time: 0:22:18 (0.802 s / it) [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 40/350] Total time: 0:22:18 (0.802 s / it) [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 40/350] Total time: 0:22:18 (0.802 s / it) [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 40/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.32 Lm: 6.846 (6.791) Lt: 6.125 (6.099) Accm: 2.53 (2.62) Acct: 3.99 (4.33) proj_loss: -0.5502 (-0.5502) time: 0.7921 data: 0.0016 [11-23 06:47:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 40/350] Total time: 0:22:18 (0.802 s / it) [11-23 06:47:35] (/home/user/VAR/train.py , line 276)=> [ep40] (training ) Lm: 6.747 (6.759), Lt: 6.021 (6.035), Acc m&t: 2.75 4.34, Remain: 4 days, 12:30:57, Finish: 2024-11-27 03:18 [11-23 06:47:35] (/home/user/VAR/train.py , line 276)=> [ep40] (training ) Lm: 6.747 (6.759), Lt: 6.021 (6.035), Acc m&t: 2.75 4.34, Remain: 4 days, 12:31:32, Finish: 2024-11-27 03:19 [11-23 06:47:35] (/home/user/VAR/train.py , line 276)=> [ep40] (training ) Lm: 6.747 (6.759), Lt: 6.021 (6.035), Acc m&t: 2.75 4.34, Remain: 4 days, 12:31:45, Finish: 2024-11-27 03:19 [11-23 06:47:35] (/home/user/VAR/train.py , line 276)=> [ep40] (training ) Lm: 6.747 (6.759), Lt: 6.021 (6.035), Acc m&t: 2.75 4.34, Remain: 4 days, 12:35:07, Finish: 2024-11-27 03:22 [11-23 06:47:35] (/home/user/VAR/train.py , line 276)=> [ep40] (training ) Lm: 6.747 (6.759), Lt: 6.021 (6.035), Acc m&t: 2.75 4.34, Remain: 4 days, 12:30:44, Finish: 2024-11-27 03:18 [11-23 06:47:35] (/home/user/VAR/train.py , line 276)=> [ep40] (training ) Lm: 6.747 (6.759), Lt: 6.021 (6.035), Acc m&t: 2.75 4.34, Remain: 4 days, 12:31:43, Finish: 2024-11-27 03:19 [11-23 06:47:35] (/home/user/VAR/train.py , line 276)=> [ep40] (training ) Lm: 6.747 (6.759), Lt: 6.021 (6.035), Acc m&t: 2.75 4.34, Remain: 4 days, 12:31:29, Finish: 2024-11-27 03:19 [11-23 06:47:35] (/home/user/VAR/train.py , line 276)=> [ep40] (training ) Lm: 6.747 (6.759), Lt: 6.021 (6.035), Acc m&t: 2.75 4.34, Remain: 4 days, 12:31:30, Finish: 2024-11-27 03:19 [11-23 06:47:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 0/1669] eta: 0:21:36 tlr: 0.00023 tnm: 0.29 Lm: 6.823 (6.823) Lt: 6.121 (6.121) Accm: 2.42 (2.42) Acct: 3.75 (3.75) proj_loss: -0.5173 (-0.5173) time: 0.7767 data: 0.0004 [11-23 06:47:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 0/1669] eta: 0:20:49 tlr: 0.00023 tnm: 0.29 Lm: 6.784 (6.784) Lt: 5.996 (5.996) Accm: 2.56 (2.56) Acct: 4.30 (4.30) proj_loss: -0.5209 (-0.5209) time: 0.7489 data: 0.0004 [11-23 06:47:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 0/1669] eta: 0:20:47 tlr: 0.00023 tnm: 0.29 Lm: 6.875 (6.875) Lt: 6.106 (6.106) Accm: 2.52 (2.52) Acct: 4.13 (4.13) proj_loss: -0.5419 (-0.5419) time: 0.7475 data: 0.0004 [11-23 06:47:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 0/1669] eta: 0:20:50 tlr: 0.00023 tnm: 0.29 Lm: 6.790 (6.790) Lt: 6.019 (6.019) Accm: 2.65 (2.65) Acct: 4.17 (4.17) proj_loss: -0.5290 (-0.5290) time: 0.7494 data: 0.0003 [11-23 06:47:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 0/1669] eta: 0:20:50 tlr: 0.00023 tnm: 0.29 Lm: 6.798 (6.798) Lt: 6.125 (6.125) Accm: 2.75 (2.75) Acct: 4.17 (4.17) proj_loss: -0.5211 (-0.5211) time: 0.7491 data: 0.0003 [11-23 06:47:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 0/1669] eta: 0:20:50 tlr: 0.00023 tnm: 0.29 Lm: 6.888 (6.888) Lt: 6.196 (6.196) Accm: 2.26 (2.26) Acct: 3.51 (3.51) proj_loss: -0.5518 (-0.5518) time: 0.7493 data: 0.0004 [11-23 06:47:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 0/1669] eta: 0:20:51 tlr: 0.00023 tnm: 0.29 Lm: 6.660 (6.660) Lt: 5.905 (5.905) Accm: 3.26 (3.26) Acct: 5.10 (5.10) proj_loss: -0.5567 (-0.5567) time: 0.7496 data: 0.0003 [11-23 06:47:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 0/1669] eta: 0:20:51 tlr: 0.00023 tnm: 0.29 Lm: 6.635 (6.635) Lt: 5.870 (5.870) Accm: 3.10 (3.10) Acct: 4.65 (4.65) proj_loss: -0.5401 (-0.5401) time: 0.7497 data: 0.0004 [11-23 06:52:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 417/1669] eta: 0:15:46 tlr: 0.00023 tnm: 0.29 Lm: 6.721 (6.721) Lt: 5.963 (5.963) Accm: 2.96 (2.96) Acct: 4.53 (4.53) proj_loss: -0.5360 (-0.5360) time: 0.7521 data: 0.0003 [11-23 06:52:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 417/1669] eta: 0:15:46 tlr: 0.00023 tnm: 0.29 Lm: 6.752 (6.752) Lt: 6.030 (6.030) Accm: 2.60 (2.60) Acct: 4.10 (4.10) proj_loss: -0.5269 (-0.5269) time: 0.7521 data: 0.0003 [11-23 06:52:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 417/1669] eta: 0:15:46 tlr: 0.00023 tnm: 0.29 Lm: 6.904 (6.904) Lt: 6.177 (6.177) Accm: 2.27 (2.27) Acct: 3.67 (3.67) proj_loss: -0.5214 (-0.5214) time: 0.7521 data: 0.0002 [11-23 06:52:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 417/1669] eta: 0:15:46 tlr: 0.00023 tnm: 0.29 Lm: 6.598 (6.598) Lt: 5.893 (5.893) Accm: 2.98 (2.98) Acct: 4.84 (4.84) proj_loss: -0.5553 (-0.5553) time: 0.7521 data: 0.0003 [11-23 06:52:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 417/1669] eta: 0:15:46 tlr: 0.00023 tnm: 0.29 Lm: 6.787 (6.787) Lt: 6.037 (6.037) Accm: 2.76 (2.76) Acct: 4.63 (4.63) proj_loss: -0.5483 (-0.5483) time: 0.7521 data: 0.0003 [11-23 06:52:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 417/1669] eta: 0:15:46 tlr: 0.00023 tnm: 0.29 Lm: 6.781 (6.781) Lt: 6.044 (6.044) Accm: 2.41 (2.41) Acct: 3.55 (3.55) proj_loss: -0.5329 (-0.5329) time: 0.7521 data: 0.0003 [11-23 06:52:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 417/1669] eta: 0:15:46 tlr: 0.00023 tnm: 0.29 Lm: 6.650 (6.650) Lt: 5.910 (5.910) Accm: 3.13 (3.13) Acct: 4.84 (4.84) proj_loss: -0.5535 (-0.5535) time: 0.7521 data: 0.0003 [11-23 06:52:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 417/1669] eta: 0:15:46 tlr: 0.00023 tnm: 0.29 Lm: 6.724 (6.724) Lt: 5.942 (5.942) Accm: 2.78 (2.78) Acct: 4.68 (4.68) proj_loss: -0.5172 (-0.5172) time: 0.7521 data: 0.0003 [11-23 06:58:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.30 Lm: 6.784 (6.797) Lt: 5.996 (6.059) Accm: 2.56 (2.60) Acct: 4.30 (4.32) proj_loss: -0.5209 (-0.5323) time: 0.7552 data: 0.0002 [11-23 06:58:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.30 Lm: 6.790 (6.835) Lt: 6.070 (6.096) Accm: 2.53 (2.45) Acct: 4.13 (3.74) proj_loss: -0.5290 (-0.5305) time: 0.7552 data: 0.0003 [11-23 06:58:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.30 Lm: 6.823 (6.810) Lt: 6.121 (6.069) Accm: 2.42 (2.54) Acct: 3.75 (4.03) proj_loss: -0.5254 (-0.5415) time: 0.7552 data: 0.0002 [11-23 06:58:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.30 Lm: 6.769 (6.757) Lt: 6.117 (6.059) Accm: 2.45 (2.55) Acct: 4.03 (4.05) proj_loss: -0.5327 (-0.5358) time: 0.7552 data: 0.0003 [11-23 06:58:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.30 Lm: 6.699 (6.750) Lt: 5.968 (5.991) Accm: 2.64 (2.72) Acct: 4.20 (4.49) proj_loss: -0.5419 (-0.5459) time: 0.7552 data: 0.0003 [11-23 06:58:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.30 Lm: 6.696 (6.631) Lt: 5.927 (5.904) Accm: 2.64 (2.87) Acct: 4.20 (4.63) proj_loss: -0.5518 (-0.5483) time: 0.7552 data: 0.0003 [11-23 06:58:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.30 Lm: 6.639 (6.642) Lt: 5.905 (5.881) Accm: 2.99 (3.06) Acct: 4.72 (4.80) proj_loss: -0.5503 (-0.5462) time: 0.7552 data: 0.0003 [11-23 06:58:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [ 834/1669] eta: 0:10:29 tlr: 0.00023 tnm: 0.30 Lm: 6.806 (6.790) Lt: 6.055 (6.046) Accm: 2.83 (2.82) Acct: 4.41 (4.40) proj_loss: -0.5401 (-0.5430) time: 0.7552 data: 0.0003 [11-23 07:03:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.30 Lm: 6.792 (6.798) Lt: 6.084 (6.064) Accm: 2.53 (2.57) Acct: 4.05 (4.11) proj_loss: -0.5276 (-0.5385) time: 0.7530 data: 0.0002 [11-23 07:03:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.30 Lm: 6.818 (6.838) Lt: 6.105 (6.107) Accm: 2.59 (2.53) Acct: 4.15 (3.94) proj_loss: -0.5329 (-0.5409) time: 0.7530 data: 0.0003 [11-23 07:03:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.30 Lm: 6.734 (6.769) Lt: 5.942 (6.015) Accm: 2.78 (2.73) Acct: 4.68 (4.61) proj_loss: -0.5307 (-0.5343) time: 0.7530 data: 0.0002 [11-23 07:03:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.30 Lm: 6.786 (6.692) Lt: 6.062 (5.983) Accm: 2.59 (2.78) Acct: 4.10 (4.47) proj_loss: -0.5534 (-0.5500) time: 0.7530 data: 0.0003 [11-23 07:03:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.30 Lm: 6.737 (6.744) Lt: 6.075 (6.052) Accm: 2.60 (2.74) Acct: 4.10 (4.25) proj_loss: -0.5357 (-0.5365) time: 0.7530 data: 0.0003 [11-23 07:03:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.30 Lm: 6.687 (6.724) Lt: 5.934 (5.966) Accm: 2.77 (2.76) Acct: 4.30 (4.47) proj_loss: -0.5416 (-0.5396) time: 0.7530 data: 0.0003 [11-23 07:03:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.30 Lm: 6.867 (6.834) Lt: 6.135 (6.116) Accm: 2.68 (2.69) Acct: 4.27 (4.07) proj_loss: -0.5360 (-0.5391) time: 0.7530 data: 0.0003 [11-23 07:03:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1251/1669] eta: 0:05:15 tlr: 0.00023 tnm: 0.30 Lm: 6.634 (6.616) Lt: 5.910 (5.894) Accm: 3.10 (3.10) Acct: 4.77 (4.80) proj_loss: -0.5535 (-0.5526) time: 0.7530 data: 0.0003 [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.761 (6.758) Lt: 6.047 (6.017) Accm: 2.65 (2.71) Acct: 4.34 (4.38) proj_loss: -0.5298 (-0.5382) time: 0.7540 data: 0.0019 [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.846 (6.841) Lt: 6.140 (6.121) Accm: 2.65 (2.56) Acct: 4.13 (3.96) proj_loss: -0.5368 (-0.5424) time: 0.7540 data: 0.0018 [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.684 (6.749) Lt: 5.968 (6.005) Accm: 2.56 (2.67) Acct: 4.30 (4.44) proj_loss: -0.5405 (-0.5378) time: 0.7540 data: 0.0015 [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.639 (6.637) Lt: 5.905 (5.893) Accm: 3.22 (3.20) Acct: 4.82 (5.03) proj_loss: -0.5503 (-0.5465) time: 0.7540 data: 0.0015 [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.696 (6.665) Lt: 5.927 (5.950) Accm: 2.64 (2.86) Acct: 4.20 (4.57) proj_loss: -0.5518 (-0.5484) time: 0.7540 data: 0.0017 [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.699 (6.723) Lt: 5.914 (5.956) Accm: 2.88 (2.79) Acct: 4.24 (4.42) proj_loss: -0.5412 (-0.5394) time: 0.7540 data: 0.0019 [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.769 (6.767) Lt: 6.117 (6.080) Accm: 2.61 (2.71) Acct: 4.03 (4.13) proj_loss: -0.5327 (-0.5330) time: 0.7540 data: 0.0019 [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 41/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.806 (6.750) Lt: 6.055 (6.016) Accm: 2.83 (2.83) Acct: 4.41 (4.33) proj_loss: -0.5319 (-0.5330) time: 0.7540 data: 0.0020 [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 41/350] Total time: 0:20:58 (0.754 s / it) [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 41/350] Total time: 0:20:58 (0.754 s / it) [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 41/350] Total time: 0:20:58 (0.754 s / it) [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 41/350] Total time: 0:20:58 (0.754 s / it) [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 41/350] Total time: 0:20:58 (0.754 s / it) [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 41/350] Total time: 0:20:58 (0.754 s / it) [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 41/350] Total time: 0:20:58 (0.754 s / it) [11-23 07:08:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 41/350] Total time: 0:20:58 (0.754 s / it) [11-23 07:08:33] (/home/user/VAR/train.py , line 276)=> [ep41] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.023), Acc m&t: 2.76 4.37, Remain: 4 days, 12:23:23, Finish: 2024-11-27 03:31 [11-23 07:08:33] (/home/user/VAR/train.py , line 276)=> [ep41] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.023), Acc m&t: 2.76 4.37, Remain: 4 days, 12:25:18, Finish: 2024-11-27 03:33 [11-23 07:08:33] (/home/user/VAR/train.py , line 276)=> [ep41] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.023), Acc m&t: 2.76 4.37, Remain: 4 days, 12:25:13, Finish: 2024-11-27 03:33 [11-23 07:08:33] (/home/user/VAR/train.py , line 276)=> [ep41] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.023), Acc m&t: 2.76 4.37, Remain: 4 days, 12:24:46, Finish: 2024-11-27 03:33 [11-23 07:08:33] (/home/user/VAR/train.py , line 276)=> [ep41] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.023), Acc m&t: 2.76 4.37, Remain: 4 days, 12:22:54, Finish: 2024-11-27 03:31 [11-23 07:08:33] (/home/user/VAR/train.py , line 276)=> [ep41] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.023), Acc m&t: 2.76 4.37, Remain: 4 days, 12:25:08, Finish: 2024-11-27 03:33 [11-23 07:08:33] (/home/user/VAR/train.py , line 276)=> [ep41] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.023), Acc m&t: 2.76 4.37, Remain: 4 days, 12:23:47, Finish: 2024-11-27 03:32 [11-23 07:08:33] (/home/user/VAR/train.py , line 276)=> [ep41] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.023), Acc m&t: 2.76 4.37, Remain: 4 days, 12:26:21, Finish: 2024-11-27 03:34 [11-23 07:08:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 0/1669] eta: 0:20:36 tlr: 0.00023 tnm: 0.29 Lm: 6.968 (6.968) Lt: 6.252 (6.252) Accm: 2.17 (2.17) Acct: 3.65 (3.65) proj_loss: -0.5354 (-0.5354) time: 0.7411 data: 0.0003 [11-23 07:08:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 0/1669] eta: 0:20:39 tlr: 0.00023 tnm: 0.29 Lm: 6.590 (6.590) Lt: 5.810 (5.810) Accm: 3.00 (3.00) Acct: 4.48 (4.48) proj_loss: -0.5343 (-0.5343) time: 0.7426 data: 0.0004 [11-23 07:08:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 0/1669] eta: 0:20:40 tlr: 0.00023 tnm: 0.29 Lm: 6.848 (6.848) Lt: 6.071 (6.071) Accm: 2.46 (2.46) Acct: 4.03 (4.03) proj_loss: -0.5189 (-0.5189) time: 0.7430 data: 0.0003 [11-23 07:08:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 0/1669] eta: 0:20:39 tlr: 0.00023 tnm: 0.29 Lm: 6.781 (6.781) Lt: 6.142 (6.142) Accm: 2.24 (2.24) Acct: 3.17 (3.17) proj_loss: -0.5732 (-0.5732) time: 0.7424 data: 0.0003 [11-23 07:08:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 0/1669] eta: 0:20:40 tlr: 0.00023 tnm: 0.29 Lm: 6.796 (6.796) Lt: 6.092 (6.092) Accm: 2.59 (2.59) Acct: 4.13 (4.13) proj_loss: -0.5195 (-0.5195) time: 0.7434 data: 0.0003 [11-23 07:08:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 0/1669] eta: 0:20:40 tlr: 0.00023 tnm: 0.29 Lm: 6.496 (6.496) Lt: 5.765 (5.765) Accm: 3.03 (3.03) Acct: 4.72 (4.72) proj_loss: -0.5474 (-0.5474) time: 0.7431 data: 0.0004 [11-23 07:08:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 0/1669] eta: 0:20:41 tlr: 0.00023 tnm: 0.29 Lm: 6.710 (6.710) Lt: 6.004 (6.004) Accm: 2.74 (2.74) Acct: 4.30 (4.30) proj_loss: -0.5406 (-0.5406) time: 0.7439 data: 0.0004 [11-23 07:08:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 0/1669] eta: 0:20:43 tlr: 0.00023 tnm: 0.29 Lm: 6.395 (6.395) Lt: 5.641 (5.641) Accm: 3.54 (3.54) Acct: 5.37 (5.37) proj_loss: -0.5224 (-0.5224) time: 0.7449 data: 0.0003 [11-23 07:14:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 417/1669] eta: 0:16:40 tlr: 0.00023 tnm: 0.30 Lm: 6.954 (6.954) Lt: 6.257 (6.257) Accm: 2.14 (2.14) Acct: 3.29 (3.29) proj_loss: -0.5364 (-0.5364) time: 0.9674 data: 0.0002 [11-23 07:14:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 417/1669] eta: 0:16:40 tlr: 0.00023 tnm: 0.30 Lm: 6.626 (6.626) Lt: 5.891 (5.891) Accm: 2.98 (2.98) Acct: 4.58 (4.58) proj_loss: -0.5276 (-0.5276) time: 0.9674 data: 0.0003 [11-23 07:14:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 417/1669] eta: 0:16:40 tlr: 0.00023 tnm: 0.30 Lm: 6.770 (6.770) Lt: 6.009 (6.009) Accm: 2.53 (2.53) Acct: 4.15 (4.15) proj_loss: -0.5260 (-0.5260) time: 0.9674 data: 0.0003 [11-23 07:14:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 417/1669] eta: 0:16:40 tlr: 0.00023 tnm: 0.30 Lm: 6.806 (6.806) Lt: 6.067 (6.067) Accm: 2.66 (2.66) Acct: 4.58 (4.58) proj_loss: -0.5310 (-0.5310) time: 0.9673 data: 0.0003 [11-23 07:14:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 417/1669] eta: 0:16:40 tlr: 0.00023 tnm: 0.30 Lm: 6.672 (6.672) Lt: 5.967 (5.967) Accm: 2.72 (2.72) Acct: 4.20 (4.20) proj_loss: -0.5285 (-0.5285) time: 0.9674 data: 0.0003 [11-23 07:14:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 417/1669] eta: 0:16:40 tlr: 0.00023 tnm: 0.30 Lm: 6.778 (6.778) Lt: 6.078 (6.078) Accm: 2.62 (2.62) Acct: 4.03 (4.03) proj_loss: -0.5411 (-0.5411) time: 0.9674 data: 0.0003 [11-23 07:14:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 417/1669] eta: 0:16:40 tlr: 0.00023 tnm: 0.30 Lm: 6.732 (6.732) Lt: 6.052 (6.052) Accm: 2.61 (2.61) Acct: 3.98 (3.98) proj_loss: -0.5513 (-0.5513) time: 0.9674 data: 0.0003 [11-23 07:14:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 417/1669] eta: 0:16:40 tlr: 0.00023 tnm: 0.30 Lm: 6.743 (6.743) Lt: 5.994 (5.994) Accm: 2.77 (2.77) Acct: 4.25 (4.25) proj_loss: -0.5307 (-0.5307) time: 0.9674 data: 0.0003 [11-23 07:20:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 834/1669] eta: 0:11:55 tlr: 0.00023 tnm: 0.29 Lm: 6.776 (6.778) Lt: 6.004 (6.024) Accm: 2.74 (2.74) Acct: 4.30 (4.29) proj_loss: -0.5406 (-0.5416) time: 0.7498 data: 0.0003 [11-23 07:20:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 834/1669] eta: 0:11:55 tlr: 0.00023 tnm: 0.29 Lm: 6.816 (6.819) Lt: 6.092 (6.093) Accm: 2.59 (2.57) Acct: 4.13 (4.19) proj_loss: -0.5343 (-0.5321) time: 0.7498 data: 0.0002 [11-23 07:20:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 834/1669] eta: 0:11:55 tlr: 0.00023 tnm: 0.29 Lm: 6.590 (6.698) Lt: 5.810 (5.962) Accm: 3.00 (2.81) Acct: 4.48 (4.42) proj_loss: -0.5343 (-0.5342) time: 0.7498 data: 0.0002 [11-23 07:20:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 834/1669] eta: 0:11:55 tlr: 0.00023 tnm: 0.29 Lm: 6.848 (6.834) Lt: 6.071 (6.109) Accm: 2.46 (2.33) Acct: 4.03 (3.86) proj_loss: -0.5330 (-0.5324) time: 0.7498 data: 0.0003 [11-23 07:20:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 834/1669] eta: 0:11:55 tlr: 0.00023 tnm: 0.29 Lm: 6.683 (6.680) Lt: 5.961 (5.969) Accm: 2.97 (2.91) Acct: 4.79 (4.59) proj_loss: -0.5307 (-0.5444) time: 0.7498 data: 0.0003 [11-23 07:20:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 834/1669] eta: 0:11:55 tlr: 0.00023 tnm: 0.29 Lm: 6.767 (6.703) Lt: 5.969 (5.968) Accm: 2.49 (2.65) Acct: 4.27 (4.22) proj_loss: -0.5127 (-0.5233) time: 0.7499 data: 0.0003 [11-23 07:20:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 834/1669] eta: 0:11:55 tlr: 0.00023 tnm: 0.29 Lm: 6.714 (6.655) Lt: 5.998 (5.927) Accm: 2.75 (2.90) Acct: 4.17 (4.44) proj_loss: -0.5329 (-0.5319) time: 0.7498 data: 0.0003 [11-23 07:20:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [ 834/1669] eta: 0:11:55 tlr: 0.00023 tnm: 0.29 Lm: 6.940 (6.899) Lt: 6.252 (6.188) Accm: 2.17 (2.28) Acct: 3.65 (3.49) proj_loss: -0.5373 (-0.5376) time: 0.7499 data: 0.0002 [11-23 07:25:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1251/1669] eta: 0:05:43 tlr: 0.00023 tnm: 0.30 Lm: 6.891 (6.885) Lt: 6.171 (6.164) Accm: 2.36 (2.40) Acct: 3.77 (3.72) proj_loss: -0.5364 (-0.5343) time: 0.7524 data: 0.0002 [11-23 07:25:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1251/1669] eta: 0:05:43 tlr: 0.00023 tnm: 0.30 Lm: 6.806 (6.761) Lt: 6.067 (6.046) Accm: 2.66 (2.72) Acct: 4.49 (4.36) proj_loss: -0.5384 (-0.5419) time: 0.7523 data: 0.0003 [11-23 07:25:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1251/1669] eta: 0:05:43 tlr: 0.00023 tnm: 0.30 Lm: 6.698 (6.725) Lt: 5.965 (6.001) Accm: 2.94 (2.82) Acct: 4.51 (4.45) proj_loss: -0.5411 (-0.5403) time: 0.7524 data: 0.0002 [11-23 07:25:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1251/1669] eta: 0:05:43 tlr: 0.00023 tnm: 0.30 Lm: 6.802 (6.737) Lt: 6.028 (5.998) Accm: 2.46 (2.59) Acct: 4.15 (4.18) proj_loss: -0.5219 (-0.5252) time: 0.7524 data: 0.0003 [11-23 07:25:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1251/1669] eta: 0:05:43 tlr: 0.00023 tnm: 0.30 Lm: 6.674 (6.650) Lt: 5.965 (5.928) Accm: 2.86 (2.92) Acct: 4.44 (4.51) proj_loss: -0.5367 (-0.5357) time: 0.7523 data: 0.0003 [11-23 07:25:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1251/1669] eta: 0:05:43 tlr: 0.00023 tnm: 0.30 Lm: 6.709 (6.694) Lt: 6.004 (5.989) Accm: 2.86 (2.87) Acct: 4.65 (4.57) proj_loss: -0.5300 (-0.5391) time: 0.7524 data: 0.0003 [11-23 07:25:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1251/1669] eta: 0:05:43 tlr: 0.00023 tnm: 0.30 Lm: 6.743 (6.740) Lt: 5.994 (5.991) Accm: 2.77 (2.78) Acct: 4.34 (4.38) proj_loss: -0.5519 (-0.5511) time: 0.7524 data: 0.0003 [11-23 07:25:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1251/1669] eta: 0:05:43 tlr: 0.00023 tnm: 0.30 Lm: 6.770 (6.790) Lt: 6.009 (6.066) Accm: 2.53 (2.45) Acct: 4.05 (3.91) proj_loss: -0.5391 (-0.5369) time: 0.7524 data: 0.0003 [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.803 (6.793) Lt: 6.071 (6.070) Accm: 2.46 (2.40) Acct: 4.03 (3.85) proj_loss: -0.5452 (-0.5406) time: 0.7576 data: 0.0018 [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 42/350] Total time: 0:22:21 (0.804 s / it) [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.708 (6.721) Lt: 6.087 (6.018) Accm: 2.88 (2.84) Acct: 4.48 (4.46) proj_loss: -0.5478 (-0.5509) time: 0.7576 data: 0.0016 [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.796 (6.742) Lt: 6.041 (6.026) Accm: 2.72 (2.76) Acct: 4.27 (4.34) proj_loss: -0.5424 (-0.5460) time: 0.7576 data: 0.0017 [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.911 (6.890) Lt: 6.236 (6.178) Accm: 2.39 (2.40) Acct: 3.89 (3.79) proj_loss: -0.5354 (-0.5342) time: 0.7576 data: 0.0019 [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.683 (6.690) Lt: 6.030 (5.997) Accm: 2.84 (2.86) Acct: 4.75 (4.61) proj_loss: -0.5307 (-0.5463) time: 0.7576 data: 0.0017 [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.776 (6.750) Lt: 6.004 (6.006) Accm: 2.80 (2.81) Acct: 4.37 (4.41) proj_loss: -0.5406 (-0.5489) time: 0.7576 data: 0.0020 [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.767 (6.723) Lt: 5.969 (5.988) Accm: 2.49 (2.58) Acct: 4.03 (4.13) proj_loss: -0.5311 (-0.5344) time: 0.7576 data: 0.0018 [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 42/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.31 Lm: 6.714 (6.683) Lt: 5.998 (5.958) Accm: 2.75 (2.83) Acct: 4.17 (4.39) proj_loss: -0.5404 (-0.5376) time: 0.7576 data: 0.0018 [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 42/350] Total time: 0:22:21 (0.804 s / it) [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 42/350] Total time: 0:22:21 (0.804 s / it) [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 42/350] Total time: 0:22:21 (0.804 s / it) [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 42/350] Total time: 0:22:21 (0.804 s / it) [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 42/350] Total time: 0:22:21 (0.804 s / it) [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 42/350] Total time: 0:22:21 (0.804 s / it) [11-23 07:30:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 42/350] Total time: 0:22:21 (0.804 s / it) [11-23 07:30:55] (/home/user/VAR/train.py , line 276)=> [ep42] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.025), Acc m&t: 2.76 4.37, Remain: 4 days, 12:21:52, Finish: 2024-11-27 03:52 [11-23 07:30:55] (/home/user/VAR/train.py , line 276)=> [ep42] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.025), Acc m&t: 2.76 4.37, Remain: 4 days, 12:22:52, Finish: 2024-11-27 03:53 [11-23 07:30:55] (/home/user/VAR/train.py , line 276)=> [ep42] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.025), Acc m&t: 2.76 4.37, Remain: 4 days, 12:22:49, Finish: 2024-11-27 03:53 [11-23 07:30:55] (/home/user/VAR/train.py , line 276)=> [ep42] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.025), Acc m&t: 2.76 4.37, Remain: 4 days, 12:22:40, Finish: 2024-11-27 03:53 [11-23 07:30:55] (/home/user/VAR/train.py , line 276)=> [ep42] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.025), Acc m&t: 2.76 4.37, Remain: 4 days, 12:21:53, Finish: 2024-11-27 03:52 [11-23 07:30:55] (/home/user/VAR/train.py , line 276)=> [ep42] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.025), Acc m&t: 2.76 4.37, Remain: 4 days, 12:22:06, Finish: 2024-11-27 03:53 [11-23 07:30:55] (/home/user/VAR/train.py , line 276)=> [ep42] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.025), Acc m&t: 2.76 4.37, Remain: 4 days, 12:23:28, Finish: 2024-11-27 03:54 [11-23 07:30:55] (/home/user/VAR/train.py , line 276)=> [ep42] (training ) Lm: 6.746 (6.746), Lt: 6.021 (6.025), Acc m&t: 2.76 4.37, Remain: 4 days, 12:22:19, Finish: 2024-11-27 03:53 [11-23 07:30:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 0/1669] eta: 0:20:03 tlr: 0.00023 tnm: 0.28 Lm: 6.688 (6.688) Lt: 5.930 (5.930) Accm: 2.90 (2.90) Acct: 4.68 (4.68) proj_loss: -0.5493 (-0.5493) time: 0.7210 data: 0.0003 [11-23 07:30:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 0/1669] eta: 0:20:02 tlr: 0.00023 tnm: 0.28 Lm: 6.608 (6.608) Lt: 5.831 (5.831) Accm: 2.87 (2.87) Acct: 4.20 (4.20) proj_loss: -0.5422 (-0.5422) time: 0.7207 data: 0.0003 [11-23 07:30:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 0/1669] eta: 0:20:01 tlr: 0.00023 tnm: 0.28 Lm: 6.876 (6.876) Lt: 6.194 (6.194) Accm: 2.26 (2.26) Acct: 3.65 (3.65) proj_loss: -0.5310 (-0.5310) time: 0.7201 data: 0.0004 [11-23 07:30:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 0/1669] eta: 0:20:06 tlr: 0.00023 tnm: 0.28 Lm: 6.861 (6.861) Lt: 6.187 (6.187) Accm: 2.23 (2.23) Acct: 3.41 (3.41) proj_loss: -0.5417 (-0.5417) time: 0.7228 data: 0.0004 [11-23 07:30:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 0/1669] eta: 0:20:03 tlr: 0.00023 tnm: 0.28 Lm: 7.067 (7.067) Lt: 6.415 (6.415) Accm: 1.86 (1.86) Acct: 3.20 (3.20) proj_loss: -0.5721 (-0.5721) time: 0.7210 data: 0.0004 [11-23 07:30:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 0/1669] eta: 0:20:12 tlr: 0.00023 tnm: 0.28 Lm: 6.735 (6.735) Lt: 6.039 (6.039) Accm: 2.70 (2.70) Acct: 4.34 (4.34) proj_loss: -0.5825 (-0.5825) time: 0.7263 data: 0.0004 [11-23 07:30:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 0/1669] eta: 0:20:03 tlr: 0.00023 tnm: 0.28 Lm: 6.625 (6.625) Lt: 5.854 (5.854) Accm: 3.03 (3.03) Acct: 4.65 (4.65) proj_loss: -0.5492 (-0.5492) time: 0.7208 data: 0.0004 [11-23 07:30:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 0/1669] eta: 0:20:06 tlr: 0.00023 tnm: 0.28 Lm: 6.745 (6.745) Lt: 5.992 (5.992) Accm: 2.55 (2.55) Acct: 3.96 (3.96) proj_loss: -0.5531 (-0.5531) time: 0.7229 data: 0.0004 [11-23 07:36:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 417/1669] eta: 0:15:40 tlr: 0.00023 tnm: 0.29 Lm: 6.646 (6.646) Lt: 5.905 (5.905) Accm: 2.70 (2.70) Acct: 3.98 (3.98) proj_loss: -0.5557 (-0.5557) time: 0.7499 data: 0.0003 [11-23 07:36:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 417/1669] eta: 0:15:40 tlr: 0.00023 tnm: 0.29 Lm: 6.738 (6.738) Lt: 6.008 (6.008) Accm: 2.74 (2.74) Acct: 4.51 (4.51) proj_loss: -0.5410 (-0.5410) time: 0.7499 data: 0.0003 [11-23 07:36:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 417/1669] eta: 0:15:40 tlr: 0.00023 tnm: 0.29 Lm: 6.746 (6.746) Lt: 6.009 (6.009) Accm: 2.45 (2.45) Acct: 3.72 (3.72) proj_loss: -0.5521 (-0.5521) time: 0.7499 data: 0.0003 [11-23 07:36:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 417/1669] eta: 0:15:40 tlr: 0.00023 tnm: 0.29 Lm: 6.742 (6.742) Lt: 6.063 (6.063) Accm: 2.74 (2.74) Acct: 4.15 (4.15) proj_loss: -0.5499 (-0.5499) time: 0.7499 data: 0.0003 [11-23 07:36:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 417/1669] eta: 0:15:40 tlr: 0.00023 tnm: 0.29 Lm: 6.893 (6.893) Lt: 6.202 (6.202) Accm: 2.37 (2.37) Acct: 3.75 (3.75) proj_loss: -0.5659 (-0.5659) time: 0.7499 data: 0.0003 [11-23 07:36:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 417/1669] eta: 0:15:40 tlr: 0.00023 tnm: 0.29 Lm: 6.815 (6.815) Lt: 6.109 (6.109) Accm: 2.37 (2.37) Acct: 3.63 (3.63) proj_loss: -0.5428 (-0.5428) time: 0.7499 data: 0.0003 [11-23 07:36:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 417/1669] eta: 0:15:40 tlr: 0.00023 tnm: 0.29 Lm: 6.802 (6.802) Lt: 6.083 (6.083) Accm: 2.53 (2.53) Acct: 4.10 (4.10) proj_loss: -0.5728 (-0.5728) time: 0.7499 data: 0.0003 [11-23 07:36:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 417/1669] eta: 0:15:40 tlr: 0.00023 tnm: 0.29 Lm: 6.714 (6.714) Lt: 5.940 (5.940) Accm: 2.66 (2.66) Acct: 4.18 (4.18) proj_loss: -0.5436 (-0.5436) time: 0.7499 data: 0.0003 [11-23 07:41:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 834/1669] eta: 0:10:27 tlr: 0.00023 tnm: 0.27 Lm: 6.707 (6.712) Lt: 5.989 (5.956) Accm: 2.86 (2.72) Acct: 4.61 (4.33) proj_loss: -0.5492 (-0.5539) time: 0.7489 data: 0.0003 [11-23 07:41:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 834/1669] eta: 0:10:27 tlr: 0.00023 tnm: 0.27 Lm: 6.684 (6.673) Lt: 5.980 (5.939) Accm: 2.87 (2.77) Acct: 4.20 (4.13) proj_loss: -0.5444 (-0.5519) time: 0.7489 data: 0.0002 [11-23 07:41:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 834/1669] eta: 0:10:27 tlr: 0.00023 tnm: 0.27 Lm: 6.738 (6.841) Lt: 6.039 (6.124) Accm: 2.51 (2.42) Acct: 4.10 (3.87) proj_loss: -0.5492 (-0.5572) time: 0.7489 data: 0.0003 [11-23 07:41:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 834/1669] eta: 0:10:27 tlr: 0.00023 tnm: 0.27 Lm: 6.705 (6.769) Lt: 5.996 (6.054) Accm: 2.42 (2.50) Acct: 3.72 (3.97) proj_loss: -0.5721 (-0.5665) time: 0.7489 data: 0.0003 [11-23 07:41:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 834/1669] eta: 0:10:27 tlr: 0.00023 tnm: 0.27 Lm: 6.723 (6.733) Lt: 6.047 (6.021) Accm: 2.90 (2.81) Acct: 4.65 (4.56) proj_loss: -0.5428 (-0.5416) time: 0.7489 data: 0.0003 [11-23 07:41:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 834/1669] eta: 0:10:27 tlr: 0.00023 tnm: 0.27 Lm: 6.802 (6.762) Lt: 5.951 (6.026) Accm: 2.87 (2.78) Acct: 4.89 (4.43) proj_loss: -0.5417 (-0.5387) time: 0.7489 data: 0.0003 [11-23 07:41:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 834/1669] eta: 0:10:27 tlr: 0.00023 tnm: 0.27 Lm: 6.754 (6.757) Lt: 6.024 (6.036) Accm: 2.49 (2.60) Acct: 3.65 (4.10) proj_loss: -0.5310 (-0.5386) time: 0.7489 data: 0.0003 [11-23 07:41:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [ 834/1669] eta: 0:10:27 tlr: 0.00023 tnm: 0.27 Lm: 6.746 (6.787) Lt: 6.027 (6.077) Accm: 2.35 (2.36) Acct: 3.75 (3.73) proj_loss: -0.5511 (-0.5468) time: 0.7489 data: 0.0003 [11-23 07:47:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1251/1669] eta: 0:05:23 tlr: 0.00023 tnm: 0.29 Lm: 6.746 (6.743) Lt: 6.009 (6.030) Accm: 2.45 (2.49) Acct: 3.86 (3.93) proj_loss: -0.5437 (-0.5427) time: 1.1137 data: 0.0003 [11-23 07:47:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1251/1669] eta: 0:05:23 tlr: 0.00023 tnm: 0.29 Lm: 6.710 (6.724) Lt: 5.989 (5.997) Accm: 2.88 (2.82) Acct: 4.58 (4.55) proj_loss: -0.5458 (-0.5434) time: 1.1137 data: 0.0003 [11-23 07:47:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1251/1669] eta: 0:05:23 tlr: 0.00023 tnm: 0.29 Lm: 6.706 (6.703) Lt: 5.993 (5.987) Accm: 2.80 (2.76) Acct: 4.06 (4.08) proj_loss: -0.5466 (-0.5511) time: 1.1137 data: 0.0002 [11-23 07:47:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1251/1669] eta: 0:05:23 tlr: 0.00023 tnm: 0.29 Lm: 6.788 (6.795) Lt: 6.106 (6.094) Accm: 2.21 (2.37) Acct: 3.53 (3.81) proj_loss: -0.5630 (-0.5554) time: 1.1137 data: 0.0003 [11-23 07:47:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1251/1669] eta: 0:05:23 tlr: 0.00023 tnm: 0.29 Lm: 6.738 (6.740) Lt: 5.945 (5.997) Accm: 2.65 (2.70) Acct: 4.41 (4.30) proj_loss: -0.5345 (-0.5359) time: 1.1137 data: 0.0003 [11-23 07:47:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1251/1669] eta: 0:05:23 tlr: 0.00023 tnm: 0.29 Lm: 6.763 (6.761) Lt: 6.020 (6.031) Accm: 2.59 (2.62) Acct: 3.89 (4.11) proj_loss: -0.5428 (-0.5466) time: 1.1137 data: 0.0003 [11-23 07:47:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1251/1669] eta: 0:05:23 tlr: 0.00023 tnm: 0.29 Lm: 6.753 (6.823) Lt: 6.008 (6.087) Accm: 2.56 (2.47) Acct: 4.22 (3.99) proj_loss: -0.5445 (-0.5520) time: 1.1137 data: 0.0003 [11-23 07:47:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1251/1669] eta: 0:05:23 tlr: 0.00023 tnm: 0.29 Lm: 6.696 (6.705) Lt: 5.973 (5.956) Accm: 2.81 (2.74) Acct: 4.60 (4.39) proj_loss: -0.5517 (-0.5540) time: 1.1137 data: 0.0003 [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.698 (6.704) Lt: 5.957 (5.944) Accm: 2.86 (2.78) Acct: 4.61 (4.51) proj_loss: -0.5532 (-0.5538) time: 0.7548 data: 0.0016 [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 43/350] Total time: 0:22:33 (0.811 s / it) [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.723 (6.756) Lt: 6.047 (6.040) Accm: 2.86 (2.74) Acct: 4.51 (4.48) proj_loss: -0.5488 (-0.5447) time: 0.7548 data: 0.0015 [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.684 (6.667) Lt: 5.980 (5.938) Accm: 2.87 (2.91) Acct: 4.20 (4.39) proj_loss: -0.5487 (-0.5525) time: 0.7548 data: 0.0018 [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.705 (6.753) Lt: 5.996 (6.045) Accm: 2.42 (2.57) Acct: 3.72 (4.08) proj_loss: -0.5539 (-0.5540) time: 0.7548 data: 0.0015 [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.771 (6.746) Lt: 5.951 (6.029) Accm: 2.67 (2.69) Acct: 4.27 (4.30) proj_loss: -0.5417 (-0.5442) time: 0.7548 data: 0.0017 [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.738 (6.792) Lt: 5.977 (6.060) Accm: 2.62 (2.54) Acct: 4.34 (4.09) proj_loss: -0.5398 (-0.5469) time: 0.7548 data: 0.0017 [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.772 (6.772) Lt: 6.024 (6.058) Accm: 2.49 (2.57) Acct: 3.72 (4.03) proj_loss: -0.5545 (-0.5488) time: 0.7548 data: 0.0015 [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 43/350] [1668/1669] eta: 0:00:00 tlr: 0.00023 tnm: 0.28 Lm: 6.746 (6.765) Lt: 6.027 (6.045) Accm: 2.55 (2.51) Acct: 3.96 (4.02) proj_loss: -0.5511 (-0.5462) time: 0.7548 data: 0.0016 [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 43/350] Total time: 0:22:33 (0.811 s / it) [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 43/350] Total time: 0:22:33 (0.811 s / it) [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 43/350] Total time: 0:22:33 (0.811 s / it) [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 43/350] Total time: 0:22:33 (0.811 s / it) [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 43/350] Total time: 0:22:34 (0.811 s / it) [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 43/350] Total time: 0:22:33 (0.811 s / it) [11-23 07:53:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 43/350] Total time: 0:22:33 (0.811 s / it) [11-23 07:53:29] (/home/user/VAR/train.py , line 276)=> [ep43] (training ) Lm: 6.732 (6.732), Lt: 6.012 (6.012), Acc m&t: 2.76 4.37, Remain: 4 days, 11:43:33, Finish: 2024-11-27 03:37 [11-23 07:53:29] (/home/user/VAR/train.py , line 276)=> [ep43] (training ) Lm: 6.732 (6.732), Lt: 6.012 (6.012), Acc m&t: 2.76 4.37, Remain: 4 days, 11:43:28, Finish: 2024-11-27 03:36 [11-23 07:53:29] (/home/user/VAR/train.py , line 276)=> [ep43] (training ) Lm: 6.732 (6.732), Lt: 6.012 (6.012), Acc m&t: 2.76 4.37, Remain: 4 days, 11:43:32, Finish: 2024-11-27 03:37 [11-23 07:53:29] (/home/user/VAR/train.py , line 276)=> [ep43] (training ) Lm: 6.732 (6.732), Lt: 6.012 (6.012), Acc m&t: 2.76 4.37, Remain: 4 days, 11:44:32, Finish: 2024-11-27 03:38 [11-23 07:53:29] (/home/user/VAR/train.py , line 276)=> [ep43] (training ) Lm: 6.732 (6.732), Lt: 6.012 (6.012), Acc m&t: 2.76 4.37, Remain: 4 days, 11:43:54, Finish: 2024-11-27 03:37 [11-23 07:53:29] (/home/user/VAR/train.py , line 276)=> [ep43] (training ) Lm: 6.732 (6.732), Lt: 6.012 (6.012), Acc m&t: 2.76 4.37, Remain: 4 days, 11:44:10, Finish: 2024-11-27 03:37 [11-23 07:53:29] (/home/user/VAR/train.py , line 276)=> [ep43] (training ) Lm: 6.732 (6.732), Lt: 6.012 (6.012), Acc m&t: 2.76 4.37, Remain: 4 days, 11:43:23, Finish: 2024-11-27 03:36 [11-23 07:53:29] (/home/user/VAR/train.py , line 276)=> [ep43] (training ) Lm: 6.732 (6.732), Lt: 6.012 (6.012), Acc m&t: 2.76 4.37, Remain: 4 days, 11:44:05, Finish: 2024-11-27 03:37 [11-23 07:53:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 0/1669] eta: 0:20:37 tlr: 0.00023 tnm: 0.30 Lm: 6.522 (6.522) Lt: 5.749 (5.749) Accm: 3.45 (3.45) Acct: 5.34 (5.34) proj_loss: -0.5504 (-0.5504) time: 0.7417 data: 0.0003 [11-23 07:53:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 0/1669] eta: 0:20:38 tlr: 0.00023 tnm: 0.30 Lm: 6.413 (6.413) Lt: 5.742 (5.742) Accm: 3.70 (3.70) Acct: 5.72 (5.72) proj_loss: -0.5397 (-0.5397) time: 0.7418 data: 0.0004 [11-23 07:53:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 0/1669] eta: 0:20:37 tlr: 0.00023 tnm: 0.30 Lm: 6.645 (6.645) Lt: 5.906 (5.906) Accm: 2.67 (2.67) Acct: 4.17 (4.17) proj_loss: -0.5347 (-0.5347) time: 0.7413 data: 0.0003 [11-23 07:53:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 0/1669] eta: 0:20:37 tlr: 0.00023 tnm: 0.30 Lm: 6.691 (6.691) Lt: 5.942 (5.942) Accm: 2.70 (2.70) Acct: 4.55 (4.55) proj_loss: -0.5452 (-0.5452) time: 0.7413 data: 0.0004 [11-23 07:53:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 0/1669] eta: 0:20:38 tlr: 0.00023 tnm: 0.30 Lm: 6.681 (6.681) Lt: 5.936 (5.936) Accm: 2.83 (2.83) Acct: 4.72 (4.72) proj_loss: -0.5631 (-0.5631) time: 0.7418 data: 0.0004 [11-23 07:53:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 0/1669] eta: 0:20:38 tlr: 0.00023 tnm: 0.30 Lm: 6.769 (6.769) Lt: 5.979 (5.979) Accm: 2.71 (2.71) Acct: 4.55 (4.55) proj_loss: -0.5517 (-0.5517) time: 0.7422 data: 0.0003 [11-23 07:53:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 0/1669] eta: 0:20:39 tlr: 0.00023 tnm: 0.30 Lm: 6.544 (6.544) Lt: 5.821 (5.821) Accm: 3.18 (3.18) Acct: 4.72 (4.72) proj_loss: -0.5475 (-0.5475) time: 0.7428 data: 0.0004 [11-23 07:53:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 0/1669] eta: 0:20:55 tlr: 0.00023 tnm: 0.30 Lm: 6.895 (6.895) Lt: 6.247 (6.247) Accm: 2.32 (2.32) Acct: 3.37 (3.37) proj_loss: -0.5628 (-0.5628) time: 0.7524 data: 0.0004 [11-23 07:58:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.30 Lm: 6.602 (6.602) Lt: 5.887 (5.887) Accm: 3.02 (3.02) Acct: 4.63 (4.63) proj_loss: -0.5393 (-0.5393) time: 0.7532 data: 0.0002 [11-23 07:58:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.30 Lm: 6.522 (6.522) Lt: 5.795 (5.795) Accm: 3.20 (3.20) Acct: 4.92 (4.92) proj_loss: -0.5557 (-0.5557) time: 0.7532 data: 0.0003 [11-23 07:58:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.30 Lm: 6.620 (6.620) Lt: 5.912 (5.912) Accm: 2.99 (2.99) Acct: 4.73 (4.73) proj_loss: -0.5375 (-0.5375) time: 0.7532 data: 0.0004 [11-23 07:58:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.30 Lm: 6.771 (6.771) Lt: 6.068 (6.068) Accm: 2.44 (2.44) Acct: 3.77 (3.77) proj_loss: -0.5596 (-0.5596) time: 0.7532 data: 0.0003 [11-23 07:58:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.30 Lm: 6.639 (6.639) Lt: 5.924 (5.924) Accm: 3.01 (3.01) Acct: 4.63 (4.63) proj_loss: -0.5546 (-0.5546) time: 0.7532 data: 0.0003 [11-23 07:58:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.30 Lm: 6.581 (6.581) Lt: 5.862 (5.862) Accm: 3.39 (3.39) Acct: 5.23 (5.23) proj_loss: -0.5598 (-0.5598) time: 0.7532 data: 0.0003 [11-23 07:58:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.30 Lm: 6.632 (6.632) Lt: 5.866 (5.866) Accm: 2.68 (2.68) Acct: 4.18 (4.18) proj_loss: -0.5520 (-0.5520) time: 0.7532 data: 0.0003 [11-23 07:58:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 417/1669] eta: 0:15:41 tlr: 0.00023 tnm: 0.30 Lm: 6.693 (6.693) Lt: 5.950 (5.950) Accm: 2.94 (2.94) Acct: 4.79 (4.79) proj_loss: -0.5515 (-0.5515) time: 0.7532 data: 0.0003 [11-23 08:03:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.769 (6.740) Lt: 5.979 (5.991) Accm: 2.71 (2.73) Acct: 4.55 (4.42) proj_loss: -0.5513 (-0.5424) time: 0.7537 data: 0.0003 [11-23 08:03:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.681 (6.749) Lt: 5.936 (6.067) Accm: 2.83 (2.81) Acct: 4.55 (4.33) proj_loss: -0.5628 (-0.5574) time: 0.7537 data: 0.0003 [11-23 08:03:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.618 (6.623) Lt: 5.869 (5.867) Accm: 2.70 (2.77) Acct: 4.20 (4.35) proj_loss: -0.5391 (-0.5477) time: 0.7537 data: 0.0003 [11-23 08:03:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.806 (6.783) Lt: 6.060 (6.065) Accm: 2.39 (2.42) Acct: 3.75 (3.76) proj_loss: -0.5581 (-0.5591) time: 0.7537 data: 0.0003 [11-23 08:03:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.630 (6.597) Lt: 5.823 (5.849) Accm: 3.41 (3.40) Acct: 5.34 (5.28) proj_loss: -0.5504 (-0.5491) time: 0.7537 data: 0.0003 [11-23 08:03:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.645 (6.616) Lt: 5.964 (5.913) Accm: 3.19 (3.08) Acct: 4.99 (4.75) proj_loss: -0.5397 (-0.5450) time: 0.7537 data: 0.0002 [11-23 08:03:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.544 (6.597) Lt: 5.821 (5.891) Accm: 3.18 (2.96) Acct: 4.72 (4.45) proj_loss: -0.5583 (-0.5566) time: 0.7537 data: 0.0003 [11-23 08:03:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [ 834/1669] eta: 0:10:28 tlr: 0.00023 tnm: 0.31 Lm: 6.691 (6.748) Lt: 5.942 (6.047) Accm: 2.70 (2.69) Acct: 4.55 (4.30) proj_loss: -0.5452 (-0.5428) time: 0.7537 data: 0.0003 [11-23 08:09:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.27 Lm: 6.732 (6.754) Lt: 5.977 (6.038) Accm: 2.64 (2.66) Acct: 4.32 (4.25) proj_loss: -0.5493 (-0.5484) time: 0.7539 data: 0.0003 [11-23 08:09:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.27 Lm: 6.647 (6.625) Lt: 5.975 (5.931) Accm: 3.18 (3.10) Acct: 4.91 (4.77) proj_loss: -0.5481 (-0.5484) time: 0.7539 data: 0.0002 [11-23 08:09:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.27 Lm: 6.618 (6.621) Lt: 5.864 (5.895) Accm: 2.86 (2.86) Acct: 4.46 (4.39) proj_loss: -0.5612 (-0.5600) time: 0.7539 data: 0.0003 [11-23 08:09:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.27 Lm: 6.639 (6.692) Lt: 5.924 (6.002) Accm: 3.01 (2.93) Acct: 4.63 (4.45) proj_loss: -0.5575 (-0.5561) time: 0.7539 data: 0.0003 [11-23 08:09:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.27 Lm: 6.635 (6.651) Lt: 5.899 (5.914) Accm: 3.37 (3.26) Acct: 5.23 (5.16) proj_loss: -0.5462 (-0.5474) time: 0.7539 data: 0.0003 [11-23 08:09:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.27 Lm: 6.632 (6.678) Lt: 5.887 (5.938) Accm: 2.68 (2.71) Acct: 4.18 (4.17) proj_loss: -0.5405 (-0.5463) time: 0.7539 data: 0.0003 [11-23 08:09:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.27 Lm: 6.732 (6.751) Lt: 5.998 (6.033) Accm: 2.48 (2.59) Acct: 3.96 (4.09) proj_loss: -0.5584 (-0.5590) time: 0.7539 data: 0.0003 [11-23 08:09:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.27 Lm: 6.801 (6.773) Lt: 6.025 (6.031) Accm: 2.51 (2.58) Acct: 4.12 (4.21) proj_loss: -0.5383 (-0.5381) time: 0.7539 data: 0.0003 [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.28 Lm: 6.640 (6.670) Lt: 5.975 (5.946) Accm: 3.34 (3.16) Acct: 5.13 (4.94) proj_loss: -0.5504 (-0.5489) time: 0.7511 data: 0.0017 [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.28 Lm: 6.650 (6.655) Lt: 5.986 (5.964) Accm: 3.18 (3.07) Acct: 4.82 (4.74) proj_loss: -0.5449 (-0.5477) time: 0.7511 data: 0.0016 [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.28 Lm: 6.772 (6.762) Lt: 6.012 (6.041) Accm: 2.62 (2.65) Acct: 4.44 (4.29) proj_loss: -0.5452 (-0.5462) time: 0.7511 data: 0.0017 [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.28 Lm: 6.645 (6.694) Lt: 5.906 (5.979) Accm: 2.67 (2.66) Acct: 4.17 (4.07) proj_loss: -0.5419 (-0.5545) time: 0.7511 data: 0.0018 [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.28 Lm: 6.790 (6.777) Lt: 6.072 (6.052) Accm: 2.71 (2.63) Acct: 4.24 (4.21) proj_loss: -0.5513 (-0.5419) time: 0.7511 data: 0.0016 [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.28 Lm: 6.681 (6.724) Lt: 5.936 (6.051) Accm: 2.83 (2.87) Acct: 4.55 (4.30) proj_loss: -0.5628 (-0.5592) time: 0.7511 data: 0.0019 [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.28 Lm: 6.693 (6.646) Lt: 5.907 (5.913) Accm: 2.87 (2.86) Acct: 4.41 (4.39) proj_loss: -0.5583 (-0.5485) time: 0.7511 data: 0.0017 [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 44/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.28 Lm: 6.657 (6.721) Lt: 5.937 (6.006) Accm: 2.56 (2.72) Acct: 4.17 (4.21) proj_loss: -0.5581 (-0.5585) time: 0.7511 data: 0.0018 [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 44/350] Total time: 0:20:55 (0.752 s / it) [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 44/350] Total time: 0:20:55 (0.752 s / it) [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 44/350] Total time: 0:20:55 (0.752 s / it) [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 44/350] Total time: 0:20:55 (0.752 s / it) [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 44/350] Total time: 0:20:55 (0.752 s / it) [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 44/350] Total time: 0:20:55 (0.752 s / it) [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 44/350] Total time: 0:20:55 (0.752 s / it) [11-23 08:14:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 44/350] Total time: 0:20:55 (0.752 s / it) [11-23 08:14:24] (/home/user/VAR/train.py , line 276)=> [ep44] (training ) Lm: 6.729 (6.729), Lt: 6.006 (6.006), Acc m&t: 2.80 4.40, Remain: 4 days, 10:54:28, Finish: 2024-11-27 03:08 [11-23 08:14:24] (/home/user/VAR/train.py , line 276)=> [ep44] (training ) Lm: 6.729 (6.729), Lt: 6.006 (6.006), Acc m&t: 2.80 4.40, Remain: 4 days, 10:53:33, Finish: 2024-11-27 03:07 [11-23 08:14:24] (/home/user/VAR/train.py , line 276)=> [ep44] (training ) Lm: 6.729 (6.729), Lt: 6.006 (6.006), Acc m&t: 2.80 4.40, Remain: 4 days, 10:54:47, Finish: 2024-11-27 03:09 [11-23 08:14:24] (/home/user/VAR/train.py , line 276)=> [ep44] (training ) Lm: 6.729 (6.729), Lt: 6.006 (6.006), Acc m&t: 2.80 4.40, Remain: 4 days, 10:53:43, Finish: 2024-11-27 03:08 [11-23 08:14:24] (/home/user/VAR/train.py , line 276)=> [ep44] (training ) Lm: 6.729 (6.729), Lt: 6.006 (6.006), Acc m&t: 2.80 4.40, Remain: 4 days, 10:54:01, Finish: 2024-11-27 03:08 [11-23 08:14:24] (/home/user/VAR/train.py , line 276)=> [ep44] (training ) Lm: 6.729 (6.729), Lt: 6.006 (6.006), Acc m&t: 2.80 4.40, Remain: 4 days, 10:54:25, Finish: 2024-11-27 03:08 [11-23 08:14:24] (/home/user/VAR/train.py , line 276)=> [ep44] (training ) Lm: 6.729 (6.729), Lt: 6.006 (6.006), Acc m&t: 2.80 4.40, Remain: 4 days, 10:53:51, Finish: 2024-11-27 03:08 [11-23 08:14:24] (/home/user/VAR/train.py , line 276)=> [ep44] (training ) Lm: 6.729 (6.729), Lt: 6.006 (6.006), Acc m&t: 2.80 4.40, Remain: 4 days, 10:53:30, Finish: 2024-11-27 03:07 [11-23 08:14:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 0/1669] eta: 0:20:34 tlr: 0.00022 tnm: 0.28 Lm: 6.728 (6.728) Lt: 5.969 (5.969) Accm: 2.84 (2.84) Acct: 4.79 (4.79) proj_loss: -0.5401 (-0.5401) time: 0.7396 data: 0.0003 [11-23 08:14:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 0/1669] eta: 0:20:27 tlr: 0.00022 tnm: 0.28 Lm: 6.825 (6.825) Lt: 6.122 (6.122) Accm: 2.40 (2.40) Acct: 3.79 (3.79) proj_loss: -0.5513 (-0.5513) time: 0.7356 data: 0.0004 [11-23 08:14:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 0/1669] eta: 0:20:33 tlr: 0.00022 tnm: 0.28 Lm: 6.745 (6.745) Lt: 5.937 (5.937) Accm: 2.75 (2.75) Acct: 4.58 (4.58) proj_loss: -0.5520 (-0.5520) time: 0.7389 data: 0.0003 [11-23 08:14:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 0/1669] eta: 0:20:32 tlr: 0.00022 tnm: 0.28 Lm: 6.626 (6.626) Lt: 5.876 (5.876) Accm: 2.86 (2.86) Acct: 3.86 (3.86) proj_loss: -0.5317 (-0.5317) time: 0.7386 data: 0.0004 [11-23 08:14:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 0/1669] eta: 0:20:33 tlr: 0.00022 tnm: 0.28 Lm: 6.673 (6.673) Lt: 5.971 (5.971) Accm: 2.55 (2.55) Acct: 3.96 (3.96) proj_loss: -0.5586 (-0.5586) time: 0.7391 data: 0.0004 [11-23 08:14:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 0/1669] eta: 0:20:35 tlr: 0.00022 tnm: 0.28 Lm: 6.379 (6.379) Lt: 5.629 (5.629) Accm: 4.02 (4.02) Acct: 6.30 (6.30) proj_loss: -0.5489 (-0.5489) time: 0.7404 data: 0.0004 [11-23 08:14:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 0/1669] eta: 0:20:33 tlr: 0.00022 tnm: 0.28 Lm: 6.524 (6.524) Lt: 5.811 (5.811) Accm: 3.44 (3.44) Acct: 5.41 (5.41) proj_loss: -0.5338 (-0.5338) time: 0.7392 data: 0.0003 [11-23 08:14:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 0/1669] eta: 0:20:32 tlr: 0.00022 tnm: 0.28 Lm: 6.874 (6.874) Lt: 6.129 (6.129) Accm: 2.49 (2.49) Acct: 4.13 (4.13) proj_loss: -0.5446 (-0.5446) time: 0.7385 data: 0.0004 [11-23 08:20:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 417/1669] eta: 0:17:19 tlr: 0.00022 tnm: 0.29 Lm: 6.834 (6.834) Lt: 6.082 (6.082) Accm: 2.67 (2.67) Acct: 4.30 (4.30) proj_loss: -0.5658 (-0.5658) time: 0.9802 data: 0.0003 [11-23 08:20:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 417/1669] eta: 0:17:19 tlr: 0.00022 tnm: 0.29 Lm: 6.761 (6.761) Lt: 6.018 (6.018) Accm: 2.61 (2.61) Acct: 4.24 (4.24) proj_loss: -0.5348 (-0.5348) time: 0.9802 data: 0.0003 [11-23 08:20:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 417/1669] eta: 0:17:19 tlr: 0.00022 tnm: 0.29 Lm: 6.704 (6.704) Lt: 5.926 (5.926) Accm: 2.88 (2.88) Acct: 4.68 (4.68) proj_loss: -0.5395 (-0.5395) time: 0.9802 data: 0.0003 [11-23 08:20:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 417/1669] eta: 0:17:19 tlr: 0.00022 tnm: 0.29 Lm: 6.780 (6.780) Lt: 6.000 (6.000) Accm: 2.57 (2.57) Acct: 4.22 (4.22) proj_loss: -0.5457 (-0.5457) time: 0.9802 data: 0.0003 [11-23 08:20:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 417/1669] eta: 0:17:19 tlr: 0.00022 tnm: 0.29 Lm: 6.759 (6.759) Lt: 6.116 (6.116) Accm: 2.55 (2.55) Acct: 3.82 (3.82) proj_loss: -0.5468 (-0.5468) time: 0.9802 data: 0.0003 [11-23 08:20:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 417/1669] eta: 0:17:19 tlr: 0.00022 tnm: 0.29 Lm: 6.490 (6.490) Lt: 5.748 (5.748) Accm: 3.53 (3.53) Acct: 5.54 (5.54) proj_loss: -0.5478 (-0.5478) time: 0.9802 data: 0.0003 [11-23 08:20:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 417/1669] eta: 0:17:19 tlr: 0.00022 tnm: 0.29 Lm: 6.568 (6.568) Lt: 5.877 (5.877) Accm: 3.18 (3.18) Acct: 4.79 (4.79) proj_loss: -0.5315 (-0.5315) time: 0.9802 data: 0.0003 [11-23 08:20:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 417/1669] eta: 0:17:19 tlr: 0.00022 tnm: 0.29 Lm: 6.601 (6.601) Lt: 5.877 (5.877) Accm: 3.15 (3.15) Acct: 4.60 (4.60) proj_loss: -0.5410 (-0.5410) time: 0.9802 data: 0.0003 [11-23 08:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 834/1669] eta: 0:11:59 tlr: 0.00022 tnm: 0.29 Lm: 6.626 (6.653) Lt: 5.877 (5.901) Accm: 2.86 (3.02) Acct: 4.72 (4.64) proj_loss: -0.5317 (-0.5288) time: 0.7531 data: 0.0003 [11-23 08:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 834/1669] eta: 0:11:59 tlr: 0.00022 tnm: 0.29 Lm: 6.673 (6.705) Lt: 5.971 (6.006) Accm: 2.55 (2.73) Acct: 3.96 (4.14) proj_loss: -0.5350 (-0.5424) time: 0.7531 data: 0.0003 [11-23 08:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 834/1669] eta: 0:11:59 tlr: 0.00022 tnm: 0.29 Lm: 6.815 (6.803) Lt: 6.063 (6.043) Accm: 2.59 (2.58) Acct: 4.58 (4.35) proj_loss: -0.5520 (-0.5550) time: 0.7531 data: 0.0003 [11-23 08:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 834/1669] eta: 0:11:59 tlr: 0.00022 tnm: 0.29 Lm: 6.746 (6.718) Lt: 6.008 (5.953) Accm: 2.49 (2.75) Acct: 3.96 (4.44) proj_loss: -0.5356 (-0.5382) time: 0.7531 data: 0.0002 [11-23 08:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 834/1669] eta: 0:11:59 tlr: 0.00022 tnm: 0.29 Lm: 6.601 (6.541) Lt: 5.863 (5.787) Accm: 3.03 (3.33) Acct: 4.79 (5.27) proj_loss: -0.5489 (-0.5569) time: 0.7531 data: 0.0003 [11-23 08:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 834/1669] eta: 0:11:59 tlr: 0.00022 tnm: 0.29 Lm: 6.874 (6.859) Lt: 6.129 (6.133) Accm: 2.49 (2.56) Acct: 4.13 (4.20) proj_loss: -0.5536 (-0.5618) time: 0.7531 data: 0.0003 [11-23 08:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 834/1669] eta: 0:11:59 tlr: 0.00022 tnm: 0.29 Lm: 6.729 (6.750) Lt: 6.011 (6.015) Accm: 2.49 (2.57) Acct: 4.41 (4.29) proj_loss: -0.5401 (-0.5446) time: 0.7531 data: 0.0003 [11-23 08:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [ 834/1669] eta: 0:11:59 tlr: 0.00022 tnm: 0.29 Lm: 6.612 (6.627) Lt: 5.943 (5.932) Accm: 2.93 (2.95) Acct: 4.17 (4.52) proj_loss: -0.5338 (-0.5373) time: 0.7531 data: 0.0003 [11-23 08:31:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.27 Lm: 6.762 (6.795) Lt: 6.039 (6.085) Accm: 2.43 (2.51) Acct: 4.05 (4.10) proj_loss: -0.5521 (-0.5537) time: 0.7529 data: 0.0003 [11-23 08:31:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.27 Lm: 6.699 (6.701) Lt: 5.974 (5.950) Accm: 2.84 (2.86) Acct: 4.32 (4.50) proj_loss: -0.5434 (-0.5446) time: 0.7529 data: 0.0003 [11-23 08:31:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.27 Lm: 6.678 (6.717) Lt: 5.993 (6.030) Accm: 2.71 (2.70) Acct: 4.08 (4.14) proj_loss: -0.5373 (-0.5382) time: 0.7529 data: 0.0003 [11-23 08:31:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.27 Lm: 6.622 (6.588) Lt: 5.865 (5.833) Accm: 2.98 (3.22) Acct: 4.75 (5.11) proj_loss: -0.5478 (-0.5513) time: 0.7529 data: 0.0003 [11-23 08:31:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.27 Lm: 6.623 (6.644) Lt: 5.884 (5.898) Accm: 3.12 (3.11) Acct: 5.03 (4.88) proj_loss: -0.5410 (-0.5367) time: 0.7529 data: 0.0003 [11-23 08:31:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.27 Lm: 6.832 (6.819) Lt: 6.096 (6.068) Accm: 2.49 (2.52) Acct: 4.22 (4.20) proj_loss: -0.5487 (-0.5526) time: 0.7529 data: 0.0003 [11-23 08:31:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.27 Lm: 6.834 (6.787) Lt: 6.082 (6.024) Accm: 2.67 (2.76) Acct: 4.30 (4.57) proj_loss: -0.5491 (-0.5552) time: 0.7529 data: 0.0003 [11-23 08:31:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.27 Lm: 6.702 (6.711) Lt: 5.968 (5.996) Accm: 2.67 (2.75) Acct: 4.20 (4.22) proj_loss: -0.5468 (-0.5499) time: 0.7529 data: 0.0003 [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.673 (6.670) Lt: 5.966 (5.926) Accm: 2.80 (2.86) Acct: 4.44 (4.54) proj_loss: -0.5350 (-0.5436) time: 0.7522 data: 0.0017 [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 45/350] Total time: 0:22:27 (0.807 s / it) [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.715 (6.704) Lt: 6.008 (5.969) Accm: 2.49 (2.78) Acct: 4.41 (4.48) proj_loss: -0.5472 (-0.5451) time: 0.7521 data: 0.0018 [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.601 (6.589) Lt: 5.863 (5.832) Accm: 3.03 (3.25) Acct: 4.79 (5.24) proj_loss: -0.5489 (-0.5537) time: 0.7521 data: 0.0017 [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.794 (6.798) Lt: 6.067 (6.084) Accm: 2.49 (2.51) Acct: 3.96 (4.07) proj_loss: -0.5515 (-0.5533) time: 0.7522 data: 0.0018 [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.626 (6.646) Lt: 5.877 (5.893) Accm: 2.94 (3.08) Acct: 4.72 (4.85) proj_loss: -0.5502 (-0.5422) time: 0.7521 data: 0.0020 [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.815 (6.783) Lt: 6.063 (6.036) Accm: 2.59 (2.60) Acct: 4.51 (4.26) proj_loss: -0.5454 (-0.5511) time: 0.7521 data: 0.0019 [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.794 (6.763) Lt: 6.034 (6.004) Accm: 2.84 (2.80) Acct: 4.48 (4.59) proj_loss: -0.5536 (-0.5583) time: 0.7521 data: 0.0016 [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 45/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.710 (6.716) Lt: 5.963 (6.017) Accm: 2.93 (2.76) Acct: 4.17 (4.35) proj_loss: -0.5338 (-0.5355) time: 0.7521 data: 0.0018 [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 45/350] Total time: 0:22:27 (0.807 s / it) [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 45/350] Total time: 0:22:27 (0.807 s / it) [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 45/350] Total time: 0:22:27 (0.807 s / it) [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 45/350] Total time: 0:22:27 (0.807 s / it) [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 45/350] Total time: 0:22:27 (0.807 s / it) [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 45/350] Total time: 0:22:27 (0.807 s / it) [11-23 08:36:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 45/350] Total time: 0:22:27 (0.807 s / it) [11-23 08:36:52] (/home/user/VAR/train.py , line 276)=> [ep45] (training ) Lm: 6.729 (6.730), Lt: 6.002 (6.002), Acc m&t: 2.80 4.40, Remain: 4 days, 10:36:35, Finish: 2024-11-27 03:13 [11-23 08:36:52] (/home/user/VAR/train.py , line 276)=> [ep45] (training ) Lm: 6.729 (6.730), Lt: 6.002 (6.002), Acc m&t: 2.80 4.40, Remain: 4 days, 10:34:23, Finish: 2024-11-27 03:11 [11-23 08:36:52] (/home/user/VAR/train.py , line 276)=> [ep45] (training ) Lm: 6.729 (6.730), Lt: 6.002 (6.002), Acc m&t: 2.80 4.40, Remain: 4 days, 10:34:52, Finish: 2024-11-27 03:11 [11-23 08:36:52] (/home/user/VAR/train.py , line 276)=> [ep45] (training ) Lm: 6.729 (6.730), Lt: 6.002 (6.002), Acc m&t: 2.80 4.40, Remain: 4 days, 10:34:46, Finish: 2024-11-27 03:11 [11-23 08:36:52] (/home/user/VAR/train.py , line 276)=> [ep45] (training ) Lm: 6.729 (6.730), Lt: 6.002 (6.002), Acc m&t: 2.80 4.40, Remain: 4 days, 10:34:03, Finish: 2024-11-27 03:10 [11-23 08:36:52] (/home/user/VAR/train.py , line 276)=> [ep45] (training ) Lm: 6.729 (6.730), Lt: 6.002 (6.002), Acc m&t: 2.80 4.40, Remain: 4 days, 10:34:57, Finish: 2024-11-27 03:11 [11-23 08:36:52] (/home/user/VAR/train.py , line 276)=> [ep45] (training ) Lm: 6.729 (6.730), Lt: 6.002 (6.002), Acc m&t: 2.80 4.40, Remain: 4 days, 10:36:48, Finish: 2024-11-27 03:13 [11-23 08:36:52] (/home/user/VAR/train.py , line 276)=> [ep45] (training ) Lm: 6.729 (6.730), Lt: 6.002 (6.002), Acc m&t: 2.80 4.40, Remain: 4 days, 10:37:59, Finish: 2024-11-27 03:14 [11-23 08:36:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 0/1669] eta: 0:20:07 tlr: 0.00022 tnm: 0.30 Lm: 6.791 (6.791) Lt: 6.043 (6.043) Accm: 2.90 (2.90) Acct: 4.72 (4.72) proj_loss: -0.5429 (-0.5429) time: 0.7235 data: 0.0005 [11-23 08:36:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 0/1669] eta: 0:20:08 tlr: 0.00022 tnm: 0.30 Lm: 6.571 (6.571) Lt: 5.858 (5.858) Accm: 2.94 (2.94) Acct: 4.61 (4.61) proj_loss: -0.5395 (-0.5395) time: 0.7239 data: 0.0004 [11-23 08:36:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 0/1669] eta: 0:20:08 tlr: 0.00022 tnm: 0.30 Lm: 6.698 (6.698) Lt: 5.935 (5.935) Accm: 3.06 (3.06) Acct: 4.82 (4.82) proj_loss: -0.5548 (-0.5548) time: 0.7244 data: 0.0004 [11-23 08:36:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 0/1669] eta: 0:20:08 tlr: 0.00022 tnm: 0.30 Lm: 6.697 (6.697) Lt: 5.966 (5.966) Accm: 2.96 (2.96) Acct: 4.44 (4.44) proj_loss: -0.5355 (-0.5355) time: 0.7241 data: 0.0004 [11-23 08:36:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 0/1669] eta: 0:20:09 tlr: 0.00022 tnm: 0.30 Lm: 6.976 (6.976) Lt: 6.280 (6.280) Accm: 2.40 (2.40) Acct: 3.79 (3.79) proj_loss: -0.5117 (-0.5117) time: 0.7245 data: 0.0004 [11-23 08:36:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 0/1669] eta: 0:20:09 tlr: 0.00022 tnm: 0.30 Lm: 6.596 (6.596) Lt: 5.900 (5.900) Accm: 3.58 (3.58) Acct: 5.34 (5.34) proj_loss: -0.5389 (-0.5389) time: 0.7248 data: 0.0004 [11-23 08:36:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 0/1669] eta: 0:20:10 tlr: 0.00022 tnm: 0.30 Lm: 6.812 (6.812) Lt: 6.077 (6.077) Accm: 2.62 (2.62) Acct: 4.34 (4.34) proj_loss: -0.5516 (-0.5516) time: 0.7252 data: 0.0004 [11-23 08:36:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 0/1669] eta: 0:20:10 tlr: 0.00022 tnm: 0.30 Lm: 6.715 (6.715) Lt: 6.021 (6.021) Accm: 2.86 (2.86) Acct: 4.41 (4.41) proj_loss: -0.5466 (-0.5466) time: 0.7254 data: 0.0004 [11-23 08:42:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 6.800 (6.800) Lt: 6.069 (6.069) Accm: 2.73 (2.73) Acct: 4.44 (4.44) proj_loss: -0.5420 (-0.5420) time: 0.7540 data: 0.0003 [11-23 08:42:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 6.640 (6.640) Lt: 5.921 (5.921) Accm: 2.68 (2.68) Acct: 4.29 (4.29) proj_loss: -0.5455 (-0.5455) time: 0.7540 data: 0.0002 [11-23 08:42:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 6.629 (6.629) Lt: 5.847 (5.847) Accm: 3.23 (3.23) Acct: 5.11 (5.11) proj_loss: -0.5466 (-0.5466) time: 0.7540 data: 0.0003 [11-23 08:42:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 6.813 (6.813) Lt: 6.067 (6.067) Accm: 2.90 (2.90) Acct: 4.77 (4.77) proj_loss: -0.5552 (-0.5552) time: 0.7540 data: 0.0003 [11-23 08:42:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 6.708 (6.708) Lt: 5.988 (5.988) Accm: 2.97 (2.97) Acct: 4.53 (4.53) proj_loss: -0.5520 (-0.5520) time: 0.7540 data: 0.0003 [11-23 08:42:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 7.007 (7.007) Lt: 6.323 (6.323) Accm: 2.24 (2.24) Acct: 3.48 (3.48) proj_loss: -0.5392 (-0.5392) time: 0.7540 data: 0.0003 [11-23 08:42:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 6.593 (6.593) Lt: 5.877 (5.877) Accm: 3.18 (3.18) Acct: 5.10 (5.10) proj_loss: -0.5476 (-0.5476) time: 0.7540 data: 0.0003 [11-23 08:42:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 6.836 (6.836) Lt: 6.166 (6.166) Accm: 2.55 (2.55) Acct: 4.12 (4.12) proj_loss: -0.5588 (-0.5588) time: 0.7540 data: 0.0003 [11-23 08:47:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.29 Lm: 6.629 (6.636) Lt: 5.896 (5.913) Accm: 2.94 (2.78) Acct: 4.48 (4.35) proj_loss: -0.5515 (-0.5536) time: 0.7519 data: 0.0003 [11-23 08:47:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.29 Lm: 6.791 (6.755) Lt: 6.043 (6.024) Accm: 2.90 (3.04) Acct: 4.82 (4.92) proj_loss: -0.5506 (-0.5536) time: 0.7519 data: 0.0003 [11-23 08:47:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.29 Lm: 6.746 (6.720) Lt: 5.967 (5.981) Accm: 3.18 (3.04) Acct: 4.96 (4.67) proj_loss: -0.5389 (-0.5416) time: 0.7519 data: 0.0003 [11-23 08:47:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.29 Lm: 6.812 (6.701) Lt: 6.077 (5.972) Accm: 2.62 (2.97) Acct: 4.34 (4.71) proj_loss: -0.5559 (-0.5578) time: 0.7519 data: 0.0003 [11-23 08:47:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.29 Lm: 6.976 (6.932) Lt: 6.280 (6.229) Accm: 2.40 (2.30) Acct: 3.58 (3.51) proj_loss: -0.5124 (-0.5303) time: 0.7519 data: 0.0003 [11-23 08:47:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.29 Lm: 6.698 (6.670) Lt: 5.935 (5.904) Accm: 3.06 (3.02) Acct: 4.82 (4.86) proj_loss: -0.5548 (-0.5546) time: 0.7519 data: 0.0003 [11-23 08:47:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.29 Lm: 6.624 (6.603) Lt: 5.924 (5.893) Accm: 3.03 (3.13) Acct: 4.44 (4.88) proj_loss: -0.5597 (-0.5567) time: 0.7519 data: 0.0003 [11-23 08:47:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.29 Lm: 6.715 (6.731) Lt: 6.021 (5.992) Accm: 2.86 (2.92) Acct: 4.48 (4.86) proj_loss: -0.5450 (-0.5430) time: 0.7519 data: 0.0003 [11-23 08:53:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1251/1669] eta: 0:05:26 tlr: 0.00022 tnm: 0.29 Lm: 6.600 (6.618) Lt: 5.877 (5.893) Accm: 2.96 (2.96) Acct: 4.55 (4.59) proj_loss: -0.5455 (-0.5497) time: 0.9494 data: 0.0003 [11-23 08:53:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1251/1669] eta: 0:05:26 tlr: 0.00022 tnm: 0.29 Lm: 6.714 (6.674) Lt: 5.990 (5.922) Accm: 3.11 (3.25) Acct: 5.03 (5.42) proj_loss: -0.5549 (-0.5550) time: 0.9494 data: 0.0003 [11-23 08:53:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1251/1669] eta: 0:05:26 tlr: 0.00022 tnm: 0.29 Lm: 6.701 (6.704) Lt: 5.945 (5.966) Accm: 2.98 (2.98) Acct: 4.60 (4.56) proj_loss: -0.5449 (-0.5439) time: 0.9494 data: 0.0003 [11-23 08:53:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1251/1669] eta: 0:05:26 tlr: 0.00022 tnm: 0.29 Lm: 6.880 (6.812) Lt: 6.160 (6.092) Accm: 2.42 (2.77) Acct: 3.68 (4.20) proj_loss: -0.5318 (-0.5355) time: 0.9494 data: 0.0003 [11-23 08:53:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1251/1669] eta: 0:05:26 tlr: 0.00022 tnm: 0.29 Lm: 6.660 (6.638) Lt: 5.945 (5.926) Accm: 2.99 (2.98) Acct: 4.44 (4.64) proj_loss: -0.5476 (-0.5512) time: 0.9494 data: 0.0003 [11-23 08:53:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1251/1669] eta: 0:05:26 tlr: 0.00022 tnm: 0.29 Lm: 6.658 (6.657) Lt: 5.915 (5.902) Accm: 3.19 (3.09) Acct: 5.06 (4.97) proj_loss: -0.5466 (-0.5460) time: 0.9494 data: 0.0003 [11-23 08:53:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1251/1669] eta: 0:05:26 tlr: 0.00022 tnm: 0.29 Lm: 6.710 (6.725) Lt: 6.011 (5.994) Accm: 2.86 (2.90) Acct: 4.44 (4.72) proj_loss: -0.5458 (-0.5441) time: 0.9494 data: 0.0003 [11-23 08:53:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1251/1669] eta: 0:05:26 tlr: 0.00022 tnm: 0.29 Lm: 6.727 (6.686) Lt: 5.919 (5.919) Accm: 2.84 (2.99) Acct: 4.77 (4.83) proj_loss: -0.5537 (-0.5465) time: 0.9494 data: 0.0003 [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.629 (6.635) Lt: 5.896 (5.909) Accm: 2.94 (2.95) Acct: 4.61 (4.62) proj_loss: -0.5395 (-0.5457) time: 0.7535 data: 0.0019 [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.791 (6.735) Lt: 6.043 (5.985) Accm: 2.90 (3.02) Acct: 4.82 (5.08) proj_loss: -0.5506 (-0.5472) time: 0.7535 data: 0.0020 [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.976 (6.857) Lt: 6.280 (6.152) Accm: 2.40 (2.63) Acct: 3.58 (3.97) proj_loss: -0.5512 (-0.5424) time: 0.7535 data: 0.0018 [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.669 (6.660) Lt: 5.935 (5.930) Accm: 3.06 (3.02) Acct: 4.82 (4.89) proj_loss: -0.5548 (-0.5537) time: 0.7535 data: 0.0015 [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.624 (6.624) Lt: 5.924 (5.909) Accm: 3.03 (3.08) Acct: 4.44 (4.79) proj_loss: -0.5355 (-0.5479) time: 0.7535 data: 0.0022 [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.746 (6.727) Lt: 5.967 (5.989) Accm: 2.84 (2.95) Acct: 4.41 (4.53) proj_loss: -0.5510 (-0.5458) time: 0.7535 data: 0.0017 [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.704 (6.712) Lt: 6.001 (5.984) Accm: 2.86 (2.91) Acct: 4.48 (4.67) proj_loss: -0.5466 (-0.5470) time: 0.7535 data: 0.0019 [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 46/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.745 (6.698) Lt: 5.964 (5.928) Accm: 2.97 (2.99) Acct: 4.58 (4.78) proj_loss: -0.5516 (-0.5450) time: 0.7536 data: 0.0018 [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 46/350] Total time: 0:22:29 (0.809 s / it) [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 46/350] Total time: 0:22:29 (0.809 s / it) [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 46/350] Total time: 0:22:29 (0.809 s / it) [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 46/350] Total time: 0:22:29 (0.809 s / it) [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 46/350] Total time: 0:22:29 (0.809 s / it) [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 46/350] Total time: 0:22:29 (0.809 s / it) [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 46/350] Total time: 0:22:29 (0.809 s / it) [11-23 08:59:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 46/350] Total time: 0:22:29 (0.809 s / it) [11-23 08:59:22] (/home/user/VAR/train.py , line 276)=> [ep46] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 10:25:48, Finish: 2024-11-27 03:25 [11-23 08:59:22] (/home/user/VAR/train.py , line 276)=> [ep46] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 10:23:15, Finish: 2024-11-27 03:22 [11-23 08:59:22] (/home/user/VAR/train.py , line 276)=> [ep46] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 10:23:38, Finish: 2024-11-27 03:22 [11-23 08:59:22] (/home/user/VAR/train.py , line 276)=> [ep46] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 10:24:17, Finish: 2024-11-27 03:23 [11-23 08:59:22] (/home/user/VAR/train.py , line 276)=> [ep46] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 10:23:11, Finish: 2024-11-27 03:22 [11-23 08:59:22] (/home/user/VAR/train.py , line 276)=> [ep46] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 10:24:33, Finish: 2024-11-27 03:23 [11-23 08:59:22] (/home/user/VAR/train.py , line 276)=> [ep46] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 10:22:51, Finish: 2024-11-27 03:22 [11-23 08:59:22] (/home/user/VAR/train.py , line 276)=> [ep46] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 10:24:16, Finish: 2024-11-27 03:23 [11-23 08:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 0/1669] eta: 0:20:11 tlr: 0.00022 tnm: 0.28 Lm: 6.638 (6.638) Lt: 5.826 (5.826) Accm: 3.22 (3.22) Acct: 5.17 (5.17) proj_loss: -0.5494 (-0.5494) time: 0.7262 data: 0.0003 [11-23 08:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 0/1669] eta: 0:20:11 tlr: 0.00022 tnm: 0.28 Lm: 6.688 (6.688) Lt: 6.010 (6.010) Accm: 2.86 (2.86) Acct: 4.13 (4.13) proj_loss: -0.5518 (-0.5518) time: 0.7257 data: 0.0003 [11-23 08:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 0/1669] eta: 0:20:12 tlr: 0.00022 tnm: 0.28 Lm: 6.787 (6.787) Lt: 5.973 (5.973) Accm: 2.97 (2.97) Acct: 4.75 (4.75) proj_loss: -0.5051 (-0.5051) time: 0.7265 data: 0.0003 [11-23 08:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 0/1669] eta: 0:20:12 tlr: 0.00022 tnm: 0.28 Lm: 6.932 (6.932) Lt: 6.277 (6.277) Accm: 2.23 (2.23) Acct: 3.55 (3.55) proj_loss: -0.5271 (-0.5271) time: 0.7266 data: 0.0003 [11-23 08:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 0/1669] eta: 0:20:12 tlr: 0.00022 tnm: 0.28 Lm: 6.755 (6.755) Lt: 5.959 (5.959) Accm: 2.75 (2.75) Acct: 4.92 (4.92) proj_loss: -0.5291 (-0.5291) time: 0.7266 data: 0.0004 [11-23 08:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 0/1669] eta: 0:20:16 tlr: 0.00022 tnm: 0.28 Lm: 6.559 (6.559) Lt: 5.776 (5.776) Accm: 3.12 (3.12) Acct: 5.13 (5.13) proj_loss: -0.5159 (-0.5159) time: 0.7291 data: 0.0005 [11-23 08:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 0/1669] eta: 0:20:14 tlr: 0.00022 tnm: 0.28 Lm: 6.632 (6.632) Lt: 5.928 (5.928) Accm: 3.26 (3.26) Acct: 4.79 (4.79) proj_loss: -0.5664 (-0.5664) time: 0.7276 data: 0.0004 [11-23 08:59:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 0/1669] eta: 0:20:14 tlr: 0.00022 tnm: 0.28 Lm: 6.963 (6.963) Lt: 6.274 (6.274) Accm: 2.14 (2.14) Acct: 3.58 (3.58) proj_loss: -0.5392 (-0.5392) time: 0.7279 data: 0.0004 [11-23 09:04:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.28 Lm: 6.631 (6.631) Lt: 5.931 (5.931) Accm: 2.88 (2.88) Acct: 4.32 (4.32) proj_loss: -0.5560 (-0.5560) time: 0.7516 data: 0.0003 [11-23 09:04:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.28 Lm: 6.697 (6.697) Lt: 5.930 (5.930) Accm: 3.10 (3.10) Acct: 4.98 (4.98) proj_loss: -0.5476 (-0.5476) time: 0.7516 data: 0.0002 [11-23 09:04:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.28 Lm: 6.660 (6.660) Lt: 5.947 (5.947) Accm: 2.91 (2.91) Acct: 4.51 (4.51) proj_loss: -0.5356 (-0.5356) time: 0.7516 data: 0.0003 [11-23 09:04:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.28 Lm: 6.820 (6.820) Lt: 6.039 (6.039) Accm: 2.64 (2.64) Acct: 4.29 (4.29) proj_loss: -0.5091 (-0.5091) time: 0.7516 data: 0.0002 [11-23 09:04:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.28 Lm: 6.857 (6.857) Lt: 6.138 (6.138) Accm: 2.48 (2.48) Acct: 4.13 (4.13) proj_loss: -0.5372 (-0.5372) time: 0.7516 data: 0.0003 [11-23 09:04:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.28 Lm: 6.815 (6.815) Lt: 6.064 (6.064) Accm: 2.45 (2.45) Acct: 3.99 (3.99) proj_loss: -0.5414 (-0.5414) time: 0.7516 data: 0.0003 [11-23 09:04:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.28 Lm: 6.820 (6.820) Lt: 6.126 (6.126) Accm: 2.45 (2.45) Acct: 3.81 (3.81) proj_loss: -0.5431 (-0.5431) time: 0.7516 data: 0.0003 [11-23 09:04:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.28 Lm: 6.721 (6.721) Lt: 5.959 (5.959) Accm: 2.93 (2.93) Acct: 4.55 (4.55) proj_loss: -0.5538 (-0.5538) time: 0.7516 data: 0.0003 [11-23 09:09:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.29 Lm: 6.705 (6.715) Lt: 5.985 (5.968) Accm: 2.86 (2.90) Acct: 4.61 (4.57) proj_loss: -0.5412 (-0.5486) time: 0.7518 data: 0.0003 [11-23 09:09:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.29 Lm: 6.638 (6.659) Lt: 5.826 (5.894) Accm: 3.22 (3.18) Acct: 5.17 (5.15) proj_loss: -0.5494 (-0.5508) time: 0.7518 data: 0.0003 [11-23 09:09:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.29 Lm: 6.574 (6.603) Lt: 5.852 (5.878) Accm: 2.91 (3.06) Acct: 4.51 (4.71) proj_loss: -0.5518 (-0.5527) time: 0.7518 data: 0.0003 [11-23 09:09:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.29 Lm: 6.886 (6.842) Lt: 6.102 (6.118) Accm: 2.23 (2.34) Acct: 3.58 (3.73) proj_loss: -0.5292 (-0.5385) time: 0.7518 data: 0.0003 [11-23 09:09:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.29 Lm: 6.850 (6.855) Lt: 6.134 (6.137) Accm: 2.21 (2.37) Acct: 3.55 (3.94) proj_loss: -0.5291 (-0.5338) time: 0.7518 data: 0.0003 [11-23 09:09:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.29 Lm: 6.844 (6.828) Lt: 6.104 (6.092) Accm: 2.35 (2.54) Acct: 3.82 (3.97) proj_loss: -0.5131 (-0.5246) time: 0.7518 data: 0.0002 [11-23 09:09:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.29 Lm: 6.631 (6.651) Lt: 5.853 (5.916) Accm: 3.12 (3.06) Acct: 5.13 (4.76) proj_loss: -0.5514 (-0.5409) time: 0.7518 data: 0.0003 [11-23 09:09:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.29 Lm: 6.666 (6.757) Lt: 5.871 (6.000) Accm: 2.77 (2.66) Acct: 4.41 (4.28) proj_loss: -0.5392 (-0.5385) time: 0.7518 data: 0.0003 [11-23 09:15:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.28 Lm: 6.611 (6.636) Lt: 5.850 (5.889) Accm: 3.15 (3.16) Acct: 4.98 (5.02) proj_loss: -0.5476 (-0.5474) time: 0.7520 data: 0.0002 [11-23 09:15:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.28 Lm: 6.848 (6.853) Lt: 6.152 (6.124) Accm: 2.32 (2.46) Acct: 3.72 (3.88) proj_loss: -0.5343 (-0.5366) time: 0.7520 data: 0.0002 [11-23 09:15:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.28 Lm: 6.803 (6.819) Lt: 6.067 (6.103) Accm: 2.48 (2.52) Acct: 4.24 (4.20) proj_loss: -0.5372 (-0.5437) time: 0.7520 data: 0.0003 [11-23 09:15:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.28 Lm: 6.725 (6.764) Lt: 5.911 (5.987) Accm: 2.86 (2.73) Acct: 4.51 (4.36) proj_loss: -0.5414 (-0.5415) time: 0.7521 data: 0.0003 [11-23 09:15:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.28 Lm: 6.797 (6.797) Lt: 6.038 (6.082) Accm: 2.45 (2.45) Acct: 3.82 (3.90) proj_loss: -0.5442 (-0.5453) time: 0.7521 data: 0.0003 [11-23 09:15:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.28 Lm: 6.696 (6.701) Lt: 5.986 (5.976) Accm: 2.91 (2.87) Acct: 4.51 (4.41) proj_loss: -0.5507 (-0.5432) time: 0.7521 data: 0.0003 [11-23 09:15:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.28 Lm: 6.757 (6.744) Lt: 5.988 (6.032) Accm: 2.79 (2.86) Acct: 4.48 (4.51) proj_loss: -0.5538 (-0.5535) time: 0.7520 data: 0.0003 [11-23 09:15:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.28 Lm: 6.631 (6.628) Lt: 5.901 (5.896) Accm: 2.89 (3.02) Acct: 4.65 (4.73) proj_loss: -0.5489 (-0.5487) time: 0.7520 data: 0.0003 [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.688 (6.675) Lt: 5.950 (5.946) Accm: 2.91 (3.01) Acct: 4.79 (4.75) proj_loss: -0.5518 (-0.5500) time: 0.7527 data: 0.0017 [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 47/350] Total time: 0:20:55 (0.752 s / it) [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.844 (6.831) Lt: 6.104 (6.100) Accm: 2.35 (2.50) Acct: 3.82 (3.98) proj_loss: -0.5525 (-0.5398) time: 0.7527 data: 0.0015 [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.583 (6.606) Lt: 5.826 (5.866) Accm: 3.22 (3.21) Acct: 5.06 (5.03) proj_loss: -0.5494 (-0.5496) time: 0.7527 data: 0.0015 [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.653 (6.691) Lt: 5.895 (5.960) Accm: 2.96 (2.88) Acct: 4.92 (4.51) proj_loss: -0.5500 (-0.5427) time: 0.7527 data: 0.0017 [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.886 (6.839) Lt: 6.102 (6.143) Accm: 2.23 (2.38) Acct: 3.58 (3.71) proj_loss: -0.5591 (-0.5499) time: 0.7527 data: 0.0020 [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.755 (6.793) Lt: 6.000 (6.071) Accm: 2.75 (2.66) Acct: 4.92 (4.40) proj_loss: -0.5453 (-0.5463) time: 0.7527 data: 0.0017 [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.666 (6.738) Lt: 5.898 (5.970) Accm: 2.86 (2.76) Acct: 4.41 (4.37) proj_loss: -0.5435 (-0.5526) time: 0.7527 data: 0.0016 [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 47/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.809 (6.759) Lt: 5.990 (6.053) Accm: 2.72 (2.79) Acct: 4.34 (4.36) proj_loss: -0.5664 (-0.5562) time: 0.7527 data: 0.0016 [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 47/350] Total time: 0:20:55 (0.752 s / it) [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 47/350] Total time: 0:20:55 (0.752 s / it) [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 47/350] Total time: 0:20:55 (0.752 s / it) [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 47/350] Total time: 0:20:55 (0.752 s / it) [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 47/350] Total time: 0:20:55 (0.752 s / it) [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 47/350] Total time: 0:20:55 (0.752 s / it) [11-23 09:20:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 47/350] Total time: 0:20:55 (0.752 s / it) [11-23 09:20:18] (/home/user/VAR/train.py , line 276)=> [ep47] (training ) Lm: 6.708 (6.722), Lt: 5.977 (5.992), Acc m&t: 2.86 4.51, Remain: 4 days, 10:02:29, Finish: 2024-11-27 03:22 [11-23 09:20:18] (/home/user/VAR/train.py , line 276)=> [ep47] (training ) Lm: 6.708 (6.722), Lt: 5.977 (5.992), Acc m&t: 2.86 4.51, Remain: 4 days, 10:03:41, Finish: 2024-11-27 03:23 [11-23 09:20:18] (/home/user/VAR/train.py , line 276)=> [ep47] (training ) Lm: 6.708 (6.722), Lt: 5.977 (5.992), Acc m&t: 2.86 4.51, Remain: 4 days, 10:04:23, Finish: 2024-11-27 03:24 [11-23 09:20:18] (/home/user/VAR/train.py , line 276)=> [ep47] (training ) Lm: 6.708 (6.722), Lt: 5.977 (5.992), Acc m&t: 2.86 4.51, Remain: 4 days, 10:02:45, Finish: 2024-11-27 03:23 [11-23 09:20:18] (/home/user/VAR/train.py , line 276)=> [ep47] (training ) Lm: 6.708 (6.722), Lt: 5.977 (5.992), Acc m&t: 2.86 4.51, Remain: 4 days, 10:03:20, Finish: 2024-11-27 03:23 [11-23 09:20:18] (/home/user/VAR/train.py , line 276)=> [ep47] (training ) Lm: 6.708 (6.722), Lt: 5.977 (5.992), Acc m&t: 2.86 4.51, Remain: 4 days, 10:02:39, Finish: 2024-11-27 03:22 [11-23 09:20:18] (/home/user/VAR/train.py , line 276)=> [ep47] (training ) Lm: 6.708 (6.722), Lt: 5.977 (5.992), Acc m&t: 2.86 4.51, Remain: 4 days, 10:02:18, Finish: 2024-11-27 03:22 [11-23 09:20:18] (/home/user/VAR/train.py , line 276)=> [ep47] (training ) Lm: 6.708 (6.722), Lt: 5.977 (5.992), Acc m&t: 2.86 4.51, Remain: 4 days, 10:03:20, Finish: 2024-11-27 03:23 [11-23 09:20:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 0/1669] eta: 0:21:14 tlr: 0.00022 tnm: 0.30 Lm: 6.497 (6.497) Lt: 5.741 (5.741) Accm: 3.19 (3.19) Acct: 5.17 (5.17) proj_loss: -0.5541 (-0.5541) time: 0.7635 data: 0.0003 [11-23 09:20:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 0/1669] eta: 0:21:14 tlr: 0.00022 tnm: 0.30 Lm: 6.698 (6.698) Lt: 5.931 (5.931) Accm: 2.70 (2.70) Acct: 4.51 (4.51) proj_loss: -0.5615 (-0.5615) time: 0.7636 data: 0.0004 [11-23 09:20:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 0/1669] eta: 0:19:32 tlr: 0.00022 tnm: 0.30 Lm: 6.810 (6.810) Lt: 6.132 (6.132) Accm: 2.42 (2.42) Acct: 3.65 (3.65) proj_loss: -0.5512 (-0.5512) time: 0.7025 data: 0.0004 [11-23 09:20:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 0/1669] eta: 0:22:16 tlr: 0.00022 tnm: 0.30 Lm: 6.549 (6.549) Lt: 5.764 (5.764) Accm: 3.04 (3.04) Acct: 5.06 (5.06) proj_loss: -0.5674 (-0.5674) time: 0.8007 data: 0.0004 [11-23 09:20:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 0/1669] eta: 0:22:24 tlr: 0.00022 tnm: 0.30 Lm: 6.567 (6.567) Lt: 5.911 (5.911) Accm: 3.09 (3.09) Acct: 4.72 (4.72) proj_loss: -0.5658 (-0.5658) time: 0.8055 data: 0.0004 [11-23 09:20:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 0/1669] eta: 0:20:39 tlr: 0.00022 tnm: 0.30 Lm: 6.598 (6.598) Lt: 5.827 (5.827) Accm: 3.04 (3.04) Acct: 4.79 (4.79) proj_loss: -0.5463 (-0.5463) time: 0.7425 data: 0.0003 [11-23 09:20:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 0/1669] eta: 0:20:02 tlr: 0.00022 tnm: 0.30 Lm: 6.711 (6.711) Lt: 5.974 (5.974) Accm: 3.09 (3.09) Acct: 5.13 (5.13) proj_loss: -0.5430 (-0.5430) time: 0.7205 data: 0.0004 [11-23 09:20:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 0/1669] eta: 0:23:40 tlr: 0.00022 tnm: 0.30 Lm: 6.739 (6.739) Lt: 6.072 (6.072) Accm: 2.97 (2.97) Acct: 4.27 (4.27) proj_loss: -0.5781 (-0.5781) time: 0.8509 data: 0.0003 [11-23 09:26:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 417/1669] eta: 0:17:47 tlr: 0.00022 tnm: 0.28 Lm: 6.825 (6.825) Lt: 6.136 (6.136) Accm: 2.73 (2.73) Acct: 3.98 (3.98) proj_loss: -0.5684 (-0.5684) time: 0.9391 data: 0.0003 [11-23 09:26:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 417/1669] eta: 0:17:47 tlr: 0.00022 tnm: 0.28 Lm: 6.733 (6.733) Lt: 6.034 (6.034) Accm: 2.75 (2.75) Acct: 4.30 (4.30) proj_loss: -0.5521 (-0.5521) time: 0.9391 data: 0.0003 [11-23 09:26:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 417/1669] eta: 0:17:47 tlr: 0.00022 tnm: 0.28 Lm: 6.593 (6.593) Lt: 5.900 (5.900) Accm: 3.12 (3.12) Acct: 4.92 (4.92) proj_loss: -0.5587 (-0.5587) time: 0.9392 data: 0.0003 [11-23 09:26:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 417/1669] eta: 0:17:47 tlr: 0.00022 tnm: 0.28 Lm: 6.663 (6.663) Lt: 5.913 (5.913) Accm: 2.96 (2.96) Acct: 4.63 (4.63) proj_loss: -0.5347 (-0.5347) time: 0.9392 data: 0.0003 [11-23 09:26:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 417/1669] eta: 0:17:47 tlr: 0.00022 tnm: 0.28 Lm: 6.766 (6.766) Lt: 6.025 (6.025) Accm: 2.70 (2.70) Acct: 4.27 (4.27) proj_loss: -0.5497 (-0.5497) time: 0.9391 data: 0.0003 [11-23 09:26:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 417/1669] eta: 0:17:47 tlr: 0.00022 tnm: 0.28 Lm: 6.625 (6.625) Lt: 5.836 (5.836) Accm: 3.18 (3.18) Acct: 5.29 (5.29) proj_loss: -0.5324 (-0.5324) time: 0.9392 data: 0.0003 [11-23 09:26:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 417/1669] eta: 0:17:47 tlr: 0.00022 tnm: 0.28 Lm: 6.517 (6.517) Lt: 5.783 (5.783) Accm: 3.02 (3.02) Acct: 4.96 (4.96) proj_loss: -0.5613 (-0.5613) time: 0.9392 data: 0.0003 [11-23 09:26:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 417/1669] eta: 0:17:51 tlr: 0.00022 tnm: 0.28 Lm: 6.701 (6.701) Lt: 5.957 (5.957) Accm: 2.91 (2.91) Acct: 4.56 (4.56) proj_loss: -0.5394 (-0.5394) time: 0.9838 data: 0.0003 [11-23 09:32:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 834/1669] eta: 0:12:02 tlr: 0.00022 tnm: 0.30 Lm: 6.775 (6.726) Lt: 5.991 (5.968) Accm: 2.71 (2.84) Acct: 4.41 (4.51) proj_loss: -0.5459 (-0.5416) time: 0.7536 data: 0.0003 [11-23 09:32:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 834/1669] eta: 0:12:02 tlr: 0.00022 tnm: 0.30 Lm: 6.698 (6.665) Lt: 5.931 (5.907) Accm: 2.70 (3.05) Acct: 4.51 (4.94) proj_loss: -0.5460 (-0.5485) time: 0.7536 data: 0.0003 [11-23 09:32:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 834/1669] eta: 0:12:02 tlr: 0.00022 tnm: 0.30 Lm: 6.706 (6.724) Lt: 6.002 (6.023) Accm: 2.74 (2.74) Acct: 4.58 (4.40) proj_loss: -0.5541 (-0.5565) time: 0.7536 data: 0.0002 [11-23 09:32:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 834/1669] eta: 0:12:02 tlr: 0.00022 tnm: 0.30 Lm: 6.567 (6.582) Lt: 5.889 (5.870) Accm: 3.16 (3.20) Acct: 5.13 (5.00) proj_loss: -0.5516 (-0.5499) time: 0.7536 data: 0.0003 [11-23 09:32:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 834/1669] eta: 0:12:02 tlr: 0.00022 tnm: 0.30 Lm: 6.739 (6.780) Lt: 6.072 (6.069) Accm: 2.97 (2.87) Acct: 4.27 (4.40) proj_loss: -0.5586 (-0.5529) time: 0.7536 data: 0.0003 [11-23 09:32:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 834/1669] eta: 0:12:02 tlr: 0.00022 tnm: 0.30 Lm: 6.549 (6.606) Lt: 5.803 (5.866) Accm: 2.99 (2.84) Acct: 4.86 (4.67) proj_loss: -0.5552 (-0.5557) time: 0.7536 data: 0.0003 [11-23 09:32:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 834/1669] eta: 0:12:02 tlr: 0.00022 tnm: 0.30 Lm: 6.598 (6.604) Lt: 5.827 (5.842) Accm: 3.04 (2.99) Acct: 4.79 (4.83) proj_loss: -0.5231 (-0.5271) time: 0.7536 data: 0.0003 [11-23 09:32:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [ 834/1669] eta: 0:12:02 tlr: 0.00022 tnm: 0.30 Lm: 6.711 (6.761) Lt: 5.974 (6.022) Accm: 3.09 (2.75) Acct: 5.13 (4.38) proj_loss: -0.5430 (-0.5448) time: 0.7536 data: 0.0003 [11-23 09:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.29 Lm: 6.751 (6.775) Lt: 6.048 (6.057) Accm: 2.80 (2.81) Acct: 4.15 (4.30) proj_loss: -0.5648 (-0.5574) time: 0.7532 data: 0.0002 [11-23 09:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.29 Lm: 6.737 (6.692) Lt: 6.011 (5.953) Accm: 2.70 (2.91) Acct: 4.34 (4.74) proj_loss: -0.5537 (-0.5528) time: 0.7532 data: 0.0003 [11-23 09:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.29 Lm: 6.629 (6.681) Lt: 5.872 (5.950) Accm: 2.96 (2.88) Acct: 4.87 (4.66) proj_loss: -0.5597 (-0.5596) time: 0.7532 data: 0.0003 [11-23 09:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.29 Lm: 6.667 (6.667) Lt: 5.917 (5.923) Accm: 2.73 (2.74) Acct: 4.48 (4.50) proj_loss: -0.5498 (-0.5487) time: 0.7532 data: 0.0003 [11-23 09:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.29 Lm: 6.785 (6.786) Lt: 6.043 (6.045) Accm: 2.64 (2.61) Acct: 4.44 (4.23) proj_loss: -0.5425 (-0.5441) time: 0.7532 data: 0.0003 [11-23 09:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.29 Lm: 6.793 (6.749) Lt: 6.022 (5.990) Accm: 2.56 (2.73) Acct: 4.13 (4.35) proj_loss: -0.5486 (-0.5497) time: 0.7532 data: 0.0003 [11-23 09:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.29 Lm: 6.589 (6.598) Lt: 5.790 (5.820) Accm: 3.04 (3.04) Acct: 5.01 (4.97) proj_loss: -0.5241 (-0.5266) time: 0.7532 data: 0.0003 [11-23 09:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1251/1669] eta: 0:05:45 tlr: 0.00022 tnm: 0.29 Lm: 6.593 (6.596) Lt: 5.894 (5.878) Accm: 3.12 (3.16) Acct: 4.92 (4.89) proj_loss: -0.5438 (-0.5464) time: 0.7532 data: 0.0003 [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.567 (6.578) Lt: 5.889 (5.871) Accm: 3.16 (3.19) Acct: 5.10 (4.93) proj_loss: -0.5516 (-0.5497) time: 0.7540 data: 0.0019 [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 48/350] Total time: 0:22:29 (0.808 s / it) [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.739 (6.741) Lt: 6.024 (6.021) Accm: 2.97 (2.87) Acct: 4.27 (4.50) proj_loss: -0.5710 (-0.5630) time: 0.7540 data: 0.0015 [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.551 (6.654) Lt: 5.745 (5.909) Accm: 3.19 (2.98) Acct: 5.17 (4.83) proj_loss: -0.5541 (-0.5539) time: 0.7540 data: 0.0019 [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.698 (6.692) Lt: 6.064 (5.975) Accm: 2.70 (2.88) Acct: 4.24 (4.64) proj_loss: -0.5615 (-0.5591) time: 0.7540 data: 0.0019 [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.673 (6.668) Lt: 5.930 (5.925) Accm: 2.99 (2.84) Acct: 4.86 (4.72) proj_loss: -0.5444 (-0.5453) time: 0.7540 data: 0.0020 [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.775 (6.738) Lt: 5.991 (5.988) Accm: 2.71 (2.75) Acct: 4.41 (4.41) proj_loss: -0.5512 (-0.5573) time: 0.7540 data: 0.0016 [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.580 (6.592) Lt: 5.817 (5.819) Accm: 3.04 (3.11) Acct: 5.23 (5.07) proj_loss: -0.5251 (-0.5331) time: 0.7540 data: 0.0017 [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 48/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.711 (6.747) Lt: 5.974 (5.998) Accm: 3.09 (2.71) Acct: 4.65 (4.31) proj_loss: -0.5430 (-0.5481) time: 0.7540 data: 0.0018 [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 48/350] Total time: 0:22:29 (0.809 s / it) [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 48/350] Total time: 0:22:29 (0.808 s / it) [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 48/350] Total time: 0:22:29 (0.808 s / it) [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 48/350] Total time: 0:22:29 (0.809 s / it) [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 48/350] Total time: 0:22:29 (0.808 s / it) [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 48/350] Total time: 0:22:29 (0.808 s / it) [11-23 09:42:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 48/350] Total time: 0:22:29 (0.808 s / it) [11-23 09:42:47] (/home/user/VAR/train.py , line 276)=> [ep48] (training ) Lm: 6.708 (6.716), Lt: 5.977 (5.985), Acc m&t: 2.86 4.51, Remain: 4 days, 9:42:53, Finish: 2024-11-27 03:25 [11-23 09:42:47] (/home/user/VAR/train.py , line 276)=> [ep48] (training ) Lm: 6.708 (6.716), Lt: 5.977 (5.985), Acc m&t: 2.86 4.51, Remain: 4 days, 9:43:06, Finish: 2024-11-27 03:25 [11-23 09:42:47] (/home/user/VAR/train.py , line 276)=> [ep48] (training ) Lm: 6.708 (6.716), Lt: 5.977 (5.985), Acc m&t: 2.86 4.51, Remain: 4 days, 9:42:16, Finish: 2024-11-27 03:25 [11-23 09:42:47] (/home/user/VAR/train.py , line 276)=> [ep48] (training ) Lm: 6.708 (6.716), Lt: 5.977 (5.985), Acc m&t: 2.86 4.51, Remain: 4 days, 9:41:12, Finish: 2024-11-27 03:23 [11-23 09:42:47] (/home/user/VAR/train.py , line 276)=> [ep48] (training ) Lm: 6.708 (6.716), Lt: 5.977 (5.985), Acc m&t: 2.86 4.51, Remain: 4 days, 9:42:55, Finish: 2024-11-27 03:25 [11-23 09:42:47] (/home/user/VAR/train.py , line 276)=> [ep48] (training ) Lm: 6.708 (6.716), Lt: 5.977 (5.985), Acc m&t: 2.86 4.51, Remain: 4 days, 9:43:03, Finish: 2024-11-27 03:25 [11-23 09:42:47] (/home/user/VAR/train.py , line 276)=> [ep48] (training ) Lm: 6.708 (6.716), Lt: 5.977 (5.985), Acc m&t: 2.86 4.51, Remain: 4 days, 9:42:33, Finish: 2024-11-27 03:25 [11-23 09:42:47] (/home/user/VAR/train.py , line 276)=> [ep48] (training ) Lm: 6.708 (6.716), Lt: 5.977 (5.985), Acc m&t: 2.86 4.51, Remain: 4 days, 9:40:22, Finish: 2024-11-27 03:23 [11-23 09:42:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 0/1669] eta: 0:19:58 tlr: 0.00022 tnm: 0.29 Lm: 6.753 (6.753) Lt: 6.136 (6.136) Accm: 2.78 (2.78) Acct: 4.30 (4.30) proj_loss: -0.5772 (-0.5772) time: 0.7178 data: 0.0003 [11-23 09:42:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 0/1669] eta: 0:20:00 tlr: 0.00022 tnm: 0.29 Lm: 6.590 (6.590) Lt: 5.808 (5.808) Accm: 3.35 (3.35) Acct: 5.48 (5.48) proj_loss: -0.5277 (-0.5277) time: 0.7190 data: 0.0004 [11-23 09:42:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 0/1669] eta: 0:20:00 tlr: 0.00022 tnm: 0.29 Lm: 6.801 (6.801) Lt: 6.063 (6.063) Accm: 2.65 (2.65) Acct: 4.30 (4.30) proj_loss: -0.5489 (-0.5489) time: 0.7192 data: 0.0003 [11-23 09:42:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 0/1669] eta: 0:19:59 tlr: 0.00022 tnm: 0.29 Lm: 6.607 (6.607) Lt: 5.904 (5.904) Accm: 2.97 (2.97) Acct: 4.55 (4.55) proj_loss: -0.5406 (-0.5406) time: 0.7184 data: 0.0004 [11-23 09:42:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 0/1669] eta: 0:19:59 tlr: 0.00022 tnm: 0.29 Lm: 6.791 (6.791) Lt: 6.139 (6.139) Accm: 3.21 (3.21) Acct: 4.68 (4.68) proj_loss: -0.5827 (-0.5827) time: 0.7189 data: 0.0004 [11-23 09:42:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 0/1669] eta: 0:20:01 tlr: 0.00022 tnm: 0.29 Lm: 6.862 (6.862) Lt: 6.119 (6.119) Accm: 2.00 (2.00) Acct: 3.34 (3.34) proj_loss: -0.5272 (-0.5272) time: 0.7200 data: 0.0004 [11-23 09:42:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 0/1669] eta: 0:20:01 tlr: 0.00022 tnm: 0.29 Lm: 6.562 (6.562) Lt: 5.916 (5.916) Accm: 3.12 (3.12) Acct: 4.30 (4.30) proj_loss: -0.5653 (-0.5653) time: 0.7199 data: 0.0003 [11-23 09:42:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 0/1669] eta: 0:20:02 tlr: 0.00022 tnm: 0.29 Lm: 6.896 (6.896) Lt: 6.200 (6.200) Accm: 2.16 (2.16) Acct: 3.48 (3.48) proj_loss: -0.5481 (-0.5481) time: 0.7206 data: 0.0004 [11-23 09:48:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 417/1669] eta: 0:15:40 tlr: 0.00022 tnm: 0.25 Lm: 6.743 (6.743) Lt: 5.996 (5.996) Accm: 2.91 (2.91) Acct: 4.61 (4.61) proj_loss: -0.5350 (-0.5350) time: 0.7505 data: 0.0002 [11-23 09:48:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 417/1669] eta: 0:15:40 tlr: 0.00022 tnm: 0.25 Lm: 6.829 (6.829) Lt: 6.145 (6.145) Accm: 2.87 (2.87) Acct: 4.32 (4.32) proj_loss: -0.5568 (-0.5568) time: 0.7505 data: 0.0003 [11-23 09:48:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 417/1669] eta: 0:15:40 tlr: 0.00022 tnm: 0.25 Lm: 6.675 (6.675) Lt: 5.985 (5.985) Accm: 2.99 (2.99) Acct: 4.42 (4.42) proj_loss: -0.5476 (-0.5476) time: 0.7505 data: 0.0002 [11-23 09:48:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 417/1669] eta: 0:15:40 tlr: 0.00022 tnm: 0.25 Lm: 6.725 (6.725) Lt: 5.957 (5.957) Accm: 2.81 (2.81) Acct: 4.58 (4.58) proj_loss: -0.5542 (-0.5542) time: 0.7505 data: 0.0003 [11-23 09:48:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 417/1669] eta: 0:15:40 tlr: 0.00022 tnm: 0.25 Lm: 6.666 (6.666) Lt: 5.968 (5.968) Accm: 2.94 (2.94) Acct: 4.75 (4.75) proj_loss: -0.5551 (-0.5551) time: 0.7505 data: 0.0003 [11-23 09:48:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 417/1669] eta: 0:15:40 tlr: 0.00022 tnm: 0.25 Lm: 6.705 (6.705) Lt: 5.895 (5.895) Accm: 2.62 (2.62) Acct: 4.30 (4.30) proj_loss: -0.5418 (-0.5418) time: 0.7505 data: 0.0003 [11-23 09:48:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 417/1669] eta: 0:15:40 tlr: 0.00022 tnm: 0.25 Lm: 6.694 (6.694) Lt: 5.969 (5.969) Accm: 2.90 (2.90) Acct: 4.63 (4.63) proj_loss: -0.5729 (-0.5729) time: 0.7505 data: 0.0003 [11-23 09:48:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 417/1669] eta: 0:15:40 tlr: 0.00022 tnm: 0.25 Lm: 6.684 (6.684) Lt: 6.005 (6.005) Accm: 2.88 (2.88) Acct: 4.22 (4.22) proj_loss: -0.5503 (-0.5503) time: 0.7505 data: 0.0003 [11-23 09:53:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.27 Lm: 6.680 (6.722) Lt: 5.929 (5.973) Accm: 2.96 (2.93) Acct: 4.72 (4.65) proj_loss: -0.5283 (-0.5327) time: 0.7528 data: 0.0003 [11-23 09:53:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.27 Lm: 6.710 (6.706) Lt: 5.957 (5.916) Accm: 3.07 (2.77) Acct: 4.75 (4.45) proj_loss: -0.5565 (-0.5488) time: 0.7528 data: 0.0003 [11-23 09:53:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.27 Lm: 6.744 (6.748) Lt: 6.067 (6.025) Accm: 2.97 (2.73) Acct: 4.30 (4.16) proj_loss: -0.5508 (-0.5487) time: 0.7528 data: 0.0002 [11-23 09:53:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.27 Lm: 6.768 (6.739) Lt: 6.063 (5.993) Accm: 2.65 (2.74) Acct: 4.34 (4.50) proj_loss: -0.5595 (-0.5562) time: 0.7528 data: 0.0003 [11-23 09:53:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.27 Lm: 6.791 (6.748) Lt: 6.139 (6.047) Accm: 3.21 (3.00) Acct: 4.65 (4.43) proj_loss: -0.5516 (-0.5551) time: 0.7528 data: 0.0003 [11-23 09:53:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.27 Lm: 6.777 (6.715) Lt: 6.088 (6.032) Accm: 2.65 (2.73) Acct: 4.13 (4.12) proj_loss: -0.5647 (-0.5551) time: 0.7528 data: 0.0003 [11-23 09:53:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.27 Lm: 6.896 (6.746) Lt: 6.161 (6.032) Accm: 2.29 (2.72) Acct: 3.75 (4.42) proj_loss: -0.5481 (-0.5511) time: 0.7528 data: 0.0003 [11-23 09:53:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [ 834/1669] eta: 0:10:27 tlr: 0.00022 tnm: 0.27 Lm: 6.753 (6.716) Lt: 6.016 (5.985) Accm: 2.78 (2.85) Acct: 4.51 (4.59) proj_loss: -0.5686 (-0.5611) time: 0.7528 data: 0.0003 [11-23 09:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1251/1669] eta: 0:05:29 tlr: 0.00022 tnm: 0.29 Lm: 6.757 (6.737) Lt: 6.061 (6.015) Accm: 2.77 (2.82) Acct: 4.41 (4.48) proj_loss: -0.5530 (-0.5543) time: 1.0164 data: 0.0002 [11-23 09:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1251/1669] eta: 0:05:29 tlr: 0.00022 tnm: 0.29 Lm: 6.710 (6.718) Lt: 6.023 (5.990) Accm: 2.81 (2.84) Acct: 4.53 (4.55) proj_loss: -0.5565 (-0.5555) time: 1.0164 data: 0.0003 [11-23 09:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1251/1669] eta: 0:05:29 tlr: 0.00022 tnm: 0.29 Lm: 6.736 (6.740) Lt: 5.965 (5.981) Accm: 2.89 (2.90) Acct: 4.51 (4.56) proj_loss: -0.5342 (-0.5346) time: 1.0164 data: 0.0003 [11-23 09:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1251/1669] eta: 0:05:29 tlr: 0.00022 tnm: 0.29 Lm: 6.736 (6.720) Lt: 5.964 (5.930) Accm: 2.97 (2.79) Acct: 4.68 (4.49) proj_loss: -0.5418 (-0.5420) time: 1.0164 data: 0.0003 [11-23 09:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1251/1669] eta: 0:05:29 tlr: 0.00022 tnm: 0.29 Lm: 6.732 (6.741) Lt: 6.007 (6.006) Accm: 2.90 (2.75) Acct: 4.42 (4.30) proj_loss: -0.5527 (-0.5538) time: 1.0165 data: 0.0003 [11-23 09:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1251/1669] eta: 0:05:29 tlr: 0.00022 tnm: 0.29 Lm: 6.742 (6.734) Lt: 6.064 (6.032) Accm: 3.18 (3.04) Acct: 4.67 (4.61) proj_loss: -0.5521 (-0.5545) time: 1.0164 data: 0.0003 [11-23 09:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1251/1669] eta: 0:05:29 tlr: 0.00022 tnm: 0.29 Lm: 6.830 (6.751) Lt: 6.112 (6.040) Accm: 2.39 (2.67) Acct: 3.75 (4.25) proj_loss: -0.5468 (-0.5497) time: 1.0164 data: 0.0004 [11-23 09:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1251/1669] eta: 0:05:29 tlr: 0.00022 tnm: 0.29 Lm: 6.753 (6.719) Lt: 6.018 (6.011) Accm: 2.70 (2.74) Acct: 4.22 (4.17) proj_loss: -0.5501 (-0.5502) time: 1.0164 data: 0.0003 [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.680 (6.710) Lt: 5.929 (5.949) Accm: 2.93 (2.91) Acct: 4.72 (4.59) proj_loss: -0.5400 (-0.5369) time: 0.7549 data: 0.0016 [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.732 (6.721) Lt: 5.998 (5.992) Accm: 2.81 (2.83) Acct: 4.34 (4.47) proj_loss: -0.5595 (-0.5566) time: 0.7549 data: 0.0016 [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.719 (6.704) Lt: 5.946 (5.951) Accm: 2.97 (2.88) Acct: 4.55 (4.53) proj_loss: -0.5543 (-0.5539) time: 0.7549 data: 0.0015 [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.693 (6.725) Lt: 5.989 (6.009) Accm: 3.16 (3.04) Acct: 4.68 (4.71) proj_loss: -0.5516 (-0.5493) time: 0.7549 data: 0.0017 [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.753 (6.725) Lt: 6.016 (5.991) Accm: 2.78 (2.86) Acct: 4.51 (4.55) proj_loss: -0.5374 (-0.5489) time: 0.7549 data: 0.0018 [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.754 (6.726) Lt: 5.990 (6.007) Accm: 2.75 (2.75) Acct: 4.30 (4.21) proj_loss: -0.5354 (-0.5416) time: 0.7549 data: 0.0021 [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.849 (6.770) Lt: 6.161 (6.074) Accm: 2.35 (2.60) Acct: 3.75 (4.07) proj_loss: -0.5481 (-0.5531) time: 0.7549 data: 0.0019 [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 49/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.31 Lm: 6.710 (6.708) Lt: 5.957 (5.921) Accm: 2.87 (2.78) Acct: 4.61 (4.50) proj_loss: -0.5347 (-0.5405) time: 0.7549 data: 0.0016 [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 49/350] Total time: 0:22:26 (0.806 s / it) [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 49/350] Total time: 0:22:26 (0.806 s / it) [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 49/350] Total time: 0:22:26 (0.807 s / it) [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 49/350] Total time: 0:22:26 (0.807 s / it) [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 49/350] Total time: 0:22:26 (0.807 s / it) [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 49/350] Total time: 0:22:26 (0.807 s / it) [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 49/350] Total time: 0:22:26 (0.807 s / it) [11-23 10:05:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 49/350] Total time: 0:22:26 (0.807 s / it) [11-23 10:07:12] (home/user/VAR/trainer.py, line 114)=> FID: 4.601020503010432 [11-23 10:07:12] (/home/user/VAR/train.py , line 259)=> [*] [ep49] (val 50000) Lm: 6.7076, Lt: 5.9767, Acc m&t: 2.86 4.49, Val cost: 118.72s [11-23 10:07:12] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-23 10:07:49] (/home/user/VAR/train.py , line 276)=> [ep49] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 9:42:44, Finish: 2024-11-27 03:47 [11-23 10:07:49] (/home/user/VAR/train.py , line 276)=> [ep49] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 9:42:24, Finish: 2024-11-27 03:47 [11-23 10:07:49] (/home/user/VAR/train.py , line 276)=> [ep49] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 9:42:49, Finish: 2024-11-27 03:48 [11-23 10:07:49] (/home/user/VAR/train.py , line 276)=> [ep49] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 9:43:03, Finish: 2024-11-27 03:48 [11-23 10:07:49] (/home/user/VAR/train.py , line 276)=> [ep49] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 9:43:13, Finish: 2024-11-27 03:48 [11-23 10:07:49] (/home/user/VAR/train.py , line 276)=> [ep49] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 9:42:53, Finish: 2024-11-27 03:48 [11-23 10:07:49] (/home/user/VAR/train.py , line 276)=> [ep49] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 9:42:38, Finish: 2024-11-27 03:47 [11-23 10:07:49] (/home/user/VAR/train.py , line 276)=> [ep49] (training ) Lm: 6.708 (6.708), Lt: 5.977 (5.977), Acc m&t: 2.86 4.51, Remain: 4 days, 9:41:44, Finish: 2024-11-27 03:46 [11-23 10:07:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 0:20:47 tlr: 0.00022 tnm: 0.29 Lm: 6.689 (6.689) Lt: 6.019 (6.019) Accm: 3.21 (3.21) Acct: 4.79 (4.79) proj_loss: -0.5485 (-0.5485) time: 0.7477 data: 0.0004 [11-23 10:07:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 0:21:33 tlr: 0.00022 tnm: 0.29 Lm: 6.557 (6.557) Lt: 5.772 (5.772) Accm: 3.32 (3.32) Acct: 5.41 (5.41) proj_loss: -0.5651 (-0.5651) time: 0.7752 data: 0.0004 [11-23 10:07:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 0:20:46 tlr: 0.00022 tnm: 0.29 Lm: 6.701 (6.701) Lt: 5.935 (5.935) Accm: 2.70 (2.70) Acct: 4.65 (4.65) proj_loss: -0.5633 (-0.5633) time: 0.7469 data: 0.0004 [11-23 10:07:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 0:21:08 tlr: 0.00022 tnm: 0.29 Lm: 6.802 (6.802) Lt: 6.073 (6.073) Accm: 2.87 (2.87) Acct: 4.96 (4.96) proj_loss: -0.5755 (-0.5755) time: 0.7598 data: 0.0004 [11-23 10:07:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 0:20:49 tlr: 0.00022 tnm: 0.29 Lm: 6.589 (6.589) Lt: 5.802 (5.802) Accm: 3.39 (3.39) Acct: 4.72 (4.72) proj_loss: -0.5836 (-0.5836) time: 0.7488 data: 0.0004 [11-23 10:07:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 0:20:48 tlr: 0.00022 tnm: 0.29 Lm: 6.522 (6.522) Lt: 5.757 (5.757) Accm: 3.09 (3.09) Acct: 5.23 (5.23) proj_loss: -0.5522 (-0.5522) time: 0.7483 data: 0.0004 [11-23 10:07:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 0:20:49 tlr: 0.00022 tnm: 0.29 Lm: 6.938 (6.938) Lt: 6.310 (6.310) Accm: 2.51 (2.51) Acct: 4.03 (4.03) proj_loss: -0.5910 (-0.5910) time: 0.7486 data: 0.0003 [11-23 10:07:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 0:20:51 tlr: 0.00022 tnm: 0.29 Lm: 6.704 (6.704) Lt: 5.931 (5.931) Accm: 2.80 (2.80) Acct: 4.55 (4.55) proj_loss: -0.5463 (-0.5463) time: 0.7497 data: 0.0004 [11-23 10:13:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 6.701 (6.701) Lt: 5.921 (5.921) Accm: 2.90 (2.90) Acct: 4.80 (4.80) proj_loss: -0.5521 (-0.5521) time: 0.7522 data: 0.0003 [11-23 10:13:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 6.764 (6.764) Lt: 6.055 (6.055) Accm: 2.81 (2.81) Acct: 4.53 (4.53) proj_loss: -0.5549 (-0.5549) time: 0.7522 data: 0.0003 [11-23 10:13:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 6.712 (6.712) Lt: 6.002 (6.002) Accm: 3.01 (3.01) Acct: 4.68 (4.68) proj_loss: -0.5438 (-0.5438) time: 0.7522 data: 0.0003 [11-23 10:13:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 6.744 (6.744) Lt: 6.016 (6.016) Accm: 2.91 (2.91) Acct: 4.75 (4.75) proj_loss: -0.5653 (-0.5653) time: 0.7522 data: 0.0003 [11-23 10:13:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 6.603 (6.603) Lt: 5.862 (5.862) Accm: 3.10 (3.10) Acct: 4.68 (4.68) proj_loss: -0.5580 (-0.5580) time: 0.7522 data: 0.0003 [11-23 10:13:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 6.632 (6.632) Lt: 5.812 (5.812) Accm: 2.96 (2.96) Acct: 4.89 (4.89) proj_loss: -0.5468 (-0.5468) time: 0.7522 data: 0.0003 [11-23 10:13:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 6.577 (6.577) Lt: 5.803 (5.803) Accm: 2.95 (2.95) Acct: 4.82 (4.82) proj_loss: -0.5437 (-0.5437) time: 0.7522 data: 0.0003 [11-23 10:13:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 417/1669] eta: 0:15:41 tlr: 0.00022 tnm: 0.29 Lm: 6.822 (6.822) Lt: 6.126 (6.126) Accm: 2.55 (2.55) Acct: 4.22 (4.22) proj_loss: -0.5632 (-0.5632) time: 0.7522 data: 0.0003 [11-23 10:18:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.30 Lm: 6.840 (6.828) Lt: 6.132 (6.128) Accm: 2.51 (2.53) Acct: 4.03 (4.12) proj_loss: -0.5399 (-0.5554) time: 0.7516 data: 0.0003 [11-23 10:18:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.30 Lm: 6.686 (6.724) Lt: 5.958 (5.993) Accm: 2.96 (3.13) Acct: 4.96 (4.98) proj_loss: -0.5551 (-0.5592) time: 0.7516 data: 0.0003 [11-23 10:18:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.30 Lm: 6.616 (6.637) Lt: 5.922 (5.902) Accm: 2.81 (2.94) Acct: 4.65 (4.51) proj_loss: -0.5325 (-0.5453) time: 0.7516 data: 0.0003 [11-23 10:18:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.30 Lm: 6.690 (6.704) Lt: 5.993 (5.999) Accm: 2.81 (2.91) Acct: 4.58 (4.60) proj_loss: -0.5485 (-0.5563) time: 0.7516 data: 0.0002 [11-23 10:18:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.30 Lm: 6.641 (6.723) Lt: 5.902 (6.004) Accm: 2.78 (2.80) Acct: 4.17 (4.41) proj_loss: -0.5598 (-0.5565) time: 0.7516 data: 0.0003 [11-23 10:18:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.30 Lm: 6.701 (6.622) Lt: 5.908 (5.816) Accm: 3.10 (3.13) Acct: 4.96 (5.07) proj_loss: -0.5517 (-0.5520) time: 0.7516 data: 0.0003 [11-23 10:18:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.30 Lm: 6.633 (6.652) Lt: 5.849 (5.876) Accm: 2.81 (2.76) Acct: 4.41 (4.65) proj_loss: -0.5522 (-0.5488) time: 0.7516 data: 0.0003 [11-23 10:18:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 834/1669] eta: 0:10:28 tlr: 0.00022 tnm: 0.30 Lm: 6.704 (6.685) Lt: 5.931 (5.903) Accm: 2.80 (2.83) Acct: 4.55 (4.57) proj_loss: -0.5463 (-0.5464) time: 0.7516 data: 0.0003 [11-23 10:23:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.31 Lm: 6.685 (6.629) Lt: 5.953 (5.892) Accm: 3.26 (3.33) Acct: 5.20 (5.17) proj_loss: -0.5510 (-0.5550) time: 0.7514 data: 0.0003 [11-23 10:23:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.31 Lm: 6.730 (6.747) Lt: 5.999 (6.027) Accm: 2.67 (2.74) Acct: 4.08 (4.30) proj_loss: -0.5522 (-0.5495) time: 0.7514 data: 0.0003 [11-23 10:23:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.31 Lm: 6.685 (6.634) Lt: 5.879 (5.825) Accm: 3.07 (3.11) Acct: 5.04 (5.09) proj_loss: -0.5575 (-0.5569) time: 0.7514 data: 0.0003 [11-23 10:23:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.31 Lm: 6.717 (6.699) Lt: 5.935 (5.946) Accm: 2.73 (2.73) Acct: 4.36 (4.45) proj_loss: -0.5555 (-0.5551) time: 0.7514 data: 0.0003 [11-23 10:23:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.31 Lm: 6.661 (6.673) Lt: 5.952 (5.948) Accm: 2.71 (2.84) Acct: 4.41 (4.36) proj_loss: -0.5559 (-0.5538) time: 0.7514 data: 0.0003 [11-23 10:23:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.31 Lm: 6.748 (6.726) Lt: 6.008 (5.966) Accm: 2.68 (2.72) Acct: 4.24 (4.35) proj_loss: -0.5468 (-0.5495) time: 0.7514 data: 0.0003 [11-23 10:23:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.31 Lm: 6.712 (6.803) Lt: 6.006 (6.108) Accm: 2.77 (2.68) Acct: 4.51 (4.29) proj_loss: -0.5438 (-0.5468) time: 0.7514 data: 0.0003 [11-23 10:23:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1251/1669] eta: 0:05:14 tlr: 0.00022 tnm: 0.31 Lm: 6.831 (6.826) Lt: 6.091 (6.108) Accm: 2.55 (2.66) Acct: 4.22 (4.33) proj_loss: -0.5491 (-0.5562) time: 0.7514 data: 0.0003 [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.685 (6.619) Lt: 5.948 (5.876) Accm: 3.15 (3.30) Acct: 4.96 (5.12) proj_loss: -0.5468 (-0.5518) time: 0.7551 data: 0.0015 [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.668 (6.606) Lt: 5.851 (5.814) Accm: 3.10 (3.27) Acct: 5.13 (5.30) proj_loss: -0.5517 (-0.5536) time: 0.7552 data: 0.0019 [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.705 (6.691) Lt: 5.983 (5.978) Accm: 2.72 (2.82) Acct: 4.17 (4.30) proj_loss: -0.5423 (-0.5515) time: 0.7551 data: 0.0017 [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.802 (6.720) Lt: 6.021 (5.991) Accm: 2.65 (2.71) Acct: 4.30 (4.33) proj_loss: -0.5588 (-0.5619) time: 0.7551 data: 0.0019 [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.785 (6.755) Lt: 6.063 (6.034) Accm: 2.59 (2.71) Acct: 4.06 (4.26) proj_loss: -0.5588 (-0.5514) time: 0.7551 data: 0.0019 [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.735 (6.824) Lt: 6.019 (6.118) Accm: 2.72 (2.58) Acct: 4.44 (4.14) proj_loss: -0.5391 (-0.5415) time: 0.7551 data: 0.0016 [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.821 (6.762) Lt: 6.049 (6.028) Accm: 2.59 (2.91) Acct: 4.41 (4.68) proj_loss: -0.5399 (-0.5503) time: 0.7552 data: 0.0020 [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.704 (6.712) Lt: 5.931 (5.940) Accm: 2.80 (2.74) Acct: 4.48 (4.37) proj_loss: -0.5474 (-0.5507) time: 0.7551 data: 0.0016 [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 50/350] Total time: 0:20:55 (0.752 s / it) [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 50/350] Total time: 0:20:55 (0.752 s / it) [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 50/350] Total time: 0:20:55 (0.752 s / it) [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 50/350] Total time: 0:20:55 (0.752 s / it) [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 50/350] Total time: 0:20:55 (0.752 s / it) [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 50/350] Total time: 0:20:55 (0.752 s / it) [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 50/350] Total time: 0:20:55 (0.752 s / it) [11-23 10:28:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 50/350] Total time: 0:20:55 (0.752 s / it) [11-23 10:28:44] (/home/user/VAR/train.py , line 276)=> [ep50] (training ) Lm: 6.708 (6.708), Lt: 5.971 (5.971), Acc m&t: 2.86 4.52, Remain: 4 days, 9:18:11, Finish: 2024-11-27 03:46 [11-23 10:28:44] (/home/user/VAR/train.py , line 276)=> [ep50] (training ) Lm: 6.708 (6.708), Lt: 5.971 (5.971), Acc m&t: 2.86 4.52, Remain: 4 days, 9:17:35, Finish: 2024-11-27 03:46 [11-23 10:28:44] (/home/user/VAR/train.py , line 276)=> [ep50] (training ) Lm: 6.708 (6.708), Lt: 5.971 (5.971), Acc m&t: 2.86 4.52, Remain: 4 days, 9:18:02, Finish: 2024-11-27 03:46 [11-23 10:28:44] (/home/user/VAR/train.py , line 276)=> [ep50] (training ) Lm: 6.708 (6.708), Lt: 5.971 (5.971), Acc m&t: 2.86 4.52, Remain: 4 days, 9:18:39, Finish: 2024-11-27 03:47 [11-23 10:28:44] (/home/user/VAR/train.py , line 276)=> [ep50] (training ) Lm: 6.708 (6.708), Lt: 5.971 (5.971), Acc m&t: 2.86 4.52, Remain: 4 days, 9:17:33, Finish: 2024-11-27 03:46 [11-23 10:28:44] (/home/user/VAR/train.py , line 276)=> [ep50] (training ) Lm: 6.708 (6.708), Lt: 5.971 (5.971), Acc m&t: 2.86 4.52, Remain: 4 days, 9:17:04, Finish: 2024-11-27 03:45 [11-23 10:28:44] (/home/user/VAR/train.py , line 276)=> [ep50] (training ) Lm: 6.708 (6.708), Lt: 5.971 (5.971), Acc m&t: 2.86 4.52, Remain: 4 days, 9:19:01, Finish: 2024-11-27 03:47 [11-23 10:28:44] (/home/user/VAR/train.py , line 276)=> [ep50] (training ) Lm: 6.708 (6.708), Lt: 5.971 (5.971), Acc m&t: 2.86 4.52, Remain: 4 days, 9:17:09, Finish: 2024-11-27 03:45 [11-23 10:28:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 0/1669] eta: 0:20:27 tlr: 0.00022 tnm: 0.30 Lm: 6.835 (6.835) Lt: 6.028 (6.028) Accm: 2.51 (2.51) Acct: 3.68 (3.68) proj_loss: -0.5347 (-0.5347) time: 0.7355 data: 0.0004 [11-23 10:28:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 0/1669] eta: 0:20:28 tlr: 0.00022 tnm: 0.30 Lm: 6.758 (6.758) Lt: 6.075 (6.075) Accm: 2.43 (2.43) Acct: 3.68 (3.68) proj_loss: -0.5773 (-0.5773) time: 0.7361 data: 0.0004 [11-23 10:28:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 0/1669] eta: 0:20:56 tlr: 0.00022 tnm: 0.30 Lm: 6.750 (6.750) Lt: 6.001 (6.001) Accm: 2.83 (2.83) Acct: 4.55 (4.55) proj_loss: -0.4965 (-0.4965) time: 0.7528 data: 0.0003 [11-23 10:28:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 0/1669] eta: 0:20:29 tlr: 0.00022 tnm: 0.30 Lm: 6.766 (6.766) Lt: 6.006 (6.006) Accm: 2.58 (2.58) Acct: 4.24 (4.24) proj_loss: -0.5241 (-0.5241) time: 0.7367 data: 0.0003 [11-23 10:28:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 0/1669] eta: 0:20:55 tlr: 0.00022 tnm: 0.30 Lm: 6.557 (6.557) Lt: 5.767 (5.767) Accm: 3.45 (3.45) Acct: 5.79 (5.79) proj_loss: -0.5524 (-0.5524) time: 0.7522 data: 0.0003 [11-23 10:28:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 0/1669] eta: 0:20:55 tlr: 0.00022 tnm: 0.30 Lm: 6.782 (6.782) Lt: 6.003 (6.003) Accm: 2.58 (2.58) Acct: 4.20 (4.20) proj_loss: -0.5506 (-0.5506) time: 0.7522 data: 0.0033 [11-23 10:28:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 0/1669] eta: 0:20:29 tlr: 0.00022 tnm: 0.30 Lm: 6.835 (6.835) Lt: 6.138 (6.138) Accm: 2.27 (2.27) Acct: 3.24 (3.24) proj_loss: -0.5498 (-0.5498) time: 0.7368 data: 0.0004 [11-23 10:28:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 0/1669] eta: 0:20:30 tlr: 0.00022 tnm: 0.30 Lm: 6.440 (6.440) Lt: 5.546 (5.546) Accm: 3.79 (3.79) Acct: 6.61 (6.61) proj_loss: -0.5412 (-0.5412) time: 0.7373 data: 0.0005 [11-23 10:34:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 417/1669] eta: 0:18:17 tlr: 0.00022 tnm: 0.26 Lm: 6.710 (6.710) Lt: 5.978 (5.978) Accm: 2.98 (2.98) Acct: 4.94 (4.94) proj_loss: -0.5718 (-0.5718) time: 0.9459 data: 0.0003 [11-23 10:34:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 417/1669] eta: 0:18:17 tlr: 0.00022 tnm: 0.26 Lm: 6.756 (6.756) Lt: 5.935 (5.935) Accm: 2.92 (2.92) Acct: 4.73 (4.73) proj_loss: -0.5196 (-0.5196) time: 0.9459 data: 0.0003 [11-23 10:34:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 417/1669] eta: 0:18:17 tlr: 0.00022 tnm: 0.26 Lm: 6.732 (6.732) Lt: 6.031 (6.031) Accm: 2.56 (2.56) Acct: 3.94 (3.94) proj_loss: -0.5734 (-0.5734) time: 0.9459 data: 0.0003 [11-23 10:34:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 417/1669] eta: 0:18:17 tlr: 0.00022 tnm: 0.26 Lm: 6.809 (6.809) Lt: 6.085 (6.085) Accm: 2.59 (2.59) Acct: 4.10 (4.10) proj_loss: -0.5265 (-0.5265) time: 0.9459 data: 0.0003 [11-23 10:34:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 417/1669] eta: 0:18:17 tlr: 0.00022 tnm: 0.26 Lm: 6.594 (6.594) Lt: 5.795 (5.795) Accm: 3.23 (3.23) Acct: 5.32 (5.32) proj_loss: -0.5478 (-0.5478) time: 0.9459 data: 0.0003 [11-23 10:34:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 417/1669] eta: 0:18:17 tlr: 0.00022 tnm: 0.26 Lm: 6.755 (6.755) Lt: 6.008 (6.008) Accm: 2.68 (2.68) Acct: 4.30 (4.30) proj_loss: -0.5485 (-0.5485) time: 0.9459 data: 0.0003 [11-23 10:34:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 417/1669] eta: 0:18:17 tlr: 0.00022 tnm: 0.26 Lm: 6.697 (6.697) Lt: 5.981 (5.981) Accm: 2.85 (2.85) Acct: 4.46 (4.46) proj_loss: -0.5301 (-0.5301) time: 0.9459 data: 0.0003 [11-23 10:34:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 417/1669] eta: 0:18:17 tlr: 0.00022 tnm: 0.26 Lm: 6.789 (6.789) Lt: 6.081 (6.081) Accm: 2.53 (2.53) Acct: 3.63 (3.63) proj_loss: -0.5424 (-0.5424) time: 0.9459 data: 0.0003 ======================================================= RESTART [11-23 11:27:15] ======================================================= ======================================================= RESTART [11-23 11:27:15] ======================================================= ======================================================= RESTART [11-23 11:27:15] ======================================================= ======================================================= RESTART [11-23 11:27:15] ======================================================= ======================================================= RESTART [11-23 11:27:15] ======================================================= ======================================================= RESTART [11-23 11:27:15] ======================================================= ======================================================= RESTART [11-23 11:27:15] ======================================================= ======================================================= RESTART [11-23 11:27:15] ======================================================= [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-23 11:28:11] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-23 11:28:11] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 branch : main } [11-23 11:28:11] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-23 11:28:14] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep50, it0 [11-23 11:28:14] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-23 11:27:15] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-23 11:28:11] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-23 11:28:11] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add branch : main commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 } [11-23 11:28:11] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-23 11:28:14] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep50, it0 [11-23 11:28:14] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-23 11:27:15] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-23 11:28:11] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-23 11:28:11] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 branch : main } [11-23 11:28:11] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-23 11:28:14] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep50, it0 [11-23 11:28:14] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-23 11:27:15] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-23 11:28:11] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-23 11:28:11] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add branch : main commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 } [11-23 11:28:11] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-23 11:28:14] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep50, it0 [11-23 11:28:14] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-23 11:27:15] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-23 11:28:11] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-23 11:28:11] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 commit_msg : add branch : main } [11-23 11:28:11] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-23 11:28:14] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep50, it0 [11-23 11:28:14] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-23 11:27:15] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-23 11:28:11] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-23 11:28:11] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 commit_msg : add } [11-23 11:28:11] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-23 11:28:14] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep50, it0 [11-23 11:28:14] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-23 11:27:15] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-23 11:28:11] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-23 11:28:11] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 branch : main } [11-23 11:28:11] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-23 11:28:14] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep50, it0 [11-23 11:28:14] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-23 11:27:15] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-23 11:27:15] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-23 11:28:11] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=12 [11-23 11:28:11] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 12 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 commit_msg : add } [11-23 11:28:11] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-23 11:28:14] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 11:28:14] (e/user/VAR/utils/data.py, line 51)=> [11-23 11:28:14] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-23 11:28:14] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep50, it0 [11-23 11:28:14] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ... [dataloader multi processing](*) finished! (46.68s) [dataloader multi processing](*) finished! (46.77s) [dataloader multi processing](*) finished! (47.04s) [dataloader multi processing](*) finished! (47.41s) [dataloader multi processing](*) finished! (48.07s) [11-23 11:29:01] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [dataloader multi processing](*) finished! (49.62s) [dataloader multi processing](*) finished! (49.75s) [dataloader multi processing](*) finished! (51.21s) [11-23 11:29:06] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:06] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:07] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-23 11:29:02] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-23 11:29:06] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:06] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:08] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-23 11:29:02] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-23 11:29:07] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:07] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:08] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-23 11:29:01] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-23 11:29:08] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:08] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:09] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-23 11:29:01] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-23 11:29:08] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:08] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:09] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-23 11:29:04] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-23 11:29:09] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:09] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:10] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-23 11:29:05] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-23 11:29:10] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:10] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:11] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-23 11:29:04] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=12, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-23 11:29:10] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:10] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 11:29:11] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-23 11:29:09] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-23 11:29:36] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-23 11:29:36] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-23 11:29:36] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-23 11:29:36] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " 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_orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-23 11:29:37] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank0] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-23 11:29:11] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-23 11:29:36] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-23 11:29:36] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-23 11:29:36] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-23 11:29:36] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-23 11:29:37] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank8] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-23 11:29:11] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-23 11:29:36] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-23 11:29:36] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-23 11:29:36] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-23 11:29:36] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-23 11:29:37] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank16] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-23 11:29:13] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-23 11:29:36] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-23 11:29:36] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-23 11:29:36] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-23 11:29:36] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-23 11:29:37] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank24] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-23 11:29:10] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-23 11:29:36] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-23 11:29:36] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-23 11:29:36] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-23 11:29:36] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-23 11:29:37] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank32] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-23 11:29:13] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-23 11:29:36] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-23 11:29:36] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-23 11:29:36] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-23 11:29:36] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-23 11:29:37] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank40] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-23 11:29:09] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-23 11:29:36] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-23 11:29:36] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-23 11:29:36] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-23 11:29:36] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-23 11:29:37] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank48] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-23 11:29:12] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-23 11:29:36] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-23 11:29:36] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-23 11:29:36] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-23 11:29:36] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, 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'_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-23 11:29:37] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank56] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-23 11:29:37] (/VAR/utils/lr_control.py, line 105)=> [11-23 11:29:37] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-23 11:29:39] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-23 11:45:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 17 days, 16:46:16 tlr: 0.00022 tnm: 0.29 Lm: 6.582 (6.582) Lt: 5.824 (5.824) Accm: 2.97 (2.97) Acct: 4.92 (4.92) proj_loss: -0.5143 (-0.5143) time: 916.2229 data: 0.0005 [11-23 11:29:37] (/VAR/utils/lr_control.py, line 105)=> [11-23 11:29:37] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-23 11:29:39] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-23 11:45:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 17 days, 17:12:11 tlr: 0.00022 tnm: 0.29 Lm: 6.733 (6.733) Lt: 6.010 (6.010) Accm: 2.51 (2.51) Acct: 3.75 (3.75) proj_loss: -0.5257 (-0.5257) time: 917.1550 data: 0.0006 [11-23 11:29:37] (/VAR/utils/lr_control.py, line 105)=> [11-23 11:29:37] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-23 11:29:39] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-23 11:45:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 17 days, 17:11:51 tlr: 0.00022 tnm: 0.29 Lm: 6.688 (6.688) Lt: 5.911 (5.911) Accm: 2.71 (2.71) Acct: 4.10 (4.10) proj_loss: -0.5886 (-0.5886) time: 917.1428 data: 0.0006 [11-23 11:29:37] (/VAR/utils/lr_control.py, line 105)=> [11-23 11:29:37] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-23 11:29:38] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-23 11:29:38] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-23 11:45:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 17 days, 17:08:35 tlr: 0.00022 tnm: 0.29 Lm: 6.483 (6.483) Lt: 5.726 (5.726) Accm: 3.39 (3.39) Acct: 5.82 (5.82) proj_loss: -0.5580 (-0.5580) time: 917.0255 data: 0.0006 [11-23 11:29:37] (/VAR/utils/lr_control.py, line 105)=> [11-23 11:29:37] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-23 11:29:39] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-23 11:45:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 17 days, 17:18:18 tlr: 0.00022 tnm: 0.29 Lm: 6.658 (6.658) Lt: 5.947 (5.947) Accm: 2.77 (2.77) Acct: 4.20 (4.20) proj_loss: -0.5665 (-0.5665) time: 917.3749 data: 0.0007 [11-23 11:29:37] (/VAR/utils/lr_control.py, line 105)=> [11-23 11:29:37] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-23 11:29:39] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-23 11:45:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 17 days, 17:12:32 tlr: 0.00022 tnm: 0.29 Lm: 6.781 (6.781) Lt: 6.067 (6.067) Accm: 2.71 (2.71) Acct: 3.72 (3.72) proj_loss: -0.5693 (-0.5693) time: 917.1675 data: 0.0006 [11-23 11:29:37] (/VAR/utils/lr_control.py, line 105)=> [11-23 11:29:37] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-23 11:29:39] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-23 11:45:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 17 days, 17:19:01 tlr: 0.00022 tnm: 0.29 Lm: 6.763 (6.763) Lt: 6.023 (6.023) Accm: 2.62 (2.62) Acct: 3.96 (3.96) proj_loss: -0.5569 (-0.5569) time: 917.4005 data: 0.0007 [11-23 11:29:37] (/VAR/utils/lr_control.py, line 105)=> [11-23 11:29:37] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-23 11:29:39] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-23 11:29:39] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-23 11:45:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 17 days, 17:14:34 tlr: 0.00022 tnm: 0.29 Lm: 6.933 (6.933) Lt: 6.368 (6.368) Accm: 2.35 (2.35) Acct: 3.24 (3.24) proj_loss: -0.5929 (-0.5929) time: 917.2405 data: 0.0006 ======================================================= RESTART [11-23 15:39:50] ======================================================= ======================================================= RESTART [11-23 15:39:50] ======================================================= ======================================================= RESTART [11-23 15:39:50] ======================================================= ======================================================= RESTART [11-23 15:39:50] ======================================================= [11-23 15:39:50] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-23 15:39:50] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-23 15:39:50] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-23 15:40:47] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-23 15:40:47] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 commit_msg : add } [11-23 15:40:47] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-23 15:40:49] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-23 15:40:49] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> [11-23 15:40:49] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 15:40:49] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> [11-23 15:40:49] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 15:40:49] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-23 15:40:49] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep50, it0 [11-23 15:40:49] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-23 15:39:50] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-23 15:39:50] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-23 15:39:50] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-23 15:40:47] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-23 15:40:47] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 branch : main } [11-23 15:40:47] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-23 15:40:49] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-23 15:40:49] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> [11-23 15:40:49] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 15:40:49] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> [11-23 15:40:49] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 15:40:49] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-23 15:40:49] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep50, it0 [11-23 15:40:49] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-23 15:39:50] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-23 15:39:50] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-23 15:39:50] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-23 15:40:47] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-23 15:40:47] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 branch : main commit_msg : add } [11-23 15:40:47] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-23 15:40:49] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-23 15:40:49] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> [11-23 15:40:49] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 15:40:49] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> [11-23 15:40:49] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 15:40:49] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-23 15:40:49] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep50, it0 [11-23 15:40:49] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-23 15:39:50] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-23 15:39:50] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-23 15:39:50] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-23 15:40:47] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-23 15:40:47] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 branch : main commit_msg : add } [11-23 15:40:47] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-23 15:40:49] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-23 15:40:49] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> [11-23 15:40:49] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 15:40:49] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-23 15:40:49] (e/user/VAR/utils/data.py, line 51)=> [11-23 15:40:49] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-23 15:40:49] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-23 15:40:49] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep50, it0 [11-23 15:40:49] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ... [dataloader multi processing](*) finished! (45.60s) [dataloader multi processing](*) finished! (48.19s) [dataloader multi processing](*) finished! (48.60s) [dataloader multi processing](*) finished! (48.87s) [11-23 15:41:38] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-23 15:41:35] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-23 15:41:41] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 15:41:41] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 15:41:43] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-23 15:41:38] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-23 15:41:43] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 15:41:43] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 15:41:44] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-23 15:41:43] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 15:41:43] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 15:41:44] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-23 15:41:38] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-23 15:41:43] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 15:41:43] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-23 15:41:44] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-23 15:41:46] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-23 15:42:07] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-23 15:42:07] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-23 15:42:07] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-23 15:42:07] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-23 15:42:07] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank0] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-23 15:41:44] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-23 15:42:07] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-23 15:42:07] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-23 15:42:07] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-23 15:42:07] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-23 15:42:08] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank8] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-23 15:41:46] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-23 15:42:07] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-23 15:42:07] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-23 15:42:07] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-23 15:42:07] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-23 15:42:08] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank16] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-23 15:41:45] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-23 15:42:07] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-23 15:42:07] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-23 15:42:07] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-23 15:42:07] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, 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'_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-23 15:42:08] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank24] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-23 15:42:08] (/VAR/utils/lr_control.py, line 105)=> [11-23 15:42:08] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-23 15:42:09] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-23 15:42:09] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-23 15:42:09] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-23 15:42:09] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-23 15:57:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 17 days, 12:24:59 tlr: 0.00022 tnm: 0.28 Lm: 6.614 (6.614) Lt: 5.841 (5.841) Accm: 3.02 (3.02) Acct: 4.96 (4.96) proj_loss: -0.5687 (-0.5687) time: 906.8302 data: 0.0006 [11-23 15:42:08] (/VAR/utils/lr_control.py, line 105)=> [11-23 15:42:08] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-23 15:42:09] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-23 15:42:09] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-23 15:42:09] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-23 15:42:09] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-23 15:57:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 17 days, 12:29:09 tlr: 0.00022 tnm: 0.28 Lm: 6.822 (6.822) Lt: 6.117 (6.117) Accm: 2.40 (2.40) Acct: 3.68 (3.68) proj_loss: -0.5533 (-0.5533) time: 906.9800 data: 0.0005 [11-23 15:42:08] (/VAR/utils/lr_control.py, line 105)=> [11-23 15:42:08] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-23 15:42:09] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-23 15:42:09] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-23 15:42:09] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-23 15:42:09] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-23 15:57:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 17 days, 12:30:04 tlr: 0.00022 tnm: 0.28 Lm: 6.728 (6.728) Lt: 5.947 (5.947) Accm: 2.65 (2.65) Acct: 4.10 (4.10) proj_loss: -0.5449 (-0.5449) time: 907.0131 data: 0.0006 [11-23 15:42:08] (/VAR/utils/lr_control.py, line 105)=> [11-23 15:42:08] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-23 15:42:09] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-23 15:42:09] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-23 15:42:10] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-23 15:42:10] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-23 15:57:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 0/1669] eta: 17 days, 12:04:51 tlr: 0.00022 tnm: 0.28 Lm: 6.579 (6.579) Lt: 5.855 (5.855) Accm: 3.16 (3.16) Acct: 4.87 (4.87) proj_loss: -0.5397 (-0.5397) time: 906.1061 data: 0.0005 [11-23 16:07:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 417/1669] eta: 1:16:06 tlr: 0.00022 tnm: 0.29 Lm: 6.673 (6.673) Lt: 5.964 (5.964) Accm: 2.89 (2.89) Acct: 4.41 (4.41) proj_loss: -0.5466 (-0.5466) time: 0.6757 data: 0.0003 [11-23 16:07:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 417/1669] eta: 1:16:08 tlr: 0.00022 tnm: 0.29 Lm: 6.661 (6.661) Lt: 5.926 (5.926) Accm: 2.95 (2.95) Acct: 4.71 (4.71) proj_loss: -0.5573 (-0.5573) time: 0.6757 data: 0.0003 [11-23 16:07:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 417/1669] eta: 1:16:08 tlr: 0.00022 tnm: 0.29 Lm: 6.823 (6.823) Lt: 6.126 (6.126) Accm: 2.61 (2.61) Acct: 4.12 (4.12) proj_loss: -0.5510 (-0.5510) time: 0.6757 data: 0.0002 [11-23 16:07:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 417/1669] eta: 1:16:08 tlr: 0.00022 tnm: 0.29 Lm: 6.778 (6.778) Lt: 6.047 (6.047) Accm: 2.50 (2.50) Acct: 3.90 (3.90) proj_loss: -0.5541 (-0.5541) time: 0.6757 data: 0.0003 [11-23 16:12:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 834/1669] eta: 0:30:06 tlr: 0.00022 tnm: 0.29 Lm: 6.728 (6.706) Lt: 5.947 (5.989) Accm: 2.65 (2.73) Acct: 4.10 (4.32) proj_loss: -0.5561 (-0.5547) time: 0.6776 data: 0.0003 [11-23 16:12:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 834/1669] eta: 0:30:05 tlr: 0.00022 tnm: 0.29 Lm: 6.607 (6.651) Lt: 5.855 (5.911) Accm: 3.07 (2.95) Acct: 4.87 (4.60) proj_loss: -0.5535 (-0.5491) time: 0.6776 data: 0.0003 [11-23 16:12:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 834/1669] eta: 0:30:06 tlr: 0.00022 tnm: 0.29 Lm: 6.709 (6.701) Lt: 6.011 (5.955) Accm: 2.87 (2.88) Acct: 4.46 (4.60) proj_loss: -0.5460 (-0.5514) time: 0.6776 data: 0.0003 [11-23 16:12:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [ 834/1669] eta: 0:30:06 tlr: 0.00022 tnm: 0.29 Lm: 6.822 (6.787) Lt: 6.117 (6.067) Accm: 2.81 (2.70) Acct: 4.55 (4.31) proj_loss: -0.5533 (-0.5522) time: 0.6776 data: 0.0003 [11-23 16:17:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1251/1669] eta: 0:11:36 tlr: 0.00022 tnm: 0.28 Lm: 6.807 (6.788) Lt: 6.084 (6.063) Accm: 2.68 (2.66) Acct: 4.34 (4.27) proj_loss: -0.5527 (-0.5522) time: 0.6738 data: 0.0003 [11-23 16:17:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1251/1669] eta: 0:11:36 tlr: 0.00022 tnm: 0.28 Lm: 6.744 (6.722) Lt: 6.012 (5.976) Accm: 2.80 (2.82) Acct: 4.42 (4.48) proj_loss: -0.5469 (-0.5505) time: 0.6738 data: 0.0002 [11-23 16:17:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1251/1669] eta: 0:11:37 tlr: 0.00022 tnm: 0.28 Lm: 6.751 (6.723) Lt: 5.992 (6.001) Accm: 2.77 (2.77) Acct: 4.36 (4.39) proj_loss: -0.5597 (-0.5578) time: 0.6738 data: 0.0002 [11-23 16:17:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1251/1669] eta: 0:11:36 tlr: 0.00022 tnm: 0.28 Lm: 6.625 (6.649) Lt: 5.847 (5.893) Accm: 2.87 (2.88) Acct: 4.65 (4.56) proj_loss: -0.5481 (-0.5474) time: 0.6738 data: 0.0002 [11-23 16:21:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1668/1669] eta: 0:00:01 tlr: 0.00022 tnm: 0.28 Lm: 6.642 (6.703) Lt: 5.855 (5.953) Accm: 2.67 (2.74) Acct: 4.42 (4.37) proj_loss: -0.5463 (-0.5472) time: 0.6771 data: 0.0018 [11-23 16:21:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 50/350] Total time: 0:39:27 (1.419 s / it) [11-23 16:21:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1668/1669] eta: 0:00:01 tlr: 0.00022 tnm: 0.28 Lm: 6.793 (6.779) Lt: 6.051 (6.055) Accm: 2.76 (2.68) Acct: 4.13 (4.23) proj_loss: -0.5521 (-0.5495) time: 0.6771 data: 0.0014 [11-23 16:21:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1668/1669] eta: 0:00:01 tlr: 0.00022 tnm: 0.28 Lm: 6.728 (6.717) Lt: 5.958 (5.993) Accm: 2.86 (2.79) Acct: 4.44 (4.40) proj_loss: -0.5561 (-0.5564) time: 0.6771 data: 0.0024 [11-23 16:21:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 50/350] [1668/1669] eta: 0:00:01 tlr: 0.00022 tnm: 0.28 Lm: 6.780 (6.738) Lt: 6.012 (5.990) Accm: 2.74 (2.73) Acct: 4.37 (4.30) proj_loss: -0.5477 (-0.5515) time: 0.6772 data: 0.0014 [11-23 16:21:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 50/350] Total time: 0:39:28 (1.419 s / it) [11-23 16:21:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 50/350] Total time: 0:39:28 (1.419 s / it) [11-23 16:21:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 50/350] Total time: 0:39:28 (1.419 s / it) [11-23 16:21:42] (/home/user/VAR/train.py , line 276)=> [ep50] (training ) Lm: 6.712 (6.712), Lt: 5.976 (5.976), Acc m&t: 2.83 4.48, Remain: 3 days, 22:28:34, Finish: 2024-11-26 22:50 [11-23 16:21:42] (/home/user/VAR/train.py , line 276)=> [ep50] (training ) Lm: 6.712 (6.712), Lt: 5.976 (5.976), Acc m&t: 2.83 4.48, Remain: 3 days, 22:29:17, Finish: 2024-11-26 22:50 [11-23 16:21:42] (/home/user/VAR/train.py , line 276)=> [ep50] (training ) Lm: 6.712 (6.712), Lt: 5.976 (5.976), Acc m&t: 2.83 4.48, Remain: 3 days, 22:30:04, Finish: 2024-11-26 22:51 [11-23 16:21:42] (/home/user/VAR/train.py , line 276)=> [ep50] (training ) Lm: 6.712 (6.712), Lt: 5.976 (5.976), Acc m&t: 2.83 4.48, Remain: 3 days, 22:28:20, Finish: 2024-11-26 22:50 [11-23 16:21:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 0/1669] eta: 0:18:03 tlr: 0.00022 tnm: 0.29 Lm: 6.769 (6.769) Lt: 5.979 (5.979) Accm: 2.73 (2.73) Acct: 4.44 (4.44) proj_loss: -0.5431 (-0.5431) time: 0.6494 data: 0.0003 [11-23 16:21:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 0/1669] eta: 0:18:05 tlr: 0.00022 tnm: 0.29 Lm: 6.691 (6.691) Lt: 5.972 (5.972) Accm: 2.94 (2.94) Acct: 4.53 (4.53) proj_loss: -0.5559 (-0.5559) time: 0.6505 data: 0.0004 [11-23 16:21:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 0/1669] eta: 0:18:05 tlr: 0.00022 tnm: 0.29 Lm: 6.835 (6.835) Lt: 6.143 (6.143) Accm: 2.46 (2.46) Acct: 3.70 (3.70) proj_loss: -0.5503 (-0.5503) time: 0.6501 data: 0.0004 [11-23 16:21:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 0/1669] eta: 0:18:44 tlr: 0.00022 tnm: 0.29 Lm: 6.659 (6.659) Lt: 5.882 (5.882) Accm: 2.99 (2.99) Acct: 4.82 (4.82) proj_loss: -0.5557 (-0.5557) time: 0.6737 data: 0.0004 [11-23 16:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 417/1669] eta: 0:14:03 tlr: 0.00022 tnm: 0.27 Lm: 6.601 (6.601) Lt: 5.809 (5.809) Accm: 3.05 (3.05) Acct: 4.94 (4.94) proj_loss: -0.5501 (-0.5501) time: 0.6739 data: 0.0003 [11-23 16:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 417/1669] eta: 0:14:03 tlr: 0.00022 tnm: 0.27 Lm: 6.733 (6.733) Lt: 6.016 (6.016) Accm: 2.71 (2.71) Acct: 4.12 (4.12) proj_loss: -0.5478 (-0.5478) time: 0.6738 data: 0.0003 [11-23 16:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 417/1669] eta: 0:14:03 tlr: 0.00022 tnm: 0.27 Lm: 6.693 (6.693) Lt: 5.923 (5.923) Accm: 2.96 (2.96) Acct: 4.72 (4.72) proj_loss: -0.5427 (-0.5427) time: 0.6739 data: 0.0003 [11-23 16:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 417/1669] eta: 0:14:03 tlr: 0.00022 tnm: 0.27 Lm: 6.729 (6.729) Lt: 6.004 (6.004) Accm: 2.76 (2.76) Acct: 4.40 (4.40) proj_loss: -0.5574 (-0.5574) time: 0.6739 data: 0.0003 [11-23 16:31:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 834/1669] eta: 0:09:28 tlr: 0.00022 tnm: 0.30 Lm: 6.768 (6.742) Lt: 6.005 (6.005) Accm: 2.70 (2.74) Acct: 4.27 (4.30) proj_loss: -0.5559 (-0.5527) time: 0.6700 data: 0.0003 [11-23 16:31:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 834/1669] eta: 0:09:28 tlr: 0.00022 tnm: 0.30 Lm: 6.713 (6.726) Lt: 5.999 (6.011) Accm: 2.83 (2.75) Acct: 4.30 (4.18) proj_loss: -0.5503 (-0.5488) time: 0.6700 data: 0.0003 [11-23 16:31:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 834/1669] eta: 0:09:28 tlr: 0.00022 tnm: 0.30 Lm: 6.676 (6.687) Lt: 5.924 (5.924) Accm: 2.91 (2.94) Acct: 4.60 (4.68) proj_loss: -0.5431 (-0.5434) time: 0.6700 data: 0.0003 [11-23 16:31:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [ 834/1669] eta: 0:09:28 tlr: 0.00022 tnm: 0.30 Lm: 6.659 (6.637) Lt: 5.882 (5.849) Accm: 3.11 (3.14) Acct: 5.01 (4.96) proj_loss: -0.5557 (-0.5541) time: 0.6700 data: 0.0003 [11-23 16:35:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [1251/1669] eta: 0:04:43 tlr: 0.00022 tnm: 0.28 Lm: 6.681 (6.653) Lt: 5.906 (5.872) Accm: 3.05 (3.07) Acct: 4.92 (4.84) proj_loss: -0.5513 (-0.5523) time: 0.6752 data: 0.0003 [11-23 16:35:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [1251/1669] eta: 0:04:43 tlr: 0.00022 tnm: 0.28 Lm: 6.746 (6.739) Lt: 6.025 (6.021) Accm: 2.77 (2.74) Acct: 4.30 (4.21) proj_loss: -0.5505 (-0.5521) time: 0.6752 data: 0.0003 [11-23 16:35:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [1251/1669] eta: 0:04:43 tlr: 0.00022 tnm: 0.28 Lm: 6.722 (6.718) Lt: 5.951 (5.959) Accm: 2.82 (2.85) Acct: 4.52 (4.48) proj_loss: -0.5439 (-0.5476) time: 0.6752 data: 0.0002 [11-23 16:35:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [1251/1669] eta: 0:04:43 tlr: 0.00022 tnm: 0.28 Lm: 6.748 (6.739) Lt: 5.989 (5.995) Accm: 2.79 (2.78) Acct: 4.40 (4.41) proj_loss: -0.5525 (-0.5518) time: 0.6752 data: 0.0003 [11-23 16:40:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.728 (6.718) Lt: 5.972 (5.981) Accm: 2.89 (2.86) Acct: 4.53 (4.53) proj_loss: -0.5552 (-0.5525) time: 0.6755 data: 0.0015 [11-23 16:40:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 51/350] Total time: 0:18:50 (0.677 s / it) [11-23 16:40:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.713 (6.728) Lt: 5.999 (6.006) Accm: 2.83 (2.77) Acct: 4.30 (4.28) proj_loss: -0.5508 (-0.5527) time: 0.6755 data: 0.0017 [11-23 16:40:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.711 (6.717) Lt: 5.979 (5.976) Accm: 2.73 (2.82) Acct: 4.44 (4.41) proj_loss: -0.5446 (-0.5527) time: 0.6755 data: 0.0018 [11-23 16:40:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 51/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.659 (6.648) Lt: 5.882 (5.868) Accm: 3.11 (3.09) Acct: 5.01 (4.87) proj_loss: -0.5468 (-0.5481) time: 0.6755 data: 0.0019 [11-23 16:40:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 51/350] Total time: 0:18:50 (0.677 s / it) [11-23 16:40:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 51/350] Total time: 0:18:50 (0.677 s / it) [11-23 16:40:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 51/350] Total time: 0:18:50 (0.677 s / it) [11-23 16:40:33] (/home/user/VAR/train.py , line 276)=> [ep51] (training ) Lm: 6.708 (6.708), Lt: 5.972 (5.972), Acc m&t: 2.84 4.49, Remain: 3 days, 22:06:40, Finish: 2024-11-26 22:47 [11-23 16:40:33] (/home/user/VAR/train.py , line 276)=> [ep51] (training ) Lm: 6.708 (6.708), Lt: 5.972 (5.972), Acc m&t: 2.84 4.49, Remain: 3 days, 22:06:58, Finish: 2024-11-26 22:47 [11-23 16:40:33] (/home/user/VAR/train.py , line 276)=> [ep51] (training ) Lm: 6.708 (6.708), Lt: 5.972 (5.972), Acc m&t: 2.84 4.49, Remain: 3 days, 22:07:47, Finish: 2024-11-26 22:48 [11-23 16:40:33] (/home/user/VAR/train.py , line 276)=> [ep51] (training ) Lm: 6.708 (6.708), Lt: 5.972 (5.972), Acc m&t: 2.84 4.49, Remain: 3 days, 22:07:44, Finish: 2024-11-26 22:48 [11-23 16:40:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [ 0/1669] eta: 0:18:23 tlr: 0.00022 tnm: 0.30 Lm: 6.897 (6.897) Lt: 6.155 (6.155) Accm: 2.53 (2.53) Acct: 4.05 (4.05) proj_loss: -0.5465 (-0.5465) time: 0.6611 data: 0.0003 [11-23 16:40:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [ 0/1669] eta: 0:18:23 tlr: 0.00022 tnm: 0.30 Lm: 6.765 (6.765) Lt: 6.032 (6.032) Accm: 2.78 (2.78) Acct: 4.44 (4.44) proj_loss: -0.5288 (-0.5288) time: 0.6611 data: 0.0003 [11-23 16:40:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [ 0/1669] eta: 0:18:22 tlr: 0.00022 tnm: 0.30 Lm: 6.809 (6.809) Lt: 6.158 (6.158) Accm: 2.54 (2.54) Acct: 3.67 (3.67) proj_loss: -0.5611 (-0.5611) time: 0.6605 data: 0.0003 [11-23 16:40:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [ 0/1669] eta: 0:18:25 tlr: 0.00022 tnm: 0.30 Lm: 6.774 (6.774) Lt: 6.100 (6.100) Accm: 2.78 (2.78) Acct: 4.24 (4.24) proj_loss: -0.5565 (-0.5565) time: 0.6626 data: 0.0004 [11-23 16:45:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [ 417/1669] eta: 0:14:03 tlr: 0.00022 tnm: 0.28 Lm: 6.763 (6.763) Lt: 6.057 (6.057) Accm: 2.76 (2.76) Acct: 4.30 (4.30) proj_loss: -0.5500 (-0.5500) time: 0.6748 data: 0.0002 [11-23 16:45:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [ 417/1669] eta: 0:14:03 tlr: 0.00022 tnm: 0.28 Lm: 6.685 (6.685) Lt: 5.960 (5.960) Accm: 2.80 (2.80) Acct: 4.39 (4.39) proj_loss: -0.5324 (-0.5324) time: 0.6748 data: 0.0002 [11-23 16:45:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [ 417/1669] eta: 0:14:03 tlr: 0.00022 tnm: 0.28 Lm: 6.894 (6.894) Lt: 6.179 (6.179) Accm: 2.46 (2.46) Acct: 4.05 (4.05) proj_loss: -0.5626 (-0.5626) time: 0.6748 data: 0.0002 [11-23 16:45:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [ 417/1669] eta: 0:14:03 tlr: 0.00022 tnm: 0.28 Lm: 6.808 (6.808) Lt: 6.118 (6.118) Accm: 2.58 (2.58) Acct: 3.95 (3.95) proj_loss: -0.5438 (-0.5438) time: 0.6748 data: 0.0003 [11-23 16:49:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [ 834/1669] eta: 0:09:22 tlr: 0.00022 tnm: 0.29 Lm: 6.806 (6.782) Lt: 6.079 (6.069) Accm: 2.61 (2.63) Acct: 4.24 (4.13) proj_loss: -0.5383 (-0.5420) time: 0.6758 data: 0.0003 [11-23 16:49:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [ 834/1669] eta: 0:09:22 tlr: 0.00022 tnm: 0.29 Lm: 6.891 (6.811) Lt: 6.155 (6.065) Accm: 2.53 (2.59) Acct: 4.05 (4.18) proj_loss: -0.5465 (-0.5531) time: 0.6757 data: 0.0003 [11-23 16:49:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [ 834/1669] eta: 0:09:22 tlr: 0.00022 tnm: 0.29 Lm: 6.751 (6.738) Lt: 6.013 (6.019) Accm: 2.78 (2.81) Acct: 4.36 (4.36) proj_loss: -0.5434 (-0.5449) time: 0.6757 data: 0.0003 [11-23 16:49:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [ 834/1669] eta: 0:09:22 tlr: 0.00022 tnm: 0.29 Lm: 6.624 (6.665) Lt: 5.889 (5.931) Accm: 2.82 (2.87) Acct: 4.44 (4.46) proj_loss: -0.5360 (-0.5376) time: 0.6758 data: 0.0003 [11-23 16:54:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [1251/1669] eta: 0:04:44 tlr: 0.00022 tnm: 0.30 Lm: 6.653 (6.669) Lt: 5.918 (5.935) Accm: 2.81 (2.86) Acct: 4.49 (4.48) proj_loss: -0.5420 (-0.5441) time: 0.6748 data: 0.0003 [11-23 16:54:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [1251/1669] eta: 0:04:44 tlr: 0.00022 tnm: 0.30 Lm: 6.760 (6.745) Lt: 6.017 (6.020) Accm: 2.76 (2.77) Acct: 4.35 (4.36) proj_loss: -0.5408 (-0.5433) time: 0.6748 data: 0.0002 [11-23 16:54:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [1251/1669] eta: 0:04:44 tlr: 0.00022 tnm: 0.30 Lm: 6.782 (6.776) Lt: 6.035 (6.027) Accm: 2.66 (2.64) Acct: 4.25 (4.26) proj_loss: -0.5444 (-0.5504) time: 0.6748 data: 0.0002 [11-23 16:54:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [1251/1669] eta: 0:04:44 tlr: 0.00022 tnm: 0.30 Lm: 6.769 (6.744) Lt: 6.024 (6.020) Accm: 2.67 (2.71) Acct: 4.36 (4.26) proj_loss: -0.5424 (-0.5431) time: 0.6748 data: 0.0003 [11-23 16:59:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.30 Lm: 6.806 (6.763) Lt: 6.079 (6.032) Accm: 2.68 (2.70) Acct: 4.24 (4.22) proj_loss: -0.5464 (-0.5446) time: 0.6762 data: 0.0015 [11-23 16:59:26] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 52/350] Total time: 0:18:52 (0.679 s / it) [11-23 16:59:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.30 Lm: 6.683 (6.693) Lt: 5.947 (5.959) Accm: 2.80 (2.83) Acct: 4.44 (4.42) proj_loss: -0.5457 (-0.5444) time: 0.6762 data: 0.0016 [11-23 16:59:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.30 Lm: 6.751 (6.721) Lt: 6.013 (5.982) Accm: 2.78 (2.82) Acct: 4.36 (4.42) proj_loss: -0.5382 (-0.5421) time: 0.6762 data: 0.0021 [11-23 16:59:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 52/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.30 Lm: 6.736 (6.768) Lt: 6.000 (6.022) Accm: 2.73 (2.66) Acct: 4.25 (4.26) proj_loss: -0.5465 (-0.5497) time: 0.6762 data: 0.0015 [11-23 16:59:26] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 52/350] Total time: 0:18:52 (0.679 s / it) [11-23 16:59:26] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 52/350] Total time: 0:18:52 (0.679 s / it) [11-23 16:59:26] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 52/350] Total time: 0:18:52 (0.679 s / it) [11-23 16:59:26] (/home/user/VAR/train.py , line 276)=> [ep52] (training ) Lm: 6.696 (6.696), Lt: 5.959 (5.959), Acc m&t: 2.88 4.55, Remain: 3 days, 21:43:59, Finish: 2024-11-26 22:43 [11-23 16:59:26] (/home/user/VAR/train.py , line 276)=> [ep52] (training ) Lm: 6.696 (6.696), Lt: 5.959 (5.959), Acc m&t: 2.88 4.55, Remain: 3 days, 21:44:12, Finish: 2024-11-26 22:43 [11-23 16:59:26] (/home/user/VAR/train.py , line 276)=> [ep52] (training ) Lm: 6.696 (6.696), Lt: 5.959 (5.959), Acc m&t: 2.88 4.55, Remain: 3 days, 21:43:23, Finish: 2024-11-26 22:42 [11-23 16:59:26] (/home/user/VAR/train.py , line 276)=> [ep52] (training ) Lm: 6.696 (6.696), Lt: 5.959 (5.959), Acc m&t: 2.88 4.55, Remain: 3 days, 21:39:59, Finish: 2024-11-26 22:39 [11-23 16:59:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [ 0/1669] eta: 0:18:22 tlr: 0.00022 tnm: 0.27 Lm: 6.710 (6.710) Lt: 5.976 (5.976) Accm: 2.92 (2.92) Acct: 4.84 (4.84) proj_loss: -0.5409 (-0.5409) time: 0.6603 data: 0.0003 [11-23 16:59:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [ 0/1669] eta: 0:18:21 tlr: 0.00022 tnm: 0.27 Lm: 6.740 (6.740) Lt: 5.992 (5.992) Accm: 2.77 (2.77) Acct: 4.22 (4.22) proj_loss: -0.5287 (-0.5287) time: 0.6597 data: 0.0004 [11-23 16:59:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [ 0/1669] eta: 0:18:22 tlr: 0.00022 tnm: 0.27 Lm: 6.752 (6.752) Lt: 6.104 (6.104) Accm: 2.59 (2.59) Acct: 3.96 (3.96) proj_loss: -0.5747 (-0.5747) time: 0.6608 data: 0.0003 [11-23 16:59:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [ 0/1669] eta: 0:18:22 tlr: 0.00022 tnm: 0.27 Lm: 6.808 (6.808) Lt: 6.086 (6.086) Accm: 2.75 (2.75) Acct: 4.48 (4.48) proj_loss: -0.5322 (-0.5322) time: 0.6607 data: 0.0004 [11-23 17:04:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [ 417/1669] eta: 0:14:04 tlr: 0.00022 tnm: 0.33 Lm: 6.740 (6.740) Lt: 6.014 (6.014) Accm: 2.78 (2.78) Acct: 4.60 (4.60) proj_loss: -0.5374 (-0.5374) time: 0.6733 data: 0.0002 [11-23 17:04:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [ 417/1669] eta: 0:14:04 tlr: 0.00022 tnm: 0.33 Lm: 6.742 (6.742) Lt: 6.054 (6.054) Accm: 2.70 (2.70) Acct: 4.35 (4.35) proj_loss: -0.5463 (-0.5463) time: 0.6733 data: 0.0003 [11-23 17:04:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [ 417/1669] eta: 0:14:04 tlr: 0.00022 tnm: 0.33 Lm: 6.758 (6.758) Lt: 6.055 (6.055) Accm: 2.68 (2.68) Acct: 4.09 (4.09) proj_loss: -0.5367 (-0.5367) time: 0.6733 data: 0.0003 [11-23 17:04:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [ 417/1669] eta: 0:14:04 tlr: 0.00022 tnm: 0.33 Lm: 6.785 (6.785) Lt: 6.095 (6.095) Accm: 2.62 (2.62) Acct: 4.10 (4.10) proj_loss: -0.5590 (-0.5590) time: 0.6733 data: 0.0003 [11-23 17:08:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [ 834/1669] eta: 0:09:22 tlr: 0.00022 tnm: 0.29 Lm: 6.786 (6.786) Lt: 6.086 (6.074) Accm: 2.59 (2.59) Acct: 3.96 (4.05) proj_loss: -0.5481 (-0.5554) time: 0.6744 data: 0.0003 [11-23 17:08:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [ 834/1669] eta: 0:09:22 tlr: 0.00022 tnm: 0.29 Lm: 6.710 (6.706) Lt: 5.976 (5.999) Accm: 2.92 (2.93) Acct: 4.84 (4.70) proj_loss: -0.5517 (-0.5544) time: 0.6744 data: 0.0002 [11-23 17:08:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [ 834/1669] eta: 0:09:22 tlr: 0.00022 tnm: 0.29 Lm: 6.687 (6.722) Lt: 5.941 (5.988) Accm: 2.75 (2.76) Acct: 4.48 (4.46) proj_loss: -0.5427 (-0.5436) time: 0.6744 data: 0.0002 [11-23 17:08:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [ 834/1669] eta: 0:09:22 tlr: 0.00022 tnm: 0.29 Lm: 6.740 (6.711) Lt: 5.992 (5.981) Accm: 2.77 (2.74) Acct: 4.22 (4.28) proj_loss: -0.5378 (-0.5371) time: 0.6744 data: 0.0002 [11-23 17:13:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [1251/1669] eta: 0:04:41 tlr: 0.00022 tnm: 0.29 Lm: 6.758 (6.779) Lt: 6.055 (6.067) Accm: 2.68 (2.55) Acct: 4.09 (4.02) proj_loss: -0.5412 (-0.5429) time: 0.6746 data: 0.0002 [11-23 17:13:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [1251/1669] eta: 0:04:41 tlr: 0.00022 tnm: 0.29 Lm: 6.736 (6.720) Lt: 5.986 (5.998) Accm: 2.70 (2.79) Acct: 4.35 (4.43) proj_loss: -0.5463 (-0.5496) time: 0.6746 data: 0.0003 [11-23 17:13:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [1251/1669] eta: 0:04:41 tlr: 0.00022 tnm: 0.29 Lm: 6.747 (6.757) Lt: 6.014 (6.030) Accm: 2.74 (2.72) Acct: 4.33 (4.39) proj_loss: -0.5493 (-0.5481) time: 0.6746 data: 0.0003 [11-23 17:13:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [1251/1669] eta: 0:04:41 tlr: 0.00022 tnm: 0.29 Lm: 6.769 (6.772) Lt: 6.059 (6.048) Accm: 2.60 (2.60) Acct: 3.99 (4.05) proj_loss: -0.5457 (-0.5507) time: 0.6746 data: 0.0003 [11-23 17:18:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.752 (6.756) Lt: 6.032 (6.034) Accm: 2.62 (2.64) Acct: 4.03 (4.18) proj_loss: -0.5433 (-0.5490) time: 0.6766 data: 0.0020 [11-23 17:18:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 53/350] Total time: 0:18:46 (0.675 s / it) [11-23 17:18:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.710 (6.702) Lt: 5.976 (5.975) Accm: 2.92 (2.85) Acct: 4.84 (4.54) proj_loss: -0.5507 (-0.5498) time: 0.6766 data: 0.0015 [11-23 17:18:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.687 (6.714) Lt: 5.941 (5.984) Accm: 2.75 (2.80) Acct: 4.48 (4.49) proj_loss: -0.5559 (-0.5517) time: 0.6766 data: 0.0015 [11-23 17:18:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 53/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.776 (6.779) Lt: 6.053 (6.064) Accm: 2.59 (2.52) Acct: 3.96 (3.97) proj_loss: -0.5447 (-0.5432) time: 0.6766 data: 0.0017 [11-23 17:18:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 53/350] Total time: 0:18:46 (0.675 s / it) [11-23 17:18:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 53/350] Total time: 0:18:46 (0.675 s / it) [11-23 17:18:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 53/350] Total time: 0:18:46 (0.675 s / it) [11-23 17:18:12] (/home/user/VAR/train.py , line 276)=> [ep53] (training ) Lm: 6.696 (6.697), Lt: 5.959 (5.960), Acc m&t: 2.88 4.55, Remain: 3 days, 21:32:45, Finish: 2024-11-26 22:50 [11-23 17:18:12] (/home/user/VAR/train.py , line 276)=> [ep53] (training ) Lm: 6.696 (6.697), Lt: 5.959 (5.960), Acc m&t: 2.88 4.55, Remain: 3 days, 21:30:37, Finish: 2024-11-26 22:48 [11-23 17:18:12] (/home/user/VAR/train.py , line 276)=> [ep53] (training ) Lm: 6.696 (6.697), Lt: 5.959 (5.960), Acc m&t: 2.88 4.55, Remain: 3 days, 21:29:18, Finish: 2024-11-26 22:47 [11-23 17:18:12] (/home/user/VAR/train.py , line 276)=> [ep53] (training ) Lm: 6.696 (6.697), Lt: 5.959 (5.960), Acc m&t: 2.88 4.55, Remain: 3 days, 21:31:44, Finish: 2024-11-26 22:49 [11-23 17:18:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [ 0/1669] eta: 0:18:24 tlr: 0.00022 tnm: 0.30 Lm: 6.728 (6.728) Lt: 5.964 (5.964) Accm: 2.80 (2.80) Acct: 4.22 (4.22) proj_loss: -0.5404 (-0.5404) time: 0.6619 data: 0.0004 [11-23 17:18:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [ 0/1669] eta: 0:18:34 tlr: 0.00022 tnm: 0.30 Lm: 6.793 (6.793) Lt: 6.050 (6.050) Accm: 2.82 (2.82) Acct: 4.46 (4.46) proj_loss: -0.5488 (-0.5488) time: 0.6675 data: 0.0003 [11-23 17:18:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [ 0/1669] eta: 0:18:25 tlr: 0.00022 tnm: 0.30 Lm: 6.777 (6.777) Lt: 6.031 (6.031) Accm: 2.56 (2.56) Acct: 4.15 (4.15) proj_loss: -0.5469 (-0.5469) time: 0.6626 data: 0.0004 [11-23 17:18:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [ 0/1669] eta: 0:18:26 tlr: 0.00022 tnm: 0.30 Lm: 6.670 (6.670) Lt: 5.960 (5.960) Accm: 2.86 (2.86) Acct: 4.32 (4.32) proj_loss: -0.5641 (-0.5641) time: 0.6628 data: 0.0004 [11-23 17:23:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [ 417/1669] eta: 0:14:32 tlr: 0.00022 tnm: 0.29 Lm: 6.730 (6.730) Lt: 6.036 (6.036) Accm: 2.70 (2.70) Acct: 4.18 (4.18) proj_loss: -0.5667 (-0.5667) time: 0.6724 data: 0.0003 [11-23 17:23:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [ 417/1669] eta: 0:14:32 tlr: 0.00022 tnm: 0.29 Lm: 6.814 (6.814) Lt: 6.094 (6.094) Accm: 2.60 (2.60) Acct: 4.06 (4.06) proj_loss: -0.5513 (-0.5513) time: 0.6724 data: 0.0002 [11-23 17:23:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [ 417/1669] eta: 0:14:32 tlr: 0.00022 tnm: 0.29 Lm: 6.724 (6.724) Lt: 5.997 (5.997) Accm: 2.70 (2.70) Acct: 4.20 (4.20) proj_loss: -0.5383 (-0.5383) time: 0.6724 data: 0.0003 [11-23 17:23:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [ 417/1669] eta: 0:14:32 tlr: 0.00022 tnm: 0.29 Lm: 6.772 (6.772) Lt: 6.014 (6.014) Accm: 2.67 (2.67) Acct: 4.19 (4.19) proj_loss: -0.5371 (-0.5371) time: 0.6724 data: 0.0003 [11-23 17:27:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [ 834/1669] eta: 0:09:32 tlr: 0.00022 tnm: 0.30 Lm: 6.728 (6.724) Lt: 5.964 (5.975) Accm: 2.80 (2.81) Acct: 4.22 (4.38) proj_loss: -0.5404 (-0.5451) time: 0.6737 data: 0.0002 [11-23 17:27:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [ 834/1669] eta: 0:09:32 tlr: 0.00022 tnm: 0.30 Lm: 6.793 (6.802) Lt: 6.050 (6.071) Accm: 2.74 (2.65) Acct: 4.46 (4.28) proj_loss: -0.5537 (-0.5597) time: 0.6737 data: 0.0002 [11-23 17:27:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [ 834/1669] eta: 0:09:32 tlr: 0.00022 tnm: 0.30 Lm: 6.671 (6.679) Lt: 5.963 (5.926) Accm: 2.83 (2.86) Acct: 4.25 (4.47) proj_loss: -0.5469 (-0.5415) time: 0.6737 data: 0.0003 [11-23 17:27:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [ 834/1669] eta: 0:09:32 tlr: 0.00022 tnm: 0.30 Lm: 6.676 (6.712) Lt: 5.992 (6.022) Accm: 2.86 (2.78) Acct: 4.32 (4.33) proj_loss: -0.5641 (-0.5644) time: 0.6737 data: 0.0003 [11-23 17:32:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [1251/1669] eta: 0:04:44 tlr: 0.00022 tnm: 0.28 Lm: 6.698 (6.714) Lt: 5.990 (6.013) Accm: 2.71 (2.72) Acct: 4.18 (4.24) proj_loss: -0.5618 (-0.5630) time: 0.6756 data: 0.0003 [11-23 17:32:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [1251/1669] eta: 0:04:44 tlr: 0.00022 tnm: 0.28 Lm: 6.786 (6.769) Lt: 6.038 (6.038) Accm: 2.78 (2.72) Acct: 4.49 (4.34) proj_loss: -0.5513 (-0.5543) time: 0.6756 data: 0.0003 [11-23 17:32:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [1251/1669] eta: 0:04:44 tlr: 0.00022 tnm: 0.28 Lm: 6.667 (6.675) Lt: 5.947 (5.928) Accm: 2.94 (2.91) Acct: 4.52 (4.55) proj_loss: -0.5474 (-0.5460) time: 0.6756 data: 0.0002 [11-23 17:32:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [1251/1669] eta: 0:04:44 tlr: 0.00022 tnm: 0.28 Lm: 6.679 (6.688) Lt: 5.931 (5.934) Accm: 2.95 (2.88) Acct: 4.49 (4.55) proj_loss: -0.5428 (-0.5451) time: 0.6756 data: 0.0002 [11-23 17:37:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.728 (6.707) Lt: 5.964 (5.950) Accm: 2.80 (2.85) Acct: 4.61 (4.56) proj_loss: -0.5451 (-0.5456) time: 0.6759 data: 0.0014 [11-23 17:37:09] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 54/350] Total time: 0:18:54 (0.680 s / it) [11-23 17:37:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.778 (6.747) Lt: 6.026 (6.014) Accm: 2.74 (2.69) Acct: 4.46 (4.33) proj_loss: -0.5537 (-0.5584) time: 0.6759 data: 0.0019 [11-23 17:37:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.664 (6.669) Lt: 5.932 (5.926) Accm: 2.86 (2.90) Acct: 4.48 (4.53) proj_loss: -0.5479 (-0.5475) time: 0.6759 data: 0.0014 [11-23 17:37:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 54/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.676 (6.705) Lt: 5.992 (6.009) Accm: 2.83 (2.74) Acct: 4.15 (4.22) proj_loss: -0.5596 (-0.5621) time: 0.6759 data: 0.0016 [11-23 17:37:09] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 54/350] Total time: 0:18:54 (0.680 s / it) [11-23 17:37:09] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 54/350] Total time: 0:18:54 (0.680 s / it) [11-23 17:37:09] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 54/350] Total time: 0:18:54 (0.680 s / it) [11-23 17:37:09] (/home/user/VAR/train.py , line 276)=> [ep54] (training ) Lm: 6.692 (6.692), Lt: 5.955 (5.955), Acc m&t: 2.88 4.56, Remain: 3 days, 21:00:10, Finish: 2024-11-26 22:37 [11-23 17:37:09] (/home/user/VAR/train.py , line 276)=> [ep54] (training ) Lm: 6.692 (6.692), Lt: 5.955 (5.955), Acc m&t: 2.88 4.56, Remain: 3 days, 21:02:00, Finish: 2024-11-26 22:39 [11-23 17:37:09] (/home/user/VAR/train.py , line 276)=> [ep54] (training ) Lm: 6.692 (6.692), Lt: 5.955 (5.955), Acc m&t: 2.88 4.56, Remain: 3 days, 21:00:11, Finish: 2024-11-26 22:37 [11-23 17:37:09] (/home/user/VAR/train.py , line 276)=> [ep54] (training ) Lm: 6.692 (6.692), Lt: 5.955 (5.955), Acc m&t: 2.88 4.56, Remain: 3 days, 21:00:48, Finish: 2024-11-26 22:37 [11-23 17:37:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [ 0/1669] eta: 0:18:27 tlr: 0.00022 tnm: 0.27 Lm: 6.602 (6.602) Lt: 5.827 (5.827) Accm: 3.06 (3.06) Acct: 4.79 (4.79) proj_loss: -0.5582 (-0.5582) time: 0.6634 data: 0.0003 [11-23 17:37:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [ 0/1669] eta: 0:18:28 tlr: 0.00022 tnm: 0.27 Lm: 6.710 (6.710) Lt: 6.030 (6.030) Accm: 2.59 (2.59) Acct: 3.72 (3.72) proj_loss: -0.5552 (-0.5552) time: 0.6639 data: 0.0003 [11-23 17:37:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [ 0/1669] eta: 0:18:27 tlr: 0.00022 tnm: 0.27 Lm: 6.671 (6.671) Lt: 5.912 (5.912) Accm: 2.81 (2.81) Acct: 4.48 (4.48) proj_loss: -0.5170 (-0.5170) time: 0.6636 data: 0.0003 [11-23 17:37:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [ 0/1669] eta: 0:18:28 tlr: 0.00022 tnm: 0.27 Lm: 6.759 (6.759) Lt: 6.047 (6.047) Accm: 2.84 (2.84) Acct: 4.49 (4.49) proj_loss: -0.5384 (-0.5384) time: 0.6640 data: 0.0003 [11-23 17:42:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [ 417/1669] eta: 0:14:39 tlr: 0.00022 tnm: 0.28 Lm: 6.690 (6.690) Lt: 5.933 (5.933) Accm: 3.02 (3.02) Acct: 4.82 (4.82) proj_loss: -0.5379 (-0.5379) time: 0.6745 data: 0.0003 [11-23 17:42:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [ 417/1669] eta: 0:14:39 tlr: 0.00022 tnm: 0.28 Lm: 6.728 (6.728) Lt: 5.995 (5.995) Accm: 2.77 (2.77) Acct: 4.35 (4.35) proj_loss: -0.5297 (-0.5297) time: 0.6745 data: 0.0003 [11-23 17:42:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [ 417/1669] eta: 0:14:39 tlr: 0.00022 tnm: 0.28 Lm: 6.775 (6.775) Lt: 6.081 (6.081) Accm: 2.55 (2.55) Acct: 3.97 (3.97) proj_loss: -0.5544 (-0.5544) time: 0.6745 data: 0.0002 [11-23 17:42:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [ 417/1669] eta: 0:14:39 tlr: 0.00022 tnm: 0.28 Lm: 6.630 (6.630) Lt: 5.899 (5.899) Accm: 3.12 (3.12) Acct: 4.96 (4.96) proj_loss: -0.5591 (-0.5591) time: 0.6745 data: 0.0003 [11-23 17:46:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [ 834/1669] eta: 0:09:34 tlr: 0.00022 tnm: 0.30 Lm: 6.655 (6.638) Lt: 5.920 (5.906) Accm: 3.06 (3.10) Acct: 4.79 (4.90) proj_loss: -0.5600 (-0.5595) time: 0.6738 data: 0.0003 [11-23 17:46:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [ 834/1669] eta: 0:09:34 tlr: 0.00022 tnm: 0.30 Lm: 6.671 (6.668) Lt: 5.912 (5.936) Accm: 2.81 (2.88) Acct: 4.48 (4.46) proj_loss: -0.5425 (-0.5449) time: 0.6738 data: 0.0002 [11-23 17:46:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [ 834/1669] eta: 0:09:34 tlr: 0.00022 tnm: 0.30 Lm: 6.710 (6.705) Lt: 6.030 (5.993) Accm: 2.59 (2.74) Acct: 4.22 (4.25) proj_loss: -0.5552 (-0.5578) time: 0.6738 data: 0.0002 [11-23 17:46:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [ 834/1669] eta: 0:09:34 tlr: 0.00022 tnm: 0.30 Lm: 6.707 (6.695) Lt: 5.996 (5.954) Accm: 2.84 (2.92) Acct: 4.49 (4.69) proj_loss: -0.5384 (-0.5497) time: 0.6738 data: 0.0002 [11-23 17:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [1251/1669] eta: 0:04:45 tlr: 0.00022 tnm: 0.31 Lm: 6.664 (6.668) Lt: 5.928 (5.931) Accm: 3.02 (3.00) Acct: 4.82 (4.84) proj_loss: -0.5529 (-0.5541) time: 0.6723 data: 0.0002 [11-23 17:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [1251/1669] eta: 0:04:45 tlr: 0.00022 tnm: 0.31 Lm: 6.656 (6.679) Lt: 5.946 (5.953) Accm: 3.05 (2.97) Acct: 4.79 (4.68) proj_loss: -0.5591 (-0.5589) time: 0.6723 data: 0.0003 [11-23 17:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [1251/1669] eta: 0:04:45 tlr: 0.00022 tnm: 0.31 Lm: 6.667 (6.667) Lt: 5.921 (5.934) Accm: 2.88 (2.90) Acct: 4.58 (4.52) proj_loss: -0.5577 (-0.5519) time: 0.6723 data: 0.0002 [11-23 17:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [1251/1669] eta: 0:04:45 tlr: 0.00022 tnm: 0.31 Lm: 6.700 (6.702) Lt: 6.003 (5.989) Accm: 2.75 (2.79) Acct: 4.40 (4.33) proj_loss: -0.5599 (-0.5615) time: 0.6723 data: 0.0003 [11-23 17:56:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.690 (6.693) Lt: 5.975 (5.972) Accm: 2.92 (2.84) Acct: 4.58 (4.41) proj_loss: -0.5645 (-0.5621) time: 0.6761 data: 0.0020 [11-23 17:56:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 55/350] Total time: 0:18:56 (0.681 s / it) [11-23 17:56:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.658 (6.698) Lt: 5.972 (5.985) Accm: 3.04 (2.89) Acct: 4.79 (4.52) proj_loss: -0.5582 (-0.5580) time: 0.6761 data: 0.0015 [11-23 17:56:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.621 (6.655) Lt: 5.869 (5.918) Accm: 2.84 (2.94) Acct: 4.49 (4.69) proj_loss: -0.5604 (-0.5554) time: 0.6761 data: 0.0015 [11-23 17:56:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 55/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.29 Lm: 6.670 (6.668) Lt: 5.930 (5.938) Accm: 2.81 (2.87) Acct: 4.48 (4.42) proj_loss: -0.5494 (-0.5514) time: 0.6761 data: 0.0016 [11-23 17:56:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 55/350] Total time: 0:18:56 (0.681 s / it) [11-23 17:56:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 55/350] Total time: 0:18:56 (0.681 s / it) [11-23 17:56:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 55/350] Total time: 0:18:56 (0.681 s / it) [11-23 17:56:05] (/home/user/VAR/train.py , line 276)=> [ep55] (training ) Lm: 6.692 (6.703), Lt: 5.955 (5.970), Acc m&t: 2.88 4.56, Remain: 3 days, 20:50:07, Finish: 2024-11-26 22:46 [11-23 17:56:05] (/home/user/VAR/train.py , line 276)=> [ep55] (training ) Lm: 6.692 (6.703), Lt: 5.955 (5.970), Acc m&t: 2.88 4.56, Remain: 3 days, 20:50:22, Finish: 2024-11-26 22:46 [11-23 17:56:05] (/home/user/VAR/train.py , line 276)=> [ep55] (training ) Lm: 6.692 (6.703), Lt: 5.955 (5.970), Acc m&t: 2.88 4.56, Remain: 3 days, 20:50:28, Finish: 2024-11-26 22:46 [11-23 17:56:05] (/home/user/VAR/train.py , line 276)=> [ep55] (training ) Lm: 6.692 (6.703), Lt: 5.955 (5.970), Acc m&t: 2.88 4.56, Remain: 3 days, 20:49:59, Finish: 2024-11-26 22:46 [11-23 17:56:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [ 0/1669] eta: 0:18:14 tlr: 0.00022 tnm: 0.30 Lm: 6.814 (6.814) Lt: 6.070 (6.070) Accm: 2.49 (2.49) Acct: 3.81 (3.81) proj_loss: -0.5421 (-0.5421) time: 0.6558 data: 0.0003 [11-23 17:56:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [ 0/1669] eta: 4:46:09 tlr: 0.00022 tnm: 0.30 Lm: 6.523 (6.523) Lt: 5.779 (5.779) Accm: 2.93 (2.93) Acct: 4.58 (4.58) proj_loss: -0.5608 (-0.5608) time: 10.2873 data: 0.0003 [11-23 17:56:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [ 0/1669] eta: 4:46:09 tlr: 0.00022 tnm: 0.30 Lm: 6.867 (6.867) Lt: 6.134 (6.134) Accm: 2.55 (2.55) Acct: 4.08 (4.08) proj_loss: -0.5407 (-0.5407) time: 10.2875 data: 0.0004 [11-23 17:56:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [ 0/1669] eta: 4:46:09 tlr: 0.00022 tnm: 0.30 Lm: 6.846 (6.846) Lt: 6.130 (6.130) Accm: 2.41 (2.41) Acct: 3.96 (3.96) proj_loss: -0.5299 (-0.5299) time: 10.2876 data: 0.0004 [11-23 18:00:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [ 417/1669] eta: 0:14:32 tlr: 0.00022 tnm: 0.27 Lm: 6.729 (6.729) Lt: 6.033 (6.033) Accm: 2.64 (2.64) Acct: 4.14 (4.14) proj_loss: -0.5506 (-0.5506) time: 0.6718 data: 0.0003 [11-23 18:00:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [ 417/1669] eta: 0:14:03 tlr: 0.00022 tnm: 0.27 Lm: 6.816 (6.816) Lt: 6.110 (6.110) Accm: 2.45 (2.45) Acct: 3.93 (3.93) proj_loss: -0.5547 (-0.5547) time: 0.6718 data: 0.0002 [11-23 18:00:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [ 417/1669] eta: 0:14:32 tlr: 0.00022 tnm: 0.27 Lm: 6.531 (6.531) Lt: 5.791 (5.791) Accm: 3.12 (3.12) Acct: 4.88 (4.88) proj_loss: -0.5586 (-0.5586) time: 0.6718 data: 0.0002 [11-23 18:00:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [ 417/1669] eta: 0:14:32 tlr: 0.00022 tnm: 0.27 Lm: 6.809 (6.809) Lt: 6.079 (6.079) Accm: 2.64 (2.64) Acct: 4.21 (4.21) proj_loss: -0.5553 (-0.5553) time: 0.6718 data: 0.0003 [11-23 18:05:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [ 834/1669] eta: 0:09:31 tlr: 0.00022 tnm: 0.29 Lm: 6.750 (6.738) Lt: 6.025 (6.007) Accm: 2.73 (2.74) Acct: 4.34 (4.40) proj_loss: -0.5592 (-0.5566) time: 0.6732 data: 0.0003 [11-23 18:05:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [ 834/1669] eta: 0:09:31 tlr: 0.00022 tnm: 0.29 Lm: 6.665 (6.707) Lt: 5.936 (5.992) Accm: 2.86 (2.75) Acct: 4.32 (4.36) proj_loss: -0.5712 (-0.5579) time: 0.6732 data: 0.0002 [11-23 18:05:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [ 834/1669] eta: 0:09:31 tlr: 0.00022 tnm: 0.29 Lm: 6.539 (6.545) Lt: 5.803 (5.801) Accm: 3.31 (3.21) Acct: 5.18 (5.03) proj_loss: -0.5563 (-0.5532) time: 0.6732 data: 0.0003 [11-23 18:05:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [ 834/1669] eta: 0:09:22 tlr: 0.00022 tnm: 0.29 Lm: 6.814 (6.755) Lt: 6.070 (6.041) Accm: 2.49 (2.63) Acct: 4.05 (4.18) proj_loss: -0.5456 (-0.5517) time: 0.6732 data: 0.0003 [11-23 18:10:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [1251/1669] eta: 0:04:45 tlr: 0.00022 tnm: 0.26 Lm: 6.766 (6.746) Lt: 6.019 (6.023) Accm: 2.65 (2.67) Acct: 4.30 (4.27) proj_loss: -0.5460 (-0.5504) time: 0.6729 data: 0.0003 [11-23 18:10:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [1251/1669] eta: 0:04:49 tlr: 0.00022 tnm: 0.26 Lm: 6.556 (6.573) Lt: 5.812 (5.845) Accm: 3.12 (3.06) Acct: 4.88 (4.74) proj_loss: -0.5586 (-0.5562) time: 0.6729 data: 0.0003 [11-23 18:10:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [1251/1669] eta: 0:04:49 tlr: 0.00022 tnm: 0.26 Lm: 6.729 (6.729) Lt: 6.011 (6.016) Accm: 2.74 (2.72) Acct: 4.18 (4.28) proj_loss: -0.5631 (-0.5572) time: 0.6729 data: 0.0002 [11-23 18:10:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [1251/1669] eta: 0:04:49 tlr: 0.00022 tnm: 0.26 Lm: 6.759 (6.746) Lt: 6.044 (6.021) Accm: 2.75 (2.74) Acct: 4.31 (4.37) proj_loss: -0.5646 (-0.5625) time: 0.6729 data: 0.0003 [11-23 18:15:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.28 Lm: 6.758 (6.748) Lt: 6.025 (6.022) Accm: 2.76 (2.77) Acct: 4.34 (4.40) proj_loss: -0.5592 (-0.5596) time: 0.6758 data: 0.0018 [11-23 18:15:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 56/350] Total time: 0:19:07 (0.687 s / it) [11-23 18:15:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.28 Lm: 6.677 (6.718) Lt: 5.961 (6.005) Accm: 2.86 (2.80) Acct: 4.32 (4.40) proj_loss: -0.5693 (-0.5596) time: 0.6758 data: 0.0016 [11-23 18:15:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.28 Lm: 6.717 (6.725) Lt: 5.967 (6.003) Accm: 2.80 (2.74) Acct: 4.55 (4.35) proj_loss: -0.5464 (-0.5526) time: 0.6758 data: 0.0018 [11-23 18:15:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 56/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.28 Lm: 6.572 (6.603) Lt: 5.820 (5.879) Accm: 2.93 (2.97) Acct: 4.58 (4.62) proj_loss: -0.5608 (-0.5591) time: 0.6758 data: 0.0018 [11-23 18:15:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 56/350] Total time: 0:19:07 (0.687 s / it) [11-23 18:15:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 56/350] Total time: 0:18:57 (0.682 s / it) [11-23 18:15:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 56/350] Total time: 0:19:07 (0.687 s / it) [11-23 18:15:12] (/home/user/VAR/train.py , line 276)=> [ep56] (training ) Lm: 6.692 (6.697), Lt: 5.955 (5.968), Acc m&t: 2.88 4.56, Remain: 3 days, 20:28:12, Finish: 2024-11-26 22:43 [11-23 18:15:12] (/home/user/VAR/train.py , line 276)=> [ep56] (training ) Lm: 6.692 (6.697), Lt: 5.955 (5.968), Acc m&t: 2.88 4.56, Remain: 3 days, 20:29:30, Finish: 2024-11-26 22:44 [11-23 18:15:12] (/home/user/VAR/train.py , line 276)=> [ep56] (training ) Lm: 6.692 (6.697), Lt: 5.955 (5.968), Acc m&t: 2.88 4.56, Remain: 3 days, 20:29:49, Finish: 2024-11-26 22:45 [11-23 18:15:12] (/home/user/VAR/train.py , line 276)=> [ep56] (training ) Lm: 6.692 (6.697), Lt: 5.955 (5.968), Acc m&t: 2.88 4.56, Remain: 3 days, 20:24:56, Finish: 2024-11-26 22:40 [11-23 18:16:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [ 0/1669] eta: 0:19:30 tlr: 0.00022 tnm: 0.26 Lm: 6.618 (6.618) Lt: 5.896 (5.896) Accm: 3.08 (3.08) Acct: 5.03 (5.03) proj_loss: -0.5517 (-0.5517) time: 0.7014 data: 0.0004 [11-23 18:16:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [ 0/1669] eta: 0:18:34 tlr: 0.00022 tnm: 0.26 Lm: 6.613 (6.613) Lt: 5.811 (5.811) Accm: 3.29 (3.29) Acct: 5.42 (5.42) proj_loss: -0.5442 (-0.5442) time: 0.6680 data: 0.0004 [11-23 18:16:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [ 0/1669] eta: 0:22:27 tlr: 0.00022 tnm: 0.26 Lm: 6.844 (6.844) Lt: 6.160 (6.160) Accm: 2.53 (2.53) Acct: 3.93 (3.93) proj_loss: -0.5493 (-0.5493) time: 0.8072 data: 0.0004 [11-23 18:16:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [ 0/1669] eta: 0:20:23 tlr: 0.00022 tnm: 0.26 Lm: 6.736 (6.736) Lt: 5.990 (5.990) Accm: 2.86 (2.86) Acct: 4.73 (4.73) proj_loss: -0.5729 (-0.5729) time: 0.7329 data: 0.0003 [11-23 18:21:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [ 417/1669] eta: 0:14:04 tlr: 0.00022 tnm: 0.27 Lm: 6.739 (6.739) Lt: 6.012 (6.012) Accm: 2.71 (2.71) Acct: 4.44 (4.44) proj_loss: -0.5621 (-0.5621) time: 0.6726 data: 0.0003 [11-23 18:21:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [ 417/1669] eta: 0:14:04 tlr: 0.00022 tnm: 0.27 Lm: 6.729 (6.729) Lt: 5.969 (5.969) Accm: 2.78 (2.78) Acct: 4.44 (4.44) proj_loss: -0.5429 (-0.5429) time: 0.6726 data: 0.0003 [11-23 18:21:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [ 417/1669] eta: 0:14:04 tlr: 0.00022 tnm: 0.27 Lm: 6.674 (6.674) Lt: 5.966 (5.966) Accm: 2.81 (2.81) Acct: 4.55 (4.55) proj_loss: -0.5555 (-0.5555) time: 0.6726 data: 0.0002 [11-23 18:21:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [ 417/1669] eta: 0:14:03 tlr: 0.00022 tnm: 0.27 Lm: 6.680 (6.680) Lt: 5.901 (5.901) Accm: 3.01 (3.01) Acct: 4.86 (4.86) proj_loss: -0.5495 (-0.5495) time: 0.6727 data: 0.0003 [11-23 18:25:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [ 834/1669] eta: 0:09:22 tlr: 0.00022 tnm: 0.28 Lm: 6.613 (6.612) Lt: 5.811 (5.818) Accm: 3.29 (3.24) Acct: 5.42 (5.27) proj_loss: -0.5547 (-0.5527) time: 0.6753 data: 0.0003 [11-23 18:25:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [ 834/1669] eta: 0:09:22 tlr: 0.00022 tnm: 0.28 Lm: 6.753 (6.737) Lt: 6.000 (5.979) Accm: 3.02 (2.89) Acct: 4.96 (4.74) proj_loss: -0.5441 (-0.5433) time: 0.6753 data: 0.0003 [11-23 18:25:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [ 834/1669] eta: 0:09:22 tlr: 0.00022 tnm: 0.28 Lm: 6.718 (6.689) Lt: 5.999 (5.977) Accm: 2.64 (2.75) Acct: 4.29 (4.46) proj_loss: -0.5517 (-0.5516) time: 0.6753 data: 0.0002 [11-23 18:25:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [ 834/1669] eta: 0:09:22 tlr: 0.00022 tnm: 0.28 Lm: 6.736 (6.694) Lt: 5.990 (5.970) Accm: 2.86 (2.89) Acct: 4.73 (4.67) proj_loss: -0.5524 (-0.5589) time: 0.6753 data: 0.0003 [11-23 18:30:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [1251/1669] eta: 0:04:46 tlr: 0.00022 tnm: 0.27 Lm: 6.739 (6.716) Lt: 6.012 (5.998) Accm: 2.71 (2.80) Acct: 4.44 (4.52) proj_loss: -0.5593 (-0.5607) time: 0.6740 data: 0.0003 [11-23 18:30:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [1251/1669] eta: 0:04:46 tlr: 0.00022 tnm: 0.27 Lm: 6.743 (6.736) Lt: 5.994 (5.982) Accm: 2.98 (2.90) Acct: 4.79 (4.71) proj_loss: -0.5421 (-0.5425) time: 0.6740 data: 0.0003 [11-23 18:30:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [1251/1669] eta: 0:04:46 tlr: 0.00022 tnm: 0.27 Lm: 6.724 (6.710) Lt: 5.989 (5.977) Accm: 2.60 (2.71) Acct: 4.21 (4.38) proj_loss: -0.5550 (-0.5533) time: 0.6740 data: 0.0003 [11-23 18:30:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [1251/1669] eta: 0:04:46 tlr: 0.00022 tnm: 0.27 Lm: 6.680 (6.662) Lt: 5.901 (5.881) Accm: 3.04 (3.13) Acct: 4.91 (5.05) proj_loss: -0.5518 (-0.5517) time: 0.6740 data: 0.0003 [11-23 18:35:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.635 (6.657) Lt: 5.904 (5.885) Accm: 3.18 (3.14) Acct: 4.86 (5.01) proj_loss: -0.5489 (-0.5482) time: 0.6763 data: 0.0014 [11-23 18:35:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 57/350] Total time: 0:18:58 (0.682 s / it) [11-23 18:35:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.736 (6.707) Lt: 5.990 (5.990) Accm: 2.86 (2.86) Acct: 4.73 (4.60) proj_loss: -0.5662 (-0.5636) time: 0.6764 data: 0.0016 [11-23 18:35:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.718 (6.697) Lt: 5.978 (5.959) Accm: 2.64 (2.76) Acct: 4.29 (4.48) proj_loss: -0.5583 (-0.5556) time: 0.6764 data: 0.0015 [11-23 18:35:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 57/350] [1668/1669] eta: 0:00:00 tlr: 0.00022 tnm: 0.27 Lm: 6.733 (6.708) Lt: 5.988 (5.953) Accm: 2.97 (2.92) Acct: 4.79 (4.72) proj_loss: -0.5441 (-0.5466) time: 0.6763 data: 0.0016 [11-23 18:35:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 57/350] Total time: 0:18:58 (0.682 s / it) [11-23 18:35:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 57/350] Total time: 0:18:58 (0.682 s / it) [11-23 18:35:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 57/350] Total time: 0:18:58 (0.682 s / it) [11-23 18:35:28] (/home/user/VAR/train.py , line 276)=> [ep57] (training ) Lm: 6.679 (6.679), Lt: 5.937 (5.937), Acc m&t: 2.92 4.65, Remain: 3 days, 20:10:02, Finish: 2024-11-26 22:45 [11-23 18:35:28] (/home/user/VAR/train.py , line 276)=> [ep57] (training ) Lm: 6.679 (6.679), Lt: 5.937 (5.937), Acc m&t: 2.92 4.65, Remain: 3 days, 20:09:09, Finish: 2024-11-26 22:44 [11-23 18:35:28] (/home/user/VAR/train.py , line 276)=> [ep57] (training ) Lm: 6.679 (6.679), Lt: 5.937 (5.937), Acc m&t: 2.92 4.65, Remain: 3 days, 20:08:41, Finish: 2024-11-26 22:44 [11-23 18:35:28] (/home/user/VAR/train.py , line 276)=> [ep57] (training ) Lm: 6.679 (6.679), Lt: 5.937 (5.937), Acc m&t: 2.92 4.65, Remain: 3 days, 20:09:00, Finish: 2024-11-26 22:44 [11-23 18:35:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [ 0/1669] eta: 0:18:20 tlr: 0.00022 tnm: 0.29 Lm: 6.691 (6.691) Lt: 5.938 (5.938) Accm: 2.94 (2.94) Acct: 4.65 (4.65) proj_loss: -0.5545 (-0.5545) time: 0.6595 data: 0.0004 [11-23 18:35:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [ 0/1669] eta: 0:18:20 tlr: 0.00022 tnm: 0.29 Lm: 6.735 (6.735) Lt: 6.039 (6.039) Accm: 2.75 (2.75) Acct: 4.34 (4.34) proj_loss: -0.5495 (-0.5495) time: 0.6597 data: 0.0004 [11-23 18:35:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [ 0/1669] eta: 0:18:21 tlr: 0.00022 tnm: 0.29 Lm: 6.877 (6.877) Lt: 6.114 (6.114) Accm: 2.51 (2.51) Acct: 4.20 (4.20) proj_loss: -0.5421 (-0.5421) time: 0.6599 data: 0.0003 [11-23 18:35:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [ 0/1669] eta: 0:18:21 tlr: 0.00022 tnm: 0.29 Lm: 6.727 (6.727) Lt: 5.958 (5.958) Accm: 2.83 (2.83) Acct: 4.84 (4.84) proj_loss: -0.5554 (-0.5554) time: 0.6599 data: 0.0003 [11-23 18:40:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [ 417/1669] eta: 0:14:03 tlr: 0.00022 tnm: 0.28 Lm: 6.760 (6.760) Lt: 6.012 (6.012) Accm: 2.70 (2.70) Acct: 4.48 (4.48) proj_loss: -0.5569 (-0.5569) time: 0.6745 data: 0.0002 [11-23 18:40:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [ 417/1669] eta: 0:14:03 tlr: 0.00022 tnm: 0.28 Lm: 6.831 (6.831) Lt: 6.111 (6.111) Accm: 2.46 (2.46) Acct: 3.90 (3.90) proj_loss: -0.5528 (-0.5528) time: 0.6745 data: 0.0002 [11-23 18:40:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [ 417/1669] eta: 0:14:03 tlr: 0.00022 tnm: 0.28 Lm: 6.623 (6.623) Lt: 5.874 (5.874) Accm: 3.08 (3.08) Acct: 4.93 (4.93) proj_loss: -0.5482 (-0.5482) time: 0.6745 data: 0.0003 [11-23 18:40:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [ 417/1669] eta: 0:14:03 tlr: 0.00022 tnm: 0.28 Lm: 6.755 (6.755) Lt: 6.015 (6.015) Accm: 2.70 (2.70) Acct: 4.27 (4.27) proj_loss: -0.5683 (-0.5683) time: 0.6745 data: 0.0003 [11-23 18:44:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [ 834/1669] eta: 0:09:22 tlr: 0.00021 tnm: 0.28 Lm: 6.691 (6.718) Lt: 5.938 (5.986) Accm: 2.94 (2.81) Acct: 4.65 (4.50) proj_loss: -0.5798 (-0.5721) time: 0.6729 data: 0.0003 [11-23 18:44:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [ 834/1669] eta: 0:09:22 tlr: 0.00021 tnm: 0.28 Lm: 6.686 (6.644) Lt: 6.021 (5.923) Accm: 2.88 (3.01) Acct: 4.34 (4.70) proj_loss: -0.5495 (-0.5518) time: 0.6729 data: 0.0002 [11-23 18:44:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [ 834/1669] eta: 0:09:22 tlr: 0.00021 tnm: 0.28 Lm: 6.727 (6.681) Lt: 5.958 (5.904) Accm: 2.83 (2.99) Acct: 4.84 (5.00) proj_loss: -0.5554 (-0.5560) time: 0.6729 data: 0.0002 [11-23 18:44:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [ 834/1669] eta: 0:09:22 tlr: 0.00021 tnm: 0.28 Lm: 6.786 (6.776) Lt: 6.108 (6.058) Accm: 2.51 (2.56) Acct: 4.20 (4.09) proj_loss: -0.5583 (-0.5546) time: 0.6729 data: 0.0002 [11-23 18:49:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [1251/1669] eta: 0:04:41 tlr: 0.00021 tnm: 0.28 Lm: 6.726 (6.740) Lt: 6.030 (6.011) Accm: 2.64 (2.70) Acct: 4.33 (4.36) proj_loss: -0.5609 (-0.5572) time: 0.6727 data: 0.0003 [11-23 18:49:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [1251/1669] eta: 0:04:41 tlr: 0.00021 tnm: 0.28 Lm: 6.687 (6.673) Lt: 5.928 (5.903) Accm: 2.97 (3.02) Acct: 4.89 (4.98) proj_loss: -0.5569 (-0.5590) time: 0.6728 data: 0.0002 [11-23 18:49:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [1251/1669] eta: 0:04:41 tlr: 0.00021 tnm: 0.28 Lm: 6.598 (6.598) Lt: 5.876 (5.875) Accm: 3.14 (3.13) Acct: 4.66 (4.77) proj_loss: -0.5542 (-0.5553) time: 0.6727 data: 0.0003 [11-23 18:49:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [1251/1669] eta: 0:04:41 tlr: 0.00021 tnm: 0.28 Lm: 6.728 (6.729) Lt: 5.992 (6.001) Accm: 2.72 (2.73) Acct: 4.37 (4.40) proj_loss: -0.5672 (-0.5664) time: 0.6728 data: 0.0003 [11-23 18:54:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.691 (6.705) Lt: 5.938 (5.971) Accm: 2.94 (2.80) Acct: 4.65 (4.50) proj_loss: -0.5670 (-0.5665) time: 0.6762 data: 0.0018 [11-23 18:54:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 58/350] Total time: 0:18:45 (0.674 s / it) [11-23 18:54:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.654 (6.669) Lt: 5.899 (5.902) Accm: 2.94 (3.00) Acct: 4.84 (4.90) proj_loss: -0.5554 (-0.5525) time: 0.6762 data: 0.0014 [11-23 18:54:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.686 (6.621) Lt: 6.004 (5.901) Accm: 2.88 (3.05) Acct: 4.34 (4.68) proj_loss: -0.5589 (-0.5571) time: 0.6762 data: 0.0016 [11-23 18:54:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 58/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.739 (6.740) Lt: 5.964 (6.001) Accm: 2.67 (2.70) Acct: 4.36 (4.36) proj_loss: -0.5583 (-0.5561) time: 0.6762 data: 0.0016 [11-23 18:54:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 58/350] Total time: 0:18:45 (0.674 s / it) [11-23 18:54:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 58/350] Total time: 0:18:45 (0.674 s / it) [11-23 18:54:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 58/350] Total time: 0:18:45 (0.674 s / it) [11-23 18:54:14] (/home/user/VAR/train.py , line 276)=> [ep58] (training ) Lm: 6.679 (6.685), Lt: 5.937 (5.948), Acc m&t: 2.92 4.65, Remain: 3 days, 19:44:20, Finish: 2024-11-26 22:38 [11-23 18:54:14] (/home/user/VAR/train.py , line 276)=> [ep58] (training ) Lm: 6.679 (6.685), Lt: 5.937 (5.948), Acc m&t: 2.92 4.65, Remain: 3 days, 19:46:45, Finish: 2024-11-26 22:40 [11-23 18:54:14] (/home/user/VAR/train.py , line 276)=> [ep58] (training ) Lm: 6.679 (6.685), Lt: 5.937 (5.948), Acc m&t: 2.92 4.65, Remain: 3 days, 19:45:52, Finish: 2024-11-26 22:40 [11-23 18:54:14] (/home/user/VAR/train.py , line 276)=> [ep58] (training ) Lm: 6.679 (6.685), Lt: 5.937 (5.948), Acc m&t: 2.92 4.65, Remain: 3 days, 19:45:50, Finish: 2024-11-26 22:40 [11-23 18:54:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [ 0/1669] eta: 0:18:24 tlr: 0.00021 tnm: 0.28 Lm: 6.747 (6.747) Lt: 6.043 (6.043) Accm: 2.67 (2.67) Acct: 4.20 (4.20) proj_loss: -0.5606 (-0.5606) time: 0.6615 data: 0.0003 [11-23 18:54:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [ 0/1669] eta: 0:18:24 tlr: 0.00021 tnm: 0.28 Lm: 6.719 (6.719) Lt: 6.009 (6.009) Accm: 2.94 (2.94) Acct: 4.60 (4.60) proj_loss: -0.5685 (-0.5685) time: 0.6617 data: 0.0003 [11-23 18:54:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [ 0/1669] eta: 0:18:24 tlr: 0.00021 tnm: 0.28 Lm: 6.628 (6.628) Lt: 5.804 (5.804) Accm: 3.21 (3.21) Acct: 5.39 (5.39) proj_loss: -0.5520 (-0.5520) time: 0.6619 data: 0.0004 [11-23 18:54:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [ 0/1669] eta: 0:18:24 tlr: 0.00021 tnm: 0.28 Lm: 6.563 (6.563) Lt: 5.819 (5.819) Accm: 3.27 (3.27) Acct: 4.92 (4.92) proj_loss: -0.5565 (-0.5565) time: 0.6616 data: 0.0003 [11-23 18:59:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [ 417/1669] eta: 0:14:43 tlr: 0.00021 tnm: 0.30 Lm: 6.660 (6.660) Lt: 5.948 (5.948) Accm: 2.90 (2.90) Acct: 4.50 (4.50) proj_loss: -0.5534 (-0.5534) time: 0.6734 data: 0.0003 [11-23 18:59:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [ 417/1669] eta: 0:14:43 tlr: 0.00021 tnm: 0.30 Lm: 6.642 (6.642) Lt: 5.925 (5.925) Accm: 3.07 (3.07) Acct: 4.71 (4.71) proj_loss: -0.5750 (-0.5750) time: 0.6734 data: 0.0003 [11-23 18:59:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [ 417/1669] eta: 0:14:43 tlr: 0.00021 tnm: 0.30 Lm: 6.614 (6.614) Lt: 5.842 (5.842) Accm: 3.10 (3.10) Acct: 5.12 (5.12) proj_loss: -0.5613 (-0.5613) time: 0.6734 data: 0.0002 [11-23 18:59:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [ 417/1669] eta: 0:14:43 tlr: 0.00021 tnm: 0.30 Lm: 6.562 (6.562) Lt: 5.787 (5.787) Accm: 3.33 (3.33) Acct: 5.28 (5.28) proj_loss: -0.5625 (-0.5625) time: 0.6734 data: 0.0003 [11-23 19:03:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [ 834/1669] eta: 0:09:36 tlr: 0.00021 tnm: 0.30 Lm: 6.579 (6.568) Lt: 5.837 (5.804) Accm: 3.34 (3.33) Acct: 5.11 (5.22) proj_loss: -0.5564 (-0.5585) time: 0.6751 data: 0.0003 [11-23 19:03:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [ 834/1669] eta: 0:09:36 tlr: 0.00021 tnm: 0.30 Lm: 6.628 (6.626) Lt: 5.880 (5.868) Accm: 2.98 (3.01) Acct: 4.86 (4.84) proj_loss: -0.5587 (-0.5604) time: 0.6751 data: 0.0002 [11-23 19:03:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [ 834/1669] eta: 0:09:36 tlr: 0.00021 tnm: 0.30 Lm: 6.660 (6.648) Lt: 5.896 (5.915) Accm: 2.87 (3.00) Acct: 4.53 (4.65) proj_loss: -0.5606 (-0.5672) time: 0.6751 data: 0.0003 [11-23 19:03:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [ 834/1669] eta: 0:09:36 tlr: 0.00021 tnm: 0.30 Lm: 6.705 (6.675) Lt: 6.007 (5.967) Accm: 3.06 (2.95) Acct: 4.91 (4.64) proj_loss: -0.5565 (-0.5578) time: 0.6751 data: 0.0003 [11-23 19:08:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [1251/1669] eta: 0:04:46 tlr: 0.00021 tnm: 0.29 Lm: 6.663 (6.661) Lt: 5.926 (5.937) Accm: 3.12 (3.01) Acct: 4.92 (4.76) proj_loss: -0.5591 (-0.5588) time: 0.6750 data: 0.0003 [11-23 19:08:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [1251/1669] eta: 0:04:46 tlr: 0.00021 tnm: 0.29 Lm: 6.639 (6.649) Lt: 5.899 (5.894) Accm: 2.91 (2.94) Acct: 4.57 (4.67) proj_loss: -0.5554 (-0.5573) time: 0.6750 data: 0.0002 [11-23 19:08:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [1251/1669] eta: 0:04:46 tlr: 0.00021 tnm: 0.29 Lm: 6.645 (6.603) Lt: 5.913 (5.850) Accm: 3.14 (3.23) Acct: 4.89 (5.08) proj_loss: -0.5579 (-0.5587) time: 0.6750 data: 0.0003 [11-23 19:08:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [1251/1669] eta: 0:04:46 tlr: 0.00021 tnm: 0.29 Lm: 6.688 (6.665) Lt: 5.924 (5.925) Accm: 2.83 (2.95) Acct: 4.50 (4.61) proj_loss: -0.5561 (-0.5584) time: 0.6750 data: 0.0003 [11-23 19:13:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.692 (6.670) Lt: 5.923 (5.924) Accm: 2.87 (2.96) Acct: 4.53 (4.66) proj_loss: -0.5606 (-0.5607) time: 0.6787 data: 0.0018 [11-23 19:13:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 59/350] Total time: 0:18:59 (0.683 s / it) [11-23 19:13:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.579 (6.579) Lt: 5.837 (5.827) Accm: 3.21 (3.23) Acct: 4.98 (5.06) proj_loss: -0.5594 (-0.5608) time: 0.6786 data: 0.0019 [11-23 19:13:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.705 (6.688) Lt: 6.007 (5.966) Accm: 3.06 (2.93) Acct: 4.91 (4.65) proj_loss: -0.5618 (-0.5624) time: 0.6786 data: 0.0021 [11-23 19:13:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 59/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.651 (6.676) Lt: 5.918 (5.935) Accm: 2.84 (2.89) Acct: 4.29 (4.56) proj_loss: -0.5587 (-0.5604) time: 0.6787 data: 0.0018 [11-23 19:13:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 59/350] Total time: 0:18:59 (0.683 s / it) [11-23 19:13:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 59/350] Total time: 0:18:59 (0.683 s / it) [11-23 19:13:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 59/350] Total time: 0:18:59 (0.683 s / it) [11-23 19:17:38] (me/user/VAR/evaluator.py, line 590)=> downloading InceptionV3 model... [11-23 19:18:51] (home/user/VAR/trainer.py, line 114)=> FID: 4.417994869935228 [11-23 19:18:52] (/home/user/VAR/train.py , line 259)=> [*] [ep59] (val 50000) Lm: 6.6766, Lt: 5.9408, Acc m&t: 2.93 4.64, Val cost: 338.23s [11-23 19:18:52] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-23 19:19:29] (/home/user/VAR/train.py , line 276)=> [ep59] (training ) Lm: 6.677 (6.677), Lt: 5.937 (5.941), Acc m&t: 2.93 4.65, Remain: 3 days, 19:43:39, Finish: 2024-11-26 22:56 [11-23 19:19:29] (/home/user/VAR/train.py , line 276)=> [ep59] (training ) Lm: 6.677 (6.677), Lt: 5.937 (5.941), Acc m&t: 2.93 4.65, Remain: 3 days, 19:43:51, Finish: 2024-11-26 22:57 [11-23 19:19:29] (/home/user/VAR/train.py , line 276)=> [ep59] (training ) Lm: 6.677 (6.677), Lt: 5.937 (5.941), Acc m&t: 2.93 4.65, Remain: 3 days, 19:43:51, Finish: 2024-11-26 22:57 [11-23 19:19:29] (/home/user/VAR/train.py , line 276)=> [ep59] (training ) Lm: 6.677 (6.677), Lt: 5.937 (5.941), Acc m&t: 2.93 4.65, Remain: 3 days, 19:43:52, Finish: 2024-11-26 22:57 [11-23 19:19:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [ 0/1669] eta: 0:20:48 tlr: 0.00021 tnm: 0.27 Lm: 6.710 (6.710) Lt: 5.951 (5.951) Accm: 2.67 (2.67) Acct: 4.27 (4.27) proj_loss: -0.5578 (-0.5578) time: 0.7480 data: 0.0004 [11-23 19:19:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [ 0/1669] eta: 0:20:38 tlr: 0.00021 tnm: 0.27 Lm: 6.638 (6.638) Lt: 5.872 (5.872) Accm: 2.76 (2.76) Acct: 4.53 (4.53) proj_loss: -0.5464 (-0.5464) time: 0.7423 data: 0.0004 [11-23 19:19:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [ 0/1669] eta: 0:20:40 tlr: 0.00021 tnm: 0.27 Lm: 6.761 (6.761) Lt: 6.053 (6.053) Accm: 2.45 (2.45) Acct: 4.06 (4.06) proj_loss: -0.5550 (-0.5550) time: 0.7430 data: 0.0004 [11-23 19:19:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [ 0/1669] eta: 0:20:40 tlr: 0.00021 tnm: 0.27 Lm: 6.812 (6.812) Lt: 6.093 (6.093) Accm: 2.80 (2.80) Acct: 4.30 (4.30) proj_loss: -0.5631 (-0.5631) time: 0.7435 data: 0.0004 [11-23 19:24:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [ 417/1669] eta: 0:14:05 tlr: 0.00021 tnm: 0.29 Lm: 6.777 (6.777) Lt: 6.053 (6.053) Accm: 2.82 (2.82) Acct: 4.49 (4.49) proj_loss: -0.5633 (-0.5633) time: 0.6751 data: 0.0003 [11-23 19:24:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [ 417/1669] eta: 0:14:05 tlr: 0.00021 tnm: 0.29 Lm: 6.740 (6.740) Lt: 6.016 (6.016) Accm: 2.65 (2.65) Acct: 4.27 (4.27) proj_loss: -0.5543 (-0.5543) time: 0.6751 data: 0.0003 [11-23 19:24:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [ 417/1669] eta: 0:14:05 tlr: 0.00021 tnm: 0.29 Lm: 6.731 (6.731) Lt: 6.007 (6.007) Accm: 2.74 (2.74) Acct: 4.40 (4.40) proj_loss: -0.5607 (-0.5607) time: 0.6751 data: 0.0003 [11-23 19:24:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [ 417/1669] eta: 0:14:05 tlr: 0.00021 tnm: 0.29 Lm: 6.656 (6.656) Lt: 5.897 (5.897) Accm: 2.83 (2.83) Acct: 4.55 (4.55) proj_loss: -0.5463 (-0.5463) time: 0.6751 data: 0.0002 [11-23 19:28:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.29 Lm: 6.602 (6.637) Lt: 5.842 (5.874) Accm: 2.99 (2.96) Acct: 4.84 (4.78) proj_loss: -0.5542 (-0.5489) time: 0.6761 data: 0.0003 [11-23 19:28:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.29 Lm: 6.742 (6.750) Lt: 6.013 (6.024) Accm: 2.83 (2.89) Acct: 4.67 (4.67) proj_loss: -0.5635 (-0.5645) time: 0.6761 data: 0.0003 [11-23 19:28:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.29 Lm: 6.733 (6.732) Lt: 5.986 (6.000) Accm: 2.61 (2.70) Acct: 4.20 (4.33) proj_loss: -0.5550 (-0.5581) time: 0.6761 data: 0.0003 [11-23 19:28:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.29 Lm: 6.812 (6.764) Lt: 6.117 (6.050) Accm: 2.64 (2.64) Acct: 4.20 (4.25) proj_loss: -0.5621 (-0.5612) time: 0.6761 data: 0.0003 [11-23 19:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.725 (6.720) Lt: 5.995 (6.004) Accm: 2.70 (2.77) Acct: 4.36 (4.34) proj_loss: -0.5621 (-0.5614) time: 0.6768 data: 0.0002 [11-23 19:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.747 (6.742) Lt: 6.019 (6.016) Accm: 2.71 (2.73) Acct: 4.30 (4.35) proj_loss: -0.5607 (-0.5608) time: 0.6768 data: 0.0003 [11-23 19:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.719 (6.718) Lt: 5.989 (5.986) Accm: 2.92 (2.92) Acct: 4.86 (4.76) proj_loss: -0.5633 (-0.5639) time: 0.6768 data: 0.0003 [11-23 19:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.656 (6.681) Lt: 5.897 (5.927) Accm: 2.83 (2.86) Acct: 4.56 (4.65) proj_loss: -0.5560 (-0.5517) time: 0.6768 data: 0.0003 [11-23 19:38:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.615 (6.668) Lt: 5.855 (5.913) Accm: 2.90 (2.87) Acct: 4.63 (4.65) proj_loss: -0.5578 (-0.5539) time: 0.6758 data: 0.0015 [11-23 19:38:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 60/350] Total time: 0:18:47 (0.676 s / it) [11-23 19:38:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.743 (6.742) Lt: 5.986 (6.008) Accm: 2.80 (2.74) Acct: 4.39 (4.41) proj_loss: -0.5576 (-0.5602) time: 0.6758 data: 0.0016 [11-23 19:38:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.638 (6.689) Lt: 5.872 (5.972) Accm: 2.76 (2.88) Acct: 4.53 (4.52) proj_loss: -0.5620 (-0.5595) time: 0.6758 data: 0.0018 [11-23 19:38:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 60/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.742 (6.725) Lt: 6.013 (5.993) Accm: 2.83 (2.86) Acct: 4.67 (4.62) proj_loss: -0.5631 (-0.5612) time: 0.6758 data: 0.0016 [11-23 19:38:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 60/350] Total time: 0:18:47 (0.676 s / it) [11-23 19:38:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 60/350] Total time: 0:18:47 (0.676 s / it) [11-23 19:38:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 60/350] Total time: 0:18:47 (0.676 s / it) [11-23 19:38:16] (/home/user/VAR/train.py , line 276)=> [ep60] (training ) Lm: 6.677 (6.690), Lt: 5.937 (5.951), Acc m&t: 2.93 4.65, Remain: 3 days, 19:09:34, Finish: 2024-11-26 22:47 [11-23 19:38:16] (/home/user/VAR/train.py , line 276)=> [ep60] (training ) Lm: 6.677 (6.690), Lt: 5.937 (5.951), Acc m&t: 2.93 4.65, Remain: 3 days, 19:09:01, Finish: 2024-11-26 22:47 [11-23 19:38:16] (/home/user/VAR/train.py , line 276)=> [ep60] (training ) Lm: 6.677 (6.690), Lt: 5.937 (5.951), Acc m&t: 2.93 4.65, Remain: 3 days, 19:08:19, Finish: 2024-11-26 22:46 [11-23 19:38:16] (/home/user/VAR/train.py , line 276)=> [ep60] (training ) Lm: 6.677 (6.690), Lt: 5.937 (5.951), Acc m&t: 2.93 4.65, Remain: 3 days, 19:09:49, Finish: 2024-11-26 22:48 [11-23 19:38:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [ 0/1669] eta: 0:18:32 tlr: 0.00021 tnm: 0.28 Lm: 6.647 (6.647) Lt: 5.842 (5.842) Accm: 2.96 (2.96) Acct: 4.63 (4.63) proj_loss: -0.5474 (-0.5474) time: 0.6664 data: 0.0003 [11-23 19:38:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [ 0/1669] eta: 0:18:23 tlr: 0.00021 tnm: 0.28 Lm: 6.889 (6.889) Lt: 6.241 (6.241) Accm: 2.32 (2.32) Acct: 3.39 (3.39) proj_loss: -0.5598 (-0.5598) time: 0.6613 data: 0.0004 [11-23 19:38:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [ 0/1669] eta: 0:18:16 tlr: 0.00021 tnm: 0.28 Lm: 6.582 (6.582) Lt: 5.842 (5.842) Accm: 3.13 (3.13) Acct: 4.49 (4.49) proj_loss: -0.5720 (-0.5720) time: 0.6570 data: 0.0003 [11-23 19:38:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [ 0/1669] eta: 0:18:24 tlr: 0.00021 tnm: 0.28 Lm: 6.623 (6.623) Lt: 5.855 (5.855) Accm: 3.23 (3.23) Acct: 4.92 (4.92) proj_loss: -0.5641 (-0.5641) time: 0.6618 data: 0.0003 [11-23 19:43:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [ 417/1669] eta: 0:14:10 tlr: 0.00021 tnm: 0.28 Lm: 6.599 (6.599) Lt: 5.823 (5.823) Accm: 3.14 (3.14) Acct: 4.94 (4.94) proj_loss: -0.5562 (-0.5562) time: 0.6784 data: 0.0002 [11-23 19:43:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [ 417/1669] eta: 0:14:10 tlr: 0.00021 tnm: 0.28 Lm: 6.670 (6.670) Lt: 5.902 (5.902) Accm: 2.87 (2.87) Acct: 4.55 (4.55) proj_loss: -0.5607 (-0.5607) time: 0.6784 data: 0.0002 [11-23 19:43:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [ 417/1669] eta: 0:14:10 tlr: 0.00021 tnm: 0.28 Lm: 6.657 (6.657) Lt: 5.932 (5.932) Accm: 3.02 (3.02) Acct: 4.53 (4.53) proj_loss: -0.5540 (-0.5540) time: 0.6784 data: 0.0002 [11-23 19:43:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [ 417/1669] eta: 0:14:10 tlr: 0.00021 tnm: 0.28 Lm: 6.774 (6.774) Lt: 6.050 (6.050) Accm: 2.59 (2.59) Acct: 4.05 (4.05) proj_loss: -0.5535 (-0.5535) time: 0.6784 data: 0.0003 [11-23 19:47:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [ 834/1669] eta: 0:09:35 tlr: 0.00021 tnm: 0.28 Lm: 6.647 (6.652) Lt: 5.845 (5.883) Accm: 2.96 (2.97) Acct: 4.63 (4.74) proj_loss: -0.5740 (-0.5689) time: 0.6761 data: 0.0002 [11-23 19:47:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [ 834/1669] eta: 0:09:35 tlr: 0.00021 tnm: 0.28 Lm: 6.615 (6.643) Lt: 5.904 (5.922) Accm: 2.98 (3.01) Acct: 4.56 (4.56) proj_loss: -0.5720 (-0.5607) time: 0.6762 data: 0.0003 [11-23 19:47:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [ 834/1669] eta: 0:09:35 tlr: 0.00021 tnm: 0.28 Lm: 6.748 (6.765) Lt: 6.066 (6.055) Accm: 2.74 (2.64) Acct: 4.18 (4.10) proj_loss: -0.5598 (-0.5589) time: 0.6762 data: 0.0003 [11-23 19:47:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [ 834/1669] eta: 0:09:35 tlr: 0.00021 tnm: 0.28 Lm: 6.582 (6.593) Lt: 5.833 (5.826) Accm: 3.23 (3.21) Acct: 4.96 (4.98) proj_loss: -0.5598 (-0.5574) time: 0.6762 data: 0.0002 [11-23 19:52:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [1251/1669] eta: 0:04:45 tlr: 0.00021 tnm: 0.28 Lm: 6.603 (6.614) Lt: 5.844 (5.845) Accm: 3.14 (3.16) Acct: 4.94 (4.94) proj_loss: -0.5619 (-0.5626) time: 0.6733 data: 0.0002 [11-23 19:52:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [1251/1669] eta: 0:04:45 tlr: 0.00021 tnm: 0.28 Lm: 6.632 (6.638) Lt: 5.870 (5.886) Accm: 3.05 (3.02) Acct: 4.66 (4.73) proj_loss: -0.5765 (-0.5714) time: 0.6733 data: 0.0002 [11-23 19:52:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [1251/1669] eta: 0:04:45 tlr: 0.00021 tnm: 0.28 Lm: 6.735 (6.755) Lt: 6.031 (6.040) Accm: 2.77 (2.68) Acct: 4.37 (4.21) proj_loss: -0.5648 (-0.5621) time: 0.6733 data: 0.0003 [11-23 19:52:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [1251/1669] eta: 0:04:45 tlr: 0.00021 tnm: 0.28 Lm: 6.602 (6.630) Lt: 5.882 (5.907) Accm: 3.05 (3.04) Acct: 4.59 (4.58) proj_loss: -0.5717 (-0.5634) time: 0.6733 data: 0.0003 [11-23 19:57:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.615 (6.627) Lt: 5.904 (5.908) Accm: 2.98 (3.02) Acct: 4.61 (4.62) proj_loss: -0.5714 (-0.5626) time: 0.6759 data: 0.0016 [11-23 19:57:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 61/350] Total time: 0:18:58 (0.682 s / it) [11-23 19:57:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.723 (6.746) Lt: 5.995 (6.029) Accm: 2.80 (2.74) Acct: 4.56 (4.31) proj_loss: -0.5618 (-0.5621) time: 0.6759 data: 0.0020 [11-23 19:57:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.623 (6.619) Lt: 5.853 (5.847) Accm: 3.07 (3.14) Acct: 4.92 (4.91) proj_loss: -0.5598 (-0.5577) time: 0.6759 data: 0.0014 [11-23 19:57:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 61/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.616 (6.629) Lt: 5.868 (5.882) Accm: 3.07 (3.03) Acct: 4.68 (4.77) proj_loss: -0.5740 (-0.5700) time: 0.6759 data: 0.0017 [11-23 19:57:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 61/350] Total time: 0:18:58 (0.682 s / it) [11-23 19:57:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 61/350] Total time: 0:18:58 (0.682 s / it) [11-23 19:57:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 61/350] Total time: 0:18:58 (0.682 s / it) [11-23 19:57:15] (/home/user/VAR/train.py , line 276)=> [ep61] (training ) Lm: 6.670 (6.670), Lt: 5.933 (5.933), Acc m&t: 2.96 4.65, Remain: 3 days, 18:57:30, Finish: 2024-11-26 22:54 [11-23 19:57:15] (/home/user/VAR/train.py , line 276)=> [ep61] (training ) Lm: 6.670 (6.670), Lt: 5.933 (5.933), Acc m&t: 2.96 4.65, Remain: 3 days, 18:58:18, Finish: 2024-11-26 22:55 [11-23 19:57:15] (/home/user/VAR/train.py , line 276)=> [ep61] (training ) Lm: 6.670 (6.670), Lt: 5.933 (5.933), Acc m&t: 2.96 4.65, Remain: 3 days, 18:58:01, Finish: 2024-11-26 22:55 [11-23 19:57:15] (/home/user/VAR/train.py , line 276)=> [ep61] (training ) Lm: 6.670 (6.670), Lt: 5.933 (5.933), Acc m&t: 2.96 4.65, Remain: 3 days, 18:58:03, Finish: 2024-11-26 22:55 [11-23 19:57:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [ 0/1669] eta: 0:18:17 tlr: 0.00021 tnm: 0.26 Lm: 6.709 (6.709) Lt: 5.958 (5.958) Accm: 2.75 (2.75) Acct: 4.61 (4.61) proj_loss: -0.5847 (-0.5847) time: 0.6578 data: 0.0003 [11-23 19:57:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [ 0/1669] eta: 0:18:18 tlr: 0.00021 tnm: 0.26 Lm: 6.685 (6.685) Lt: 5.980 (5.980) Accm: 2.66 (2.66) Acct: 4.34 (4.34) proj_loss: -0.5692 (-0.5692) time: 0.6584 data: 0.0004 [11-23 19:57:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [ 0/1669] eta: 0:18:19 tlr: 0.00021 tnm: 0.26 Lm: 6.823 (6.823) Lt: 6.137 (6.137) Accm: 2.45 (2.45) Acct: 3.91 (3.91) proj_loss: -0.5723 (-0.5723) time: 0.6586 data: 0.0003 [11-23 19:57:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [ 0/1669] eta: 0:18:18 tlr: 0.00021 tnm: 0.26 Lm: 6.730 (6.730) Lt: 6.019 (6.019) Accm: 2.63 (2.63) Acct: 4.20 (4.20) proj_loss: -0.5575 (-0.5575) time: 0.6584 data: 0.0004 [11-23 20:01:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [ 417/1669] eta: 0:14:04 tlr: 0.00021 tnm: 0.30 Lm: 6.725 (6.725) Lt: 5.971 (5.971) Accm: 2.63 (2.63) Acct: 4.19 (4.19) proj_loss: -0.5626 (-0.5626) time: 0.6721 data: 0.0003 [11-23 20:01:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [ 417/1669] eta: 0:14:04 tlr: 0.00021 tnm: 0.30 Lm: 6.612 (6.612) Lt: 5.842 (5.842) Accm: 3.15 (3.15) Acct: 5.16 (5.16) proj_loss: -0.5802 (-0.5802) time: 0.6721 data: 0.0003 [11-23 20:01:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [ 417/1669] eta: 0:14:04 tlr: 0.00021 tnm: 0.30 Lm: 6.815 (6.815) Lt: 6.076 (6.076) Accm: 2.67 (2.67) Acct: 4.29 (4.29) proj_loss: -0.5646 (-0.5646) time: 0.6721 data: 0.0003 [11-23 20:01:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [ 417/1669] eta: 0:14:04 tlr: 0.00021 tnm: 0.30 Lm: 6.658 (6.658) Lt: 5.912 (5.912) Accm: 2.88 (2.88) Acct: 4.63 (4.63) proj_loss: -0.5591 (-0.5591) time: 0.6721 data: 0.0003 [11-23 20:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [ 834/1669] eta: 0:09:23 tlr: 0.00021 tnm: 0.32 Lm: 6.685 (6.677) Lt: 5.980 (5.961) Accm: 2.67 (2.81) Acct: 4.34 (4.45) proj_loss: -0.5640 (-0.5607) time: 0.6747 data: 0.0003 [11-23 20:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [ 834/1669] eta: 0:09:23 tlr: 0.00021 tnm: 0.32 Lm: 6.709 (6.666) Lt: 5.958 (5.910) Accm: 2.75 (2.97) Acct: 4.61 (4.86) proj_loss: -0.5756 (-0.5739) time: 0.6747 data: 0.0002 [11-23 20:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [ 834/1669] eta: 0:09:23 tlr: 0.00021 tnm: 0.32 Lm: 6.806 (6.721) Lt: 6.015 (5.972) Accm: 2.90 (2.88) Acct: 4.67 (4.71) proj_loss: -0.5678 (-0.5657) time: 0.6747 data: 0.0003 [11-23 20:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [ 834/1669] eta: 0:09:23 tlr: 0.00021 tnm: 0.32 Lm: 6.720 (6.692) Lt: 5.923 (5.914) Accm: 2.64 (2.78) Acct: 4.20 (4.48) proj_loss: -0.5575 (-0.5601) time: 0.6747 data: 0.0003 [11-23 20:11:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.710 (6.694) Lt: 5.956 (5.933) Accm: 2.74 (2.80) Acct: 4.40 (4.51) proj_loss: -0.5626 (-0.5638) time: 0.6770 data: 0.0003 [11-23 20:11:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.683 (6.678) Lt: 5.999 (5.975) Accm: 2.77 (2.82) Acct: 4.39 (4.45) proj_loss: -0.5647 (-0.5619) time: 0.6770 data: 0.0003 [11-23 20:11:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.646 (6.645) Lt: 5.887 (5.886) Accm: 2.90 (2.99) Acct: 4.72 (4.85) proj_loss: -0.5685 (-0.5668) time: 0.6770 data: 0.0002 [11-23 20:11:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.748 (6.713) Lt: 5.993 (5.972) Accm: 2.94 (2.91) Acct: 4.63 (4.68) proj_loss: -0.5701 (-0.5679) time: 0.6770 data: 0.0003 [11-23 20:16:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.29 Lm: 6.689 (6.684) Lt: 5.972 (5.956) Accm: 2.99 (3.00) Acct: 4.67 (4.76) proj_loss: -0.5723 (-0.5745) time: 0.8601 data: 0.0016 [11-23 20:16:19] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 62/350] Total time: 0:19:03 (0.685 s / it) [11-23 20:16:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.29 Lm: 6.709 (6.672) Lt: 5.958 (5.928) Accm: 2.75 (2.89) Acct: 4.61 (4.58) proj_loss: -0.5615 (-0.5658) time: 0.8601 data: 0.0018 [11-23 20:16:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.29 Lm: 6.685 (6.687) Lt: 6.002 (5.981) Accm: 2.80 (2.82) Acct: 4.34 (4.42) proj_loss: -0.5640 (-0.5578) time: 0.8601 data: 0.0018 [11-23 20:16:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 62/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.29 Lm: 6.720 (6.715) Lt: 5.990 (5.963) Accm: 2.64 (2.76) Acct: 4.39 (4.49) proj_loss: -0.5575 (-0.5610) time: 0.8601 data: 0.0018 [11-23 20:16:19] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 62/350] Total time: 0:19:03 (0.685 s / it) [11-23 20:16:19] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 62/350] Total time: 0:19:03 (0.685 s / it) [11-23 20:16:19] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 62/350] Total time: 0:19:03 (0.685 s / it) [11-23 20:16:19] (/home/user/VAR/train.py , line 276)=> [ep62] (training ) Lm: 6.670 (6.682), Lt: 5.933 (5.942), Acc m&t: 2.96 4.65, Remain: 3 days, 18:42:30, Finish: 2024-11-26 22:58 [11-23 20:16:19] (/home/user/VAR/train.py , line 276)=> [ep62] (training ) Lm: 6.670 (6.682), Lt: 5.933 (5.942), Acc m&t: 2.96 4.65, Remain: 3 days, 18:43:54, Finish: 2024-11-26 23:00 [11-23 20:16:19] (/home/user/VAR/train.py , line 276)=> [ep62] (training ) Lm: 6.670 (6.682), Lt: 5.933 (5.942), Acc m&t: 2.96 4.65, Remain: 3 days, 18:44:07, Finish: 2024-11-26 23:00 [11-23 20:16:19] (/home/user/VAR/train.py , line 276)=> [ep62] (training ) Lm: 6.670 (6.682), Lt: 5.933 (5.942), Acc m&t: 2.96 4.65, Remain: 3 days, 18:43:11, Finish: 2024-11-26 22:59 [11-23 20:16:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [ 0/1669] eta: 0:18:27 tlr: 0.00021 tnm: 0.29 Lm: 6.745 (6.745) Lt: 6.059 (6.059) Accm: 2.76 (2.76) Acct: 4.44 (4.44) proj_loss: -0.5871 (-0.5871) time: 0.6635 data: 0.0003 [11-23 20:16:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [ 0/1669] eta: 0:18:30 tlr: 0.00021 tnm: 0.29 Lm: 6.801 (6.801) Lt: 6.114 (6.114) Accm: 2.44 (2.44) Acct: 3.84 (3.84) proj_loss: -0.5604 (-0.5604) time: 0.6653 data: 0.0003 [11-23 20:16:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [ 0/1669] eta: 0:18:30 tlr: 0.00021 tnm: 0.29 Lm: 6.621 (6.621) Lt: 5.800 (5.800) Accm: 2.81 (2.81) Acct: 4.60 (4.60) proj_loss: -0.5583 (-0.5583) time: 0.6652 data: 0.0003 [11-23 20:16:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [ 0/1669] eta: 0:18:30 tlr: 0.00021 tnm: 0.29 Lm: 6.500 (6.500) Lt: 5.766 (5.766) Accm: 3.14 (3.14) Acct: 4.77 (4.77) proj_loss: -0.5973 (-0.5973) time: 0.6652 data: 0.0006 [11-23 20:21:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [ 417/1669] eta: 0:14:05 tlr: 0.00021 tnm: 0.28 Lm: 6.566 (6.566) Lt: 5.810 (5.810) Accm: 3.03 (3.03) Acct: 4.73 (4.73) proj_loss: -0.5727 (-0.5727) time: 0.6753 data: 0.0003 [11-23 20:21:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [ 417/1669] eta: 0:14:05 tlr: 0.00021 tnm: 0.28 Lm: 6.640 (6.640) Lt: 5.939 (5.939) Accm: 3.06 (3.06) Acct: 4.93 (4.93) proj_loss: -0.5665 (-0.5665) time: 0.6753 data: 0.0003 [11-23 20:21:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [ 417/1669] eta: 0:14:05 tlr: 0.00021 tnm: 0.28 Lm: 6.800 (6.800) Lt: 6.082 (6.082) Accm: 2.52 (2.52) Acct: 3.99 (3.99) proj_loss: -0.5580 (-0.5580) time: 0.6753 data: 0.0003 [11-23 20:21:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [ 417/1669] eta: 0:14:05 tlr: 0.00021 tnm: 0.28 Lm: 6.663 (6.663) Lt: 5.863 (5.863) Accm: 2.88 (2.88) Acct: 4.70 (4.70) proj_loss: -0.5563 (-0.5563) time: 0.6753 data: 0.0003 [11-23 20:25:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [ 834/1669] eta: 0:09:23 tlr: 0.00021 tnm: 0.29 Lm: 6.704 (6.686) Lt: 5.925 (5.921) Accm: 2.81 (2.78) Acct: 4.60 (4.48) proj_loss: -0.5571 (-0.5566) time: 0.6778 data: 0.0003 [11-23 20:25:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [ 834/1669] eta: 0:09:23 tlr: 0.00021 tnm: 0.29 Lm: 6.702 (6.661) Lt: 5.946 (5.942) Accm: 2.88 (3.00) Acct: 4.56 (4.81) proj_loss: -0.5542 (-0.5624) time: 0.6778 data: 0.0002 [11-23 20:25:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [ 834/1669] eta: 0:09:23 tlr: 0.00021 tnm: 0.29 Lm: 6.800 (6.749) Lt: 6.050 (6.009) Accm: 2.60 (2.56) Acct: 4.13 (4.12) proj_loss: -0.5580 (-0.5580) time: 0.6778 data: 0.0003 [11-23 20:25:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [ 834/1669] eta: 0:09:23 tlr: 0.00021 tnm: 0.29 Lm: 6.608 (6.580) Lt: 5.855 (5.832) Accm: 2.91 (2.94) Acct: 4.70 (4.56) proj_loss: -0.5484 (-0.5646) time: 0.6778 data: 0.0003 [11-23 20:30:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.620 (6.632) Lt: 5.866 (5.891) Accm: 2.84 (2.85) Acct: 4.46 (4.43) proj_loss: -0.5566 (-0.5647) time: 0.6769 data: 0.0003 [11-23 20:30:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.685 (6.681) Lt: 5.918 (5.919) Accm: 2.80 (2.78) Acct: 4.49 (4.46) proj_loss: -0.5577 (-0.5574) time: 0.6769 data: 0.0003 [11-23 20:30:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.646 (6.643) Lt: 5.915 (5.927) Accm: 2.92 (2.99) Acct: 4.61 (4.77) proj_loss: -0.5643 (-0.5654) time: 0.6769 data: 0.0002 [11-23 20:30:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.723 (6.723) Lt: 5.996 (5.993) Accm: 2.63 (2.65) Acct: 4.26 (4.22) proj_loss: -0.5592 (-0.5643) time: 0.6769 data: 0.0002 [11-23 20:35:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.647 (6.703) Lt: 5.943 (5.977) Accm: 2.65 (2.69) Acct: 4.39 (4.29) proj_loss: -0.5604 (-0.5680) time: 0.6779 data: 0.0019 [11-23 20:35:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 63/350] Total time: 0:18:47 (0.675 s / it) [11-23 20:35:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.702 (6.676) Lt: 5.946 (5.974) Accm: 2.88 (2.92) Acct: 4.56 (4.63) proj_loss: -0.5744 (-0.5690) time: 0.6779 data: 0.0015 [11-23 20:35:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.696 (6.684) Lt: 5.925 (5.927) Accm: 2.78 (2.77) Acct: 4.51 (4.47) proj_loss: -0.5583 (-0.5620) time: 0.6779 data: 0.0020 [11-23 20:35:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 63/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.633 (6.637) Lt: 5.877 (5.898) Accm: 2.88 (2.86) Acct: 4.56 (4.46) proj_loss: -0.5649 (-0.5652) time: 0.6779 data: 0.0018 [11-23 20:35:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 63/350] Total time: 0:18:47 (0.675 s / it) [11-23 20:35:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 63/350] Total time: 0:18:47 (0.675 s / it) [11-23 20:35:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 63/350] Total time: 0:18:47 (0.675 s / it) [11-23 20:35:06] (/home/user/VAR/train.py , line 276)=> [ep63] (training ) Lm: 6.670 (6.674), Lt: 5.933 (5.934), Acc m&t: 2.96 4.65, Remain: 3 days, 18:28:57, Finish: 2024-11-26 23:04 [11-23 20:35:06] (/home/user/VAR/train.py , line 276)=> [ep63] (training ) Lm: 6.670 (6.674), Lt: 5.933 (5.934), Acc m&t: 2.96 4.65, Remain: 3 days, 18:28:43, Finish: 2024-11-26 23:03 [11-23 20:35:06] (/home/user/VAR/train.py , line 276)=> [ep63] (training ) Lm: 6.670 (6.674), Lt: 5.933 (5.934), Acc m&t: 2.96 4.65, Remain: 3 days, 18:28:58, Finish: 2024-11-26 23:04 [11-23 20:35:06] (/home/user/VAR/train.py , line 276)=> [ep63] (training ) Lm: 6.670 (6.674), Lt: 5.933 (5.934), Acc m&t: 2.96 4.65, Remain: 3 days, 18:29:53, Finish: 2024-11-26 23:04 [11-23 20:35:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [ 0/1669] eta: 0:18:20 tlr: 0.00021 tnm: 0.29 Lm: 6.525 (6.525) Lt: 5.778 (5.778) Accm: 3.28 (3.28) Acct: 5.22 (5.22) proj_loss: -0.5617 (-0.5617) time: 0.6594 data: 0.0003 [11-23 20:35:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [ 0/1669] eta: 0:18:21 tlr: 0.00021 tnm: 0.29 Lm: 6.443 (6.443) Lt: 5.643 (5.643) Accm: 3.55 (3.55) Acct: 6.13 (6.13) proj_loss: -0.5455 (-0.5455) time: 0.6600 data: 0.0004 [11-23 20:35:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [ 0/1669] eta: 0:18:21 tlr: 0.00021 tnm: 0.29 Lm: 6.698 (6.698) Lt: 5.907 (5.907) Accm: 2.78 (2.78) Acct: 4.46 (4.46) proj_loss: -0.5578 (-0.5578) time: 0.6598 data: 0.0003 [11-23 20:35:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [ 0/1669] eta: 0:18:20 tlr: 0.00021 tnm: 0.29 Lm: 6.869 (6.869) Lt: 6.125 (6.125) Accm: 2.43 (2.43) Acct: 3.91 (3.91) proj_loss: -0.5271 (-0.5271) time: 0.6596 data: 0.0004 [11-23 20:39:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [ 417/1669] eta: 0:14:10 tlr: 0.00021 tnm: 0.26 Lm: 6.752 (6.752) Lt: 5.989 (5.989) Accm: 2.82 (2.82) Acct: 4.38 (4.38) proj_loss: -0.5358 (-0.5358) time: 0.6764 data: 0.0003 [11-23 20:39:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [ 417/1669] eta: 0:14:10 tlr: 0.00021 tnm: 0.26 Lm: 6.556 (6.556) Lt: 5.795 (5.795) Accm: 3.37 (3.37) Acct: 5.43 (5.43) proj_loss: -0.5618 (-0.5618) time: 0.6764 data: 0.0003 [11-23 20:39:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [ 417/1669] eta: 0:14:10 tlr: 0.00021 tnm: 0.26 Lm: 6.716 (6.716) Lt: 5.956 (5.956) Accm: 2.83 (2.83) Acct: 4.42 (4.42) proj_loss: -0.5610 (-0.5610) time: 0.6764 data: 0.0003 [11-23 20:39:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [ 417/1669] eta: 0:14:10 tlr: 0.00021 tnm: 0.26 Lm: 6.595 (6.595) Lt: 5.794 (5.794) Accm: 3.25 (3.25) Acct: 5.46 (5.46) proj_loss: -0.5493 (-0.5493) time: 0.6764 data: 0.0003 [11-23 20:44:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [ 834/1669] eta: 0:09:41 tlr: 0.00021 tnm: 0.28 Lm: 6.588 (6.593) Lt: 5.862 (5.817) Accm: 3.13 (3.21) Acct: 4.87 (5.26) proj_loss: -0.5530 (-0.5517) time: 0.6762 data: 0.0003 [11-23 20:44:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [ 834/1669] eta: 0:09:41 tlr: 0.00021 tnm: 0.28 Lm: 6.698 (6.696) Lt: 5.907 (5.932) Accm: 2.88 (2.94) Acct: 4.46 (4.59) proj_loss: -0.5578 (-0.5523) time: 0.6762 data: 0.0003 [11-23 20:44:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [ 834/1669] eta: 0:09:41 tlr: 0.00021 tnm: 0.28 Lm: 6.586 (6.623) Lt: 5.812 (5.866) Accm: 3.28 (3.17) Acct: 5.22 (5.10) proj_loss: -0.5619 (-0.5678) time: 0.6762 data: 0.0003 [11-23 20:44:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [ 834/1669] eta: 0:09:41 tlr: 0.00021 tnm: 0.28 Lm: 6.635 (6.705) Lt: 5.863 (5.947) Accm: 3.20 (3.01) Acct: 4.86 (4.66) proj_loss: -0.5445 (-0.5483) time: 0.6762 data: 0.0003 [11-23 20:49:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [1251/1669] eta: 0:04:48 tlr: 0.00021 tnm: 0.29 Lm: 6.711 (6.725) Lt: 5.981 (5.985) Accm: 2.82 (2.86) Acct: 4.38 (4.39) proj_loss: -0.5589 (-0.5589) time: 0.6763 data: 0.0003 [11-23 20:49:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [1251/1669] eta: 0:04:48 tlr: 0.00021 tnm: 0.29 Lm: 6.673 (6.661) Lt: 5.911 (5.911) Accm: 3.06 (3.09) Acct: 4.88 (4.96) proj_loss: -0.5622 (-0.5665) time: 0.6763 data: 0.0002 [11-23 20:49:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [1251/1669] eta: 0:04:48 tlr: 0.00021 tnm: 0.29 Lm: 6.667 (6.650) Lt: 5.903 (5.890) Accm: 3.04 (3.03) Acct: 4.83 (4.94) proj_loss: -0.5527 (-0.5519) time: 0.6763 data: 0.0003 [11-23 20:49:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [1251/1669] eta: 0:04:48 tlr: 0.00021 tnm: 0.29 Lm: 6.677 (6.657) Lt: 5.895 (5.874) Accm: 3.02 (3.02) Acct: 4.70 (4.76) proj_loss: -0.5610 (-0.5557) time: 0.6763 data: 0.0002 [11-23 20:54:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.30 Lm: 6.657 (6.657) Lt: 5.907 (5.882) Accm: 2.96 (3.01) Acct: 4.92 (4.80) proj_loss: -0.5641 (-0.5580) time: 0.6753 data: 0.0015 [11-23 20:54:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 64/350] Total time: 0:19:04 (0.686 s / it) [11-23 20:54:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.30 Lm: 6.678 (6.665) Lt: 5.925 (5.914) Accm: 3.28 (3.16) Acct: 5.22 (5.04) proj_loss: -0.5619 (-0.5625) time: 0.6753 data: 0.0017 [11-23 20:54:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.30 Lm: 6.752 (6.730) Lt: 6.025 (5.993) Accm: 2.43 (2.76) Acct: 3.91 (4.26) proj_loss: -0.5684 (-0.5608) time: 0.6753 data: 0.0021 [11-23 20:54:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 64/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.30 Lm: 6.744 (6.669) Lt: 5.945 (5.918) Accm: 2.96 (2.95) Acct: 4.79 (4.73) proj_loss: -0.5524 (-0.5477) time: 0.6753 data: 0.0021 [11-23 20:54:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 64/350] Total time: 0:19:04 (0.686 s / it) [11-23 20:54:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 64/350] Total time: 0:19:04 (0.686 s / it) [11-23 20:54:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 64/350] Total time: 0:19:04 (0.686 s / it) [11-23 20:54:10] (/home/user/VAR/train.py , line 276)=> [ep64] (training ) Lm: 6.670 (6.679), Lt: 5.933 (5.936), Acc m&t: 2.96 4.65, Remain: 3 days, 17:51:05, Finish: 2024-11-26 22:45 [11-23 20:54:10] (/home/user/VAR/train.py , line 276)=> [ep64] (training ) Lm: 6.670 (6.679), Lt: 5.933 (5.936), Acc m&t: 2.96 4.65, Remain: 3 days, 17:50:58, Finish: 2024-11-26 22:45 [11-23 20:54:10] (/home/user/VAR/train.py , line 276)=> [ep64] (training ) Lm: 6.670 (6.679), Lt: 5.933 (5.936), Acc m&t: 2.96 4.65, Remain: 3 days, 17:50:48, Finish: 2024-11-26 22:44 [11-23 20:54:10] (/home/user/VAR/train.py , line 276)=> [ep64] (training ) Lm: 6.670 (6.679), Lt: 5.933 (5.936), Acc m&t: 2.96 4.65, Remain: 3 days, 17:51:31, Finish: 2024-11-26 22:45 [11-23 20:54:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [ 0/1669] eta: 0:18:10 tlr: 0.00021 tnm: 0.26 Lm: 6.704 (6.704) Lt: 5.983 (5.983) Accm: 2.72 (2.72) Acct: 4.36 (4.36) proj_loss: -0.5636 (-0.5636) time: 0.6536 data: 0.0003 [11-23 20:54:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [ 0/1669] eta: 0:18:12 tlr: 0.00021 tnm: 0.26 Lm: 6.643 (6.643) Lt: 5.884 (5.884) Accm: 3.20 (3.20) Acct: 5.22 (5.22) proj_loss: -0.5628 (-0.5628) time: 0.6545 data: 0.0004 [11-23 20:54:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [ 0/1669] eta: 0:18:11 tlr: 0.00021 tnm: 0.26 Lm: 6.404 (6.404) Lt: 5.597 (5.597) Accm: 3.77 (3.77) Acct: 5.91 (5.91) proj_loss: -0.5631 (-0.5631) time: 0.6543 data: 0.0003 [11-23 20:54:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [ 0/1669] eta: 0:18:12 tlr: 0.00021 tnm: 0.26 Lm: 6.594 (6.594) Lt: 5.827 (5.827) Accm: 3.15 (3.15) Acct: 5.10 (5.10) proj_loss: -0.5719 (-0.5719) time: 0.6546 data: 0.0004 [11-23 20:58:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [ 417/1669] eta: 0:14:07 tlr: 0.00021 tnm: 0.27 Lm: 6.668 (6.668) Lt: 5.935 (5.935) Accm: 2.90 (2.90) Acct: 4.64 (4.64) proj_loss: -0.5693 (-0.5693) time: 0.6768 data: 0.0003 [11-23 20:58:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [ 417/1669] eta: 0:14:07 tlr: 0.00021 tnm: 0.27 Lm: 6.601 (6.601) Lt: 5.830 (5.830) Accm: 3.24 (3.24) Acct: 5.18 (5.18) proj_loss: -0.5626 (-0.5626) time: 0.6768 data: 0.0003 [11-23 20:58:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [ 417/1669] eta: 0:14:07 tlr: 0.00021 tnm: 0.27 Lm: 6.552 (6.552) Lt: 5.804 (5.804) Accm: 3.45 (3.45) Acct: 5.30 (5.30) proj_loss: -0.5638 (-0.5638) time: 0.6768 data: 0.0003 [11-23 20:58:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [ 417/1669] eta: 0:14:07 tlr: 0.00021 tnm: 0.27 Lm: 6.546 (6.546) Lt: 5.793 (5.793) Accm: 3.20 (3.20) Acct: 4.97 (4.97) proj_loss: -0.5637 (-0.5637) time: 0.6768 data: 0.0003 [11-23 21:03:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.28 Lm: 6.643 (6.580) Lt: 5.884 (5.834) Accm: 3.20 (3.12) Acct: 4.72 (4.88) proj_loss: -0.5630 (-0.5635) time: 0.6781 data: 0.0003 [11-23 21:03:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.28 Lm: 6.570 (6.590) Lt: 5.834 (5.831) Accm: 3.06 (3.18) Acct: 4.73 (5.03) proj_loss: -0.5636 (-0.5693) time: 0.6781 data: 0.0002 [11-23 21:03:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.28 Lm: 6.674 (6.670) Lt: 5.908 (5.926) Accm: 3.09 (2.96) Acct: 4.65 (4.64) proj_loss: -0.5667 (-0.5589) time: 0.6781 data: 0.0002 [11-23 21:03:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.28 Lm: 6.518 (6.541) Lt: 5.723 (5.777) Accm: 3.66 (3.52) Acct: 5.91 (5.55) proj_loss: -0.5631 (-0.5566) time: 0.6781 data: 0.0003 [11-23 21:08:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.609 (6.594) Lt: 5.867 (5.846) Accm: 3.39 (3.29) Acct: 5.30 (5.23) proj_loss: -0.5624 (-0.5579) time: 0.6736 data: 0.0002 [11-23 21:08:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.634 (6.639) Lt: 5.868 (5.900) Accm: 3.07 (2.98) Acct: 4.67 (4.65) proj_loss: -0.5662 (-0.5606) time: 0.6736 data: 0.0005 [11-23 21:08:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.570 (6.585) Lt: 5.863 (5.847) Accm: 3.17 (3.21) Acct: 4.96 (5.07) proj_loss: -0.5641 (-0.5681) time: 0.6736 data: 0.0002 [11-23 21:08:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.27 Lm: 6.646 (6.640) Lt: 5.899 (5.904) Accm: 3.08 (3.03) Acct: 4.71 (4.77) proj_loss: -0.5629 (-0.5612) time: 0.6736 data: 0.0003 [11-23 21:13:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.649 (6.652) Lt: 5.914 (5.908) Accm: 2.96 (2.99) Acct: 4.70 (4.72) proj_loss: -0.5628 (-0.5552) time: 1.0982 data: 0.0025 [11-23 21:13:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 65/350] Total time: 0:19:10 (0.689 s / it) [11-23 21:13:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.570 (6.578) Lt: 5.834 (5.827) Accm: 3.29 (3.23) Acct: 5.18 (5.15) proj_loss: -0.5636 (-0.5659) time: 1.0982 data: 0.0016 [11-23 21:13:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.674 (6.664) Lt: 5.908 (5.922) Accm: 3.04 (2.95) Acct: 4.65 (4.65) proj_loss: -0.5656 (-0.5608) time: 1.0982 data: 0.0020 [11-23 21:13:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 65/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.518 (6.578) Lt: 5.763 (5.829) Accm: 3.13 (3.26) Acct: 4.80 (5.15) proj_loss: -0.5617 (-0.5575) time: 1.0982 data: 0.0016 [11-23 21:13:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 65/350] Total time: 0:19:10 (0.689 s / it) [11-23 21:13:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 65/350] Total time: 0:19:10 (0.689 s / it) [11-23 21:13:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 65/350] Total time: 0:19:10 (0.689 s / it) [11-23 21:13:21] (/home/user/VAR/train.py , line 276)=> [ep65] (training ) Lm: 6.665 (6.665), Lt: 5.923 (5.923), Acc m&t: 2.96 4.69, Remain: 3 days, 18:40:49, Finish: 2024-11-26 23:54 [11-23 21:13:21] (/home/user/VAR/train.py , line 276)=> [ep65] (training ) Lm: 6.665 (6.665), Lt: 5.923 (5.923), Acc m&t: 2.96 4.69, Remain: 3 days, 18:39:15, Finish: 2024-11-26 23:52 [11-23 21:13:21] (/home/user/VAR/train.py , line 276)=> [ep65] (training ) Lm: 6.665 (6.665), Lt: 5.923 (5.923), Acc m&t: 2.96 4.69, Remain: 3 days, 18:40:37, Finish: 2024-11-26 23:53 [11-23 21:13:21] (/home/user/VAR/train.py , line 276)=> [ep65] (training ) Lm: 6.665 (6.665), Lt: 5.923 (5.923), Acc m&t: 2.96 4.69, Remain: 3 days, 18:41:40, Finish: 2024-11-26 23:55 [11-23 21:13:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [ 0/1669] eta: 0:18:19 tlr: 0.00021 tnm: 0.30 Lm: 6.708 (6.708) Lt: 5.958 (5.958) Accm: 2.86 (2.86) Acct: 4.56 (4.56) proj_loss: -0.5463 (-0.5463) time: 0.6586 data: 0.0003 [11-23 21:13:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [ 0/1669] eta: 0:18:20 tlr: 0.00021 tnm: 0.30 Lm: 6.632 (6.632) Lt: 5.929 (5.929) Accm: 3.01 (3.01) Acct: 4.58 (4.58) proj_loss: -0.5664 (-0.5664) time: 0.6591 data: 0.0003 [11-23 21:13:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [ 0/1669] eta: 0:18:20 tlr: 0.00021 tnm: 0.30 Lm: 6.775 (6.775) Lt: 6.071 (6.071) Accm: 2.53 (2.53) Acct: 4.08 (4.08) proj_loss: -0.5465 (-0.5465) time: 0.6596 data: 0.0003 [11-23 21:13:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [ 0/1669] eta: 0:18:22 tlr: 0.00021 tnm: 0.30 Lm: 6.789 (6.789) Lt: 6.073 (6.073) Accm: 2.36 (2.36) Acct: 3.81 (3.81) proj_loss: -0.5712 (-0.5712) time: 0.6604 data: 0.0004 [11-23 21:18:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [ 417/1669] eta: 0:14:05 tlr: 0.00021 tnm: 0.26 Lm: 6.744 (6.744) Lt: 6.017 (6.017) Accm: 2.57 (2.57) Acct: 4.16 (4.16) proj_loss: -0.5633 (-0.5633) time: 0.6765 data: 0.0003 [11-23 21:18:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [ 417/1669] eta: 0:14:05 tlr: 0.00021 tnm: 0.26 Lm: 6.681 (6.681) Lt: 5.957 (5.957) Accm: 2.86 (2.86) Acct: 4.57 (4.57) proj_loss: -0.5560 (-0.5560) time: 0.6765 data: 0.0002 [11-23 21:18:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [ 417/1669] eta: 0:14:05 tlr: 0.00021 tnm: 0.26 Lm: 6.584 (6.584) Lt: 5.827 (5.827) Accm: 3.17 (3.17) Acct: 4.92 (4.92) proj_loss: -0.5658 (-0.5658) time: 0.6765 data: 0.0003 [11-23 21:18:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [ 417/1669] eta: 0:14:05 tlr: 0.00021 tnm: 0.26 Lm: 6.713 (6.713) Lt: 6.005 (6.005) Accm: 2.78 (2.78) Acct: 4.44 (4.44) proj_loss: -0.5590 (-0.5590) time: 0.6765 data: 0.0003 [11-23 21:22:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.27 Lm: 6.775 (6.743) Lt: 6.068 (6.026) Accm: 2.55 (2.70) Acct: 4.08 (4.30) proj_loss: -0.5716 (-0.5667) time: 0.6757 data: 0.0003 [11-23 21:22:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.27 Lm: 6.708 (6.763) Lt: 5.958 (6.037) Accm: 2.86 (2.73) Acct: 4.56 (4.33) proj_loss: -0.5463 (-0.5507) time: 0.6757 data: 0.0002 [11-23 21:22:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.27 Lm: 6.700 (6.714) Lt: 5.962 (5.985) Accm: 2.78 (2.64) Acct: 4.44 (4.25) proj_loss: -0.5554 (-0.5587) time: 0.6757 data: 0.0003 [11-23 21:22:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.27 Lm: 6.632 (6.688) Lt: 5.929 (5.960) Accm: 3.01 (2.89) Acct: 4.58 (4.60) proj_loss: -0.5651 (-0.5623) time: 0.6757 data: 0.0002 [11-23 21:27:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.30 Lm: 6.660 (6.688) Lt: 5.937 (5.956) Accm: 2.96 (2.90) Acct: 4.61 (4.61) proj_loss: -0.5658 (-0.5671) time: 0.6771 data: 0.0003 [11-23 21:27:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.30 Lm: 6.681 (6.719) Lt: 5.957 (5.990) Accm: 2.86 (2.80) Acct: 4.57 (4.40) proj_loss: -0.5528 (-0.5529) time: 0.6771 data: 0.0002 [11-23 21:27:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.30 Lm: 6.684 (6.702) Lt: 5.961 (5.979) Accm: 2.78 (2.73) Acct: 4.48 (4.32) proj_loss: -0.5591 (-0.5597) time: 0.6771 data: 0.0003 [11-23 21:27:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.30 Lm: 6.789 (6.771) Lt: 6.070 (6.050) Accm: 2.68 (2.73) Acct: 4.33 (4.37) proj_loss: -0.5662 (-0.5652) time: 0.6771 data: 0.0003 [11-23 21:32:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.775 (6.748) Lt: 6.068 (6.026) Accm: 2.80 (2.74) Acct: 4.53 (4.40) proj_loss: -0.5716 (-0.5681) time: 0.6789 data: 0.0020 [11-23 21:32:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 66/350] Total time: 0:18:48 (0.676 s / it) [11-23 21:32:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.692 (6.700) Lt: 5.960 (5.974) Accm: 2.78 (2.74) Acct: 4.44 (4.31) proj_loss: -0.5629 (-0.5622) time: 0.6788 data: 0.0016 [11-23 21:32:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.654 (6.685) Lt: 5.955 (5.953) Accm: 2.86 (2.92) Acct: 4.58 (4.64) proj_loss: -0.5463 (-0.5510) time: 0.6789 data: 0.0015 [11-23 21:32:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 66/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.664 (6.683) Lt: 5.929 (5.947) Accm: 2.92 (2.87) Acct: 4.58 (4.61) proj_loss: -0.5664 (-0.5694) time: 0.6789 data: 0.0016 [11-23 21:32:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 66/350] Total time: 0:18:48 (0.676 s / it) [11-23 21:32:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 66/350] Total time: 0:18:48 (0.676 s / it) [11-23 21:32:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 66/350] Total time: 0:18:48 (0.676 s / it) [11-23 21:32:10] (/home/user/VAR/train.py , line 276)=> [ep66] (training ) Lm: 6.665 (6.674), Lt: 5.923 (5.936), Acc m&t: 2.96 4.69, Remain: 3 days, 17:43:17, Finish: 2024-11-26 23:15 [11-23 21:32:10] (/home/user/VAR/train.py , line 276)=> [ep66] (training ) Lm: 6.665 (6.674), Lt: 5.923 (5.936), Acc m&t: 2.96 4.69, Remain: 3 days, 17:43:37, Finish: 2024-11-26 23:15 [11-23 21:32:10] (/home/user/VAR/train.py , line 276)=> [ep66] (training ) Lm: 6.665 (6.674), Lt: 5.923 (5.936), Acc m&t: 2.96 4.69, Remain: 3 days, 17:42:42, Finish: 2024-11-26 23:14 [11-23 21:32:10] (/home/user/VAR/train.py , line 276)=> [ep66] (training ) Lm: 6.665 (6.674), Lt: 5.923 (5.936), Acc m&t: 2.96 4.69, Remain: 3 days, 17:44:11, Finish: 2024-11-26 23:16 [11-23 21:32:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [ 0/1669] eta: 0:18:20 tlr: 0.00021 tnm: 0.29 Lm: 6.709 (6.709) Lt: 5.983 (5.983) Accm: 2.73 (2.73) Acct: 4.08 (4.08) proj_loss: -0.5636 (-0.5636) time: 0.6595 data: 0.0003 [11-23 21:32:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [ 0/1669] eta: 0:18:22 tlr: 0.00021 tnm: 0.29 Lm: 6.558 (6.558) Lt: 5.816 (5.816) Accm: 3.08 (3.08) Acct: 4.96 (4.96) proj_loss: -0.5874 (-0.5874) time: 0.6603 data: 0.0004 [11-23 21:32:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [ 0/1669] eta: 0:18:21 tlr: 0.00021 tnm: 0.29 Lm: 6.704 (6.704) Lt: 5.954 (5.954) Accm: 2.83 (2.83) Acct: 4.44 (4.44) proj_loss: -0.5360 (-0.5360) time: 0.6603 data: 0.0004 [11-23 21:32:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [ 0/1669] eta: 0:18:22 tlr: 0.00021 tnm: 0.29 Lm: 6.668 (6.668) Lt: 5.946 (5.946) Accm: 3.21 (3.21) Acct: 5.03 (5.03) proj_loss: -0.5818 (-0.5818) time: 0.6605 data: 0.0003 [11-23 21:36:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [ 417/1669] eta: 0:14:12 tlr: 0.00021 tnm: 0.28 Lm: 6.668 (6.668) Lt: 5.917 (5.917) Accm: 3.11 (3.11) Acct: 5.13 (5.13) proj_loss: -0.5667 (-0.5667) time: 0.6742 data: 0.0003 [11-23 21:36:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [ 417/1669] eta: 0:14:12 tlr: 0.00021 tnm: 0.28 Lm: 6.776 (6.776) Lt: 6.055 (6.055) Accm: 2.55 (2.55) Acct: 3.97 (3.97) proj_loss: -0.5665 (-0.5665) time: 0.6741 data: 0.0002 [11-23 21:36:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [ 417/1669] eta: 0:14:12 tlr: 0.00021 tnm: 0.28 Lm: 6.603 (6.603) Lt: 5.873 (5.873) Accm: 2.90 (2.90) Acct: 4.53 (4.53) proj_loss: -0.5776 (-0.5776) time: 0.6741 data: 0.0003 [11-23 21:36:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [ 417/1669] eta: 0:14:12 tlr: 0.00021 tnm: 0.28 Lm: 6.683 (6.683) Lt: 5.953 (5.953) Accm: 2.85 (2.85) Acct: 4.57 (4.57) proj_loss: -0.5473 (-0.5473) time: 0.6742 data: 0.0003 [11-23 21:41:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [ 834/1669] eta: 0:09:48 tlr: 0.00021 tnm: 0.29 Lm: 6.661 (6.599) Lt: 5.952 (5.841) Accm: 2.87 (3.16) Acct: 4.70 (5.02) proj_loss: -0.5586 (-0.5561) time: 0.6772 data: 0.0003 [11-23 21:41:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [ 834/1669] eta: 0:09:48 tlr: 0.00021 tnm: 0.29 Lm: 6.709 (6.752) Lt: 5.983 (6.012) Accm: 2.71 (2.61) Acct: 4.08 (4.16) proj_loss: -0.5636 (-0.5610) time: 0.6772 data: 0.0002 [11-23 21:41:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [ 834/1669] eta: 0:09:48 tlr: 0.00021 tnm: 0.29 Lm: 6.607 (6.605) Lt: 5.845 (5.863) Accm: 3.06 (2.95) Acct: 4.96 (4.68) proj_loss: -0.5709 (-0.5754) time: 0.6772 data: 0.0002 [11-23 21:41:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [ 834/1669] eta: 0:09:48 tlr: 0.00021 tnm: 0.29 Lm: 6.669 (6.675) Lt: 5.925 (5.920) Accm: 3.01 (3.07) Acct: 5.03 (5.06) proj_loss: -0.5818 (-0.5718) time: 0.6772 data: 0.0002 [11-23 21:46:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [1251/1669] eta: 0:04:50 tlr: 0.00021 tnm: 0.28 Lm: 6.668 (6.667) Lt: 5.920 (5.918) Accm: 3.06 (3.08) Acct: 4.98 (5.03) proj_loss: -0.5781 (-0.5725) time: 0.6759 data: 0.0002 [11-23 21:46:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [1251/1669] eta: 0:04:50 tlr: 0.00021 tnm: 0.28 Lm: 6.628 (6.619) Lt: 5.885 (5.879) Accm: 2.89 (2.89) Acct: 4.58 (4.56) proj_loss: -0.5693 (-0.5733) time: 0.6759 data: 0.0002 [11-23 21:46:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [1251/1669] eta: 0:04:50 tlr: 0.00021 tnm: 0.28 Lm: 6.707 (6.735) Lt: 5.977 (6.001) Accm: 2.67 (2.61) Acct: 4.19 (4.19) proj_loss: -0.5602 (-0.5600) time: 0.6759 data: 0.0002 [11-23 21:46:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [1251/1669] eta: 0:04:50 tlr: 0.00021 tnm: 0.28 Lm: 6.661 (6.615) Lt: 5.951 (5.868) Accm: 2.90 (3.10) Acct: 4.61 (4.89) proj_loss: -0.5574 (-0.5561) time: 0.6759 data: 0.0003 [11-23 21:51:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.26 Lm: 6.661 (6.623) Lt: 5.950 (5.874) Accm: 2.94 (3.08) Acct: 4.70 (4.90) proj_loss: -0.5562 (-0.5553) time: 0.6765 data: 0.0016 [11-23 21:51:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 67/350] Total time: 0:19:11 (0.690 s / it) [11-23 21:51:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.26 Lm: 6.704 (6.706) Lt: 5.970 (5.964) Accm: 2.71 (2.72) Acct: 4.30 (4.38) proj_loss: -0.5636 (-0.5628) time: 0.6765 data: 0.0017 [11-23 21:51:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.26 Lm: 6.648 (6.654) Lt: 5.926 (5.915) Accm: 2.72 (2.81) Acct: 4.20 (4.45) proj_loss: -0.5677 (-0.5706) time: 0.6765 data: 0.0016 [11-23 21:51:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 67/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.26 Lm: 6.668 (6.626) Lt: 5.914 (5.879) Accm: 3.10 (3.13) Acct: 5.03 (5.06) proj_loss: -0.5818 (-0.5745) time: 0.6765 data: 0.0016 [11-23 21:51:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 67/350] Total time: 0:19:11 (0.690 s / it) [11-23 21:51:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 67/350] Total time: 0:19:11 (0.690 s / it) [11-23 21:51:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 67/350] Total time: 0:19:11 (0.690 s / it) [11-23 21:51:21] (/home/user/VAR/train.py , line 276)=> [ep67] (training ) Lm: 6.665 (6.668), Lt: 5.923 (5.927), Acc m&t: 2.96 4.69, Remain: 3 days, 16:55:40, Finish: 2024-11-26 22:47 [11-23 21:51:21] (/home/user/VAR/train.py , line 276)=> [ep67] (training ) Lm: 6.665 (6.668), Lt: 5.923 (5.927), Acc m&t: 2.96 4.69, Remain: 3 days, 16:56:43, Finish: 2024-11-26 22:48 [11-23 21:51:21] (/home/user/VAR/train.py , line 276)=> [ep67] (training ) Lm: 6.665 (6.668), Lt: 5.923 (5.927), Acc m&t: 2.96 4.69, Remain: 3 days, 16:59:42, Finish: 2024-11-26 22:51 [11-23 21:51:21] (/home/user/VAR/train.py , line 276)=> [ep67] (training ) Lm: 6.665 (6.668), Lt: 5.923 (5.927), Acc m&t: 2.96 4.69, Remain: 3 days, 16:56:33, Finish: 2024-11-26 22:47 [11-23 21:51:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [ 0/1669] eta: 0:18:21 tlr: 0.00021 tnm: 0.28 Lm: 6.600 (6.600) Lt: 5.822 (5.822) Accm: 3.15 (3.15) Acct: 4.82 (4.82) proj_loss: -0.5594 (-0.5594) time: 0.6598 data: 0.0004 [11-23 21:51:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [ 0/1669] eta: 0:18:22 tlr: 0.00021 tnm: 0.28 Lm: 6.761 (6.761) Lt: 5.957 (5.957) Accm: 2.80 (2.80) Acct: 4.70 (4.70) proj_loss: -0.5571 (-0.5571) time: 0.6604 data: 0.0003 [11-23 21:51:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [ 0/1669] eta: 0:18:22 tlr: 0.00021 tnm: 0.28 Lm: 6.681 (6.681) Lt: 6.069 (6.069) Accm: 2.83 (2.83) Acct: 4.13 (4.13) proj_loss: -0.5902 (-0.5902) time: 0.6606 data: 0.0003 [11-23 21:51:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [ 0/1669] eta: 0:18:23 tlr: 0.00021 tnm: 0.28 Lm: 6.743 (6.743) Lt: 6.003 (6.003) Accm: 2.71 (2.71) Acct: 4.27 (4.27) proj_loss: -0.5533 (-0.5533) time: 0.6609 data: 0.0003 [11-23 21:56:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [ 417/1669] eta: 0:14:05 tlr: 0.00021 tnm: 0.28 Lm: 6.649 (6.649) Lt: 5.866 (5.866) Accm: 3.11 (3.11) Acct: 4.98 (4.98) proj_loss: -0.5559 (-0.5559) time: 0.6776 data: 0.0003 [11-23 21:56:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [ 417/1669] eta: 0:14:06 tlr: 0.00021 tnm: 0.28 Lm: 6.740 (6.740) Lt: 5.966 (5.966) Accm: 2.78 (2.78) Acct: 4.59 (4.59) proj_loss: -0.5621 (-0.5621) time: 0.6776 data: 0.0002 [11-23 21:56:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [ 417/1669] eta: 0:14:06 tlr: 0.00021 tnm: 0.28 Lm: 6.663 (6.663) Lt: 5.966 (5.966) Accm: 2.80 (2.80) Acct: 4.42 (4.42) proj_loss: -0.5833 (-0.5833) time: 0.6776 data: 0.0003 [11-23 21:56:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [ 417/1669] eta: 0:14:06 tlr: 0.00021 tnm: 0.28 Lm: 6.639 (6.639) Lt: 5.897 (5.897) Accm: 3.10 (3.10) Acct: 4.68 (4.68) proj_loss: -0.5770 (-0.5770) time: 0.6776 data: 0.0002 [11-23 22:00:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.26 Lm: 6.637 (6.638) Lt: 5.828 (5.874) Accm: 3.04 (3.03) Acct: 4.82 (4.77) proj_loss: -0.5594 (-0.5663) time: 0.6764 data: 0.0003 [11-23 22:00:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.26 Lm: 6.718 (6.698) Lt: 5.957 (5.916) Accm: 2.80 (2.89) Acct: 4.70 (4.76) proj_loss: -0.5671 (-0.5640) time: 0.6764 data: 0.0002 [11-23 22:00:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.26 Lm: 6.681 (6.710) Lt: 6.069 (6.010) Accm: 2.78 (2.67) Acct: 4.13 (4.17) proj_loss: -0.5764 (-0.5798) time: 0.6764 data: 0.0002 [11-23 22:00:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [ 834/1669] eta: 0:09:24 tlr: 0.00021 tnm: 0.26 Lm: 6.743 (6.685) Lt: 6.003 (5.927) Accm: 2.71 (2.90) Acct: 4.27 (4.59) proj_loss: -0.5585 (-0.5600) time: 0.6764 data: 0.0003 [11-23 22:05:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.28 Lm: 6.678 (6.667) Lt: 5.959 (5.924) Accm: 2.88 (2.94) Acct: 4.42 (4.58) proj_loss: -0.5633 (-0.5654) time: 0.6777 data: 0.0003 [11-23 22:05:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.28 Lm: 6.723 (6.705) Lt: 5.945 (5.920) Accm: 2.85 (2.89) Acct: 4.61 (4.70) proj_loss: -0.5675 (-0.5654) time: 0.6778 data: 0.0003 [11-23 22:05:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.28 Lm: 6.736 (6.730) Lt: 6.047 (6.014) Accm: 2.80 (2.73) Acct: 4.42 (4.30) proj_loss: -0.5746 (-0.5719) time: 0.6778 data: 0.0003 [11-23 22:05:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.28 Lm: 6.647 (6.643) Lt: 5.874 (5.886) Accm: 3.10 (3.10) Acct: 4.88 (4.86) proj_loss: -0.5578 (-0.5638) time: 0.6778 data: 0.0003 [11-23 22:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.657 (6.656) Lt: 5.921 (5.909) Accm: 3.04 (3.02) Acct: 4.82 (4.75) proj_loss: -0.5562 (-0.5605) time: 1.0950 data: 0.0018 [11-23 22:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.686 (6.721) Lt: 6.025 (6.004) Accm: 2.83 (2.78) Acct: 4.53 (4.35) proj_loss: -0.5728 (-0.5686) time: 1.0950 data: 0.0016 [11-23 22:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.727 (6.714) Lt: 5.957 (5.929) Accm: 2.81 (2.87) Acct: 4.51 (4.63) proj_loss: -0.5671 (-0.5607) time: 1.0950 data: 0.0018 [11-23 22:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 68/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.27 Lm: 6.678 (6.669) Lt: 5.927 (5.925) Accm: 2.89 (2.93) Acct: 4.58 (4.59) proj_loss: -0.5665 (-0.5656) time: 1.0950 data: 0.0016 [11-23 22:10:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 68/350] Total time: 0:19:08 (0.688 s / it) [11-23 22:10:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 68/350] Total time: 0:19:08 (0.688 s / it) [11-23 22:10:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 68/350] Total time: 0:19:08 (0.688 s / it) [11-23 22:10:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 68/350] Total time: 0:19:08 (0.688 s / it) [11-23 22:10:30] (/home/user/VAR/train.py , line 276)=> [ep68] (training ) Lm: 6.665 (6.680), Lt: 5.923 (5.938), Acc m&t: 2.96 4.69, Remain: 3 days, 17:17:40, Finish: 2024-11-26 23:28 [11-23 22:10:30] (/home/user/VAR/train.py , line 276)=> [ep68] (training ) Lm: 6.665 (6.680), Lt: 5.923 (5.938), Acc m&t: 2.96 4.69, Remain: 3 days, 17:19:08, Finish: 2024-11-26 23:29 [11-23 22:10:30] (/home/user/VAR/train.py , line 276)=> [ep68] (training ) Lm: 6.665 (6.680), Lt: 5.923 (5.938), Acc m&t: 2.96 4.69, Remain: 3 days, 17:19:06, Finish: 2024-11-26 23:29 [11-23 22:10:30] (/home/user/VAR/train.py , line 276)=> [ep68] (training ) Lm: 6.665 (6.680), Lt: 5.923 (5.938), Acc m&t: 2.96 4.69, Remain: 3 days, 17:19:29, Finish: 2024-11-26 23:29 [11-23 22:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [ 0/1669] eta: 0:18:14 tlr: 0.00021 tnm: 0.27 Lm: 6.648 (6.648) Lt: 5.920 (5.920) Accm: 3.20 (3.20) Acct: 5.10 (5.10) proj_loss: -0.5776 (-0.5776) time: 0.6560 data: 0.0004 [11-23 22:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [ 0/1669] eta: 0:18:16 tlr: 0.00021 tnm: 0.27 Lm: 6.536 (6.536) Lt: 5.816 (5.816) Accm: 3.49 (3.49) Acct: 5.61 (5.61) proj_loss: -0.5785 (-0.5785) time: 0.6571 data: 0.0003 [11-23 22:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [ 0/1669] eta: 0:18:17 tlr: 0.00021 tnm: 0.27 Lm: 6.807 (6.807) Lt: 6.084 (6.084) Accm: 2.56 (2.56) Acct: 4.18 (4.18) proj_loss: -0.5740 (-0.5740) time: 0.6574 data: 0.0003 [11-23 22:10:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [ 0/1669] eta: 0:18:16 tlr: 0.00021 tnm: 0.27 Lm: 6.585 (6.585) Lt: 5.803 (5.803) Accm: 3.26 (3.26) Acct: 5.34 (5.34) proj_loss: -0.5821 (-0.5821) time: 0.6572 data: 0.0004 [11-23 22:15:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [ 417/1669] eta: 0:14:17 tlr: 0.00021 tnm: 0.29 Lm: 6.627 (6.627) Lt: 5.868 (5.868) Accm: 3.08 (3.08) Acct: 5.05 (5.05) proj_loss: -0.5635 (-0.5635) time: 0.6807 data: 0.0003 [11-23 22:15:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [ 417/1669] eta: 0:14:17 tlr: 0.00021 tnm: 0.29 Lm: 6.713 (6.713) Lt: 5.997 (5.997) Accm: 2.95 (2.95) Acct: 4.67 (4.67) proj_loss: -0.5698 (-0.5698) time: 0.6807 data: 0.0003 [11-23 22:15:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [ 417/1669] eta: 0:14:17 tlr: 0.00021 tnm: 0.29 Lm: 6.651 (6.651) Lt: 5.946 (5.946) Accm: 3.10 (3.10) Acct: 4.98 (4.98) proj_loss: -0.5800 (-0.5800) time: 0.6807 data: 0.0003 [11-23 22:15:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [ 417/1669] eta: 0:14:17 tlr: 0.00021 tnm: 0.29 Lm: 6.709 (6.709) Lt: 5.976 (5.976) Accm: 2.61 (2.61) Acct: 4.21 (4.21) proj_loss: -0.5802 (-0.5802) time: 0.6807 data: 0.0003 [11-23 22:19:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [ 834/1669] eta: 0:09:28 tlr: 0.00021 tnm: 0.31 Lm: 6.701 (6.706) Lt: 5.936 (5.963) Accm: 2.65 (2.62) Acct: 4.24 (4.25) proj_loss: -0.5743 (-0.5783) time: 0.6781 data: 0.0003 [11-23 22:19:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [ 834/1669] eta: 0:09:28 tlr: 0.00021 tnm: 0.31 Lm: 6.684 (6.662) Lt: 5.951 (5.948) Accm: 2.70 (2.95) Acct: 4.36 (4.75) proj_loss: -0.5785 (-0.5795) time: 0.6781 data: 0.0003 [11-23 22:19:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [ 834/1669] eta: 0:09:28 tlr: 0.00021 tnm: 0.31 Lm: 6.585 (6.607) Lt: 5.804 (5.846) Accm: 3.19 (3.12) Acct: 4.94 (5.02) proj_loss: -0.5809 (-0.5693) time: 0.6781 data: 0.0003 [11-23 22:19:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [ 834/1669] eta: 0:09:28 tlr: 0.00021 tnm: 0.31 Lm: 6.688 (6.704) Lt: 5.942 (5.978) Accm: 2.89 (2.93) Acct: 4.72 (4.69) proj_loss: -0.5776 (-0.5740) time: 0.6781 data: 0.0003 [11-23 22:24:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [1251/1669] eta: 0:04:44 tlr: 0.00021 tnm: 0.28 Lm: 6.678 (6.695) Lt: 5.931 (5.957) Accm: 2.89 (2.92) Acct: 4.63 (4.65) proj_loss: -0.5763 (-0.5743) time: 0.6800 data: 0.0003 [11-23 22:24:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [1251/1669] eta: 0:04:44 tlr: 0.00021 tnm: 0.28 Lm: 6.690 (6.671) Lt: 5.930 (5.938) Accm: 2.73 (2.90) Acct: 4.31 (4.61) proj_loss: -0.5785 (-0.5682) time: 0.6800 data: 0.0003 [11-23 22:24:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [1251/1669] eta: 0:04:44 tlr: 0.00021 tnm: 0.28 Lm: 6.627 (6.625) Lt: 5.868 (5.885) Accm: 3.15 (3.12) Acct: 4.89 (4.97) proj_loss: -0.5747 (-0.5691) time: 0.6800 data: 0.0003 [11-23 22:24:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [1251/1669] eta: 0:04:44 tlr: 0.00021 tnm: 0.28 Lm: 6.672 (6.690) Lt: 5.915 (5.946) Accm: 2.65 (2.71) Acct: 4.28 (4.38) proj_loss: -0.5741 (-0.5734) time: 0.6800 data: 0.0003 [11-23 22:29:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.30 Lm: 6.701 (6.720) Lt: 5.936 (5.981) Accm: 2.65 (2.68) Acct: 4.30 (4.36) proj_loss: -0.5740 (-0.5718) time: 0.6787 data: 0.0019 [11-23 22:29:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 69/350] Total time: 0:18:53 (0.679 s / it) [11-23 22:29:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.30 Lm: 6.669 (6.637) Lt: 5.932 (5.902) Accm: 3.12 (3.07) Acct: 4.84 (4.82) proj_loss: -0.5684 (-0.5672) time: 0.6787 data: 0.0015 [11-23 22:29:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.30 Lm: 6.684 (6.651) Lt: 5.910 (5.906) Accm: 2.75 (3.02) Acct: 4.36 (4.85) proj_loss: -0.5785 (-0.5684) time: 0.6787 data: 0.0020 [11-23 22:29:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 69/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.30 Lm: 6.668 (6.668) Lt: 5.920 (5.928) Accm: 2.89 (3.01) Acct: 4.72 (4.81) proj_loss: -0.5776 (-0.5765) time: 0.6787 data: 0.0017 [11-23 22:29:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 69/350] Total time: 0:18:53 (0.679 s / it) [11-23 22:29:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 69/350] Total time: 0:18:53 (0.679 s / it) [11-23 22:29:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 69/350] Total time: 0:18:53 (0.679 s / it) [11-23 22:31:46] (home/user/VAR/trainer.py, line 114)=> FID: 4.338071101540663 [11-23 22:31:47] (/home/user/VAR/train.py , line 259)=> [*] [ep69] (val 50000) Lm: 6.6674, Lt: 5.9257, Acc m&t: 2.94 4.65, Val cost: 143.57s [11-23 22:31:47] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-23 22:32:20] (/home/user/VAR/train.py , line 276)=> [ep69] (training ) Lm: 6.665 (6.667), Lt: 5.923 (5.926), Acc m&t: 2.96 4.69, Remain: 3 days, 16:41:29, Finish: 2024-11-26 23:10 [11-23 22:32:20] (/home/user/VAR/train.py , line 276)=> [ep69] (training ) Lm: 6.665 (6.667), Lt: 5.923 (5.926), Acc m&t: 2.96 4.69, Remain: 3 days, 16:40:44, Finish: 2024-11-26 23:10 [11-23 22:32:20] (/home/user/VAR/train.py , line 276)=> [ep69] (training ) Lm: 6.665 (6.667), Lt: 5.923 (5.926), Acc m&t: 2.96 4.69, Remain: 3 days, 16:42:09, Finish: 2024-11-26 23:11 [11-23 22:32:20] (/home/user/VAR/train.py , line 276)=> [ep69] (training ) Lm: 6.665 (6.667), Lt: 5.923 (5.926), Acc m&t: 2.96 4.69, Remain: 3 days, 16:41:39, Finish: 2024-11-26 23:11 [11-23 22:32:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 0/1669] eta: 0:18:56 tlr: 0.00021 tnm: 0.28 Lm: 6.714 (6.714) Lt: 6.049 (6.049) Accm: 2.80 (2.80) Acct: 4.36 (4.36) proj_loss: -0.5828 (-0.5828) time: 0.6810 data: 0.0004 [11-23 22:32:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 0/1669] eta: 0:18:55 tlr: 0.00021 tnm: 0.28 Lm: 6.692 (6.692) Lt: 5.897 (5.897) Accm: 3.26 (3.26) Acct: 5.11 (5.11) proj_loss: -0.5599 (-0.5599) time: 0.6805 data: 0.0004 [11-23 22:32:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 0/1669] eta: 0:19:09 tlr: 0.00021 tnm: 0.28 Lm: 6.584 (6.584) Lt: 5.844 (5.844) Accm: 3.18 (3.18) Acct: 5.03 (5.03) proj_loss: -0.5601 (-0.5601) time: 0.6890 data: 0.0003 [11-23 22:32:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 0/1669] eta: 0:18:56 tlr: 0.00021 tnm: 0.28 Lm: 6.681 (6.681) Lt: 5.872 (5.872) Accm: 2.86 (2.86) Acct: 4.46 (4.46) proj_loss: -0.5583 (-0.5583) time: 0.6810 data: 0.0004 [11-23 22:37:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 417/1669] eta: 0:14:07 tlr: 0.00021 tnm: 0.30 Lm: 6.700 (6.700) Lt: 5.935 (5.935) Accm: 2.88 (2.88) Acct: 4.34 (4.34) proj_loss: -0.5653 (-0.5653) time: 0.6756 data: 0.0003 [11-23 22:37:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 417/1669] eta: 0:14:07 tlr: 0.00021 tnm: 0.30 Lm: 6.680 (6.680) Lt: 5.887 (5.887) Accm: 3.08 (3.08) Acct: 4.80 (4.80) proj_loss: -0.5612 (-0.5612) time: 0.6756 data: 0.0003 [11-23 22:37:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 417/1669] eta: 0:14:07 tlr: 0.00021 tnm: 0.30 Lm: 6.762 (6.762) Lt: 6.089 (6.089) Accm: 2.78 (2.78) Acct: 4.36 (4.36) proj_loss: -0.5785 (-0.5785) time: 0.6756 data: 0.0003 [11-23 22:37:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 417/1669] eta: 0:14:07 tlr: 0.00021 tnm: 0.30 Lm: 6.625 (6.625) Lt: 5.889 (5.889) Accm: 3.05 (3.05) Acct: 4.90 (4.90) proj_loss: -0.5722 (-0.5722) time: 0.6756 data: 0.0003 [11-23 22:41:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 834/1669] eta: 0:09:25 tlr: 0.00021 tnm: 0.28 Lm: 6.584 (6.602) Lt: 5.844 (5.865) Accm: 3.18 (3.11) Acct: 5.03 (4.98) proj_loss: -0.5769 (-0.5738) time: 0.6773 data: 0.0003 [11-23 22:41:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 834/1669] eta: 0:09:25 tlr: 0.00021 tnm: 0.28 Lm: 6.692 (6.702) Lt: 5.897 (5.927) Accm: 2.91 (2.87) Acct: 4.49 (4.44) proj_loss: -0.5599 (-0.5591) time: 0.6774 data: 0.0003 [11-23 22:41:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 834/1669] eta: 0:09:25 tlr: 0.00021 tnm: 0.28 Lm: 6.745 (6.756) Lt: 6.049 (6.061) Accm: 2.80 (2.79) Acct: 4.36 (4.39) proj_loss: -0.5743 (-0.5658) time: 0.6773 data: 0.0003 [11-23 22:41:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 834/1669] eta: 0:09:25 tlr: 0.00021 tnm: 0.28 Lm: 6.681 (6.663) Lt: 5.872 (5.913) Accm: 2.91 (2.93) Acct: 4.46 (4.38) proj_loss: -0.5723 (-0.5707) time: 0.6774 data: 0.0002 [11-23 22:46:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.30 Lm: 6.700 (6.684) Lt: 5.935 (5.938) Accm: 2.88 (2.86) Acct: 4.40 (4.37) proj_loss: -0.5707 (-0.5703) time: 0.6768 data: 0.0002 [11-23 22:46:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.30 Lm: 6.680 (6.656) Lt: 5.887 (5.891) Accm: 3.08 (3.02) Acct: 4.80 (4.63) proj_loss: -0.5612 (-0.5627) time: 0.6768 data: 0.0003 [11-23 22:46:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.30 Lm: 6.625 (6.624) Lt: 5.889 (5.899) Accm: 3.05 (2.99) Acct: 4.90 (4.77) proj_loss: -0.5685 (-0.5688) time: 0.6768 data: 0.0003 [11-23 22:46:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.30 Lm: 6.730 (6.742) Lt: 6.027 (6.034) Accm: 2.81 (2.84) Acct: 4.41 (4.48) proj_loss: -0.5635 (-0.5625) time: 0.6768 data: 0.0003 [11-23 22:51:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.26 Lm: 6.720 (6.737) Lt: 6.004 (6.023) Accm: 2.80 (2.81) Acct: 4.36 (4.39) proj_loss: -0.5644 (-0.5629) time: 0.6782 data: 0.0016 [11-23 22:51:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 70/350] Total time: 0:18:49 (0.677 s / it) [11-23 22:51:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.26 Lm: 6.668 (6.635) Lt: 5.877 (5.863) Accm: 3.26 (3.07) Acct: 5.11 (4.73) proj_loss: -0.5602 (-0.5622) time: 0.6782 data: 0.0016 [11-23 22:51:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.26 Lm: 6.584 (6.616) Lt: 5.844 (5.888) Accm: 3.18 (3.04) Acct: 5.03 (4.86) proj_loss: -0.5769 (-0.5710) time: 0.6782 data: 0.0019 [11-23 22:51:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.26 Lm: 6.720 (6.692) Lt: 5.997 (5.957) Accm: 2.86 (2.81) Acct: 4.34 (4.31) proj_loss: -0.5723 (-0.5735) time: 0.6782 data: 0.0016 [11-23 22:51:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 70/350] Total time: 0:18:49 (0.677 s / it) [11-23 22:51:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 70/350] Total time: 0:18:49 (0.677 s / it) [11-23 22:51:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 70/350] Total time: 0:18:49 (0.677 s / it) [11-23 22:51:10] (/home/user/VAR/train.py , line 276)=> [ep70] (training ) Lm: 6.654 (6.654), Lt: 5.913 (5.913), Acc m&t: 2.97 4.70, Remain: 3 days, 16:36:37, Finish: 2024-11-26 23:27 [11-23 22:51:10] (/home/user/VAR/train.py , line 276)=> [ep70] (training ) Lm: 6.654 (6.654), Lt: 5.913 (5.913), Acc m&t: 2.97 4.70, Remain: 3 days, 16:36:16, Finish: 2024-11-26 23:27 [11-23 22:51:10] (/home/user/VAR/train.py , line 276)=> [ep70] (training ) Lm: 6.654 (6.654), Lt: 5.913 (5.913), Acc m&t: 2.97 4.70, Remain: 3 days, 16:37:15, Finish: 2024-11-26 23:28 [11-23 22:51:10] (/home/user/VAR/train.py , line 276)=> [ep70] (training ) Lm: 6.654 (6.654), Lt: 5.913 (5.913), Acc m&t: 2.97 4.70, Remain: 3 days, 16:36:25, Finish: 2024-11-26 23:27 [11-23 22:51:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 0/1669] eta: 0:18:16 tlr: 0.00021 tnm: 0.30 Lm: 6.473 (6.473) Lt: 5.642 (5.642) Accm: 3.60 (3.60) Acct: 5.97 (5.97) proj_loss: -0.5536 (-0.5536) time: 0.6567 data: 0.0004 [11-23 22:51:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 0/1669] eta: 0:18:35 tlr: 0.00021 tnm: 0.30 Lm: 6.498 (6.498) Lt: 5.713 (5.713) Accm: 3.47 (3.47) Acct: 5.46 (5.46) proj_loss: -0.5472 (-0.5472) time: 0.6686 data: 0.0003 [11-23 22:51:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 0/1669] eta: 0:18:16 tlr: 0.00021 tnm: 0.30 Lm: 6.584 (6.584) Lt: 5.834 (5.834) Accm: 2.91 (2.91) Acct: 4.70 (4.70) proj_loss: -0.5735 (-0.5735) time: 0.6573 data: 0.0003 [11-23 22:51:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 0/1669] eta: 0:18:16 tlr: 0.00021 tnm: 0.30 Lm: 6.637 (6.637) Lt: 5.916 (5.916) Accm: 2.88 (2.88) Acct: 4.37 (4.37) proj_loss: -0.5613 (-0.5613) time: 0.6569 data: 0.0003 [11-23 22:55:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 417/1669] eta: 0:14:07 tlr: 0.00021 tnm: 0.29 Lm: 6.721 (6.721) Lt: 5.992 (5.992) Accm: 2.76 (2.76) Acct: 4.27 (4.27) proj_loss: -0.5566 (-0.5566) time: 0.6734 data: 0.0003 [11-23 22:55:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 417/1669] eta: 0:14:07 tlr: 0.00021 tnm: 0.29 Lm: 6.533 (6.533) Lt: 5.735 (5.735) Accm: 3.43 (3.43) Acct: 5.75 (5.75) proj_loss: -0.5570 (-0.5570) time: 0.6734 data: 0.0003 [11-23 22:55:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 417/1669] eta: 0:14:07 tlr: 0.00021 tnm: 0.29 Lm: 6.563 (6.563) Lt: 5.796 (5.796) Accm: 3.30 (3.30) Acct: 5.12 (5.12) proj_loss: -0.5577 (-0.5577) time: 0.6734 data: 0.0002 [11-23 22:55:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 417/1669] eta: 0:14:07 tlr: 0.00021 tnm: 0.29 Lm: 6.597 (6.597) Lt: 5.845 (5.845) Accm: 3.10 (3.10) Acct: 4.99 (4.99) proj_loss: -0.5621 (-0.5621) time: 0.6734 data: 0.0003 [11-23 23:00:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 834/1669] eta: 0:09:30 tlr: 0.00021 tnm: 0.28 Lm: 6.610 (6.610) Lt: 5.856 (5.866) Accm: 3.02 (3.07) Acct: 4.70 (4.90) proj_loss: -0.5508 (-0.5584) time: 0.6748 data: 0.0003 [11-23 23:00:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 834/1669] eta: 0:09:30 tlr: 0.00021 tnm: 0.28 Lm: 6.625 (6.583) Lt: 5.879 (5.825) Accm: 3.14 (3.14) Acct: 4.79 (4.94) proj_loss: -0.5553 (-0.5569) time: 0.6748 data: 0.0003 [11-23 23:00:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 834/1669] eta: 0:09:30 tlr: 0.00021 tnm: 0.28 Lm: 6.643 (6.695) Lt: 5.916 (5.953) Accm: 2.88 (2.91) Acct: 4.37 (4.57) proj_loss: -0.5613 (-0.5596) time: 0.6748 data: 0.0003 [11-23 23:00:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 834/1669] eta: 0:09:30 tlr: 0.00021 tnm: 0.28 Lm: 6.592 (6.557) Lt: 5.812 (5.761) Accm: 3.26 (3.33) Acct: 5.53 (5.49) proj_loss: -0.5604 (-0.5583) time: 0.6748 data: 0.0003 [11-23 23:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [1251/1669] eta: 0:04:45 tlr: 0.00021 tnm: 0.29 Lm: 6.599 (6.572) Lt: 5.801 (5.768) Accm: 3.19 (3.26) Acct: 5.29 (5.38) proj_loss: -0.5571 (-0.5572) time: 0.6766 data: 0.0003 [11-23 23:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [1251/1669] eta: 0:04:45 tlr: 0.00021 tnm: 0.29 Lm: 6.699 (6.710) Lt: 5.943 (5.957) Accm: 2.80 (2.86) Acct: 4.33 (4.50) proj_loss: -0.5566 (-0.5574) time: 0.6766 data: 0.0003 [11-23 23:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [1251/1669] eta: 0:04:45 tlr: 0.00021 tnm: 0.29 Lm: 6.602 (6.582) Lt: 5.841 (5.820) Accm: 3.17 (3.15) Acct: 4.93 (4.98) proj_loss: -0.5618 (-0.5631) time: 0.6766 data: 0.0003 [11-23 23:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [1251/1669] eta: 0:04:45 tlr: 0.00021 tnm: 0.29 Lm: 6.623 (6.618) Lt: 5.882 (5.877) Accm: 2.97 (3.01) Acct: 4.70 (4.74) proj_loss: -0.5560 (-0.5591) time: 0.6766 data: 0.0003 ======================================================= RESTART [11-24 00:21:36] ======================================================= ======================================================= RESTART [11-24 00:21:36] ======================================================= ======================================================= RESTART [11-24 00:21:36] ======================================================= ======================================================= RESTART [11-24 00:21:36] ======================================================= [11-24 00:21:36] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-24 00:21:36] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-24 00:21:36] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-24 00:22:31] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-24 00:22:31] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 commit_msg : add branch : main } [11-24 00:22:31] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-24 00:22:34] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-24 00:22:34] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> [11-24 00:22:34] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-24 00:22:34] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> [11-24 00:22:34] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-24 00:22:34] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-24 00:22:34] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep70, it0 [11-24 00:22:34] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-24 00:21:36] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-24 00:21:36] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-24 00:21:36] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-24 00:22:31] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-24 00:22:31] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_msg : add commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 } [11-24 00:22:31] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-24 00:22:34] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-24 00:22:34] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> [11-24 00:22:34] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-24 00:22:34] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> [11-24 00:22:34] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-24 00:22:34] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-24 00:22:34] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep70, it0 [11-24 00:22:34] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-24 00:21:36] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-24 00:21:36] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-24 00:21:36] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-24 00:22:31] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-24 00:22:31] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add branch : main commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 } [11-24 00:22:31] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-24 00:22:34] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-24 00:22:34] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> [11-24 00:22:34] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-24 00:22:34] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> [11-24 00:22:34] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-24 00:22:34] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-24 00:22:34] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep70, it0 [11-24 00:22:34] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-24 00:21:36] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-24 00:21:36] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-24 00:21:36] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-24 00:22:31] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-24 00:22:31] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add branch : main commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 } [11-24 00:22:31] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-24 00:22:34] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-24 00:22:34] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> [11-24 00:22:34] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-24 00:22:34] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-24 00:22:34] (e/user/VAR/utils/data.py, line 51)=> [11-24 00:22:34] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-24 00:22:34] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-24 00:22:34] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep70, it0 [11-24 00:22:34] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ... [dataloader multi processing](*) finished! (46.71s) [11-24 00:23:21] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [dataloader multi processing](*) finished! (48.79s) [dataloader multi processing](*) finished! (49.07s) [dataloader multi processing](*) finished! (51.83s) [11-24 00:23:25] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-24 00:23:25] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-24 00:23:27] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-24 00:23:23] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-24 00:23:28] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-24 00:23:28] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-24 00:23:29] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-24 00:23:23] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-24 00:23:28] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-24 00:23:28] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-24 00:23:29] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-24 00:23:26] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-24 00:23:31] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-24 00:23:31] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-24 00:23:32] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-24 00:23:29] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-24 00:23:56] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-24 00:23:56] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-24 00:23:56] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-24 00:23:56] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-24 00:23:56] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank0] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-24 00:23:34] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-24 00:23:56] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-24 00:23:56] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-24 00:23:56] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-24 00:23:56] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-24 00:23:56] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank8] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-24 00:23:31] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-24 00:23:56] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-24 00:23:56] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-24 00:23:56] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-24 00:23:56] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-24 00:23:56] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank16] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-24 00:23:30] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-24 00:23:56] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-24 00:23:56] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-24 00:23:56] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-24 00:23:56] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, 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'_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-24 00:23:57] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank24] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-24 00:23:57] (/VAR/utils/lr_control.py, line 105)=> [11-24 00:23:57] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-24 00:23:58] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-24 00:23:58] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-24 00:23:58] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-24 00:23:58] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-24 00:38:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 0/1669] eta: 16 days, 23:49:36 tlr: 0.00021 tnm: 0.27 Lm: 6.693 (6.693) Lt: 5.993 (5.993) Accm: 3.07 (3.07) Acct: 5.01 (5.01) proj_loss: -0.5796 (-0.5796) time: 879.6742 data: 0.0005 [11-24 00:23:57] (/VAR/utils/lr_control.py, line 105)=> [11-24 00:23:57] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-24 00:23:58] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-24 00:23:58] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-24 00:23:59] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-24 00:23:59] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-24 00:38:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 0/1669] eta: 16 days, 23:45:21 tlr: 0.00021 tnm: 0.27 Lm: 6.678 (6.678) Lt: 5.887 (5.887) Accm: 2.65 (2.65) Acct: 4.32 (4.32) proj_loss: -0.5627 (-0.5627) time: 879.5217 data: 0.0007 [11-24 00:23:57] (/VAR/utils/lr_control.py, line 105)=> [11-24 00:23:57] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-24 00:23:58] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-24 00:23:58] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-24 00:23:58] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-24 00:23:58] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-24 00:38:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 0/1669] eta: 16 days, 23:49:14 tlr: 0.00021 tnm: 0.27 Lm: 6.704 (6.704) Lt: 5.916 (5.916) Accm: 2.80 (2.80) Acct: 4.53 (4.53) proj_loss: -0.5565 (-0.5565) time: 879.6613 data: 0.0007 [11-24 00:23:57] (/VAR/utils/lr_control.py, line 105)=> [11-24 00:23:57] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-24 00:23:58] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-24 00:23:58] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-24 00:23:58] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-24 00:23:58] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-24 00:38:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 0/1669] eta: 16 days, 23:37:48 tlr: 0.00021 tnm: 0.27 Lm: 6.609 (6.609) Lt: 5.859 (5.859) Accm: 2.99 (2.99) Acct: 4.67 (4.67) proj_loss: -0.5600 (-0.5600) time: 879.2503 data: 0.0006 [11-24 00:49:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 417/1669] eta: 1:14:47 tlr: 0.00021 tnm: 0.28 Lm: 6.618 (6.618) Lt: 5.865 (5.865) Accm: 2.95 (2.95) Acct: 4.61 (4.61) proj_loss: -0.5688 (-0.5688) time: 0.6693 data: 0.0003 [11-24 00:49:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 417/1669] eta: 1:14:48 tlr: 0.00021 tnm: 0.28 Lm: 6.749 (6.749) Lt: 6.063 (6.063) Accm: 2.79 (2.79) Acct: 4.49 (4.49) proj_loss: -0.5724 (-0.5724) time: 0.6693 data: 0.0003 [11-24 00:49:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 417/1669] eta: 1:14:48 tlr: 0.00021 tnm: 0.28 Lm: 6.660 (6.660) Lt: 5.892 (5.892) Accm: 2.92 (2.92) Acct: 4.72 (4.72) proj_loss: -0.5604 (-0.5604) time: 0.6693 data: 0.0003 [11-24 00:49:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 417/1669] eta: 1:14:48 tlr: 0.00021 tnm: 0.28 Lm: 6.699 (6.699) Lt: 5.933 (5.933) Accm: 2.62 (2.62) Acct: 4.25 (4.25) proj_loss: -0.5594 (-0.5594) time: 0.6693 data: 0.0003 [11-24 00:53:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 834/1669] eta: 0:29:38 tlr: 0.00021 tnm: 0.26 Lm: 6.678 (6.666) Lt: 5.887 (5.913) Accm: 2.65 (2.79) Acct: 4.32 (4.41) proj_loss: -0.5627 (-0.5663) time: 0.6703 data: 0.0003 [11-24 00:53:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 834/1669] eta: 0:29:38 tlr: 0.00021 tnm: 0.26 Lm: 6.704 (6.691) Lt: 5.916 (5.942) Accm: 2.80 (2.83) Acct: 4.53 (4.56) proj_loss: -0.5565 (-0.5583) time: 0.6703 data: 0.0003 [11-24 00:53:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 834/1669] eta: 0:29:38 tlr: 0.00021 tnm: 0.26 Lm: 6.748 (6.749) Lt: 6.015 (6.047) Accm: 2.90 (2.83) Acct: 4.63 (4.54) proj_loss: -0.5652 (-0.5593) time: 0.6703 data: 0.0003 [11-24 00:53:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [ 834/1669] eta: 0:29:38 tlr: 0.00021 tnm: 0.26 Lm: 6.609 (6.608) Lt: 5.861 (5.864) Accm: 2.99 (3.15) Acct: 4.67 (4.90) proj_loss: -0.5776 (-0.5732) time: 0.6703 data: 0.0003 [11-24 00:58:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1251/1669] eta: 0:11:27 tlr: 0.00021 tnm: 0.27 Lm: 6.613 (6.610) Lt: 5.866 (5.868) Accm: 3.10 (3.16) Acct: 4.71 (4.86) proj_loss: -0.5688 (-0.5680) time: 0.6701 data: 0.0002 [11-24 00:58:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1251/1669] eta: 0:11:27 tlr: 0.00021 tnm: 0.27 Lm: 6.699 (6.694) Lt: 5.933 (5.935) Accm: 2.64 (2.75) Acct: 4.25 (4.33) proj_loss: -0.5629 (-0.5655) time: 0.6701 data: 0.0003 [11-24 00:58:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1251/1669] eta: 0:11:27 tlr: 0.00021 tnm: 0.27 Lm: 6.733 (6.741) Lt: 6.004 (6.029) Accm: 2.71 (2.74) Acct: 4.36 (4.43) proj_loss: -0.5636 (-0.5600) time: 0.6701 data: 0.0003 [11-24 00:58:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1251/1669] eta: 0:11:27 tlr: 0.00021 tnm: 0.27 Lm: 6.660 (6.654) Lt: 5.892 (5.901) Accm: 2.92 (2.94) Acct: 4.72 (4.73) proj_loss: -0.5577 (-0.5585) time: 0.6701 data: 0.0002 [11-24 01:03:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1668/1669] eta: 0:00:01 tlr: 0.00021 tnm: 0.27 Lm: 6.615 (6.640) Lt: 5.869 (5.867) Accm: 3.00 (2.95) Acct: 4.80 (4.75) proj_loss: -0.5565 (-0.5568) time: 0.6752 data: 0.0015 [11-24 01:03:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 70/350] Total time: 0:38:58 (1.401 s / it) [11-24 01:03:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1668/1669] eta: 0:00:01 tlr: 0.00021 tnm: 0.27 Lm: 6.719 (6.734) Lt: 5.993 (6.008) Accm: 2.78 (2.74) Acct: 4.55 (4.45) proj_loss: -0.5652 (-0.5613) time: 0.6752 data: 0.0015 [11-24 01:03:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1668/1669] eta: 0:00:01 tlr: 0.00021 tnm: 0.27 Lm: 6.721 (6.706) Lt: 5.980 (5.956) Accm: 2.65 (2.73) Acct: 4.18 (4.29) proj_loss: -0.5632 (-0.5693) time: 0.6752 data: 0.0016 [11-24 01:03:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 70/350] [1668/1669] eta: 0:00:01 tlr: 0.00021 tnm: 0.27 Lm: 6.617 (6.616) Lt: 5.870 (5.878) Accm: 3.21 (3.19) Acct: 4.75 (4.94) proj_loss: -0.5776 (-0.5723) time: 0.6752 data: 0.0016 [11-24 01:03:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 70/350] Total time: 0:38:58 (1.401 s / it) [11-24 01:03:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 70/350] Total time: 0:38:58 (1.401 s / it) [11-24 01:03:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 70/350] Total time: 0:38:57 (1.401 s / it) [11-24 01:03:01] (/home/user/VAR/train.py , line 276)=> [ep70] (training ) Lm: 6.654 (6.654), Lt: 5.912 (5.912), Acc m&t: 2.98 4.72, Remain: 3 days, 15:58:52, Finish: 2024-11-27 01:01 [11-24 01:03:01] (/home/user/VAR/train.py , line 276)=> [ep70] (training ) Lm: 6.654 (6.654), Lt: 5.912 (5.912), Acc m&t: 2.98 4.72, Remain: 3 days, 16:01:35, Finish: 2024-11-27 01:04 [11-24 01:03:01] (/home/user/VAR/train.py , line 276)=> [ep70] (training ) Lm: 6.654 (6.654), Lt: 5.912 (5.912), Acc m&t: 2.98 4.72, Remain: 3 days, 16:01:24, Finish: 2024-11-27 01:04 [11-24 01:03:01] (/home/user/VAR/train.py , line 276)=> [ep70] (training ) Lm: 6.654 (6.654), Lt: 5.912 (5.912), Acc m&t: 2.98 4.72, Remain: 3 days, 16:02:04, Finish: 2024-11-27 01:05 [11-24 01:03:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 0/1669] eta: 0:18:09 tlr: 0.00021 tnm: 0.29 Lm: 6.517 (6.517) Lt: 5.704 (5.704) Accm: 3.17 (3.17) Acct: 5.08 (5.08) proj_loss: -0.5594 (-0.5594) time: 0.6527 data: 0.0004 [11-24 01:03:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 0/1669] eta: 0:18:09 tlr: 0.00021 tnm: 0.29 Lm: 6.575 (6.575) Lt: 5.798 (5.798) Accm: 3.31 (3.31) Acct: 5.18 (5.18) proj_loss: -0.5746 (-0.5746) time: 0.6528 data: 0.0003 [11-24 01:03:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 0/1669] eta: 0:18:45 tlr: 0.00021 tnm: 0.29 Lm: 6.508 (6.508) Lt: 5.714 (5.714) Accm: 3.31 (3.31) Acct: 5.27 (5.27) proj_loss: -0.5496 (-0.5496) time: 0.6743 data: 0.0004 [11-24 01:03:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 0/1669] eta: 0:18:09 tlr: 0.00021 tnm: 0.29 Lm: 6.651 (6.651) Lt: 5.893 (5.893) Accm: 3.19 (3.19) Acct: 4.87 (4.87) proj_loss: -0.5478 (-0.5478) time: 0.6528 data: 0.0003 [11-24 01:07:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 417/1669] eta: 0:14:00 tlr: 0.00021 tnm: 0.28 Lm: 6.731 (6.731) Lt: 5.989 (5.989) Accm: 2.82 (2.82) Acct: 4.56 (4.56) proj_loss: -0.5454 (-0.5454) time: 0.6714 data: 0.0002 [11-24 01:07:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 417/1669] eta: 0:14:00 tlr: 0.00021 tnm: 0.28 Lm: 6.657 (6.657) Lt: 5.899 (5.899) Accm: 3.02 (3.02) Acct: 4.78 (4.78) proj_loss: -0.5622 (-0.5622) time: 0.6714 data: 0.0003 [11-24 01:07:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 417/1669] eta: 0:14:00 tlr: 0.00021 tnm: 0.28 Lm: 6.576 (6.576) Lt: 5.796 (5.796) Accm: 3.23 (3.23) Acct: 5.23 (5.23) proj_loss: -0.5623 (-0.5623) time: 0.6714 data: 0.0003 [11-24 01:07:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 417/1669] eta: 0:14:00 tlr: 0.00021 tnm: 0.28 Lm: 6.591 (6.591) Lt: 5.803 (5.803) Accm: 3.11 (3.11) Acct: 5.04 (5.04) proj_loss: -0.5573 (-0.5573) time: 0.6714 data: 0.0003 [11-24 01:12:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 834/1669] eta: 0:09:26 tlr: 0.00021 tnm: 0.26 Lm: 6.611 (6.598) Lt: 5.861 (5.822) Accm: 3.06 (3.08) Acct: 5.01 (4.92) proj_loss: -0.5594 (-0.5583) time: 0.6710 data: 0.0003 [11-24 01:12:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 834/1669] eta: 0:09:26 tlr: 0.00021 tnm: 0.26 Lm: 6.618 (6.644) Lt: 5.914 (5.904) Accm: 2.87 (2.97) Acct: 4.68 (4.75) proj_loss: -0.5532 (-0.5592) time: 0.6710 data: 0.0003 [11-24 01:12:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 834/1669] eta: 0:09:26 tlr: 0.00021 tnm: 0.26 Lm: 6.644 (6.608) Lt: 5.878 (5.851) Accm: 3.15 (3.09) Acct: 5.18 (4.91) proj_loss: -0.5570 (-0.5605) time: 0.6710 data: 0.0002 [11-24 01:12:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [ 834/1669] eta: 0:09:26 tlr: 0.00021 tnm: 0.26 Lm: 6.670 (6.710) Lt: 5.893 (5.950) Accm: 2.78 (2.81) Acct: 4.70 (4.61) proj_loss: -0.5478 (-0.5498) time: 0.6710 data: 0.0003 [11-24 01:17:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.29 Lm: 6.711 (6.721) Lt: 5.944 (5.961) Accm: 2.91 (2.86) Acct: 4.71 (4.64) proj_loss: -0.5454 (-0.5477) time: 0.6702 data: 0.0002 [11-24 01:17:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.29 Lm: 6.632 (6.612) Lt: 5.865 (5.834) Accm: 3.04 (3.02) Acct: 4.84 (4.82) proj_loss: -0.5573 (-0.5572) time: 0.6702 data: 0.0002 [11-24 01:17:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.29 Lm: 6.627 (6.609) Lt: 5.874 (5.856) Accm: 3.01 (3.04) Acct: 5.00 (4.89) proj_loss: -0.5660 (-0.5693) time: 0.6702 data: 0.0002 [11-24 01:17:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [1251/1669] eta: 0:04:42 tlr: 0.00021 tnm: 0.29 Lm: 6.618 (6.637) Lt: 5.903 (5.901) Accm: 3.07 (3.05) Acct: 4.90 (4.84) proj_loss: -0.5604 (-0.5613) time: 0.6702 data: 0.0003 [11-24 01:21:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.619 (6.647) Lt: 5.914 (5.916) Accm: 2.88 (3.01) Acct: 4.68 (4.78) proj_loss: -0.5676 (-0.5653) time: 0.6727 data: 0.0017 [11-24 01:21:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 71/350] Total time: 0:18:45 (0.675 s / it) [11-24 01:21:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.611 (6.607) Lt: 5.868 (5.848) Accm: 3.03 (3.02) Acct: 4.67 (4.74) proj_loss: -0.5594 (-0.5682) time: 0.6727 data: 0.0017 [11-24 01:21:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.635 (6.614) Lt: 5.878 (5.861) Accm: 3.15 (3.10) Acct: 5.10 (4.93) proj_loss: -0.5585 (-0.5671) time: 0.6727 data: 0.0017 [11-24 01:21:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 71/350] [1668/1669] eta: 0:00:00 tlr: 0.00021 tnm: 0.28 Lm: 6.670 (6.703) Lt: 5.893 (5.944) Accm: 3.03 (2.93) Acct: 4.72 (4.68) proj_loss: -0.5478 (-0.5509) time: 0.6727 data: 0.0018 [11-24 01:21:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 71/350] Total time: 0:18:45 (0.675 s / it) [11-24 01:21:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 71/350] Total time: 0:18:45 (0.675 s / it) [11-24 01:21:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 71/350] Total time: 0:18:45 (0.675 s / it) [11-24 01:21:47] (/home/user/VAR/train.py , line 276)=> [ep71] (training ) Lm: 6.648 (6.648), Lt: 5.902 (5.902), Acc m&t: 2.99 4.74, Remain: 3 days, 15:24:29, Finish: 2024-11-27 00:46 [11-24 01:21:47] (/home/user/VAR/train.py , line 276)=> [ep71] (training ) Lm: 6.648 (6.648), Lt: 5.902 (5.902), Acc m&t: 2.99 4.74, Remain: 3 days, 15:23:44, Finish: 2024-11-27 00:45 [11-24 01:21:47] (/home/user/VAR/train.py , line 276)=> [ep71] (training ) Lm: 6.648 (6.648), Lt: 5.902 (5.902), Acc m&t: 2.99 4.74, Remain: 3 days, 15:23:43, Finish: 2024-11-27 00:45 [11-24 01:21:47] (/home/user/VAR/train.py , line 276)=> [ep71] (training ) Lm: 6.648 (6.648), Lt: 5.902 (5.902), Acc m&t: 2.99 4.74, Remain: 3 days, 15:25:25, Finish: 2024-11-27 00:47 [11-24 01:21:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [ 0/1669] eta: 0:18:16 tlr: 0.00021 tnm: 0.28 Lm: 6.535 (6.535) Lt: 5.810 (5.810) Accm: 3.35 (3.35) Acct: 5.34 (5.34) proj_loss: -0.5752 (-0.5752) time: 0.6568 data: 0.0003 [11-24 01:21:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [ 0/1669] eta: 0:18:17 tlr: 0.00021 tnm: 0.28 Lm: 6.498 (6.498) Lt: 5.737 (5.737) Accm: 3.36 (3.36) Acct: 5.27 (5.27) proj_loss: -0.5874 (-0.5874) time: 0.6574 data: 0.0004 [11-24 01:21:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [ 0/1669] eta: 0:18:19 tlr: 0.00021 tnm: 0.28 Lm: 6.653 (6.653) Lt: 5.954 (5.954) Accm: 2.81 (2.81) Acct: 4.44 (4.44) proj_loss: -0.5633 (-0.5633) time: 0.6587 data: 0.0003 [11-24 01:21:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [ 0/1669] eta: 0:18:17 tlr: 0.00021 tnm: 0.28 Lm: 6.561 (6.561) Lt: 5.826 (5.826) Accm: 3.18 (3.18) Acct: 5.10 (5.10) proj_loss: -0.5564 (-0.5564) time: 0.6574 data: 0.0003 [11-24 01:26:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.27 Lm: 6.605 (6.605) Lt: 5.861 (5.861) Accm: 3.04 (3.04) Acct: 4.88 (4.88) proj_loss: -0.5608 (-0.5608) time: 0.6709 data: 0.0003 [11-24 01:26:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.27 Lm: 6.634 (6.634) Lt: 5.922 (5.922) Accm: 3.04 (3.04) Acct: 4.66 (4.66) proj_loss: -0.5643 (-0.5643) time: 0.6709 data: 0.0002 [11-24 01:26:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.27 Lm: 6.630 (6.630) Lt: 5.889 (5.889) Accm: 2.92 (2.92) Acct: 4.61 (4.61) proj_loss: -0.5725 (-0.5725) time: 0.6709 data: 0.0003 [11-24 01:26:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.27 Lm: 6.605 (6.605) Lt: 5.867 (5.867) Accm: 3.16 (3.16) Acct: 5.09 (5.09) proj_loss: -0.5635 (-0.5635) time: 0.6709 data: 0.0003 [11-24 01:31:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [ 834/1669] eta: 0:09:19 tlr: 0.0002 tnm: 0.27 Lm: 6.675 (6.683) Lt: 5.923 (5.978) Accm: 2.97 (2.90) Acct: 4.84 (4.62) proj_loss: -0.5752 (-0.5676) time: 0.6717 data: 0.0003 [11-24 01:31:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [ 834/1669] eta: 0:09:19 tlr: 0.0002 tnm: 0.27 Lm: 6.653 (6.669) Lt: 5.954 (5.973) Accm: 2.81 (2.92) Acct: 4.44 (4.55) proj_loss: -0.5653 (-0.5676) time: 0.6717 data: 0.0002 [11-24 01:31:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [ 834/1669] eta: 0:09:19 tlr: 0.0002 tnm: 0.27 Lm: 6.763 (6.702) Lt: 6.041 (5.976) Accm: 2.52 (2.79) Acct: 3.94 (4.32) proj_loss: -0.5794 (-0.5748) time: 0.6717 data: 0.0003 [11-24 01:31:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [ 834/1669] eta: 0:09:19 tlr: 0.0002 tnm: 0.27 Lm: 6.648 (6.643) Lt: 5.895 (5.921) Accm: 2.90 (2.90) Acct: 4.67 (4.60) proj_loss: -0.5652 (-0.5703) time: 0.6717 data: 0.0002 [11-24 01:35:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [1251/1669] eta: 0:04:42 tlr: 0.0002 tnm: 0.28 Lm: 6.605 (6.612) Lt: 5.861 (5.869) Accm: 3.04 (3.02) Acct: 4.88 (4.81) proj_loss: -0.5709 (-0.5718) time: 0.6674 data: 0.0002 [11-24 01:35:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [1251/1669] eta: 0:04:42 tlr: 0.0002 tnm: 0.28 Lm: 6.646 (6.662) Lt: 5.922 (5.952) Accm: 2.94 (2.95) Acct: 4.64 (4.62) proj_loss: -0.5698 (-0.5725) time: 0.6674 data: 0.0002 [11-24 01:35:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [1251/1669] eta: 0:04:42 tlr: 0.0002 tnm: 0.28 Lm: 6.694 (6.683) Lt: 5.983 (5.963) Accm: 2.75 (2.83) Acct: 4.22 (4.36) proj_loss: -0.5818 (-0.5771) time: 0.6674 data: 0.0002 [11-24 01:35:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [1251/1669] eta: 0:04:42 tlr: 0.0002 tnm: 0.28 Lm: 6.679 (6.683) Lt: 5.943 (5.974) Accm: 2.85 (2.86) Acct: 4.63 (4.57) proj_loss: -0.5755 (-0.5710) time: 0.6674 data: 0.0003 [11-24 01:40:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.25 Lm: 6.675 (6.670) Lt: 5.923 (5.945) Accm: 2.97 (2.92) Acct: 4.84 (4.72) proj_loss: -0.5758 (-0.5720) time: 0.6719 data: 0.0016 [11-24 01:40:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 72/350] Total time: 0:18:45 (0.675 s / it) [11-24 01:40:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.25 Lm: 6.561 (6.599) Lt: 5.826 (5.852) Accm: 3.18 (3.10) Acct: 5.08 (4.87) proj_loss: -0.5766 (-0.5750) time: 0.6719 data: 0.0016 [11-24 01:40:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.25 Lm: 6.640 (6.640) Lt: 5.891 (5.913) Accm: 3.06 (2.99) Acct: 4.84 (4.69) proj_loss: -0.5653 (-0.5703) time: 0.6719 data: 0.0015 [11-24 01:40:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 72/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.25 Lm: 6.667 (6.680) Lt: 5.952 (5.961) Accm: 2.97 (2.86) Acct: 4.49 (4.48) proj_loss: -0.5794 (-0.5775) time: 0.6719 data: 0.0017 [11-24 01:40:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 72/350] Total time: 0:18:45 (0.675 s / it) [11-24 01:40:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 72/350] Total time: 0:18:45 (0.675 s / it) [11-24 01:40:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 72/350] Total time: 0:18:45 (0.675 s / it) [11-24 01:40:33] (/home/user/VAR/train.py , line 276)=> [ep72] (training ) Lm: 6.648 (6.659), Lt: 5.902 (5.919), Acc m&t: 2.99 4.74, Remain: 3 days, 14:59:44, Finish: 2024-11-27 00:40 [11-24 01:40:33] (/home/user/VAR/train.py , line 276)=> [ep72] (training ) Lm: 6.648 (6.659), Lt: 5.902 (5.919), Acc m&t: 2.99 4.74, Remain: 3 days, 15:01:24, Finish: 2024-11-27 00:41 [11-24 01:40:33] (/home/user/VAR/train.py , line 276)=> [ep72] (training ) Lm: 6.648 (6.659), Lt: 5.902 (5.919), Acc m&t: 2.99 4.74, Remain: 3 days, 15:01:51, Finish: 2024-11-27 00:42 [11-24 01:40:33] (/home/user/VAR/train.py , line 276)=> [ep72] (training ) Lm: 6.648 (6.659), Lt: 5.902 (5.919), Acc m&t: 2.99 4.74, Remain: 3 days, 15:00:47, Finish: 2024-11-27 00:41 [11-24 01:40:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [ 0/1669] eta: 0:18:31 tlr: 0.0002 tnm: 0.29 Lm: 6.733 (6.733) Lt: 5.977 (5.977) Accm: 3.03 (3.03) Acct: 4.98 (4.98) proj_loss: -0.5681 (-0.5681) time: 0.6660 data: 0.0003 [11-24 01:40:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [ 0/1669] eta: 0:18:31 tlr: 0.0002 tnm: 0.29 Lm: 6.846 (6.846) Lt: 6.123 (6.123) Accm: 2.58 (2.58) Acct: 4.10 (4.10) proj_loss: -0.5661 (-0.5661) time: 0.6662 data: 0.0003 [11-24 01:40:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [ 0/1669] eta: 0:18:32 tlr: 0.0002 tnm: 0.29 Lm: 6.408 (6.408) Lt: 5.599 (5.599) Accm: 3.37 (3.37) Acct: 5.41 (5.41) proj_loss: -0.5638 (-0.5638) time: 0.6666 data: 0.0004 [11-24 01:40:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [ 0/1669] eta: 0:18:32 tlr: 0.0002 tnm: 0.29 Lm: 6.543 (6.543) Lt: 5.790 (5.790) Accm: 3.25 (3.25) Acct: 5.23 (5.23) proj_loss: -0.5499 (-0.5499) time: 0.6667 data: 0.0004 [11-24 01:45:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.27 Lm: 6.684 (6.684) Lt: 5.948 (5.948) Accm: 2.91 (2.91) Acct: 4.66 (4.66) proj_loss: -0.5549 (-0.5549) time: 0.6711 data: 0.0003 [11-24 01:45:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.27 Lm: 6.527 (6.527) Lt: 5.745 (5.745) Accm: 3.31 (3.31) Acct: 5.34 (5.34) proj_loss: -0.5635 (-0.5635) time: 0.6711 data: 0.0002 [11-24 01:45:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.27 Lm: 6.656 (6.656) Lt: 5.886 (5.886) Accm: 3.13 (3.13) Acct: 4.97 (4.97) proj_loss: -0.5678 (-0.5678) time: 0.6711 data: 0.0002 [11-24 01:45:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.27 Lm: 6.595 (6.595) Lt: 5.844 (5.844) Accm: 3.29 (3.29) Acct: 5.21 (5.21) proj_loss: -0.5755 (-0.5755) time: 0.6711 data: 0.0003 [11-24 01:49:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [ 834/1669] eta: 0:09:19 tlr: 0.0002 tnm: 0.28 Lm: 6.647 (6.612) Lt: 5.865 (5.851) Accm: 3.03 (3.18) Acct: 4.98 (4.99) proj_loss: -0.5681 (-0.5694) time: 0.6696 data: 0.0003 [11-24 01:49:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [ 834/1669] eta: 0:09:19 tlr: 0.0002 tnm: 0.28 Lm: 6.571 (6.628) Lt: 5.841 (5.871) Accm: 3.33 (3.20) Acct: 5.27 (5.07) proj_loss: -0.5661 (-0.5654) time: 0.6696 data: 0.0002 [11-24 01:49:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [ 834/1669] eta: 0:09:19 tlr: 0.0002 tnm: 0.28 Lm: 6.523 (6.526) Lt: 5.843 (5.778) Accm: 3.29 (3.30) Acct: 5.34 (5.34) proj_loss: -0.5638 (-0.5681) time: 0.6696 data: 0.0002 [11-24 01:49:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [ 834/1669] eta: 0:09:19 tlr: 0.0002 tnm: 0.28 Lm: 6.750 (6.706) Lt: 6.003 (5.966) Accm: 2.56 (2.77) Acct: 4.08 (4.41) proj_loss: -0.5599 (-0.5657) time: 0.6696 data: 0.0002 [11-24 01:54:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [1251/1669] eta: 0:04:40 tlr: 0.0002 tnm: 0.28 Lm: 6.762 (6.723) Lt: 6.014 (5.981) Accm: 2.63 (2.75) Acct: 4.20 (4.39) proj_loss: -0.5664 (-0.5675) time: 0.6727 data: 0.0002 [11-24 01:54:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [1251/1669] eta: 0:04:40 tlr: 0.0002 tnm: 0.28 Lm: 6.655 (6.656) Lt: 5.905 (5.896) Accm: 3.06 (3.09) Acct: 4.85 (4.91) proj_loss: -0.5633 (-0.5582) time: 0.6727 data: 0.0002 [11-24 01:54:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [1251/1669] eta: 0:04:40 tlr: 0.0002 tnm: 0.28 Lm: 6.585 (6.559) Lt: 5.867 (5.822) Accm: 3.27 (3.22) Acct: 5.30 (5.17) proj_loss: -0.5684 (-0.5693) time: 0.6727 data: 0.0002 [11-24 01:54:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [1251/1669] eta: 0:04:40 tlr: 0.0002 tnm: 0.28 Lm: 6.690 (6.671) Lt: 5.921 (5.941) Accm: 2.99 (2.99) Acct: 4.77 (4.64) proj_loss: -0.5755 (-0.5767) time: 0.6727 data: 0.0003 [11-24 01:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.26 Lm: 6.647 (6.651) Lt: 5.865 (5.916) Accm: 3.03 (3.00) Acct: 4.75 (4.66) proj_loss: -0.5742 (-0.5762) time: 0.6722 data: 0.0018 [11-24 01:59:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 73/350] Total time: 0:18:40 (0.671 s / it) [11-24 01:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.26 Lm: 6.739 (6.683) Lt: 5.969 (5.933) Accm: 2.78 (2.96) Acct: 4.42 (4.70) proj_loss: -0.5605 (-0.5557) time: 0.6721 data: 0.0016 [11-24 01:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.26 Lm: 6.646 (6.579) Lt: 5.892 (5.838) Accm: 3.26 (3.13) Acct: 5.27 (5.00) proj_loss: -0.5648 (-0.5684) time: 0.6722 data: 0.0019 [11-24 01:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 73/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.26 Lm: 6.750 (6.711) Lt: 6.003 (5.957) Accm: 2.69 (2.82) Acct: 4.32 (4.48) proj_loss: -0.5729 (-0.5691) time: 0.6722 data: 0.0020 [11-24 01:59:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 73/350] Total time: 0:18:40 (0.671 s / it) [11-24 01:59:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 73/350] Total time: 0:18:40 (0.671 s / it) [11-24 01:59:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 73/350] Total time: 0:18:40 (0.671 s / it) [11-24 01:59:14] (/home/user/VAR/train.py , line 276)=> [ep73] (training ) Lm: 6.648 (6.652), Lt: 5.902 (5.910), Acc m&t: 2.99 4.74, Remain: 3 days, 14:29:29, Finish: 2024-11-27 00:28 [11-24 01:59:14] (/home/user/VAR/train.py , line 276)=> [ep73] (training ) Lm: 6.648 (6.652), Lt: 5.902 (5.910), Acc m&t: 2.99 4.74, Remain: 3 days, 14:30:25, Finish: 2024-11-27 00:29 [11-24 01:59:14] (/home/user/VAR/train.py , line 276)=> [ep73] (training ) Lm: 6.648 (6.652), Lt: 5.902 (5.910), Acc m&t: 2.99 4.74, Remain: 3 days, 14:29:51, Finish: 2024-11-27 00:29 [11-24 01:59:14] (/home/user/VAR/train.py , line 276)=> [ep73] (training ) Lm: 6.648 (6.652), Lt: 5.902 (5.910), Acc m&t: 2.99 4.74, Remain: 3 days, 14:27:23, Finish: 2024-11-27 00:26 [11-24 01:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [ 0/1669] eta: 0:18:17 tlr: 0.0002 tnm: 0.27 Lm: 6.711 (6.711) Lt: 5.983 (5.983) Accm: 2.79 (2.79) Acct: 4.27 (4.27) proj_loss: -0.5458 (-0.5458) time: 0.6575 data: 0.0003 [11-24 01:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [ 0/1669] eta: 0:18:17 tlr: 0.0002 tnm: 0.27 Lm: 6.555 (6.555) Lt: 5.832 (5.832) Accm: 3.10 (3.10) Acct: 4.94 (4.94) proj_loss: -0.5692 (-0.5692) time: 0.6577 data: 0.0003 [11-24 01:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [ 0/1669] eta: 0:18:17 tlr: 0.0002 tnm: 0.27 Lm: 6.603 (6.603) Lt: 5.822 (5.822) Accm: 2.97 (2.97) Acct: 4.91 (4.91) proj_loss: -0.5709 (-0.5709) time: 0.6578 data: 0.0004 [11-24 01:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [ 0/1669] eta: 0:18:17 tlr: 0.0002 tnm: 0.27 Lm: 6.743 (6.743) Lt: 5.974 (5.974) Accm: 2.83 (2.83) Acct: 4.41 (4.41) proj_loss: -0.5557 (-0.5557) time: 0.6573 data: 0.0004 [11-24 02:04:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [ 417/1669] eta: 0:14:27 tlr: 0.0002 tnm: 0.27 Lm: 6.677 (6.677) Lt: 5.930 (5.930) Accm: 2.84 (2.84) Acct: 4.33 (4.33) proj_loss: -0.5539 (-0.5539) time: 0.6706 data: 0.0003 [11-24 02:04:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [ 417/1669] eta: 0:14:27 tlr: 0.0002 tnm: 0.27 Lm: 6.642 (6.642) Lt: 5.901 (5.901) Accm: 2.98 (2.98) Acct: 4.61 (4.61) proj_loss: -0.5633 (-0.5633) time: 0.6706 data: 0.0002 [11-24 02:04:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [ 417/1669] eta: 0:14:27 tlr: 0.0002 tnm: 0.27 Lm: 6.609 (6.609) Lt: 5.825 (5.825) Accm: 2.96 (2.96) Acct: 4.73 (4.73) proj_loss: -0.5570 (-0.5570) time: 0.6706 data: 0.0003 [11-24 02:04:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [ 417/1669] eta: 0:14:27 tlr: 0.0002 tnm: 0.27 Lm: 6.682 (6.682) Lt: 5.950 (5.950) Accm: 2.86 (2.86) Acct: 4.46 (4.46) proj_loss: -0.5623 (-0.5623) time: 0.6706 data: 0.0002 [11-24 02:08:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [ 834/1669] eta: 0:09:29 tlr: 0.0002 tnm: 0.28 Lm: 6.656 (6.673) Lt: 5.916 (5.912) Accm: 2.93 (2.95) Acct: 4.65 (4.60) proj_loss: -0.5599 (-0.5615) time: 0.6701 data: 0.0003 [11-24 02:08:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [ 834/1669] eta: 0:09:29 tlr: 0.0002 tnm: 0.28 Lm: 6.724 (6.670) Lt: 5.970 (5.933) Accm: 2.86 (2.93) Acct: 4.41 (4.55) proj_loss: -0.5692 (-0.5681) time: 0.6701 data: 0.0002 [11-24 02:08:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [ 834/1669] eta: 0:09:29 tlr: 0.0002 tnm: 0.28 Lm: 6.614 (6.623) Lt: 5.828 (5.843) Accm: 2.96 (2.92) Acct: 4.77 (4.75) proj_loss: -0.5613 (-0.5584) time: 0.6701 data: 0.0003 [11-24 02:08:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [ 834/1669] eta: 0:09:29 tlr: 0.0002 tnm: 0.28 Lm: 6.709 (6.688) Lt: 5.974 (5.964) Accm: 2.83 (2.82) Acct: 4.27 (4.31) proj_loss: -0.5557 (-0.5584) time: 0.6701 data: 0.0003 [11-24 02:13:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [1251/1669] eta: 0:04:43 tlr: 0.0002 tnm: 0.26 Lm: 6.689 (6.683) Lt: 5.950 (5.954) Accm: 2.83 (2.82) Acct: 4.34 (4.36) proj_loss: -0.5616 (-0.5644) time: 0.6723 data: 0.0002 [11-24 02:13:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [1251/1669] eta: 0:04:43 tlr: 0.0002 tnm: 0.26 Lm: 6.708 (6.675) Lt: 5.956 (5.935) Accm: 2.91 (2.94) Acct: 4.52 (4.57) proj_loss: -0.5722 (-0.5699) time: 0.6723 data: 0.0002 [11-24 02:13:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [1251/1669] eta: 0:04:43 tlr: 0.0002 tnm: 0.26 Lm: 6.654 (6.616) Lt: 5.876 (5.849) Accm: 3.03 (3.10) Acct: 4.76 (4.89) proj_loss: -0.5693 (-0.5698) time: 0.6723 data: 0.0003 [11-24 02:13:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [1251/1669] eta: 0:04:43 tlr: 0.0002 tnm: 0.26 Lm: 6.609 (6.610) Lt: 5.825 (5.831) Accm: 2.96 (2.95) Acct: 4.78 (4.76) proj_loss: -0.5602 (-0.5586) time: 0.6723 data: 0.0003 [11-24 02:18:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.614 (6.620) Lt: 5.828 (5.843) Accm: 2.96 (2.91) Acct: 4.77 (4.67) proj_loss: -0.5613 (-0.5629) time: 0.6747 data: 0.0020 [11-24 02:18:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 74/350] Total time: 0:18:49 (0.677 s / it) [11-24 02:18:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.724 (6.689) Lt: 5.970 (5.954) Accm: 2.86 (2.88) Acct: 4.41 (4.52) proj_loss: -0.5752 (-0.5723) time: 0.6747 data: 0.0016 [11-24 02:18:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.652 (6.622) Lt: 5.845 (5.848) Accm: 2.94 (3.07) Acct: 4.87 (4.89) proj_loss: -0.5731 (-0.5704) time: 0.6747 data: 0.0021 [11-24 02:18:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 74/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.669 (6.673) Lt: 5.925 (5.936) Accm: 2.83 (2.83) Acct: 4.41 (4.41) proj_loss: -0.5639 (-0.5643) time: 0.6747 data: 0.0019 [11-24 02:18:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 74/350] Total time: 0:18:49 (0.677 s / it) [11-24 02:18:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 74/350] Total time: 0:18:49 (0.677 s / it) [11-24 02:18:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 74/350] Total time: 0:18:49 (0.677 s / it) [11-24 02:18:04] (/home/user/VAR/train.py , line 276)=> [ep74] (training ) Lm: 6.648 (6.649), Lt: 5.902 (5.905), Acc m&t: 3.00 4.74, Remain: 3 days, 14:41:39, Finish: 2024-11-27 00:59 [11-24 02:18:04] (/home/user/VAR/train.py , line 276)=> [ep74] (training ) Lm: 6.648 (6.649), Lt: 5.902 (5.905), Acc m&t: 3.00 4.74, Remain: 3 days, 14:43:23, Finish: 2024-11-27 01:01 [11-24 02:18:04] (/home/user/VAR/train.py , line 276)=> [ep74] (training ) Lm: 6.648 (6.649), Lt: 5.902 (5.905), Acc m&t: 3.00 4.74, Remain: 3 days, 14:41:28, Finish: 2024-11-27 00:59 [11-24 02:18:04] (/home/user/VAR/train.py , line 276)=> [ep74] (training ) Lm: 6.648 (6.649), Lt: 5.902 (5.905), Acc m&t: 3.00 4.74, Remain: 3 days, 14:43:40, Finish: 2024-11-27 01:01 [11-24 02:18:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [ 0/1669] eta: 0:18:32 tlr: 0.0002 tnm: 0.28 Lm: 6.523 (6.523) Lt: 5.774 (5.774) Accm: 3.36 (3.36) Acct: 5.54 (5.54) proj_loss: -0.5689 (-0.5689) time: 0.6664 data: 0.0003 [11-24 02:18:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [ 0/1669] eta: 0:18:32 tlr: 0.0002 tnm: 0.28 Lm: 6.608 (6.608) Lt: 5.899 (5.899) Accm: 2.88 (2.88) Acct: 4.73 (4.73) proj_loss: -0.5909 (-0.5909) time: 0.6665 data: 0.0003 [11-24 02:18:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [ 0/1669] eta: 0:18:32 tlr: 0.0002 tnm: 0.28 Lm: 6.533 (6.533) Lt: 5.714 (5.714) Accm: 3.27 (3.27) Acct: 5.46 (5.46) proj_loss: -0.5896 (-0.5896) time: 0.6666 data: 0.0004 [11-24 02:18:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [ 0/1669] eta: 0:18:32 tlr: 0.0002 tnm: 0.28 Lm: 6.723 (6.723) Lt: 5.971 (5.971) Accm: 2.90 (2.90) Acct: 4.49 (4.49) proj_loss: -0.5590 (-0.5590) time: 0.6666 data: 0.0003 [11-24 02:22:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [ 417/1669] eta: 0:14:37 tlr: 0.0002 tnm: 0.28 Lm: 6.585 (6.585) Lt: 5.814 (5.814) Accm: 3.26 (3.26) Acct: 5.18 (5.18) proj_loss: -0.5606 (-0.5606) time: 0.6734 data: 0.0003 [11-24 02:22:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [ 417/1669] eta: 0:14:37 tlr: 0.0002 tnm: 0.28 Lm: 6.621 (6.621) Lt: 5.901 (5.901) Accm: 3.11 (3.11) Acct: 5.04 (5.04) proj_loss: -0.5744 (-0.5744) time: 0.6734 data: 0.0002 [11-24 02:22:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [ 417/1669] eta: 0:14:37 tlr: 0.0002 tnm: 0.28 Lm: 6.646 (6.646) Lt: 5.956 (5.956) Accm: 2.95 (2.95) Acct: 4.70 (4.70) proj_loss: -0.5883 (-0.5883) time: 0.6734 data: 0.0002 [11-24 02:22:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [ 417/1669] eta: 0:14:37 tlr: 0.0002 tnm: 0.28 Lm: 6.539 (6.539) Lt: 5.741 (5.741) Accm: 3.22 (3.22) Acct: 5.18 (5.18) proj_loss: -0.5764 (-0.5764) time: 0.6734 data: 0.0003 [11-24 02:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [ 834/1669] eta: 0:09:32 tlr: 0.0002 tnm: 0.27 Lm: 6.545 (6.574) Lt: 5.767 (5.802) Accm: 3.17 (3.10) Acct: 4.91 (4.93) proj_loss: -0.5880 (-0.5803) time: 0.6729 data: 0.0003 [11-24 02:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [ 834/1669] eta: 0:09:32 tlr: 0.0002 tnm: 0.27 Lm: 6.665 (6.635) Lt: 5.915 (5.906) Accm: 2.87 (3.03) Acct: 4.53 (4.83) proj_loss: -0.5689 (-0.5721) time: 0.6729 data: 0.0002 [11-24 02:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [ 834/1669] eta: 0:09:32 tlr: 0.0002 tnm: 0.27 Lm: 6.609 (6.593) Lt: 5.866 (5.832) Accm: 2.92 (3.15) Acct: 5.06 (5.14) proj_loss: -0.5622 (-0.5628) time: 0.6729 data: 0.0003 [11-24 02:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [ 834/1669] eta: 0:09:32 tlr: 0.0002 tnm: 0.27 Lm: 6.644 (6.645) Lt: 5.899 (5.929) Accm: 2.88 (2.86) Acct: 4.67 (4.64) proj_loss: -0.5858 (-0.5828) time: 0.6729 data: 0.0003 [11-24 02:32:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [1251/1669] eta: 0:04:44 tlr: 0.0002 tnm: 0.28 Lm: 6.664 (6.686) Lt: 5.956 (5.964) Accm: 2.78 (2.75) Acct: 4.60 (4.43) proj_loss: -0.5788 (-0.5795) time: 0.6707 data: 0.0003 [11-24 02:32:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [1251/1669] eta: 0:04:44 tlr: 0.0002 tnm: 0.28 Lm: 6.594 (6.627) Lt: 5.846 (5.863) Accm: 3.02 (2.97) Acct: 4.67 (4.68) proj_loss: -0.5818 (-0.5791) time: 0.6707 data: 0.0003 [11-24 02:32:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [1251/1669] eta: 0:04:44 tlr: 0.0002 tnm: 0.28 Lm: 6.602 (6.594) Lt: 5.830 (5.822) Accm: 3.11 (3.19) Acct: 5.35 (5.26) proj_loss: -0.5606 (-0.5570) time: 0.6707 data: 0.0002 [11-24 02:32:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [1251/1669] eta: 0:04:44 tlr: 0.0002 tnm: 0.28 Lm: 6.626 (6.624) Lt: 5.873 (5.887) Accm: 3.02 (3.06) Acct: 4.90 (4.94) proj_loss: -0.5721 (-0.5729) time: 0.6707 data: 0.0002 [11-24 02:36:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.600 (6.619) Lt: 5.853 (5.880) Accm: 3.12 (3.08) Acct: 5.08 (4.97) proj_loss: -0.5689 (-0.5696) time: 0.6710 data: 0.0015 [11-24 02:36:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 75/350] Total time: 0:18:52 (0.679 s / it) [11-24 02:36:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.684 (6.701) Lt: 5.990 (5.969) Accm: 2.73 (2.75) Acct: 4.53 (4.42) proj_loss: -0.5718 (-0.5760) time: 0.6710 data: 0.0017 [11-24 02:36:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.609 (6.617) Lt: 5.866 (5.858) Accm: 2.92 (3.11) Acct: 5.06 (5.10) proj_loss: -0.5622 (-0.5597) time: 0.6710 data: 0.0018 [11-24 02:36:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 75/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.603 (6.622) Lt: 5.815 (5.853) Accm: 3.17 (3.02) Acct: 4.91 (4.81) proj_loss: -0.5756 (-0.5725) time: 0.6710 data: 0.0016 [11-24 02:36:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 75/350] Total time: 0:18:52 (0.679 s / it) [11-24 02:36:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 75/350] Total time: 0:18:52 (0.679 s / it) [11-24 02:36:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 75/350] Total time: 0:18:52 (0.679 s / it) [11-24 02:36:56] (/home/user/VAR/train.py , line 276)=> [ep75] (training ) Lm: 6.648 (6.652), Lt: 5.902 (5.905), Acc m&t: 3.00 4.74, Remain: 3 days, 13:57:04, Finish: 2024-11-27 00:34 [11-24 02:36:56] (/home/user/VAR/train.py , line 276)=> [ep75] (training ) Lm: 6.648 (6.652), Lt: 5.902 (5.905), Acc m&t: 3.00 4.74, Remain: 3 days, 13:56:18, Finish: 2024-11-27 00:33 [11-24 02:36:56] (/home/user/VAR/train.py , line 276)=> [ep75] (training ) Lm: 6.648 (6.652), Lt: 5.902 (5.905), Acc m&t: 3.00 4.74, Remain: 3 days, 13:57:07, Finish: 2024-11-27 00:34 [11-24 02:36:56] (/home/user/VAR/train.py , line 276)=> [ep75] (training ) Lm: 6.648 (6.652), Lt: 5.902 (5.905), Acc m&t: 3.00 4.74, Remain: 3 days, 13:56:37, Finish: 2024-11-27 00:33 [11-24 02:36:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [ 0/1669] eta: 0:18:16 tlr: 0.0002 tnm: 0.30 Lm: 6.628 (6.628) Lt: 5.837 (5.837) Accm: 3.10 (3.10) Acct: 4.92 (4.92) proj_loss: -0.5757 (-0.5757) time: 0.6568 data: 0.0004 [11-24 02:36:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [ 0/1669] eta: 0:18:16 tlr: 0.0002 tnm: 0.30 Lm: 6.810 (6.810) Lt: 6.040 (6.040) Accm: 2.50 (2.50) Acct: 4.27 (4.27) proj_loss: -0.5590 (-0.5590) time: 0.6569 data: 0.0004 [11-24 02:36:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [ 0/1669] eta: 0:18:16 tlr: 0.0002 tnm: 0.30 Lm: 6.515 (6.515) Lt: 5.788 (5.788) Accm: 3.32 (3.32) Acct: 4.80 (4.80) proj_loss: -0.5757 (-0.5757) time: 0.6571 data: 0.0003 [11-24 02:36:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [ 0/1669] eta: 0:18:16 tlr: 0.0002 tnm: 0.30 Lm: 6.744 (6.744) Lt: 6.052 (6.052) Accm: 2.51 (2.51) Acct: 3.84 (3.84) proj_loss: -0.5791 (-0.5791) time: 0.6568 data: 0.0003 [11-24 02:41:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.27 Lm: 6.618 (6.618) Lt: 5.900 (5.900) Accm: 3.05 (3.05) Acct: 4.87 (4.87) proj_loss: -0.5698 (-0.5698) time: 0.6703 data: 0.0003 [11-24 02:41:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.27 Lm: 6.565 (6.565) Lt: 5.776 (5.776) Accm: 3.25 (3.25) Acct: 5.11 (5.11) proj_loss: -0.5672 (-0.5672) time: 0.6703 data: 0.0003 [11-24 02:41:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.27 Lm: 6.699 (6.699) Lt: 5.908 (5.908) Accm: 2.91 (2.91) Acct: 4.86 (4.86) proj_loss: -0.5596 (-0.5596) time: 0.6703 data: 0.0003 [11-24 02:41:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.27 Lm: 6.508 (6.508) Lt: 5.753 (5.753) Accm: 3.46 (3.46) Acct: 5.20 (5.20) proj_loss: -0.5774 (-0.5774) time: 0.6703 data: 0.0002 [11-24 02:46:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [ 834/1669] eta: 0:09:20 tlr: 0.0002 tnm: 0.31 Lm: 6.501 (6.500) Lt: 5.728 (5.745) Accm: 3.34 (3.42) Acct: 5.60 (5.34) proj_loss: -0.5757 (-0.5750) time: 0.6715 data: 0.0002 [11-24 02:46:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [ 834/1669] eta: 0:09:20 tlr: 0.0002 tnm: 0.31 Lm: 6.628 (6.610) Lt: 5.837 (5.834) Accm: 3.10 (3.04) Acct: 4.92 (4.72) proj_loss: -0.5757 (-0.5735) time: 0.6715 data: 0.0003 [11-24 02:46:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [ 834/1669] eta: 0:09:20 tlr: 0.0002 tnm: 0.31 Lm: 6.588 (6.648) Lt: 5.795 (5.870) Accm: 3.33 (3.10) Acct: 5.44 (5.09) proj_loss: -0.5601 (-0.5687) time: 0.6715 data: 0.0002 [11-24 02:46:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [ 834/1669] eta: 0:09:20 tlr: 0.0002 tnm: 0.31 Lm: 6.523 (6.586) Lt: 5.821 (5.874) Accm: 3.58 (3.23) Acct: 5.48 (5.07) proj_loss: -0.5783 (-0.5726) time: 0.6715 data: 0.0003 [11-24 02:51:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [1251/1669] eta: 0:04:44 tlr: 0.0002 tnm: 0.28 Lm: 6.536 (6.577) Lt: 5.802 (5.851) Accm: 3.57 (3.32) Acct: 5.66 (5.26) proj_loss: -0.5721 (-0.5709) time: 0.6702 data: 0.0003 [11-24 02:51:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [1251/1669] eta: 0:04:44 tlr: 0.0002 tnm: 0.28 Lm: 6.664 (6.666) Lt: 5.894 (5.922) Accm: 2.85 (2.86) Acct: 4.43 (4.45) proj_loss: -0.5748 (-0.5736) time: 0.6702 data: 0.0003 [11-24 02:51:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [1251/1669] eta: 0:04:44 tlr: 0.0002 tnm: 0.28 Lm: 6.508 (6.542) Lt: 5.758 (5.792) Accm: 3.33 (3.29) Acct: 5.21 (5.21) proj_loss: -0.5730 (-0.5671) time: 0.6702 data: 0.0003 [11-24 02:51:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [1251/1669] eta: 0:04:44 tlr: 0.0002 tnm: 0.28 Lm: 6.617 (6.648) Lt: 5.854 (5.881) Accm: 3.09 (3.04) Acct: 5.04 (4.98) proj_loss: -0.5703 (-0.5717) time: 0.6702 data: 0.0002 [11-24 02:55:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.30 Lm: 6.647 (6.660) Lt: 5.913 (5.907) Accm: 2.99 (3.03) Acct: 4.63 (4.90) proj_loss: -0.5805 (-0.5741) time: 0.6707 data: 0.0019 [11-24 02:55:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 76/350] Total time: 0:18:52 (0.679 s / it) [11-24 02:55:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.30 Lm: 6.515 (6.566) Lt: 5.788 (5.810) Accm: 3.32 (3.28) Acct: 5.10 (5.19) proj_loss: -0.5703 (-0.5642) time: 0.6707 data: 0.0015 [11-24 02:55:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.30 Lm: 6.700 (6.675) Lt: 5.952 (5.935) Accm: 2.72 (2.83) Acct: 4.41 (4.44) proj_loss: -0.5757 (-0.5745) time: 0.6707 data: 0.0016 [11-24 02:55:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 76/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.30 Lm: 6.550 (6.604) Lt: 5.821 (5.875) Accm: 3.56 (3.21) Acct: 5.48 (5.10) proj_loss: -0.5660 (-0.5688) time: 0.6708 data: 0.0017 [11-24 02:55:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 76/350] Total time: 0:18:52 (0.679 s / it) [11-24 02:55:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 76/350] Total time: 0:18:52 (0.679 s / it) [11-24 02:55:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 76/350] Total time: 0:18:52 (0.679 s / it) [11-24 02:55:49] (/home/user/VAR/train.py , line 276)=> [ep76] (training ) Lm: 6.643 (6.643), Lt: 5.895 (5.895), Acc m&t: 3.00 4.74, Remain: 3 days, 13:26:06, Finish: 2024-11-27 00:21 [11-24 02:55:49] (/home/user/VAR/train.py , line 276)=> [ep76] (training ) Lm: 6.643 (6.643), Lt: 5.895 (5.895), Acc m&t: 3.00 4.74, Remain: 3 days, 13:26:16, Finish: 2024-11-27 00:22 [11-24 02:55:49] (/home/user/VAR/train.py , line 276)=> [ep76] (training ) Lm: 6.643 (6.643), Lt: 5.895 (5.895), Acc m&t: 3.00 4.74, Remain: 3 days, 13:26:25, Finish: 2024-11-27 00:22 [11-24 02:55:49] (/home/user/VAR/train.py , line 276)=> [ep76] (training ) Lm: 6.643 (6.643), Lt: 5.895 (5.895), Acc m&t: 3.00 4.74, Remain: 3 days, 13:26:06, Finish: 2024-11-27 00:21 [11-24 02:55:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [ 0/1669] eta: 0:18:23 tlr: 0.0002 tnm: 0.30 Lm: 6.626 (6.626) Lt: 5.905 (5.905) Accm: 3.10 (3.10) Acct: 4.73 (4.73) proj_loss: -0.5679 (-0.5679) time: 0.6610 data: 0.0003 [11-24 02:55:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [ 0/1669] eta: 0:18:14 tlr: 0.0002 tnm: 0.30 Lm: 6.620 (6.620) Lt: 5.897 (5.897) Accm: 2.94 (2.94) Acct: 4.51 (4.51) proj_loss: -0.5677 (-0.5677) time: 0.6556 data: 0.0003 [11-24 02:55:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [ 0/1669] eta: 0:18:23 tlr: 0.0002 tnm: 0.30 Lm: 6.729 (6.729) Lt: 6.005 (6.005) Accm: 2.81 (2.81) Acct: 4.27 (4.27) proj_loss: -0.5951 (-0.5951) time: 0.6614 data: 0.0003 [11-24 02:55:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [ 0/1669] eta: 0:18:23 tlr: 0.0002 tnm: 0.30 Lm: 6.755 (6.755) Lt: 6.021 (6.021) Accm: 2.74 (2.74) Acct: 4.06 (4.06) proj_loss: -0.5766 (-0.5766) time: 0.6614 data: 0.0004 [11-24 03:00:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [ 417/1669] eta: 0:14:00 tlr: 0.0002 tnm: 0.28 Lm: 6.688 (6.688) Lt: 5.953 (5.953) Accm: 2.83 (2.83) Acct: 4.32 (4.32) proj_loss: -0.5760 (-0.5760) time: 0.6717 data: 0.0003 [11-24 03:00:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [ 417/1669] eta: 0:14:00 tlr: 0.0002 tnm: 0.28 Lm: 6.671 (6.671) Lt: 5.940 (5.940) Accm: 2.98 (2.98) Acct: 4.54 (4.54) proj_loss: -0.5897 (-0.5897) time: 0.6717 data: 0.0002 [11-24 03:00:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [ 417/1669] eta: 0:14:00 tlr: 0.0002 tnm: 0.28 Lm: 6.657 (6.657) Lt: 5.933 (5.933) Accm: 2.99 (2.99) Acct: 4.60 (4.60) proj_loss: -0.5629 (-0.5629) time: 0.6717 data: 0.0003 [11-24 03:00:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [ 417/1669] eta: 0:14:00 tlr: 0.0002 tnm: 0.28 Lm: 6.711 (6.711) Lt: 6.013 (6.013) Accm: 2.76 (2.76) Acct: 4.14 (4.14) proj_loss: -0.5717 (-0.5717) time: 0.6717 data: 0.0002 [11-24 03:05:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [ 834/1669] eta: 0:09:20 tlr: 0.0002 tnm: 0.29 Lm: 6.620 (6.653) Lt: 5.897 (5.947) Accm: 2.94 (2.94) Acct: 4.51 (4.48) proj_loss: -0.5756 (-0.5748) time: 0.6709 data: 0.0002 [11-24 03:05:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [ 834/1669] eta: 0:09:20 tlr: 0.0002 tnm: 0.29 Lm: 6.641 (6.672) Lt: 5.907 (5.938) Accm: 2.86 (2.84) Acct: 4.58 (4.48) proj_loss: -0.5766 (-0.5825) time: 0.6709 data: 0.0003 [11-24 03:05:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [ 834/1669] eta: 0:09:20 tlr: 0.0002 tnm: 0.29 Lm: 6.688 (6.669) Lt: 5.960 (5.952) Accm: 2.97 (2.98) Acct: 4.73 (4.64) proj_loss: -0.5679 (-0.5670) time: 0.6709 data: 0.0003 [11-24 03:05:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [ 834/1669] eta: 0:09:20 tlr: 0.0002 tnm: 0.29 Lm: 6.614 (6.617) Lt: 5.875 (5.869) Accm: 3.15 (3.07) Acct: 4.80 (4.72) proj_loss: -0.5843 (-0.5855) time: 0.6709 data: 0.0002 [11-24 03:10:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [1251/1669] eta: 0:04:45 tlr: 0.0002 tnm: 0.28 Lm: 6.591 (6.604) Lt: 5.835 (5.850) Accm: 3.19 (3.12) Acct: 4.94 (4.82) proj_loss: -0.5893 (-0.5876) time: 0.6705 data: 0.0002 [11-24 03:10:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [1251/1669] eta: 0:04:45 tlr: 0.0002 tnm: 0.28 Lm: 6.631 (6.651) Lt: 5.897 (5.921) Accm: 2.90 (2.94) Acct: 4.69 (4.65) proj_loss: -0.5804 (-0.5829) time: 0.6705 data: 0.0003 [11-24 03:10:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [1251/1669] eta: 0:04:45 tlr: 0.0002 tnm: 0.28 Lm: 6.690 (6.676) Lt: 5.975 (5.973) Accm: 2.92 (2.92) Acct: 4.60 (4.59) proj_loss: -0.5716 (-0.5709) time: 0.6705 data: 0.0003 [11-24 03:10:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [1251/1669] eta: 0:04:45 tlr: 0.0002 tnm: 0.28 Lm: 6.580 (6.594) Lt: 5.856 (5.865) Accm: 3.11 (3.12) Acct: 4.84 (4.83) proj_loss: -0.5717 (-0.5727) time: 0.6705 data: 0.0003 [11-24 03:14:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.596 (6.594) Lt: 5.897 (5.875) Accm: 2.94 (3.07) Acct: 4.51 (4.72) proj_loss: -0.5756 (-0.5766) time: 0.6705 data: 0.0017 [11-24 03:14:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 77/350] Total time: 0:18:53 (0.679 s / it) [11-24 03:14:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.688 (6.671) Lt: 5.960 (5.955) Accm: 2.88 (2.89) Acct: 4.55 (4.58) proj_loss: -0.5737 (-0.5715) time: 0.6705 data: 0.0016 [11-24 03:14:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.621 (6.615) Lt: 5.886 (5.868) Accm: 2.93 (3.01) Acct: 4.80 (4.80) proj_loss: -0.5766 (-0.5810) time: 0.6705 data: 0.0016 [11-24 03:14:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 77/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.614 (6.618) Lt: 5.875 (5.868) Accm: 3.15 (3.06) Acct: 4.80 (4.75) proj_loss: -0.5843 (-0.5856) time: 0.6705 data: 0.0016 [11-24 03:14:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 77/350] Total time: 0:18:53 (0.679 s / it) [11-24 03:14:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 77/350] Total time: 0:18:53 (0.679 s / it) [11-24 03:14:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 77/350] Total time: 0:18:53 (0.679 s / it) [11-24 03:14:43] (/home/user/VAR/train.py , line 276)=> [ep77] (training ) Lm: 6.643 (6.643), Lt: 5.895 (5.898), Acc m&t: 3.00 4.75, Remain: 3 days, 12:59:17, Finish: 2024-11-27 00:14 [11-24 03:14:43] (/home/user/VAR/train.py , line 276)=> [ep77] (training ) Lm: 6.643 (6.643), Lt: 5.895 (5.898), Acc m&t: 3.00 4.75, Remain: 3 days, 12:59:33, Finish: 2024-11-27 00:14 [11-24 03:14:43] (/home/user/VAR/train.py , line 276)=> [ep77] (training ) Lm: 6.643 (6.643), Lt: 5.895 (5.898), Acc m&t: 3.00 4.75, Remain: 3 days, 12:58:03, Finish: 2024-11-27 00:12 [11-24 03:14:43] (/home/user/VAR/train.py , line 276)=> [ep77] (training ) Lm: 6.643 (6.643), Lt: 5.895 (5.898), Acc m&t: 3.00 4.75, Remain: 3 days, 12:58:54, Finish: 2024-11-27 00:13 [11-24 03:14:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [ 0/1669] eta: 0:18:27 tlr: 0.0002 tnm: 0.30 Lm: 6.657 (6.657) Lt: 5.860 (5.860) Accm: 2.88 (2.88) Acct: 4.60 (4.60) proj_loss: -0.5542 (-0.5542) time: 0.6633 data: 0.0004 [11-24 03:14:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [ 0/1669] eta: 0:18:27 tlr: 0.0002 tnm: 0.30 Lm: 6.775 (6.775) Lt: 6.043 (6.043) Accm: 2.56 (2.56) Acct: 3.96 (3.96) proj_loss: -0.5565 (-0.5565) time: 0.6634 data: 0.0004 [11-24 03:14:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [ 0/1669] eta: 0:18:27 tlr: 0.0002 tnm: 0.30 Lm: 6.656 (6.656) Lt: 5.887 (5.887) Accm: 2.94 (2.94) Acct: 4.32 (4.32) proj_loss: -0.5795 (-0.5795) time: 0.6635 data: 0.0004 [11-24 03:14:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [ 0/1669] eta: 0:18:27 tlr: 0.0002 tnm: 0.30 Lm: 6.610 (6.610) Lt: 5.897 (5.897) Accm: 3.03 (3.03) Acct: 4.80 (4.80) proj_loss: -0.5794 (-0.5794) time: 0.6637 data: 0.0003 [11-24 03:19:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.28 Lm: 6.658 (6.658) Lt: 5.924 (5.924) Accm: 2.92 (2.92) Acct: 4.68 (4.68) proj_loss: -0.5803 (-0.5803) time: 0.6701 data: 0.0002 [11-24 03:19:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.28 Lm: 6.678 (6.678) Lt: 5.947 (5.947) Accm: 2.85 (2.85) Acct: 4.48 (4.48) proj_loss: -0.5690 (-0.5690) time: 0.6701 data: 0.0002 [11-24 03:19:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.28 Lm: 6.639 (6.639) Lt: 5.869 (5.869) Accm: 2.98 (2.98) Acct: 4.59 (4.59) proj_loss: -0.5766 (-0.5766) time: 0.6701 data: 0.0003 [11-24 03:19:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [ 417/1669] eta: 0:13:58 tlr: 0.0002 tnm: 0.28 Lm: 6.550 (6.550) Lt: 5.775 (5.775) Accm: 3.15 (3.15) Acct: 4.86 (4.86) proj_loss: -0.5654 (-0.5654) time: 0.6701 data: 0.0003 [11-24 03:24:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [ 834/1669] eta: 0:09:19 tlr: 0.0002 tnm: 0.28 Lm: 6.607 (6.569) Lt: 5.813 (5.788) Accm: 3.14 (3.14) Acct: 4.98 (4.90) proj_loss: -0.5765 (-0.5693) time: 0.6736 data: 0.0003 [11-24 03:24:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [ 834/1669] eta: 0:09:19 tlr: 0.0002 tnm: 0.28 Lm: 6.590 (6.649) Lt: 5.851 (5.893) Accm: 3.13 (3.01) Acct: 4.99 (4.78) proj_loss: -0.5664 (-0.5681) time: 0.6736 data: 0.0003 [11-24 03:24:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [ 834/1669] eta: 0:09:19 tlr: 0.0002 tnm: 0.28 Lm: 6.656 (6.691) Lt: 5.887 (5.937) Accm: 2.94 (2.86) Acct: 4.36 (4.51) proj_loss: -0.5738 (-0.5745) time: 0.6736 data: 0.0002 [11-24 03:24:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [ 834/1669] eta: 0:09:19 tlr: 0.0002 tnm: 0.28 Lm: 6.637 (6.651) Lt: 5.897 (5.910) Accm: 2.86 (2.90) Acct: 4.58 (4.65) proj_loss: -0.5812 (-0.5827) time: 0.6736 data: 0.0002 [11-24 03:28:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [1251/1669] eta: 0:04:40 tlr: 0.0002 tnm: 0.27 Lm: 6.623 (6.627) Lt: 5.890 (5.880) Accm: 2.95 (2.96) Acct: 4.69 (4.70) proj_loss: -0.5827 (-0.5831) time: 0.6707 data: 0.0003 [11-24 03:28:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [1251/1669] eta: 0:04:40 tlr: 0.0002 tnm: 0.27 Lm: 6.603 (6.641) Lt: 5.869 (5.892) Accm: 3.09 (3.02) Acct: 4.75 (4.71) proj_loss: -0.5624 (-0.5657) time: 0.6707 data: 0.0003 [11-24 03:28:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [1251/1669] eta: 0:04:40 tlr: 0.0002 tnm: 0.27 Lm: 6.697 (6.703) Lt: 5.953 (5.957) Accm: 2.79 (2.81) Acct: 4.34 (4.46) proj_loss: -0.5746 (-0.5747) time: 0.6707 data: 0.0002 [11-24 03:28:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [1251/1669] eta: 0:04:40 tlr: 0.0002 tnm: 0.27 Lm: 6.610 (6.580) Lt: 5.827 (5.801) Accm: 3.13 (3.14) Acct: 5.05 (4.96) proj_loss: -0.5716 (-0.5686) time: 0.6707 data: 0.0003 [11-24 03:33:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.613 (6.593) Lt: 5.841 (5.824) Accm: 3.12 (3.13) Acct: 4.98 (4.92) proj_loss: -0.5667 (-0.5679) time: 0.6745 data: 0.0019 [11-24 03:33:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 78/350] Total time: 0:18:39 (0.671 s / it) [11-24 03:33:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.738 (6.718) Lt: 6.018 (5.985) Accm: 2.65 (2.78) Acct: 4.32 (4.37) proj_loss: -0.5755 (-0.5795) time: 0.6745 data: 0.0018 [11-24 03:33:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.637 (6.635) Lt: 5.897 (5.883) Accm: 3.02 (2.97) Acct: 4.80 (4.76) proj_loss: -0.5842 (-0.5848) time: 0.6745 data: 0.0018 [11-24 03:33:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 78/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.617 (6.643) Lt: 5.886 (5.891) Accm: 3.04 (3.00) Acct: 4.86 (4.74) proj_loss: -0.5664 (-0.5699) time: 0.6745 data: 0.0020 [11-24 03:33:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 78/350] Total time: 0:18:39 (0.671 s / it) [11-24 03:33:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 78/350] Total time: 0:18:39 (0.671 s / it) [11-24 03:33:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 78/350] Total time: 0:18:39 (0.671 s / it) [11-24 03:33:23] (/home/user/VAR/train.py , line 276)=> [ep78] (training ) Lm: 6.643 (6.653), Lt: 5.895 (5.907), Acc m&t: 3.00 4.75, Remain: 3 days, 13:27:18, Finish: 2024-11-27 01:00 [11-24 03:33:23] (/home/user/VAR/train.py , line 276)=> [ep78] (training ) Lm: 6.643 (6.653), Lt: 5.895 (5.907), Acc m&t: 3.00 4.75, Remain: 3 days, 13:27:34, Finish: 2024-11-27 01:00 [11-24 03:33:23] (/home/user/VAR/train.py , line 276)=> [ep78] (training ) Lm: 6.643 (6.653), Lt: 5.895 (5.907), Acc m&t: 3.00 4.75, Remain: 3 days, 13:27:35, Finish: 2024-11-27 01:00 [11-24 03:33:23] (/home/user/VAR/train.py , line 276)=> [ep78] (training ) Lm: 6.643 (6.653), Lt: 5.895 (5.907), Acc m&t: 3.00 4.75, Remain: 3 days, 13:27:12, Finish: 2024-11-27 01:00 [11-24 03:33:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [ 0/1669] eta: 0:18:32 tlr: 0.0002 tnm: 0.27 Lm: 6.736 (6.736) Lt: 6.009 (6.009) Accm: 2.59 (2.59) Acct: 3.99 (3.99) proj_loss: -0.5948 (-0.5948) time: 0.6663 data: 0.0004 [11-24 03:33:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [ 0/1669] eta: 0:18:23 tlr: 0.0002 tnm: 0.27 Lm: 6.820 (6.820) Lt: 6.075 (6.075) Accm: 2.48 (2.48) Acct: 4.15 (4.15) proj_loss: -0.5573 (-0.5573) time: 0.6613 data: 0.0003 [11-24 03:33:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [ 0/1669] eta: 0:18:32 tlr: 0.0002 tnm: 0.27 Lm: 6.596 (6.596) Lt: 5.883 (5.883) Accm: 3.22 (3.22) Acct: 5.04 (5.04) proj_loss: -0.5561 (-0.5561) time: 0.6665 data: 0.0003 [11-24 03:33:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [ 0/1669] eta: 0:18:32 tlr: 0.0002 tnm: 0.27 Lm: 6.572 (6.572) Lt: 5.822 (5.822) Accm: 3.34 (3.34) Acct: 5.29 (5.29) proj_loss: -0.5938 (-0.5938) time: 0.6665 data: 0.0004 [11-24 03:38:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [ 417/1669] eta: 0:14:53 tlr: 0.0002 tnm: 0.28 Lm: 6.617 (6.617) Lt: 5.851 (5.851) Accm: 3.11 (3.11) Acct: 5.02 (5.02) proj_loss: -0.5842 (-0.5842) time: 0.6751 data: 0.0003 [11-24 03:38:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [ 417/1669] eta: 0:14:53 tlr: 0.0002 tnm: 0.28 Lm: 6.727 (6.727) Lt: 5.994 (5.994) Accm: 2.53 (2.53) Acct: 3.93 (3.93) proj_loss: -0.5854 (-0.5854) time: 0.6751 data: 0.0003 [11-24 03:38:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [ 417/1669] eta: 0:14:53 tlr: 0.0002 tnm: 0.28 Lm: 6.656 (6.656) Lt: 5.951 (5.951) Accm: 3.00 (3.00) Acct: 4.66 (4.66) proj_loss: -0.5616 (-0.5616) time: 0.6751 data: 0.0003 [11-24 03:38:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [ 417/1669] eta: 0:14:53 tlr: 0.0002 tnm: 0.28 Lm: 6.772 (6.772) Lt: 6.019 (6.019) Accm: 2.61 (2.61) Acct: 4.24 (4.24) proj_loss: -0.5626 (-0.5626) time: 0.6751 data: 0.0003 [11-24 03:43:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [ 834/1669] eta: 0:09:39 tlr: 0.0002 tnm: 0.29 Lm: 6.725 (6.726) Lt: 5.964 (5.971) Accm: 2.75 (2.84) Acct: 4.34 (4.56) proj_loss: -0.5679 (-0.5685) time: 0.6751 data: 0.0002 [11-24 03:43:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [ 834/1669] eta: 0:09:39 tlr: 0.0002 tnm: 0.29 Lm: 6.644 (6.652) Lt: 5.883 (5.928) Accm: 3.10 (3.03) Acct: 4.87 (4.73) proj_loss: -0.5621 (-0.5618) time: 0.6751 data: 0.0003 [11-24 03:43:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [ 834/1669] eta: 0:09:39 tlr: 0.0002 tnm: 0.29 Lm: 6.613 (6.616) Lt: 5.827 (5.843) Accm: 3.34 (3.18) Acct: 5.29 (5.19) proj_loss: -0.5745 (-0.5800) time: 0.6751 data: 0.0003 [11-24 03:43:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [ 834/1669] eta: 0:09:39 tlr: 0.0002 tnm: 0.29 Lm: 6.730 (6.728) Lt: 6.009 (6.016) Accm: 2.56 (2.54) Acct: 3.99 (4.02) proj_loss: -0.5897 (-0.5868) time: 0.6751 data: 0.0003 [11-24 03:47:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [1251/1669] eta: 0:04:46 tlr: 0.0002 tnm: 0.28 Lm: 6.724 (6.704) Lt: 5.994 (5.988) Accm: 2.57 (2.69) Acct: 4.10 (4.30) proj_loss: -0.5828 (-0.5805) time: 0.6729 data: 0.0003 [11-24 03:47:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [1251/1669] eta: 0:04:46 tlr: 0.0002 tnm: 0.28 Lm: 6.723 (6.725) Lt: 5.997 (5.986) Accm: 2.74 (2.81) Acct: 4.36 (4.52) proj_loss: -0.5741 (-0.5717) time: 0.6729 data: 0.0002 [11-24 03:47:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [1251/1669] eta: 0:04:46 tlr: 0.0002 tnm: 0.28 Lm: 6.680 (6.668) Lt: 5.916 (5.933) Accm: 3.00 (3.00) Acct: 4.79 (4.72) proj_loss: -0.5610 (-0.5613) time: 0.6729 data: 0.0003 [11-24 03:47:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [1251/1669] eta: 0:04:46 tlr: 0.0002 tnm: 0.28 Lm: 6.638 (6.652) Lt: 5.853 (5.888) Accm: 3.10 (3.06) Acct: 5.02 (4.93) proj_loss: -0.5731 (-0.5745) time: 0.6729 data: 0.0003 [11-24 03:52:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.613 (6.633) Lt: 5.827 (5.874) Accm: 3.27 (3.10) Acct: 5.13 (4.97) proj_loss: -0.5745 (-0.5783) time: 0.6736 data: 0.0018 [11-24 03:52:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 79/350] Total time: 0:18:59 (0.683 s / it) [11-24 03:52:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.694 (6.673) Lt: 5.949 (5.940) Accm: 3.10 (3.05) Acct: 4.87 (4.85) proj_loss: -0.5600 (-0.5602) time: 0.6736 data: 0.0015 [11-24 03:52:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.721 (6.674) Lt: 5.964 (5.935) Accm: 2.75 (2.97) Acct: 4.39 (4.71) proj_loss: -0.5803 (-0.5750) time: 0.6736 data: 0.0016 [11-24 03:52:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 79/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.718 (6.694) Lt: 5.979 (5.979) Accm: 2.59 (2.81) Acct: 4.20 (4.50) proj_loss: -0.5760 (-0.5785) time: 0.6736 data: 0.0022 [11-24 03:52:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 79/350] Total time: 0:19:00 (0.683 s / it) [11-24 03:52:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 79/350] Total time: 0:18:59 (0.683 s / it) [11-24 03:52:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 79/350] Total time: 0:19:00 (0.683 s / it) [11-24 03:56:49] (me/user/VAR/evaluator.py, line 590)=> downloading InceptionV3 model... [11-24 03:58:02] (home/user/VAR/trainer.py, line 114)=> FID: 4.5353316177273655 [11-24 03:58:04] (/home/user/VAR/train.py , line 259)=> [*] [ep79] (val 50000) Lm: 6.6402, Lt: 5.8985, Acc m&t: 3.02 4.80, Val cost: 340.73s [11-24 03:58:04] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-24 03:58:37] (/home/user/VAR/train.py , line 276)=> [ep79] (training ) Lm: 6.640 (6.640), Lt: 5.895 (5.899), Acc m&t: 3.02 4.80, Remain: 3 days, 12:43:25, Finish: 2024-11-27 00:35 [11-24 03:58:37] (/home/user/VAR/train.py , line 276)=> [ep79] (training ) Lm: 6.640 (6.640), Lt: 5.895 (5.899), Acc m&t: 3.02 4.80, Remain: 3 days, 12:42:06, Finish: 2024-11-27 00:34 [11-24 03:58:37] (/home/user/VAR/train.py , line 276)=> [ep79] (training ) Lm: 6.640 (6.640), Lt: 5.895 (5.899), Acc m&t: 3.02 4.80, Remain: 3 days, 12:41:09, Finish: 2024-11-27 00:33 [11-24 03:58:37] (/home/user/VAR/train.py , line 276)=> [ep79] (training ) Lm: 6.640 (6.640), Lt: 5.895 (5.899), Acc m&t: 3.02 4.80, Remain: 3 days, 12:41:58, Finish: 2024-11-27 00:34 [11-24 03:58:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [ 0/1669] eta: 0:22:38 tlr: 0.0002 tnm: 0.28 Lm: 6.731 (6.731) Lt: 5.972 (5.972) Accm: 2.65 (2.65) Acct: 4.12 (4.12) proj_loss: -0.5880 (-0.5880) time: 0.8139 data: 0.0003 [11-24 03:58:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [ 0/1669] eta: 0:22:52 tlr: 0.0002 tnm: 0.28 Lm: 6.651 (6.651) Lt: 5.923 (5.923) Accm: 3.07 (3.07) Acct: 5.04 (5.04) proj_loss: -0.5668 (-0.5668) time: 0.8226 data: 0.0003 [11-24 03:58:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [ 0/1669] eta: 0:22:53 tlr: 0.0002 tnm: 0.28 Lm: 6.597 (6.597) Lt: 5.805 (5.805) Accm: 3.15 (3.15) Acct: 5.22 (5.22) proj_loss: -0.5432 (-0.5432) time: 0.8230 data: 0.0004 [11-24 03:58:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [ 0/1669] eta: 0:22:39 tlr: 0.0002 tnm: 0.28 Lm: 6.623 (6.623) Lt: 5.889 (5.889) Accm: 3.19 (3.19) Acct: 4.68 (4.68) proj_loss: -0.5863 (-0.5863) time: 0.8146 data: 0.0004 [11-24 04:03:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [ 417/1669] eta: 0:14:03 tlr: 0.0002 tnm: 0.29 Lm: 6.633 (6.633) Lt: 5.913 (5.913) Accm: 3.01 (3.01) Acct: 4.55 (4.55) proj_loss: -0.5879 (-0.5879) time: 0.6730 data: 0.0003 [11-24 04:03:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [ 417/1669] eta: 0:14:03 tlr: 0.0002 tnm: 0.29 Lm: 6.656 (6.656) Lt: 5.890 (5.890) Accm: 2.86 (2.86) Acct: 4.73 (4.73) proj_loss: -0.5568 (-0.5568) time: 0.6730 data: 0.0003 [11-24 04:03:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [ 417/1669] eta: 0:14:03 tlr: 0.0002 tnm: 0.29 Lm: 6.624 (6.624) Lt: 5.897 (5.897) Accm: 3.22 (3.22) Acct: 5.04 (5.04) proj_loss: -0.5628 (-0.5628) time: 0.6730 data: 0.0002 [11-24 04:03:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [ 417/1669] eta: 0:14:03 tlr: 0.0002 tnm: 0.29 Lm: 6.698 (6.698) Lt: 5.983 (5.983) Accm: 2.78 (2.78) Acct: 4.36 (4.36) proj_loss: -0.5866 (-0.5866) time: 0.6730 data: 0.0003 [11-24 04:08:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.28 Lm: 6.665 (6.648) Lt: 5.972 (5.912) Accm: 2.91 (3.04) Acct: 4.61 (4.93) proj_loss: -0.5851 (-0.5834) time: 0.6745 data: 0.0003 [11-24 04:08:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.28 Lm: 6.651 (6.680) Lt: 5.923 (5.961) Accm: 3.07 (3.03) Acct: 5.04 (4.72) proj_loss: -0.5668 (-0.5642) time: 0.6746 data: 0.0003 [11-24 04:08:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.28 Lm: 6.715 (6.694) Lt: 5.976 (5.968) Accm: 2.64 (2.79) Acct: 4.25 (4.57) proj_loss: -0.5705 (-0.5667) time: 0.6746 data: 0.0003 [11-24 04:08:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.28 Lm: 6.642 (6.689) Lt: 5.938 (5.975) Accm: 2.83 (2.92) Acct: 4.42 (4.44) proj_loss: -0.5863 (-0.5862) time: 0.6746 data: 0.0003 [11-24 04:12:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.28 Lm: 6.633 (6.671) Lt: 5.913 (5.940) Accm: 2.91 (2.94) Acct: 4.55 (4.52) proj_loss: -0.5846 (-0.5823) time: 0.6732 data: 0.0003 [11-24 04:12:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.28 Lm: 6.672 (6.677) Lt: 5.936 (5.950) Accm: 2.81 (2.84) Acct: 4.36 (4.54) proj_loss: -0.5785 (-0.5724) time: 0.6732 data: 0.0003 [11-24 04:12:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.28 Lm: 6.650 (6.672) Lt: 5.919 (5.950) Accm: 3.17 (3.09) Acct: 5.04 (4.84) proj_loss: -0.5649 (-0.5639) time: 0.6732 data: 0.0003 [11-24 04:12:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.28 Lm: 6.698 (6.683) Lt: 5.983 (5.951) Accm: 2.78 (2.88) Acct: 4.36 (4.66) proj_loss: -0.5850 (-0.5838) time: 0.6732 data: 0.0003 [11-24 04:17:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.665 (6.639) Lt: 5.972 (5.901) Accm: 2.91 (2.98) Acct: 4.61 (4.80) proj_loss: -0.5849 (-0.5829) time: 0.6706 data: 0.0019 [11-24 04:17:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 80/350] Total time: 0:18:44 (0.674 s / it) [11-24 04:17:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.652 (6.672) Lt: 5.895 (5.934) Accm: 2.98 (2.90) Acct: 4.46 (4.62) proj_loss: -0.5865 (-0.5753) time: 0.6706 data: 0.0015 [11-24 04:17:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.642 (6.678) Lt: 5.920 (5.936) Accm: 2.83 (2.91) Acct: 4.53 (4.52) proj_loss: -0.5829 (-0.5801) time: 0.6706 data: 0.0018 [11-24 04:17:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 80/350] Total time: 0:18:44 (0.674 s / it) [11-24 04:17:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 80/350] Total time: 0:18:44 (0.674 s / it) [11-24 04:17:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 80/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.649 (6.644) Lt: 5.916 (5.914) Accm: 3.26 (3.15) Acct: 5.04 (5.00) proj_loss: -0.5630 (-0.5615) time: 0.6706 data: 0.0019 [11-24 04:17:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 80/350] Total time: 0:18:44 (0.674 s / it) [11-24 04:17:22] (/home/user/VAR/train.py , line 276)=> [ep80] (training ) Lm: 6.640 (6.653), Lt: 5.895 (5.911), Acc m&t: 3.02 4.80, Remain: 3 days, 11:58:40, Finish: 2024-11-27 00:16 [11-24 04:17:22] (/home/user/VAR/train.py , line 276)=> [ep80] (training ) Lm: 6.640 (6.653), Lt: 5.895 (5.911), Acc m&t: 3.02 4.80, Remain: 3 days, 11:59:52, Finish: 2024-11-27 00:17 [11-24 04:17:22] (/home/user/VAR/train.py , line 276)=> [ep80] (training ) Lm: 6.640 (6.653), Lt: 5.895 (5.911), Acc m&t: 3.02 4.80, Remain: 3 days, 11:58:43, Finish: 2024-11-27 00:16 [11-24 04:17:22] (/home/user/VAR/train.py , line 276)=> [ep80] (training ) Lm: 6.640 (6.653), Lt: 5.895 (5.911), Acc m&t: 3.02 4.80, Remain: 3 days, 11:58:44, Finish: 2024-11-27 00:16 [11-24 04:17:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [ 0/1669] eta: 0:18:08 tlr: 0.0002 tnm: 0.30 Lm: 6.598 (6.598) Lt: 5.881 (5.881) Accm: 3.15 (3.15) Acct: 5.04 (5.04) proj_loss: -0.5678 (-0.5678) time: 0.6523 data: 0.0003 [11-24 04:17:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [ 0/1669] eta: 0:18:09 tlr: 0.0002 tnm: 0.30 Lm: 6.669 (6.669) Lt: 5.920 (5.920) Accm: 3.09 (3.09) Acct: 4.92 (4.92) proj_loss: -0.5644 (-0.5644) time: 0.6528 data: 0.0004 [11-24 04:17:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [ 0/1669] eta: 0:18:09 tlr: 0.0002 tnm: 0.30 Lm: 6.516 (6.516) Lt: 5.719 (5.719) Accm: 3.07 (3.07) Acct: 4.70 (4.70) proj_loss: -0.5844 (-0.5844) time: 0.6527 data: 0.0004 [11-24 04:17:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [ 0/1669] eta: 0:18:40 tlr: 0.0002 tnm: 0.30 Lm: 6.488 (6.488) Lt: 5.688 (5.688) Accm: 3.53 (3.53) Acct: 5.84 (5.84) proj_loss: -0.5752 (-0.5752) time: 0.6715 data: 0.0003 [11-24 04:22:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [ 417/1669] eta: 0:14:10 tlr: 0.0002 tnm: 0.30 Lm: 6.601 (6.601) Lt: 5.857 (5.857) Accm: 3.13 (3.13) Acct: 5.11 (5.11) proj_loss: -0.5827 (-0.5827) time: 0.6742 data: 0.0002 [11-24 04:22:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [ 417/1669] eta: 0:14:09 tlr: 0.0002 tnm: 0.30 Lm: 6.601 (6.601) Lt: 5.814 (5.814) Accm: 2.97 (2.97) Acct: 4.63 (4.63) proj_loss: -0.5784 (-0.5784) time: 0.6742 data: 0.0002 [11-24 04:22:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [ 417/1669] eta: 0:14:09 tlr: 0.0002 tnm: 0.30 Lm: 6.673 (6.673) Lt: 5.935 (5.935) Accm: 2.96 (2.96) Acct: 4.84 (4.84) proj_loss: -0.5608 (-0.5608) time: 0.6742 data: 0.0003 [11-24 04:22:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [ 417/1669] eta: 0:14:09 tlr: 0.0002 tnm: 0.30 Lm: 6.654 (6.654) Lt: 5.920 (5.920) Accm: 2.95 (2.95) Acct: 4.80 (4.80) proj_loss: -0.5622 (-0.5622) time: 0.6742 data: 0.0002 [11-24 04:26:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [ 834/1669] eta: 0:09:32 tlr: 0.0002 tnm: 0.28 Lm: 6.655 (6.654) Lt: 5.959 (5.934) Accm: 2.90 (2.93) Acct: 4.73 (4.78) proj_loss: -0.5678 (-0.5678) time: 0.6754 data: 0.0003 [11-24 04:26:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [ 834/1669] eta: 0:09:32 tlr: 0.0002 tnm: 0.28 Lm: 6.558 (6.586) Lt: 5.801 (5.838) Accm: 3.13 (3.13) Acct: 4.91 (5.04) proj_loss: -0.5885 (-0.5846) time: 0.6754 data: 0.0003 [11-24 04:26:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [ 834/1669] eta: 0:09:32 tlr: 0.0002 tnm: 0.28 Lm: 6.669 (6.639) Lt: 5.920 (5.889) Accm: 3.09 (3.08) Acct: 4.92 (4.94) proj_loss: -0.5644 (-0.5625) time: 0.6754 data: 0.0003 [11-24 04:26:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [ 834/1669] eta: 0:09:32 tlr: 0.0002 tnm: 0.28 Lm: 6.687 (6.649) Lt: 5.909 (5.877) Accm: 2.88 (2.87) Acct: 4.56 (4.43) proj_loss: -0.5723 (-0.5751) time: 0.6754 data: 0.0003 [11-24 04:31:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [1251/1669] eta: 0:04:45 tlr: 0.0002 tnm: 0.28 Lm: 6.682 (6.656) Lt: 5.893 (5.877) Accm: 2.97 (2.93) Acct: 4.63 (4.58) proj_loss: -0.5740 (-0.5752) time: 0.6707 data: 0.0002 [11-24 04:31:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [1251/1669] eta: 0:04:45 tlr: 0.0002 tnm: 0.28 Lm: 6.683 (6.677) Lt: 5.960 (5.961) Accm: 2.90 (2.92) Acct: 4.72 (4.76) proj_loss: -0.5662 (-0.5670) time: 0.6707 data: 0.0002 [11-24 04:31:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [1251/1669] eta: 0:04:45 tlr: 0.0002 tnm: 0.28 Lm: 6.529 (6.565) Lt: 5.749 (5.803) Accm: 3.33 (3.25) Acct: 5.29 (5.20) proj_loss: -0.5832 (-0.5830) time: 0.6707 data: 0.0002 [11-24 04:31:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [1251/1669] eta: 0:04:45 tlr: 0.0002 tnm: 0.28 Lm: 6.673 (6.654) Lt: 5.892 (5.882) Accm: 3.18 (3.13) Acct: 5.03 (5.06) proj_loss: -0.5614 (-0.5615) time: 0.6707 data: 0.0003 [11-24 04:36:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.669 (6.644) Lt: 5.864 (5.867) Accm: 3.15 (3.13) Acct: 4.92 (5.02) proj_loss: -0.5637 (-0.5619) time: 0.6734 data: 0.0016 [11-24 04:36:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 81/350] Total time: 0:18:54 (0.680 s / it) [11-24 04:36:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.655 (6.641) Lt: 5.959 (5.912) Accm: 2.90 (3.02) Acct: 4.73 (4.83) proj_loss: -0.5678 (-0.5694) time: 0.6733 data: 0.0019 [11-24 04:36:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.558 (6.604) Lt: 5.801 (5.860) Accm: 3.13 (3.12) Acct: 4.91 (4.94) proj_loss: -0.5780 (-0.5801) time: 0.6734 data: 0.0018 [11-24 04:36:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 81/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.28 Lm: 6.678 (6.660) Lt: 5.909 (5.893) Accm: 2.88 (2.89) Acct: 4.56 (4.53) proj_loss: -0.5756 (-0.5759) time: 0.6734 data: 0.0015 [11-24 04:36:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 81/350] Total time: 0:18:54 (0.680 s / it) [11-24 04:36:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 81/350] Total time: 0:18:54 (0.680 s / it) [11-24 04:36:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 81/350] Total time: 0:18:54 (0.680 s / it) [11-24 04:36:17] (/home/user/VAR/train.py , line 276)=> [ep81] (training ) Lm: 6.639 (6.639), Lt: 5.894 (5.894), Acc m&t: 3.03 4.80, Remain: 3 days, 12:14:06, Finish: 2024-11-27 00:50 [11-24 04:36:17] (/home/user/VAR/train.py , line 276)=> [ep81] (training ) Lm: 6.639 (6.639), Lt: 5.894 (5.894), Acc m&t: 3.03 4.80, Remain: 3 days, 12:12:50, Finish: 2024-11-27 00:49 [11-24 04:36:17] (/home/user/VAR/train.py , line 276)=> [ep81] (training ) Lm: 6.639 (6.639), Lt: 5.894 (5.894), Acc m&t: 3.03 4.80, Remain: 3 days, 12:13:35, Finish: 2024-11-27 00:49 [11-24 04:36:17] (/home/user/VAR/train.py , line 276)=> [ep81] (training ) Lm: 6.639 (6.639), Lt: 5.894 (5.894), Acc m&t: 3.03 4.80, Remain: 3 days, 12:14:09, Finish: 2024-11-27 00:50 [11-24 04:36:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [ 0/1669] eta: 0:18:18 tlr: 0.0002 tnm: 0.28 Lm: 6.753 (6.753) Lt: 6.038 (6.038) Accm: 2.95 (2.95) Acct: 4.70 (4.70) proj_loss: -0.5604 (-0.5604) time: 0.6584 data: 0.0003 [11-24 04:36:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [ 0/1669] eta: 0:18:20 tlr: 0.0002 tnm: 0.28 Lm: 6.545 (6.545) Lt: 5.707 (5.707) Accm: 3.25 (3.25) Acct: 5.34 (5.34) proj_loss: -0.5742 (-0.5742) time: 0.6591 data: 0.0004 [11-24 04:36:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [ 0/1669] eta: 0:18:20 tlr: 0.0002 tnm: 0.28 Lm: 6.618 (6.618) Lt: 5.939 (5.939) Accm: 3.00 (3.00) Acct: 4.61 (4.61) proj_loss: -0.5787 (-0.5787) time: 0.6592 data: 0.0003 [11-24 04:36:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [ 0/1669] eta: 0:18:20 tlr: 0.0002 tnm: 0.28 Lm: 6.542 (6.542) Lt: 5.813 (5.813) Accm: 3.22 (3.22) Acct: 4.92 (4.92) proj_loss: -0.5903 (-0.5903) time: 0.6592 data: 0.0003 [11-24 04:40:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [ 417/1669] eta: 0:14:03 tlr: 0.0002 tnm: 0.27 Lm: 6.608 (6.608) Lt: 5.837 (5.837) Accm: 3.01 (3.01) Acct: 4.83 (4.83) proj_loss: -0.5673 (-0.5673) time: 0.6717 data: 0.0002 [11-24 04:40:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [ 417/1669] eta: 0:14:03 tlr: 0.0002 tnm: 0.27 Lm: 6.624 (6.624) Lt: 5.902 (5.902) Accm: 3.19 (3.19) Acct: 5.08 (5.08) proj_loss: -0.5708 (-0.5708) time: 0.6717 data: 0.0002 [11-24 04:40:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [ 417/1669] eta: 0:14:03 tlr: 0.0002 tnm: 0.27 Lm: 6.689 (6.689) Lt: 5.997 (5.997) Accm: 2.83 (2.83) Acct: 4.48 (4.48) proj_loss: -0.5836 (-0.5836) time: 0.6718 data: 0.0003 [11-24 04:40:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [ 417/1669] eta: 0:14:03 tlr: 0.0002 tnm: 0.27 Lm: 6.578 (6.578) Lt: 5.774 (5.774) Accm: 3.17 (3.17) Acct: 5.13 (5.13) proj_loss: -0.5727 (-0.5727) time: 0.6718 data: 0.0003 [11-24 04:45:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.29 Lm: 6.610 (6.645) Lt: 5.840 (5.869) Accm: 3.10 (3.03) Acct: 4.92 (4.80) proj_loss: -0.5742 (-0.5753) time: 0.6733 data: 0.0003 [11-24 04:45:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.29 Lm: 6.621 (6.623) Lt: 5.861 (5.888) Accm: 3.01 (3.13) Acct: 4.70 (4.89) proj_loss: -0.5637 (-0.5684) time: 0.6733 data: 0.0002 [11-24 04:45:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.29 Lm: 6.618 (6.625) Lt: 5.939 (5.897) Accm: 3.00 (3.04) Acct: 4.61 (4.88) proj_loss: -0.5796 (-0.5823) time: 0.6733 data: 0.0003 [11-24 04:45:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.29 Lm: 6.674 (6.637) Lt: 5.860 (5.901) Accm: 2.80 (2.93) Acct: 4.73 (4.64) proj_loss: -0.5903 (-0.5771) time: 0.6733 data: 0.0002 [11-24 04:50:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.28 Lm: 6.670 (6.644) Lt: 5.869 (5.896) Accm: 2.90 (2.94) Acct: 4.83 (4.73) proj_loss: -0.5812 (-0.5759) time: 0.6731 data: 0.0002 [11-24 04:50:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.28 Lm: 6.618 (6.624) Lt: 5.890 (5.882) Accm: 3.05 (3.06) Acct: 4.97 (4.99) proj_loss: -0.5792 (-0.5795) time: 0.6731 data: 0.0003 [11-24 04:50:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.28 Lm: 6.634 (6.648) Lt: 5.860 (5.872) Accm: 3.05 (3.02) Acct: 4.95 (4.84) proj_loss: -0.5727 (-0.5700) time: 0.6731 data: 0.0003 [11-24 04:50:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.28 Lm: 6.662 (6.643) Lt: 5.891 (5.897) Accm: 3.01 (3.10) Acct: 4.79 (4.89) proj_loss: -0.5724 (-0.5722) time: 0.6731 data: 0.0002 [11-24 04:55:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.696 (6.654) Lt: 5.921 (5.916) Accm: 3.02 (3.14) Acct: 4.87 (4.97) proj_loss: -0.5811 (-0.5757) time: 0.8476 data: 0.0021 [11-24 04:55:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 82/350] Total time: 0:18:58 (0.682 s / it) [11-24 04:55:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.614 (6.641) Lt: 5.880 (5.881) Accm: 3.10 (3.04) Acct: 4.96 (4.87) proj_loss: -0.5742 (-0.5711) time: 0.8476 data: 0.0017 [11-24 04:55:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.667 (6.643) Lt: 5.879 (5.896) Accm: 2.99 (2.98) Acct: 4.92 (4.81) proj_loss: -0.5722 (-0.5736) time: 0.8476 data: 0.0017 [11-24 04:55:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 82/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.618 (6.631) Lt: 5.883 (5.882) Accm: 3.10 (3.07) Acct: 5.13 (5.02) proj_loss: -0.5787 (-0.5786) time: 0.8476 data: 0.0020 [11-24 04:55:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 82/350] Total time: 0:18:58 (0.682 s / it) [11-24 04:55:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 82/350] Total time: 0:18:58 (0.682 s / it) [11-24 04:55:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 82/350] Total time: 0:18:58 (0.682 s / it) [11-24 04:55:16] (/home/user/VAR/train.py , line 276)=> [ep82] (training ) Lm: 6.630 (6.630), Lt: 5.879 (5.879), Acc m&t: 3.05 4.85, Remain: 3 days, 12:08:07, Finish: 2024-11-27 01:03 [11-24 04:55:16] (/home/user/VAR/train.py , line 276)=> [ep82] (training ) Lm: 6.630 (6.630), Lt: 5.879 (5.879), Acc m&t: 3.05 4.85, Remain: 3 days, 12:08:34, Finish: 2024-11-27 01:03 [11-24 04:55:16] (/home/user/VAR/train.py , line 276)=> [ep82] (training ) Lm: 6.630 (6.630), Lt: 5.879 (5.879), Acc m&t: 3.05 4.85, Remain: 3 days, 12:07:58, Finish: 2024-11-27 01:03 [11-24 04:55:16] (/home/user/VAR/train.py , line 276)=> [ep82] (training ) Lm: 6.630 (6.630), Lt: 5.879 (5.879), Acc m&t: 3.05 4.85, Remain: 3 days, 12:09:21, Finish: 2024-11-27 01:04 [11-24 04:55:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [ 0/1669] eta: 0:18:25 tlr: 0.0002 tnm: 0.28 Lm: 6.637 (6.637) Lt: 5.871 (5.871) Accm: 3.07 (3.07) Acct: 5.06 (5.06) proj_loss: -0.5781 (-0.5781) time: 0.6622 data: 0.0004 [11-24 04:55:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [ 0/1669] eta: 0:18:25 tlr: 0.0002 tnm: 0.28 Lm: 6.496 (6.496) Lt: 5.800 (5.800) Accm: 3.30 (3.30) Acct: 4.98 (4.98) proj_loss: -0.5912 (-0.5912) time: 0.6626 data: 0.0004 [11-24 04:55:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [ 0/1669] eta: 0:18:25 tlr: 0.0002 tnm: 0.28 Lm: 6.722 (6.722) Lt: 5.965 (5.965) Accm: 2.86 (2.86) Acct: 4.77 (4.77) proj_loss: -0.5697 (-0.5697) time: 0.6622 data: 0.0004 [11-24 04:55:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [ 0/1669] eta: 0:18:26 tlr: 0.0002 tnm: 0.28 Lm: 6.464 (6.464) Lt: 5.655 (5.655) Accm: 3.33 (3.33) Acct: 5.27 (5.27) proj_loss: -0.5504 (-0.5504) time: 0.6628 data: 0.0003 [11-24 04:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [ 417/1669] eta: 0:14:03 tlr: 0.0002 tnm: 0.29 Lm: 6.618 (6.618) Lt: 5.885 (5.885) Accm: 2.87 (2.87) Acct: 4.48 (4.48) proj_loss: -0.5599 (-0.5599) time: 0.6746 data: 0.0002 [11-24 04:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [ 417/1669] eta: 0:14:03 tlr: 0.0002 tnm: 0.29 Lm: 6.647 (6.647) Lt: 5.885 (5.885) Accm: 3.03 (3.03) Acct: 4.87 (4.87) proj_loss: -0.5655 (-0.5655) time: 0.6746 data: 0.0003 [11-24 04:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [ 417/1669] eta: 0:14:03 tlr: 0.0002 tnm: 0.29 Lm: 6.743 (6.743) Lt: 6.044 (6.044) Accm: 2.82 (2.82) Acct: 4.50 (4.50) proj_loss: -0.5712 (-0.5712) time: 0.6746 data: 0.0003 [11-24 04:59:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [ 417/1669] eta: 0:14:03 tlr: 0.0002 tnm: 0.29 Lm: 6.574 (6.574) Lt: 5.870 (5.870) Accm: 3.14 (3.14) Acct: 4.83 (4.83) proj_loss: -0.5830 (-0.5830) time: 0.6746 data: 0.0003 [11-24 05:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.28 Lm: 6.640 (6.596) Lt: 5.900 (5.880) Accm: 3.11 (3.13) Acct: 4.98 (4.88) proj_loss: -0.5748 (-0.5767) time: 0.6716 data: 0.0003 [11-24 05:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.28 Lm: 6.656 (6.664) Lt: 5.900 (5.909) Accm: 3.07 (3.06) Acct: 5.06 (4.95) proj_loss: -0.5752 (-0.5688) time: 0.6716 data: 0.0003 [11-24 05:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.28 Lm: 6.675 (6.637) Lt: 5.939 (5.903) Accm: 3.02 (2.92) Acct: 4.87 (4.61) proj_loss: -0.5585 (-0.5594) time: 0.6716 data: 0.0002 [11-24 05:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.28 Lm: 6.722 (6.688) Lt: 5.965 (5.978) Accm: 2.86 (2.90) Acct: 4.70 (4.57) proj_loss: -0.5726 (-0.5726) time: 0.6717 data: 0.0003 [11-24 05:09:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.29 Lm: 6.650 (6.640) Lt: 5.905 (5.920) Accm: 2.97 (3.10) Acct: 4.73 (4.80) proj_loss: -0.5741 (-0.5750) time: 0.6744 data: 0.0003 [11-24 05:09:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.29 Lm: 6.598 (6.586) Lt: 5.859 (5.865) Accm: 3.21 (3.19) Acct: 4.98 (5.06) proj_loss: -0.5735 (-0.5756) time: 0.6744 data: 0.0003 [11-24 05:09:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.29 Lm: 6.682 (6.650) Lt: 5.943 (5.914) Accm: 2.81 (2.84) Acct: 4.59 (4.53) proj_loss: -0.5633 (-0.5616) time: 0.6744 data: 0.0002 [11-24 05:09:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.29 Lm: 6.678 (6.697) Lt: 5.929 (5.951) Accm: 3.03 (2.95) Acct: 4.87 (4.80) proj_loss: -0.5724 (-0.5690) time: 0.6744 data: 0.0002 [11-24 05:14:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.30 Lm: 6.699 (6.708) Lt: 5.958 (5.970) Accm: 2.99 (2.90) Acct: 4.68 (4.72) proj_loss: -0.5715 (-0.5695) time: 0.6737 data: 0.0018 [11-24 05:14:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 83/350] Total time: 0:18:44 (0.674 s / it) [11-24 05:14:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.30 Lm: 6.556 (6.558) Lt: 5.818 (5.825) Accm: 3.30 (3.32) Acct: 4.98 (5.29) proj_loss: -0.5722 (-0.5723) time: 0.6737 data: 0.0015 [11-24 05:14:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.30 Lm: 6.684 (6.649) Lt: 5.965 (5.936) Accm: 2.86 (3.00) Acct: 4.70 (4.64) proj_loss: -0.5726 (-0.5725) time: 0.6737 data: 0.0015 [11-24 05:14:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 83/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.30 Lm: 6.689 (6.658) Lt: 5.948 (5.925) Accm: 2.83 (2.84) Acct: 4.30 (4.46) proj_loss: -0.5681 (-0.5659) time: 0.6737 data: 0.0020 [11-24 05:14:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 83/350] Total time: 0:18:44 (0.674 s / it) [11-24 05:14:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 83/350] Total time: 0:18:44 (0.674 s / it) [11-24 05:14:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 83/350] Total time: 0:18:44 (0.674 s / it) [11-24 05:14:01] (/home/user/VAR/train.py , line 276)=> [ep83] (training ) Lm: 6.630 (6.635), Lt: 5.879 (5.889), Acc m&t: 3.05 4.85, Remain: 3 days, 11:44:04, Finish: 2024-11-27 00:58 [11-24 05:14:01] (/home/user/VAR/train.py , line 276)=> [ep83] (training ) Lm: 6.630 (6.635), Lt: 5.879 (5.889), Acc m&t: 3.05 4.85, Remain: 3 days, 11:43:12, Finish: 2024-11-27 00:57 [11-24 05:14:01] (/home/user/VAR/train.py , line 276)=> [ep83] (training ) Lm: 6.630 (6.635), Lt: 5.879 (5.889), Acc m&t: 3.05 4.85, Remain: 3 days, 11:44:43, Finish: 2024-11-27 00:58 [11-24 05:14:01] (/home/user/VAR/train.py , line 276)=> [ep83] (training ) Lm: 6.630 (6.635), Lt: 5.879 (5.889), Acc m&t: 3.05 4.85, Remain: 3 days, 11:44:10, Finish: 2024-11-27 00:58 [11-24 05:14:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [ 0/1669] eta: 0:18:08 tlr: 0.0002 tnm: 0.30 Lm: 6.611 (6.611) Lt: 5.911 (5.911) Accm: 3.11 (3.11) Acct: 4.86 (4.86) proj_loss: -0.6116 (-0.6116) time: 0.6522 data: 0.0003 [11-24 05:14:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [ 0/1669] eta: 0:18:09 tlr: 0.0002 tnm: 0.30 Lm: 6.476 (6.476) Lt: 5.642 (5.642) Accm: 3.47 (3.47) Acct: 6.08 (6.08) proj_loss: -0.5922 (-0.5922) time: 0.6528 data: 0.0004 [11-24 05:14:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [ 0/1669] eta: 0:18:19 tlr: 0.0002 tnm: 0.30 Lm: 6.658 (6.658) Lt: 5.861 (5.861) Accm: 3.13 (3.13) Acct: 5.23 (5.23) proj_loss: -0.5676 (-0.5676) time: 0.6585 data: 0.0003 [11-24 05:14:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [ 0/1669] eta: 0:18:19 tlr: 0.0002 tnm: 0.30 Lm: 6.720 (6.720) Lt: 5.952 (5.952) Accm: 2.82 (2.82) Acct: 4.53 (4.53) proj_loss: -0.5720 (-0.5720) time: 0.6587 data: 0.0003 [11-24 05:18:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [ 417/1669] eta: 0:14:07 tlr: 0.0002 tnm: 0.28 Lm: 6.635 (6.635) Lt: 5.880 (5.880) Accm: 3.20 (3.20) Acct: 5.22 (5.22) proj_loss: -0.5705 (-0.5705) time: 0.6694 data: 0.0002 [11-24 05:18:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [ 417/1669] eta: 0:14:07 tlr: 0.0002 tnm: 0.28 Lm: 6.553 (6.553) Lt: 5.753 (5.753) Accm: 3.35 (3.35) Acct: 5.70 (5.70) proj_loss: -0.5780 (-0.5780) time: 0.6694 data: 0.0003 [11-24 05:18:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [ 417/1669] eta: 0:14:07 tlr: 0.0002 tnm: 0.28 Lm: 6.632 (6.632) Lt: 5.835 (5.835) Accm: 3.04 (3.04) Acct: 5.13 (5.13) proj_loss: -0.5706 (-0.5706) time: 0.6694 data: 0.0003 [11-24 05:18:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [ 417/1669] eta: 0:14:07 tlr: 0.0002 tnm: 0.28 Lm: 6.601 (6.601) Lt: 5.864 (5.864) Accm: 3.07 (3.07) Acct: 4.90 (4.90) proj_loss: -0.5916 (-0.5916) time: 0.6694 data: 0.0003 [11-24 05:23:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [ 834/1669] eta: 0:09:36 tlr: 0.0002 tnm: 0.29 Lm: 6.611 (6.675) Lt: 5.911 (5.938) Accm: 3.03 (2.91) Acct: 4.86 (4.64) proj_loss: -0.5716 (-0.5807) time: 0.6721 data: 0.0003 [11-24 05:23:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [ 834/1669] eta: 0:09:36 tlr: 0.0002 tnm: 0.29 Lm: 6.630 (6.610) Lt: 5.863 (5.843) Accm: 3.23 (3.15) Acct: 5.32 (5.22) proj_loss: -0.5639 (-0.5729) time: 0.6721 data: 0.0003 [11-24 05:23:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [ 834/1669] eta: 0:09:36 tlr: 0.0002 tnm: 0.29 Lm: 6.551 (6.595) Lt: 5.809 (5.846) Accm: 3.20 (3.20) Acct: 5.03 (5.15) proj_loss: -0.5720 (-0.5749) time: 0.6721 data: 0.0002 [11-24 05:23:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [ 834/1669] eta: 0:09:36 tlr: 0.0002 tnm: 0.29 Lm: 6.611 (6.625) Lt: 5.809 (5.821) Accm: 3.13 (3.08) Acct: 5.10 (5.12) proj_loss: -0.5737 (-0.5728) time: 0.6721 data: 0.0003 [11-24 05:28:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [1251/1669] eta: 0:04:46 tlr: 0.0002 tnm: 0.28 Lm: 6.624 (6.628) Lt: 5.835 (5.846) Accm: 3.06 (3.06) Acct: 5.06 (4.97) proj_loss: -0.5709 (-0.5717) time: 0.6706 data: 0.0003 [11-24 05:28:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [1251/1669] eta: 0:04:46 tlr: 0.0002 tnm: 0.28 Lm: 6.674 (6.691) Lt: 5.952 (5.952) Accm: 2.93 (2.89) Acct: 4.73 (4.63) proj_loss: -0.5653 (-0.5732) time: 0.6706 data: 0.0002 [11-24 05:28:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [1251/1669] eta: 0:04:46 tlr: 0.0002 tnm: 0.28 Lm: 6.593 (6.597) Lt: 5.825 (5.830) Accm: 3.18 (3.15) Acct: 5.18 (5.17) proj_loss: -0.5643 (-0.5709) time: 0.6706 data: 0.0003 [11-24 05:28:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [1251/1669] eta: 0:04:46 tlr: 0.0002 tnm: 0.28 Lm: 6.596 (6.607) Lt: 5.869 (5.867) Accm: 3.12 (3.16) Acct: 4.97 (5.09) proj_loss: -0.5779 (-0.5803) time: 0.6706 data: 0.0002 [11-24 05:32:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.642 (6.640) Lt: 5.929 (5.897) Accm: 3.04 (3.09) Acct: 4.91 (4.96) proj_loss: -0.5720 (-0.5745) time: 0.6741 data: 0.0018 [11-24 05:32:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 84/350] Total time: 0:18:58 (0.682 s / it) [11-24 05:32:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.557 (6.573) Lt: 5.788 (5.820) Accm: 3.23 (3.21) Acct: 5.18 (5.18) proj_loss: -0.5639 (-0.5668) time: 0.6742 data: 0.0015 [11-24 05:32:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.738 (6.718) Lt: 5.994 (5.981) Accm: 2.83 (2.85) Acct: 4.60 (4.59) proj_loss: -0.5716 (-0.5790) time: 0.6741 data: 0.0016 [11-24 05:32:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 84/350] [1668/1669] eta: 0:00:00 tlr: 0.0002 tnm: 0.29 Lm: 6.612 (6.625) Lt: 5.861 (5.852) Accm: 3.13 (3.10) Acct: 5.03 (4.98) proj_loss: -0.5737 (-0.5746) time: 0.6741 data: 0.0017 [11-24 05:32:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 84/350] Total time: 0:18:58 (0.682 s / it) [11-24 05:32:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 84/350] Total time: 0:18:58 (0.682 s / it) [11-24 05:32:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 84/350] Total time: 0:18:58 (0.682 s / it) [11-24 05:32:59] (/home/user/VAR/train.py , line 276)=> [ep84] (training ) Lm: 6.630 (6.634), Lt: 5.879 (5.893), Acc m&t: 3.05 4.85, Remain: 3 days, 11:29:46, Finish: 2024-11-27 01:02 [11-24 05:32:59] (/home/user/VAR/train.py , line 276)=> [ep84] (training ) Lm: 6.630 (6.634), Lt: 5.879 (5.893), Acc m&t: 3.05 4.85, Remain: 3 days, 11:30:23, Finish: 2024-11-27 01:03 [11-24 05:32:59] (/home/user/VAR/train.py , line 276)=> [ep84] (training ) Lm: 6.630 (6.634), Lt: 5.879 (5.893), Acc m&t: 3.05 4.85, Remain: 3 days, 11:30:12, Finish: 2024-11-27 01:03 [11-24 05:32:59] (/home/user/VAR/train.py , line 276)=> [ep84] (training ) Lm: 6.630 (6.634), Lt: 5.879 (5.893), Acc m&t: 3.05 4.85, Remain: 3 days, 11:30:15, Finish: 2024-11-27 01:03 [11-24 05:33:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [ 0/1669] eta: 0:18:21 tlr: 0.0002 tnm: 0.28 Lm: 6.722 (6.722) Lt: 5.998 (5.998) Accm: 2.99 (2.99) Acct: 4.77 (4.77) proj_loss: -0.5838 (-0.5838) time: 0.6599 data: 0.0003 [11-24 05:33:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [ 0/1669] eta: 0:18:21 tlr: 0.0002 tnm: 0.28 Lm: 6.712 (6.712) Lt: 5.941 (5.941) Accm: 2.68 (2.68) Acct: 4.34 (4.34) proj_loss: -0.5788 (-0.5788) time: 0.6599 data: 0.0004 [11-24 05:33:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [ 0/1669] eta: 0:18:21 tlr: 0.0002 tnm: 0.28 Lm: 6.787 (6.787) Lt: 6.099 (6.099) Accm: 2.70 (2.70) Acct: 4.30 (4.30) proj_loss: -0.5710 (-0.5710) time: 0.6601 data: 0.0004 [11-24 05:33:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [ 0/1669] eta: 0:18:21 tlr: 0.0002 tnm: 0.28 Lm: 6.750 (6.750) Lt: 6.021 (6.021) Accm: 2.88 (2.88) Acct: 4.51 (4.51) proj_loss: -0.5759 (-0.5759) time: 0.6602 data: 0.0004 [11-24 05:37:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [ 417/1669] eta: 0:14:02 tlr: 0.0002 tnm: 0.29 Lm: 6.604 (6.604) Lt: 5.860 (5.860) Accm: 2.98 (2.98) Acct: 4.65 (4.65) proj_loss: -0.5688 (-0.5688) time: 0.6720 data: 0.0003 [11-24 05:37:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [ 417/1669] eta: 0:14:02 tlr: 0.0002 tnm: 0.29 Lm: 6.654 (6.654) Lt: 5.908 (5.908) Accm: 2.86 (2.86) Acct: 4.63 (4.63) proj_loss: -0.5797 (-0.5797) time: 0.6719 data: 0.0003 [11-24 05:37:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [ 417/1669] eta: 0:14:02 tlr: 0.0002 tnm: 0.29 Lm: 6.555 (6.555) Lt: 5.801 (5.801) Accm: 3.50 (3.50) Acct: 5.54 (5.54) proj_loss: -0.5783 (-0.5783) time: 0.6720 data: 0.0003 [11-24 05:37:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [ 417/1669] eta: 0:14:02 tlr: 0.0002 tnm: 0.29 Lm: 6.707 (6.707) Lt: 5.962 (5.962) Accm: 2.80 (2.80) Acct: 4.41 (4.41) proj_loss: -0.5694 (-0.5694) time: 0.6720 data: 0.0003 [11-24 05:42:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.29 Lm: 6.702 (6.697) Lt: 5.941 (5.943) Accm: 2.93 (2.88) Acct: 4.48 (4.62) proj_loss: -0.5788 (-0.5736) time: 0.6751 data: 0.0003 [11-24 05:42:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.29 Lm: 6.689 (6.600) Lt: 5.961 (5.855) Accm: 2.99 (3.31) Acct: 4.82 (5.30) proj_loss: -0.5728 (-0.5754) time: 0.6751 data: 0.0002 [11-24 05:42:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.29 Lm: 6.609 (6.639) Lt: 5.864 (5.893) Accm: 3.02 (2.95) Acct: 4.87 (4.71) proj_loss: -0.5884 (-0.5856) time: 0.6751 data: 0.0003 [11-24 05:42:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [ 834/1669] eta: 0:09:22 tlr: 0.0002 tnm: 0.29 Lm: 6.724 (6.644) Lt: 5.963 (5.894) Accm: 2.88 (2.89) Acct: 4.51 (4.54) proj_loss: -0.5618 (-0.5613) time: 0.6751 data: 0.0002 [11-24 05:47:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.29 Lm: 6.700 (6.652) Lt: 5.934 (5.897) Accm: 2.85 (2.88) Acct: 4.54 (4.55) proj_loss: -0.5660 (-0.5635) time: 0.6774 data: 0.0002 [11-24 05:47:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.29 Lm: 6.672 (6.614) Lt: 5.928 (5.865) Accm: 3.00 (3.23) Acct: 4.86 (5.20) proj_loss: -0.5740 (-0.5753) time: 0.6774 data: 0.0002 [11-24 05:47:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.29 Lm: 6.707 (6.725) Lt: 5.962 (5.985) Accm: 2.80 (2.76) Acct: 4.41 (4.39) proj_loss: -0.5804 (-0.5770) time: 0.6774 data: 0.0002 [11-24 05:47:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [1251/1669] eta: 0:04:41 tlr: 0.0002 tnm: 0.29 Lm: 6.609 (6.631) Lt: 5.874 (5.891) Accm: 2.96 (2.93) Acct: 4.65 (4.64) proj_loss: -0.5886 (-0.5864) time: 0.6774 data: 0.0003 [11-24 05:52:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.30 Lm: 6.609 (6.649) Lt: 5.884 (5.906) Accm: 2.89 (2.91) Acct: 4.58 (4.63) proj_loss: -0.5884 (-0.5849) time: 1.0780 data: 0.0017 [11-24 05:52:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 85/350] Total time: 0:19:03 (0.685 s / it) [11-24 05:52:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.30 Lm: 6.676 (6.654) Lt: 5.939 (5.906) Accm: 2.88 (2.89) Acct: 4.56 (4.56) proj_loss: -0.5627 (-0.5634) time: 1.0780 data: 0.0016 [11-24 05:52:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.30 Lm: 6.689 (6.656) Lt: 5.961 (5.916) Accm: 2.99 (3.07) Acct: 4.82 (4.89) proj_loss: -0.5728 (-0.5724) time: 1.0780 data: 0.0017 [11-24 05:52:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 85/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.30 Lm: 6.702 (6.676) Lt: 5.941 (5.932) Accm: 2.93 (2.89) Acct: 4.48 (4.57) proj_loss: -0.5788 (-0.5770) time: 1.0780 data: 0.0019 [11-24 05:52:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 85/350] Total time: 0:19:03 (0.685 s / it) [11-24 05:52:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 85/350] Total time: 0:19:03 (0.685 s / it) [11-24 05:52:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 85/350] Total time: 0:19:03 (0.685 s / it) [11-24 05:52:03] (/home/user/VAR/train.py , line 276)=> [ep85] (training ) Lm: 6.630 (6.637), Lt: 5.879 (5.891), Acc m&t: 3.05 4.85, Remain: 3 days, 11:48:48, Finish: 2024-11-27 01:40 [11-24 05:52:03] (/home/user/VAR/train.py , line 276)=> [ep85] (training ) Lm: 6.630 (6.637), Lt: 5.879 (5.891), Acc m&t: 3.05 4.85, Remain: 3 days, 11:49:00, Finish: 2024-11-27 01:41 [11-24 05:52:03] (/home/user/VAR/train.py , line 276)=> [ep85] (training ) Lm: 6.630 (6.637), Lt: 5.879 (5.891), Acc m&t: 3.05 4.85, Remain: 3 days, 11:49:53, Finish: 2024-11-27 01:41 [11-24 05:52:03] (/home/user/VAR/train.py , line 276)=> [ep85] (training ) Lm: 6.630 (6.637), Lt: 5.879 (5.891), Acc m&t: 3.05 4.85, Remain: 3 days, 11:48:45, Finish: 2024-11-27 01:40 [11-24 05:52:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [ 0/1669] eta: 0:18:14 tlr: 0.00019 tnm: 0.28 Lm: 6.458 (6.458) Lt: 5.668 (5.668) Accm: 3.62 (3.62) Acct: 5.77 (5.77) proj_loss: -0.5819 (-0.5819) time: 0.6560 data: 0.0004 [11-24 05:52:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [ 0/1669] eta: 0:18:15 tlr: 0.00019 tnm: 0.28 Lm: 6.444 (6.444) Lt: 5.695 (5.695) Accm: 3.66 (3.66) Acct: 5.87 (5.87) proj_loss: -0.5822 (-0.5822) time: 0.6563 data: 0.0003 [11-24 05:52:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [ 0/1669] eta: 0:18:15 tlr: 0.00019 tnm: 0.28 Lm: 6.573 (6.573) Lt: 5.795 (5.795) Accm: 3.47 (3.47) Acct: 5.56 (5.56) proj_loss: -0.5680 (-0.5680) time: 0.6566 data: 0.0004 [11-24 05:52:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [ 0/1669] eta: 0:18:16 tlr: 0.00019 tnm: 0.28 Lm: 6.654 (6.654) Lt: 5.939 (5.939) Accm: 2.60 (2.60) Acct: 4.25 (4.25) proj_loss: -0.5831 (-0.5831) time: 0.6569 data: 0.0003 [11-24 05:56:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [ 417/1669] eta: 0:14:03 tlr: 0.00019 tnm: 0.27 Lm: 6.615 (6.615) Lt: 5.861 (5.861) Accm: 2.84 (2.84) Acct: 4.71 (4.71) proj_loss: -0.5743 (-0.5743) time: 0.6742 data: 0.0003 [11-24 05:56:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [ 417/1669] eta: 0:14:03 tlr: 0.00019 tnm: 0.27 Lm: 6.649 (6.649) Lt: 5.904 (5.904) Accm: 3.22 (3.22) Acct: 5.29 (5.29) proj_loss: -0.5689 (-0.5689) time: 0.6742 data: 0.0002 [11-24 05:56:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [ 417/1669] eta: 0:14:03 tlr: 0.00019 tnm: 0.27 Lm: 6.594 (6.594) Lt: 5.848 (5.848) Accm: 3.21 (3.21) Acct: 4.96 (4.96) proj_loss: -0.5814 (-0.5814) time: 0.6742 data: 0.0003 [11-24 05:56:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [ 417/1669] eta: 0:14:03 tlr: 0.00019 tnm: 0.27 Lm: 6.545 (6.545) Lt: 5.806 (5.806) Accm: 3.35 (3.35) Acct: 5.30 (5.30) proj_loss: -0.5788 (-0.5788) time: 0.6742 data: 0.0003 [11-24 06:01:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [ 834/1669] eta: 0:09:22 tlr: 0.00019 tnm: 0.27 Lm: 6.617 (6.569) Lt: 5.836 (5.816) Accm: 3.26 (3.32) Acct: 5.54 (5.38) proj_loss: -0.5822 (-0.5832) time: 0.6739 data: 0.0003 [11-24 06:01:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [ 834/1669] eta: 0:09:22 tlr: 0.00019 tnm: 0.27 Lm: 6.724 (6.713) Lt: 6.014 (5.976) Accm: 2.96 (3.03) Acct: 5.01 (4.99) proj_loss: -0.5699 (-0.5697) time: 0.6739 data: 0.0003 [11-24 06:01:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [ 834/1669] eta: 0:09:22 tlr: 0.00019 tnm: 0.27 Lm: 6.596 (6.595) Lt: 5.865 (5.854) Accm: 3.10 (3.18) Acct: 4.99 (4.97) proj_loss: -0.5808 (-0.5787) time: 0.6739 data: 0.0002 [11-24 06:01:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [ 834/1669] eta: 0:09:22 tlr: 0.00019 tnm: 0.27 Lm: 6.654 (6.652) Lt: 5.939 (5.917) Accm: 2.93 (2.87) Acct: 4.77 (4.73) proj_loss: -0.5831 (-0.5802) time: 0.6739 data: 0.0003 [11-24 06:06:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.30 Lm: 6.689 (6.682) Lt: 5.984 (5.963) Accm: 2.85 (2.84) Acct: 4.57 (4.64) proj_loss: -0.5822 (-0.5805) time: 0.6740 data: 0.0003 [11-24 06:06:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.30 Lm: 6.578 (6.561) Lt: 5.811 (5.809) Accm: 3.22 (3.28) Acct: 5.17 (5.24) proj_loss: -0.5803 (-0.5820) time: 0.6740 data: 0.0003 [11-24 06:06:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.30 Lm: 6.649 (6.673) Lt: 5.904 (5.927) Accm: 3.13 (3.10) Acct: 5.12 (5.05) proj_loss: -0.5706 (-0.5705) time: 0.6740 data: 0.0003 [11-24 06:06:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.30 Lm: 6.604 (6.599) Lt: 5.875 (5.861) Accm: 3.22 (3.22) Acct: 4.99 (4.98) proj_loss: -0.5809 (-0.5793) time: 0.6740 data: 0.0002 [11-24 06:10:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.27 Lm: 6.596 (6.571) Lt: 5.865 (5.830) Accm: 3.34 (3.33) Acct: 4.99 (5.09) proj_loss: -0.5808 (-0.5776) time: 0.6789 data: 0.0014 [11-24 06:10:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 86/350] Total time: 0:18:44 (0.674 s / it) [11-24 06:10:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.27 Lm: 6.724 (6.699) Lt: 6.014 (5.953) Accm: 2.96 (2.98) Acct: 5.01 (4.83) proj_loss: -0.5713 (-0.5710) time: 0.6789 data: 0.0016 [11-24 06:10:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.27 Lm: 6.617 (6.597) Lt: 5.836 (5.846) Accm: 3.18 (3.14) Acct: 4.80 (5.00) proj_loss: -0.5822 (-0.5822) time: 0.6789 data: 0.0015 [11-24 06:10:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 86/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.27 Lm: 6.683 (6.682) Lt: 5.989 (5.968) Accm: 2.85 (2.84) Acct: 4.37 (4.57) proj_loss: -0.5814 (-0.5767) time: 0.6789 data: 0.0015 [11-24 06:10:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 86/350] Total time: 0:18:44 (0.674 s / it) [11-24 06:10:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 86/350] Total time: 0:18:44 (0.674 s / it) [11-24 06:10:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 86/350] Total time: 0:18:44 (0.674 s / it) [11-24 06:10:47] (/home/user/VAR/train.py , line 276)=> [ep86] (training ) Lm: 6.630 (6.634), Lt: 5.879 (5.894), Acc m&t: 3.05 4.85, Remain: 3 days, 11:21:52, Finish: 2024-11-27 01:32 [11-24 06:10:47] (/home/user/VAR/train.py , line 276)=> [ep86] (training ) Lm: 6.630 (6.634), Lt: 5.879 (5.894), Acc m&t: 3.05 4.85, Remain: 3 days, 11:21:28, Finish: 2024-11-27 01:32 [11-24 06:10:47] (/home/user/VAR/train.py , line 276)=> [ep86] (training ) Lm: 6.630 (6.634), Lt: 5.879 (5.894), Acc m&t: 3.05 4.85, Remain: 3 days, 11:22:15, Finish: 2024-11-27 01:33 [11-24 06:10:47] (/home/user/VAR/train.py , line 276)=> [ep86] (training ) Lm: 6.630 (6.634), Lt: 5.879 (5.894), Acc m&t: 3.05 4.85, Remain: 3 days, 11:20:33, Finish: 2024-11-27 01:31 [11-24 06:10:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [ 0/1669] eta: 0:18:27 tlr: 0.00019 tnm: 0.28 Lm: 6.561 (6.561) Lt: 5.843 (5.843) Accm: 3.34 (3.34) Acct: 5.39 (5.39) proj_loss: -0.5943 (-0.5943) time: 0.6637 data: 0.0004 [11-24 06:10:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [ 0/1669] eta: 0:18:27 tlr: 0.00019 tnm: 0.28 Lm: 6.654 (6.654) Lt: 5.873 (5.873) Accm: 3.10 (3.10) Acct: 5.01 (5.01) proj_loss: -0.5673 (-0.5673) time: 0.6636 data: 0.0004 [11-24 06:10:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [ 0/1669] eta: 0:18:27 tlr: 0.00019 tnm: 0.28 Lm: 6.702 (6.702) Lt: 5.955 (5.955) Accm: 2.88 (2.88) Acct: 4.60 (4.60) proj_loss: -0.5550 (-0.5550) time: 0.6635 data: 0.0004 [11-24 06:10:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [ 0/1669] eta: 0:18:28 tlr: 0.00019 tnm: 0.28 Lm: 6.615 (6.615) Lt: 5.863 (5.863) Accm: 3.07 (3.07) Acct: 5.01 (5.01) proj_loss: -0.5742 (-0.5742) time: 0.6644 data: 0.0004 [11-24 06:15:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [ 417/1669] eta: 0:14:10 tlr: 0.00019 tnm: 0.27 Lm: 6.622 (6.622) Lt: 5.871 (5.871) Accm: 3.06 (3.06) Acct: 4.84 (4.84) proj_loss: -0.5693 (-0.5693) time: 0.6765 data: 0.0003 [11-24 06:15:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [ 417/1669] eta: 0:14:10 tlr: 0.00019 tnm: 0.27 Lm: 6.730 (6.730) Lt: 5.987 (5.987) Accm: 2.94 (2.94) Acct: 4.67 (4.67) proj_loss: -0.5759 (-0.5759) time: 0.6765 data: 0.0003 [11-24 06:15:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [ 417/1669] eta: 0:14:10 tlr: 0.00019 tnm: 0.27 Lm: 6.652 (6.652) Lt: 5.932 (5.932) Accm: 2.90 (2.90) Acct: 4.65 (4.65) proj_loss: -0.5855 (-0.5855) time: 0.6765 data: 0.0002 [11-24 06:15:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [ 417/1669] eta: 0:14:10 tlr: 0.00019 tnm: 0.27 Lm: 6.586 (6.586) Lt: 5.814 (5.814) Accm: 3.13 (3.13) Acct: 5.07 (5.07) proj_loss: -0.5722 (-0.5722) time: 0.6765 data: 0.0003 [11-24 06:20:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [ 834/1669] eta: 0:09:43 tlr: 0.00019 tnm: 0.31 Lm: 6.614 (6.596) Lt: 5.873 (5.835) Accm: 3.10 (3.09) Acct: 5.01 (4.93) proj_loss: -0.5770 (-0.5755) time: 0.6735 data: 0.0003 [11-24 06:20:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [ 834/1669] eta: 0:09:43 tlr: 0.00019 tnm: 0.31 Lm: 6.702 (6.656) Lt: 5.955 (5.915) Accm: 2.99 (3.12) Acct: 4.73 (4.92) proj_loss: -0.5839 (-0.5785) time: 0.6735 data: 0.0003 [11-24 06:20:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [ 834/1669] eta: 0:09:43 tlr: 0.00019 tnm: 0.31 Lm: 6.561 (6.620) Lt: 5.843 (5.884) Accm: 3.21 (3.00) Acct: 5.06 (4.79) proj_loss: -0.5823 (-0.5844) time: 0.6735 data: 0.0002 [11-24 06:20:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [ 834/1669] eta: 0:09:43 tlr: 0.00019 tnm: 0.31 Lm: 6.630 (6.629) Lt: 5.878 (5.874) Accm: 3.05 (2.97) Acct: 4.67 (4.67) proj_loss: -0.5742 (-0.5738) time: 0.6735 data: 0.0003 [11-24 06:25:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [1251/1669] eta: 0:04:48 tlr: 0.00019 tnm: 0.29 Lm: 6.637 (6.677) Lt: 5.880 (5.924) Accm: 2.92 (2.85) Acct: 4.49 (4.39) proj_loss: -0.5745 (-0.5740) time: 0.6726 data: 0.0003 [11-24 06:25:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [1251/1669] eta: 0:04:48 tlr: 0.00019 tnm: 0.29 Lm: 6.630 (6.632) Lt: 5.864 (5.880) Accm: 3.03 (3.10) Acct: 4.81 (4.92) proj_loss: -0.5743 (-0.5751) time: 0.6726 data: 0.0003 [11-24 06:25:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [1251/1669] eta: 0:04:48 tlr: 0.00019 tnm: 0.29 Lm: 6.634 (6.617) Lt: 5.875 (5.861) Accm: 3.06 (3.07) Acct: 4.89 (4.89) proj_loss: -0.5750 (-0.5749) time: 0.6726 data: 0.0003 [11-24 06:25:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [1251/1669] eta: 0:04:48 tlr: 0.00019 tnm: 0.29 Lm: 6.620 (6.635) Lt: 5.906 (5.906) Accm: 2.95 (2.92) Acct: 4.72 (4.68) proj_loss: -0.5883 (-0.5872) time: 0.6726 data: 0.0002 [11-24 06:29:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.647 (6.637) Lt: 5.883 (5.901) Accm: 3.12 (2.96) Acct: 5.01 (4.75) proj_loss: -0.5823 (-0.5825) time: 0.6752 data: 0.0016 [11-24 06:29:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 87/350] Total time: 0:19:04 (0.686 s / it) [11-24 06:29:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.641 (6.622) Lt: 5.873 (5.863) Accm: 3.03 (3.03) Acct: 4.77 (4.80) proj_loss: -0.5770 (-0.5755) time: 0.6752 data: 0.0016 [11-24 06:29:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.657 (6.637) Lt: 5.864 (5.876) Accm: 2.99 (3.06) Acct: 4.73 (4.84) proj_loss: -0.5826 (-0.5766) time: 0.6752 data: 0.0020 [11-24 06:29:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 87/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.644 (6.688) Lt: 5.882 (5.938) Accm: 2.93 (2.87) Acct: 4.49 (4.41) proj_loss: -0.5748 (-0.5746) time: 0.6752 data: 0.0017 [11-24 06:29:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 87/350] Total time: 0:19:04 (0.686 s / it) [11-24 06:29:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 87/350] Total time: 0:19:04 (0.686 s / it) [11-24 06:29:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 87/350] Total time: 0:19:04 (0.686 s / it) [11-24 06:29:52] (/home/user/VAR/train.py , line 276)=> [ep87] (training ) Lm: 6.628 (6.628), Lt: 5.879 (5.886), Acc m&t: 3.05 4.85, Remain: 3 days, 10:40:14, Finish: 2024-11-27 01:10 [11-24 06:29:52] (/home/user/VAR/train.py , line 276)=> [ep87] (training ) Lm: 6.628 (6.628), Lt: 5.879 (5.886), Acc m&t: 3.05 4.85, Remain: 3 days, 10:40:54, Finish: 2024-11-27 01:10 [11-24 06:29:52] (/home/user/VAR/train.py , line 276)=> [ep87] (training ) Lm: 6.628 (6.628), Lt: 5.879 (5.886), Acc m&t: 3.05 4.85, Remain: 3 days, 10:39:03, Finish: 2024-11-27 01:08 [11-24 06:29:52] (/home/user/VAR/train.py , line 276)=> [ep87] (training ) Lm: 6.628 (6.628), Lt: 5.879 (5.886), Acc m&t: 3.05 4.85, Remain: 3 days, 10:41:08, Finish: 2024-11-27 01:11 [11-24 06:29:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [ 0/1669] eta: 0:18:23 tlr: 0.00019 tnm: 0.30 Lm: 6.674 (6.674) Lt: 5.919 (5.919) Accm: 2.83 (2.83) Acct: 4.34 (4.34) proj_loss: -0.5610 (-0.5610) time: 0.6612 data: 0.0003 [11-24 06:29:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [ 0/1669] eta: 0:18:23 tlr: 0.00019 tnm: 0.30 Lm: 6.517 (6.517) Lt: 5.773 (5.773) Accm: 3.25 (3.25) Acct: 4.94 (4.94) proj_loss: -0.5762 (-0.5762) time: 0.6611 data: 0.0004 [11-24 06:29:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [ 0/1669] eta: 0:18:24 tlr: 0.00019 tnm: 0.30 Lm: 6.688 (6.688) Lt: 5.903 (5.903) Accm: 2.67 (2.67) Acct: 4.46 (4.46) proj_loss: -0.5731 (-0.5731) time: 0.6618 data: 0.0004 [11-24 06:29:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [ 0/1669] eta: 0:18:24 tlr: 0.00019 tnm: 0.30 Lm: 6.698 (6.698) Lt: 5.933 (5.933) Accm: 2.78 (2.78) Acct: 4.42 (4.42) proj_loss: -0.5544 (-0.5544) time: 0.6615 data: 0.0003 [11-24 06:34:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [ 417/1669] eta: 0:14:01 tlr: 0.00019 tnm: 0.29 Lm: 6.685 (6.685) Lt: 5.935 (5.935) Accm: 2.69 (2.69) Acct: 4.36 (4.36) proj_loss: -0.5719 (-0.5719) time: 0.6696 data: 0.0003 [11-24 06:34:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [ 417/1669] eta: 0:14:01 tlr: 0.00019 tnm: 0.29 Lm: 6.639 (6.639) Lt: 5.902 (5.902) Accm: 2.98 (2.98) Acct: 4.44 (4.44) proj_loss: -0.5683 (-0.5683) time: 0.6695 data: 0.0003 [11-24 06:34:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [ 417/1669] eta: 0:14:01 tlr: 0.00019 tnm: 0.29 Lm: 6.670 (6.670) Lt: 5.943 (5.943) Accm: 2.92 (2.92) Acct: 4.50 (4.50) proj_loss: -0.5714 (-0.5714) time: 0.6696 data: 0.0002 [11-24 06:34:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [ 417/1669] eta: 0:14:01 tlr: 0.00019 tnm: 0.29 Lm: 6.540 (6.540) Lt: 5.758 (5.758) Accm: 3.24 (3.24) Acct: 5.35 (5.35) proj_loss: -0.5715 (-0.5715) time: 0.6696 data: 0.0003 [11-24 06:39:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [ 834/1669] eta: 0:09:21 tlr: 0.00019 tnm: 0.30 Lm: 6.661 (6.581) Lt: 5.903 (5.811) Accm: 3.02 (3.17) Acct: 4.67 (5.13) proj_loss: -0.5700 (-0.5697) time: 0.6753 data: 0.0003 [11-24 06:39:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [ 834/1669] eta: 0:09:21 tlr: 0.00019 tnm: 0.30 Lm: 6.530 (6.624) Lt: 5.807 (5.897) Accm: 3.10 (2.98) Acct: 4.91 (4.64) proj_loss: -0.5704 (-0.5711) time: 0.6753 data: 0.0003 [11-24 06:39:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [ 834/1669] eta: 0:09:21 tlr: 0.00019 tnm: 0.30 Lm: 6.604 (6.624) Lt: 5.895 (5.900) Accm: 2.92 (2.96) Acct: 4.34 (4.37) proj_loss: -0.5756 (-0.5740) time: 0.6753 data: 0.0002 [11-24 06:39:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [ 834/1669] eta: 0:09:21 tlr: 0.00019 tnm: 0.30 Lm: 6.673 (6.649) Lt: 5.933 (5.889) Accm: 2.78 (2.92) Acct: 4.42 (4.69) proj_loss: -0.5713 (-0.5717) time: 0.6753 data: 0.0002 [11-24 06:43:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.30 Lm: 6.685 (6.665) Lt: 5.935 (5.914) Accm: 2.80 (2.90) Acct: 4.39 (4.61) proj_loss: -0.5718 (-0.5718) time: 0.6738 data: 0.0003 [11-24 06:43:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.30 Lm: 6.524 (6.593) Lt: 5.790 (5.863) Accm: 3.17 (3.10) Acct: 4.92 (4.80) proj_loss: -0.5733 (-0.5760) time: 0.6738 data: 0.0003 [11-24 06:43:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.30 Lm: 6.599 (6.596) Lt: 5.890 (5.870) Accm: 3.03 (3.08) Acct: 4.44 (4.69) proj_loss: -0.5802 (-0.5767) time: 0.6738 data: 0.0002 [11-24 06:43:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.30 Lm: 6.675 (6.616) Lt: 5.911 (5.878) Accm: 2.85 (3.03) Acct: 4.56 (4.92) proj_loss: -0.5715 (-0.5740) time: 0.6738 data: 0.0003 [11-24 06:48:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.28 Lm: 6.661 (6.588) Lt: 5.903 (5.842) Accm: 3.02 (3.10) Acct: 4.67 (5.04) proj_loss: -0.5731 (-0.5759) time: 1.0533 data: 0.0022 [11-24 06:48:54] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 88/350] Total time: 0:19:01 (0.684 s / it) [11-24 06:48:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.28 Lm: 6.673 (6.651) Lt: 5.933 (5.901) Accm: 2.83 (2.96) Acct: 4.42 (4.68) proj_loss: -0.5723 (-0.5742) time: 1.0533 data: 0.0017 [11-24 06:48:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.28 Lm: 6.530 (6.598) Lt: 5.807 (5.859) Accm: 3.25 (3.15) Acct: 4.94 (4.97) proj_loss: -0.5704 (-0.5721) time: 1.0533 data: 0.0018 [11-24 06:48:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 88/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.28 Lm: 6.595 (6.590) Lt: 5.886 (5.857) Accm: 3.13 (3.19) Acct: 4.55 (4.95) proj_loss: -0.5756 (-0.5749) time: 1.0534 data: 0.0014 [11-24 06:48:54] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 88/350] Total time: 0:19:01 (0.684 s / it) [11-24 06:48:54] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 88/350] Total time: 0:19:01 (0.684 s / it) [11-24 06:48:54] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 88/350] Total time: 0:19:01 (0.684 s / it) [11-24 06:48:54] (/home/user/VAR/train.py , line 276)=> [ep88] (training ) Lm: 6.628 (6.630), Lt: 5.879 (5.884), Acc m&t: 3.05 4.85, Remain: 3 days, 10:34:39, Finish: 2024-11-27 01:23 [11-24 06:48:54] (/home/user/VAR/train.py , line 276)=> [ep88] (training ) Lm: 6.628 (6.630), Lt: 5.879 (5.884), Acc m&t: 3.05 4.85, Remain: 3 days, 10:41:35, Finish: 2024-11-27 01:30 [11-24 06:48:54] (/home/user/VAR/train.py , line 276)=> [ep88] (training ) Lm: 6.628 (6.630), Lt: 5.879 (5.884), Acc m&t: 3.05 4.85, Remain: 3 days, 10:41:59, Finish: 2024-11-27 01:30 [11-24 06:48:54] (/home/user/VAR/train.py , line 276)=> [ep88] (training ) Lm: 6.628 (6.630), Lt: 5.879 (5.884), Acc m&t: 3.05 4.85, Remain: 3 days, 10:42:25, Finish: 2024-11-27 01:31 [11-24 06:48:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [ 0/1669] eta: 0:18:14 tlr: 0.00019 tnm: 0.28 Lm: 6.679 (6.679) Lt: 5.955 (5.955) Accm: 2.88 (2.88) Acct: 4.72 (4.72) proj_loss: -0.5710 (-0.5710) time: 0.6557 data: 0.0004 [11-24 06:48:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [ 0/1669] eta: 0:18:12 tlr: 0.00019 tnm: 0.28 Lm: 6.661 (6.661) Lt: 5.937 (5.937) Accm: 3.23 (3.23) Acct: 4.99 (4.99) proj_loss: -0.6092 (-0.6092) time: 0.6547 data: 0.0004 [11-24 06:48:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [ 0/1669] eta: 0:18:13 tlr: 0.00019 tnm: 0.28 Lm: 6.564 (6.564) Lt: 5.762 (5.762) Accm: 3.29 (3.29) Acct: 5.32 (5.32) proj_loss: -0.5569 (-0.5569) time: 0.6553 data: 0.0004 [11-24 06:48:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [ 0/1669] eta: 0:18:14 tlr: 0.00019 tnm: 0.28 Lm: 6.670 (6.670) Lt: 6.020 (6.020) Accm: 2.86 (2.86) Acct: 4.18 (4.18) proj_loss: -0.5954 (-0.5954) time: 0.6556 data: 0.0004 [11-24 06:53:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [ 417/1669] eta: 0:14:07 tlr: 0.00019 tnm: 0.28 Lm: 6.626 (6.626) Lt: 5.916 (5.916) Accm: 3.11 (3.11) Acct: 4.76 (4.76) proj_loss: -0.5884 (-0.5884) time: 0.6709 data: 0.0003 [11-24 06:53:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [ 417/1669] eta: 0:14:07 tlr: 0.00019 tnm: 0.28 Lm: 6.633 (6.633) Lt: 5.893 (5.893) Accm: 3.13 (3.13) Acct: 4.97 (4.97) proj_loss: -0.5888 (-0.5888) time: 0.6709 data: 0.0002 [11-24 06:53:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [ 417/1669] eta: 0:14:07 tlr: 0.00019 tnm: 0.28 Lm: 6.575 (6.575) Lt: 5.802 (5.802) Accm: 3.26 (3.26) Acct: 5.23 (5.23) proj_loss: -0.5769 (-0.5769) time: 0.6709 data: 0.0003 [11-24 06:53:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [ 417/1669] eta: 0:14:07 tlr: 0.00019 tnm: 0.28 Lm: 6.577 (6.577) Lt: 5.826 (5.826) Accm: 3.11 (3.11) Acct: 4.92 (4.92) proj_loss: -0.5740 (-0.5740) time: 0.6709 data: 0.0003 [11-24 06:58:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [ 834/1669] eta: 0:09:23 tlr: 0.00019 tnm: 0.30 Lm: 6.517 (6.557) Lt: 5.729 (5.793) Accm: 3.33 (3.22) Acct: 5.11 (5.17) proj_loss: -0.5770 (-0.5760) time: 0.6722 data: 0.0003 [11-24 06:58:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [ 834/1669] eta: 0:09:23 tlr: 0.00019 tnm: 0.30 Lm: 6.564 (6.554) Lt: 5.784 (5.796) Accm: 3.29 (3.34) Acct: 5.32 (5.35) proj_loss: -0.5780 (-0.5773) time: 0.6722 data: 0.0003 [11-24 06:58:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [ 834/1669] eta: 0:09:23 tlr: 0.00019 tnm: 0.30 Lm: 6.670 (6.653) Lt: 5.957 (5.929) Accm: 3.07 (3.09) Acct: 4.48 (4.67) proj_loss: -0.5814 (-0.5815) time: 0.6722 data: 0.0003 [11-24 06:58:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [ 834/1669] eta: 0:09:23 tlr: 0.00019 tnm: 0.30 Lm: 6.661 (6.648) Lt: 5.937 (5.918) Accm: 3.03 (2.99) Acct: 4.94 (4.72) proj_loss: -0.5788 (-0.5855) time: 0.6722 data: 0.0002 [11-24 07:02:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.28 Lm: 6.664 (6.652) Lt: 5.950 (5.929) Accm: 2.94 (2.96) Acct: 4.72 (4.66) proj_loss: -0.5816 (-0.5852) time: 0.6720 data: 0.0003 [11-24 07:02:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.28 Lm: 6.575 (6.602) Lt: 5.813 (5.844) Accm: 3.26 (3.21) Acct: 5.23 (5.13) proj_loss: -0.5752 (-0.5761) time: 0.6720 data: 0.0003 [11-24 07:02:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.28 Lm: 6.626 (6.616) Lt: 5.884 (5.889) Accm: 3.21 (3.21) Acct: 4.89 (4.83) proj_loss: -0.5787 (-0.5801) time: 0.6720 data: 0.0003 [11-24 07:02:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.28 Lm: 6.598 (6.593) Lt: 5.842 (5.847) Accm: 3.11 (3.04) Acct: 4.92 (4.89) proj_loss: -0.5785 (-0.5806) time: 0.6720 data: 0.0003 [11-24 07:07:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.600 (6.594) Lt: 5.862 (5.850) Accm: 2.88 (2.99) Acct: 4.72 (4.80) proj_loss: -0.5770 (-0.5777) time: 0.6762 data: 0.0016 [11-24 07:07:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 89/350] Total time: 0:18:45 (0.674 s / it) [11-24 07:07:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.661 (6.610) Lt: 5.937 (5.862) Accm: 3.03 (3.10) Acct: 4.94 (4.94) proj_loss: -0.5788 (-0.5779) time: 0.6762 data: 0.0020 [11-24 07:07:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.581 (6.591) Lt: 5.811 (5.858) Accm: 3.35 (3.24) Acct: 5.30 (4.93) proj_loss: -0.5760 (-0.5788) time: 0.6762 data: 0.0016 [11-24 07:07:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 89/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.564 (6.593) Lt: 5.820 (5.839) Accm: 3.23 (3.22) Acct: 5.32 (5.18) proj_loss: -0.5780 (-0.5783) time: 0.6762 data: 0.0020 [11-24 07:07:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 89/350] Total time: 0:18:45 (0.674 s / it) [11-24 07:07:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 89/350] Total time: 0:18:45 (0.674 s / it) [11-24 07:07:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 89/350] Total time: 0:18:45 (0.674 s / it) [11-24 07:09:59] (home/user/VAR/trainer.py, line 114)=> FID: 4.105984780553001 [11-24 07:10:00] (/home/user/VAR/train.py , line 259)=> [*] [ep89] (val 50000) Lm: 6.6033, Lt: 5.8562, Acc m&t: 3.11 4.93, Val cost: 140.09s [11-24 07:10:00] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-24 07:10:36] (/home/user/VAR/train.py , line 276)=> [ep89] (training ) Lm: 6.603 (6.603), Lt: 5.856 (5.856), Acc m&t: 3.11 4.93, Remain: 3 days, 10:08:59, Finish: 2024-11-27 01:16 [11-24 07:10:36] (/home/user/VAR/train.py , line 276)=> [ep89] (training ) Lm: 6.603 (6.603), Lt: 5.856 (5.856), Acc m&t: 3.11 4.93, Remain: 3 days, 10:09:11, Finish: 2024-11-27 01:16 [11-24 07:10:36] (/home/user/VAR/train.py , line 276)=> [ep89] (training ) Lm: 6.603 (6.603), Lt: 5.856 (5.856), Acc m&t: 3.11 4.93, Remain: 3 days, 10:10:02, Finish: 2024-11-27 01:17 [11-24 07:10:36] (/home/user/VAR/train.py , line 276)=> [ep89] (training ) Lm: 6.603 (6.603), Lt: 5.856 (5.856), Acc m&t: 3.11 4.93, Remain: 3 days, 10:09:47, Finish: 2024-11-27 01:17 [11-24 07:10:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [ 0/1669] eta: 0:18:44 tlr: 0.00019 tnm: 0.29 Lm: 6.563 (6.563) Lt: 5.818 (5.818) Accm: 3.31 (3.31) Acct: 5.18 (5.18) proj_loss: -0.5694 (-0.5694) time: 0.6736 data: 0.0004 [11-24 07:10:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [ 0/1669] eta: 0:18:43 tlr: 0.00019 tnm: 0.29 Lm: 6.598 (6.598) Lt: 5.778 (5.778) Accm: 2.96 (2.96) Acct: 4.98 (4.98) proj_loss: -0.5696 (-0.5696) time: 0.6733 data: 0.0003 [11-24 07:10:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [ 0/1669] eta: 0:18:38 tlr: 0.00019 tnm: 0.29 Lm: 6.620 (6.620) Lt: 5.807 (5.807) Accm: 2.83 (2.83) Acct: 4.67 (4.67) proj_loss: -0.5746 (-0.5746) time: 0.6701 data: 0.0004 [11-24 07:10:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [ 0/1669] eta: 0:18:38 tlr: 0.00019 tnm: 0.29 Lm: 6.589 (6.589) Lt: 5.861 (5.861) Accm: 3.29 (3.29) Acct: 5.29 (5.29) proj_loss: -0.5833 (-0.5833) time: 0.6704 data: 0.0004 [11-24 07:15:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [ 417/1669] eta: 0:14:03 tlr: 0.00019 tnm: 0.28 Lm: 6.578 (6.578) Lt: 5.842 (5.842) Accm: 3.29 (3.29) Acct: 5.24 (5.24) proj_loss: -0.5870 (-0.5870) time: 0.6768 data: 0.0003 [11-24 07:15:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [ 417/1669] eta: 0:14:03 tlr: 0.00019 tnm: 0.28 Lm: 6.609 (6.609) Lt: 5.868 (5.868) Accm: 3.05 (3.05) Acct: 4.81 (4.81) proj_loss: -0.5664 (-0.5664) time: 0.6768 data: 0.0003 [11-24 07:15:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [ 417/1669] eta: 0:14:03 tlr: 0.00019 tnm: 0.28 Lm: 6.596 (6.596) Lt: 5.835 (5.835) Accm: 3.00 (3.00) Acct: 4.80 (4.80) proj_loss: -0.5812 (-0.5812) time: 0.6768 data: 0.0002 [11-24 07:15:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [ 417/1669] eta: 0:14:03 tlr: 0.00019 tnm: 0.28 Lm: 6.654 (6.654) Lt: 5.877 (5.877) Accm: 2.87 (2.87) Acct: 4.63 (4.63) proj_loss: -0.5691 (-0.5691) time: 0.6768 data: 0.0003 [11-24 07:19:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [ 834/1669] eta: 0:09:22 tlr: 0.00019 tnm: 0.29 Lm: 6.688 (6.677) Lt: 5.946 (5.933) Accm: 2.83 (2.86) Acct: 4.60 (4.42) proj_loss: -0.5636 (-0.5667) time: 0.6708 data: 0.0003 [11-24 07:19:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [ 834/1669] eta: 0:09:22 tlr: 0.00019 tnm: 0.29 Lm: 6.654 (6.660) Lt: 5.919 (5.931) Accm: 2.79 (2.92) Acct: 4.44 (4.56) proj_loss: -0.5694 (-0.5784) time: 0.6708 data: 0.0003 [11-24 07:19:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [ 834/1669] eta: 0:09:22 tlr: 0.00019 tnm: 0.29 Lm: 6.598 (6.602) Lt: 5.863 (5.844) Accm: 3.03 (3.02) Acct: 4.98 (4.90) proj_loss: -0.5772 (-0.5799) time: 0.6708 data: 0.0003 [11-24 07:19:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [ 834/1669] eta: 0:09:22 tlr: 0.00019 tnm: 0.29 Lm: 6.589 (6.610) Lt: 5.861 (5.876) Accm: 3.29 (3.13) Acct: 5.20 (5.05) proj_loss: -0.5833 (-0.5813) time: 0.6708 data: 0.0003 [11-24 07:24:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.27 Lm: 6.617 (6.619) Lt: 5.853 (5.868) Accm: 3.21 (3.13) Acct: 5.10 (5.04) proj_loss: -0.5870 (-0.5844) time: 0.6733 data: 0.0003 [11-24 07:24:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.27 Lm: 6.607 (6.621) Lt: 5.877 (5.877) Accm: 3.01 (3.01) Acct: 4.89 (4.87) proj_loss: -0.5735 (-0.5774) time: 0.6733 data: 0.0002 [11-24 07:24:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.27 Lm: 6.645 (6.654) Lt: 5.890 (5.914) Accm: 2.84 (2.92) Acct: 4.52 (4.57) proj_loss: -0.5664 (-0.5741) time: 0.6733 data: 0.0003 [11-24 07:24:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.27 Lm: 6.673 (6.672) Lt: 5.896 (5.911) Accm: 2.83 (2.84) Acct: 4.49 (4.41) proj_loss: -0.5663 (-0.5673) time: 0.6733 data: 0.0003 [11-24 07:29:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.28 Lm: 6.659 (6.669) Lt: 5.884 (5.906) Accm: 2.83 (2.87) Acct: 4.60 (4.51) proj_loss: -0.5690 (-0.5716) time: 0.6771 data: 0.0017 [11-24 07:29:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 90/350] Total time: 0:18:44 (0.674 s / it) [11-24 07:29:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.28 Lm: 6.637 (6.640) Lt: 5.861 (5.900) Accm: 2.90 (2.99) Acct: 4.60 (4.72) proj_loss: -0.5694 (-0.5732) time: 0.6771 data: 0.0016 [11-24 07:29:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.28 Lm: 6.615 (6.645) Lt: 5.892 (5.906) Accm: 2.99 (2.97) Acct: 4.80 (4.77) proj_loss: -0.5772 (-0.5808) time: 0.6771 data: 0.0015 [11-24 07:29:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 90/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.28 Lm: 6.589 (6.591) Lt: 5.845 (5.834) Accm: 3.29 (3.21) Acct: 5.20 (5.16) proj_loss: -0.5856 (-0.5846) time: 0.6771 data: 0.0016 [11-24 07:29:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 90/350] Total time: 0:18:44 (0.674 s / it) [11-24 07:29:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 90/350] Total time: 0:18:44 (0.674 s / it) [11-24 07:29:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 90/350] Total time: 0:18:44 (0.674 s / it) [11-24 07:29:21] (/home/user/VAR/train.py , line 276)=> [ep90] (training ) Lm: 6.603 (6.629), Lt: 5.856 (5.878), Acc m&t: 3.11 4.93, Remain: 3 days, 9:38:21, Finish: 2024-11-27 01:07 [11-24 07:29:21] (/home/user/VAR/train.py , line 276)=> [ep90] (training ) Lm: 6.603 (6.629), Lt: 5.856 (5.878), Acc m&t: 3.11 4.93, Remain: 3 days, 9:37:59, Finish: 2024-11-27 01:07 [11-24 07:29:21] (/home/user/VAR/train.py , line 276)=> [ep90] (training ) Lm: 6.603 (6.629), Lt: 5.856 (5.878), Acc m&t: 3.11 4.93, Remain: 3 days, 9:38:11, Finish: 2024-11-27 01:07 [11-24 07:29:21] (/home/user/VAR/train.py , line 276)=> [ep90] (training ) Lm: 6.603 (6.629), Lt: 5.856 (5.878), Acc m&t: 3.11 4.93, Remain: 3 days, 9:39:00, Finish: 2024-11-27 01:08 [11-24 07:29:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [ 0/1669] eta: 0:18:05 tlr: 0.00019 tnm: 0.28 Lm: 6.660 (6.660) Lt: 5.991 (5.991) Accm: 2.75 (2.75) Acct: 4.34 (4.34) proj_loss: -0.5633 (-0.5633) time: 0.6501 data: 0.0003 [11-24 07:29:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [ 0/1669] eta: 0:18:19 tlr: 0.00019 tnm: 0.28 Lm: 6.466 (6.466) Lt: 5.726 (5.726) Accm: 3.41 (3.41) Acct: 5.51 (5.51) proj_loss: -0.5865 (-0.5865) time: 0.6589 data: 0.0004 [11-24 07:29:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [ 0/1669] eta: 0:18:05 tlr: 0.00019 tnm: 0.28 Lm: 6.532 (6.532) Lt: 5.795 (5.795) Accm: 3.10 (3.10) Acct: 4.89 (4.89) proj_loss: -0.5739 (-0.5739) time: 0.6507 data: 0.0004 [11-24 07:29:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [ 0/1669] eta: 0:18:05 tlr: 0.00019 tnm: 0.28 Lm: 6.591 (6.591) Lt: 5.887 (5.887) Accm: 3.20 (3.20) Acct: 5.20 (5.20) proj_loss: -0.5733 (-0.5733) time: 0.6504 data: 0.0003 [11-24 07:34:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [ 417/1669] eta: 0:14:03 tlr: 0.00019 tnm: 0.29 Lm: 6.625 (6.625) Lt: 5.926 (5.926) Accm: 3.15 (3.15) Acct: 5.01 (5.01) proj_loss: -0.5894 (-0.5894) time: 0.6742 data: 0.0003 [11-24 07:34:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [ 417/1669] eta: 0:14:03 tlr: 0.00019 tnm: 0.29 Lm: 6.669 (6.669) Lt: 5.957 (5.957) Accm: 2.83 (2.83) Acct: 4.47 (4.47) proj_loss: -0.5652 (-0.5652) time: 0.6742 data: 0.0002 [11-24 07:34:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [ 417/1669] eta: 0:14:03 tlr: 0.00019 tnm: 0.29 Lm: 6.637 (6.637) Lt: 5.866 (5.866) Accm: 2.95 (2.95) Acct: 4.66 (4.66) proj_loss: -0.5713 (-0.5713) time: 0.6742 data: 0.0003 [11-24 07:34:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [ 417/1669] eta: 0:14:03 tlr: 0.00019 tnm: 0.29 Lm: 6.570 (6.570) Lt: 5.829 (5.829) Accm: 3.29 (3.29) Acct: 5.36 (5.36) proj_loss: -0.5709 (-0.5709) time: 0.6742 data: 0.0003 [11-24 07:38:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [ 834/1669] eta: 0:09:27 tlr: 0.00019 tnm: 0.32 Lm: 6.645 (6.595) Lt: 5.847 (5.835) Accm: 3.16 (3.20) Acct: 5.22 (5.22) proj_loss: -0.5619 (-0.5679) time: 0.6733 data: 0.0003 [11-24 07:38:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [ 834/1669] eta: 0:09:27 tlr: 0.00019 tnm: 0.32 Lm: 6.591 (6.607) Lt: 5.887 (5.891) Accm: 3.11 (3.11) Acct: 4.82 (4.93) proj_loss: -0.5733 (-0.5794) time: 0.6733 data: 0.0003 [11-24 07:38:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [ 834/1669] eta: 0:09:27 tlr: 0.00019 tnm: 0.32 Lm: 6.660 (6.618) Lt: 5.923 (5.897) Accm: 2.91 (2.96) Acct: 4.60 (4.61) proj_loss: -0.5670 (-0.5674) time: 0.6733 data: 0.0002 [11-24 07:38:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [ 834/1669] eta: 0:09:27 tlr: 0.00019 tnm: 0.32 Lm: 6.605 (6.626) Lt: 5.902 (5.878) Accm: 3.10 (3.03) Acct: 4.89 (4.75) proj_loss: -0.5739 (-0.5808) time: 0.6733 data: 0.0003 [11-24 07:43:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [1251/1669] eta: 0:04:43 tlr: 0.00019 tnm: 0.27 Lm: 6.604 (6.620) Lt: 5.874 (5.870) Accm: 3.14 (3.09) Acct: 4.85 (4.76) proj_loss: -0.5713 (-0.5753) time: 0.6763 data: 0.0003 [11-24 07:43:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [1251/1669] eta: 0:04:43 tlr: 0.00019 tnm: 0.27 Lm: 6.559 (6.565) Lt: 5.787 (5.797) Accm: 3.29 (3.27) Acct: 5.35 (5.29) proj_loss: -0.5674 (-0.5691) time: 0.6763 data: 0.0003 [11-24 07:43:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [1251/1669] eta: 0:04:43 tlr: 0.00019 tnm: 0.27 Lm: 6.669 (6.668) Lt: 5.957 (5.962) Accm: 2.83 (2.83) Acct: 4.47 (4.41) proj_loss: -0.5695 (-0.5734) time: 0.6763 data: 0.0003 [11-24 07:43:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [1251/1669] eta: 0:04:43 tlr: 0.00019 tnm: 0.27 Lm: 6.584 (6.599) Lt: 5.854 (5.866) Accm: 3.15 (3.15) Acct: 4.93 (4.96) proj_loss: -0.5744 (-0.5784) time: 0.6763 data: 0.0003 [11-24 07:48:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.27 Lm: 6.591 (6.605) Lt: 5.857 (5.864) Accm: 3.11 (3.13) Acct: 4.86 (4.94) proj_loss: -0.5750 (-0.5777) time: 0.6772 data: 0.0015 [11-24 07:48:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 91/350] Total time: 0:18:51 (0.678 s / it) [11-24 07:48:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.27 Lm: 6.678 (6.673) Lt: 5.953 (5.960) Accm: 2.91 (2.85) Acct: 4.60 (4.45) proj_loss: -0.5719 (-0.5736) time: 0.6772 data: 0.0016 [11-24 07:48:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.27 Lm: 6.645 (6.585) Lt: 5.847 (5.815) Accm: 3.16 (3.18) Acct: 5.22 (5.15) proj_loss: -0.5706 (-0.5694) time: 0.6772 data: 0.0019 [11-24 07:48:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 91/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.27 Lm: 6.603 (6.589) Lt: 5.846 (5.832) Accm: 3.19 (3.22) Acct: 4.89 (5.02) proj_loss: -0.5739 (-0.5778) time: 0.6773 data: 0.0018 [11-24 07:48:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 91/350] Total time: 0:18:51 (0.678 s / it) [11-24 07:48:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 91/350] Total time: 0:18:51 (0.678 s / it) [11-24 07:48:12] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 91/350] Total time: 0:18:51 (0.678 s / it) [11-24 07:48:12] (/home/user/VAR/train.py , line 276)=> [ep91] (training ) Lm: 6.603 (6.619), Lt: 5.856 (5.868), Acc m&t: 3.11 4.93, Remain: 3 days, 9:41:19, Finish: 2024-11-27 01:29 [11-24 07:48:12] (/home/user/VAR/train.py , line 276)=> [ep91] (training ) Lm: 6.603 (6.619), Lt: 5.856 (5.868), Acc m&t: 3.11 4.93, Remain: 3 days, 9:41:29, Finish: 2024-11-27 01:29 [11-24 07:48:12] (/home/user/VAR/train.py , line 276)=> [ep91] (training ) Lm: 6.603 (6.619), Lt: 5.856 (5.868), Acc m&t: 3.11 4.93, Remain: 3 days, 9:42:04, Finish: 2024-11-27 01:30 [11-24 07:48:12] (/home/user/VAR/train.py , line 276)=> [ep91] (training ) Lm: 6.603 (6.619), Lt: 5.856 (5.868), Acc m&t: 3.11 4.93, Remain: 3 days, 9:41:19, Finish: 2024-11-27 01:29 [11-24 07:48:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [ 0/1669] eta: 0:18:07 tlr: 0.00019 tnm: 0.28 Lm: 6.519 (6.519) Lt: 5.752 (5.752) Accm: 3.36 (3.36) Acct: 5.32 (5.32) proj_loss: -0.5846 (-0.5846) time: 0.6513 data: 0.0003 [11-24 07:48:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [ 0/1669] eta: 0:18:06 tlr: 0.00019 tnm: 0.28 Lm: 6.742 (6.742) Lt: 6.038 (6.038) Accm: 2.35 (2.35) Acct: 3.65 (3.65) proj_loss: -0.5850 (-0.5850) time: 0.6512 data: 0.0003 [11-24 07:48:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [ 0/1669] eta: 0:18:07 tlr: 0.00019 tnm: 0.28 Lm: 6.584 (6.584) Lt: 5.842 (5.842) Accm: 3.19 (3.19) Acct: 5.13 (5.13) proj_loss: -0.5777 (-0.5777) time: 0.6516 data: 0.0003 [11-24 07:48:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [ 0/1669] eta: 0:18:07 tlr: 0.00019 tnm: 0.28 Lm: 6.625 (6.625) Lt: 5.870 (5.870) Accm: 3.15 (3.15) Acct: 4.86 (4.86) proj_loss: -0.5853 (-0.5853) time: 0.6516 data: 0.0003 [11-24 07:52:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [ 417/1669] eta: 0:14:04 tlr: 0.00019 tnm: 0.30 Lm: 6.622 (6.622) Lt: 5.863 (5.863) Accm: 3.18 (3.18) Acct: 4.98 (4.98) proj_loss: -0.5808 (-0.5808) time: 0.6742 data: 0.0003 [11-24 07:52:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [ 417/1669] eta: 0:14:04 tlr: 0.00019 tnm: 0.30 Lm: 6.694 (6.694) Lt: 5.977 (5.977) Accm: 2.52 (2.52) Acct: 3.99 (3.99) proj_loss: -0.5884 (-0.5884) time: 0.6742 data: 0.0003 [11-24 07:52:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [ 417/1669] eta: 0:14:04 tlr: 0.00019 tnm: 0.30 Lm: 6.631 (6.631) Lt: 5.886 (5.886) Accm: 3.02 (3.02) Acct: 4.78 (4.78) proj_loss: -0.5893 (-0.5893) time: 0.6742 data: 0.0003 [11-24 07:52:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [ 417/1669] eta: 0:14:04 tlr: 0.00019 tnm: 0.30 Lm: 6.656 (6.656) Lt: 5.929 (5.929) Accm: 2.98 (2.98) Acct: 4.86 (4.86) proj_loss: -0.5798 (-0.5798) time: 0.6742 data: 0.0002 [11-24 07:57:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [ 834/1669] eta: 0:09:22 tlr: 0.00019 tnm: 0.30 Lm: 6.584 (6.623) Lt: 5.842 (5.892) Accm: 3.18 (3.05) Acct: 4.99 (4.91) proj_loss: -0.5777 (-0.5773) time: 0.6751 data: 0.0003 [11-24 07:57:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [ 834/1669] eta: 0:09:22 tlr: 0.00019 tnm: 0.30 Lm: 6.619 (6.606) Lt: 5.857 (5.841) Accm: 3.21 (3.25) Acct: 5.10 (5.13) proj_loss: -0.5763 (-0.5721) time: 0.6750 data: 0.0003 [11-24 07:57:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [ 834/1669] eta: 0:09:22 tlr: 0.00019 tnm: 0.30 Lm: 6.598 (6.620) Lt: 5.829 (5.867) Accm: 3.04 (3.03) Acct: 4.89 (4.82) proj_loss: -0.5846 (-0.5829) time: 0.6751 data: 0.0003 [11-24 07:57:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [ 834/1669] eta: 0:09:22 tlr: 0.00019 tnm: 0.30 Lm: 6.724 (6.704) Lt: 5.949 (5.968) Accm: 2.69 (2.70) Acct: 4.34 (4.26) proj_loss: -0.5850 (-0.5811) time: 0.6751 data: 0.0003 [11-24 08:02:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.30 Lm: 6.700 (6.697) Lt: 5.963 (5.970) Accm: 2.68 (2.69) Acct: 4.35 (4.29) proj_loss: -0.5829 (-0.5810) time: 0.6774 data: 0.0003 [11-24 08:02:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.30 Lm: 6.574 (6.602) Lt: 5.800 (5.843) Accm: 3.12 (3.07) Acct: 4.98 (4.88) proj_loss: -0.5804 (-0.5812) time: 0.6774 data: 0.0003 [11-24 08:02:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.30 Lm: 6.622 (6.613) Lt: 5.863 (5.848) Accm: 3.18 (3.22) Acct: 5.04 (5.10) proj_loss: -0.5739 (-0.5720) time: 0.6774 data: 0.0003 [11-24 08:02:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [1251/1669] eta: 0:04:41 tlr: 0.00019 tnm: 0.30 Lm: 6.646 (6.644) Lt: 5.906 (5.911) Accm: 2.98 (2.92) Acct: 4.80 (4.72) proj_loss: -0.5750 (-0.5755) time: 0.6774 data: 0.0002 [11-24 08:07:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.30 Lm: 6.584 (6.619) Lt: 5.842 (5.890) Accm: 3.18 (3.00) Acct: 4.99 (4.78) proj_loss: -0.5777 (-0.5765) time: 0.7416 data: 0.0020 [11-24 08:07:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 92/350] Total time: 0:18:51 (0.678 s / it) [11-24 08:07:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.30 Lm: 6.598 (6.614) Lt: 5.829 (5.852) Accm: 3.04 (3.03) Acct: 4.89 (4.85) proj_loss: -0.5846 (-0.5833) time: 0.7416 data: 0.0015 [11-24 08:07:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.30 Lm: 6.708 (6.699) Lt: 5.953 (5.967) Accm: 2.69 (2.74) Acct: 4.36 (4.35) proj_loss: -0.5808 (-0.5765) time: 0.7416 data: 0.0016 [11-24 08:07:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 92/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.30 Lm: 6.619 (6.599) Lt: 5.857 (5.825) Accm: 3.15 (3.20) Acct: 4.99 (5.06) proj_loss: -0.5763 (-0.5771) time: 0.7416 data: 0.0020 [11-24 08:07:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 92/350] Total time: 0:18:51 (0.678 s / it) [11-24 08:07:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 92/350] Total time: 0:18:51 (0.678 s / it) [11-24 08:07:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 92/350] Total time: 0:18:51 (0.678 s / it) [11-24 08:07:04] (/home/user/VAR/train.py , line 276)=> [ep92] (training ) Lm: 6.603 (6.614), Lt: 5.856 (5.865), Acc m&t: 3.11 4.93, Remain: 3 days, 9:16:42, Finish: 2024-11-27 01:23 [11-24 08:07:04] (/home/user/VAR/train.py , line 276)=> [ep92] (training ) Lm: 6.603 (6.614), Lt: 5.856 (5.865), Acc m&t: 3.11 4.93, Remain: 3 days, 9:17:51, Finish: 2024-11-27 01:24 [11-24 08:07:04] (/home/user/VAR/train.py , line 276)=> [ep92] (training ) Lm: 6.603 (6.614), Lt: 5.856 (5.865), Acc m&t: 3.11 4.93, Remain: 3 days, 9:16:23, Finish: 2024-11-27 01:23 [11-24 08:07:04] (/home/user/VAR/train.py , line 276)=> [ep92] (training ) Lm: 6.603 (6.614), Lt: 5.856 (5.865), Acc m&t: 3.11 4.93, Remain: 3 days, 9:17:04, Finish: 2024-11-27 01:24 [11-24 08:07:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [ 0/1669] eta: 0:18:16 tlr: 0.00019 tnm: 0.29 Lm: 6.634 (6.634) Lt: 5.933 (5.933) Accm: 2.99 (2.99) Acct: 4.63 (4.63) proj_loss: -0.5747 (-0.5747) time: 0.6573 data: 0.0003 [11-24 08:07:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [ 0/1669] eta: 0:18:13 tlr: 0.00019 tnm: 0.29 Lm: 6.787 (6.787) Lt: 6.056 (6.056) Accm: 2.84 (2.84) Acct: 4.61 (4.61) proj_loss: -0.5675 (-0.5675) time: 0.6554 data: 0.0004 [11-24 08:07:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [ 0/1669] eta: 0:18:18 tlr: 0.00019 tnm: 0.29 Lm: 6.673 (6.673) Lt: 5.927 (5.927) Accm: 3.02 (3.02) Acct: 4.99 (4.99) proj_loss: -0.5667 (-0.5667) time: 0.6581 data: 0.0004 [11-24 08:07:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [ 0/1669] eta: 0:18:19 tlr: 0.00019 tnm: 0.29 Lm: 6.467 (6.467) Lt: 5.688 (5.688) Accm: 3.77 (3.77) Acct: 5.84 (5.84) proj_loss: -0.5824 (-0.5824) time: 0.6586 data: 0.0003 [11-24 08:11:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [ 417/1669] eta: 0:14:21 tlr: 0.00019 tnm: 0.27 Lm: 6.545 (6.545) Lt: 5.781 (5.781) Accm: 3.46 (3.46) Acct: 5.48 (5.48) proj_loss: -0.5766 (-0.5766) time: 0.6750 data: 0.0003 [11-24 08:11:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [ 417/1669] eta: 0:14:21 tlr: 0.00019 tnm: 0.27 Lm: 6.743 (6.743) Lt: 6.013 (6.013) Accm: 2.92 (2.92) Acct: 4.67 (4.67) proj_loss: -0.5636 (-0.5636) time: 0.6750 data: 0.0003 [11-24 08:11:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [ 417/1669] eta: 0:14:21 tlr: 0.00019 tnm: 0.27 Lm: 6.639 (6.639) Lt: 5.890 (5.890) Accm: 2.99 (2.99) Acct: 4.93 (4.93) proj_loss: -0.5747 (-0.5747) time: 0.6750 data: 0.0003 [11-24 08:11:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [ 417/1669] eta: 0:14:21 tlr: 0.00019 tnm: 0.27 Lm: 6.637 (6.637) Lt: 5.900 (5.900) Accm: 3.11 (3.11) Acct: 5.05 (5.05) proj_loss: -0.5757 (-0.5757) time: 0.6750 data: 0.0002 [11-24 08:16:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [ 834/1669] eta: 0:09:29 tlr: 0.00019 tnm: 0.28 Lm: 6.632 (6.635) Lt: 5.874 (5.891) Accm: 3.02 (3.02) Acct: 4.99 (4.86) proj_loss: -0.5823 (-0.5779) time: 0.6741 data: 0.0003 [11-24 08:16:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [ 834/1669] eta: 0:09:29 tlr: 0.00019 tnm: 0.28 Lm: 6.634 (6.623) Lt: 5.860 (5.880) Accm: 2.99 (3.08) Acct: 5.18 (5.02) proj_loss: -0.5747 (-0.5711) time: 0.6741 data: 0.0003 [11-24 08:16:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [ 834/1669] eta: 0:09:29 tlr: 0.00019 tnm: 0.28 Lm: 6.467 (6.514) Lt: 5.739 (5.767) Accm: 3.38 (3.44) Acct: 5.13 (5.33) proj_loss: -0.5824 (-0.5798) time: 0.6741 data: 0.0003 [11-24 08:16:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [ 834/1669] eta: 0:09:29 tlr: 0.00019 tnm: 0.28 Lm: 6.751 (6.746) Lt: 5.996 (6.007) Accm: 2.84 (2.87) Acct: 4.61 (4.51) proj_loss: -0.5675 (-0.5651) time: 0.6741 data: 0.0003 [11-24 08:21:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [1251/1669] eta: 0:04:43 tlr: 0.00019 tnm: 0.28 Lm: 6.725 (6.717) Lt: 5.983 (5.972) Accm: 2.91 (2.90) Acct: 4.67 (4.66) proj_loss: -0.5679 (-0.5707) time: 0.6749 data: 0.0003 [11-24 08:21:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [1251/1669] eta: 0:04:43 tlr: 0.00019 tnm: 0.28 Lm: 6.613 (6.610) Lt: 5.853 (5.852) Accm: 3.13 (3.15) Acct: 5.21 (5.09) proj_loss: -0.5747 (-0.5757) time: 0.6749 data: 0.0002 [11-24 08:21:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [1251/1669] eta: 0:04:43 tlr: 0.00019 tnm: 0.28 Lm: 6.538 (6.538) Lt: 5.778 (5.780) Accm: 3.35 (3.41) Acct: 5.37 (5.40) proj_loss: -0.5793 (-0.5788) time: 0.6749 data: 0.0003 [11-24 08:21:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [1251/1669] eta: 0:04:43 tlr: 0.00019 tnm: 0.28 Lm: 6.616 (6.613) Lt: 5.873 (5.857) Accm: 3.10 (3.06) Acct: 4.88 (4.83) proj_loss: -0.5830 (-0.5793) time: 0.6749 data: 0.0003 [11-24 08:25:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.601 (6.592) Lt: 5.873 (5.835) Accm: 3.19 (3.15) Acct: 4.99 (4.97) proj_loss: -0.5837 (-0.5818) time: 0.6779 data: 0.0018 [11-24 08:25:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 93/350] Total time: 0:18:52 (0.678 s / it) [11-24 08:25:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.698 (6.702) Lt: 5.971 (5.951) Accm: 2.99 (2.94) Acct: 4.73 (4.74) proj_loss: -0.5683 (-0.5722) time: 0.6779 data: 0.0016 [11-24 08:25:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.629 (6.614) Lt: 5.860 (5.857) Accm: 2.99 (3.09) Acct: 5.18 (4.98) proj_loss: -0.5747 (-0.5728) time: 0.6779 data: 0.0015 [11-24 08:25:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 93/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.479 (6.526) Lt: 5.739 (5.763) Accm: 3.33 (3.38) Acct: 5.27 (5.38) proj_loss: -0.5824 (-0.5799) time: 0.6779 data: 0.0016 [11-24 08:25:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 93/350] Total time: 0:18:52 (0.678 s / it) [11-24 08:25:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 93/350] Total time: 0:18:52 (0.678 s / it) [11-24 08:25:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 93/350] Total time: 0:18:52 (0.678 s / it) [11-24 08:25:56] (/home/user/VAR/train.py , line 276)=> [ep93] (training ) Lm: 6.603 (6.624), Lt: 5.856 (5.873), Acc m&t: 3.11 4.93, Remain: 3 days, 8:58:56, Finish: 2024-11-27 01:24 [11-24 08:25:56] (/home/user/VAR/train.py , line 276)=> [ep93] (training ) Lm: 6.603 (6.624), Lt: 5.856 (5.873), Acc m&t: 3.11 4.93, Remain: 3 days, 8:59:13, Finish: 2024-11-27 01:25 [11-24 08:25:56] (/home/user/VAR/train.py , line 276)=> [ep93] (training ) Lm: 6.603 (6.624), Lt: 5.856 (5.873), Acc m&t: 3.11 4.93, Remain: 3 days, 9:00:02, Finish: 2024-11-27 01:25 [11-24 08:25:56] (/home/user/VAR/train.py , line 276)=> [ep93] (training ) Lm: 6.603 (6.624), Lt: 5.856 (5.873), Acc m&t: 3.11 4.93, Remain: 3 days, 8:58:45, Finish: 2024-11-27 01:24 [11-24 08:25:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [ 0/1669] eta: 0:18:09 tlr: 0.00019 tnm: 0.31 Lm: 6.588 (6.588) Lt: 5.838 (5.838) Accm: 3.17 (3.17) Acct: 4.80 (4.80) proj_loss: -0.5812 (-0.5812) time: 0.6528 data: 0.0003 [11-24 08:25:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [ 0/1669] eta: 0:18:10 tlr: 0.00019 tnm: 0.31 Lm: 6.745 (6.745) Lt: 6.020 (6.020) Accm: 2.86 (2.86) Acct: 4.92 (4.92) proj_loss: -0.5655 (-0.5655) time: 0.6531 data: 0.0004 [11-24 08:25:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [ 0/1669] eta: 0:18:10 tlr: 0.00019 tnm: 0.31 Lm: 6.712 (6.712) Lt: 6.004 (6.004) Accm: 2.75 (2.75) Acct: 4.42 (4.42) proj_loss: -0.5646 (-0.5646) time: 0.6533 data: 0.0004 [11-24 08:25:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [ 0/1669] eta: 0:18:11 tlr: 0.00019 tnm: 0.31 Lm: 6.644 (6.644) Lt: 5.912 (5.912) Accm: 2.94 (2.94) Acct: 4.60 (4.60) proj_loss: -0.5828 (-0.5828) time: 0.6538 data: 0.0004 [11-24 08:30:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [ 417/1669] eta: 0:14:05 tlr: 0.00019 tnm: 0.27 Lm: 6.648 (6.648) Lt: 5.936 (5.936) Accm: 2.85 (2.85) Acct: 4.48 (4.48) proj_loss: -0.5938 (-0.5938) time: 0.6752 data: 0.0003 [11-24 08:30:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [ 417/1669] eta: 0:14:05 tlr: 0.00019 tnm: 0.27 Lm: 6.611 (6.611) Lt: 5.876 (5.876) Accm: 3.14 (3.14) Acct: 5.01 (5.01) proj_loss: -0.5669 (-0.5669) time: 0.6752 data: 0.0003 [11-24 08:30:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [ 417/1669] eta: 0:14:05 tlr: 0.00019 tnm: 0.27 Lm: 6.657 (6.657) Lt: 5.930 (5.930) Accm: 3.02 (3.02) Acct: 4.67 (4.67) proj_loss: -0.5803 (-0.5803) time: 0.6752 data: 0.0003 [11-24 08:30:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [ 417/1669] eta: 0:14:05 tlr: 0.00019 tnm: 0.27 Lm: 6.690 (6.690) Lt: 5.955 (5.955) Accm: 2.78 (2.78) Acct: 4.52 (4.52) proj_loss: -0.5745 (-0.5745) time: 0.6752 data: 0.0003 [11-24 08:35:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [ 834/1669] eta: 0:09:33 tlr: 0.00019 tnm: 0.31 Lm: 6.712 (6.707) Lt: 5.982 (5.964) Accm: 2.75 (2.72) Acct: 4.42 (4.37) proj_loss: -0.5646 (-0.5673) time: 0.6773 data: 0.0003 [11-24 08:35:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [ 834/1669] eta: 0:09:33 tlr: 0.00019 tnm: 0.31 Lm: 6.682 (6.635) Lt: 5.936 (5.896) Accm: 2.86 (3.01) Acct: 4.92 (4.84) proj_loss: -0.5683 (-0.5723) time: 0.6773 data: 0.0003 [11-24 08:35:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [ 834/1669] eta: 0:09:33 tlr: 0.00019 tnm: 0.31 Lm: 6.589 (6.635) Lt: 5.838 (5.899) Accm: 3.17 (3.10) Acct: 4.80 (4.80) proj_loss: -0.5812 (-0.5828) time: 0.6773 data: 0.0003 [11-24 08:35:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [ 834/1669] eta: 0:09:33 tlr: 0.00019 tnm: 0.31 Lm: 6.653 (6.659) Lt: 5.938 (5.937) Accm: 2.76 (2.77) Acct: 4.36 (4.34) proj_loss: -0.5847 (-0.5908) time: 0.6773 data: 0.0003 [11-24 08:40:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [1251/1669] eta: 0:04:46 tlr: 0.00019 tnm: 0.28 Lm: 6.648 (6.638) Lt: 5.925 (5.916) Accm: 2.85 (2.83) Acct: 4.48 (4.49) proj_loss: -0.5869 (-0.5904) time: 0.6776 data: 0.0003 [11-24 08:40:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [1251/1669] eta: 0:04:46 tlr: 0.00019 tnm: 0.28 Lm: 6.690 (6.643) Lt: 5.944 (5.902) Accm: 2.78 (3.01) Acct: 4.52 (4.78) proj_loss: -0.5745 (-0.5761) time: 0.6776 data: 0.0003 [11-24 08:40:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [1251/1669] eta: 0:04:46 tlr: 0.00019 tnm: 0.28 Lm: 6.588 (6.622) Lt: 5.837 (5.868) Accm: 3.21 (3.14) Acct: 4.93 (4.94) proj_loss: -0.5803 (-0.5784) time: 0.6776 data: 0.0003 [11-24 08:40:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [1251/1669] eta: 0:04:46 tlr: 0.00019 tnm: 0.28 Lm: 6.600 (6.605) Lt: 5.833 (5.848) Accm: 3.00 (3.05) Acct: 4.95 (4.87) proj_loss: -0.5669 (-0.5684) time: 0.6776 data: 0.0003 [11-24 08:44:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.28 Lm: 6.582 (6.601) Lt: 5.863 (5.851) Accm: 3.15 (3.09) Acct: 4.98 (4.94) proj_loss: -0.5683 (-0.5691) time: 0.6790 data: 0.0019 [11-24 08:44:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 94/350] Total time: 0:19:00 (0.683 s / it) [11-24 08:44:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.28 Lm: 6.668 (6.640) Lt: 5.906 (5.895) Accm: 2.81 (3.07) Acct: 4.61 (4.94) proj_loss: -0.5844 (-0.5781) time: 0.6791 data: 0.0018 [11-24 08:44:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.28 Lm: 6.644 (6.618) Lt: 5.912 (5.894) Accm: 2.94 (2.91) Acct: 4.60 (4.55) proj_loss: -0.5847 (-0.5885) time: 0.6791 data: 0.0022 [11-24 08:44:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 94/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.28 Lm: 6.589 (6.641) Lt: 5.838 (5.897) Accm: 3.17 (3.08) Acct: 4.80 (4.85) proj_loss: -0.5812 (-0.5798) time: 0.6791 data: 0.0016 [11-24 08:44:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 94/350] Total time: 0:19:00 (0.683 s / it) [11-24 08:44:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 94/350] Total time: 0:19:00 (0.683 s / it) [11-24 08:44:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 94/350] Total time: 0:19:00 (0.683 s / it) [11-24 08:44:57] (/home/user/VAR/train.py , line 276)=> [ep94] (training ) Lm: 6.603 (6.608), Lt: 5.856 (5.859), Acc m&t: 3.11 4.93, Remain: 3 days, 8:45:28, Finish: 2024-11-27 01:30 [11-24 08:44:57] (/home/user/VAR/train.py , line 276)=> [ep94] (training ) Lm: 6.603 (6.608), Lt: 5.856 (5.859), Acc m&t: 3.11 4.93, Remain: 3 days, 8:47:22, Finish: 2024-11-27 01:32 [11-24 08:44:57] (/home/user/VAR/train.py , line 276)=> [ep94] (training ) Lm: 6.603 (6.608), Lt: 5.856 (5.859), Acc m&t: 3.11 4.93, Remain: 3 days, 8:48:43, Finish: 2024-11-27 01:33 [11-24 08:44:57] (/home/user/VAR/train.py , line 276)=> [ep94] (training ) Lm: 6.603 (6.608), Lt: 5.856 (5.859), Acc m&t: 3.11 4.93, Remain: 3 days, 8:47:02, Finish: 2024-11-27 01:31 [11-24 08:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [ 0/1669] eta: 0:18:36 tlr: 0.00019 tnm: 0.30 Lm: 6.682 (6.682) Lt: 5.980 (5.980) Accm: 3.01 (3.01) Acct: 4.72 (4.72) proj_loss: -0.5905 (-0.5905) time: 0.6689 data: 0.0004 [11-24 08:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [ 0/1669] eta: 0:18:38 tlr: 0.00019 tnm: 0.30 Lm: 6.623 (6.623) Lt: 5.901 (5.901) Accm: 3.02 (3.02) Acct: 4.63 (4.63) proj_loss: -0.5738 (-0.5738) time: 0.6700 data: 0.0004 [11-24 08:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [ 0/1669] eta: 0:18:38 tlr: 0.00019 tnm: 0.30 Lm: 6.604 (6.604) Lt: 5.848 (5.848) Accm: 2.95 (2.95) Acct: 4.48 (4.48) proj_loss: -0.5918 (-0.5918) time: 0.6704 data: 0.0003 [11-24 08:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [ 0/1669] eta: 0:18:39 tlr: 0.00019 tnm: 0.30 Lm: 6.632 (6.632) Lt: 5.885 (5.885) Accm: 3.06 (3.06) Acct: 4.82 (4.82) proj_loss: -0.5779 (-0.5779) time: 0.6706 data: 0.0004 [11-24 08:49:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [ 417/1669] eta: 0:14:06 tlr: 0.00019 tnm: 0.28 Lm: 6.571 (6.571) Lt: 5.832 (5.832) Accm: 3.26 (3.26) Acct: 5.00 (5.00) proj_loss: -0.5775 (-0.5775) time: 0.6789 data: 0.0003 [11-24 08:49:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [ 417/1669] eta: 0:14:06 tlr: 0.00019 tnm: 0.28 Lm: 6.736 (6.736) Lt: 5.987 (5.987) Accm: 2.80 (2.80) Acct: 4.56 (4.56) proj_loss: -0.5729 (-0.5729) time: 0.6789 data: 0.0003 [11-24 08:49:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [ 417/1669] eta: 0:14:06 tlr: 0.00019 tnm: 0.28 Lm: 6.631 (6.631) Lt: 5.902 (5.902) Accm: 3.00 (3.00) Acct: 4.57 (4.57) proj_loss: -0.5822 (-0.5822) time: 0.6789 data: 0.0003 [11-24 08:49:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [ 417/1669] eta: 0:14:06 tlr: 0.00019 tnm: 0.28 Lm: 6.564 (6.564) Lt: 5.815 (5.815) Accm: 3.17 (3.17) Acct: 4.89 (4.89) proj_loss: -0.5802 (-0.5802) time: 0.6788 data: 0.0003 [11-24 08:54:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [ 834/1669] eta: 0:09:24 tlr: 0.00019 tnm: 0.28 Lm: 6.604 (6.583) Lt: 5.848 (5.842) Accm: 3.24 (3.19) Acct: 5.10 (4.96) proj_loss: -0.5918 (-0.5849) time: 0.6759 data: 0.0005 [11-24 08:54:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [ 834/1669] eta: 0:09:24 tlr: 0.00019 tnm: 0.28 Lm: 6.682 (6.698) Lt: 5.980 (5.969) Accm: 3.01 (3.03) Acct: 4.72 (4.78) proj_loss: -0.5905 (-0.5833) time: 0.6759 data: 0.0003 [11-24 08:54:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [ 834/1669] eta: 0:09:24 tlr: 0.00019 tnm: 0.28 Lm: 6.510 (6.533) Lt: 5.779 (5.788) Accm: 3.42 (3.31) Acct: 5.18 (5.19) proj_loss: -0.5779 (-0.5793) time: 0.6759 data: 0.0003 [11-24 08:54:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [ 834/1669] eta: 0:09:24 tlr: 0.00019 tnm: 0.28 Lm: 6.639 (6.651) Lt: 5.903 (5.915) Accm: 2.98 (2.98) Acct: 4.63 (4.62) proj_loss: -0.5838 (-0.5828) time: 0.6759 data: 0.0003 [11-24 08:59:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [1251/1669] eta: 0:04:42 tlr: 0.00019 tnm: 0.27 Lm: 6.631 (6.629) Lt: 5.902 (5.893) Accm: 3.00 (3.02) Acct: 4.67 (4.68) proj_loss: -0.5833 (-0.5828) time: 0.6776 data: 0.0002 [11-24 08:59:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [1251/1669] eta: 0:04:42 tlr: 0.00019 tnm: 0.27 Lm: 6.653 (6.654) Lt: 5.956 (5.922) Accm: 3.24 (3.18) Acct: 4.97 (5.00) proj_loss: -0.5856 (-0.5827) time: 0.6776 data: 0.0003 [11-24 08:59:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [1251/1669] eta: 0:04:42 tlr: 0.00019 tnm: 0.27 Lm: 6.483 (6.504) Lt: 5.740 (5.746) Accm: 3.44 (3.44) Acct: 5.37 (5.40) proj_loss: -0.5804 (-0.5825) time: 0.6776 data: 0.0003 [11-24 08:59:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [1251/1669] eta: 0:04:42 tlr: 0.00019 tnm: 0.27 Lm: 6.612 (6.628) Lt: 5.872 (5.881) Accm: 3.10 (3.14) Acct: 4.96 (4.92) proj_loss: -0.5802 (-0.5778) time: 0.6776 data: 0.0003 [11-24 09:03:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.623 (6.616) Lt: 5.901 (5.866) Accm: 3.02 (3.08) Acct: 4.72 (4.84) proj_loss: -0.5827 (-0.5816) time: 0.7452 data: 0.0019 [11-24 09:03:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.623 (6.626) Lt: 5.932 (5.897) Accm: 3.26 (3.19) Acct: 5.04 (5.01) proj_loss: -0.5862 (-0.5834) time: 0.7452 data: 0.0016 [11-24 09:03:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.510 (6.556) Lt: 5.779 (5.806) Accm: 3.42 (3.30) Acct: 5.18 (5.14) proj_loss: -0.5829 (-0.5828) time: 0.7452 data: 0.0017 [11-24 09:03:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 95/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.604 (6.614) Lt: 5.848 (5.870) Accm: 3.15 (3.14) Acct: 4.84 (4.91) proj_loss: -0.5765 (-0.5775) time: 0.7453 data: 0.0018 [11-24 09:03:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 95/350] Total time: 0:18:52 (0.679 s / it) [11-24 09:03:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 95/350] Total time: 0:18:52 (0.679 s / it) [11-24 09:03:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 95/350] Total time: 0:18:52 (0.679 s / it) [11-24 09:03:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 95/350] Total time: 0:18:52 (0.679 s / it) [11-24 09:03:50] (/home/user/VAR/train.py , line 276)=> [ep95] (training ) Lm: 6.603 (6.626), Lt: 5.856 (5.882), Acc m&t: 3.11 4.93, Remain: 3 days, 8:37:40, Finish: 2024-11-27 01:41 [11-24 09:03:50] (/home/user/VAR/train.py , line 276)=> [ep95] (training ) Lm: 6.603 (6.626), Lt: 5.856 (5.882), Acc m&t: 3.11 4.93, Remain: 3 days, 8:37:33, Finish: 2024-11-27 01:41 [11-24 09:03:50] (/home/user/VAR/train.py , line 276)=> [ep95] (training ) Lm: 6.603 (6.626), Lt: 5.856 (5.882), Acc m&t: 3.11 4.93, Remain: 3 days, 8:37:17, Finish: 2024-11-27 01:41 [11-24 09:03:50] (/home/user/VAR/train.py , line 276)=> [ep95] (training ) Lm: 6.603 (6.626), Lt: 5.856 (5.882), Acc m&t: 3.11 4.93, Remain: 3 days, 8:36:54, Finish: 2024-11-27 01:40 [11-24 09:03:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [ 0/1669] eta: 0:18:38 tlr: 0.00019 tnm: 0.28 Lm: 6.600 (6.600) Lt: 5.849 (5.849) Accm: 2.84 (2.84) Acct: 4.67 (4.67) proj_loss: -0.5843 (-0.5843) time: 0.6702 data: 0.0004 [11-24 09:03:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [ 0/1669] eta: 0:18:38 tlr: 0.00019 tnm: 0.28 Lm: 6.740 (6.740) Lt: 5.975 (5.975) Accm: 2.55 (2.55) Acct: 4.20 (4.20) proj_loss: -0.5915 (-0.5915) time: 0.6702 data: 0.0004 [11-24 09:03:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [ 0/1669] eta: 0:18:39 tlr: 0.00019 tnm: 0.28 Lm: 6.653 (6.653) Lt: 5.922 (5.922) Accm: 3.01 (3.01) Acct: 4.91 (4.91) proj_loss: -0.5917 (-0.5917) time: 0.6707 data: 0.0003 [11-24 09:03:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [ 0/1669] eta: 0:18:39 tlr: 0.00019 tnm: 0.28 Lm: 6.682 (6.682) Lt: 5.973 (5.973) Accm: 2.99 (2.99) Acct: 4.56 (4.56) proj_loss: -0.5839 (-0.5839) time: 0.6705 data: 0.0004 [11-24 09:08:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [ 417/1669] eta: 0:14:38 tlr: 0.00019 tnm: 0.28 Lm: 6.625 (6.625) Lt: 5.925 (5.925) Accm: 3.14 (3.14) Acct: 4.69 (4.69) proj_loss: -0.5884 (-0.5884) time: 0.6771 data: 0.0003 [11-24 09:08:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [ 417/1669] eta: 0:14:38 tlr: 0.00019 tnm: 0.28 Lm: 6.600 (6.600) Lt: 5.864 (5.864) Accm: 3.12 (3.12) Acct: 4.92 (4.92) proj_loss: -0.5836 (-0.5836) time: 0.6771 data: 0.0003 [11-24 09:08:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [ 417/1669] eta: 0:14:38 tlr: 0.00019 tnm: 0.28 Lm: 6.641 (6.641) Lt: 5.883 (5.883) Accm: 2.98 (2.98) Acct: 4.76 (4.76) proj_loss: -0.5801 (-0.5801) time: 0.6771 data: 0.0002 [11-24 09:08:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [ 417/1669] eta: 0:14:38 tlr: 0.00019 tnm: 0.28 Lm: 6.666 (6.666) Lt: 5.930 (5.930) Accm: 2.86 (2.86) Acct: 4.55 (4.55) proj_loss: -0.6016 (-0.6016) time: 0.6772 data: 0.0003 [11-24 09:13:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [ 834/1669] eta: 0:09:35 tlr: 0.00019 tnm: 0.29 Lm: 6.740 (6.699) Lt: 5.975 (5.948) Accm: 2.77 (2.83) Acct: 4.79 (4.63) proj_loss: -0.5915 (-0.5897) time: 0.6767 data: 0.0003 [11-24 09:13:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [ 834/1669] eta: 0:09:35 tlr: 0.00019 tnm: 0.29 Lm: 6.653 (6.671) Lt: 5.922 (5.922) Accm: 2.94 (2.90) Acct: 4.61 (4.61) proj_loss: -0.5794 (-0.5798) time: 0.6767 data: 0.0002 [11-24 09:13:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [ 834/1669] eta: 0:09:35 tlr: 0.00019 tnm: 0.29 Lm: 6.601 (6.626) Lt: 5.878 (5.888) Accm: 2.97 (3.07) Acct: 4.67 (4.83) proj_loss: -0.5829 (-0.5752) time: 0.6767 data: 0.0003 [11-24 09:13:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [ 834/1669] eta: 0:09:35 tlr: 0.00019 tnm: 0.29 Lm: 6.568 (6.594) Lt: 5.878 (5.865) Accm: 3.19 (3.16) Acct: 4.82 (4.83) proj_loss: -0.5839 (-0.5812) time: 0.6767 data: 0.0003 [11-24 09:18:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [1251/1669] eta: 0:04:46 tlr: 0.00019 tnm: 0.29 Lm: 6.600 (6.604) Lt: 5.886 (5.872) Accm: 3.11 (3.12) Acct: 4.86 (4.85) proj_loss: -0.5853 (-0.5826) time: 0.6737 data: 0.0002 [11-24 09:18:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [1251/1669] eta: 0:04:46 tlr: 0.00019 tnm: 0.29 Lm: 6.742 (6.710) Lt: 5.979 (5.965) Accm: 2.85 (2.85) Acct: 4.74 (4.65) proj_loss: -0.5813 (-0.5851) time: 0.6737 data: 0.0003 [11-24 09:18:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [1251/1669] eta: 0:04:46 tlr: 0.00019 tnm: 0.29 Lm: 6.641 (6.600) Lt: 5.883 (5.838) Accm: 2.98 (3.13) Acct: 4.76 (4.97) proj_loss: -0.5812 (-0.5806) time: 0.6737 data: 0.0002 [11-24 09:18:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [1251/1669] eta: 0:04:46 tlr: 0.00019 tnm: 0.29 Lm: 6.605 (6.622) Lt: 5.882 (5.888) Accm: 3.09 (3.10) Acct: 4.66 (4.74) proj_loss: -0.5836 (-0.5790) time: 0.6737 data: 0.0003 [11-24 09:22:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.601 (6.608) Lt: 5.878 (5.862) Accm: 2.99 (3.08) Acct: 4.67 (4.81) proj_loss: -0.5829 (-0.5765) time: 0.6772 data: 0.0017 [11-24 09:22:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 96/350] Total time: 0:18:59 (0.683 s / it) [11-24 09:22:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.568 (6.568) Lt: 5.878 (5.828) Accm: 3.19 (3.23) Acct: 4.91 (5.07) proj_loss: -0.5867 (-0.5879) time: 0.6772 data: 0.0024 [11-24 09:22:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.629 (6.579) Lt: 5.845 (5.809) Accm: 3.01 (3.17) Acct: 4.91 (5.04) proj_loss: -0.5830 (-0.5826) time: 0.6772 data: 0.0018 [11-24 09:22:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 96/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.740 (6.679) Lt: 5.975 (5.918) Accm: 2.93 (2.99) Acct: 4.79 (4.90) proj_loss: -0.5710 (-0.5776) time: 0.6772 data: 0.0016 [11-24 09:22:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 96/350] Total time: 0:18:59 (0.683 s / it) [11-24 09:22:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 96/350] Total time: 0:18:59 (0.683 s / it) [11-24 09:22:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 96/350] Total time: 0:18:59 (0.683 s / it) [11-24 09:22:50] (/home/user/VAR/train.py , line 276)=> [ep96] (training ) Lm: 6.603 (6.616), Lt: 5.856 (5.866), Acc m&t: 3.11 4.93, Remain: 3 days, 7:54:53, Finish: 2024-11-27 01:17 [11-24 09:22:50] (/home/user/VAR/train.py , line 276)=> [ep96] (training ) Lm: 6.603 (6.616), Lt: 5.856 (5.866), Acc m&t: 3.11 4.93, Remain: 3 days, 7:55:34, Finish: 2024-11-27 01:18 [11-24 09:22:50] (/home/user/VAR/train.py , line 276)=> [ep96] (training ) Lm: 6.603 (6.616), Lt: 5.856 (5.866), Acc m&t: 3.11 4.93, Remain: 3 days, 7:54:24, Finish: 2024-11-27 01:17 [11-24 09:22:50] (/home/user/VAR/train.py , line 276)=> [ep96] (training ) Lm: 6.603 (6.616), Lt: 5.856 (5.866), Acc m&t: 3.11 4.93, Remain: 3 days, 7:54:44, Finish: 2024-11-27 01:17 [11-24 09:22:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [ 0/1669] eta: 0:18:21 tlr: 0.00019 tnm: 0.29 Lm: 6.643 (6.643) Lt: 5.922 (5.922) Accm: 3.08 (3.08) Acct: 4.51 (4.51) proj_loss: -0.5663 (-0.5663) time: 0.6599 data: 0.0003 [11-24 09:22:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [ 0/1669] eta: 0:18:22 tlr: 0.00019 tnm: 0.29 Lm: 6.556 (6.556) Lt: 5.720 (5.720) Accm: 3.15 (3.15) Acct: 5.01 (5.01) proj_loss: -0.5663 (-0.5663) time: 0.6603 data: 0.0004 [11-24 09:22:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [ 0/1669] eta: 0:18:22 tlr: 0.00019 tnm: 0.29 Lm: 6.618 (6.618) Lt: 5.829 (5.829) Accm: 2.92 (2.92) Acct: 4.72 (4.72) proj_loss: -0.5893 (-0.5893) time: 0.6607 data: 0.0004 [11-24 09:22:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [ 0/1669] eta: 0:18:22 tlr: 0.00019 tnm: 0.29 Lm: 6.694 (6.694) Lt: 5.950 (5.950) Accm: 2.72 (2.72) Acct: 4.22 (4.22) proj_loss: -0.5773 (-0.5773) time: 0.6606 data: 0.0003 [11-24 09:27:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [ 417/1669] eta: 0:14:04 tlr: 0.00019 tnm: 0.28 Lm: 6.742 (6.742) Lt: 6.025 (6.025) Accm: 2.71 (2.71) Acct: 4.19 (4.19) proj_loss: -0.5560 (-0.5560) time: 0.6745 data: 0.0003 [11-24 09:27:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [ 417/1669] eta: 0:14:04 tlr: 0.00019 tnm: 0.28 Lm: 6.546 (6.546) Lt: 5.739 (5.739) Accm: 3.21 (3.21) Acct: 5.23 (5.23) proj_loss: -0.5835 (-0.5835) time: 0.6745 data: 0.0003 [11-24 09:27:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [ 417/1669] eta: 0:14:04 tlr: 0.00019 tnm: 0.28 Lm: 6.574 (6.574) Lt: 5.823 (5.823) Accm: 3.19 (3.19) Acct: 4.86 (4.86) proj_loss: -0.5749 (-0.5749) time: 0.6745 data: 0.0003 [11-24 09:27:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [ 417/1669] eta: 0:14:04 tlr: 0.00019 tnm: 0.28 Lm: 6.622 (6.622) Lt: 5.809 (5.809) Accm: 3.10 (3.10) Acct: 5.10 (5.10) proj_loss: -0.5732 (-0.5732) time: 0.6745 data: 0.0002 [11-24 09:32:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [ 834/1669] eta: 0:09:38 tlr: 0.00019 tnm: 0.29 Lm: 6.653 (6.632) Lt: 5.899 (5.842) Accm: 3.05 (3.08) Acct: 5.01 (4.94) proj_loss: -0.5800 (-0.5781) time: 0.6735 data: 0.0002 [11-24 09:32:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [ 834/1669] eta: 0:09:38 tlr: 0.00019 tnm: 0.29 Lm: 6.694 (6.676) Lt: 5.950 (5.956) Accm: 2.72 (2.88) Acct: 4.22 (4.57) proj_loss: -0.5685 (-0.5601) time: 0.6735 data: 0.0003 [11-24 09:32:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [ 834/1669] eta: 0:09:38 tlr: 0.00019 tnm: 0.29 Lm: 6.643 (6.624) Lt: 5.922 (5.880) Accm: 3.08 (3.00) Acct: 4.51 (4.66) proj_loss: -0.5835 (-0.5783) time: 0.6735 data: 0.0003 [11-24 09:32:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [ 834/1669] eta: 0:09:38 tlr: 0.00019 tnm: 0.29 Lm: 6.618 (6.574) Lt: 5.829 (5.788) Accm: 3.10 (3.17) Acct: 4.72 (5.04) proj_loss: -0.5777 (-0.5801) time: 0.6735 data: 0.0003 [11-24 09:37:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [1251/1669] eta: 0:04:48 tlr: 0.00019 tnm: 0.28 Lm: 6.561 (6.556) Lt: 5.784 (5.776) Accm: 3.30 (3.27) Acct: 5.15 (5.17) proj_loss: -0.5755 (-0.5771) time: 0.6729 data: 0.0003 [11-24 09:37:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [1251/1669] eta: 0:04:48 tlr: 0.00019 tnm: 0.28 Lm: 6.638 (6.626) Lt: 5.899 (5.879) Accm: 3.03 (3.00) Acct: 4.69 (4.71) proj_loss: -0.5749 (-0.5740) time: 0.6729 data: 0.0003 [11-24 09:37:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [1251/1669] eta: 0:04:48 tlr: 0.00019 tnm: 0.28 Lm: 6.659 (6.641) Lt: 5.893 (5.854) Accm: 3.04 (3.06) Acct: 4.88 (4.89) proj_loss: -0.5740 (-0.5756) time: 0.6729 data: 0.0002 [11-24 09:37:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [1251/1669] eta: 0:04:48 tlr: 0.00019 tnm: 0.28 Lm: 6.652 (6.659) Lt: 5.901 (5.930) Accm: 2.96 (2.96) Acct: 4.49 (4.62) proj_loss: -0.5707 (-0.5633) time: 0.6729 data: 0.0003 [11-24 09:41:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.31 Lm: 6.632 (6.654) Lt: 5.881 (5.920) Accm: 2.85 (2.94) Acct: 4.53 (4.60) proj_loss: -0.5685 (-0.5629) time: 0.6799 data: 0.0019 [11-24 09:41:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 97/350] Total time: 0:19:05 (0.686 s / it) [11-24 09:41:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.31 Lm: 6.632 (6.608) Lt: 5.876 (5.873) Accm: 3.07 (3.01) Acct: 4.72 (4.71) proj_loss: -0.5835 (-0.5786) time: 0.6799 data: 0.0018 [11-24 09:41:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.31 Lm: 6.666 (6.670) Lt: 5.899 (5.911) Accm: 3.03 (2.97) Acct: 4.75 (4.68) proj_loss: -0.5800 (-0.5818) time: 0.6799 data: 0.0017 [11-24 09:41:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 97/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.31 Lm: 6.618 (6.581) Lt: 5.829 (5.808) Accm: 3.10 (3.21) Acct: 4.72 (5.08) proj_loss: -0.5733 (-0.5761) time: 0.6799 data: 0.0021 [11-24 09:41:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 97/350] Total time: 0:19:05 (0.686 s / it) [11-24 09:41:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 97/350] Total time: 0:19:05 (0.686 s / it) [11-24 09:41:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 97/350] Total time: 0:19:05 (0.686 s / it) [11-24 09:41:55] (/home/user/VAR/train.py , line 276)=> [ep97] (training ) Lm: 6.603 (6.615), Lt: 5.856 (5.865), Acc m&t: 3.11 4.93, Remain: 3 days, 7:55:37, Finish: 2024-11-27 01:37 [11-24 09:41:55] (/home/user/VAR/train.py , line 276)=> [ep97] (training ) Lm: 6.603 (6.615), Lt: 5.856 (5.865), Acc m&t: 3.11 4.93, Remain: 3 days, 7:54:44, Finish: 2024-11-27 01:36 [11-24 09:41:55] (/home/user/VAR/train.py , line 276)=> [ep97] (training ) Lm: 6.603 (6.615), Lt: 5.856 (5.865), Acc m&t: 3.11 4.93, Remain: 3 days, 7:57:06, Finish: 2024-11-27 01:39 [11-24 09:41:55] (/home/user/VAR/train.py , line 276)=> [ep97] (training ) Lm: 6.603 (6.615), Lt: 5.856 (5.865), Acc m&t: 3.11 4.93, Remain: 3 days, 7:54:39, Finish: 2024-11-27 01:36 [11-24 09:42:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [ 0/1669] eta: 18:04:41 tlr: 0.00019 tnm: 0.29 Lm: 6.579 (6.579) Lt: 5.786 (5.786) Accm: 3.35 (3.35) Acct: 5.32 (5.32) proj_loss: -0.5576 (-0.5576) time: 38.9946 data: 0.0004 [11-24 09:42:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [ 0/1669] eta: 10:55:21 tlr: 0.00019 tnm: 0.29 Lm: 6.627 (6.627) Lt: 5.884 (5.884) Accm: 3.07 (3.07) Acct: 4.80 (4.80) proj_loss: -0.5873 (-0.5873) time: 23.5602 data: 0.0004 [11-24 09:42:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [ 0/1669] eta: 0:18:07 tlr: 0.00019 tnm: 0.29 Lm: 6.350 (6.350) Lt: 5.607 (5.607) Accm: 3.88 (3.88) Acct: 5.85 (5.85) proj_loss: -0.5942 (-0.5942) time: 0.6514 data: 0.0003 [11-24 09:42:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [ 0/1669] eta: 13:57:33 tlr: 0.00019 tnm: 0.29 Lm: 6.596 (6.596) Lt: 5.853 (5.853) Accm: 3.31 (3.31) Acct: 5.08 (5.08) proj_loss: -0.5680 (-0.5680) time: 30.1102 data: 0.0004 [11-24 09:47:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [ 417/1669] eta: 0:15:35 tlr: 0.00019 tnm: 0.28 Lm: 6.556 (6.556) Lt: 5.784 (5.784) Accm: 3.33 (3.33) Acct: 5.24 (5.24) proj_loss: -0.5693 (-0.5693) time: 0.6799 data: 0.0003 [11-24 09:47:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [ 417/1669] eta: 0:15:16 tlr: 0.00019 tnm: 0.28 Lm: 6.636 (6.636) Lt: 5.934 (5.934) Accm: 2.94 (2.94) Acct: 4.61 (4.61) proj_loss: -0.5892 (-0.5892) time: 0.6799 data: 0.0003 [11-24 09:47:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [ 417/1669] eta: 0:16:02 tlr: 0.00019 tnm: 0.28 Lm: 6.638 (6.638) Lt: 5.894 (5.894) Accm: 2.98 (2.98) Acct: 4.82 (4.82) proj_loss: -0.5682 (-0.5682) time: 0.6799 data: 0.0003 [11-24 09:47:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [ 417/1669] eta: 0:14:07 tlr: 0.00019 tnm: 0.28 Lm: 6.459 (6.459) Lt: 5.742 (5.742) Accm: 3.51 (3.51) Acct: 5.35 (5.35) proj_loss: -0.5895 (-0.5895) time: 0.6799 data: 0.0003 [11-24 09:51:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [ 834/1669] eta: 0:09:24 tlr: 0.00019 tnm: 0.30 Lm: 6.568 (6.523) Lt: 5.877 (5.832) Accm: 3.14 (3.27) Acct: 4.86 (4.96) proj_loss: -0.5900 (-0.5897) time: 0.6744 data: 0.0003 [11-24 09:51:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [ 834/1669] eta: 0:09:54 tlr: 0.00019 tnm: 0.30 Lm: 6.516 (6.519) Lt: 5.715 (5.734) Accm: 3.35 (3.44) Acct: 5.41 (5.44) proj_loss: -0.5707 (-0.5734) time: 0.6744 data: 0.0003 [11-24 09:51:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [ 834/1669] eta: 0:10:03 tlr: 0.00019 tnm: 0.30 Lm: 6.663 (6.647) Lt: 5.915 (5.901) Accm: 2.83 (2.93) Acct: 4.44 (4.69) proj_loss: -0.5695 (-0.5686) time: 0.6744 data: 0.0003 [11-24 09:51:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [ 834/1669] eta: 0:09:47 tlr: 0.00019 tnm: 0.30 Lm: 6.645 (6.643) Lt: 5.909 (5.925) Accm: 2.84 (2.91) Acct: 4.42 (4.55) proj_loss: -0.5911 (-0.5961) time: 0.6744 data: 0.0002 [11-24 09:56:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [1251/1669] eta: 0:04:50 tlr: 0.00019 tnm: 0.31 Lm: 6.636 (6.635) Lt: 5.898 (5.916) Accm: 2.91 (2.92) Acct: 4.56 (4.59) proj_loss: -0.5892 (-0.5917) time: 0.6762 data: 0.0003 [11-24 09:56:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [1251/1669] eta: 0:04:42 tlr: 0.00019 tnm: 0.31 Lm: 6.610 (6.559) Lt: 5.900 (5.855) Accm: 2.96 (3.12) Acct: 4.52 (4.74) proj_loss: -0.5874 (-0.5812) time: 0.6762 data: 0.0003 [11-24 09:56:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [1251/1669] eta: 0:04:52 tlr: 0.00019 tnm: 0.31 Lm: 6.487 (6.504) Lt: 5.689 (5.717) Accm: 3.33 (3.37) Acct: 5.24 (5.31) proj_loss: -0.5761 (-0.5765) time: 0.6762 data: 0.0003 [11-24 09:56:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [1251/1669] eta: 0:04:55 tlr: 0.00019 tnm: 0.31 Lm: 6.621 (6.628) Lt: 5.871 (5.882) Accm: 3.09 (3.05) Acct: 4.80 (4.81) proj_loss: -0.5741 (-0.5719) time: 0.6762 data: 0.0003 [11-24 10:01:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.579 (6.605) Lt: 5.826 (5.847) Accm: 3.35 (3.14) Acct: 5.17 (5.00) proj_loss: -0.5695 (-0.5712) time: 0.7958 data: 0.0017 [11-24 10:01:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 98/350] Total time: 0:19:32 (0.702 s / it) [11-24 10:01:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.568 (6.535) Lt: 5.877 (5.800) Accm: 3.14 (3.23) Acct: 4.86 (5.05) proj_loss: -0.5900 (-0.5831) time: 0.7958 data: 0.0016 [11-24 10:01:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.631 (6.635) Lt: 5.909 (5.923) Accm: 2.97 (2.96) Acct: 4.70 (4.61) proj_loss: -0.5911 (-0.5944) time: 0.7958 data: 0.0019 [11-24 10:01:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 98/350] [1668/1669] eta: 0:00:00 tlr: 0.00019 tnm: 0.29 Lm: 6.516 (6.509) Lt: 5.715 (5.730) Accm: 3.35 (3.42) Acct: 5.41 (5.33) proj_loss: -0.5812 (-0.5774) time: 0.7958 data: 0.0018 [11-24 10:01:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 98/350] Total time: 0:18:53 (0.679 s / it) [11-24 10:01:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 98/350] Total time: 0:19:16 (0.693 s / it) [11-24 10:01:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 98/350] Total time: 0:19:23 (0.697 s / it) [11-24 10:01:27] (/home/user/VAR/train.py , line 276)=> [ep98] (training ) Lm: 6.603 (6.609), Lt: 5.856 (5.861), Acc m&t: 3.11 4.93, Remain: 3 days, 7:23:47, Finish: 2024-11-27 01:25 [11-24 10:01:27] (/home/user/VAR/train.py , line 276)=> [ep98] (training ) Lm: 6.603 (6.609), Lt: 5.856 (5.861), Acc m&t: 3.11 4.93, Remain: 3 days, 7:23:34, Finish: 2024-11-27 01:25 [11-24 10:01:27] (/home/user/VAR/train.py , line 276)=> [ep98] (training ) Lm: 6.603 (6.609), Lt: 5.856 (5.861), Acc m&t: 3.11 4.93, Remain: 3 days, 7:23:55, Finish: 2024-11-27 01:25 [11-24 10:01:27] (/home/user/VAR/train.py , line 276)=> [ep98] (training ) Lm: 6.603 (6.609), Lt: 5.856 (5.861), Acc m&t: 3.11 4.93, Remain: 3 days, 7:23:30, Finish: 2024-11-27 01:24 [11-24 10:01:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [ 0/1669] eta: 0:18:21 tlr: 0.00019 tnm: 0.28 Lm: 6.588 (6.588) Lt: 5.831 (5.831) Accm: 3.10 (3.10) Acct: 4.67 (4.67) proj_loss: -0.5761 (-0.5761) time: 0.6600 data: 0.0003 [11-24 10:01:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [ 0/1669] eta: 0:18:33 tlr: 0.00019 tnm: 0.28 Lm: 6.569 (6.569) Lt: 5.828 (5.828) Accm: 2.99 (2.99) Acct: 4.67 (4.67) proj_loss: -0.5826 (-0.5826) time: 0.6672 data: 0.0004 [11-24 10:01:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [ 0/1669] eta: 0:18:35 tlr: 0.00019 tnm: 0.28 Lm: 6.654 (6.654) Lt: 5.933 (5.933) Accm: 2.97 (2.97) Acct: 4.34 (4.34) proj_loss: -0.5805 (-0.5805) time: 0.6686 data: 0.0003 [11-24 10:01:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [ 0/1669] eta: 0:18:23 tlr: 0.00019 tnm: 0.28 Lm: 6.419 (6.419) Lt: 5.614 (5.614) Accm: 3.69 (3.69) Acct: 6.13 (6.13) proj_loss: -0.5676 (-0.5676) time: 0.6610 data: 0.0004 [11-24 10:06:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [ 417/1669] eta: 0:14:45 tlr: 0.00019 tnm: 0.31 Lm: 6.483 (6.483) Lt: 5.687 (5.687) Accm: 3.45 (3.45) Acct: 5.55 (5.55) proj_loss: -0.5786 (-0.5786) time: 0.6743 data: 0.0003 [11-24 10:06:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [ 417/1669] eta: 0:14:45 tlr: 0.00019 tnm: 0.31 Lm: 6.660 (6.660) Lt: 5.939 (5.939) Accm: 2.98 (2.98) Acct: 4.55 (4.55) proj_loss: -0.5797 (-0.5797) time: 0.6743 data: 0.0003 [11-24 10:06:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [ 417/1669] eta: 0:14:45 tlr: 0.00019 tnm: 0.31 Lm: 6.623 (6.623) Lt: 5.867 (5.867) Accm: 2.93 (2.93) Acct: 4.58 (4.58) proj_loss: -0.5735 (-0.5735) time: 0.6743 data: 0.0003 [11-24 10:06:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [ 417/1669] eta: 0:14:45 tlr: 0.00019 tnm: 0.31 Lm: 6.583 (6.583) Lt: 5.858 (5.858) Accm: 3.01 (3.01) Acct: 4.54 (4.54) proj_loss: -0.5835 (-0.5835) time: 0.6743 data: 0.0003 [11-24 10:11:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [ 834/1669] eta: 0:09:37 tlr: 0.00019 tnm: 0.27 Lm: 6.596 (6.616) Lt: 5.889 (5.879) Accm: 2.99 (2.97) Acct: 4.63 (4.57) proj_loss: -0.5826 (-0.5800) time: 0.6760 data: 0.0002 [11-24 10:11:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [ 834/1669] eta: 0:09:37 tlr: 0.00019 tnm: 0.27 Lm: 6.547 (6.528) Lt: 5.759 (5.752) Accm: 3.21 (3.28) Acct: 4.98 (5.26) proj_loss: -0.5676 (-0.5740) time: 0.6760 data: 0.0003 [11-24 10:11:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [ 834/1669] eta: 0:09:37 tlr: 0.00019 tnm: 0.27 Lm: 6.654 (6.644) Lt: 5.933 (5.899) Accm: 2.97 (2.96) Acct: 4.61 (4.57) proj_loss: -0.5789 (-0.5762) time: 0.6760 data: 0.0003 [11-24 10:11:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [ 834/1669] eta: 0:09:37 tlr: 0.00019 tnm: 0.27 Lm: 6.588 (6.601) Lt: 5.831 (5.841) Accm: 3.10 (3.09) Acct: 4.67 (5.01) proj_loss: -0.5761 (-0.5758) time: 0.6760 data: 0.0003 [11-24 10:15:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [1251/1669] eta: 0:04:46 tlr: 0.00018 tnm: 0.28 Lm: 6.572 (6.551) Lt: 5.810 (5.784) Accm: 3.26 (3.27) Acct: 5.27 (5.29) proj_loss: -0.5782 (-0.5775) time: 0.6751 data: 0.0003 [11-24 10:15:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [1251/1669] eta: 0:04:46 tlr: 0.00018 tnm: 0.28 Lm: 6.598 (6.612) Lt: 5.858 (5.857) Accm: 3.01 (3.00) Acct: 4.65 (4.69) proj_loss: -0.5787 (-0.5787) time: 0.6751 data: 0.0003 [11-24 10:15:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [1251/1669] eta: 0:04:46 tlr: 0.00018 tnm: 0.28 Lm: 6.565 (6.542) Lt: 5.771 (5.759) Accm: 3.28 (3.30) Acct: 5.13 (5.27) proj_loss: -0.5763 (-0.5767) time: 0.6751 data: 0.0003 [11-24 10:15:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [1251/1669] eta: 0:04:46 tlr: 0.00018 tnm: 0.28 Lm: 6.660 (6.658) Lt: 5.939 (5.917) Accm: 2.95 (2.86) Acct: 4.48 (4.46) proj_loss: -0.5797 (-0.5823) time: 0.6751 data: 0.0003 [11-24 10:20:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.27 Lm: 6.666 (6.693) Lt: 5.945 (5.959) Accm: 2.93 (2.85) Acct: 4.34 (4.42) proj_loss: -0.5805 (-0.5864) time: 0.6773 data: 0.0020 [11-24 10:20:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 99/350] Total time: 0:19:01 (0.684 s / it) [11-24 10:20:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.27 Lm: 6.588 (6.566) Lt: 5.831 (5.812) Accm: 3.10 (3.19) Acct: 4.67 (5.14) proj_loss: -0.5798 (-0.5780) time: 0.6773 data: 0.0020 [11-24 10:20:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.27 Lm: 6.600 (6.616) Lt: 5.889 (5.867) Accm: 3.02 (3.01) Acct: 4.67 (4.71) proj_loss: -0.5826 (-0.5809) time: 0.6773 data: 0.0017 [11-24 10:20:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 99/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.27 Lm: 6.547 (6.540) Lt: 5.759 (5.756) Accm: 3.34 (3.32) Acct: 5.29 (5.28) proj_loss: -0.5763 (-0.5767) time: 0.6773 data: 0.0016 [11-24 10:20:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 99/350] Total time: 0:19:01 (0.684 s / it) [11-24 10:20:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 99/350] Total time: 0:19:01 (0.684 s / it) [11-24 10:20:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 99/350] Total time: 0:19:01 (0.684 s / it) [11-24 10:22:46] (home/user/VAR/trainer.py, line 114)=> FID: 4.0945410567669 [11-24 10:22:47] (/home/user/VAR/train.py , line 259)=> [*] [ep99] (val 50000) Lm: 6.6018, Lt: 5.8539, Acc m&t: 3.11 4.89, Val cost: 137.75s [11-24 10:22:47] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-24 10:23:30] (/home/user/VAR/train.py , line 276)=> [ep99] (training ) Lm: 6.602 (6.602), Lt: 5.854 (5.854), Acc m&t: 3.11 4.93, Remain: 3 days, 7:06:56, Finish: 2024-11-27 01:27 [11-24 10:23:30] (/home/user/VAR/train.py , line 276)=> [ep99] (training ) Lm: 6.602 (6.602), Lt: 5.854 (5.854), Acc m&t: 3.11 4.93, Remain: 3 days, 7:07:21, Finish: 2024-11-27 01:27 [11-24 10:23:30] (/home/user/VAR/train.py , line 276)=> [ep99] (training ) Lm: 6.602 (6.602), Lt: 5.854 (5.854), Acc m&t: 3.11 4.93, Remain: 3 days, 7:08:31, Finish: 2024-11-27 01:28 [11-24 10:23:30] (/home/user/VAR/train.py , line 276)=> [ep99] (training ) Lm: 6.602 (6.602), Lt: 5.854 (5.854), Acc m&t: 3.11 4.93, Remain: 3 days, 7:07:06, Finish: 2024-11-27 01:27 [11-24 10:23:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [ 0/1669] eta: 0:18:34 tlr: 0.00018 tnm: 0.27 Lm: 6.592 (6.592) Lt: 5.830 (5.830) Accm: 2.96 (2.96) Acct: 4.87 (4.87) proj_loss: -0.5837 (-0.5837) time: 0.6676 data: 0.0003 [11-24 10:23:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [ 0/1669] eta: 0:18:32 tlr: 0.00018 tnm: 0.27 Lm: 6.544 (6.544) Lt: 5.746 (5.746) Accm: 3.67 (3.67) Acct: 5.91 (5.91) proj_loss: -0.5889 (-0.5889) time: 0.6666 data: 0.0004 [11-24 10:23:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [ 0/1669] eta: 0:18:32 tlr: 0.00018 tnm: 0.27 Lm: 6.720 (6.720) Lt: 5.983 (5.983) Accm: 2.79 (2.79) Acct: 4.60 (4.60) proj_loss: -0.5819 (-0.5819) time: 0.6668 data: 0.0004 [11-24 10:23:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [ 0/1669] eta: 0:18:25 tlr: 0.00018 tnm: 0.27 Lm: 6.556 (6.556) Lt: 5.828 (5.828) Accm: 3.10 (3.10) Acct: 4.80 (4.80) proj_loss: -0.5872 (-0.5872) time: 0.6621 data: 0.0004 [11-24 10:28:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.30 Lm: 6.634 (6.634) Lt: 5.910 (5.910) Accm: 2.94 (2.94) Acct: 4.61 (4.61) proj_loss: -0.5861 (-0.5861) time: 0.6751 data: 0.0003 [11-24 10:28:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.30 Lm: 6.661 (6.661) Lt: 5.906 (5.906) Accm: 3.10 (3.10) Acct: 4.97 (4.97) proj_loss: -0.5847 (-0.5847) time: 0.6751 data: 0.0003 [11-24 10:28:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.30 Lm: 6.590 (6.590) Lt: 5.845 (5.845) Accm: 3.49 (3.49) Acct: 5.41 (5.41) proj_loss: -0.5809 (-0.5809) time: 0.6751 data: 0.0003 [11-24 10:28:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.30 Lm: 6.619 (6.619) Lt: 5.894 (5.894) Accm: 3.01 (3.01) Acct: 4.77 (4.77) proj_loss: -0.5856 (-0.5856) time: 0.6751 data: 0.0003 [11-24 10:33:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [ 834/1669] eta: 0:09:38 tlr: 0.00018 tnm: 0.30 Lm: 6.647 (6.661) Lt: 5.958 (5.921) Accm: 2.96 (2.90) Acct: 4.67 (4.59) proj_loss: -0.5837 (-0.5715) time: 0.6794 data: 0.0003 [11-24 10:33:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [ 834/1669] eta: 0:09:38 tlr: 0.00018 tnm: 0.30 Lm: 6.720 (6.684) Lt: 5.983 (5.934) Accm: 2.94 (3.04) Acct: 4.67 (4.87) proj_loss: -0.5819 (-0.5790) time: 0.6794 data: 0.0003 [11-24 10:33:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [ 834/1669] eta: 0:09:38 tlr: 0.00018 tnm: 0.30 Lm: 6.556 (6.608) Lt: 5.828 (5.865) Accm: 3.10 (3.04) Acct: 4.80 (4.77) proj_loss: -0.5872 (-0.5876) time: 0.6794 data: 0.0003 [11-24 10:33:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [ 834/1669] eta: 0:09:38 tlr: 0.00018 tnm: 0.30 Lm: 6.631 (6.603) Lt: 5.913 (5.867) Accm: 3.31 (3.36) Acct: 4.99 (5.27) proj_loss: -0.5889 (-0.5854) time: 0.6794 data: 0.0003 [11-24 10:37:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [1251/1669] eta: 0:04:48 tlr: 0.00018 tnm: 0.29 Lm: 6.633 (6.635) Lt: 5.928 (5.896) Accm: 3.20 (3.21) Acct: 4.96 (5.11) proj_loss: -0.5809 (-0.5816) time: 0.6753 data: 0.0003 [11-24 10:37:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [1251/1669] eta: 0:04:48 tlr: 0.00018 tnm: 0.29 Lm: 6.571 (6.602) Lt: 5.842 (5.863) Accm: 3.00 (3.00) Acct: 4.61 (4.67) proj_loss: -0.5887 (-0.5883) time: 0.6753 data: 0.0003 [11-24 10:37:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [1251/1669] eta: 0:04:48 tlr: 0.00018 tnm: 0.29 Lm: 6.661 (6.654) Lt: 5.907 (5.908) Accm: 3.03 (3.06) Acct: 4.72 (4.84) proj_loss: -0.5792 (-0.5784) time: 0.6753 data: 0.0003 [11-24 10:37:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [1251/1669] eta: 0:04:48 tlr: 0.00018 tnm: 0.29 Lm: 6.695 (6.690) Lt: 5.966 (5.964) Accm: 2.89 (2.88) Acct: 4.47 (4.51) proj_loss: -0.5845 (-0.5750) time: 0.6753 data: 0.0003 [11-24 10:42:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.647 (6.657) Lt: 5.958 (5.923) Accm: 2.96 (2.98) Acct: 4.67 (4.69) proj_loss: -0.5837 (-0.5766) time: 0.6801 data: 0.0019 [11-24 10:42:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 100/350] Total time: 0:19:06 (0.687 s / it) [11-24 10:42:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.610 (6.645) Lt: 5.832 (5.893) Accm: 3.13 (3.08) Acct: 4.77 (4.87) proj_loss: -0.5819 (-0.5809) time: 0.6801 data: 0.0015 [11-24 10:42:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.586 (6.603) Lt: 5.828 (5.848) Accm: 3.10 (3.04) Acct: 4.80 (4.78) proj_loss: -0.5872 (-0.5871) time: 0.6801 data: 0.0014 [11-24 10:42:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 100/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.635 (6.644) Lt: 5.927 (5.902) Accm: 3.10 (3.11) Acct: 4.92 (4.97) proj_loss: -0.5791 (-0.5811) time: 0.6801 data: 0.0016 [11-24 10:42:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 100/350] Total time: 0:19:06 (0.687 s / it) [11-24 10:42:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 100/350] Total time: 0:19:06 (0.687 s / it) [11-24 10:42:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 100/350] Total time: 0:19:06 (0.687 s / it) [11-24 10:42:36] (/home/user/VAR/train.py , line 276)=> [ep100] (training ) Lm: 6.602 (6.609), Lt: 5.854 (5.856), Acc m&t: 3.11 4.95, Remain: 3 days, 7:03:27, Finish: 2024-11-27 01:46 [11-24 10:42:36] (/home/user/VAR/train.py , line 276)=> [ep100] (training ) Lm: 6.602 (6.609), Lt: 5.854 (5.856), Acc m&t: 3.11 4.95, Remain: 3 days, 7:06:22, Finish: 2024-11-27 01:48 [11-24 10:42:36] (/home/user/VAR/train.py , line 276)=> [ep100] (training ) Lm: 6.602 (6.609), Lt: 5.854 (5.856), Acc m&t: 3.11 4.95, Remain: 3 days, 7:02:01, Finish: 2024-11-27 01:44 [11-24 10:42:36] (/home/user/VAR/train.py , line 276)=> [ep100] (training ) Lm: 6.602 (6.609), Lt: 5.854 (5.856), Acc m&t: 3.11 4.95, Remain: 3 days, 7:03:11, Finish: 2024-11-27 01:45 [11-24 10:42:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [ 0/1669] eta: 0:18:19 tlr: 0.00018 tnm: 0.30 Lm: 6.719 (6.719) Lt: 6.025 (6.025) Accm: 2.70 (2.70) Acct: 4.39 (4.39) proj_loss: -0.5912 (-0.5912) time: 0.6586 data: 0.0004 [11-24 10:42:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [ 0/1669] eta: 0:18:19 tlr: 0.00018 tnm: 0.30 Lm: 6.753 (6.753) Lt: 6.002 (6.002) Accm: 2.73 (2.73) Acct: 4.51 (4.51) proj_loss: -0.5783 (-0.5783) time: 0.6589 data: 0.0004 [11-24 10:42:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [ 0/1669] eta: 0:18:19 tlr: 0.00018 tnm: 0.30 Lm: 6.496 (6.496) Lt: 5.747 (5.747) Accm: 3.39 (3.39) Acct: 5.10 (5.10) proj_loss: -0.5737 (-0.5737) time: 0.6587 data: 0.0004 [11-24 10:42:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [ 0/1669] eta: 0:18:20 tlr: 0.00018 tnm: 0.30 Lm: 6.628 (6.628) Lt: 5.875 (5.875) Accm: 2.80 (2.80) Acct: 4.41 (4.41) proj_loss: -0.5668 (-0.5668) time: 0.6592 data: 0.0003 [11-24 10:47:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.29 Lm: 6.564 (6.564) Lt: 5.796 (5.796) Accm: 3.01 (3.01) Acct: 4.92 (4.92) proj_loss: -0.5748 (-0.5748) time: 0.6765 data: 0.0003 [11-24 10:47:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.29 Lm: 6.600 (6.600) Lt: 5.850 (5.850) Accm: 3.16 (3.16) Acct: 4.85 (4.85) proj_loss: -0.5854 (-0.5854) time: 0.6765 data: 0.0003 [11-24 10:47:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.29 Lm: 6.716 (6.716) Lt: 5.969 (5.969) Accm: 2.78 (2.78) Acct: 4.66 (4.66) proj_loss: -0.5651 (-0.5651) time: 0.6765 data: 0.0003 [11-24 10:47:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.29 Lm: 6.679 (6.679) Lt: 5.974 (5.974) Accm: 2.84 (2.84) Acct: 4.51 (4.51) proj_loss: -0.5911 (-0.5911) time: 0.6765 data: 0.0003 [11-24 10:52:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [ 834/1669] eta: 0:09:24 tlr: 0.00018 tnm: 0.29 Lm: 6.638 (6.621) Lt: 5.924 (5.897) Accm: 2.97 (2.95) Acct: 4.63 (4.74) proj_loss: -0.5909 (-0.5842) time: 0.6772 data: 0.0003 [11-24 10:52:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [ 834/1669] eta: 0:09:24 tlr: 0.00018 tnm: 0.29 Lm: 6.628 (6.686) Lt: 5.875 (5.943) Accm: 2.80 (2.73) Acct: 4.41 (4.43) proj_loss: -0.5827 (-0.5801) time: 0.6772 data: 0.0003 [11-24 10:52:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [ 834/1669] eta: 0:09:24 tlr: 0.00018 tnm: 0.29 Lm: 6.550 (6.583) Lt: 5.768 (5.823) Accm: 3.12 (3.15) Acct: 4.92 (4.87) proj_loss: -0.5853 (-0.5854) time: 0.6772 data: 0.0002 [11-24 10:52:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [ 834/1669] eta: 0:09:24 tlr: 0.00018 tnm: 0.29 Lm: 6.705 (6.713) Lt: 6.002 (5.980) Accm: 2.80 (2.79) Acct: 4.65 (4.65) proj_loss: -0.5783 (-0.5747) time: 0.6772 data: 0.0003 [11-24 10:56:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.29 Lm: 6.692 (6.697) Lt: 5.969 (5.960) Accm: 2.82 (2.89) Acct: 4.73 (4.83) proj_loss: -0.5792 (-0.5761) time: 0.6800 data: 0.0003 [11-24 10:56:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.29 Lm: 6.560 (6.580) Lt: 5.798 (5.824) Accm: 3.09 (3.13) Acct: 4.96 (4.90) proj_loss: -0.5912 (-0.5895) time: 0.6799 data: 0.0002 [11-24 10:56:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.29 Lm: 6.610 (6.663) Lt: 5.869 (5.923) Accm: 3.01 (2.89) Acct: 4.86 (4.65) proj_loss: -0.5867 (-0.5838) time: 0.6799 data: 0.0003 [11-24 10:56:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.29 Lm: 6.604 (6.609) Lt: 5.878 (5.881) Accm: 3.07 (3.02) Acct: 4.86 (4.83) proj_loss: -0.5806 (-0.5769) time: 0.6799 data: 0.0003 [11-24 11:01:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.638 (6.640) Lt: 5.924 (5.916) Accm: 2.97 (2.96) Acct: 4.63 (4.70) proj_loss: -0.5880 (-0.5791) time: 0.7416 data: 0.0015 [11-24 11:01:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 101/350] Total time: 0:18:53 (0.679 s / it) [11-24 11:01:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.564 (6.577) Lt: 5.823 (5.824) Accm: 3.12 (3.14) Acct: 4.99 (4.93) proj_loss: -0.5853 (-0.5886) time: 0.7416 data: 0.0017 [11-24 11:01:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.679 (6.662) Lt: 5.935 (5.923) Accm: 2.83 (2.96) Acct: 4.80 (4.85) proj_loss: -0.5783 (-0.5763) time: 0.7416 data: 0.0019 [11-24 11:01:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 101/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.628 (6.663) Lt: 5.875 (5.916) Accm: 2.91 (2.90) Acct: 4.75 (4.67) proj_loss: -0.5842 (-0.5839) time: 0.7416 data: 0.0020 [11-24 11:01:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 101/350] Total time: 0:18:53 (0.679 s / it) [11-24 11:01:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 101/350] Total time: 0:18:53 (0.679 s / it) [11-24 11:01:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 101/350] Total time: 0:18:53 (0.679 s / it) [11-24 11:01:30] (/home/user/VAR/train.py , line 276)=> [ep101] (training ) Lm: 6.602 (6.603), Lt: 5.850 (5.850), Acc m&t: 3.11 4.95, Remain: 3 days, 6:34:49, Finish: 2024-11-27 01:36 [11-24 11:01:30] (/home/user/VAR/train.py , line 276)=> [ep101] (training ) Lm: 6.602 (6.603), Lt: 5.850 (5.850), Acc m&t: 3.11 4.95, Remain: 3 days, 6:33:21, Finish: 2024-11-27 01:34 [11-24 11:01:30] (/home/user/VAR/train.py , line 276)=> [ep101] (training ) Lm: 6.602 (6.603), Lt: 5.850 (5.850), Acc m&t: 3.11 4.95, Remain: 3 days, 6:32:51, Finish: 2024-11-27 01:34 [11-24 11:01:30] (/home/user/VAR/train.py , line 276)=> [ep101] (training ) Lm: 6.602 (6.603), Lt: 5.850 (5.850), Acc m&t: 3.11 4.95, Remain: 3 days, 6:33:21, Finish: 2024-11-27 01:34 [11-24 11:01:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [ 0/1669] eta: 0:18:18 tlr: 0.00018 tnm: 0.29 Lm: 6.699 (6.699) Lt: 5.973 (5.973) Accm: 2.67 (2.67) Acct: 3.99 (3.99) proj_loss: -0.5859 (-0.5859) time: 0.6579 data: 0.0003 [11-24 11:01:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [ 0/1669] eta: 0:18:17 tlr: 0.00018 tnm: 0.29 Lm: 6.801 (6.801) Lt: 6.117 (6.117) Accm: 2.39 (2.39) Acct: 3.63 (3.63) proj_loss: -0.5847 (-0.5847) time: 0.6577 data: 0.0004 [11-24 11:01:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [ 0/1669] eta: 0:18:17 tlr: 0.00018 tnm: 0.29 Lm: 6.324 (6.324) Lt: 5.547 (5.547) Accm: 4.25 (4.25) Acct: 6.61 (6.61) proj_loss: -0.5783 (-0.5783) time: 0.6576 data: 0.0004 [11-24 11:01:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [ 0/1669] eta: 0:18:18 tlr: 0.00018 tnm: 0.29 Lm: 6.830 (6.830) Lt: 6.089 (6.089) Accm: 2.74 (2.74) Acct: 4.20 (4.20) proj_loss: -0.5832 (-0.5832) time: 0.6580 data: 0.0003 [11-24 11:06:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [ 417/1669] eta: 0:14:48 tlr: 0.00018 tnm: 0.30 Lm: 6.695 (6.695) Lt: 5.959 (5.959) Accm: 2.84 (2.84) Acct: 4.48 (4.48) proj_loss: -0.5808 (-0.5808) time: 0.6757 data: 0.0003 [11-24 11:06:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [ 417/1669] eta: 0:14:48 tlr: 0.00018 tnm: 0.30 Lm: 6.675 (6.675) Lt: 5.961 (5.961) Accm: 2.76 (2.76) Acct: 4.26 (4.26) proj_loss: -0.5867 (-0.5867) time: 0.6756 data: 0.0003 [11-24 11:06:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [ 417/1669] eta: 0:14:48 tlr: 0.00018 tnm: 0.30 Lm: 6.701 (6.701) Lt: 5.948 (5.948) Accm: 2.66 (2.66) Acct: 4.06 (4.06) proj_loss: -0.5714 (-0.5714) time: 0.6756 data: 0.0003 [11-24 11:06:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [ 417/1669] eta: 0:14:48 tlr: 0.00018 tnm: 0.30 Lm: 6.489 (6.489) Lt: 5.735 (5.735) Accm: 3.47 (3.47) Acct: 5.36 (5.36) proj_loss: -0.5822 (-0.5822) time: 0.6757 data: 0.0003 [11-24 11:11:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [ 834/1669] eta: 0:09:38 tlr: 0.00018 tnm: 0.29 Lm: 6.567 (6.515) Lt: 5.814 (5.761) Accm: 3.04 (3.33) Acct: 4.79 (5.17) proj_loss: -0.5861 (-0.5844) time: 0.6734 data: 0.0003 [11-24 11:11:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [ 834/1669] eta: 0:09:38 tlr: 0.00018 tnm: 0.29 Lm: 6.699 (6.640) Lt: 5.923 (5.889) Accm: 2.67 (2.94) Acct: 4.13 (4.56) proj_loss: -0.5848 (-0.5759) time: 0.6734 data: 0.0003 [11-24 11:11:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [ 834/1669] eta: 0:09:38 tlr: 0.00018 tnm: 0.29 Lm: 6.560 (6.607) Lt: 5.829 (5.865) Accm: 2.94 (3.11) Acct: 4.75 (4.84) proj_loss: -0.5784 (-0.5726) time: 0.6734 data: 0.0003 [11-24 11:11:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [ 834/1669] eta: 0:09:38 tlr: 0.00018 tnm: 0.29 Lm: 6.761 (6.704) Lt: 6.030 (5.984) Accm: 2.68 (2.74) Acct: 4.34 (4.29) proj_loss: -0.5847 (-0.5799) time: 0.6734 data: 0.0003 [11-24 11:15:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [1251/1669] eta: 0:04:47 tlr: 0.00018 tnm: 0.31 Lm: 6.722 (6.698) Lt: 5.999 (5.980) Accm: 2.87 (2.82) Acct: 4.61 (4.46) proj_loss: -0.5799 (-0.5787) time: 0.6723 data: 0.0003 [11-24 11:15:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [1251/1669] eta: 0:04:47 tlr: 0.00018 tnm: 0.31 Lm: 6.582 (6.606) Lt: 5.838 (5.860) Accm: 2.99 (3.09) Acct: 4.83 (4.86) proj_loss: -0.5808 (-0.5770) time: 0.6723 data: 0.0003 [11-24 11:15:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [1251/1669] eta: 0:04:47 tlr: 0.00018 tnm: 0.31 Lm: 6.608 (6.601) Lt: 5.847 (5.841) Accm: 3.05 (3.06) Acct: 4.61 (4.69) proj_loss: -0.5854 (-0.5820) time: 0.6723 data: 0.0003 [11-24 11:15:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [1251/1669] eta: 0:04:47 tlr: 0.00018 tnm: 0.31 Lm: 6.545 (6.517) Lt: 5.814 (5.775) Accm: 3.31 (3.39) Acct: 5.11 (5.24) proj_loss: -0.5822 (-0.5798) time: 0.6723 data: 0.0003 [11-24 11:20:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.567 (6.545) Lt: 5.814 (5.801) Accm: 3.04 (3.31) Acct: 4.79 (5.15) proj_loss: -0.5861 (-0.5865) time: 0.6780 data: 0.0019 [11-24 11:20:32] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 102/350] Total time: 0:19:02 (0.684 s / it) [11-24 11:20:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.659 (6.613) Lt: 5.923 (5.857) Accm: 2.98 (3.05) Acct: 5.03 (4.76) proj_loss: -0.5848 (-0.5780) time: 0.6780 data: 0.0014 [11-24 11:20:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.566 (6.598) Lt: 5.829 (5.853) Accm: 3.01 (3.08) Acct: 4.75 (4.81) proj_loss: -0.5832 (-0.5814) time: 0.6780 data: 0.0018 [11-24 11:20:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 102/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.682 (6.693) Lt: 5.968 (5.975) Accm: 2.91 (2.84) Acct: 4.42 (4.45) proj_loss: -0.5847 (-0.5833) time: 0.6780 data: 0.0022 [11-24 11:20:32] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 102/350] Total time: 0:19:02 (0.684 s / it) [11-24 11:20:32] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 102/350] Total time: 0:19:02 (0.684 s / it) [11-24 11:20:32] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 102/350] Total time: 0:19:02 (0.684 s / it) [11-24 11:20:32] (/home/user/VAR/train.py , line 276)=> [ep102] (training ) Lm: 6.599 (6.599), Lt: 5.842 (5.842), Acc m&t: 3.13 4.95, Remain: 3 days, 6:11:28, Finish: 2024-11-27 01:31 [11-24 11:20:32] (/home/user/VAR/train.py , line 276)=> [ep102] (training ) Lm: 6.599 (6.599), Lt: 5.842 (5.842), Acc m&t: 3.13 4.95, Remain: 3 days, 6:09:25, Finish: 2024-11-27 01:29 [11-24 11:20:32] (/home/user/VAR/train.py , line 276)=> [ep102] (training ) Lm: 6.599 (6.599), Lt: 5.842 (5.842), Acc m&t: 3.13 4.95, Remain: 3 days, 6:10:24, Finish: 2024-11-27 01:30 [11-24 11:20:32] (/home/user/VAR/train.py , line 276)=> [ep102] (training ) Lm: 6.599 (6.599), Lt: 5.842 (5.842), Acc m&t: 3.13 4.95, Remain: 3 days, 6:11:14, Finish: 2024-11-27 01:31 [11-24 11:20:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [ 0/1669] eta: 0:18:03 tlr: 0.00018 tnm: 0.29 Lm: 6.605 (6.605) Lt: 5.874 (5.874) Accm: 2.91 (2.91) Acct: 4.75 (4.75) proj_loss: -0.5718 (-0.5718) time: 0.6495 data: 0.0004 [11-24 11:20:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [ 0/1669] eta: 0:18:04 tlr: 0.00018 tnm: 0.29 Lm: 6.609 (6.609) Lt: 5.816 (5.816) Accm: 3.27 (3.27) Acct: 5.46 (5.46) proj_loss: -0.5697 (-0.5697) time: 0.6496 data: 0.0004 [11-24 11:20:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [ 0/1669] eta: 0:18:04 tlr: 0.00018 tnm: 0.29 Lm: 6.577 (6.577) Lt: 5.843 (5.843) Accm: 3.09 (3.09) Acct: 5.13 (5.13) proj_loss: -0.5729 (-0.5729) time: 0.6496 data: 0.0003 [11-24 11:20:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [ 0/1669] eta: 0:18:04 tlr: 0.00018 tnm: 0.29 Lm: 6.684 (6.684) Lt: 5.892 (5.892) Accm: 2.85 (2.85) Acct: 4.60 (4.60) proj_loss: -0.5670 (-0.5670) time: 0.6495 data: 0.0004 [11-24 11:25:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.30 Lm: 6.594 (6.594) Lt: 5.790 (5.790) Accm: 3.13 (3.13) Acct: 5.08 (5.08) proj_loss: -0.5627 (-0.5627) time: 0.6726 data: 0.0003 [11-24 11:25:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.30 Lm: 6.578 (6.578) Lt: 5.838 (5.838) Accm: 2.92 (2.92) Acct: 4.73 (4.73) proj_loss: -0.5889 (-0.5889) time: 0.6726 data: 0.0002 [11-24 11:25:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.30 Lm: 6.597 (6.597) Lt: 5.849 (5.849) Accm: 3.29 (3.29) Acct: 5.13 (5.13) proj_loss: -0.5762 (-0.5762) time: 0.6726 data: 0.0003 [11-24 11:25:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.30 Lm: 6.550 (6.550) Lt: 5.795 (5.795) Accm: 3.25 (3.25) Acct: 5.32 (5.32) proj_loss: -0.5668 (-0.5668) time: 0.6726 data: 0.0003 [11-24 11:30:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [ 834/1669] eta: 0:09:40 tlr: 0.00018 tnm: 0.29 Lm: 6.577 (6.606) Lt: 5.843 (5.864) Accm: 3.09 (3.15) Acct: 5.13 (5.10) proj_loss: -0.5729 (-0.5702) time: 0.6761 data: 0.0003 [11-24 11:30:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [ 834/1669] eta: 0:09:40 tlr: 0.00018 tnm: 0.29 Lm: 6.551 (6.527) Lt: 5.802 (5.788) Accm: 2.94 (3.16) Acct: 4.75 (5.02) proj_loss: -0.5922 (-0.5900) time: 0.6761 data: 0.0002 [11-24 11:30:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [ 834/1669] eta: 0:09:40 tlr: 0.00018 tnm: 0.29 Lm: 6.567 (6.585) Lt: 5.835 (5.805) Accm: 3.39 (3.21) Acct: 5.18 (5.11) proj_loss: -0.5670 (-0.5686) time: 0.6761 data: 0.0003 [11-24 11:30:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [ 834/1669] eta: 0:09:40 tlr: 0.00018 tnm: 0.29 Lm: 6.603 (6.599) Lt: 5.816 (5.832) Accm: 3.27 (3.25) Acct: 4.98 (5.08) proj_loss: -0.5697 (-0.5703) time: 0.6761 data: 0.0003 [11-24 11:34:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.28 Lm: 6.593 (6.582) Lt: 5.807 (5.823) Accm: 3.23 (3.24) Acct: 4.96 (5.04) proj_loss: -0.5762 (-0.5741) time: 0.6780 data: 0.0003 [11-24 11:34:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.28 Lm: 6.615 (6.605) Lt: 5.848 (5.819) Accm: 3.23 (3.18) Acct: 5.18 (5.13) proj_loss: -0.5715 (-0.5705) time: 0.6780 data: 0.0003 [11-24 11:34:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.28 Lm: 6.552 (6.533) Lt: 5.791 (5.786) Accm: 3.19 (3.23) Acct: 5.08 (5.12) proj_loss: -0.5825 (-0.5857) time: 0.6780 data: 0.0003 [11-24 11:34:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.28 Lm: 6.580 (6.601) Lt: 5.816 (5.845) Accm: 3.03 (3.10) Acct: 4.93 (5.01) proj_loss: -0.5749 (-0.5760) time: 0.6780 data: 0.0003 [11-24 11:39:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.584 (6.602) Lt: 5.825 (5.841) Accm: 3.09 (3.15) Acct: 5.13 (5.08) proj_loss: -0.5729 (-0.5749) time: 0.6782 data: 0.0016 [11-24 11:39:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 103/350] Total time: 0:19:07 (0.688 s / it) [11-24 11:39:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.552 (6.563) Lt: 5.802 (5.811) Accm: 2.94 (3.11) Acct: 4.75 (4.90) proj_loss: -0.5922 (-0.5904) time: 0.6782 data: 0.0017 [11-24 11:39:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.567 (6.590) Lt: 5.835 (5.816) Accm: 3.39 (3.23) Acct: 5.18 (5.15) proj_loss: -0.5760 (-0.5722) time: 0.6782 data: 0.0015 [11-24 11:39:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 103/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.584 (6.574) Lt: 5.797 (5.808) Accm: 3.27 (3.26) Acct: 4.98 (5.11) proj_loss: -0.5828 (-0.5763) time: 0.6782 data: 0.0016 [11-24 11:39:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 103/350] Total time: 0:19:07 (0.688 s / it) [11-24 11:39:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 103/350] Total time: 0:19:07 (0.688 s / it) [11-24 11:39:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 103/350] Total time: 0:19:07 (0.688 s / it) [11-24 11:39:40] (/home/user/VAR/train.py , line 276)=> [ep103] (training ) Lm: 6.599 (6.605), Lt: 5.842 (5.857), Acc m&t: 3.13 4.95, Remain: 3 days, 5:41:37, Finish: 2024-11-27 01:21 [11-24 11:39:40] (/home/user/VAR/train.py , line 276)=> [ep103] (training ) Lm: 6.599 (6.605), Lt: 5.842 (5.857), Acc m&t: 3.13 4.95, Remain: 3 days, 5:41:11, Finish: 2024-11-27 01:20 [11-24 11:39:40] (/home/user/VAR/train.py , line 276)=> [ep103] (training ) Lm: 6.599 (6.605), Lt: 5.842 (5.857), Acc m&t: 3.13 4.95, Remain: 3 days, 5:41:27, Finish: 2024-11-27 01:21 [11-24 11:39:40] (/home/user/VAR/train.py , line 276)=> [ep103] (training ) Lm: 6.599 (6.605), Lt: 5.842 (5.857), Acc m&t: 3.13 4.95, Remain: 3 days, 5:42:10, Finish: 2024-11-27 01:21 [11-24 11:39:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [ 0/1669] eta: 0:18:20 tlr: 0.00018 tnm: 0.29 Lm: 6.600 (6.600) Lt: 5.836 (5.836) Accm: 3.10 (3.10) Acct: 4.94 (4.94) proj_loss: -0.5700 (-0.5700) time: 0.6595 data: 0.0003 [11-24 11:39:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [ 0/1669] eta: 0:18:20 tlr: 0.00018 tnm: 0.29 Lm: 6.670 (6.670) Lt: 5.917 (5.917) Accm: 2.83 (2.83) Acct: 4.65 (4.65) proj_loss: -0.5731 (-0.5731) time: 0.6596 data: 0.0003 [11-24 11:39:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [ 0/1669] eta: 0:18:20 tlr: 0.00018 tnm: 0.29 Lm: 6.404 (6.404) Lt: 5.631 (5.631) Accm: 3.43 (3.43) Acct: 5.18 (5.18) proj_loss: -0.5801 (-0.5801) time: 0.6596 data: 0.0004 [11-24 11:39:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [ 0/1669] eta: 0:18:20 tlr: 0.00018 tnm: 0.29 Lm: 6.428 (6.428) Lt: 5.655 (5.655) Accm: 3.73 (3.73) Acct: 6.08 (6.08) proj_loss: -0.5717 (-0.5717) time: 0.6596 data: 0.0004 [11-24 11:44:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.28 Lm: 6.542 (6.542) Lt: 5.809 (5.809) Accm: 3.31 (3.31) Acct: 5.32 (5.32) proj_loss: -0.5751 (-0.5751) time: 0.6751 data: 0.0002 [11-24 11:44:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.28 Lm: 6.460 (6.460) Lt: 5.683 (5.683) Accm: 3.36 (3.36) Acct: 5.09 (5.09) proj_loss: -0.5721 (-0.5721) time: 0.6751 data: 0.0003 [11-24 11:44:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.28 Lm: 6.620 (6.620) Lt: 5.882 (5.882) Accm: 2.84 (2.84) Acct: 4.53 (4.53) proj_loss: -0.5848 (-0.5848) time: 0.6751 data: 0.0003 [11-24 11:44:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.28 Lm: 6.593 (6.593) Lt: 5.818 (5.818) Accm: 3.16 (3.16) Acct: 4.94 (4.94) proj_loss: -0.5744 (-0.5744) time: 0.6751 data: 0.0003 [11-24 11:49:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [ 834/1669] eta: 0:09:23 tlr: 0.00018 tnm: 0.29 Lm: 6.600 (6.608) Lt: 5.836 (5.855) Accm: 3.15 (3.16) Acct: 4.94 (4.98) proj_loss: -0.5787 (-0.5764) time: 0.6766 data: 0.0003 [11-24 11:49:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [ 834/1669] eta: 0:09:23 tlr: 0.00018 tnm: 0.29 Lm: 6.569 (6.578) Lt: 5.848 (5.851) Accm: 2.85 (3.04) Acct: 4.65 (4.72) proj_loss: -0.5911 (-0.5869) time: 0.6766 data: 0.0003 [11-24 11:49:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [ 834/1669] eta: 0:09:23 tlr: 0.00018 tnm: 0.29 Lm: 6.516 (6.519) Lt: 5.734 (5.757) Accm: 3.29 (3.32) Acct: 5.18 (5.17) proj_loss: -0.5801 (-0.5775) time: 0.6766 data: 0.0003 [11-24 11:49:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [ 834/1669] eta: 0:09:23 tlr: 0.00018 tnm: 0.29 Lm: 6.572 (6.552) Lt: 5.827 (5.815) Accm: 3.25 (3.29) Acct: 5.08 (5.24) proj_loss: -0.5785 (-0.5819) time: 0.6766 data: 0.0002 [11-24 11:53:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.28 Lm: 6.614 (6.597) Lt: 5.895 (5.868) Accm: 3.07 (3.16) Acct: 4.82 (5.02) proj_loss: -0.5781 (-0.5809) time: 0.6789 data: 0.0003 [11-24 11:53:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.28 Lm: 6.558 (6.539) Lt: 5.771 (5.769) Accm: 3.27 (3.30) Acct: 5.25 (5.23) proj_loss: -0.5726 (-0.5744) time: 0.6789 data: 0.0003 [11-24 11:53:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.28 Lm: 6.594 (6.603) Lt: 5.818 (5.841) Accm: 3.17 (3.17) Acct: 4.95 (4.97) proj_loss: -0.5790 (-0.5771) time: 0.6789 data: 0.0003 [11-24 11:53:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.28 Lm: 6.596 (6.589) Lt: 5.882 (5.873) Accm: 2.88 (3.01) Acct: 4.53 (4.64) proj_loss: -0.5934 (-0.5891) time: 0.6789 data: 0.0003 [11-24 11:58:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.28 Lm: 6.622 (6.605) Lt: 5.917 (5.893) Accm: 2.91 (3.03) Acct: 4.65 (4.70) proj_loss: -0.5911 (-0.5820) time: 0.7420 data: 0.0016 [11-24 11:58:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 104/350] Total time: 0:18:51 (0.678 s / it) [11-24 11:58:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.28 Lm: 6.655 (6.625) Lt: 5.963 (5.893) Accm: 2.88 (3.05) Acct: 4.56 (4.87) proj_loss: -0.5785 (-0.5806) time: 0.7420 data: 0.0018 [11-24 11:58:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.28 Lm: 6.516 (6.527) Lt: 5.734 (5.756) Accm: 3.29 (3.32) Acct: 5.32 (5.30) proj_loss: -0.5801 (-0.5798) time: 0.7420 data: 0.0020 [11-24 11:58:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 104/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.28 Lm: 6.600 (6.624) Lt: 5.836 (5.876) Accm: 3.15 (3.15) Acct: 4.94 (4.92) proj_loss: -0.5793 (-0.5854) time: 0.7420 data: 0.0015 [11-24 11:58:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 104/350] Total time: 0:18:51 (0.678 s / it) [11-24 11:58:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 104/350] Total time: 0:18:51 (0.678 s / it) [11-24 11:58:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 104/350] Total time: 0:18:51 (0.678 s / it) [11-24 11:58:31] (/home/user/VAR/train.py , line 276)=> [ep104] (training ) Lm: 6.599 (6.606), Lt: 5.842 (5.857), Acc m&t: 3.13 4.95, Remain: 3 days, 5:22:31, Finish: 2024-11-27 01:21 [11-24 11:58:31] (/home/user/VAR/train.py , line 276)=> [ep104] (training ) Lm: 6.599 (6.606), Lt: 5.842 (5.857), Acc m&t: 3.13 4.95, Remain: 3 days, 5:22:52, Finish: 2024-11-27 01:21 [11-24 11:58:31] (/home/user/VAR/train.py , line 276)=> [ep104] (training ) Lm: 6.599 (6.606), Lt: 5.842 (5.857), Acc m&t: 3.13 4.95, Remain: 3 days, 5:22:28, Finish: 2024-11-27 01:20 [11-24 11:58:31] (/home/user/VAR/train.py , line 276)=> [ep104] (training ) Lm: 6.599 (6.606), Lt: 5.842 (5.857), Acc m&t: 3.13 4.95, Remain: 3 days, 5:22:46, Finish: 2024-11-27 01:21 [11-24 11:58:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [ 0/1669] eta: 0:18:01 tlr: 0.00018 tnm: 0.29 Lm: 6.534 (6.534) Lt: 5.745 (5.745) Accm: 3.31 (3.31) Acct: 5.39 (5.39) proj_loss: -0.5735 (-0.5735) time: 0.6480 data: 0.0004 [11-24 11:58:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [ 0/1669] eta: 0:18:01 tlr: 0.00018 tnm: 0.29 Lm: 6.515 (6.515) Lt: 5.765 (5.765) Accm: 3.63 (3.63) Acct: 5.79 (5.79) proj_loss: -0.5931 (-0.5931) time: 0.6482 data: 0.0003 [11-24 11:58:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [ 0/1669] eta: 0:18:01 tlr: 0.00018 tnm: 0.29 Lm: 6.576 (6.576) Lt: 5.826 (5.826) Accm: 3.14 (3.14) Acct: 4.99 (4.99) proj_loss: -0.5742 (-0.5742) time: 0.6480 data: 0.0004 [11-24 11:58:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [ 0/1669] eta: 0:18:01 tlr: 0.00018 tnm: 0.29 Lm: 6.594 (6.594) Lt: 5.875 (5.875) Accm: 3.21 (3.21) Acct: 4.68 (4.68) proj_loss: -0.5850 (-0.5850) time: 0.6479 data: 0.0004 [11-24 12:03:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [ 417/1669] eta: 0:14:58 tlr: 0.00018 tnm: 0.31 Lm: 6.529 (6.529) Lt: 5.812 (5.812) Accm: 3.30 (3.30) Acct: 4.93 (4.93) proj_loss: -0.5819 (-0.5819) time: 0.6744 data: 0.0003 [11-24 12:03:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [ 417/1669] eta: 0:14:58 tlr: 0.00018 tnm: 0.31 Lm: 6.636 (6.636) Lt: 5.875 (5.875) Accm: 3.26 (3.26) Acct: 5.17 (5.17) proj_loss: -0.5781 (-0.5781) time: 0.6744 data: 0.0003 [11-24 12:03:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [ 417/1669] eta: 0:14:58 tlr: 0.00018 tnm: 0.31 Lm: 6.571 (6.571) Lt: 5.780 (5.780) Accm: 3.20 (3.20) Acct: 5.29 (5.29) proj_loss: -0.5810 (-0.5810) time: 0.6744 data: 0.0003 [11-24 12:03:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [ 417/1669] eta: 0:14:58 tlr: 0.00018 tnm: 0.31 Lm: 6.559 (6.559) Lt: 5.817 (5.817) Accm: 3.25 (3.25) Acct: 5.13 (5.13) proj_loss: -0.5723 (-0.5723) time: 0.6744 data: 0.0002 [11-24 12:08:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [ 834/1669] eta: 0:09:42 tlr: 0.00018 tnm: 0.28 Lm: 6.574 (6.564) Lt: 5.822 (5.819) Accm: 3.34 (3.28) Acct: 5.27 (5.23) proj_loss: -0.5742 (-0.5739) time: 0.6788 data: 0.0002 [11-24 12:08:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [ 834/1669] eta: 0:09:42 tlr: 0.00018 tnm: 0.28 Lm: 6.534 (6.536) Lt: 5.745 (5.743) Accm: 3.31 (3.27) Acct: 5.39 (5.39) proj_loss: -0.5885 (-0.5873) time: 0.6788 data: 0.0003 [11-24 12:08:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [ 834/1669] eta: 0:09:42 tlr: 0.00018 tnm: 0.28 Lm: 6.525 (6.528) Lt: 5.792 (5.805) Accm: 3.29 (3.29) Acct: 5.06 (4.98) proj_loss: -0.5788 (-0.5804) time: 0.6788 data: 0.0003 [11-24 12:08:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [ 834/1669] eta: 0:09:42 tlr: 0.00018 tnm: 0.28 Lm: 6.626 (6.632) Lt: 5.873 (5.874) Accm: 2.95 (3.16) Acct: 4.55 (4.96) proj_loss: -0.5696 (-0.5753) time: 0.6788 data: 0.0003 [11-24 12:12:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [1251/1669] eta: 0:04:48 tlr: 0.00018 tnm: 0.29 Lm: 6.571 (6.601) Lt: 5.780 (5.828) Accm: 3.20 (3.14) Acct: 5.29 (5.16) proj_loss: -0.5816 (-0.5841) time: 0.6775 data: 0.0003 [11-24 12:12:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [1251/1669] eta: 0:04:48 tlr: 0.00018 tnm: 0.29 Lm: 6.575 (6.589) Lt: 5.824 (5.850) Accm: 3.24 (3.18) Acct: 5.13 (4.95) proj_loss: -0.5757 (-0.5787) time: 0.6775 data: 0.0002 [11-24 12:12:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [1251/1669] eta: 0:04:48 tlr: 0.00018 tnm: 0.29 Lm: 6.605 (6.620) Lt: 5.849 (5.862) Accm: 3.19 (3.23) Acct: 4.93 (5.05) proj_loss: -0.5664 (-0.5699) time: 0.6775 data: 0.0003 [11-24 12:12:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [1251/1669] eta: 0:04:48 tlr: 0.00018 tnm: 0.29 Lm: 6.544 (6.537) Lt: 5.800 (5.806) Accm: 3.34 (3.36) Acct: 5.12 (5.12) proj_loss: -0.5782 (-0.5769) time: 0.6775 data: 0.0003 [11-24 12:17:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.563 (6.545) Lt: 5.808 (5.811) Accm: 3.29 (3.30) Acct: 5.06 (5.07) proj_loss: -0.5788 (-0.5823) time: 0.6767 data: 0.0015 [11-24 12:17:38] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 105/350] Total time: 0:19:06 (0.687 s / it) [11-24 12:17:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.608 (6.621) Lt: 5.814 (5.847) Accm: 3.09 (3.09) Acct: 5.20 (5.03) proj_loss: -0.5746 (-0.5802) time: 0.6767 data: 0.0016 [11-24 12:17:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.618 (6.620) Lt: 5.873 (5.868) Accm: 3.42 (3.26) Acct: 5.32 (5.13) proj_loss: -0.5696 (-0.5714) time: 0.6767 data: 0.0018 [11-24 12:17:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 105/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.574 (6.576) Lt: 5.822 (5.833) Accm: 3.32 (3.21) Acct: 5.17 (5.00) proj_loss: -0.5772 (-0.5799) time: 0.6767 data: 0.0019 [11-24 12:17:38] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 105/350] Total time: 0:19:06 (0.687 s / it) [11-24 12:17:38] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 105/350] Total time: 0:19:06 (0.687 s / it) [11-24 12:17:38] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 105/350] Total time: 0:19:06 (0.687 s / it) [11-24 12:17:38] (/home/user/VAR/train.py , line 276)=> [ep105] (training ) Lm: 6.599 (6.609), Lt: 5.842 (5.862), Acc m&t: 3.13 4.95, Remain: 3 days, 5:12:31, Finish: 2024-11-27 01:30 [11-24 12:17:38] (/home/user/VAR/train.py , line 276)=> [ep105] (training ) Lm: 6.599 (6.609), Lt: 5.842 (5.862), Acc m&t: 3.13 4.95, Remain: 3 days, 5:12:19, Finish: 2024-11-27 01:29 [11-24 12:17:38] (/home/user/VAR/train.py , line 276)=> [ep105] (training ) Lm: 6.599 (6.609), Lt: 5.842 (5.862), Acc m&t: 3.13 4.95, Remain: 3 days, 5:12:49, Finish: 2024-11-27 01:30 [11-24 12:17:38] (/home/user/VAR/train.py , line 276)=> [ep105] (training ) Lm: 6.599 (6.609), Lt: 5.842 (5.862), Acc m&t: 3.13 4.95, Remain: 3 days, 5:11:44, Finish: 2024-11-27 01:29 [11-24 12:17:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [ 0/1669] eta: 0:18:22 tlr: 0.00018 tnm: 0.30 Lm: 6.613 (6.613) Lt: 5.844 (5.844) Accm: 2.91 (2.91) Acct: 4.87 (4.87) proj_loss: -0.5768 (-0.5768) time: 0.6608 data: 0.0003 [11-24 12:17:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [ 0/1669] eta: 0:18:23 tlr: 0.00018 tnm: 0.30 Lm: 6.466 (6.466) Lt: 5.648 (5.648) Accm: 3.44 (3.44) Acct: 5.17 (5.17) proj_loss: -0.6018 (-0.6018) time: 0.6610 data: 0.0004 [11-24 12:17:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [ 0/1669] eta: 0:18:24 tlr: 0.00018 tnm: 0.30 Lm: 6.729 (6.729) Lt: 6.019 (6.019) Accm: 2.94 (2.94) Acct: 4.56 (4.56) proj_loss: -0.5758 (-0.5758) time: 0.6616 data: 0.0003 [11-24 12:17:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [ 0/1669] eta: 0:18:23 tlr: 0.00018 tnm: 0.30 Lm: 6.632 (6.632) Lt: 5.912 (5.912) Accm: 2.89 (2.89) Acct: 4.42 (4.42) proj_loss: -0.5816 (-0.5816) time: 0.6614 data: 0.0004 [11-24 12:22:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.31 Lm: 6.682 (6.682) Lt: 5.972 (5.972) Accm: 2.77 (2.77) Acct: 4.35 (4.35) proj_loss: -0.5863 (-0.5863) time: 0.6749 data: 0.0003 [11-24 12:22:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.31 Lm: 6.685 (6.685) Lt: 5.932 (5.932) Accm: 3.00 (3.00) Acct: 4.77 (4.77) proj_loss: -0.5685 (-0.5685) time: 0.6749 data: 0.0002 [11-24 12:22:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.31 Lm: 6.543 (6.543) Lt: 5.751 (5.751) Accm: 3.22 (3.22) Acct: 5.29 (5.29) proj_loss: -0.5859 (-0.5859) time: 0.6749 data: 0.0003 [11-24 12:22:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.31 Lm: 6.498 (6.498) Lt: 5.709 (5.709) Accm: 3.32 (3.32) Acct: 5.11 (5.11) proj_loss: -0.5897 (-0.5897) time: 0.6749 data: 0.0003 [11-24 12:27:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [ 834/1669] eta: 0:09:42 tlr: 0.00018 tnm: 0.32 Lm: 6.466 (6.474) Lt: 5.648 (5.645) Accm: 3.44 (3.38) Acct: 5.17 (5.39) proj_loss: -0.5783 (-0.5859) time: 0.6766 data: 0.0003 [11-24 12:27:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [ 834/1669] eta: 0:09:42 tlr: 0.00018 tnm: 0.32 Lm: 6.709 (6.693) Lt: 6.019 (5.969) Accm: 2.94 (2.95) Acct: 4.56 (4.54) proj_loss: -0.5741 (-0.5704) time: 0.6766 data: 0.0003 [11-24 12:27:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [ 834/1669] eta: 0:09:42 tlr: 0.00018 tnm: 0.32 Lm: 6.613 (6.582) Lt: 5.844 (5.796) Accm: 2.91 (3.11) Acct: 4.87 (5.14) proj_loss: -0.5768 (-0.5817) time: 0.6766 data: 0.0003 [11-24 12:27:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [ 834/1669] eta: 0:09:42 tlr: 0.00018 tnm: 0.32 Lm: 6.689 (6.685) Lt: 5.915 (5.953) Accm: 2.89 (2.83) Acct: 4.42 (4.46) proj_loss: -0.5816 (-0.5832) time: 0.6766 data: 0.0003 [11-24 12:32:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.28 Lm: 6.660 (6.640) Lt: 5.913 (5.900) Accm: 2.92 (2.96) Acct: 4.55 (4.71) proj_loss: -0.5800 (-0.5820) time: 0.7357 data: 0.0003 [11-24 12:32:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.28 Lm: 6.623 (6.595) Lt: 5.852 (5.813) Accm: 3.09 (3.15) Acct: 4.98 (5.13) proj_loss: -0.5750 (-0.5790) time: 0.7357 data: 0.0003 [11-24 12:32:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.28 Lm: 6.675 (6.669) Lt: 5.932 (5.933) Accm: 2.89 (2.89) Acct: 4.50 (4.52) proj_loss: -0.5749 (-0.5720) time: 0.7358 data: 0.0003 [11-24 12:32:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.28 Lm: 6.498 (6.521) Lt: 5.709 (5.715) Accm: 3.32 (3.25) Acct: 5.11 (5.18) proj_loss: -0.5786 (-0.5841) time: 0.7357 data: 0.0003 [11-24 12:36:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.32 Lm: 6.530 (6.570) Lt: 5.770 (5.775) Accm: 3.20 (3.15) Acct: 5.04 (5.02) proj_loss: -0.5790 (-0.5843) time: 0.6779 data: 0.0016 [11-24 12:36:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 106/350] Total time: 0:19:09 (0.689 s / it) [11-24 12:36:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.32 Lm: 6.709 (6.689) Lt: 5.942 (5.935) Accm: 2.83 (2.87) Acct: 4.53 (4.52) proj_loss: -0.5741 (-0.5717) time: 0.6779 data: 0.0024 [11-24 12:36:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.32 Lm: 6.613 (6.593) Lt: 5.861 (5.822) Accm: 2.96 (3.11) Acct: 4.87 (5.06) proj_loss: -0.5768 (-0.5856) time: 0.6779 data: 0.0022 [11-24 12:36:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 106/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.32 Lm: 6.632 (6.598) Lt: 5.912 (5.844) Accm: 2.95 (3.09) Acct: 4.68 (4.94) proj_loss: -0.5783 (-0.5768) time: 0.6779 data: 0.0019 [11-24 12:36:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 106/350] Total time: 0:19:09 (0.689 s / it) [11-24 12:36:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 106/350] Total time: 0:19:09 (0.689 s / it) [11-24 12:36:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 106/350] Total time: 0:19:09 (0.689 s / it) [11-24 12:36:47] (/home/user/VAR/train.py , line 276)=> [ep106] (training ) Lm: 6.599 (6.608), Lt: 5.842 (5.855), Acc m&t: 3.13 4.96, Remain: 3 days, 4:49:26, Finish: 2024-11-27 01:26 [11-24 12:36:47] (/home/user/VAR/train.py , line 276)=> [ep106] (training ) Lm: 6.599 (6.608), Lt: 5.842 (5.855), Acc m&t: 3.13 4.96, Remain: 3 days, 4:50:03, Finish: 2024-11-27 01:26 [11-24 12:36:47] (/home/user/VAR/train.py , line 276)=> [ep106] (training ) Lm: 6.599 (6.608), Lt: 5.842 (5.855), Acc m&t: 3.13 4.96, Remain: 3 days, 4:49:51, Finish: 2024-11-27 01:26 [11-24 12:36:47] (/home/user/VAR/train.py , line 276)=> [ep106] (training ) Lm: 6.599 (6.608), Lt: 5.842 (5.855), Acc m&t: 3.13 4.96, Remain: 3 days, 4:50:27, Finish: 2024-11-27 01:27 [11-24 12:36:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [ 0/1669] eta: 0:17:58 tlr: 0.00018 tnm: 0.30 Lm: 6.433 (6.433) Lt: 5.649 (5.649) Accm: 3.66 (3.66) Acct: 5.73 (5.73) proj_loss: -0.5694 (-0.5694) time: 0.6462 data: 0.0003 [11-24 12:36:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [ 0/1669] eta: 0:18:42 tlr: 0.00018 tnm: 0.30 Lm: 6.572 (6.572) Lt: 5.846 (5.846) Accm: 3.21 (3.21) Acct: 5.10 (5.10) proj_loss: -0.5835 (-0.5835) time: 0.6727 data: 0.0003 [11-24 12:36:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [ 0/1669] eta: 0:18:42 tlr: 0.00018 tnm: 0.30 Lm: 6.501 (6.501) Lt: 5.719 (5.719) Accm: 3.40 (3.40) Acct: 5.25 (5.25) proj_loss: -0.5787 (-0.5787) time: 0.6726 data: 0.0004 [11-24 12:36:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [ 0/1669] eta: 0:18:43 tlr: 0.00018 tnm: 0.30 Lm: 6.674 (6.674) Lt: 5.950 (5.950) Accm: 3.13 (3.13) Acct: 4.84 (4.84) proj_loss: -0.5816 (-0.5816) time: 0.6731 data: 0.0004 [11-24 12:41:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.29 Lm: 6.633 (6.633) Lt: 5.887 (5.887) Accm: 3.15 (3.15) Acct: 4.90 (4.90) proj_loss: -0.5842 (-0.5842) time: 0.6756 data: 0.0003 [11-24 12:41:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.29 Lm: 6.553 (6.553) Lt: 5.780 (5.780) Accm: 3.24 (3.24) Acct: 5.03 (5.03) proj_loss: -0.5729 (-0.5729) time: 0.6756 data: 0.0003 [11-24 12:41:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.29 Lm: 6.529 (6.529) Lt: 5.754 (5.754) Accm: 3.48 (3.48) Acct: 5.45 (5.45) proj_loss: -0.5823 (-0.5823) time: 0.6756 data: 0.0003 [11-24 12:41:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.29 Lm: 6.519 (6.519) Lt: 5.763 (5.763) Accm: 3.34 (3.34) Acct: 5.33 (5.33) proj_loss: -0.5804 (-0.5804) time: 0.6756 data: 0.0003 [11-24 12:46:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [ 834/1669] eta: 0:09:24 tlr: 0.00018 tnm: 0.29 Lm: 6.605 (6.563) Lt: 5.877 (5.835) Accm: 3.02 (3.13) Acct: 4.92 (4.93) proj_loss: -0.5855 (-0.5821) time: 0.6742 data: 0.0003 [11-24 12:46:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [ 834/1669] eta: 0:09:24 tlr: 0.00018 tnm: 0.29 Lm: 6.674 (6.664) Lt: 5.950 (5.927) Accm: 3.13 (3.05) Acct: 4.84 (4.81) proj_loss: -0.5846 (-0.5843) time: 0.6742 data: 0.0003 [11-24 12:46:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [ 834/1669] eta: 0:09:24 tlr: 0.00018 tnm: 0.29 Lm: 6.572 (6.607) Lt: 5.846 (5.861) Accm: 3.21 (3.04) Acct: 4.96 (4.69) proj_loss: -0.5835 (-0.5804) time: 0.6742 data: 0.0003 [11-24 12:46:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [ 834/1669] eta: 0:09:24 tlr: 0.00018 tnm: 0.29 Lm: 6.514 (6.524) Lt: 5.725 (5.744) Accm: 3.40 (3.45) Acct: 5.58 (5.49) proj_loss: -0.5859 (-0.5836) time: 0.6742 data: 0.0003 [11-24 12:50:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.32 Lm: 6.527 (6.528) Lt: 5.746 (5.750) Accm: 3.41 (3.44) Acct: 5.54 (5.49) proj_loss: -0.5860 (-0.5896) time: 0.6756 data: 0.0003 [11-24 12:50:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.32 Lm: 6.613 (6.578) Lt: 5.872 (5.843) Accm: 2.98 (3.08) Acct: 4.62 (4.78) proj_loss: -0.5775 (-0.5785) time: 0.6756 data: 0.0003 [11-24 12:50:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.32 Lm: 6.613 (6.618) Lt: 5.855 (5.862) Accm: 3.16 (3.06) Acct: 4.98 (4.76) proj_loss: -0.5772 (-0.5781) time: 0.6756 data: 0.0003 [11-24 12:50:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.32 Lm: 6.701 (6.706) Lt: 5.980 (5.984) Accm: 2.99 (2.88) Acct: 4.73 (4.56) proj_loss: -0.5831 (-0.5814) time: 0.6756 data: 0.0003 [11-24 12:55:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.674 (6.685) Lt: 5.950 (5.959) Accm: 2.90 (2.89) Acct: 4.77 (4.60) proj_loss: -0.5846 (-0.5835) time: 0.7447 data: 0.0016 [11-24 12:55:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 107/350] Total time: 0:18:51 (0.678 s / it) [11-24 12:55:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.572 (6.550) Lt: 5.846 (5.787) Accm: 3.21 (3.25) Acct: 4.99 (5.09) proj_loss: -0.5834 (-0.5791) time: 0.7447 data: 0.0015 [11-24 12:55:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.539 (6.552) Lt: 5.767 (5.775) Accm: 3.40 (3.38) Acct: 5.49 (5.40) proj_loss: -0.5859 (-0.5873) time: 0.7447 data: 0.0019 [11-24 12:55:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 107/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.621 (6.601) Lt: 5.877 (5.859) Accm: 2.95 (3.03) Acct: 4.39 (4.70) proj_loss: -0.5737 (-0.5775) time: 0.7447 data: 0.0014 [11-24 12:55:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 107/350] Total time: 0:18:51 (0.678 s / it) [11-24 12:55:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 107/350] Total time: 0:18:51 (0.678 s / it) [11-24 12:55:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 107/350] Total time: 0:18:51 (0.678 s / it) [11-24 12:55:39] (/home/user/VAR/train.py , line 276)=> [ep107] (training ) Lm: 6.599 (6.605), Lt: 5.842 (5.851), Acc m&t: 3.13 4.96, Remain: 3 days, 4:24:39, Finish: 2024-11-27 01:20 [11-24 12:55:39] (/home/user/VAR/train.py , line 276)=> [ep107] (training ) Lm: 6.599 (6.605), Lt: 5.842 (5.851), Acc m&t: 3.13 4.96, Remain: 3 days, 4:25:07, Finish: 2024-11-27 01:20 [11-24 12:55:39] (/home/user/VAR/train.py , line 276)=> [ep107] (training ) Lm: 6.599 (6.605), Lt: 5.842 (5.851), Acc m&t: 3.13 4.96, Remain: 3 days, 4:25:07, Finish: 2024-11-27 01:20 [11-24 12:55:39] (/home/user/VAR/train.py , line 276)=> [ep107] (training ) Lm: 6.599 (6.605), Lt: 5.842 (5.851), Acc m&t: 3.13 4.96, Remain: 3 days, 4:25:50, Finish: 2024-11-27 01:21 [11-24 12:55:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [ 0/1669] eta: 0:18:28 tlr: 0.00018 tnm: 0.31 Lm: 6.510 (6.510) Lt: 5.701 (5.701) Accm: 3.22 (3.22) Acct: 5.06 (5.06) proj_loss: -0.5734 (-0.5734) time: 0.6640 data: 0.0004 [11-24 12:55:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [ 0/1669] eta: 0:18:28 tlr: 0.00018 tnm: 0.31 Lm: 6.690 (6.690) Lt: 5.930 (5.930) Accm: 2.81 (2.81) Acct: 4.12 (4.12) proj_loss: -0.5773 (-0.5773) time: 0.6640 data: 0.0003 [11-24 12:55:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [ 0/1669] eta: 0:18:29 tlr: 0.00018 tnm: 0.31 Lm: 6.621 (6.621) Lt: 5.896 (5.896) Accm: 2.93 (2.93) Acct: 4.55 (4.55) proj_loss: -0.5562 (-0.5562) time: 0.6645 data: 0.0004 [11-24 12:55:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [ 0/1669] eta: 0:18:22 tlr: 0.00018 tnm: 0.31 Lm: 6.695 (6.695) Lt: 5.923 (5.923) Accm: 2.99 (2.99) Acct: 4.79 (4.79) proj_loss: -0.5785 (-0.5785) time: 0.6606 data: 0.0003 [11-24 13:00:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [ 417/1669] eta: 0:14:59 tlr: 0.00018 tnm: 0.31 Lm: 6.598 (6.598) Lt: 5.816 (5.816) Accm: 3.18 (3.18) Acct: 5.15 (5.15) proj_loss: -0.5736 (-0.5736) time: 0.6761 data: 0.0003 [11-24 13:00:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [ 417/1669] eta: 0:14:59 tlr: 0.00018 tnm: 0.31 Lm: 6.647 (6.647) Lt: 5.916 (5.916) Accm: 2.90 (2.90) Acct: 4.46 (4.46) proj_loss: -0.5753 (-0.5753) time: 0.6761 data: 0.0003 [11-24 13:00:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [ 417/1669] eta: 0:14:59 tlr: 0.00018 tnm: 0.31 Lm: 6.662 (6.662) Lt: 5.889 (5.889) Accm: 2.96 (2.96) Acct: 4.36 (4.36) proj_loss: -0.5865 (-0.5865) time: 0.6761 data: 0.0003 [11-24 13:00:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [ 417/1669] eta: 0:14:59 tlr: 0.00018 tnm: 0.31 Lm: 6.552 (6.552) Lt: 5.785 (5.785) Accm: 3.00 (3.00) Acct: 4.58 (4.58) proj_loss: -0.5725 (-0.5725) time: 0.6761 data: 0.0003 [11-24 13:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [ 834/1669] eta: 0:09:41 tlr: 0.00018 tnm: 0.29 Lm: 6.594 (6.637) Lt: 5.868 (5.896) Accm: 2.78 (2.88) Acct: 4.13 (4.43) proj_loss: -0.5721 (-0.5724) time: 0.6758 data: 0.0003 [11-24 13:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [ 834/1669] eta: 0:09:41 tlr: 0.00018 tnm: 0.29 Lm: 6.621 (6.604) Lt: 5.896 (5.861) Accm: 2.93 (2.94) Acct: 4.55 (4.49) proj_loss: -0.5729 (-0.5745) time: 0.6758 data: 0.0003 [11-24 13:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [ 834/1669] eta: 0:09:41 tlr: 0.00018 tnm: 0.29 Lm: 6.550 (6.582) Lt: 5.807 (5.813) Accm: 3.21 (3.19) Acct: 4.96 (5.08) proj_loss: -0.5688 (-0.5700) time: 0.6758 data: 0.0003 [11-24 13:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [ 834/1669] eta: 0:09:41 tlr: 0.00018 tnm: 0.29 Lm: 6.683 (6.669) Lt: 5.913 (5.897) Accm: 3.05 (2.99) Acct: 4.61 (4.56) proj_loss: -0.5773 (-0.5813) time: 0.6758 data: 0.0003 [11-24 13:10:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [1251/1669] eta: 0:04:48 tlr: 0.00018 tnm: 0.31 Lm: 6.571 (6.563) Lt: 5.823 (5.816) Accm: 2.98 (3.15) Acct: 4.55 (4.80) proj_loss: -0.5837 (-0.5838) time: 0.6752 data: 0.0003 [11-24 13:10:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [1251/1669] eta: 0:04:48 tlr: 0.00018 tnm: 0.31 Lm: 6.648 (6.653) Lt: 5.913 (5.911) Accm: 2.75 (2.84) Acct: 4.24 (4.41) proj_loss: -0.5728 (-0.5768) time: 0.6752 data: 0.0003 [11-24 13:10:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [1251/1669] eta: 0:04:48 tlr: 0.00018 tnm: 0.31 Lm: 6.687 (6.680) Lt: 5.922 (5.941) Accm: 2.93 (2.93) Acct: 4.41 (4.47) proj_loss: -0.5865 (-0.5873) time: 0.6752 data: 0.0003 [11-24 13:10:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [1251/1669] eta: 0:04:48 tlr: 0.00018 tnm: 0.31 Lm: 6.584 (6.591) Lt: 5.841 (5.828) Accm: 3.10 (3.12) Acct: 4.87 (4.98) proj_loss: -0.5736 (-0.5769) time: 0.6752 data: 0.0003 [11-24 13:14:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.617 (6.618) Lt: 5.875 (5.855) Accm: 2.99 (3.08) Acct: 4.94 (4.98) proj_loss: -0.5776 (-0.5770) time: 0.6783 data: 0.0017 [11-24 13:14:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 108/350] Total time: 0:19:05 (0.686 s / it) [11-24 13:14:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.616 (6.573) Lt: 5.807 (5.814) Accm: 3.03 (3.16) Acct: 4.56 (4.88) proj_loss: -0.5729 (-0.5810) time: 0.6783 data: 0.0015 [11-24 13:14:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.668 (6.656) Lt: 5.935 (5.916) Accm: 2.78 (2.86) Acct: 4.36 (4.48) proj_loss: -0.5734 (-0.5779) time: 0.6783 data: 0.0018 [11-24 13:14:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 108/350] Total time: 0:19:05 (0.686 s / it) [11-24 13:14:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 108/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.683 (6.658) Lt: 5.913 (5.927) Accm: 3.05 (2.96) Acct: 4.61 (4.56) proj_loss: -0.5956 (-0.5910) time: 0.6783 data: 0.0021 [11-24 13:14:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 108/350] Total time: 0:19:05 (0.686 s / it) [11-24 13:14:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 108/350] Total time: 0:19:05 (0.686 s / it) [11-24 13:14:44] (/home/user/VAR/train.py , line 276)=> [ep108] (training ) Lm: 6.599 (6.600), Lt: 5.842 (5.848), Acc m&t: 3.13 4.96, Remain: 3 days, 4:24:07, Finish: 2024-11-27 01:38 [11-24 13:14:44] (/home/user/VAR/train.py , line 276)=> [ep108] (training ) Lm: 6.599 (6.600), Lt: 5.842 (5.848), Acc m&t: 3.13 4.96, Remain: 3 days, 4:23:24, Finish: 2024-11-27 01:38 [11-24 13:14:44] (/home/user/VAR/train.py , line 276)=> [ep108] (training ) Lm: 6.599 (6.600), Lt: 5.842 (5.848), Acc m&t: 3.13 4.96, Remain: 3 days, 4:23:11, Finish: 2024-11-27 01:37 [11-24 13:14:44] (/home/user/VAR/train.py , line 276)=> [ep108] (training ) Lm: 6.599 (6.600), Lt: 5.842 (5.848), Acc m&t: 3.13 4.96, Remain: 3 days, 4:23:40, Finish: 2024-11-27 01:38 [11-24 13:14:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [ 0/1669] eta: 0:18:16 tlr: 0.00018 tnm: 0.30 Lm: 6.481 (6.481) Lt: 5.717 (5.717) Accm: 3.84 (3.84) Acct: 5.97 (5.97) proj_loss: -0.5854 (-0.5854) time: 0.6571 data: 0.0003 [11-24 13:14:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [ 0/1669] eta: 0:18:17 tlr: 0.00018 tnm: 0.30 Lm: 6.676 (6.676) Lt: 5.940 (5.940) Accm: 2.98 (2.98) Acct: 4.67 (4.67) proj_loss: -0.5813 (-0.5813) time: 0.6573 data: 0.0004 [11-24 13:14:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [ 0/1669] eta: 0:18:17 tlr: 0.00018 tnm: 0.30 Lm: 6.742 (6.742) Lt: 5.986 (5.986) Accm: 2.79 (2.79) Acct: 4.30 (4.30) proj_loss: -0.5810 (-0.5810) time: 0.6575 data: 0.0004 [11-24 13:14:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [ 0/1669] eta: 0:18:16 tlr: 0.00018 tnm: 0.30 Lm: 6.571 (6.571) Lt: 5.833 (5.833) Accm: 3.23 (3.23) Acct: 4.98 (4.98) proj_loss: -0.5709 (-0.5709) time: 0.6568 data: 0.0004 [11-24 13:19:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.30 Lm: 6.540 (6.540) Lt: 5.775 (5.775) Accm: 3.22 (3.22) Acct: 5.12 (5.12) proj_loss: -0.5766 (-0.5766) time: 0.6749 data: 0.0003 [11-24 13:19:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.30 Lm: 6.626 (6.626) Lt: 5.888 (5.888) Accm: 3.07 (3.07) Acct: 4.78 (4.78) proj_loss: -0.5923 (-0.5923) time: 0.6749 data: 0.0003 [11-24 13:19:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.30 Lm: 6.684 (6.684) Lt: 5.918 (5.918) Accm: 2.87 (2.87) Acct: 4.45 (4.45) proj_loss: -0.5759 (-0.5759) time: 0.6749 data: 0.0003 [11-24 13:19:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.30 Lm: 6.517 (6.517) Lt: 5.751 (5.751) Accm: 3.58 (3.58) Acct: 5.74 (5.74) proj_loss: -0.5814 (-0.5814) time: 0.6749 data: 0.0003 [11-24 13:24:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [ 834/1669] eta: 0:09:44 tlr: 0.00018 tnm: 0.31 Lm: 6.616 (6.623) Lt: 5.859 (5.878) Accm: 3.02 (3.06) Acct: 4.89 (4.89) proj_loss: -0.5964 (-0.5937) time: 0.6779 data: 0.0003 [11-24 13:24:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [ 834/1669] eta: 0:09:44 tlr: 0.00018 tnm: 0.31 Lm: 6.542 (6.526) Lt: 5.761 (5.754) Accm: 3.31 (3.43) Acct: 5.51 (5.52) proj_loss: -0.5773 (-0.5786) time: 0.6779 data: 0.0003 [11-24 13:24:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [ 834/1669] eta: 0:09:44 tlr: 0.00018 tnm: 0.31 Lm: 6.626 (6.631) Lt: 5.850 (5.880) Accm: 2.94 (3.08) Acct: 4.60 (4.72) proj_loss: -0.5810 (-0.5860) time: 0.6779 data: 0.0003 [11-24 13:24:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [ 834/1669] eta: 0:09:44 tlr: 0.00018 tnm: 0.31 Lm: 6.567 (6.549) Lt: 5.810 (5.786) Accm: 3.23 (3.22) Acct: 5.11 (5.12) proj_loss: -0.5709 (-0.5729) time: 0.6779 data: 0.0003 [11-24 13:29:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.30 Lm: 6.569 (6.561) Lt: 5.801 (5.788) Accm: 3.23 (3.24) Acct: 5.19 (5.16) proj_loss: -0.5766 (-0.5776) time: 0.6766 data: 0.0003 [11-24 13:29:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.30 Lm: 6.645 (6.635) Lt: 5.899 (5.901) Accm: 3.04 (3.06) Acct: 4.78 (4.83) proj_loss: -0.5923 (-0.5923) time: 0.6766 data: 0.0003 [11-24 13:29:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.30 Lm: 6.511 (6.506) Lt: 5.739 (5.736) Accm: 3.38 (3.43) Acct: 5.64 (5.58) proj_loss: -0.5754 (-0.5773) time: 0.6766 data: 0.0003 [11-24 13:29:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.30 Lm: 6.635 (6.634) Lt: 5.851 (5.873) Accm: 3.05 (3.10) Acct: 4.82 (4.80) proj_loss: -0.5802 (-0.5843) time: 0.6766 data: 0.0003 [11-24 13:33:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.626 (6.601) Lt: 5.850 (5.839) Accm: 3.15 (3.13) Acct: 5.04 (4.85) proj_loss: -0.5810 (-0.5875) time: 0.6791 data: 0.0017 [11-24 13:33:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 109/350] Total time: 0:19:11 (0.690 s / it) [11-24 13:33:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.616 (6.613) Lt: 5.859 (5.872) Accm: 3.05 (3.12) Acct: 4.89 (4.96) proj_loss: -0.5915 (-0.5921) time: 0.6791 data: 0.0016 [11-24 13:33:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.542 (6.516) Lt: 5.761 (5.744) Accm: 3.31 (3.36) Acct: 5.51 (5.44) proj_loss: -0.5773 (-0.5776) time: 0.6791 data: 0.0015 [11-24 13:33:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 109/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.30 Lm: 6.567 (6.554) Lt: 5.792 (5.788) Accm: 3.23 (3.23) Acct: 5.11 (5.14) proj_loss: -0.5824 (-0.5797) time: 0.6791 data: 0.0019 [11-24 13:33:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 109/350] Total time: 0:19:11 (0.690 s / it) [11-24 13:33:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 109/350] Total time: 0:19:11 (0.690 s / it) [11-24 13:33:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 109/350] Total time: 0:19:11 (0.690 s / it) [11-24 13:36:15] (home/user/VAR/trainer.py, line 114)=> FID: 4.112415111951066 [11-24 13:36:16] (/home/user/VAR/train.py , line 259)=> [*] [ep109] (val 50000) Lm: 6.5996, Lt: 5.8495, Acc m&t: 3.12 4.95, Val cost: 139.91s [11-24 13:36:16] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-24 13:36:36] (/home/user/VAR/train.py , line 276)=> [ep109] (training ) Lm: 6.599 (6.600), Lt: 5.842 (5.850), Acc m&t: 3.13 4.96, Remain: 3 days, 4:08:53, Finish: 2024-11-27 01:42 [11-24 13:36:36] (/home/user/VAR/train.py , line 276)=> [ep109] (training ) Lm: 6.599 (6.600), Lt: 5.842 (5.850), Acc m&t: 3.13 4.96, Remain: 3 days, 4:10:24, Finish: 2024-11-27 01:44 [11-24 13:36:36] (/home/user/VAR/train.py , line 276)=> [ep109] (training ) Lm: 6.599 (6.600), Lt: 5.842 (5.850), Acc m&t: 3.13 4.96, Remain: 3 days, 4:09:22, Finish: 2024-11-27 01:43 [11-24 13:36:36] (/home/user/VAR/train.py , line 276)=> [ep109] (training ) Lm: 6.599 (6.600), Lt: 5.842 (5.850), Acc m&t: 3.13 4.96, Remain: 3 days, 4:09:24, Finish: 2024-11-27 01:43 [11-24 13:36:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [ 0/1669] eta: 0:18:36 tlr: 0.00018 tnm: 0.32 Lm: 6.559 (6.559) Lt: 5.841 (5.841) Accm: 3.26 (3.26) Acct: 5.06 (5.06) proj_loss: -0.5725 (-0.5725) time: 0.6687 data: 0.0004 [11-24 13:36:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [ 0/1669] eta: 0:18:37 tlr: 0.00018 tnm: 0.32 Lm: 6.690 (6.690) Lt: 5.966 (5.966) Accm: 2.86 (2.86) Acct: 4.44 (4.44) proj_loss: -0.5841 (-0.5841) time: 0.6695 data: 0.0004 [11-24 13:36:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [ 0/1669] eta: 0:18:36 tlr: 0.00018 tnm: 0.32 Lm: 6.605 (6.605) Lt: 5.902 (5.902) Accm: 2.94 (2.94) Acct: 4.77 (4.77) proj_loss: -0.5929 (-0.5929) time: 0.6688 data: 0.0004 [11-24 13:36:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [ 0/1669] eta: 0:18:30 tlr: 0.00018 tnm: 0.32 Lm: 6.704 (6.704) Lt: 5.946 (5.946) Accm: 2.82 (2.82) Acct: 4.58 (4.58) proj_loss: -0.5710 (-0.5710) time: 0.6652 data: 0.0005 [11-24 13:41:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.31 Lm: 6.747 (6.747) Lt: 5.988 (5.988) Accm: 2.73 (2.73) Acct: 4.29 (4.29) proj_loss: -0.5743 (-0.5743) time: 0.6768 data: 0.0003 [11-24 13:41:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.31 Lm: 6.628 (6.628) Lt: 5.916 (5.916) Accm: 2.96 (2.96) Acct: 4.78 (4.78) proj_loss: -0.5901 (-0.5901) time: 0.6768 data: 0.0003 [11-24 13:41:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.31 Lm: 6.630 (6.630) Lt: 5.921 (5.921) Accm: 3.10 (3.10) Acct: 4.84 (4.84) proj_loss: -0.5724 (-0.5724) time: 0.6768 data: 0.0003 [11-24 13:41:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [ 417/1669] eta: 0:14:06 tlr: 0.00018 tnm: 0.31 Lm: 6.644 (6.644) Lt: 5.906 (5.906) Accm: 3.06 (3.06) Acct: 4.79 (4.79) proj_loss: -0.5698 (-0.5698) time: 0.6768 data: 0.0003 [11-24 13:46:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [ 834/1669] eta: 0:09:24 tlr: 0.00018 tnm: 0.30 Lm: 6.559 (6.579) Lt: 5.841 (5.854) Accm: 3.18 (3.13) Acct: 5.06 (4.95) proj_loss: -0.5723 (-0.5689) time: 0.6779 data: 0.0003 [11-24 13:46:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [ 834/1669] eta: 0:09:24 tlr: 0.00018 tnm: 0.30 Lm: 6.704 (6.708) Lt: 5.946 (5.942) Accm: 2.82 (2.88) Acct: 4.58 (4.46) proj_loss: -0.5716 (-0.5734) time: 0.6779 data: 0.0003 [11-24 13:46:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [ 834/1669] eta: 0:09:24 tlr: 0.00018 tnm: 0.30 Lm: 6.605 (6.598) Lt: 5.902 (5.882) Accm: 2.97 (3.03) Acct: 4.79 (4.82) proj_loss: -0.5872 (-0.5833) time: 0.6779 data: 0.0003 [11-24 13:46:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [ 834/1669] eta: 0:09:24 tlr: 0.00018 tnm: 0.30 Lm: 6.690 (6.677) Lt: 5.966 (5.932) Accm: 2.86 (2.96) Acct: 4.44 (4.59) proj_loss: -0.5685 (-0.5693) time: 0.6779 data: 0.0003 [11-24 13:50:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.29 Lm: 6.644 (6.656) Lt: 5.906 (5.907) Accm: 2.86 (2.93) Acct: 4.43 (4.55) proj_loss: -0.5763 (-0.5732) time: 0.6747 data: 0.0003 [11-24 13:50:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.29 Lm: 6.630 (6.625) Lt: 5.921 (5.903) Accm: 3.06 (3.01) Acct: 4.84 (4.69) proj_loss: -0.5724 (-0.5734) time: 0.6747 data: 0.0003 [11-24 13:50:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.29 Lm: 6.624 (6.610) Lt: 5.901 (5.887) Accm: 3.00 (3.03) Acct: 4.84 (4.85) proj_loss: -0.5819 (-0.5816) time: 0.6747 data: 0.0003 [11-24 13:50:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [1251/1669] eta: 0:04:42 tlr: 0.00018 tnm: 0.29 Lm: 6.667 (6.688) Lt: 5.928 (5.934) Accm: 2.94 (2.92) Acct: 4.69 (4.55) proj_loss: -0.5746 (-0.5769) time: 0.6747 data: 0.0003 [11-24 13:55:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.32 Lm: 6.630 (6.640) Lt: 5.909 (5.882) Accm: 3.05 (3.02) Acct: 4.80 (4.79) proj_loss: -0.5776 (-0.5782) time: 0.7406 data: 0.0019 [11-24 13:55:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 110/350] Total time: 0:18:52 (0.679 s / it) [11-24 13:55:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.32 Lm: 6.605 (6.595) Lt: 5.900 (5.867) Accm: 3.03 (3.08) Acct: 4.89 (4.93) proj_loss: -0.5784 (-0.5810) time: 0.7406 data: 0.0015 [11-24 13:55:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.32 Lm: 6.559 (6.586) Lt: 5.841 (5.846) Accm: 3.18 (3.11) Acct: 5.06 (4.88) proj_loss: -0.5725 (-0.5739) time: 0.7406 data: 0.0016 [11-24 13:55:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 110/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.32 Lm: 6.630 (6.650) Lt: 5.857 (5.897) Accm: 2.86 (3.00) Acct: 4.44 (4.67) proj_loss: -0.5841 (-0.5771) time: 0.7406 data: 0.0018 [11-24 13:55:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 110/350] Total time: 0:18:52 (0.679 s / it) [11-24 13:55:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 110/350] Total time: 0:18:52 (0.679 s / it) [11-24 13:55:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 110/350] Total time: 0:18:52 (0.679 s / it) [11-24 13:55:29] (/home/user/VAR/train.py , line 276)=> [ep110] (training ) Lm: 6.599 (6.609), Lt: 5.842 (5.858), Acc m&t: 3.13 4.96, Remain: 3 days, 3:42:12, Finish: 2024-11-27 01:37 [11-24 13:55:29] (/home/user/VAR/train.py , line 276)=> [ep110] (training ) Lm: 6.599 (6.609), Lt: 5.842 (5.858), Acc m&t: 3.13 4.96, Remain: 3 days, 3:41:54, Finish: 2024-11-27 01:37 [11-24 13:55:29] (/home/user/VAR/train.py , line 276)=> [ep110] (training ) Lm: 6.599 (6.609), Lt: 5.842 (5.858), Acc m&t: 3.13 4.96, Remain: 3 days, 3:40:59, Finish: 2024-11-27 01:36 [11-24 13:55:29] (/home/user/VAR/train.py , line 276)=> [ep110] (training ) Lm: 6.599 (6.609), Lt: 5.842 (5.858), Acc m&t: 3.13 4.96, Remain: 3 days, 3:40:29, Finish: 2024-11-27 01:35 [11-24 13:55:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [ 0/1669] eta: 0:18:21 tlr: 0.00018 tnm: 0.30 Lm: 6.360 (6.360) Lt: 5.599 (5.599) Accm: 3.77 (3.77) Acct: 5.97 (5.97) proj_loss: -0.5893 (-0.5893) time: 0.6602 data: 0.0004 [11-24 13:55:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [ 0/1669] eta: 0:18:22 tlr: 0.00018 tnm: 0.30 Lm: 6.528 (6.528) Lt: 5.766 (5.766) Accm: 3.49 (3.49) Acct: 5.32 (5.32) proj_loss: -0.5741 (-0.5741) time: 0.6605 data: 0.0004 [11-24 13:55:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [ 0/1669] eta: 0:18:33 tlr: 0.00018 tnm: 0.30 Lm: 6.557 (6.557) Lt: 5.802 (5.802) Accm: 3.53 (3.53) Acct: 5.44 (5.44) proj_loss: -0.6006 (-0.6006) time: 0.6671 data: 0.0004 [11-24 13:55:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [ 0/1669] eta: 0:18:22 tlr: 0.00018 tnm: 0.30 Lm: 6.656 (6.656) Lt: 5.913 (5.913) Accm: 2.78 (2.78) Acct: 4.32 (4.32) proj_loss: -0.5949 (-0.5949) time: 0.6604 data: 0.0003 [11-24 14:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [ 417/1669] eta: 0:15:11 tlr: 0.00018 tnm: 0.31 Lm: 6.597 (6.597) Lt: 5.847 (5.847) Accm: 3.05 (3.05) Acct: 4.86 (4.86) proj_loss: -0.5882 (-0.5882) time: 0.6760 data: 0.0003 [11-24 14:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [ 417/1669] eta: 0:15:11 tlr: 0.00018 tnm: 0.31 Lm: 6.597 (6.597) Lt: 5.824 (5.824) Accm: 3.14 (3.14) Acct: 4.92 (4.92) proj_loss: -0.5803 (-0.5803) time: 0.6760 data: 0.0003 [11-24 14:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [ 417/1669] eta: 0:15:11 tlr: 0.00018 tnm: 0.31 Lm: 6.605 (6.605) Lt: 5.850 (5.850) Accm: 3.25 (3.25) Acct: 5.09 (5.09) proj_loss: -0.5775 (-0.5775) time: 0.6760 data: 0.0004 [11-24 14:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [ 417/1669] eta: 0:15:11 tlr: 0.00018 tnm: 0.31 Lm: 6.501 (6.501) Lt: 5.756 (5.756) Accm: 3.31 (3.31) Acct: 5.08 (5.08) proj_loss: -0.5927 (-0.5927) time: 0.6760 data: 0.0003 [11-24 14:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [ 834/1669] eta: 0:09:46 tlr: 0.00018 tnm: 0.29 Lm: 6.642 (6.592) Lt: 5.913 (5.851) Accm: 2.86 (3.14) Acct: 4.49 (4.88) proj_loss: -0.5960 (-0.5955) time: 0.6769 data: 0.0003 [11-24 14:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [ 834/1669] eta: 0:09:46 tlr: 0.00018 tnm: 0.29 Lm: 6.638 (6.645) Lt: 5.846 (5.894) Accm: 2.75 (3.01) Acct: 4.41 (4.70) proj_loss: -0.5803 (-0.5803) time: 0.6769 data: 0.0003 [11-24 14:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [ 834/1669] eta: 0:09:46 tlr: 0.00018 tnm: 0.29 Lm: 6.538 (6.570) Lt: 5.780 (5.817) Accm: 3.32 (3.18) Acct: 5.27 (4.99) proj_loss: -0.5905 (-0.5890) time: 0.6769 data: 0.0003 [11-24 14:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [ 834/1669] eta: 0:09:46 tlr: 0.00018 tnm: 0.29 Lm: 6.528 (6.576) Lt: 5.831 (5.844) Accm: 3.02 (3.17) Acct: 4.86 (4.95) proj_loss: -0.5809 (-0.5940) time: 0.6769 data: 0.0004 [11-24 14:09:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.30 Lm: 6.557 (6.579) Lt: 5.833 (5.841) Accm: 3.05 (3.15) Acct: 4.89 (4.95) proj_loss: -0.5840 (-0.5923) time: 0.6780 data: 0.0003 [11-24 14:09:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.30 Lm: 6.638 (6.603) Lt: 5.891 (5.855) Accm: 2.97 (3.13) Acct: 4.59 (4.83) proj_loss: -0.5927 (-0.5884) time: 0.6780 data: 0.0003 [11-24 14:09:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.30 Lm: 6.597 (6.594) Lt: 5.824 (5.839) Accm: 3.04 (3.09) Acct: 4.92 (4.89) proj_loss: -0.5805 (-0.5804) time: 0.6780 data: 0.0003 [11-24 14:09:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [1251/1669] eta: 0:04:49 tlr: 0.00018 tnm: 0.30 Lm: 6.597 (6.606) Lt: 5.847 (5.861) Accm: 3.08 (3.09) Acct: 4.88 (4.87) proj_loss: -0.5860 (-0.5851) time: 0.6780 data: 0.0003 [11-24 14:14:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.656 (6.625) Lt: 5.913 (5.887) Accm: 2.85 (3.04) Acct: 4.51 (4.80) proj_loss: -0.5905 (-0.5868) time: 0.6777 data: 0.0021 [11-24 14:14:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 111/350] Total time: 0:19:10 (0.689 s / it) [11-24 14:14:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.635 (6.594) Lt: 5.870 (5.855) Accm: 3.09 (3.15) Acct: 4.68 (4.83) proj_loss: -0.5893 (-0.5880) time: 0.6777 data: 0.0016 [11-24 14:14:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.634 (6.602) Lt: 5.846 (5.849) Accm: 2.89 (3.05) Acct: 4.56 (4.83) proj_loss: -0.5807 (-0.5821) time: 0.6777 data: 0.0014 [11-24 14:14:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 111/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.586 (6.581) Lt: 5.835 (5.840) Accm: 3.08 (3.14) Acct: 4.86 (4.91) proj_loss: -0.5809 (-0.5832) time: 0.6777 data: 0.0018 [11-24 14:14:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 111/350] Total time: 0:19:10 (0.689 s / it) [11-24 14:14:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 111/350] Total time: 0:19:10 (0.689 s / it) [11-24 14:14:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 111/350] Total time: 0:19:10 (0.689 s / it) [11-24 14:14:39] (/home/user/VAR/train.py , line 276)=> [ep111] (training ) Lm: 6.599 (6.601), Lt: 5.842 (5.846), Acc m&t: 3.13 4.96, Remain: 3 days, 3:09:39, Finish: 2024-11-27 01:24 [11-24 14:14:39] (/home/user/VAR/train.py , line 276)=> [ep111] (training ) Lm: 6.599 (6.601), Lt: 5.842 (5.846), Acc m&t: 3.13 4.96, Remain: 3 days, 3:09:45, Finish: 2024-11-27 01:24 [11-24 14:14:39] (/home/user/VAR/train.py , line 276)=> [ep111] (training ) Lm: 6.599 (6.601), Lt: 5.842 (5.846), Acc m&t: 3.13 4.96, Remain: 3 days, 3:09:55, Finish: 2024-11-27 01:24 [11-24 14:14:39] (/home/user/VAR/train.py , line 276)=> [ep111] (training ) Lm: 6.599 (6.601), Lt: 5.842 (5.846), Acc m&t: 3.13 4.96, Remain: 3 days, 3:10:10, Finish: 2024-11-27 01:24 [11-24 14:14:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [ 0/1669] eta: 0:18:41 tlr: 0.00018 tnm: 0.29 Lm: 6.580 (6.580) Lt: 5.804 (5.804) Accm: 2.97 (2.97) Acct: 4.73 (4.73) proj_loss: -0.5849 (-0.5849) time: 0.6720 data: 0.0003 [11-24 14:14:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [ 0/1669] eta: 0:18:42 tlr: 0.00018 tnm: 0.29 Lm: 6.648 (6.648) Lt: 5.874 (5.874) Accm: 3.03 (3.03) Acct: 4.72 (4.72) proj_loss: -0.5875 (-0.5875) time: 0.6725 data: 0.0004 [11-24 14:14:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [ 0/1669] eta: 0:18:42 tlr: 0.00018 tnm: 0.29 Lm: 6.725 (6.725) Lt: 5.998 (5.998) Accm: 2.77 (2.77) Acct: 4.42 (4.42) proj_loss: -0.5651 (-0.5651) time: 0.6725 data: 0.0004 [11-24 14:14:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [ 0/1669] eta: 0:18:41 tlr: 0.00018 tnm: 0.29 Lm: 6.705 (6.705) Lt: 6.014 (6.014) Accm: 2.64 (2.64) Acct: 3.94 (3.94) proj_loss: -0.6146 (-0.6146) time: 0.6722 data: 0.0007 [11-24 14:19:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.32 Lm: 6.699 (6.699) Lt: 5.973 (5.973) Accm: 2.68 (2.68) Acct: 4.24 (4.24) proj_loss: -0.6131 (-0.6131) time: 0.6784 data: 0.0003 [11-24 14:19:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.32 Lm: 6.651 (6.651) Lt: 5.833 (5.833) Accm: 2.92 (2.92) Acct: 4.78 (4.78) proj_loss: -0.5869 (-0.5869) time: 0.6784 data: 0.0003 [11-24 14:19:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.32 Lm: 6.644 (6.644) Lt: 5.894 (5.894) Accm: 3.06 (3.06) Acct: 5.08 (5.08) proj_loss: -0.5723 (-0.5723) time: 0.6784 data: 0.0003 [11-24 14:19:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [ 417/1669] eta: 0:14:05 tlr: 0.00018 tnm: 0.32 Lm: 6.611 (6.611) Lt: 5.850 (5.850) Accm: 2.95 (2.95) Acct: 4.67 (4.67) proj_loss: -0.5829 (-0.5829) time: 0.6784 data: 0.0003 [11-24 14:24:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [ 834/1669] eta: 0:09:46 tlr: 0.00018 tnm: 0.28 Lm: 6.588 (6.604) Lt: 5.876 (5.859) Accm: 2.97 (2.98) Acct: 4.60 (4.64) proj_loss: -0.5849 (-0.5859) time: 0.6770 data: 0.0003 [11-24 14:24:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [ 834/1669] eta: 0:09:46 tlr: 0.00018 tnm: 0.28 Lm: 6.580 (6.623) Lt: 5.864 (5.884) Accm: 3.21 (3.11) Acct: 4.89 (5.02) proj_loss: -0.5795 (-0.5803) time: 0.6770 data: 0.0003 [11-24 14:24:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [ 834/1669] eta: 0:09:46 tlr: 0.00018 tnm: 0.28 Lm: 6.692 (6.638) Lt: 5.932 (5.888) Accm: 2.72 (2.87) Acct: 4.55 (4.62) proj_loss: -0.6117 (-0.5973) time: 0.6770 data: 0.0003 [11-24 14:24:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [ 834/1669] eta: 0:09:46 tlr: 0.00018 tnm: 0.28 Lm: 6.648 (6.609) Lt: 5.792 (5.812) Accm: 3.03 (3.13) Acct: 4.84 (5.00) proj_loss: -0.5863 (-0.5853) time: 0.6771 data: 0.0003 [11-24 14:29:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [1251/1669] eta: 0:04:50 tlr: 0.00018 tnm: 0.30 Lm: 6.651 (6.625) Lt: 5.833 (5.829) Accm: 2.99 (3.09) Acct: 4.82 (4.95) proj_loss: -0.5842 (-0.5788) time: 0.6762 data: 0.0003 [11-24 14:29:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [1251/1669] eta: 0:04:50 tlr: 0.00018 tnm: 0.30 Lm: 6.584 (6.581) Lt: 5.840 (5.831) Accm: 3.01 (3.10) Acct: 4.67 (4.79) proj_loss: -0.5883 (-0.5874) time: 0.6762 data: 0.0003 [11-24 14:29:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [1251/1669] eta: 0:04:50 tlr: 0.00018 tnm: 0.30 Lm: 6.699 (6.694) Lt: 5.973 (5.957) Accm: 2.68 (2.75) Acct: 4.24 (4.36) proj_loss: -0.5985 (-0.5943) time: 0.6762 data: 0.0003 [11-24 14:29:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [1251/1669] eta: 0:04:50 tlr: 0.00018 tnm: 0.30 Lm: 6.579 (6.612) Lt: 5.827 (5.851) Accm: 3.23 (3.15) Acct: 5.24 (5.16) proj_loss: -0.5773 (-0.5790) time: 0.6762 data: 0.0003 [11-24 14:33:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.579 (6.570) Lt: 5.791 (5.810) Accm: 3.25 (3.33) Acct: 5.60 (5.47) proj_loss: -0.5795 (-0.5791) time: 0.6756 data: 0.0016 [11-24 14:33:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.653 (6.632) Lt: 5.874 (5.846) Accm: 2.95 (3.05) Acct: 4.80 (4.89) proj_loss: -0.5863 (-0.5829) time: 0.6756 data: 0.0017 [11-24 14:33:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.692 (6.681) Lt: 5.932 (5.937) Accm: 2.72 (2.78) Acct: 4.55 (4.41) proj_loss: -0.5852 (-0.5881) time: 0.6756 data: 0.0017 [11-24 14:33:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 112/350] Total time: 0:19:13 (0.691 s / it) [11-24 14:33:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 112/350] [1668/1669] eta: 0:00:00 tlr: 0.00018 tnm: 0.29 Lm: 6.588 (6.587) Lt: 5.841 (5.833) Accm: 3.04 (3.16) Acct: 4.73 (4.89) proj_loss: -0.5849 (-0.5848) time: 0.6756 data: 0.0014 [11-24 14:33:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 112/350] Total time: 0:19:13 (0.691 s / it) [11-24 14:33:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 112/350] Total time: 0:19:13 (0.691 s / it) [11-24 14:33:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 112/350] Total time: 0:19:13 (0.691 s / it) [11-24 14:33:53] (/home/user/VAR/train.py , line 276)=> [ep112] (training ) Lm: 6.598 (6.598), Lt: 5.842 (5.845), Acc m&t: 3.14 4.97, Remain: 3 days, 2:46:45, Finish: 2024-11-27 01:20 [11-24 14:33:53] (/home/user/VAR/train.py , line 276)=> [ep112] (training ) Lm: 6.598 (6.598), Lt: 5.842 (5.845), Acc m&t: 3.14 4.97, Remain: 3 days, 2:46:03, Finish: 2024-11-27 01:19 [11-24 14:33:53] (/home/user/VAR/train.py , line 276)=> [ep112] (training ) Lm: 6.598 (6.598), Lt: 5.842 (5.845), Acc m&t: 3.14 4.97, Remain: 3 days, 2:45:10, Finish: 2024-11-27 01:19 [11-24 14:33:53] (/home/user/VAR/train.py , line 276)=> [ep112] (training ) Lm: 6.598 (6.598), Lt: 5.842 (5.845), Acc m&t: 3.14 4.97, Remain: 3 days, 2:46:24, Finish: 2024-11-27 01:20 [11-24 14:33:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [ 0/1669] eta: 0:18:10 tlr: 0.00018 tnm: 0.31 Lm: 6.532 (6.532) Lt: 5.743 (5.743) Accm: 3.43 (3.43) Acct: 5.48 (5.48) proj_loss: -0.6039 (-0.6039) time: 0.6536 data: 0.0003 [11-24 14:33:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [ 0/1669] eta: 0:18:11 tlr: 0.00018 tnm: 0.31 Lm: 6.622 (6.622) Lt: 5.825 (5.825) Accm: 3.04 (3.04) Acct: 4.94 (4.94) proj_loss: -0.5665 (-0.5665) time: 0.6537 data: 0.0004 [11-24 14:33:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [ 0/1669] eta: 0:18:10 tlr: 0.00018 tnm: 0.31 Lm: 6.759 (6.759) Lt: 5.989 (5.989) Accm: 2.62 (2.62) Acct: 4.29 (4.29) proj_loss: -0.5681 (-0.5681) time: 0.6533 data: 0.0004 [11-24 14:33:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [ 0/1669] eta: 0:18:16 tlr: 0.00018 tnm: 0.31 Lm: 6.494 (6.494) Lt: 5.780 (5.780) Accm: 3.43 (3.43) Acct: 5.49 (5.49) proj_loss: -0.5845 (-0.5845) time: 0.6572 data: 0.0004 [11-24 14:38:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [ 417/1669] eta: 0:14:03 tlr: 0.00018 tnm: 0.30 Lm: 6.601 (6.601) Lt: 5.882 (5.882) Accm: 3.18 (3.18) Acct: 4.96 (4.96) proj_loss: -0.5870 (-0.5870) time: 0.6747 data: 0.0003 [11-24 14:38:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [ 417/1669] eta: 0:14:03 tlr: 0.00018 tnm: 0.30 Lm: 6.585 (6.585) Lt: 5.810 (5.810) Accm: 3.19 (3.19) Acct: 4.98 (4.98) proj_loss: -0.5949 (-0.5949) time: 0.6747 data: 0.0003 [11-24 14:38:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [ 417/1669] eta: 0:14:03 tlr: 0.00018 tnm: 0.30 Lm: 6.584 (6.584) Lt: 5.772 (5.772) Accm: 3.03 (3.03) Acct: 4.94 (4.94) proj_loss: -0.5733 (-0.5733) time: 0.6747 data: 0.0003 [11-24 14:38:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [ 417/1669] eta: 0:14:03 tlr: 0.00018 tnm: 0.30 Lm: 6.623 (6.623) Lt: 5.822 (5.822) Accm: 3.04 (3.04) Acct: 4.98 (4.98) proj_loss: -0.5686 (-0.5686) time: 0.6747 data: 0.0003 [11-24 14:43:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [ 834/1669] eta: 0:09:23 tlr: 0.00017 tnm: 0.31 Lm: 6.684 (6.643) Lt: 5.905 (5.850) Accm: 2.99 (3.03) Acct: 4.87 (4.95) proj_loss: -0.5692 (-0.5709) time: 0.6782 data: 0.0003 [11-24 14:43:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [ 834/1669] eta: 0:09:23 tlr: 0.00017 tnm: 0.31 Lm: 6.638 (6.625) Lt: 5.876 (5.857) Accm: 2.94 (3.07) Acct: 4.49 (4.72) proj_loss: -0.6039 (-0.5986) time: 0.6782 data: 0.0003 [11-24 14:43:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [ 834/1669] eta: 0:09:23 tlr: 0.00017 tnm: 0.31 Lm: 6.545 (6.569) Lt: 5.791 (5.778) Accm: 3.04 (3.13) Acct: 4.94 (4.97) proj_loss: -0.5802 (-0.5761) time: 0.6782 data: 0.0002 [11-24 14:43:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [ 834/1669] eta: 0:09:23 tlr: 0.00017 tnm: 0.31 Lm: 6.647 (6.616) Lt: 5.937 (5.901) Accm: 2.98 (3.11) Acct: 4.70 (4.87) proj_loss: -0.5872 (-0.5871) time: 0.6782 data: 0.0003 [11-24 14:47:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.31 Lm: 6.677 (6.639) Lt: 5.930 (5.906) Accm: 2.95 (3.00) Acct: 4.56 (4.69) proj_loss: -0.5858 (-0.5815) time: 0.6754 data: 0.0003 [11-24 14:47:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.31 Lm: 6.543 (6.560) Lt: 5.800 (5.786) Accm: 3.17 (3.18) Acct: 4.98 (5.05) proj_loss: -0.5809 (-0.5798) time: 0.6754 data: 0.0003 [11-24 14:47:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.31 Lm: 6.648 (6.634) Lt: 5.915 (5.882) Accm: 2.98 (3.06) Acct: 4.49 (4.66) proj_loss: -0.5980 (-0.5970) time: 0.6754 data: 0.0003 [11-24 14:47:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.31 Lm: 6.608 (6.616) Lt: 5.861 (5.842) Accm: 3.18 (3.11) Acct: 5.00 (4.99) proj_loss: -0.5722 (-0.5720) time: 0.6754 data: 0.0003 [11-24 14:52:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.29 Lm: 6.532 (6.584) Lt: 5.817 (5.805) Accm: 3.36 (3.16) Acct: 5.13 (5.07) proj_loss: -0.5751 (-0.5763) time: 0.8100 data: 0.0019 [11-24 14:52:45] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 113/350] Total time: 0:18:51 (0.678 s / it) [11-24 14:52:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.29 Lm: 6.638 (6.629) Lt: 5.876 (5.868) Accm: 3.02 (3.11) Acct: 4.49 (4.77) proj_loss: -0.5921 (-0.5911) time: 0.8100 data: 0.0016 [11-24 14:52:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.29 Lm: 6.545 (6.561) Lt: 5.810 (5.796) Accm: 3.04 (3.14) Acct: 4.94 (4.96) proj_loss: -0.5817 (-0.5827) time: 0.8100 data: 0.0018 [11-24 14:52:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 113/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.29 Lm: 6.647 (6.606) Lt: 5.922 (5.869) Accm: 2.98 (3.09) Acct: 4.70 (4.78) proj_loss: -0.5845 (-0.5820) time: 0.8100 data: 0.0022 [11-24 14:52:45] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 113/350] Total time: 0:18:51 (0.678 s / it) [11-24 14:52:45] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 113/350] Total time: 0:18:51 (0.678 s / it) [11-24 14:52:45] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 113/350] Total time: 0:18:51 (0.678 s / it) [11-24 14:52:45] (/home/user/VAR/train.py , line 276)=> [ep113] (training ) Lm: 6.595 (6.595), Lt: 5.841 (5.841), Acc m&t: 3.14 4.97, Remain: 3 days, 3:14:10, Finish: 2024-11-27 02:06 [11-24 14:52:45] (/home/user/VAR/train.py , line 276)=> [ep113] (training ) Lm: 6.595 (6.595), Lt: 5.841 (5.841), Acc m&t: 3.14 4.97, Remain: 3 days, 3:17:11, Finish: 2024-11-27 02:09 [11-24 14:52:45] (/home/user/VAR/train.py , line 276)=> [ep113] (training ) Lm: 6.595 (6.595), Lt: 5.841 (5.841), Acc m&t: 3.14 4.97, Remain: 3 days, 3:15:54, Finish: 2024-11-27 02:08 [11-24 14:52:45] (/home/user/VAR/train.py , line 276)=> [ep113] (training ) Lm: 6.595 (6.595), Lt: 5.841 (5.841), Acc m&t: 3.14 4.97, Remain: 3 days, 3:14:25, Finish: 2024-11-27 02:07 [11-24 14:52:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [ 0/1669] eta: 0:18:39 tlr: 0.00017 tnm: 0.30 Lm: 6.657 (6.657) Lt: 5.920 (5.920) Accm: 2.88 (2.88) Acct: 4.58 (4.58) proj_loss: -0.5753 (-0.5753) time: 0.6707 data: 0.0003 [11-24 14:52:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [ 0/1669] eta: 0:18:39 tlr: 0.00017 tnm: 0.30 Lm: 6.610 (6.610) Lt: 5.801 (5.801) Accm: 3.14 (3.14) Acct: 5.03 (5.03) proj_loss: -0.5733 (-0.5733) time: 0.6709 data: 0.0004 [11-24 14:52:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [ 0/1669] eta: 0:18:40 tlr: 0.00017 tnm: 0.30 Lm: 6.554 (6.554) Lt: 5.787 (5.787) Accm: 3.19 (3.19) Acct: 4.98 (4.98) proj_loss: -0.5744 (-0.5744) time: 0.6713 data: 0.0004 [11-24 14:52:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [ 0/1669] eta: 0:18:40 tlr: 0.00017 tnm: 0.30 Lm: 6.751 (6.751) Lt: 6.004 (6.004) Accm: 2.75 (2.75) Acct: 4.42 (4.42) proj_loss: -0.5533 (-0.5533) time: 0.6714 data: 0.0004 [11-24 14:57:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [ 417/1669] eta: 0:15:11 tlr: 0.00017 tnm: 0.30 Lm: 6.678 (6.678) Lt: 5.914 (5.914) Accm: 2.99 (2.99) Acct: 4.81 (4.81) proj_loss: -0.5691 (-0.5691) time: 0.6751 data: 0.0003 [11-24 14:57:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [ 417/1669] eta: 0:15:11 tlr: 0.00017 tnm: 0.30 Lm: 6.646 (6.646) Lt: 5.891 (5.891) Accm: 2.88 (2.88) Acct: 4.68 (4.68) proj_loss: -0.5823 (-0.5823) time: 0.6751 data: 0.0003 [11-24 14:57:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [ 417/1669] eta: 0:15:11 tlr: 0.00017 tnm: 0.30 Lm: 6.656 (6.656) Lt: 5.879 (5.879) Accm: 2.95 (2.95) Acct: 4.68 (4.68) proj_loss: -0.5750 (-0.5750) time: 0.6751 data: 0.0002 [11-24 14:57:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [ 417/1669] eta: 0:15:11 tlr: 0.00017 tnm: 0.30 Lm: 6.578 (6.578) Lt: 5.814 (5.814) Accm: 3.25 (3.25) Acct: 5.17 (5.17) proj_loss: -0.5754 (-0.5754) time: 0.6751 data: 0.0003 [11-24 15:02:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [ 834/1669] eta: 0:09:46 tlr: 0.00017 tnm: 0.29 Lm: 6.567 (6.574) Lt: 5.796 (5.808) Accm: 3.19 (3.13) Acct: 4.98 (4.95) proj_loss: -0.5763 (-0.5792) time: 0.6749 data: 0.0003 [11-24 15:02:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [ 834/1669] eta: 0:09:46 tlr: 0.00017 tnm: 0.29 Lm: 6.610 (6.587) Lt: 5.801 (5.806) Accm: 3.14 (3.08) Acct: 5.03 (4.87) proj_loss: -0.5733 (-0.5737) time: 0.6749 data: 0.0002 [11-24 15:02:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [ 834/1669] eta: 0:09:46 tlr: 0.00017 tnm: 0.29 Lm: 6.636 (6.618) Lt: 5.862 (5.862) Accm: 2.88 (2.94) Acct: 4.79 (4.76) proj_loss: -0.5894 (-0.5875) time: 0.6749 data: 0.0003 [11-24 15:02:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [ 834/1669] eta: 0:09:46 tlr: 0.00017 tnm: 0.29 Lm: 6.605 (6.640) Lt: 5.852 (5.893) Accm: 3.20 (3.06) Acct: 4.94 (4.86) proj_loss: -0.5850 (-0.5782) time: 0.6749 data: 0.0003 [11-24 15:07:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [1251/1669] eta: 0:04:49 tlr: 0.00017 tnm: 0.31 Lm: 6.632 (6.645) Lt: 5.878 (5.896) Accm: 3.13 (3.06) Acct: 5.04 (4.93) proj_loss: -0.5879 (-0.5813) time: 0.6755 data: 0.0003 [11-24 15:07:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [1251/1669] eta: 0:04:49 tlr: 0.00017 tnm: 0.31 Lm: 6.646 (6.657) Lt: 5.891 (5.905) Accm: 2.88 (2.89) Acct: 4.68 (4.64) proj_loss: -0.5841 (-0.5853) time: 0.6755 data: 0.0003 [11-24 15:07:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [1251/1669] eta: 0:04:49 tlr: 0.00017 tnm: 0.31 Lm: 6.580 (6.579) Lt: 5.811 (5.812) Accm: 3.05 (3.07) Acct: 4.83 (4.88) proj_loss: -0.5754 (-0.5750) time: 0.6755 data: 0.0003 [11-24 15:07:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [1251/1669] eta: 0:04:49 tlr: 0.00017 tnm: 0.31 Lm: 6.656 (6.626) Lt: 5.879 (5.852) Accm: 3.06 (3.06) Acct: 4.99 (4.89) proj_loss: -0.5750 (-0.5774) time: 0.6755 data: 0.0002 [11-24 15:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.610 (6.597) Lt: 5.801 (5.816) Accm: 3.14 (3.12) Acct: 5.03 (5.04) proj_loss: -0.5766 (-0.5793) time: 0.6760 data: 0.0017 [11-24 15:11:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 114/350] Total time: 0:19:10 (0.689 s / it) [11-24 15:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.593 (6.583) Lt: 5.826 (5.822) Accm: 2.91 (3.01) Acct: 4.68 (4.78) proj_loss: -0.5763 (-0.5822) time: 0.6760 data: 0.0020 [11-24 15:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.643 (6.644) Lt: 5.904 (5.909) Accm: 3.07 (3.00) Acct: 4.94 (4.77) proj_loss: -0.5908 (-0.5847) time: 0.6760 data: 0.0019 [11-24 15:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 114/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.636 (6.633) Lt: 5.862 (5.882) Accm: 2.88 (3.00) Acct: 4.79 (4.79) proj_loss: -0.5788 (-0.5807) time: 0.6760 data: 0.0020 [11-24 15:11:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 114/350] Total time: 0:19:10 (0.689 s / it) [11-24 15:11:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 114/350] Total time: 0:19:10 (0.689 s / it) [11-24 15:11:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 114/350] Total time: 0:19:10 (0.689 s / it) [11-24 15:11:55] (/home/user/VAR/train.py , line 276)=> [ep114] (training ) Lm: 6.595 (6.595), Lt: 5.841 (5.844), Acc m&t: 3.14 4.98, Remain: 3 days, 1:56:24, Finish: 2024-11-27 01:08 [11-24 15:11:55] (/home/user/VAR/train.py , line 276)=> [ep114] (training ) Lm: 6.595 (6.595), Lt: 5.841 (5.844), Acc m&t: 3.14 4.98, Remain: 3 days, 1:56:04, Finish: 2024-11-27 01:07 [11-24 15:11:55] (/home/user/VAR/train.py , line 276)=> [ep114] (training ) Lm: 6.595 (6.595), Lt: 5.841 (5.844), Acc m&t: 3.14 4.98, Remain: 3 days, 1:56:36, Finish: 2024-11-27 01:08 [11-24 15:11:55] (/home/user/VAR/train.py , line 276)=> [ep114] (training ) Lm: 6.595 (6.595), Lt: 5.841 (5.844), Acc m&t: 3.14 4.98, Remain: 3 days, 1:56:33, Finish: 2024-11-27 01:08 [11-24 15:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [ 0/1669] eta: 0:18:27 tlr: 0.00017 tnm: 0.29 Lm: 6.705 (6.705) Lt: 6.011 (6.011) Accm: 3.31 (3.31) Acct: 5.42 (5.42) proj_loss: -0.6013 (-0.6013) time: 0.6633 data: 0.0004 [11-24 15:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [ 0/1669] eta: 0:18:29 tlr: 0.00017 tnm: 0.29 Lm: 6.727 (6.727) Lt: 5.929 (5.929) Accm: 2.56 (2.56) Acct: 4.10 (4.10) proj_loss: -0.5943 (-0.5943) time: 0.6647 data: 0.0003 [11-24 15:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [ 0/1669] eta: 0:18:28 tlr: 0.00017 tnm: 0.29 Lm: 6.693 (6.693) Lt: 5.934 (5.934) Accm: 2.88 (2.88) Acct: 4.53 (4.53) proj_loss: -0.5872 (-0.5872) time: 0.6643 data: 0.0004 [11-24 15:11:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [ 0/1669] eta: 0:18:29 tlr: 0.00017 tnm: 0.29 Lm: 6.618 (6.618) Lt: 5.878 (5.878) Accm: 3.19 (3.19) Acct: 4.92 (4.92) proj_loss: -0.5687 (-0.5687) time: 0.6645 data: 0.0004 [11-24 15:16:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [ 417/1669] eta: 0:14:05 tlr: 0.00017 tnm: 0.30 Lm: 6.671 (6.671) Lt: 5.939 (5.939) Accm: 2.92 (2.92) Acct: 4.50 (4.50) proj_loss: -0.5708 (-0.5708) time: 0.6788 data: 0.0003 [11-24 15:16:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [ 417/1669] eta: 0:14:05 tlr: 0.00017 tnm: 0.30 Lm: 6.656 (6.656) Lt: 5.920 (5.920) Accm: 2.97 (2.97) Acct: 4.67 (4.67) proj_loss: -0.5895 (-0.5895) time: 0.6788 data: 0.0003 [11-24 15:16:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [ 417/1669] eta: 0:14:05 tlr: 0.00017 tnm: 0.30 Lm: 6.644 (6.644) Lt: 5.914 (5.914) Accm: 3.36 (3.36) Acct: 5.43 (5.43) proj_loss: -0.5918 (-0.5918) time: 0.6788 data: 0.0003 [11-24 15:16:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [ 417/1669] eta: 0:14:05 tlr: 0.00017 tnm: 0.30 Lm: 6.736 (6.736) Lt: 5.985 (5.985) Accm: 2.64 (2.64) Acct: 4.08 (4.08) proj_loss: -0.5911 (-0.5911) time: 0.6788 data: 0.0002 [11-24 15:28:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [ 834/1669] eta: 0:09:47 tlr: 0.00017 tnm: 0.30 Lm: 6.727 (6.701) Lt: 5.946 (5.972) Accm: 2.64 (2.64) Acct: 4.06 (4.02) proj_loss: -0.5943 (-0.5953) time: 0.6761 data: 0.0002 [11-24 15:28:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [ 834/1669] eta: 0:09:48 tlr: 0.00017 tnm: 0.30 Lm: 6.724 (6.698) Lt: 6.000 (5.977) Accm: 2.66 (2.82) Acct: 4.08 (4.34) proj_loss: -0.5688 (-0.5702) time: 0.6761 data: 0.0003 [11-24 15:28:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [ 834/1669] eta: 0:09:47 tlr: 0.00017 tnm: 0.30 Lm: 6.705 (6.672) Lt: 6.011 (5.951) Accm: 3.31 (3.14) Acct: 5.42 (5.03) proj_loss: -0.5822 (-0.5868) time: 0.6761 data: 0.0003 [11-24 15:28:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [ 834/1669] eta: 0:09:47 tlr: 0.00017 tnm: 0.30 Lm: 6.618 (6.616) Lt: 5.906 (5.885) Accm: 3.02 (2.99) Acct: 4.82 (4.75) proj_loss: -0.5918 (-0.5956) time: 0.6761 data: 0.0003 [11-24 15:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [1251/1669] eta: 0:04:50 tlr: 0.00017 tnm: 0.31 Lm: 6.578 (6.575) Lt: 5.860 (5.820) Accm: 3.04 (3.23) Acct: 4.86 (5.14) proj_loss: -0.5895 (-0.5918) time: 0.6775 data: 0.0003 [11-24 15:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [1251/1669] eta: 0:04:50 tlr: 0.00017 tnm: 0.31 Lm: 6.685 (6.687) Lt: 5.938 (5.957) Accm: 2.67 (2.79) Acct: 4.08 (4.25) proj_loss: -0.5950 (-0.5954) time: 0.6775 data: 0.0002 [11-24 15:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.31 Lm: 6.671 (6.660) Lt: 5.939 (5.916) Accm: 2.91 (2.91) Acct: 4.50 (4.55) proj_loss: -0.5709 (-0.5726) time: 0.6775 data: 0.0003 [11-24 15:33:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [1251/1669] eta: 0:04:50 tlr: 0.00017 tnm: 0.31 Lm: 6.644 (6.628) Lt: 5.914 (5.888) Accm: 3.26 (3.16) Acct: 5.27 (5.05) proj_loss: -0.5824 (-0.5857) time: 0.6775 data: 0.0003 [11-24 15:38:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.705 (6.652) Lt: 6.005 (5.912) Accm: 3.22 (3.07) Acct: 5.11 (4.92) proj_loss: -0.5825 (-0.5865) time: 0.6804 data: 0.0016 [11-24 15:38:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 115/350] Total time: 0:26:23 (0.949 s / it) [11-24 15:38:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.653 (6.659) Lt: 5.895 (5.912) Accm: 3.17 (2.97) Acct: 4.92 (4.63) proj_loss: -0.5729 (-0.5730) time: 0.6804 data: 0.0016 [11-24 15:38:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.646 (6.678) Lt: 5.929 (5.944) Accm: 2.68 (2.77) Acct: 4.10 (4.25) proj_loss: -0.5943 (-0.5934) time: 0.6804 data: 0.0018 [11-24 15:38:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 115/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.569 (6.574) Lt: 5.814 (5.811) Accm: 3.06 (3.25) Acct: 4.91 (5.19) proj_loss: -0.5872 (-0.5846) time: 0.6804 data: 0.0016 [11-24 15:38:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 115/350] Total time: 0:26:23 (0.949 s / it) [11-24 15:38:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 115/350] Total time: 0:26:23 (0.949 s / it) [11-24 15:38:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 115/350] Total time: 0:26:23 (0.949 s / it) [11-24 15:38:18] (/home/user/VAR/train.py , line 276)=> [ep115] (training ) Lm: 6.595 (6.597), Lt: 5.841 (5.844), Acc m&t: 3.14 5.00, Remain: 3 days, 2:18:57, Finish: 2024-11-27 01:57 [11-24 15:38:18] (/home/user/VAR/train.py , line 276)=> [ep115] (training ) Lm: 6.595 (6.597), Lt: 5.841 (5.844), Acc m&t: 3.14 5.00, Remain: 3 days, 2:18:00, Finish: 2024-11-27 01:56 [11-24 15:38:18] (/home/user/VAR/train.py , line 276)=> [ep115] (training ) Lm: 6.595 (6.597), Lt: 5.841 (5.844), Acc m&t: 3.14 5.00, Remain: 3 days, 2:18:27, Finish: 2024-11-27 01:56 [11-24 15:38:18] (/home/user/VAR/train.py , line 276)=> [ep115] (training ) Lm: 6.595 (6.597), Lt: 5.841 (5.844), Acc m&t: 3.14 5.00, Remain: 3 days, 2:18:00, Finish: 2024-11-27 01:56 [11-24 15:38:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [ 0/1669] eta: 0:18:16 tlr: 0.00017 tnm: 0.30 Lm: 6.484 (6.484) Lt: 5.732 (5.732) Accm: 3.20 (3.20) Acct: 5.01 (5.01) proj_loss: -0.5906 (-0.5906) time: 0.6571 data: 0.0004 [11-24 15:38:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [ 0/1669] eta: 0:18:17 tlr: 0.00017 tnm: 0.30 Lm: 6.696 (6.696) Lt: 5.969 (5.969) Accm: 3.12 (3.12) Acct: 5.06 (5.06) proj_loss: -0.5995 (-0.5995) time: 0.6574 data: 0.0003 [11-24 15:38:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [ 0/1669] eta: 0:18:17 tlr: 0.00017 tnm: 0.30 Lm: 6.693 (6.693) Lt: 5.941 (5.941) Accm: 2.91 (2.91) Acct: 4.46 (4.46) proj_loss: -0.5614 (-0.5614) time: 0.6579 data: 0.0003 [11-24 15:38:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [ 0/1669] eta: 0:18:16 tlr: 0.00017 tnm: 0.30 Lm: 6.520 (6.520) Lt: 5.752 (5.752) Accm: 3.24 (3.24) Acct: 5.23 (5.23) proj_loss: -0.5775 (-0.5775) time: 0.6568 data: 0.0004 [11-24 15:43:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [ 417/1669] eta: 0:14:05 tlr: 0.00017 tnm: 0.32 Lm: 6.554 (6.554) Lt: 5.825 (5.825) Accm: 3.08 (3.08) Acct: 4.85 (4.85) proj_loss: -0.5920 (-0.5920) time: 0.6739 data: 0.0003 [11-24 15:43:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [ 417/1669] eta: 0:14:05 tlr: 0.00017 tnm: 0.32 Lm: 6.603 (6.603) Lt: 5.850 (5.850) Accm: 3.25 (3.25) Acct: 5.22 (5.22) proj_loss: -0.5834 (-0.5834) time: 0.6739 data: 0.0003 [11-24 15:43:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [ 417/1669] eta: 0:14:05 tlr: 0.00017 tnm: 0.32 Lm: 6.702 (6.702) Lt: 5.992 (5.992) Accm: 2.80 (2.80) Acct: 4.30 (4.30) proj_loss: -0.5749 (-0.5749) time: 0.6739 data: 0.0002 [11-24 15:43:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [ 417/1669] eta: 0:14:05 tlr: 0.00017 tnm: 0.32 Lm: 6.474 (6.474) Lt: 5.690 (5.690) Accm: 3.30 (3.30) Acct: 5.22 (5.22) proj_loss: -0.5820 (-0.5820) time: 0.6739 data: 0.0003 [11-24 15:47:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [ 834/1669] eta: 0:09:23 tlr: 0.00017 tnm: 0.29 Lm: 6.520 (6.498) Lt: 5.752 (5.737) Accm: 3.24 (3.29) Acct: 5.23 (5.34) proj_loss: -0.5775 (-0.5869) time: 0.6755 data: 0.0003 [11-24 15:47:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [ 834/1669] eta: 0:09:23 tlr: 0.00017 tnm: 0.29 Lm: 6.710 (6.716) Lt: 6.031 (6.005) Accm: 2.73 (2.78) Acct: 4.37 (4.32) proj_loss: -0.5625 (-0.5707) time: 0.6755 data: 0.0002 [11-24 15:47:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [ 834/1669] eta: 0:09:23 tlr: 0.00017 tnm: 0.29 Lm: 6.511 (6.562) Lt: 5.731 (5.800) Accm: 3.39 (3.30) Acct: 5.15 (5.19) proj_loss: -0.5754 (-0.5807) time: 0.6755 data: 0.0003 [11-24 15:47:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [ 834/1669] eta: 0:09:23 tlr: 0.00017 tnm: 0.29 Lm: 6.484 (6.558) Lt: 5.732 (5.804) Accm: 3.20 (3.08) Acct: 5.01 (4.78) proj_loss: -0.5809 (-0.5816) time: 0.6755 data: 0.0003 [11-24 15:52:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.32 Lm: 6.517 (6.556) Lt: 5.760 (5.800) Accm: 3.18 (3.10) Acct: 4.89 (4.77) proj_loss: -0.5857 (-0.5881) time: 0.6768 data: 0.0003 [11-24 15:52:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.32 Lm: 6.554 (6.544) Lt: 5.825 (5.782) Accm: 3.08 (3.18) Acct: 4.85 (5.10) proj_loss: -0.5797 (-0.5856) time: 0.6768 data: 0.0003 [11-24 15:52:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.32 Lm: 6.713 (6.716) Lt: 6.018 (6.005) Accm: 2.81 (2.81) Acct: 4.42 (4.36) proj_loss: -0.5704 (-0.5727) time: 0.6768 data: 0.0002 [11-24 15:52:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.32 Lm: 6.546 (6.566) Lt: 5.797 (5.815) Accm: 3.25 (3.21) Acct: 5.11 (5.09) proj_loss: -0.5828 (-0.5831) time: 0.6768 data: 0.0003 [11-24 15:57:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.32 Lm: 6.511 (6.542) Lt: 5.732 (5.799) Accm: 3.39 (3.24) Acct: 5.15 (5.11) proj_loss: -0.5829 (-0.5831) time: 0.8009 data: 0.0016 [11-24 15:57:09] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 116/350] Total time: 0:18:50 (0.678 s / it) [11-24 15:57:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.32 Lm: 6.551 (6.573) Lt: 5.789 (5.807) Accm: 3.17 (3.08) Acct: 4.77 (4.71) proj_loss: -0.5809 (-0.5853) time: 0.8010 data: 0.0021 [11-24 15:57:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.32 Lm: 6.589 (6.554) Lt: 5.845 (5.795) Accm: 3.07 (3.16) Acct: 4.77 (5.03) proj_loss: -0.5818 (-0.5851) time: 0.8010 data: 0.0024 [11-24 15:57:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 116/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.32 Lm: 6.710 (6.678) Lt: 6.005 (5.963) Accm: 2.89 (2.91) Acct: 4.46 (4.55) proj_loss: -0.5784 (-0.5781) time: 0.8010 data: 0.0018 [11-24 15:57:09] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 116/350] Total time: 0:18:50 (0.678 s / it) [11-24 15:57:09] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 116/350] Total time: 0:18:50 (0.678 s / it) [11-24 15:57:09] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 116/350] Total time: 0:18:50 (0.678 s / it) [11-24 15:57:09] (/home/user/VAR/train.py , line 276)=> [ep116] (training ) Lm: 6.589 (6.589), Lt: 5.837 (5.837), Acc m&t: 3.15 5.00, Remain: 3 days, 2:16:17, Finish: 2024-11-27 02:13 [11-24 15:57:09] (/home/user/VAR/train.py , line 276)=> [ep116] (training ) Lm: 6.589 (6.589), Lt: 5.837 (5.837), Acc m&t: 3.15 5.00, Remain: 3 days, 2:15:26, Finish: 2024-11-27 02:12 [11-24 15:57:09] (/home/user/VAR/train.py , line 276)=> [ep116] (training ) Lm: 6.589 (6.589), Lt: 5.837 (5.837), Acc m&t: 3.15 5.00, Remain: 3 days, 2:15:14, Finish: 2024-11-27 02:12 [11-24 15:57:09] (/home/user/VAR/train.py , line 276)=> [ep116] (training ) Lm: 6.589 (6.589), Lt: 5.837 (5.837), Acc m&t: 3.15 5.00, Remain: 3 days, 2:16:47, Finish: 2024-11-27 02:13 [11-24 15:57:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [ 0/1669] eta: 0:18:15 tlr: 0.00017 tnm: 0.30 Lm: 6.511 (6.511) Lt: 5.768 (5.768) Accm: 3.37 (3.37) Acct: 5.18 (5.18) proj_loss: -0.5807 (-0.5807) time: 0.6565 data: 0.0004 [11-24 15:57:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [ 0/1669] eta: 0:18:15 tlr: 0.00017 tnm: 0.30 Lm: 6.506 (6.506) Lt: 5.696 (5.696) Accm: 3.65 (3.65) Acct: 5.91 (5.91) proj_loss: -0.5820 (-0.5820) time: 0.6566 data: 0.0004 [11-24 15:57:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [ 0/1669] eta: 0:18:16 tlr: 0.00017 tnm: 0.30 Lm: 6.528 (6.528) Lt: 5.775 (5.775) Accm: 3.63 (3.63) Acct: 5.49 (5.49) proj_loss: -0.5950 (-0.5950) time: 0.6570 data: 0.0003 [11-24 15:57:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [ 0/1669] eta: 0:18:16 tlr: 0.00017 tnm: 0.30 Lm: 6.599 (6.599) Lt: 5.824 (5.824) Accm: 2.94 (2.94) Acct: 4.75 (4.75) proj_loss: -0.5771 (-0.5771) time: 0.6570 data: 0.0004 [11-24 16:02:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [ 417/1669] eta: 0:15:16 tlr: 0.00017 tnm: 0.31 Lm: 6.575 (6.575) Lt: 5.805 (5.805) Accm: 3.18 (3.18) Acct: 5.11 (5.11) proj_loss: -0.5822 (-0.5822) time: 0.6767 data: 0.0003 [11-24 16:02:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [ 417/1669] eta: 0:15:16 tlr: 0.00017 tnm: 0.31 Lm: 6.534 (6.534) Lt: 5.777 (5.777) Accm: 3.30 (3.30) Acct: 5.28 (5.28) proj_loss: -0.5879 (-0.5879) time: 0.6768 data: 0.0003 [11-24 16:02:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [ 417/1669] eta: 0:15:16 tlr: 0.00017 tnm: 0.31 Lm: 6.536 (6.536) Lt: 5.786 (5.786) Accm: 3.45 (3.45) Acct: 5.30 (5.30) proj_loss: -0.5957 (-0.5957) time: 0.6768 data: 0.0003 [11-24 16:02:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [ 417/1669] eta: 0:15:16 tlr: 0.00017 tnm: 0.31 Lm: 6.614 (6.614) Lt: 5.870 (5.870) Accm: 3.10 (3.10) Acct: 4.79 (4.79) proj_loss: -0.5779 (-0.5779) time: 0.6768 data: 0.0003 [11-24 16:06:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [ 834/1669] eta: 0:09:47 tlr: 0.00017 tnm: 0.29 Lm: 6.633 (6.620) Lt: 5.851 (5.864) Accm: 3.33 (3.17) Acct: 5.18 (5.00) proj_loss: -0.5807 (-0.5831) time: 0.6771 data: 0.0003 [11-24 16:06:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [ 834/1669] eta: 0:09:47 tlr: 0.00017 tnm: 0.29 Lm: 6.599 (6.620) Lt: 5.824 (5.870) Accm: 2.94 (3.07) Acct: 4.75 (4.92) proj_loss: -0.5862 (-0.5835) time: 0.6771 data: 0.0003 [11-24 16:06:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [ 834/1669] eta: 0:09:47 tlr: 0.00017 tnm: 0.29 Lm: 6.528 (6.500) Lt: 5.775 (5.745) Accm: 3.63 (3.59) Acct: 5.49 (5.52) proj_loss: -0.5950 (-0.5934) time: 0.6771 data: 0.0003 [11-24 16:06:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [ 834/1669] eta: 0:09:47 tlr: 0.00017 tnm: 0.29 Lm: 6.562 (6.546) Lt: 5.834 (5.796) Accm: 2.97 (3.19) Acct: 4.65 (5.04) proj_loss: -0.5937 (-0.5905) time: 0.6771 data: 0.0003 [11-24 16:11:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [1251/1669] eta: 0:04:50 tlr: 0.00017 tnm: 0.30 Lm: 6.566 (6.564) Lt: 5.837 (5.807) Accm: 3.04 (3.17) Acct: 4.74 (4.99) proj_loss: -0.5897 (-0.5893) time: 0.6766 data: 0.0002 [11-24 16:11:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [1251/1669] eta: 0:04:50 tlr: 0.00017 tnm: 0.30 Lm: 6.514 (6.500) Lt: 5.738 (5.734) Accm: 3.45 (3.51) Acct: 5.36 (5.45) proj_loss: -0.5920 (-0.5917) time: 0.6767 data: 0.0003 [11-24 16:11:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [1251/1669] eta: 0:04:50 tlr: 0.00017 tnm: 0.30 Lm: 6.573 (6.593) Lt: 5.809 (5.827) Accm: 3.32 (3.21) Acct: 5.25 (5.08) proj_loss: -0.5779 (-0.5796) time: 0.6766 data: 0.0003 [11-24 16:11:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [1251/1669] eta: 0:04:50 tlr: 0.00017 tnm: 0.30 Lm: 6.655 (6.656) Lt: 5.912 (5.915) Accm: 2.90 (2.94) Acct: 4.65 (4.66) proj_loss: -0.5829 (-0.5825) time: 0.6767 data: 0.0003 [11-24 16:16:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.599 (6.628) Lt: 5.824 (5.894) Accm: 2.94 (3.04) Acct: 4.75 (4.77) proj_loss: -0.5862 (-0.5902) time: 0.6767 data: 0.0016 [11-24 16:16:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 117/350] Total time: 0:19:11 (0.690 s / it) [11-24 16:16:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.528 (6.537) Lt: 5.775 (5.779) Accm: 3.27 (3.38) Acct: 5.23 (5.26) proj_loss: -0.5950 (-0.5925) time: 0.6767 data: 0.0016 [11-24 16:16:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.523 (6.579) Lt: 5.769 (5.815) Accm: 3.33 (3.24) Acct: 5.18 (5.10) proj_loss: -0.5807 (-0.5836) time: 0.6767 data: 0.0014 [11-24 16:16:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 117/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.562 (6.540) Lt: 5.834 (5.766) Accm: 3.10 (3.24) Acct: 4.84 (5.16) proj_loss: -0.5857 (-0.5879) time: 0.6767 data: 0.0018 [11-24 16:16:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 117/350] Total time: 0:19:11 (0.690 s / it) [11-24 16:16:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 117/350] Total time: 0:19:11 (0.690 s / it) [11-24 16:16:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 117/350] Total time: 0:19:11 (0.690 s / it) [11-24 16:16:20] (/home/user/VAR/train.py , line 276)=> [ep117] (training ) Lm: 6.589 (6.595), Lt: 5.837 (5.847), Acc m&t: 3.15 5.00, Remain: 3 days, 1:22:28, Finish: 2024-11-27 01:38 [11-24 16:16:20] (/home/user/VAR/train.py , line 276)=> [ep117] (training ) Lm: 6.589 (6.595), Lt: 5.837 (5.847), Acc m&t: 3.15 5.00, Remain: 3 days, 1:21:31, Finish: 2024-11-27 01:37 [11-24 16:16:20] (/home/user/VAR/train.py , line 276)=> [ep117] (training ) Lm: 6.589 (6.595), Lt: 5.837 (5.847), Acc m&t: 3.15 5.00, Remain: 3 days, 1:21:52, Finish: 2024-11-27 01:38 [11-24 16:16:20] (/home/user/VAR/train.py , line 276)=> [ep117] (training ) Lm: 6.589 (6.595), Lt: 5.837 (5.847), Acc m&t: 3.15 5.00, Remain: 3 days, 1:20:37, Finish: 2024-11-27 01:36 [11-24 16:16:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [ 0/1669] eta: 0:18:26 tlr: 0.00017 tnm: 0.30 Lm: 6.507 (6.507) Lt: 5.767 (5.767) Accm: 3.23 (3.23) Acct: 5.15 (5.15) proj_loss: -0.5917 (-0.5917) time: 0.6629 data: 0.0003 [11-24 16:16:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [ 0/1669] eta: 0:18:24 tlr: 0.00017 tnm: 0.30 Lm: 6.650 (6.650) Lt: 5.924 (5.924) Accm: 3.17 (3.17) Acct: 5.30 (5.30) proj_loss: -0.5998 (-0.5998) time: 0.6618 data: 0.0002 [11-24 16:16:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [ 0/1669] eta: 0:18:23 tlr: 0.00017 tnm: 0.30 Lm: 6.591 (6.591) Lt: 5.867 (5.867) Accm: 3.18 (3.18) Acct: 4.96 (4.96) proj_loss: -0.5804 (-0.5804) time: 0.6610 data: 0.0004 [11-24 16:16:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [ 0/1669] eta: 0:18:27 tlr: 0.00017 tnm: 0.30 Lm: 6.567 (6.567) Lt: 5.809 (5.809) Accm: 3.13 (3.13) Acct: 5.11 (5.11) proj_loss: -0.5930 (-0.5930) time: 0.6634 data: 0.0004 [11-24 16:21:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [ 417/1669] eta: 0:14:04 tlr: 0.00017 tnm: 0.30 Lm: 6.638 (6.638) Lt: 5.937 (5.937) Accm: 2.96 (2.96) Acct: 4.76 (4.76) proj_loss: -0.5980 (-0.5980) time: 0.6748 data: 0.0003 [11-24 16:21:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [ 417/1669] eta: 0:14:04 tlr: 0.00017 tnm: 0.30 Lm: 6.599 (6.599) Lt: 5.850 (5.850) Accm: 3.32 (3.32) Acct: 5.48 (5.48) proj_loss: -0.5893 (-0.5893) time: 0.6748 data: 0.0003 [11-24 16:21:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [ 417/1669] eta: 0:14:04 tlr: 0.00017 tnm: 0.30 Lm: 6.547 (6.547) Lt: 5.762 (5.762) Accm: 3.11 (3.11) Acct: 5.11 (5.11) proj_loss: -0.5855 (-0.5855) time: 0.6748 data: 0.0003 [11-24 16:21:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [ 417/1669] eta: 0:14:04 tlr: 0.00017 tnm: 0.30 Lm: 6.618 (6.618) Lt: 5.879 (5.879) Accm: 3.08 (3.08) Acct: 4.96 (4.96) proj_loss: -0.5766 (-0.5766) time: 0.6748 data: 0.0002 [11-24 16:26:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [ 834/1669] eta: 0:09:46 tlr: 0.00017 tnm: 0.30 Lm: 6.591 (6.592) Lt: 5.867 (5.844) Accm: 3.18 (3.13) Acct: 4.96 (4.99) proj_loss: -0.5774 (-0.5768) time: 0.6766 data: 0.0002 [11-24 16:26:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [ 834/1669] eta: 0:09:46 tlr: 0.00017 tnm: 0.30 Lm: 6.567 (6.603) Lt: 5.809 (5.886) Accm: 3.13 (3.07) Acct: 5.11 (4.98) proj_loss: -0.5984 (-0.5981) time: 0.6766 data: 0.0003 [11-24 16:26:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [ 834/1669] eta: 0:09:46 tlr: 0.00017 tnm: 0.30 Lm: 6.556 (6.550) Lt: 5.759 (5.761) Accm: 3.02 (3.08) Acct: 5.08 (4.95) proj_loss: -0.5793 (-0.5824) time: 0.6766 data: 0.0003 [11-24 16:26:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [ 834/1669] eta: 0:09:46 tlr: 0.00017 tnm: 0.30 Lm: 6.650 (6.630) Lt: 5.924 (5.885) Accm: 3.17 (3.13) Acct: 5.30 (5.13) proj_loss: -0.5788 (-0.5855) time: 0.6766 data: 0.0003 [11-24 16:30:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [1251/1669] eta: 0:04:50 tlr: 0.00017 tnm: 0.31 Lm: 6.628 (6.624) Lt: 5.909 (5.888) Accm: 3.26 (3.18) Acct: 5.13 (5.09) proj_loss: -0.5860 (-0.5874) time: 0.6751 data: 0.0003 [11-24 16:30:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [1251/1669] eta: 0:04:50 tlr: 0.00017 tnm: 0.31 Lm: 6.618 (6.611) Lt: 5.879 (5.870) Accm: 3.08 (3.08) Acct: 4.96 (4.86) proj_loss: -0.5789 (-0.5798) time: 0.6751 data: 0.0002 [11-24 16:30:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [1251/1669] eta: 0:04:50 tlr: 0.00017 tnm: 0.31 Lm: 6.589 (6.605) Lt: 5.849 (5.886) Accm: 3.03 (3.04) Acct: 4.92 (4.91) proj_loss: -0.5994 (-0.5987) time: 0.6751 data: 0.0003 [11-24 16:30:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [1251/1669] eta: 0:04:50 tlr: 0.00017 tnm: 0.31 Lm: 6.572 (6.593) Lt: 5.763 (5.823) Accm: 3.01 (3.01) Acct: 4.85 (4.78) proj_loss: -0.5821 (-0.5830) time: 0.6751 data: 0.0003 [11-24 16:35:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.578 (6.590) Lt: 5.767 (5.825) Accm: 2.99 (3.00) Acct: 4.61 (4.75) proj_loss: -0.5849 (-0.5860) time: 0.6787 data: 0.0015 [11-24 16:35:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 118/350] Total time: 0:19:13 (0.691 s / it) [11-24 16:35:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.650 (6.649) Lt: 5.924 (5.917) Accm: 3.17 (3.06) Acct: 4.96 (4.89) proj_loss: -0.5886 (-0.5877) time: 0.6787 data: 0.0016 [11-24 16:35:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.567 (6.588) Lt: 5.809 (5.859) Accm: 3.13 (3.11) Acct: 5.11 (4.97) proj_loss: -0.6004 (-0.5994) time: 0.6787 data: 0.0017 [11-24 16:35:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 118/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.591 (6.578) Lt: 5.867 (5.828) Accm: 3.18 (3.20) Acct: 4.96 (5.08) proj_loss: -0.5804 (-0.5814) time: 0.6787 data: 0.0017 [11-24 16:35:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 118/350] Total time: 0:19:13 (0.691 s / it) [11-24 16:35:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 118/350] Total time: 0:19:13 (0.691 s / it) [11-24 16:35:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 118/350] Total time: 0:19:13 (0.691 s / it) [11-24 16:35:34] (/home/user/VAR/train.py , line 276)=> [ep118] (training ) Lm: 6.588 (6.588), Lt: 5.837 (5.839), Acc m&t: 3.15 5.00, Remain: 3 days, 1:06:49, Finish: 2024-11-27 01:42 [11-24 16:35:34] (/home/user/VAR/train.py , line 276)=> [ep118] (training ) Lm: 6.588 (6.588), Lt: 5.837 (5.839), Acc m&t: 3.15 5.00, Remain: 3 days, 1:06:29, Finish: 2024-11-27 01:42 [11-24 16:35:34] (/home/user/VAR/train.py , line 276)=> [ep118] (training ) Lm: 6.588 (6.588), Lt: 5.837 (5.839), Acc m&t: 3.15 5.00, Remain: 3 days, 1:07:22, Finish: 2024-11-27 01:42 [11-24 16:35:34] (/home/user/VAR/train.py , line 276)=> [ep118] (training ) Lm: 6.588 (6.588), Lt: 5.837 (5.839), Acc m&t: 3.15 5.00, Remain: 3 days, 1:07:37, Finish: 2024-11-27 01:43 [11-24 16:35:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [ 0/1669] eta: 0:18:26 tlr: 0.00017 tnm: 0.29 Lm: 6.676 (6.676) Lt: 5.906 (5.906) Accm: 2.96 (2.96) Acct: 4.92 (4.92) proj_loss: -0.5522 (-0.5522) time: 0.6632 data: 0.0004 [11-24 16:35:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [ 0/1669] eta: 0:18:27 tlr: 0.00017 tnm: 0.29 Lm: 6.538 (6.538) Lt: 5.748 (5.748) Accm: 3.38 (3.38) Acct: 5.41 (5.41) proj_loss: -0.6054 (-0.6054) time: 0.6636 data: 0.0003 [11-24 16:35:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [ 0/1669] eta: 0:18:28 tlr: 0.00017 tnm: 0.29 Lm: 6.346 (6.346) Lt: 5.511 (5.511) Accm: 3.91 (3.91) Acct: 6.46 (6.46) proj_loss: -0.5980 (-0.5980) time: 0.6639 data: 0.0004 [11-24 16:35:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [ 0/1669] eta: 0:18:28 tlr: 0.00017 tnm: 0.29 Lm: 6.493 (6.493) Lt: 5.732 (5.732) Accm: 3.41 (3.41) Acct: 5.18 (5.18) proj_loss: -0.5825 (-0.5825) time: 0.6639 data: 0.0003 [11-24 16:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [ 417/1669] eta: 0:14:06 tlr: 0.00017 tnm: 0.29 Lm: 6.671 (6.671) Lt: 5.931 (5.931) Accm: 2.99 (2.99) Acct: 4.63 (4.63) proj_loss: -0.5871 (-0.5871) time: 0.6733 data: 0.0002 [11-24 16:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [ 417/1669] eta: 0:14:06 tlr: 0.00017 tnm: 0.29 Lm: 6.604 (6.604) Lt: 5.806 (5.806) Accm: 3.11 (3.11) Acct: 4.92 (4.92) proj_loss: -0.6004 (-0.6004) time: 0.6733 data: 0.0003 [11-24 16:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [ 417/1669] eta: 0:14:06 tlr: 0.00017 tnm: 0.29 Lm: 6.551 (6.551) Lt: 5.804 (5.804) Accm: 3.27 (3.27) Acct: 5.24 (5.24) proj_loss: -0.5793 (-0.5793) time: 0.6733 data: 0.0003 [11-24 16:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [ 417/1669] eta: 0:14:06 tlr: 0.00017 tnm: 0.29 Lm: 6.385 (6.385) Lt: 5.592 (5.592) Accm: 3.64 (3.64) Acct: 5.72 (5.72) proj_loss: -0.5902 (-0.5902) time: 0.6733 data: 0.0003 [11-24 16:44:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [ 834/1669] eta: 0:09:24 tlr: 0.00017 tnm: 0.28 Lm: 6.425 (6.484) Lt: 5.673 (5.710) Accm: 3.37 (3.37) Acct: 4.99 (5.29) proj_loss: -0.5980 (-0.5966) time: 0.6758 data: 0.0003 [11-24 16:44:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [ 834/1669] eta: 0:09:24 tlr: 0.00017 tnm: 0.28 Lm: 6.596 (6.646) Lt: 5.848 (5.903) Accm: 3.07 (3.02) Acct: 4.94 (4.73) proj_loss: -0.5825 (-0.5855) time: 0.6758 data: 0.0002 [11-24 16:44:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [ 834/1669] eta: 0:09:24 tlr: 0.00017 tnm: 0.28 Lm: 6.538 (6.581) Lt: 5.798 (5.803) Accm: 3.18 (3.13) Acct: 4.67 (4.83) proj_loss: -0.5954 (-0.5946) time: 0.6758 data: 0.0003 [11-24 16:44:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [ 834/1669] eta: 0:09:24 tlr: 0.00017 tnm: 0.28 Lm: 6.464 (6.522) Lt: 5.701 (5.756) Accm: 3.15 (3.23) Acct: 5.34 (5.27) proj_loss: -0.5895 (-0.5827) time: 0.6758 data: 0.0003 [11-24 16:49:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.29 Lm: 6.530 (6.541) Lt: 5.777 (5.781) Accm: 3.06 (3.16) Acct: 5.13 (5.05) proj_loss: -0.5846 (-0.5819) time: 0.6761 data: 0.0003 [11-24 16:49:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.29 Lm: 6.604 (6.612) Lt: 5.831 (5.844) Accm: 3.01 (3.03) Acct: 4.55 (4.69) proj_loss: -0.5909 (-0.5925) time: 0.6761 data: 0.0003 [11-24 16:49:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.29 Lm: 6.702 (6.686) Lt: 5.933 (5.932) Accm: 2.82 (2.88) Acct: 4.51 (4.52) proj_loss: -0.5824 (-0.5818) time: 0.6761 data: 0.0002 [11-24 16:49:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.29 Lm: 6.552 (6.532) Lt: 5.809 (5.772) Accm: 3.10 (3.21) Acct: 4.71 (5.04) proj_loss: -0.6003 (-0.5981) time: 0.6761 data: 0.0003 [11-24 16:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.551 (6.536) Lt: 5.719 (5.762) Accm: 3.23 (3.22) Acct: 4.99 (5.10) proj_loss: -0.5980 (-0.5947) time: 0.7937 data: 0.0017 [11-24 16:54:25] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 119/350] Total time: 0:18:51 (0.678 s / it) [11-24 16:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.608 (6.611) Lt: 5.865 (5.849) Accm: 3.03 (3.03) Acct: 4.67 (4.69) proj_loss: -0.5863 (-0.5893) time: 0.7937 data: 0.0016 [11-24 16:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.606 (6.670) Lt: 5.848 (5.914) Accm: 2.90 (2.88) Acct: 4.53 (4.52) proj_loss: -0.5825 (-0.5839) time: 0.7937 data: 0.0018 [11-24 16:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 119/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.597 (6.556) Lt: 5.854 (5.798) Accm: 3.02 (3.13) Acct: 4.92 (5.00) proj_loss: -0.5895 (-0.5846) time: 0.7937 data: 0.0014 [11-24 16:54:25] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 119/350] Total time: 0:18:51 (0.678 s / it) [11-24 16:54:25] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 119/350] Total time: 0:18:51 (0.678 s / it) [11-24 16:54:25] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 119/350] Total time: 0:18:51 (0.678 s / it) [11-24 16:56:45] (home/user/VAR/trainer.py, line 114)=> FID: 3.7224401912951635 [11-24 16:56:45] (/home/user/VAR/train.py , line 259)=> [*] [ep119] (val 50000) Lm: 6.5659, Lt: 5.8104, Acc m&t: 3.20 5.04, Val cost: 139.63s [11-24 16:56:45] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-24 16:57:28] (/home/user/VAR/train.py , line 276)=> [ep119] (training ) Lm: 6.566 (6.566), Lt: 5.810 (5.810), Acc m&t: 3.20 5.04, Remain: 3 days, 0:36:36, Finish: 2024-11-27 01:31 [11-24 16:57:28] (/home/user/VAR/train.py , line 276)=> [ep119] (training ) Lm: 6.566 (6.566), Lt: 5.810 (5.810), Acc m&t: 3.20 5.04, Remain: 3 days, 0:38:21, Finish: 2024-11-27 01:32 [11-24 16:57:28] (/home/user/VAR/train.py , line 276)=> [ep119] (training ) Lm: 6.566 (6.566), Lt: 5.810 (5.810), Acc m&t: 3.20 5.04, Remain: 3 days, 0:36:59, Finish: 2024-11-27 01:31 [11-24 16:57:28] (/home/user/VAR/train.py , line 276)=> [ep119] (training ) Lm: 6.566 (6.566), Lt: 5.810 (5.810), Acc m&t: 3.20 5.04, Remain: 3 days, 0:37:45, Finish: 2024-11-27 01:32 [11-24 16:57:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [ 0/1669] eta: 0:18:44 tlr: 0.00017 tnm: 0.31 Lm: 6.623 (6.623) Lt: 5.878 (5.878) Accm: 3.13 (3.13) Acct: 5.27 (5.27) proj_loss: -0.5658 (-0.5658) time: 0.6740 data: 0.0003 [11-24 16:57:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [ 0/1669] eta: 0:18:45 tlr: 0.00017 tnm: 0.31 Lm: 6.572 (6.572) Lt: 5.818 (5.818) Accm: 3.10 (3.10) Acct: 4.98 (4.98) proj_loss: -0.5911 (-0.5911) time: 0.6744 data: 0.0004 [11-24 16:57:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [ 0/1669] eta: 0:18:43 tlr: 0.00017 tnm: 0.31 Lm: 6.638 (6.638) Lt: 5.904 (5.904) Accm: 2.89 (2.89) Acct: 4.46 (4.46) proj_loss: -0.5827 (-0.5827) time: 0.6733 data: 0.0004 [11-24 16:57:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [ 0/1669] eta: 0:18:45 tlr: 0.00017 tnm: 0.31 Lm: 6.447 (6.447) Lt: 5.703 (5.703) Accm: 3.59 (3.59) Acct: 5.61 (5.61) proj_loss: -0.5876 (-0.5876) time: 0.6742 data: 0.0003 [11-24 17:02:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [ 417/1669] eta: 0:15:16 tlr: 0.00017 tnm: 0.32 Lm: 6.483 (6.483) Lt: 5.745 (5.745) Accm: 3.40 (3.40) Acct: 5.39 (5.39) proj_loss: -0.5972 (-0.5972) time: 0.6783 data: 0.0003 [11-24 17:02:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [ 417/1669] eta: 0:15:16 tlr: 0.00017 tnm: 0.32 Lm: 6.678 (6.678) Lt: 6.003 (6.003) Accm: 2.76 (2.76) Acct: 4.11 (4.11) proj_loss: -0.5883 (-0.5883) time: 0.6782 data: 0.0003 [11-24 17:02:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [ 417/1669] eta: 0:15:16 tlr: 0.00017 tnm: 0.32 Lm: 6.620 (6.620) Lt: 5.854 (5.854) Accm: 3.05 (3.05) Acct: 4.90 (4.90) proj_loss: -0.5898 (-0.5898) time: 0.6783 data: 0.0003 [11-24 17:02:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [ 417/1669] eta: 0:15:16 tlr: 0.00017 tnm: 0.32 Lm: 6.661 (6.661) Lt: 5.932 (5.932) Accm: 2.88 (2.88) Acct: 4.86 (4.86) proj_loss: -0.5808 (-0.5808) time: 0.6783 data: 0.0003 [11-24 17:07:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [ 834/1669] eta: 0:09:48 tlr: 0.00017 tnm: 0.30 Lm: 6.623 (6.629) Lt: 5.878 (5.884) Accm: 3.13 (3.01) Acct: 5.27 (5.00) proj_loss: -0.5801 (-0.5806) time: 0.6779 data: 0.0003 [11-24 17:07:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [ 834/1669] eta: 0:09:48 tlr: 0.00017 tnm: 0.30 Lm: 6.580 (6.607) Lt: 5.869 (5.859) Accm: 3.10 (3.15) Acct: 4.98 (5.11) proj_loss: -0.5885 (-0.5882) time: 0.6779 data: 0.0003 [11-24 17:07:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [ 834/1669] eta: 0:09:48 tlr: 0.00017 tnm: 0.30 Lm: 6.519 (6.568) Lt: 5.787 (5.835) Accm: 3.21 (3.22) Acct: 5.17 (5.08) proj_loss: -0.5913 (-0.5952) time: 0.6779 data: 0.0003 [11-24 17:07:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [ 834/1669] eta: 0:09:48 tlr: 0.00017 tnm: 0.30 Lm: 6.717 (6.700) Lt: 6.045 (6.017) Accm: 2.62 (2.69) Acct: 3.93 (4.05) proj_loss: -0.5938 (-0.5922) time: 0.6779 data: 0.0003 [11-24 17:11:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [1251/1669] eta: 0:04:50 tlr: 0.00017 tnm: 0.30 Lm: 6.681 (6.686) Lt: 5.974 (5.986) Accm: 2.76 (2.76) Acct: 4.19 (4.15) proj_loss: -0.5883 (-0.5883) time: 0.6765 data: 0.0003 [11-24 17:11:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [1251/1669] eta: 0:04:50 tlr: 0.00017 tnm: 0.30 Lm: 6.576 (6.567) Lt: 5.843 (5.791) Accm: 3.23 (3.27) Acct: 5.26 (5.32) proj_loss: -0.5867 (-0.5859) time: 0.6765 data: 0.0003 [11-24 17:11:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [1251/1669] eta: 0:04:50 tlr: 0.00017 tnm: 0.30 Lm: 6.590 (6.592) Lt: 5.867 (5.863) Accm: 3.05 (3.14) Acct: 4.81 (4.90) proj_loss: -0.5910 (-0.5941) time: 0.6765 data: 0.0002 [11-24 17:11:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [1251/1669] eta: 0:04:50 tlr: 0.00017 tnm: 0.30 Lm: 6.659 (6.645) Lt: 5.910 (5.899) Accm: 3.05 (3.00) Acct: 4.99 (4.93) proj_loss: -0.5862 (-0.5835) time: 0.6765 data: 0.0003 [11-24 17:16:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.623 (6.631) Lt: 5.878 (5.876) Accm: 3.13 (3.06) Acct: 4.96 (4.93) proj_loss: -0.5835 (-0.5835) time: 0.6785 data: 0.0020 [11-24 17:16:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 120/350] Total time: 0:19:13 (0.691 s / it) [11-24 17:16:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.661 (6.614) Lt: 5.947 (5.886) Accm: 2.90 (3.09) Acct: 4.80 (4.88) proj_loss: -0.5906 (-0.5888) time: 0.6785 data: 0.0019 [11-24 17:16:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.644 (6.640) Lt: 5.904 (5.925) Accm: 2.89 (2.93) Acct: 4.46 (4.45) proj_loss: -0.5938 (-0.5915) time: 0.6786 data: 0.0017 [11-24 17:16:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 120/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.572 (6.548) Lt: 5.818 (5.776) Accm: 3.36 (3.29) Acct: 5.41 (5.34) proj_loss: -0.5850 (-0.5833) time: 0.6785 data: 0.0018 [11-24 17:16:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 120/350] Total time: 0:19:13 (0.691 s / it) [11-24 17:16:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 120/350] Total time: 0:19:13 (0.691 s / it) [11-24 17:16:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 120/350] Total time: 0:19:13 (0.691 s / it) [11-24 17:16:41] (/home/user/VAR/train.py , line 276)=> [ep120] (training ) Lm: 6.566 (6.574), Lt: 5.810 (5.821), Acc m&t: 3.20 5.04, Remain: 3 days, 0:33:02, Finish: 2024-11-27 01:49 [11-24 17:16:41] (/home/user/VAR/train.py , line 276)=> [ep120] (training ) Lm: 6.566 (6.574), Lt: 5.810 (5.821), Acc m&t: 3.20 5.04, Remain: 3 days, 0:33:50, Finish: 2024-11-27 01:50 [11-24 17:16:41] (/home/user/VAR/train.py , line 276)=> [ep120] (training ) Lm: 6.566 (6.574), Lt: 5.810 (5.821), Acc m&t: 3.20 5.04, Remain: 3 days, 0:33:47, Finish: 2024-11-27 01:50 [11-24 17:16:41] (/home/user/VAR/train.py , line 276)=> [ep120] (training ) Lm: 6.566 (6.574), Lt: 5.810 (5.821), Acc m&t: 3.20 5.04, Remain: 3 days, 0:33:21, Finish: 2024-11-27 01:50 [11-24 17:16:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [ 0/1669] eta: 0:19:03 tlr: 0.00017 tnm: 0.30 Lm: 6.451 (6.451) Lt: 5.702 (5.702) Accm: 3.31 (3.31) Acct: 5.34 (5.34) proj_loss: -0.6049 (-0.6049) time: 0.6853 data: 0.0003 [11-24 17:16:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [ 0/1669] eta: 0:18:31 tlr: 0.00017 tnm: 0.30 Lm: 6.299 (6.299) Lt: 5.537 (5.537) Accm: 4.25 (4.25) Acct: 6.65 (6.65) proj_loss: -0.6066 (-0.6066) time: 0.6659 data: 0.0004 [11-24 17:16:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [ 0/1669] eta: 0:18:31 tlr: 0.00017 tnm: 0.30 Lm: 6.487 (6.487) Lt: 5.744 (5.744) Accm: 3.34 (3.34) Acct: 5.22 (5.22) proj_loss: -0.5945 (-0.5945) time: 0.6662 data: 0.0003 [11-24 17:16:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [ 0/1669] eta: 0:18:32 tlr: 0.00017 tnm: 0.30 Lm: 6.690 (6.690) Lt: 5.934 (5.934) Accm: 2.93 (2.93) Acct: 4.73 (4.73) proj_loss: -0.5893 (-0.5893) time: 0.6668 data: 0.0004 [11-24 17:21:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [ 417/1669] eta: 0:14:07 tlr: 0.00017 tnm: 0.29 Lm: 6.631 (6.631) Lt: 5.872 (5.872) Accm: 3.05 (3.05) Acct: 4.90 (4.90) proj_loss: -0.5896 (-0.5896) time: 0.6773 data: 0.0003 [11-24 17:21:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [ 417/1669] eta: 0:14:07 tlr: 0.00017 tnm: 0.29 Lm: 6.484 (6.484) Lt: 5.727 (5.727) Accm: 3.43 (3.43) Acct: 5.41 (5.41) proj_loss: -0.5917 (-0.5917) time: 0.6773 data: 0.0003 [11-24 17:21:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [ 417/1669] eta: 0:14:07 tlr: 0.00017 tnm: 0.29 Lm: 6.524 (6.524) Lt: 5.779 (5.779) Accm: 3.26 (3.26) Acct: 5.06 (5.06) proj_loss: -0.5983 (-0.5983) time: 0.6773 data: 0.0003 [11-24 17:21:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [ 417/1669] eta: 0:14:07 tlr: 0.00017 tnm: 0.29 Lm: 6.516 (6.516) Lt: 5.784 (5.784) Accm: 3.51 (3.51) Acct: 5.53 (5.53) proj_loss: -0.5970 (-0.5970) time: 0.6773 data: 0.0003 [11-24 17:26:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [ 834/1669] eta: 0:09:48 tlr: 0.00017 tnm: 0.31 Lm: 6.572 (6.583) Lt: 5.811 (5.805) Accm: 3.17 (3.24) Acct: 5.06 (5.20) proj_loss: -0.5898 (-0.5944) time: 0.6758 data: 0.0003 [11-24 17:26:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [ 834/1669] eta: 0:09:48 tlr: 0.00017 tnm: 0.31 Lm: 6.517 (6.551) Lt: 5.751 (5.804) Accm: 3.31 (3.17) Acct: 5.34 (5.03) proj_loss: -0.5908 (-0.5914) time: 0.6758 data: 0.0003 [11-24 17:26:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [ 834/1669] eta: 0:09:48 tlr: 0.00017 tnm: 0.31 Lm: 6.561 (6.605) Lt: 5.814 (5.855) Accm: 3.19 (3.11) Acct: 4.91 (4.87) proj_loss: -0.5945 (-0.5892) time: 0.6758 data: 0.0003 [11-24 17:26:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [ 834/1669] eta: 0:09:48 tlr: 0.00017 tnm: 0.31 Lm: 6.418 (6.484) Lt: 5.606 (5.725) Accm: 3.72 (3.58) Acct: 5.54 (5.53) proj_loss: -0.5874 (-0.5877) time: 0.6758 data: 0.0003 [11-24 17:31:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.34 Lm: 6.576 (6.546) Lt: 5.818 (5.809) Accm: 3.25 (3.34) Acct: 4.98 (5.16) proj_loss: -0.5965 (-0.5922) time: 0.6796 data: 0.0003 [11-24 17:31:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.34 Lm: 6.591 (6.590) Lt: 5.843 (5.822) Accm: 3.07 (3.17) Acct: 4.91 (5.09) proj_loss: -0.5910 (-0.5938) time: 0.6796 data: 0.0003 [11-24 17:31:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.34 Lm: 6.572 (6.599) Lt: 5.807 (5.842) Accm: 3.22 (3.14) Acct: 5.02 (4.93) proj_loss: -0.5963 (-0.5914) time: 0.6796 data: 0.0003 [11-24 17:31:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.34 Lm: 6.556 (6.562) Lt: 5.774 (5.802) Accm: 3.15 (3.12) Acct: 5.15 (5.01) proj_loss: -0.5884 (-0.5900) time: 0.6796 data: 0.0003 [11-24 17:35:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.595 (6.578) Lt: 5.797 (5.819) Accm: 3.15 (3.13) Acct: 4.96 (4.96) proj_loss: -0.5859 (-0.5883) time: 0.6765 data: 0.0019 [11-24 17:35:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 121/350] Total time: 0:19:16 (0.693 s / it) [11-24 17:35:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.603 (6.558) Lt: 5.807 (5.809) Accm: 3.10 (3.29) Acct: 5.54 (5.26) proj_loss: -0.5874 (-0.5882) time: 0.6765 data: 0.0021 [11-24 17:35:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.583 (6.597) Lt: 5.814 (5.848) Accm: 3.19 (3.12) Acct: 4.91 (4.88) proj_loss: -0.5945 (-0.5895) time: 0.6765 data: 0.0019 [11-24 17:35:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 121/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.572 (6.562) Lt: 5.811 (5.799) Accm: 3.17 (3.24) Acct: 5.06 (5.17) proj_loss: -0.5898 (-0.5916) time: 0.6765 data: 0.0020 [11-24 17:35:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 121/350] Total time: 0:19:16 (0.693 s / it) [11-24 17:35:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 121/350] Total time: 0:19:16 (0.693 s / it) [11-24 17:35:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 121/350] Total time: 0:19:16 (0.693 s / it) [11-24 17:35:58] (/home/user/VAR/train.py , line 276)=> [ep121] (training ) Lm: 6.566 (6.580), Lt: 5.810 (5.825), Acc m&t: 3.20 5.04, Remain: 3 days, 0:04:20, Finish: 2024-11-27 01:40 [11-24 17:35:58] (/home/user/VAR/train.py , line 276)=> [ep121] (training ) Lm: 6.566 (6.580), Lt: 5.810 (5.825), Acc m&t: 3.20 5.04, Remain: 3 days, 0:04:39, Finish: 2024-11-27 01:40 [11-24 17:35:58] (/home/user/VAR/train.py , line 276)=> [ep121] (training ) Lm: 6.566 (6.580), Lt: 5.810 (5.825), Acc m&t: 3.20 5.04, Remain: 3 days, 0:04:20, Finish: 2024-11-27 01:40 [11-24 17:35:58] (/home/user/VAR/train.py , line 276)=> [ep121] (training ) Lm: 6.566 (6.580), Lt: 5.810 (5.825), Acc m&t: 3.20 5.04, Remain: 3 days, 0:04:24, Finish: 2024-11-27 01:40 [11-24 17:35:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [ 0/1669] eta: 0:18:12 tlr: 0.00017 tnm: 0.30 Lm: 6.710 (6.710) Lt: 5.964 (5.964) Accm: 2.91 (2.91) Acct: 4.61 (4.61) proj_loss: -0.5927 (-0.5927) time: 0.6544 data: 0.0003 [11-24 17:35:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [ 0/1669] eta: 0:18:13 tlr: 0.00017 tnm: 0.30 Lm: 6.421 (6.421) Lt: 5.625 (5.625) Accm: 3.69 (3.69) Acct: 5.96 (5.96) proj_loss: -0.5814 (-0.5814) time: 0.6554 data: 0.0004 [11-24 17:35:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [ 0/1669] eta: 0:18:13 tlr: 0.00017 tnm: 0.30 Lm: 6.600 (6.600) Lt: 5.936 (5.936) Accm: 2.96 (2.96) Acct: 4.32 (4.32) proj_loss: -0.6019 (-0.6019) time: 0.6553 data: 0.0004 [11-24 17:35:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [ 0/1669] eta: 0:18:13 tlr: 0.00017 tnm: 0.30 Lm: 6.771 (6.771) Lt: 6.035 (6.035) Accm: 2.45 (2.45) Acct: 3.87 (3.87) proj_loss: -0.6027 (-0.6027) time: 0.6555 data: 0.0003 [11-24 17:40:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [ 417/1669] eta: 0:14:07 tlr: 0.00017 tnm: 0.32 Lm: 6.674 (6.674) Lt: 5.929 (5.929) Accm: 2.83 (2.83) Acct: 4.51 (4.51) proj_loss: -0.5930 (-0.5930) time: 0.6769 data: 0.0003 [11-24 17:40:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [ 417/1669] eta: 0:14:07 tlr: 0.00017 tnm: 0.32 Lm: 6.649 (6.649) Lt: 5.919 (5.919) Accm: 3.00 (3.00) Acct: 4.58 (4.58) proj_loss: -0.5898 (-0.5898) time: 0.6769 data: 0.0003 [11-24 17:40:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [ 417/1669] eta: 0:14:07 tlr: 0.00017 tnm: 0.32 Lm: 6.608 (6.608) Lt: 5.808 (5.808) Accm: 3.22 (3.22) Acct: 5.17 (5.17) proj_loss: -0.5782 (-0.5782) time: 0.6769 data: 0.0003 [11-24 17:40:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [ 417/1669] eta: 0:14:07 tlr: 0.00017 tnm: 0.32 Lm: 6.600 (6.600) Lt: 5.916 (5.916) Accm: 3.01 (3.01) Acct: 4.49 (4.49) proj_loss: -0.5932 (-0.5932) time: 0.6769 data: 0.0003 [11-24 17:45:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [ 834/1669] eta: 0:09:24 tlr: 0.00017 tnm: 0.32 Lm: 6.600 (6.595) Lt: 5.897 (5.891) Accm: 2.98 (3.00) Acct: 4.67 (4.57) proj_loss: -0.5887 (-0.5917) time: 0.6760 data: 0.0002 [11-24 17:45:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [ 834/1669] eta: 0:09:24 tlr: 0.00017 tnm: 0.32 Lm: 6.639 (6.646) Lt: 5.889 (5.909) Accm: 2.91 (2.95) Acct: 4.61 (4.60) proj_loss: -0.5870 (-0.5857) time: 0.6760 data: 0.0003 [11-24 17:45:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [ 834/1669] eta: 0:09:24 tlr: 0.00017 tnm: 0.32 Lm: 6.583 (6.600) Lt: 5.820 (5.812) Accm: 3.60 (3.35) Acct: 5.96 (5.44) proj_loss: -0.5814 (-0.5852) time: 0.6760 data: 0.0003 [11-24 17:45:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [ 834/1669] eta: 0:09:24 tlr: 0.00017 tnm: 0.32 Lm: 6.681 (6.676) Lt: 5.885 (5.914) Accm: 2.80 (2.82) Acct: 4.63 (4.55) proj_loss: -0.6020 (-0.5960) time: 0.6761 data: 0.0003 [11-24 17:50:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.29 Lm: 6.629 (6.651) Lt: 5.870 (5.899) Accm: 3.00 (2.92) Acct: 4.79 (4.65) proj_loss: -0.5970 (-0.5950) time: 0.6779 data: 0.0003 [11-24 17:50:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.29 Lm: 6.583 (6.596) Lt: 5.826 (5.817) Accm: 3.32 (3.27) Acct: 5.41 (5.30) proj_loss: -0.5820 (-0.5845) time: 0.6779 data: 0.0003 [11-24 17:50:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.29 Lm: 6.626 (6.638) Lt: 5.900 (5.910) Accm: 2.96 (2.97) Acct: 4.63 (4.70) proj_loss: -0.5828 (-0.5839) time: 0.6779 data: 0.0003 [11-24 17:50:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [1251/1669] eta: 0:04:42 tlr: 0.00017 tnm: 0.29 Lm: 6.600 (6.615) Lt: 5.916 (5.905) Accm: 3.00 (3.00) Acct: 4.58 (4.55) proj_loss: -0.5953 (-0.5946) time: 0.6779 data: 0.0003 [11-24 17:54:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.29 Lm: 6.601 (6.646) Lt: 5.936 (5.937) Accm: 2.98 (2.95) Acct: 4.53 (4.55) proj_loss: -0.5975 (-0.5952) time: 0.7458 data: 0.0017 [11-24 17:54:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 122/350] Total time: 0:18:50 (0.678 s / it) [11-24 17:54:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.29 Lm: 6.612 (6.619) Lt: 5.889 (5.882) Accm: 3.02 (3.02) Acct: 4.65 (4.75) proj_loss: -0.5870 (-0.5857) time: 0.7458 data: 0.0018 [11-24 17:54:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.29 Lm: 6.644 (6.649) Lt: 5.885 (5.912) Accm: 2.90 (2.92) Acct: 4.63 (4.58) proj_loss: -0.5920 (-0.5915) time: 0.7458 data: 0.0020 [11-24 17:54:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 122/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.29 Lm: 6.583 (6.611) Lt: 5.832 (5.849) Accm: 3.04 (3.21) Acct: 4.87 (5.09) proj_loss: -0.5815 (-0.5839) time: 0.7458 data: 0.0020 [11-24 17:54:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 122/350] Total time: 0:18:50 (0.678 s / it) [11-24 17:54:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 122/350] Total time: 0:18:50 (0.678 s / it) [11-24 17:54:49] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 122/350] Total time: 0:18:50 (0.678 s / it) [11-24 17:54:49] (/home/user/VAR/train.py , line 276)=> [ep122] (training ) Lm: 6.566 (6.593), Lt: 5.810 (5.838), Acc m&t: 3.20 5.04, Remain: 3 days, 0:01:23, Finish: 2024-11-27 01:56 [11-24 17:54:49] (/home/user/VAR/train.py , line 276)=> [ep122] (training ) Lm: 6.566 (6.593), Lt: 5.810 (5.838), Acc m&t: 3.20 5.04, Remain: 3 days, 0:01:17, Finish: 2024-11-27 01:56 [11-24 17:54:49] (/home/user/VAR/train.py , line 276)=> [ep122] (training ) Lm: 6.566 (6.593), Lt: 5.810 (5.838), Acc m&t: 3.20 5.04, Remain: 3 days, 0:01:04, Finish: 2024-11-27 01:55 [11-24 17:54:49] (/home/user/VAR/train.py , line 276)=> [ep122] (training ) Lm: 6.566 (6.593), Lt: 5.810 (5.838), Acc m&t: 3.20 5.04, Remain: 3 days, 0:00:42, Finish: 2024-11-27 01:55 [11-24 17:54:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [ 0/1669] eta: 0:18:14 tlr: 0.00017 tnm: 0.29 Lm: 6.423 (6.423) Lt: 5.640 (5.640) Accm: 3.54 (3.54) Acct: 4.98 (4.98) proj_loss: -0.5907 (-0.5907) time: 0.6557 data: 0.0004 [11-24 17:54:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [ 0/1669] eta: 0:18:14 tlr: 0.00017 tnm: 0.29 Lm: 6.525 (6.525) Lt: 5.787 (5.787) Accm: 3.23 (3.23) Acct: 5.03 (5.03) proj_loss: -0.5928 (-0.5928) time: 0.6558 data: 0.0004 [11-24 17:54:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [ 0/1669] eta: 0:18:15 tlr: 0.00017 tnm: 0.29 Lm: 6.590 (6.590) Lt: 5.875 (5.875) Accm: 3.30 (3.30) Acct: 5.01 (5.01) proj_loss: -0.5738 (-0.5738) time: 0.6563 data: 0.0004 [11-24 17:54:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [ 0/1669] eta: 0:18:15 tlr: 0.00017 tnm: 0.29 Lm: 6.626 (6.626) Lt: 5.898 (5.898) Accm: 3.07 (3.07) Acct: 4.75 (4.75) proj_loss: -0.6029 (-0.6029) time: 0.6565 data: 0.0004 [11-24 17:59:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [ 417/1669] eta: 0:15:15 tlr: 0.00017 tnm: 0.30 Lm: 6.555 (6.555) Lt: 5.788 (5.788) Accm: 3.23 (3.23) Acct: 5.14 (5.14) proj_loss: -0.6005 (-0.6005) time: 0.6778 data: 0.0003 [11-24 17:59:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [ 417/1669] eta: 0:15:15 tlr: 0.00017 tnm: 0.30 Lm: 6.456 (6.456) Lt: 5.636 (5.636) Accm: 3.59 (3.59) Acct: 5.50 (5.50) proj_loss: -0.5800 (-0.5800) time: 0.6778 data: 0.0003 [11-24 17:59:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [ 417/1669] eta: 0:15:15 tlr: 0.00017 tnm: 0.30 Lm: 6.454 (6.454) Lt: 5.715 (5.715) Accm: 3.68 (3.68) Acct: 5.62 (5.62) proj_loss: -0.5705 (-0.5705) time: 0.6778 data: 0.0003 [11-24 17:59:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [ 417/1669] eta: 0:15:15 tlr: 0.00017 tnm: 0.30 Lm: 6.609 (6.609) Lt: 5.862 (5.862) Accm: 3.00 (3.00) Acct: 4.86 (4.86) proj_loss: -0.5861 (-0.5861) time: 0.6778 data: 0.0003 [11-24 18:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [ 834/1669] eta: 0:09:50 tlr: 0.00017 tnm: 0.30 Lm: 6.545 (6.587) Lt: 5.791 (5.838) Accm: 3.23 (3.09) Acct: 5.03 (4.92) proj_loss: -0.5890 (-0.5870) time: 0.6800 data: 0.0003 [11-24 18:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [ 834/1669] eta: 0:09:50 tlr: 0.00017 tnm: 0.30 Lm: 6.490 (6.533) Lt: 5.640 (5.736) Accm: 3.54 (3.31) Acct: 4.98 (5.11) proj_loss: -0.5907 (-0.5839) time: 0.6800 data: 0.0003 [11-24 18:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [ 834/1669] eta: 0:09:50 tlr: 0.00017 tnm: 0.30 Lm: 6.590 (6.547) Lt: 5.875 (5.810) Accm: 3.30 (3.29) Acct: 5.01 (5.00) proj_loss: -0.5738 (-0.5767) time: 0.6800 data: 0.0003 [11-24 18:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [ 834/1669] eta: 0:09:50 tlr: 0.00017 tnm: 0.30 Lm: 6.564 (6.558) Lt: 5.751 (5.776) Accm: 3.31 (3.25) Acct: 5.53 (5.35) proj_loss: -0.5981 (-0.5905) time: 0.6800 data: 0.0003 [11-24 18:09:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.31 Lm: 6.595 (6.602) Lt: 5.825 (5.814) Accm: 3.19 (3.11) Acct: 5.14 (5.13) proj_loss: -0.5843 (-0.5840) time: 0.6745 data: 0.0003 [11-24 18:09:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.31 Lm: 6.602 (6.564) Lt: 5.872 (5.825) Accm: 3.26 (3.27) Acct: 5.11 (5.05) proj_loss: -0.5815 (-0.5809) time: 0.6745 data: 0.0003 [11-24 18:09:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.31 Lm: 6.588 (6.594) Lt: 5.789 (5.822) Accm: 3.14 (3.11) Acct: 4.66 (4.84) proj_loss: -0.5912 (-0.5877) time: 0.6745 data: 0.0003 [11-24 18:09:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.31 Lm: 6.619 (6.619) Lt: 5.864 (5.883) Accm: 3.00 (2.94) Acct: 4.86 (4.71) proj_loss: -0.5902 (-0.5881) time: 0.6745 data: 0.0002 [11-24 18:14:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.545 (6.589) Lt: 5.791 (5.845) Accm: 3.23 (3.06) Acct: 5.03 (4.87) proj_loss: -0.5890 (-0.5869) time: 0.6792 data: 0.0016 [11-24 18:14:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 123/350] Total time: 0:19:15 (0.692 s / it) [11-24 18:14:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.564 (6.593) Lt: 5.882 (5.828) Accm: 3.16 (3.12) Acct: 4.75 (5.05) proj_loss: -0.5860 (-0.5844) time: 0.6792 data: 0.0016 [11-24 18:14:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.572 (6.590) Lt: 5.811 (5.820) Accm: 2.92 (3.07) Acct: 4.70 (4.81) proj_loss: -0.5907 (-0.5858) time: 0.6792 data: 0.0019 [11-24 18:14:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 123/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.30 Lm: 6.602 (6.572) Lt: 5.870 (5.822) Accm: 3.21 (3.20) Acct: 5.01 (4.99) proj_loss: -0.5892 (-0.5833) time: 0.6792 data: 0.0017 [11-24 18:14:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 123/350] Total time: 0:19:15 (0.692 s / it) [11-24 18:14:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 123/350] Total time: 0:19:15 (0.692 s / it) [11-24 18:14:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 123/350] Total time: 0:19:15 (0.692 s / it) [11-24 18:14:04] (/home/user/VAR/train.py , line 276)=> [ep123] (training ) Lm: 6.566 (6.588), Lt: 5.810 (5.836), Acc m&t: 3.20 5.04, Remain: 2 days, 23:40:09, Finish: 2024-11-27 01:54 [11-24 18:14:04] (/home/user/VAR/train.py , line 276)=> [ep123] (training ) Lm: 6.566 (6.588), Lt: 5.810 (5.836), Acc m&t: 3.20 5.04, Remain: 2 days, 23:40:56, Finish: 2024-11-27 01:55 [11-24 18:14:04] (/home/user/VAR/train.py , line 276)=> [ep123] (training ) Lm: 6.566 (6.588), Lt: 5.810 (5.836), Acc m&t: 3.20 5.04, Remain: 2 days, 23:41:27, Finish: 2024-11-27 01:55 [11-24 18:14:04] (/home/user/VAR/train.py , line 276)=> [ep123] (training ) Lm: 6.566 (6.588), Lt: 5.810 (5.836), Acc m&t: 3.20 5.04, Remain: 2 days, 23:40:14, Finish: 2024-11-27 01:54 [11-24 18:14:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [ 0/1669] eta: 0:18:15 tlr: 0.00017 tnm: 0.30 Lm: 6.493 (6.493) Lt: 5.773 (5.773) Accm: 3.21 (3.21) Acct: 4.98 (4.98) proj_loss: -0.6040 (-0.6040) time: 0.6565 data: 0.0004 [11-24 18:14:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [ 0/1669] eta: 0:18:16 tlr: 0.00017 tnm: 0.30 Lm: 6.600 (6.600) Lt: 5.870 (5.870) Accm: 2.99 (2.99) Acct: 4.80 (4.80) proj_loss: -0.5978 (-0.5978) time: 0.6568 data: 0.0004 [11-24 18:14:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [ 0/1669] eta: 0:18:16 tlr: 0.00017 tnm: 0.30 Lm: 6.498 (6.498) Lt: 5.745 (5.745) Accm: 3.55 (3.55) Acct: 5.85 (5.85) proj_loss: -0.6029 (-0.6029) time: 0.6568 data: 0.0004 [11-24 18:14:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [ 0/1669] eta: 0:18:16 tlr: 0.00017 tnm: 0.30 Lm: 6.733 (6.733) Lt: 6.046 (6.046) Accm: 2.71 (2.71) Acct: 4.32 (4.32) proj_loss: -0.6020 (-0.6020) time: 0.6569 data: 0.0004 [11-24 18:18:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [ 417/1669] eta: 0:14:07 tlr: 0.00017 tnm: 0.30 Lm: 6.645 (6.645) Lt: 5.934 (5.934) Accm: 3.04 (3.04) Acct: 4.80 (4.80) proj_loss: -0.5971 (-0.5971) time: 0.6776 data: 0.0003 [11-24 18:18:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [ 417/1669] eta: 0:14:07 tlr: 0.00017 tnm: 0.30 Lm: 6.523 (6.523) Lt: 5.751 (5.751) Accm: 3.53 (3.53) Acct: 5.77 (5.77) proj_loss: -0.5984 (-0.5984) time: 0.6776 data: 0.0003 [11-24 18:18:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [ 417/1669] eta: 0:14:07 tlr: 0.00017 tnm: 0.30 Lm: 6.627 (6.627) Lt: 5.910 (5.910) Accm: 3.18 (3.18) Acct: 4.83 (4.83) proj_loss: -0.5954 (-0.5954) time: 0.6776 data: 0.0003 [11-24 18:18:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [ 417/1669] eta: 0:14:07 tlr: 0.00017 tnm: 0.30 Lm: 6.589 (6.589) Lt: 5.872 (5.872) Accm: 3.01 (3.01) Acct: 4.72 (4.72) proj_loss: -0.5845 (-0.5845) time: 0.6776 data: 0.0003 [11-24 18:23:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [ 834/1669] eta: 0:09:48 tlr: 0.00017 tnm: 0.31 Lm: 6.534 (6.571) Lt: 5.773 (5.825) Accm: 3.10 (3.04) Acct: 4.98 (4.90) proj_loss: -0.5931 (-0.5874) time: 0.6784 data: 0.0003 [11-24 18:23:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [ 834/1669] eta: 0:09:48 tlr: 0.00017 tnm: 0.31 Lm: 6.548 (6.586) Lt: 5.756 (5.845) Accm: 3.51 (3.30) Acct: 5.68 (5.26) proj_loss: -0.5938 (-0.5968) time: 0.6784 data: 0.0003 [11-24 18:23:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [ 834/1669] eta: 0:09:48 tlr: 0.00017 tnm: 0.31 Lm: 6.600 (6.579) Lt: 5.870 (5.860) Accm: 3.33 (3.23) Acct: 4.86 (5.00) proj_loss: -0.5929 (-0.5894) time: 0.6784 data: 0.0003 [11-24 18:23:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [ 834/1669] eta: 0:09:48 tlr: 0.00017 tnm: 0.31 Lm: 6.709 (6.666) Lt: 6.001 (5.957) Accm: 2.89 (2.99) Acct: 4.67 (4.76) proj_loss: -0.5922 (-0.5912) time: 0.6784 data: 0.0003 [11-24 18:28:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.30 Lm: 6.633 (6.628) Lt: 5.912 (5.907) Accm: 3.13 (3.08) Acct: 4.98 (4.91) proj_loss: -0.5859 (-0.5866) time: 0.6750 data: 0.0003 [11-24 18:28:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.30 Lm: 6.538 (6.564) Lt: 5.778 (5.815) Accm: 3.16 (3.16) Acct: 5.11 (5.16) proj_loss: -0.5954 (-0.5900) time: 0.6750 data: 0.0003 [11-24 18:28:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.30 Lm: 6.609 (6.589) Lt: 5.872 (5.863) Accm: 3.20 (3.19) Acct: 4.96 (5.02) proj_loss: -0.5954 (-0.5953) time: 0.6750 data: 0.0003 [11-24 18:28:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.30 Lm: 6.593 (6.599) Lt: 5.836 (5.863) Accm: 3.18 (3.15) Acct: 4.96 (4.94) proj_loss: -0.5937 (-0.5925) time: 0.6750 data: 0.0003 [11-24 18:33:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.32 Lm: 6.549 (6.589) Lt: 5.756 (5.835) Accm: 3.18 (3.16) Acct: 5.22 (5.00) proj_loss: -0.5935 (-0.5914) time: 0.6801 data: 0.0019 [11-24 18:33:19] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 124/350] Total time: 0:19:15 (0.692 s / it) [11-24 18:33:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.32 Lm: 6.600 (6.588) Lt: 5.870 (5.846) Accm: 3.18 (3.19) Acct: 5.04 (5.02) proj_loss: -0.5978 (-0.5964) time: 0.6801 data: 0.0016 [11-24 18:33:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.32 Lm: 6.570 (6.616) Lt: 5.822 (5.884) Accm: 3.09 (3.09) Acct: 4.98 (4.92) proj_loss: -0.5825 (-0.5858) time: 0.6801 data: 0.0016 [11-24 18:33:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 124/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.32 Lm: 6.543 (6.573) Lt: 5.783 (5.831) Accm: 3.10 (3.13) Acct: 4.98 (5.09) proj_loss: -0.5957 (-0.5911) time: 0.6801 data: 0.0019 [11-24 18:33:19] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 124/350] Total time: 0:19:15 (0.692 s / it) [11-24 18:33:19] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 124/350] Total time: 0:19:15 (0.692 s / it) [11-24 18:33:19] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 124/350] Total time: 0:19:15 (0.692 s / it) [11-24 18:33:19] (/home/user/VAR/train.py , line 276)=> [ep124] (training ) Lm: 6.566 (6.581), Lt: 5.810 (5.828), Acc m&t: 3.20 5.04, Remain: 2 days, 23:27:52, Finish: 2024-11-27 02:01 [11-24 18:33:19] (/home/user/VAR/train.py , line 276)=> [ep124] (training ) Lm: 6.566 (6.581), Lt: 5.810 (5.828), Acc m&t: 3.20 5.04, Remain: 2 days, 23:28:32, Finish: 2024-11-27 02:01 [11-24 18:33:19] (/home/user/VAR/train.py , line 276)=> [ep124] (training ) Lm: 6.566 (6.581), Lt: 5.810 (5.828), Acc m&t: 3.20 5.04, Remain: 2 days, 23:27:36, Finish: 2024-11-27 02:00 [11-24 18:33:19] (/home/user/VAR/train.py , line 276)=> [ep124] (training ) Lm: 6.566 (6.581), Lt: 5.810 (5.828), Acc m&t: 3.20 5.04, Remain: 2 days, 23:26:55, Finish: 2024-11-27 02:00 [11-24 18:33:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [ 0/1669] eta: 0:18:40 tlr: 0.00017 tnm: 0.31 Lm: 6.763 (6.763) Lt: 5.982 (5.982) Accm: 2.73 (2.73) Acct: 4.36 (4.36) proj_loss: -0.5850 (-0.5850) time: 0.6714 data: 0.0004 [11-24 18:33:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [ 0/1669] eta: 0:18:40 tlr: 0.00017 tnm: 0.31 Lm: 6.744 (6.744) Lt: 5.939 (5.939) Accm: 2.71 (2.71) Acct: 4.42 (4.42) proj_loss: -0.5713 (-0.5713) time: 0.6714 data: 0.0003 [11-24 18:33:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [ 0/1669] eta: 0:18:41 tlr: 0.00017 tnm: 0.31 Lm: 6.503 (6.503) Lt: 5.738 (5.738) Accm: 3.31 (3.31) Acct: 5.01 (5.01) proj_loss: -0.5973 (-0.5973) time: 0.6721 data: 0.0004 [11-24 18:33:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [ 0/1669] eta: 0:18:41 tlr: 0.00017 tnm: 0.31 Lm: 6.660 (6.660) Lt: 5.975 (5.975) Accm: 2.97 (2.97) Acct: 4.51 (4.51) proj_loss: -0.6040 (-0.6040) time: 0.6719 data: 0.0005 [11-24 18:38:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [ 417/1669] eta: 0:14:12 tlr: 0.00017 tnm: 0.29 Lm: 6.706 (6.706) Lt: 6.000 (6.000) Accm: 2.79 (2.79) Acct: 4.18 (4.18) proj_loss: -0.5888 (-0.5888) time: 0.6769 data: 0.0003 [11-24 18:38:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [ 417/1669] eta: 0:14:12 tlr: 0.00017 tnm: 0.29 Lm: 6.728 (6.728) Lt: 5.954 (5.954) Accm: 2.81 (2.81) Acct: 4.43 (4.43) proj_loss: -0.5882 (-0.5882) time: 0.6769 data: 0.0003 [11-24 18:38:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [ 417/1669] eta: 0:14:12 tlr: 0.00017 tnm: 0.29 Lm: 6.542 (6.542) Lt: 5.785 (5.785) Accm: 3.25 (3.25) Acct: 5.01 (5.01) proj_loss: -0.5967 (-0.5967) time: 0.6769 data: 0.0003 [11-24 18:38:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [ 417/1669] eta: 0:14:12 tlr: 0.00017 tnm: 0.29 Lm: 6.595 (6.595) Lt: 5.806 (5.806) Accm: 3.18 (3.18) Acct: 5.04 (5.04) proj_loss: -0.5756 (-0.5756) time: 0.6769 data: 0.0003 [11-24 18:42:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [ 834/1669] eta: 0:09:26 tlr: 0.00017 tnm: 0.31 Lm: 6.586 (6.592) Lt: 5.830 (5.814) Accm: 3.23 (3.20) Acct: 5.03 (5.04) proj_loss: -0.5798 (-0.5805) time: 0.6752 data: 0.0003 [11-24 18:42:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [ 834/1669] eta: 0:09:26 tlr: 0.00017 tnm: 0.31 Lm: 6.692 (6.585) Lt: 5.927 (5.812) Accm: 2.89 (3.27) Acct: 4.51 (5.13) proj_loss: -0.5914 (-0.5921) time: 0.6752 data: 0.0003 [11-24 18:42:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [ 834/1669] eta: 0:09:26 tlr: 0.00017 tnm: 0.31 Lm: 6.660 (6.668) Lt: 5.975 (5.955) Accm: 2.86 (2.82) Acct: 4.51 (4.33) proj_loss: -0.5968 (-0.5914) time: 0.6752 data: 0.0003 [11-24 18:42:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [ 834/1669] eta: 0:09:26 tlr: 0.00017 tnm: 0.31 Lm: 6.557 (6.547) Lt: 5.833 (5.818) Accm: 3.31 (3.28) Acct: 5.01 (4.92) proj_loss: -0.5961 (-0.5931) time: 0.6752 data: 0.0003 [11-24 18:47:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [1251/1669] eta: 0:04:43 tlr: 0.00017 tnm: 0.30 Lm: 6.569 (6.600) Lt: 5.858 (5.882) Accm: 3.25 (3.08) Acct: 4.88 (4.70) proj_loss: -0.5919 (-0.5918) time: 0.6761 data: 0.0003 [11-24 18:47:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [1251/1669] eta: 0:04:43 tlr: 0.00017 tnm: 0.30 Lm: 6.626 (6.643) Lt: 5.920 (5.906) Accm: 2.92 (2.94) Acct: 4.57 (4.64) proj_loss: -0.5937 (-0.5913) time: 0.6761 data: 0.0003 [11-24 18:47:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [1251/1669] eta: 0:04:43 tlr: 0.00017 tnm: 0.30 Lm: 6.656 (6.626) Lt: 5.885 (5.863) Accm: 3.15 (3.16) Acct: 4.95 (5.00) proj_loss: -0.5851 (-0.5834) time: 0.6761 data: 0.0003 [11-24 18:47:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [1251/1669] eta: 0:04:43 tlr: 0.00017 tnm: 0.30 Lm: 6.695 (6.613) Lt: 5.943 (5.849) Accm: 2.89 (3.17) Acct: 4.54 (4.98) proj_loss: -0.5882 (-0.5896) time: 0.6761 data: 0.0003 [11-24 18:52:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.692 (6.601) Lt: 5.927 (5.834) Accm: 2.89 (3.19) Acct: 4.56 (5.00) proj_loss: -0.5914 (-0.5904) time: 0.7440 data: 0.0020 [11-24 18:52:11] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 125/350] Total time: 0:18:52 (0.679 s / it) [11-24 18:52:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.581 (6.598) Lt: 5.877 (5.881) Accm: 3.20 (3.11) Acct: 5.01 (4.78) proj_loss: -0.5961 (-0.5927) time: 0.7440 data: 0.0016 [11-24 18:52:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.653 (6.645) Lt: 5.933 (5.911) Accm: 2.86 (2.92) Acct: 4.63 (4.64) proj_loss: -0.5968 (-0.5949) time: 0.7440 data: 0.0017 [11-24 18:52:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 125/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.31 Lm: 6.586 (6.613) Lt: 5.830 (5.848) Accm: 3.13 (3.16) Acct: 4.87 (4.97) proj_loss: -0.5798 (-0.5822) time: 0.7440 data: 0.0021 [11-24 18:52:11] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 125/350] Total time: 0:18:52 (0.679 s / it) [11-24 18:52:11] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 125/350] Total time: 0:18:52 (0.679 s / it) [11-24 18:52:11] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 125/350] Total time: 0:18:52 (0.679 s / it) [11-24 18:52:11] (/home/user/VAR/train.py , line 276)=> [ep125] (training ) Lm: 6.566 (6.590), Lt: 5.810 (5.841), Acc m&t: 3.20 5.04, Remain: 2 days, 22:51:18, Finish: 2024-11-27 01:43 [11-24 18:52:11] (/home/user/VAR/train.py , line 276)=> [ep125] (training ) Lm: 6.566 (6.590), Lt: 5.810 (5.841), Acc m&t: 3.20 5.04, Remain: 2 days, 22:53:14, Finish: 2024-11-27 01:45 [11-24 18:52:11] (/home/user/VAR/train.py , line 276)=> [ep125] (training ) Lm: 6.566 (6.590), Lt: 5.810 (5.841), Acc m&t: 3.20 5.04, Remain: 2 days, 22:53:06, Finish: 2024-11-27 01:45 [11-24 18:52:11] (/home/user/VAR/train.py , line 276)=> [ep125] (training ) Lm: 6.566 (6.590), Lt: 5.810 (5.841), Acc m&t: 3.20 5.04, Remain: 2 days, 22:52:03, Finish: 2024-11-27 01:44 [11-24 18:52:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [ 0/1669] eta: 0:18:11 tlr: 0.00017 tnm: 0.33 Lm: 6.606 (6.606) Lt: 5.831 (5.831) Accm: 2.91 (2.91) Acct: 4.51 (4.51) proj_loss: -0.5830 (-0.5830) time: 0.6541 data: 0.0003 [11-24 18:52:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [ 0/1669] eta: 0:18:12 tlr: 0.00017 tnm: 0.33 Lm: 6.381 (6.381) Lt: 5.612 (5.612) Accm: 3.72 (3.72) Acct: 5.82 (5.82) proj_loss: -0.5838 (-0.5838) time: 0.6544 data: 0.0004 [11-24 18:52:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [ 0/1669] eta: 0:18:12 tlr: 0.00017 tnm: 0.33 Lm: 6.585 (6.585) Lt: 5.810 (5.810) Accm: 2.99 (2.99) Acct: 4.89 (4.89) proj_loss: -0.5838 (-0.5838) time: 0.6544 data: 0.0004 [11-24 18:52:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [ 0/1669] eta: 0:18:12 tlr: 0.00017 tnm: 0.33 Lm: 6.640 (6.640) Lt: 5.921 (5.921) Accm: 3.11 (3.11) Acct: 4.86 (4.86) proj_loss: -0.6052 (-0.6052) time: 0.6547 data: 0.0004 [11-24 18:57:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [ 417/1669] eta: 0:15:21 tlr: 0.00017 tnm: 0.30 Lm: 6.538 (6.538) Lt: 5.790 (5.790) Accm: 3.41 (3.41) Acct: 5.38 (5.38) proj_loss: -0.5958 (-0.5958) time: 0.6774 data: 0.0003 [11-24 18:57:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [ 417/1669] eta: 0:15:21 tlr: 0.00017 tnm: 0.30 Lm: 6.696 (6.696) Lt: 5.940 (5.940) Accm: 2.86 (2.86) Acct: 4.49 (4.49) proj_loss: -0.5784 (-0.5784) time: 0.6773 data: 0.0003 [11-24 18:57:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [ 417/1669] eta: 0:15:21 tlr: 0.00017 tnm: 0.30 Lm: 6.543 (6.543) Lt: 5.786 (5.786) Accm: 3.22 (3.22) Acct: 5.17 (5.17) proj_loss: -0.5863 (-0.5863) time: 0.6774 data: 0.0003 [11-24 18:57:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [ 417/1669] eta: 0:15:21 tlr: 0.00017 tnm: 0.30 Lm: 6.537 (6.537) Lt: 5.763 (5.763) Accm: 3.19 (3.19) Acct: 5.12 (5.12) proj_loss: -0.5752 (-0.5752) time: 0.6774 data: 0.0003 [11-24 19:02:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [ 834/1669] eta: 0:09:51 tlr: 0.00017 tnm: 0.31 Lm: 6.585 (6.561) Lt: 5.810 (5.785) Accm: 3.11 (3.17) Acct: 4.89 (5.02) proj_loss: -0.5714 (-0.5739) time: 0.6747 data: 0.0003 [11-24 19:02:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [ 834/1669] eta: 0:09:51 tlr: 0.00017 tnm: 0.31 Lm: 6.667 (6.686) Lt: 5.874 (5.918) Accm: 2.83 (2.85) Acct: 4.51 (4.56) proj_loss: -0.5830 (-0.5846) time: 0.6747 data: 0.0003 [11-24 19:02:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [ 834/1669] eta: 0:09:51 tlr: 0.00017 tnm: 0.31 Lm: 6.679 (6.588) Lt: 5.960 (5.844) Accm: 3.05 (3.16) Acct: 4.60 (4.98) proj_loss: -0.5887 (-0.5888) time: 0.6747 data: 0.0003 [11-24 19:02:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [ 834/1669] eta: 0:09:51 tlr: 0.00017 tnm: 0.31 Lm: 6.487 (6.521) Lt: 5.734 (5.771) Accm: 3.11 (3.28) Acct: 4.86 (5.14) proj_loss: -0.6052 (-0.6040) time: 0.6747 data: 0.0003 [11-24 19:06:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.33 Lm: 6.527 (6.532) Lt: 5.788 (5.789) Accm: 3.16 (3.26) Acct: 4.97 (5.13) proj_loss: -0.5989 (-0.6012) time: 0.6782 data: 0.0003 [11-24 19:06:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.33 Lm: 6.676 (6.686) Lt: 5.897 (5.918) Accm: 2.83 (2.84) Acct: 4.56 (4.58) proj_loss: -0.5899 (-0.5895) time: 0.6782 data: 0.0003 [11-24 19:06:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.33 Lm: 6.547 (6.545) Lt: 5.814 (5.800) Accm: 3.30 (3.26) Acct: 5.08 (5.13) proj_loss: -0.5904 (-0.5896) time: 0.6782 data: 0.0003 [11-24 19:06:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [1251/1669] eta: 0:04:51 tlr: 0.00017 tnm: 0.33 Lm: 6.580 (6.564) Lt: 5.812 (5.792) Accm: 3.14 (3.17) Acct: 5.12 (5.11) proj_loss: -0.5776 (-0.5816) time: 0.6782 data: 0.0003 [11-24 19:11:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.32 Lm: 6.575 (6.554) Lt: 5.810 (5.780) Accm: 3.16 (3.20) Acct: 4.89 (5.05) proj_loss: -0.5806 (-0.5814) time: 0.6812 data: 0.0019 [11-24 19:11:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.32 Lm: 6.516 (6.529) Lt: 5.798 (5.791) Accm: 3.21 (3.33) Acct: 5.08 (5.23) proj_loss: -0.5933 (-0.5996) time: 0.6812 data: 0.0018 [11-24 19:11:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.32 Lm: 6.686 (6.689) Lt: 5.920 (5.938) Accm: 2.82 (2.81) Acct: 4.51 (4.46) proj_loss: -0.5906 (-0.5897) time: 0.6812 data: 0.0016 [11-24 19:11:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 126/350] [1668/1669] eta: 0:00:00 tlr: 0.00017 tnm: 0.32 Lm: 6.536 (6.543) Lt: 5.798 (5.800) Accm: 3.13 (3.23) Acct: 5.01 (5.10) proj_loss: -0.5920 (-0.5958) time: 0.6812 data: 0.0019 [11-24 19:11:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 126/350] Total time: 0:19:16 (0.693 s / it) [11-24 19:11:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 126/350] Total time: 0:19:16 (0.693 s / it) [11-24 19:11:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 126/350] Total time: 0:19:16 (0.693 s / it) [11-24 19:11:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 126/350] Total time: 0:19:16 (0.693 s / it) [11-24 19:11:28] (/home/user/VAR/train.py , line 276)=> [ep126] (training ) Lm: 6.566 (6.580), Lt: 5.810 (5.825), Acc m&t: 3.20 5.04, Remain: 2 days, 22:51:28, Finish: 2024-11-27 02:02 [11-24 19:11:28] (/home/user/VAR/train.py , line 276)=> [ep126] (training ) Lm: 6.566 (6.580), Lt: 5.810 (5.825), Acc m&t: 3.20 5.04, Remain: 2 days, 22:50:08, Finish: 2024-11-27 02:01 [11-24 19:11:28] (/home/user/VAR/train.py , line 276)=> [ep126] (training ) Lm: 6.566 (6.580), Lt: 5.810 (5.825), Acc m&t: 3.20 5.04, Remain: 2 days, 22:50:36, Finish: 2024-11-27 02:02 [11-24 19:11:28] (/home/user/VAR/train.py , line 276)=> [ep126] (training ) Lm: 6.566 (6.580), Lt: 5.810 (5.825), Acc m&t: 3.20 5.04, Remain: 2 days, 22:50:53, Finish: 2024-11-27 02:02 [11-24 19:11:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [ 0/1669] eta: 0:18:20 tlr: 0.00017 tnm: 0.30 Lm: 6.608 (6.608) Lt: 5.876 (5.876) Accm: 2.75 (2.75) Acct: 4.41 (4.41) proj_loss: -0.5745 (-0.5745) time: 0.6596 data: 0.0003 [11-24 19:11:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [ 0/1669] eta: 0:18:21 tlr: 0.00017 tnm: 0.30 Lm: 6.539 (6.539) Lt: 5.807 (5.807) Accm: 3.47 (3.47) Acct: 5.49 (5.49) proj_loss: -0.5994 (-0.5994) time: 0.6601 data: 0.0003 [11-24 19:11:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [ 0/1669] eta: 0:18:21 tlr: 0.00017 tnm: 0.30 Lm: 6.479 (6.479) Lt: 5.636 (5.636) Accm: 3.67 (3.67) Acct: 6.04 (6.04) proj_loss: -0.5829 (-0.5829) time: 0.6599 data: 0.0004 [11-24 19:11:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [ 0/1669] eta: 0:18:21 tlr: 0.00017 tnm: 0.30 Lm: 6.491 (6.491) Lt: 5.738 (5.738) Accm: 3.21 (3.21) Acct: 5.15 (5.15) proj_loss: -0.6039 (-0.6039) time: 0.6599 data: 0.0004 [11-24 19:16:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.30 Lm: 6.539 (6.539) Lt: 5.799 (5.799) Accm: 3.21 (3.21) Acct: 4.98 (4.98) proj_loss: -0.6130 (-0.6130) time: 0.6766 data: 0.0003 [11-24 19:16:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.30 Lm: 6.517 (6.517) Lt: 5.743 (5.743) Accm: 3.29 (3.29) Acct: 5.33 (5.33) proj_loss: -0.5846 (-0.5846) time: 0.6766 data: 0.0003 [11-24 19:16:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.30 Lm: 6.541 (6.541) Lt: 5.821 (5.821) Accm: 3.03 (3.03) Acct: 4.80 (4.80) proj_loss: -0.5898 (-0.5898) time: 0.6766 data: 0.0003 [11-24 19:16:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.30 Lm: 6.595 (6.595) Lt: 5.869 (5.869) Accm: 3.11 (3.11) Acct: 5.00 (5.00) proj_loss: -0.6057 (-0.6057) time: 0.6766 data: 0.0003 [11-24 19:21:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.32 Lm: 6.539 (6.540) Lt: 5.807 (5.790) Accm: 3.47 (3.30) Acct: 5.49 (5.25) proj_loss: -0.5994 (-0.5935) time: 0.6746 data: 0.0003 [11-24 19:21:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.32 Lm: 6.554 (6.550) Lt: 5.850 (5.807) Accm: 2.96 (3.18) Acct: 4.67 (5.11) proj_loss: -0.5863 (-0.5868) time: 0.6746 data: 0.0003 [11-24 19:21:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.32 Lm: 6.499 (6.525) Lt: 5.738 (5.761) Accm: 3.21 (3.30) Acct: 5.15 (5.07) proj_loss: -0.6039 (-0.6007) time: 0.6746 data: 0.0003 [11-24 19:21:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.32 Lm: 6.475 (6.508) Lt: 5.766 (5.770) Accm: 3.31 (3.26) Acct: 5.20 (5.26) proj_loss: -0.5997 (-0.5931) time: 0.6746 data: 0.0003 [11-24 19:26:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.486 (6.506) Lt: 5.758 (5.765) Accm: 3.42 (3.33) Acct: 5.37 (5.33) proj_loss: -0.5993 (-0.5945) time: 0.6768 data: 0.0003 [11-24 19:26:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.586 (6.571) Lt: 5.878 (5.831) Accm: 3.06 (3.17) Acct: 4.89 (5.11) proj_loss: -0.5846 (-0.5853) time: 0.6768 data: 0.0003 [11-24 19:26:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.565 (6.552) Lt: 5.823 (5.803) Accm: 3.32 (3.27) Acct: 5.29 (5.20) proj_loss: -0.6057 (-0.5999) time: 0.6768 data: 0.0003 [11-24 19:26:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.543 (6.556) Lt: 5.799 (5.799) Accm: 3.21 (3.17) Acct: 4.98 (4.96) proj_loss: -0.5988 (-0.5990) time: 0.6768 data: 0.0003 [11-24 19:30:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.29 Lm: 6.587 (6.567) Lt: 5.825 (5.804) Accm: 3.21 (3.18) Acct: 5.15 (5.01) proj_loss: -0.5992 (-0.5990) time: 0.6778 data: 0.0020 [11-24 19:30:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 127/350] Total time: 0:19:15 (0.692 s / it) [11-24 19:30:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.29 Lm: 6.539 (6.548) Lt: 5.807 (5.774) Accm: 3.42 (3.30) Acct: 5.49 (5.30) proj_loss: -0.5994 (-0.5916) time: 0.6778 data: 0.0015 [11-24 19:30:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.29 Lm: 6.554 (6.557) Lt: 5.850 (5.817) Accm: 3.15 (3.26) Acct: 5.11 (5.26) proj_loss: -0.5863 (-0.5888) time: 0.6778 data: 0.0016 [11-24 19:30:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 127/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.29 Lm: 6.497 (6.510) Lt: 5.757 (5.764) Accm: 3.31 (3.32) Acct: 5.23 (5.31) proj_loss: -0.5988 (-0.5915) time: 0.6778 data: 0.0019 [11-24 19:30:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 127/350] Total time: 0:19:15 (0.692 s / it) [11-24 19:30:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 127/350] Total time: 0:19:15 (0.692 s / it) [11-24 19:30:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 127/350] Total time: 0:19:15 (0.692 s / it) [11-24 19:30:44] (/home/user/VAR/train.py , line 276)=> [ep127] (training ) Lm: 6.566 (6.580), Lt: 5.810 (5.823), Acc m&t: 3.20 5.06, Remain: 2 days, 22:12:16, Finish: 2024-11-27 01:43 [11-24 19:30:44] (/home/user/VAR/train.py , line 276)=> [ep127] (training ) Lm: 6.566 (6.580), Lt: 5.810 (5.823), Acc m&t: 3.20 5.06, Remain: 2 days, 22:11:52, Finish: 2024-11-27 01:42 [11-24 19:30:44] (/home/user/VAR/train.py , line 276)=> [ep127] (training ) Lm: 6.566 (6.580), Lt: 5.810 (5.823), Acc m&t: 3.20 5.06, Remain: 2 days, 22:12:15, Finish: 2024-11-27 01:42 [11-24 19:30:44] (/home/user/VAR/train.py , line 276)=> [ep127] (training ) Lm: 6.566 (6.580), Lt: 5.810 (5.823), Acc m&t: 3.20 5.06, Remain: 2 days, 22:12:41, Finish: 2024-11-27 01:43 [11-24 19:30:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [ 0/1669] eta: 0:18:18 tlr: 0.00016 tnm: 0.33 Lm: 6.393 (6.393) Lt: 5.670 (5.670) Accm: 3.63 (3.63) Acct: 5.48 (5.48) proj_loss: -0.6064 (-0.6064) time: 0.6584 data: 0.0003 [11-24 19:30:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [ 0/1669] eta: 0:18:15 tlr: 0.00016 tnm: 0.33 Lm: 6.668 (6.668) Lt: 5.918 (5.918) Accm: 3.04 (3.04) Acct: 4.82 (4.82) proj_loss: -0.5824 (-0.5824) time: 0.6563 data: 0.0004 [11-24 19:30:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [ 0/1669] eta: 0:18:24 tlr: 0.00016 tnm: 0.33 Lm: 6.641 (6.641) Lt: 5.845 (5.845) Accm: 3.18 (3.18) Acct: 4.79 (4.79) proj_loss: -0.5545 (-0.5545) time: 0.6615 data: 0.0003 [11-24 19:30:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [ 0/1669] eta: 0:18:19 tlr: 0.00016 tnm: 0.33 Lm: 6.608 (6.608) Lt: 5.829 (5.829) Accm: 3.15 (3.15) Acct: 5.10 (5.10) proj_loss: -0.5847 (-0.5847) time: 0.6589 data: 0.0004 [11-24 19:35:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [ 417/1669] eta: 0:14:13 tlr: 0.00016 tnm: 0.30 Lm: 6.603 (6.603) Lt: 5.839 (5.839) Accm: 3.19 (3.19) Acct: 5.08 (5.08) proj_loss: -0.5978 (-0.5978) time: 0.6773 data: 0.0003 [11-24 19:35:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [ 417/1669] eta: 0:14:13 tlr: 0.00016 tnm: 0.30 Lm: 6.474 (6.474) Lt: 5.751 (5.751) Accm: 3.54 (3.54) Acct: 5.41 (5.41) proj_loss: -0.5999 (-0.5999) time: 0.6773 data: 0.0003 [11-24 19:35:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [ 417/1669] eta: 0:14:13 tlr: 0.00016 tnm: 0.30 Lm: 6.634 (6.634) Lt: 5.859 (5.859) Accm: 3.11 (3.11) Acct: 4.75 (4.75) proj_loss: -0.5744 (-0.5744) time: 0.6773 data: 0.0002 [11-24 19:35:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [ 417/1669] eta: 0:14:13 tlr: 0.00016 tnm: 0.30 Lm: 6.668 (6.668) Lt: 5.934 (5.934) Accm: 3.00 (3.00) Acct: 4.63 (4.63) proj_loss: -0.5921 (-0.5921) time: 0.6773 data: 0.0003 [11-24 19:40:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [ 834/1669] eta: 0:09:26 tlr: 0.00016 tnm: 0.31 Lm: 6.669 (6.683) Lt: 5.950 (5.942) Accm: 2.96 (2.93) Acct: 4.58 (4.61) proj_loss: -0.5874 (-0.5905) time: 0.6779 data: 0.0003 [11-24 19:40:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [ 834/1669] eta: 0:09:26 tlr: 0.00016 tnm: 0.31 Lm: 6.555 (6.511) Lt: 5.824 (5.776) Accm: 3.45 (3.39) Acct: 5.35 (5.24) proj_loss: -0.5936 (-0.5978) time: 0.6779 data: 0.0003 [11-24 19:40:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [ 834/1669] eta: 0:09:26 tlr: 0.00016 tnm: 0.31 Lm: 6.608 (6.610) Lt: 5.850 (5.861) Accm: 3.15 (3.09) Acct: 5.06 (4.97) proj_loss: -0.5980 (-0.5979) time: 0.6779 data: 0.0003 [11-24 19:40:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [ 834/1669] eta: 0:09:26 tlr: 0.00016 tnm: 0.31 Lm: 6.641 (6.644) Lt: 5.872 (5.876) Accm: 3.04 (2.97) Acct: 4.72 (4.64) proj_loss: -0.5798 (-0.5762) time: 0.6779 data: 0.0002 [11-24 19:44:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.31 Lm: 6.634 (6.585) Lt: 5.859 (5.811) Accm: 3.11 (3.12) Acct: 4.75 (4.84) proj_loss: -0.5870 (-0.5817) time: 0.6740 data: 0.0003 [11-24 19:44:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.31 Lm: 6.691 (6.697) Lt: 5.954 (5.954) Accm: 2.87 (2.87) Acct: 4.51 (4.51) proj_loss: -0.5878 (-0.5899) time: 0.6741 data: 0.0003 [11-24 19:44:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.31 Lm: 6.603 (6.552) Lt: 5.839 (5.802) Accm: 3.19 (3.25) Acct: 5.08 (5.15) proj_loss: -0.5925 (-0.5952) time: 0.6741 data: 0.0003 [11-24 19:44:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.31 Lm: 6.482 (6.486) Lt: 5.747 (5.743) Accm: 3.54 (3.51) Acct: 5.41 (5.40) proj_loss: -0.5935 (-0.5960) time: 0.6740 data: 0.0003 [11-24 19:49:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.410 (6.463) Lt: 5.670 (5.720) Accm: 3.63 (3.56) Acct: 5.48 (5.47) proj_loss: -0.5936 (-0.5978) time: 0.7437 data: 0.0016 [11-24 19:49:37] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 128/350] Total time: 0:18:53 (0.679 s / it) [11-24 19:49:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.669 (6.660) Lt: 5.950 (5.909) Accm: 2.96 (3.00) Acct: 4.58 (4.78) proj_loss: -0.5874 (-0.5871) time: 0.7437 data: 0.0017 [11-24 19:49:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.627 (6.583) Lt: 5.845 (5.813) Accm: 3.18 (3.13) Acct: 4.79 (4.87) proj_loss: -0.5942 (-0.5843) time: 0.7437 data: 0.0017 [11-24 19:49:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 128/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.608 (6.565) Lt: 5.850 (5.813) Accm: 3.15 (3.19) Acct: 5.06 (5.07) proj_loss: -0.5870 (-0.5889) time: 0.7437 data: 0.0019 [11-24 19:49:37] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 128/350] Total time: 0:18:53 (0.679 s / it) [11-24 19:49:37] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 128/350] Total time: 0:18:53 (0.679 s / it) [11-24 19:49:37] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 128/350] Total time: 0:18:53 (0.679 s / it) [11-24 19:49:37] (/home/user/VAR/train.py , line 276)=> [ep128] (training ) Lm: 6.566 (6.575), Lt: 5.810 (5.819), Acc m&t: 3.20 5.06, Remain: 2 days, 21:57:36, Finish: 2024-11-27 01:47 [11-24 19:49:37] (/home/user/VAR/train.py , line 276)=> [ep128] (training ) Lm: 6.566 (6.575), Lt: 5.810 (5.819), Acc m&t: 3.20 5.06, Remain: 2 days, 21:58:07, Finish: 2024-11-27 01:47 [11-24 19:49:37] (/home/user/VAR/train.py , line 276)=> [ep128] (training ) Lm: 6.566 (6.575), Lt: 5.810 (5.819), Acc m&t: 3.20 5.06, Remain: 2 days, 21:57:59, Finish: 2024-11-27 01:47 [11-24 19:49:37] (/home/user/VAR/train.py , line 276)=> [ep128] (training ) Lm: 6.566 (6.575), Lt: 5.810 (5.819), Acc m&t: 3.20 5.06, Remain: 2 days, 21:57:37, Finish: 2024-11-27 01:47 [11-24 19:49:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [ 0/1669] eta: 0:18:45 tlr: 0.00016 tnm: 0.32 Lm: 6.661 (6.661) Lt: 5.910 (5.910) Accm: 2.96 (2.96) Acct: 4.75 (4.75) proj_loss: -0.5660 (-0.5660) time: 0.6743 data: 0.0003 [11-24 19:49:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [ 0/1669] eta: 0:18:28 tlr: 0.00016 tnm: 0.32 Lm: 6.652 (6.652) Lt: 5.908 (5.908) Accm: 3.15 (3.15) Acct: 5.13 (5.13) proj_loss: -0.5973 (-0.5973) time: 0.6640 data: 0.0004 [11-24 19:49:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [ 0/1669] eta: 0:18:46 tlr: 0.00016 tnm: 0.32 Lm: 6.577 (6.577) Lt: 5.855 (5.855) Accm: 3.17 (3.17) Acct: 5.03 (5.03) proj_loss: -0.5927 (-0.5927) time: 0.6748 data: 0.0004 [11-24 19:49:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [ 0/1669] eta: 0:18:46 tlr: 0.00016 tnm: 0.32 Lm: 6.361 (6.361) Lt: 5.562 (5.562) Accm: 4.17 (4.17) Acct: 6.46 (6.46) proj_loss: -0.5911 (-0.5911) time: 0.6749 data: 0.0004 [11-24 19:54:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [ 417/1669] eta: 0:15:23 tlr: 0.00016 tnm: 0.30 Lm: 6.449 (6.449) Lt: 5.665 (5.665) Accm: 3.57 (3.57) Acct: 5.60 (5.60) proj_loss: -0.5918 (-0.5918) time: 0.6765 data: 0.0003 [11-24 19:54:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [ 417/1669] eta: 0:15:23 tlr: 0.00016 tnm: 0.30 Lm: 6.648 (6.648) Lt: 5.913 (5.913) Accm: 2.84 (2.84) Acct: 4.48 (4.48) proj_loss: -0.5702 (-0.5702) time: 0.6765 data: 0.0003 [11-24 19:54:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [ 417/1669] eta: 0:15:23 tlr: 0.00016 tnm: 0.30 Lm: 6.575 (6.575) Lt: 5.823 (5.823) Accm: 3.06 (3.06) Acct: 4.80 (4.80) proj_loss: -0.5972 (-0.5972) time: 0.6765 data: 0.0003 [11-24 19:54:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [ 417/1669] eta: 0:15:23 tlr: 0.00016 tnm: 0.30 Lm: 6.622 (6.622) Lt: 5.895 (5.895) Accm: 3.17 (3.17) Acct: 4.98 (4.98) proj_loss: -0.5914 (-0.5914) time: 0.6765 data: 0.0003 [11-24 19:59:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [ 834/1669] eta: 0:09:52 tlr: 0.00016 tnm: 0.32 Lm: 6.592 (6.603) Lt: 5.882 (5.875) Accm: 3.20 (3.19) Acct: 4.86 (4.94) proj_loss: -0.5924 (-0.5917) time: 0.6764 data: 0.0003 [11-24 19:59:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [ 834/1669] eta: 0:09:52 tlr: 0.00016 tnm: 0.32 Lm: 6.470 (6.456) Lt: 5.681 (5.670) Accm: 3.50 (3.55) Acct: 5.56 (5.58) proj_loss: -0.5925 (-0.5946) time: 0.6763 data: 0.0003 [11-24 19:59:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [ 834/1669] eta: 0:09:52 tlr: 0.00016 tnm: 0.32 Lm: 6.574 (6.570) Lt: 5.791 (5.810) Accm: 3.04 (3.05) Acct: 4.56 (4.70) proj_loss: -0.5927 (-0.5914) time: 0.6763 data: 0.0003 [11-24 19:59:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [ 834/1669] eta: 0:09:52 tlr: 0.00016 tnm: 0.32 Lm: 6.634 (6.626) Lt: 5.910 (5.867) Accm: 2.96 (2.94) Acct: 4.75 (4.67) proj_loss: -0.5744 (-0.5784) time: 0.6764 data: 0.0003 [11-24 20:04:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.32 Lm: 6.608 (6.601) Lt: 5.852 (5.849) Accm: 3.05 (3.07) Acct: 4.90 (4.87) proj_loss: -0.5846 (-0.5831) time: 0.6750 data: 0.0003 [11-24 20:04:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.32 Lm: 6.579 (6.588) Lt: 5.858 (5.853) Accm: 3.17 (3.17) Acct: 4.92 (4.95) proj_loss: -0.5916 (-0.5915) time: 0.6750 data: 0.0003 [11-24 20:04:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.32 Lm: 6.503 (6.484) Lt: 5.724 (5.705) Accm: 3.33 (3.45) Acct: 5.31 (5.45) proj_loss: -0.5918 (-0.5873) time: 0.6751 data: 0.0003 [11-24 20:04:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.32 Lm: 6.575 (6.579) Lt: 5.822 (5.820) Accm: 3.11 (3.13) Acct: 4.80 (4.84) proj_loss: -0.5931 (-0.5919) time: 0.6751 data: 0.0003 [11-24 20:08:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.30 Lm: 6.577 (6.595) Lt: 5.852 (5.843) Accm: 3.04 (3.09) Acct: 4.56 (4.75) proj_loss: -0.5927 (-0.5886) time: 0.6781 data: 0.0018 [11-24 20:08:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.30 Lm: 6.536 (6.509) Lt: 5.768 (5.727) Accm: 3.16 (3.38) Acct: 5.06 (5.35) proj_loss: -0.5911 (-0.5877) time: 0.6781 data: 0.0017 [11-24 20:08:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 129/350] Total time: 0:19:16 (0.693 s / it) [11-24 20:08:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.30 Lm: 6.592 (6.600) Lt: 5.882 (5.869) Accm: 3.15 (3.15) Acct: 4.86 (4.93) proj_loss: -0.5924 (-0.5921) time: 0.6781 data: 0.0018 [11-24 20:08:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 129/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.30 Lm: 6.583 (6.589) Lt: 5.830 (5.845) Accm: 3.07 (3.07) Acct: 4.75 (4.82) proj_loss: -0.5948 (-0.5855) time: 0.6781 data: 0.0017 [11-24 20:08:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 129/350] Total time: 0:19:16 (0.693 s / it) [11-24 20:08:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 129/350] Total time: 0:19:16 (0.693 s / it) [11-24 20:08:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 129/350] Total time: 0:19:16 (0.693 s / it) [11-24 20:11:16] (home/user/VAR/trainer.py, line 114)=> FID: 3.798152484161278 [11-24 20:11:17] (/home/user/VAR/train.py , line 259)=> [*] [ep129] (val 50000) Lm: 6.5839, Lt: 5.8300, Acc m&t: 3.16 5.01, Val cost: 143.02s [11-24 20:11:17] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-24 20:11:36] (/home/user/VAR/train.py , line 276)=> [ep129] (training ) Lm: 6.566 (6.584), Lt: 5.810 (5.830), Acc m&t: 3.20 5.06, Remain: 2 days, 21:36:34, Finish: 2024-11-27 01:45 [11-24 20:11:36] (/home/user/VAR/train.py , line 276)=> [ep129] (training ) Lm: 6.566 (6.584), Lt: 5.810 (5.830), Acc m&t: 3.20 5.06, Remain: 2 days, 21:37:07, Finish: 2024-11-27 01:46 [11-24 20:11:36] (/home/user/VAR/train.py , line 276)=> [ep129] (training ) Lm: 6.566 (6.584), Lt: 5.810 (5.830), Acc m&t: 3.20 5.06, Remain: 2 days, 21:37:54, Finish: 2024-11-27 01:46 [11-24 20:11:36] (/home/user/VAR/train.py , line 276)=> [ep129] (training ) Lm: 6.566 (6.584), Lt: 5.810 (5.830), Acc m&t: 3.20 5.06, Remain: 2 days, 21:37:33, Finish: 2024-11-27 01:46 [11-24 20:11:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [ 0/1669] eta: 0:18:14 tlr: 0.00016 tnm: 0.29 Lm: 6.633 (6.633) Lt: 5.917 (5.917) Accm: 3.13 (3.13) Acct: 5.13 (5.13) proj_loss: -0.6175 (-0.6175) time: 0.6556 data: 0.0004 [11-24 20:11:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [ 0/1669] eta: 0:18:14 tlr: 0.00016 tnm: 0.29 Lm: 6.575 (6.575) Lt: 5.784 (5.784) Accm: 3.40 (3.40) Acct: 5.35 (5.35) proj_loss: -0.5991 (-0.5991) time: 0.6557 data: 0.0003 [11-24 20:11:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [ 0/1669] eta: 0:18:46 tlr: 0.00016 tnm: 0.29 Lm: 6.561 (6.561) Lt: 5.819 (5.819) Accm: 3.24 (3.24) Acct: 5.10 (5.10) proj_loss: -0.6009 (-0.6009) time: 0.6751 data: 0.0004 [11-24 20:11:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [ 0/1669] eta: 0:18:45 tlr: 0.00016 tnm: 0.29 Lm: 6.537 (6.537) Lt: 5.795 (5.795) Accm: 3.32 (3.32) Acct: 5.23 (5.23) proj_loss: -0.6004 (-0.6004) time: 0.6745 data: 0.0004 [11-24 20:16:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [ 417/1669] eta: 0:14:07 tlr: 0.00016 tnm: 0.34 Lm: 6.564 (6.564) Lt: 5.843 (5.843) Accm: 3.13 (3.13) Acct: 4.92 (4.92) proj_loss: -0.6042 (-0.6042) time: 0.6777 data: 0.0003 [11-24 20:16:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [ 417/1669] eta: 0:14:07 tlr: 0.00016 tnm: 0.34 Lm: 6.516 (6.516) Lt: 5.723 (5.723) Accm: 3.47 (3.47) Acct: 5.47 (5.47) proj_loss: -0.5950 (-0.5950) time: 0.6777 data: 0.0003 [11-24 20:16:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [ 417/1669] eta: 0:14:07 tlr: 0.00016 tnm: 0.34 Lm: 6.513 (6.513) Lt: 5.778 (5.778) Accm: 3.41 (3.41) Acct: 5.22 (5.22) proj_loss: -0.5857 (-0.5857) time: 0.6777 data: 0.0003 [11-24 20:16:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [ 417/1669] eta: 0:14:07 tlr: 0.00016 tnm: 0.34 Lm: 6.588 (6.588) Lt: 5.843 (5.843) Accm: 3.31 (3.31) Acct: 5.31 (5.31) proj_loss: -0.5980 (-0.5980) time: 0.6777 data: 0.0002 [11-24 20:21:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.31 Lm: 6.547 (6.574) Lt: 5.768 (5.811) Accm: 3.24 (3.29) Acct: 5.17 (5.26) proj_loss: -0.5948 (-0.5969) time: 0.6774 data: 0.0003 [11-24 20:21:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.31 Lm: 6.537 (6.553) Lt: 5.799 (5.828) Accm: 3.32 (3.22) Acct: 4.99 (4.94) proj_loss: -0.6004 (-0.5982) time: 0.6774 data: 0.0003 [11-24 20:21:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.31 Lm: 6.561 (6.529) Lt: 5.816 (5.791) Accm: 3.24 (3.28) Acct: 5.10 (4.92) proj_loss: -0.5850 (-0.5855) time: 0.6774 data: 0.0003 [11-24 20:21:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.31 Lm: 6.499 (6.510) Lt: 5.701 (5.716) Accm: 3.46 (3.47) Acct: 5.35 (5.41) proj_loss: -0.5937 (-0.5946) time: 0.6775 data: 0.0006 [11-24 20:26:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.537 (6.561) Lt: 5.742 (5.793) Accm: 3.43 (3.27) Acct: 5.33 (5.08) proj_loss: -0.5923 (-0.5914) time: 0.6780 data: 0.0003 [11-24 20:26:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.564 (6.581) Lt: 5.844 (5.853) Accm: 3.13 (3.12) Acct: 4.80 (4.83) proj_loss: -0.5965 (-0.5968) time: 0.6780 data: 0.0003 [11-24 20:26:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.545 (6.549) Lt: 5.758 (5.780) Accm: 3.36 (3.38) Acct: 5.33 (5.45) proj_loss: -0.6061 (-0.6033) time: 0.6780 data: 0.0003 [11-24 20:26:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.557 (6.535) Lt: 5.803 (5.791) Accm: 3.38 (3.34) Acct: 5.22 (5.13) proj_loss: -0.5930 (-0.5896) time: 0.6780 data: 0.0003 [11-24 20:30:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.32 Lm: 6.553 (6.533) Lt: 5.790 (5.786) Accm: 3.27 (3.32) Acct: 5.34 (5.19) proj_loss: -0.6004 (-0.5918) time: 0.6782 data: 0.0021 [11-24 20:30:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 130/350] Total time: 0:19:16 (0.693 s / it) [11-24 20:30:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.32 Lm: 6.537 (6.547) Lt: 5.799 (5.804) Accm: 3.32 (3.27) Acct: 4.99 (5.09) proj_loss: -0.5925 (-0.5958) time: 0.6782 data: 0.0016 [11-24 20:30:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.32 Lm: 6.547 (6.560) Lt: 5.768 (5.790) Accm: 3.24 (3.34) Acct: 5.17 (5.33) proj_loss: -0.5948 (-0.5989) time: 0.6782 data: 0.0018 [11-24 20:30:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 130/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.32 Lm: 6.575 (6.612) Lt: 5.784 (5.857) Accm: 3.40 (3.10) Acct: 5.30 (4.85) proj_loss: -0.5909 (-0.5898) time: 0.6782 data: 0.0019 [11-24 20:30:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 130/350] Total time: 0:19:16 (0.693 s / it) [11-24 20:30:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 130/350] Total time: 0:19:16 (0.693 s / it) [11-24 20:30:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 130/350] Total time: 0:19:16 (0.693 s / it) [11-24 20:30:52] (/home/user/VAR/train.py , line 276)=> [ep130] (training ) Lm: 6.566 (6.569), Lt: 5.810 (5.815), Acc m&t: 3.20 5.06, Remain: 2 days, 21:19:37, Finish: 2024-11-27 01:50 [11-24 20:30:52] (/home/user/VAR/train.py , line 276)=> [ep130] (training ) Lm: 6.566 (6.569), Lt: 5.810 (5.815), Acc m&t: 3.20 5.06, Remain: 2 days, 21:20:20, Finish: 2024-11-27 01:51 [11-24 20:30:52] (/home/user/VAR/train.py , line 276)=> [ep130] (training ) Lm: 6.566 (6.569), Lt: 5.810 (5.815), Acc m&t: 3.20 5.06, Remain: 2 days, 21:19:52, Finish: 2024-11-27 01:50 [11-24 20:30:52] (/home/user/VAR/train.py , line 276)=> [ep130] (training ) Lm: 6.566 (6.569), Lt: 5.810 (5.815), Acc m&t: 3.20 5.06, Remain: 2 days, 21:19:20, Finish: 2024-11-27 01:50 [11-24 20:30:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [ 0/1669] eta: 0:18:41 tlr: 0.00016 tnm: 0.30 Lm: 6.535 (6.535) Lt: 5.810 (5.810) Accm: 3.24 (3.24) Acct: 5.25 (5.25) proj_loss: -0.5931 (-0.5931) time: 0.6717 data: 0.0004 [11-24 20:30:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [ 0/1669] eta: 0:18:08 tlr: 0.00016 tnm: 0.30 Lm: 6.584 (6.584) Lt: 5.845 (5.845) Accm: 3.42 (3.42) Acct: 5.48 (5.48) proj_loss: -0.5776 (-0.5776) time: 0.6520 data: 0.0004 [11-24 20:30:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [ 0/1669] eta: 0:18:08 tlr: 0.00016 tnm: 0.30 Lm: 6.397 (6.397) Lt: 5.611 (5.611) Accm: 3.53 (3.53) Acct: 5.32 (5.32) proj_loss: -0.5797 (-0.5797) time: 0.6523 data: 0.0004 [11-24 20:30:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [ 0/1669] eta: 0:18:08 tlr: 0.00016 tnm: 0.30 Lm: 6.593 (6.593) Lt: 5.878 (5.878) Accm: 3.18 (3.18) Acct: 5.04 (5.04) proj_loss: -0.5958 (-0.5958) time: 0.6521 data: 0.0005 [11-24 20:35:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [ 417/1669] eta: 0:14:12 tlr: 0.00016 tnm: 0.33 Lm: 6.618 (6.618) Lt: 5.902 (5.902) Accm: 3.11 (3.11) Acct: 4.89 (4.89) proj_loss: -0.5999 (-0.5999) time: 0.6745 data: 0.0002 [11-24 20:35:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [ 417/1669] eta: 0:14:12 tlr: 0.00016 tnm: 0.33 Lm: 6.494 (6.494) Lt: 5.752 (5.752) Accm: 3.28 (3.28) Acct: 4.95 (4.95) proj_loss: -0.5928 (-0.5928) time: 0.6745 data: 0.0003 [11-24 20:35:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [ 417/1669] eta: 0:14:12 tlr: 0.00016 tnm: 0.33 Lm: 6.489 (6.489) Lt: 5.728 (5.728) Accm: 3.62 (3.62) Acct: 5.75 (5.75) proj_loss: -0.5816 (-0.5816) time: 0.6745 data: 0.0003 [11-24 20:35:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [ 417/1669] eta: 0:14:12 tlr: 0.00016 tnm: 0.33 Lm: 6.645 (6.645) Lt: 5.894 (5.894) Accm: 3.02 (3.02) Acct: 4.73 (4.73) proj_loss: -0.5807 (-0.5807) time: 0.6745 data: 0.0003 [11-24 20:40:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [ 834/1669] eta: 0:09:26 tlr: 0.00016 tnm: 0.31 Lm: 6.584 (6.600) Lt: 5.845 (5.843) Accm: 3.42 (3.21) Acct: 5.44 (4.97) proj_loss: -0.5839 (-0.5852) time: 0.6773 data: 0.0003 [11-24 20:40:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [ 834/1669] eta: 0:09:26 tlr: 0.00016 tnm: 0.31 Lm: 6.593 (6.609) Lt: 5.878 (5.882) Accm: 3.04 (3.08) Acct: 4.84 (4.87) proj_loss: -0.5958 (-0.5944) time: 0.6773 data: 0.0003 [11-24 20:40:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [ 834/1669] eta: 0:09:26 tlr: 0.00016 tnm: 0.31 Lm: 6.505 (6.495) Lt: 5.758 (5.738) Accm: 3.60 (3.61) Acct: 5.77 (5.76) proj_loss: -0.5931 (-0.5934) time: 0.6773 data: 0.0003 [11-24 20:40:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [ 834/1669] eta: 0:09:26 tlr: 0.00016 tnm: 0.31 Lm: 6.591 (6.534) Lt: 5.853 (5.786) Accm: 3.04 (3.19) Acct: 4.58 (4.80) proj_loss: -0.5797 (-0.5876) time: 0.6773 data: 0.0003 [11-24 20:45:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.32 Lm: 6.602 (6.591) Lt: 5.873 (5.851) Accm: 3.03 (3.10) Acct: 4.59 (4.75) proj_loss: -0.5784 (-0.5843) time: 0.6780 data: 0.0003 [11-24 20:45:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.32 Lm: 6.520 (6.550) Lt: 5.784 (5.815) Accm: 3.42 (3.36) Acct: 5.51 (5.36) proj_loss: -0.5937 (-0.5936) time: 0.6781 data: 0.0003 [11-24 20:45:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.32 Lm: 6.602 (6.605) Lt: 5.861 (5.851) Accm: 3.27 (3.19) Acct: 5.07 (4.90) proj_loss: -0.5841 (-0.5850) time: 0.6780 data: 0.0003 [11-24 20:45:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.32 Lm: 6.593 (6.583) Lt: 5.860 (5.833) Accm: 3.11 (3.24) Acct: 4.94 (5.17) proj_loss: -0.5999 (-0.5980) time: 0.6780 data: 0.0003 [11-24 20:49:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.593 (6.597) Lt: 5.878 (5.847) Accm: 3.04 (3.14) Acct: 4.84 (5.02) proj_loss: -0.5958 (-0.5935) time: 0.7439 data: 0.0019 [11-24 20:49:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 131/350] Total time: 0:18:53 (0.679 s / it) [11-24 20:49:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.591 (6.586) Lt: 5.853 (5.838) Accm: 3.04 (3.12) Acct: 4.60 (4.81) proj_loss: -0.5797 (-0.5899) time: 0.7439 data: 0.0018 [11-24 20:49:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.535 (6.554) Lt: 5.810 (5.817) Accm: 3.24 (3.33) Acct: 5.25 (5.23) proj_loss: -0.5943 (-0.5963) time: 0.7439 data: 0.0014 [11-24 20:49:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 131/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.584 (6.575) Lt: 5.845 (5.811) Accm: 3.36 (3.22) Acct: 5.44 (5.03) proj_loss: -0.5839 (-0.5814) time: 0.7439 data: 0.0020 [11-24 20:49:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 131/350] Total time: 0:18:53 (0.679 s / it) [11-24 20:49:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 131/350] Total time: 0:18:53 (0.679 s / it) [11-24 20:49:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 131/350] Total time: 0:18:53 (0.679 s / it) [11-24 20:49:46] (/home/user/VAR/train.py , line 276)=> [ep131] (training ) Lm: 6.566 (6.588), Lt: 5.810 (5.837), Acc m&t: 3.20 5.06, Remain: 2 days, 20:58:17, Finish: 2024-11-27 01:48 [11-24 20:49:46] (/home/user/VAR/train.py , line 276)=> [ep131] (training ) Lm: 6.566 (6.588), Lt: 5.810 (5.837), Acc m&t: 3.20 5.06, Remain: 2 days, 21:00:03, Finish: 2024-11-27 01:49 [11-24 20:49:46] (/home/user/VAR/train.py , line 276)=> [ep131] (training ) Lm: 6.566 (6.588), Lt: 5.810 (5.837), Acc m&t: 3.20 5.06, Remain: 2 days, 21:00:57, Finish: 2024-11-27 01:50 [11-24 20:49:46] (/home/user/VAR/train.py , line 276)=> [ep131] (training ) Lm: 6.566 (6.588), Lt: 5.810 (5.837), Acc m&t: 3.20 5.06, Remain: 2 days, 21:01:22, Finish: 2024-11-27 01:51 [11-24 20:49:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [ 0/1669] eta: 0:18:23 tlr: 0.00016 tnm: 0.30 Lm: 6.504 (6.504) Lt: 5.703 (5.703) Accm: 3.60 (3.60) Acct: 6.06 (6.06) proj_loss: -0.5848 (-0.5848) time: 0.6611 data: 0.0003 [11-24 20:49:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [ 0/1669] eta: 0:18:30 tlr: 0.00016 tnm: 0.30 Lm: 6.475 (6.475) Lt: 5.734 (5.734) Accm: 3.39 (3.39) Acct: 5.30 (5.30) proj_loss: -0.5950 (-0.5950) time: 0.6652 data: 0.0004 [11-24 20:49:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [ 0/1669] eta: 0:18:25 tlr: 0.00016 tnm: 0.30 Lm: 6.680 (6.680) Lt: 5.910 (5.910) Accm: 2.68 (2.68) Acct: 4.48 (4.48) proj_loss: -0.5628 (-0.5628) time: 0.6623 data: 0.0004 [11-24 20:49:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [ 0/1669] eta: 0:18:25 tlr: 0.00016 tnm: 0.30 Lm: 6.609 (6.609) Lt: 5.903 (5.903) Accm: 3.18 (3.18) Acct: 5.01 (5.01) proj_loss: -0.6030 (-0.6030) time: 0.6624 data: 0.0003 [11-24 20:54:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [ 417/1669] eta: 0:15:22 tlr: 0.00016 tnm: 0.30 Lm: 6.639 (6.639) Lt: 5.935 (5.935) Accm: 2.95 (2.95) Acct: 4.59 (4.59) proj_loss: -0.5948 (-0.5948) time: 0.6768 data: 0.0003 [11-24 20:54:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [ 417/1669] eta: 0:15:22 tlr: 0.00016 tnm: 0.30 Lm: 6.507 (6.507) Lt: 5.727 (5.727) Accm: 3.50 (3.50) Acct: 5.60 (5.60) proj_loss: -0.6008 (-0.6008) time: 0.6768 data: 0.0003 [11-24 20:54:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [ 417/1669] eta: 0:15:22 tlr: 0.00016 tnm: 0.30 Lm: 6.470 (6.470) Lt: 5.704 (5.704) Accm: 3.43 (3.43) Acct: 5.45 (5.45) proj_loss: -0.5897 (-0.5897) time: 0.6768 data: 0.0003 [11-24 20:54:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [ 417/1669] eta: 0:15:22 tlr: 0.00016 tnm: 0.30 Lm: 6.654 (6.654) Lt: 5.882 (5.882) Accm: 2.92 (2.92) Acct: 4.79 (4.79) proj_loss: -0.5702 (-0.5702) time: 0.6768 data: 0.0003 [11-24 20:59:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [ 834/1669] eta: 0:09:51 tlr: 0.00016 tnm: 0.30 Lm: 6.628 (6.630) Lt: 5.854 (5.848) Accm: 3.15 (3.03) Acct: 5.10 (4.92) proj_loss: -0.5729 (-0.5711) time: 0.6753 data: 0.0003 [11-24 20:59:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [ 834/1669] eta: 0:09:51 tlr: 0.00016 tnm: 0.30 Lm: 6.618 (6.632) Lt: 5.903 (5.910) Accm: 2.95 (2.95) Acct: 4.65 (4.61) proj_loss: -0.5866 (-0.5920) time: 0.6753 data: 0.0003 [11-24 20:59:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [ 834/1669] eta: 0:09:51 tlr: 0.00016 tnm: 0.30 Lm: 6.510 (6.531) Lt: 5.752 (5.757) Accm: 3.40 (3.37) Acct: 5.15 (5.40) proj_loss: -0.6163 (-0.6060) time: 0.6753 data: 0.0003 [11-24 20:59:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [ 834/1669] eta: 0:09:51 tlr: 0.00016 tnm: 0.30 Lm: 6.475 (6.516) Lt: 5.734 (5.741) Accm: 3.39 (3.28) Acct: 5.30 (5.30) proj_loss: -0.5845 (-0.5874) time: 0.6753 data: 0.0003 [11-24 21:04:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.492 (6.514) Lt: 5.727 (5.736) Accm: 3.33 (3.27) Acct: 5.35 (5.32) proj_loss: -0.5867 (-0.5878) time: 0.6785 data: 0.0003 [11-24 21:04:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.605 (6.576) Lt: 5.817 (5.799) Accm: 3.21 (3.19) Acct: 5.14 (5.13) proj_loss: -0.5752 (-0.5763) time: 0.6785 data: 0.0003 [11-24 21:04:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.507 (6.517) Lt: 5.736 (5.748) Accm: 3.50 (3.44) Acct: 5.25 (5.39) proj_loss: -0.6005 (-0.5986) time: 0.6785 data: 0.0003 [11-24 21:04:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.644 (6.657) Lt: 5.935 (5.927) Accm: 2.84 (2.87) Acct: 4.42 (4.50) proj_loss: -0.5865 (-0.5869) time: 0.6785 data: 0.0003 [11-24 21:09:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.32 Lm: 6.618 (6.616) Lt: 5.903 (5.881) Accm: 2.95 (3.02) Acct: 4.65 (4.69) proj_loss: -0.5866 (-0.5907) time: 0.6802 data: 0.0018 [11-24 21:09:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 132/350] Total time: 0:19:16 (0.693 s / it) [11-24 21:09:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.32 Lm: 6.508 (6.517) Lt: 5.734 (5.742) Accm: 3.29 (3.28) Acct: 5.30 (5.27) proj_loss: -0.5889 (-0.5914) time: 0.6801 data: 0.0016 [11-24 21:09:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.32 Lm: 6.504 (6.511) Lt: 5.721 (5.733) Accm: 3.46 (3.44) Acct: 5.35 (5.42) proj_loss: -0.5848 (-0.5917) time: 0.6802 data: 0.0014 [11-24 21:09:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 132/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.32 Lm: 6.582 (6.576) Lt: 5.780 (5.794) Accm: 3.26 (3.22) Acct: 5.18 (5.22) proj_loss: -0.5745 (-0.5759) time: 0.6802 data: 0.0020 [11-24 21:09:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 132/350] Total time: 0:19:16 (0.693 s / it) [11-24 21:09:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 132/350] Total time: 0:19:16 (0.693 s / it) [11-24 21:09:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 132/350] Total time: 0:19:16 (0.693 s / it) [11-24 21:09:02] (/home/user/VAR/train.py , line 276)=> [ep132] (training ) Lm: 6.565 (6.565), Lt: 5.810 (5.811), Acc m&t: 3.20 5.06, Remain: 2 days, 20:54:56, Finish: 2024-11-27 02:03 [11-24 21:09:02] (/home/user/VAR/train.py , line 276)=> [ep132] (training ) Lm: 6.565 (6.565), Lt: 5.810 (5.811), Acc m&t: 3.20 5.06, Remain: 2 days, 20:53:43, Finish: 2024-11-27 02:02 [11-24 21:09:02] (/home/user/VAR/train.py , line 276)=> [ep132] (training ) Lm: 6.565 (6.565), Lt: 5.810 (5.811), Acc m&t: 3.20 5.06, Remain: 2 days, 20:53:52, Finish: 2024-11-27 02:02 [11-24 21:09:02] (/home/user/VAR/train.py , line 276)=> [ep132] (training ) Lm: 6.565 (6.565), Lt: 5.810 (5.811), Acc m&t: 3.20 5.06, Remain: 2 days, 20:54:45, Finish: 2024-11-27 02:03 [11-24 21:09:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [ 0/1669] eta: 0:18:21 tlr: 0.00016 tnm: 0.30 Lm: 6.548 (6.548) Lt: 5.882 (5.882) Accm: 3.22 (3.22) Acct: 5.06 (5.06) proj_loss: -0.6024 (-0.6024) time: 0.6598 data: 0.0004 [11-24 21:09:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [ 0/1669] eta: 0:18:22 tlr: 0.00016 tnm: 0.30 Lm: 6.656 (6.656) Lt: 5.908 (5.908) Accm: 2.96 (2.96) Acct: 4.75 (4.75) proj_loss: -0.5824 (-0.5824) time: 0.6603 data: 0.0004 [11-24 21:09:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [ 0/1669] eta: 0:18:22 tlr: 0.00016 tnm: 0.30 Lm: 6.546 (6.546) Lt: 5.828 (5.828) Accm: 3.13 (3.13) Acct: 4.82 (4.82) proj_loss: -0.5949 (-0.5949) time: 0.6605 data: 0.0004 [11-24 21:09:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [ 0/1669] eta: 0:18:22 tlr: 0.00016 tnm: 0.30 Lm: 6.429 (6.429) Lt: 5.576 (5.576) Accm: 3.85 (3.85) Acct: 6.15 (6.15) proj_loss: -0.5795 (-0.5795) time: 0.6605 data: 0.0004 [11-24 21:13:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.31 Lm: 6.490 (6.490) Lt: 5.646 (5.646) Accm: 3.48 (3.48) Acct: 5.49 (5.49) proj_loss: -0.5696 (-0.5696) time: 0.6747 data: 0.0003 [11-24 21:13:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.31 Lm: 6.633 (6.633) Lt: 5.907 (5.907) Accm: 2.94 (2.94) Acct: 4.59 (4.59) proj_loss: -0.5959 (-0.5959) time: 0.6747 data: 0.0003 [11-24 21:13:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.31 Lm: 6.537 (6.537) Lt: 5.863 (5.863) Accm: 3.30 (3.30) Acct: 5.04 (5.04) proj_loss: -0.5945 (-0.5945) time: 0.6747 data: 0.0003 [11-24 21:13:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.31 Lm: 6.624 (6.624) Lt: 5.866 (5.866) Accm: 2.93 (2.93) Acct: 4.67 (4.67) proj_loss: -0.5879 (-0.5879) time: 0.6747 data: 0.0003 [11-24 21:18:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.32 Lm: 6.691 (6.646) Lt: 5.904 (5.894) Accm: 2.72 (2.86) Acct: 4.53 (4.58) proj_loss: -0.5810 (-0.5843) time: 0.6765 data: 0.0003 [11-24 21:18:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.32 Lm: 6.548 (6.584) Lt: 5.882 (5.917) Accm: 3.22 (3.19) Acct: 5.03 (4.90) proj_loss: -0.5928 (-0.5939) time: 0.6765 data: 0.0003 [11-24 21:18:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.32 Lm: 6.656 (6.647) Lt: 5.905 (5.899) Accm: 2.96 (2.97) Acct: 4.75 (4.67) proj_loss: -0.5944 (-0.5954) time: 0.6765 data: 0.0003 [11-24 21:18:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.32 Lm: 6.551 (6.513) Lt: 5.717 (5.697) Accm: 3.28 (3.41) Acct: 4.89 (5.29) proj_loss: -0.5795 (-0.5812) time: 0.6765 data: 0.0003 [11-24 21:23:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.33 Lm: 6.554 (6.554) Lt: 5.758 (5.746) Accm: 3.22 (3.35) Acct: 4.90 (5.20) proj_loss: -0.5809 (-0.5815) time: 0.7359 data: 0.0003 [11-24 21:23:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.33 Lm: 6.618 (6.603) Lt: 5.866 (5.860) Accm: 2.93 (2.99) Acct: 4.67 (4.70) proj_loss: -0.5793 (-0.5827) time: 0.7359 data: 0.0003 [11-24 21:23:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.33 Lm: 6.572 (6.587) Lt: 5.863 (5.898) Accm: 3.15 (3.16) Acct: 4.93 (4.88) proj_loss: -0.5897 (-0.5920) time: 0.7359 data: 0.0003 [11-24 21:23:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.33 Lm: 6.653 (6.648) Lt: 5.907 (5.904) Accm: 2.97 (2.97) Acct: 4.69 (4.66) proj_loss: -0.6019 (-0.6005) time: 0.7359 data: 0.0003 [11-24 21:28:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.656 (6.673) Lt: 5.908 (5.928) Accm: 2.96 (2.90) Acct: 4.63 (4.57) proj_loss: -0.5944 (-0.5975) time: 0.6791 data: 0.0020 [11-24 21:28:19] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 133/350] Total time: 0:19:16 (0.693 s / it) [11-24 21:28:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.546 (6.559) Lt: 5.828 (5.814) Accm: 3.13 (3.16) Acct: 4.82 (4.96) proj_loss: -0.5810 (-0.5859) time: 0.6791 data: 0.0015 [11-24 21:28:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.551 (6.547) Lt: 5.758 (5.749) Accm: 3.28 (3.38) Acct: 4.91 (5.25) proj_loss: -0.5822 (-0.5847) time: 0.6791 data: 0.0016 [11-24 21:28:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 133/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.596 (6.600) Lt: 5.882 (5.901) Accm: 3.07 (3.14) Acct: 4.84 (4.85) proj_loss: -0.5865 (-0.5880) time: 0.6791 data: 0.0017 [11-24 21:28:19] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 133/350] Total time: 0:19:16 (0.693 s / it) [11-24 21:28:19] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 133/350] Total time: 0:19:16 (0.693 s / it) [11-24 21:28:19] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 133/350] Total time: 0:19:16 (0.693 s / it) [11-24 21:28:19] (/home/user/VAR/train.py , line 276)=> [ep133] (training ) Lm: 6.565 (6.587), Lt: 5.810 (5.837), Acc m&t: 3.20 5.06, Remain: 2 days, 20:31:16, Finish: 2024-11-27 01:59 [11-24 21:28:19] (/home/user/VAR/train.py , line 276)=> [ep133] (training ) Lm: 6.565 (6.587), Lt: 5.810 (5.837), Acc m&t: 3.20 5.06, Remain: 2 days, 20:31:11, Finish: 2024-11-27 01:59 [11-24 21:28:19] (/home/user/VAR/train.py , line 276)=> [ep133] (training ) Lm: 6.565 (6.587), Lt: 5.810 (5.837), Acc m&t: 3.20 5.06, Remain: 2 days, 20:31:48, Finish: 2024-11-27 02:00 [11-24 21:28:19] (/home/user/VAR/train.py , line 276)=> [ep133] (training ) Lm: 6.565 (6.587), Lt: 5.810 (5.837), Acc m&t: 3.20 5.06, Remain: 2 days, 20:32:19, Finish: 2024-11-27 02:00 [11-24 21:28:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [ 0/1669] eta: 0:18:17 tlr: 0.00016 tnm: 0.31 Lm: 6.670 (6.670) Lt: 5.855 (5.855) Accm: 2.88 (2.88) Acct: 4.77 (4.77) proj_loss: -0.5534 (-0.5534) time: 0.6573 data: 0.0003 [11-24 21:28:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [ 0/1669] eta: 0:18:19 tlr: 0.00016 tnm: 0.31 Lm: 6.460 (6.460) Lt: 5.703 (5.703) Accm: 3.55 (3.55) Acct: 5.85 (5.85) proj_loss: -0.6140 (-0.6140) time: 0.6586 data: 0.0003 [11-24 21:28:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [ 0/1669] eta: 0:18:17 tlr: 0.00016 tnm: 0.31 Lm: 6.403 (6.403) Lt: 5.620 (5.620) Accm: 3.49 (3.49) Acct: 5.56 (5.56) proj_loss: -0.5916 (-0.5916) time: 0.6575 data: 0.0004 [11-24 21:28:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [ 0/1669] eta: 0:18:20 tlr: 0.00016 tnm: 0.31 Lm: 6.695 (6.695) Lt: 5.980 (5.980) Accm: 2.80 (2.80) Acct: 4.36 (4.36) proj_loss: -0.5962 (-0.5962) time: 0.6596 data: 0.0003 [11-24 21:33:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [ 417/1669] eta: 0:14:13 tlr: 0.00016 tnm: 0.32 Lm: 6.547 (6.547) Lt: 5.804 (5.804) Accm: 3.27 (3.27) Acct: 5.12 (5.12) proj_loss: -0.5947 (-0.5947) time: 0.6773 data: 0.0003 [11-24 21:33:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [ 417/1669] eta: 0:14:13 tlr: 0.00016 tnm: 0.32 Lm: 6.459 (6.459) Lt: 5.680 (5.680) Accm: 3.49 (3.49) Acct: 5.60 (5.60) proj_loss: -0.5933 (-0.5933) time: 0.6773 data: 0.0003 [11-24 21:33:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [ 417/1669] eta: 0:14:13 tlr: 0.00016 tnm: 0.32 Lm: 6.547 (6.547) Lt: 5.815 (5.815) Accm: 3.27 (3.27) Acct: 5.13 (5.13) proj_loss: -0.5922 (-0.5922) time: 0.6773 data: 0.0003 [11-24 21:33:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [ 417/1669] eta: 0:14:13 tlr: 0.00016 tnm: 0.32 Lm: 6.587 (6.587) Lt: 5.819 (5.819) Accm: 3.00 (3.00) Acct: 4.80 (4.80) proj_loss: -0.5775 (-0.5775) time: 0.6773 data: 0.0003 [11-24 21:37:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [ 834/1669] eta: 0:09:27 tlr: 0.00016 tnm: 0.32 Lm: 6.518 (6.564) Lt: 5.783 (5.788) Accm: 3.11 (3.15) Acct: 4.84 (5.00) proj_loss: -0.5931 (-0.5827) time: 0.6781 data: 0.0003 [11-24 21:37:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [ 834/1669] eta: 0:09:27 tlr: 0.00016 tnm: 0.32 Lm: 6.583 (6.559) Lt: 5.811 (5.814) Accm: 3.01 (3.19) Acct: 4.75 (5.00) proj_loss: -0.5910 (-0.5918) time: 0.6781 data: 0.0003 [11-24 21:37:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [ 834/1669] eta: 0:09:27 tlr: 0.00016 tnm: 0.32 Lm: 6.584 (6.559) Lt: 5.817 (5.808) Accm: 3.33 (3.29) Acct: 5.20 (5.15) proj_loss: -0.5933 (-0.5942) time: 0.6781 data: 0.0003 [11-24 21:37:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [ 834/1669] eta: 0:09:27 tlr: 0.00016 tnm: 0.32 Lm: 6.515 (6.538) Lt: 5.741 (5.776) Accm: 3.49 (3.31) Acct: 5.56 (5.20) proj_loss: -0.5950 (-0.5973) time: 0.6781 data: 0.0004 [11-24 21:42:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.32 Lm: 6.570 (6.560) Lt: 5.850 (5.822) Accm: 3.22 (3.20) Acct: 4.98 (4.96) proj_loss: -0.6002 (-0.6000) time: 0.6785 data: 0.0003 [11-24 21:42:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.32 Lm: 6.597 (6.572) Lt: 5.857 (5.830) Accm: 3.20 (3.23) Acct: 4.91 (5.01) proj_loss: -0.5947 (-0.5974) time: 0.6785 data: 0.0003 [11-24 21:42:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.32 Lm: 6.541 (6.564) Lt: 5.810 (5.800) Accm: 3.17 (3.17) Acct: 4.85 (4.97) proj_loss: -0.5907 (-0.5841) time: 0.6785 data: 0.0003 [11-24 21:42:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.32 Lm: 6.567 (6.557) Lt: 5.790 (5.802) Accm: 3.02 (3.15) Acct: 4.67 (4.90) proj_loss: -0.5981 (-0.5951) time: 0.6785 data: 0.0003 [11-24 21:47:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.34 Lm: 6.550 (6.527) Lt: 5.768 (5.769) Accm: 3.04 (3.31) Acct: 4.75 (5.15) proj_loss: -0.5910 (-0.5942) time: 0.6771 data: 0.0020 [11-24 21:47:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 134/350] Total time: 0:18:53 (0.679 s / it) [11-24 21:47:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.34 Lm: 6.518 (6.536) Lt: 5.783 (5.765) Accm: 3.22 (3.25) Acct: 4.86 (5.12) proj_loss: -0.5931 (-0.5862) time: 0.6771 data: 0.0014 [11-24 21:47:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.34 Lm: 6.584 (6.557) Lt: 5.817 (5.822) Accm: 3.18 (3.22) Acct: 4.72 (4.96) proj_loss: -0.5962 (-0.6002) time: 0.6771 data: 0.0017 [11-24 21:47:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 134/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.34 Lm: 6.625 (6.579) Lt: 5.956 (5.849) Accm: 2.97 (3.16) Acct: 4.42 (4.86) proj_loss: -0.5950 (-0.5986) time: 0.6771 data: 0.0017 [11-24 21:47:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 134/350] Total time: 0:18:53 (0.679 s / it) [11-24 21:47:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 134/350] Total time: 0:18:53 (0.679 s / it) [11-24 21:47:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 134/350] Total time: 0:18:53 (0.679 s / it) [11-24 21:47:13] (/home/user/VAR/train.py , line 276)=> [ep134] (training ) Lm: 6.565 (6.573), Lt: 5.810 (5.821), Acc m&t: 3.20 5.06, Remain: 2 days, 20:05:55, Finish: 2024-11-27 01:53 [11-24 21:47:13] (/home/user/VAR/train.py , line 276)=> [ep134] (training ) Lm: 6.565 (6.573), Lt: 5.810 (5.821), Acc m&t: 3.20 5.06, Remain: 2 days, 20:05:07, Finish: 2024-11-27 01:52 [11-24 21:47:13] (/home/user/VAR/train.py , line 276)=> [ep134] (training ) Lm: 6.565 (6.573), Lt: 5.810 (5.821), Acc m&t: 3.20 5.06, Remain: 2 days, 20:05:29, Finish: 2024-11-27 01:52 [11-24 21:47:13] (/home/user/VAR/train.py , line 276)=> [ep134] (training ) Lm: 6.565 (6.573), Lt: 5.810 (5.821), Acc m&t: 3.20 5.06, Remain: 2 days, 20:05:49, Finish: 2024-11-27 01:53 [11-24 21:47:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [ 0/1669] eta: 0:18:28 tlr: 0.00016 tnm: 0.32 Lm: 6.701 (6.701) Lt: 6.025 (6.025) Accm: 2.75 (2.75) Acct: 4.24 (4.24) proj_loss: -0.6080 (-0.6080) time: 0.6644 data: 0.0003 [11-24 21:47:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [ 0/1669] eta: 0:18:28 tlr: 0.00016 tnm: 0.32 Lm: 6.550 (6.550) Lt: 5.763 (5.763) Accm: 3.26 (3.26) Acct: 5.27 (5.27) proj_loss: -0.5913 (-0.5913) time: 0.6643 data: 0.0004 [11-24 21:47:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [ 0/1669] eta: 0:18:28 tlr: 0.00016 tnm: 0.32 Lm: 6.604 (6.604) Lt: 5.835 (5.835) Accm: 3.16 (3.16) Acct: 5.01 (5.01) proj_loss: -0.6074 (-0.6074) time: 0.6643 data: 0.0005 [11-24 21:47:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [ 0/1669] eta: 0:18:28 tlr: 0.00016 tnm: 0.32 Lm: 6.720 (6.720) Lt: 6.022 (6.022) Accm: 2.59 (2.59) Acct: 3.96 (3.96) proj_loss: -0.6096 (-0.6096) time: 0.6644 data: 0.0004 [11-24 21:52:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [ 417/1669] eta: 0:15:22 tlr: 0.00016 tnm: 0.33 Lm: 6.627 (6.627) Lt: 5.888 (5.888) Accm: 3.01 (3.01) Acct: 4.80 (4.80) proj_loss: -0.5954 (-0.5954) time: 0.6773 data: 0.0003 [11-24 21:52:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [ 417/1669] eta: 0:15:22 tlr: 0.00016 tnm: 0.33 Lm: 6.555 (6.555) Lt: 5.796 (5.796) Accm: 3.24 (3.24) Acct: 5.09 (5.09) proj_loss: -0.5998 (-0.5998) time: 0.6773 data: 0.0003 [11-24 21:52:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [ 417/1669] eta: 0:15:22 tlr: 0.00016 tnm: 0.33 Lm: 6.634 (6.634) Lt: 5.908 (5.908) Accm: 3.06 (3.06) Acct: 4.86 (4.86) proj_loss: -0.5825 (-0.5825) time: 0.6773 data: 0.0003 [11-24 21:52:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [ 417/1669] eta: 0:15:22 tlr: 0.00016 tnm: 0.33 Lm: 6.510 (6.510) Lt: 5.734 (5.734) Accm: 3.51 (3.51) Acct: 5.57 (5.57) proj_loss: -0.5861 (-0.5861) time: 0.6773 data: 0.0003 [11-24 21:57:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.30 Lm: 6.550 (6.550) Lt: 5.763 (5.796) Accm: 3.26 (3.24) Acct: 5.27 (5.07) proj_loss: -0.5913 (-0.5912) time: 0.6777 data: 0.0003 [11-24 21:57:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.30 Lm: 6.632 (6.629) Lt: 5.923 (5.900) Accm: 3.15 (3.06) Acct: 4.82 (4.81) proj_loss: -0.5919 (-0.5942) time: 0.6777 data: 0.0003 [11-24 21:57:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.30 Lm: 6.701 (6.666) Lt: 5.992 (5.936) Accm: 2.80 (2.97) Acct: 4.29 (4.67) proj_loss: -0.5720 (-0.5790) time: 0.6777 data: 0.0003 [11-24 21:57:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.30 Lm: 6.505 (6.494) Lt: 5.757 (5.746) Accm: 3.32 (3.36) Acct: 5.17 (5.25) proj_loss: -0.6042 (-0.6012) time: 0.6777 data: 0.0003 [11-24 22:01:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [1251/1669] eta: 0:04:52 tlr: 0.00016 tnm: 0.33 Lm: 6.609 (6.618) Lt: 5.875 (5.881) Accm: 3.05 (3.03) Acct: 4.72 (4.76) proj_loss: -0.5971 (-0.5962) time: 0.6781 data: 0.0003 [11-24 22:01:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [1251/1669] eta: 0:04:52 tlr: 0.00016 tnm: 0.33 Lm: 6.553 (6.521) Lt: 5.796 (5.774) Accm: 3.24 (3.31) Acct: 5.09 (5.15) proj_loss: -0.6045 (-0.6021) time: 0.6781 data: 0.0002 [11-24 22:01:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [1251/1669] eta: 0:04:52 tlr: 0.00016 tnm: 0.33 Lm: 6.634 (6.612) Lt: 5.892 (5.874) Accm: 3.08 (3.13) Acct: 4.88 (4.92) proj_loss: -0.5830 (-0.5827) time: 0.6781 data: 0.0003 [11-24 22:01:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [1251/1669] eta: 0:04:52 tlr: 0.00016 tnm: 0.33 Lm: 6.524 (6.537) Lt: 5.734 (5.773) Accm: 3.33 (3.28) Acct: 5.33 (5.15) proj_loss: -0.5873 (-0.5892) time: 0.6781 data: 0.0003 [11-24 22:06:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.550 (6.547) Lt: 5.763 (5.782) Accm: 3.26 (3.22) Acct: 5.27 (5.04) proj_loss: -0.5913 (-0.5904) time: 0.6777 data: 0.0018 [11-24 22:06:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 135/350] Total time: 0:19:17 (0.693 s / it) [11-24 22:06:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.568 (6.589) Lt: 5.792 (5.840) Accm: 3.18 (3.14) Acct: 5.32 (5.00) proj_loss: -0.5798 (-0.5821) time: 0.6777 data: 0.0020 [11-24 22:06:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.625 (6.620) Lt: 5.923 (5.890) Accm: 3.03 (3.03) Acct: 4.61 (4.73) proj_loss: -0.6024 (-0.5977) time: 0.6777 data: 0.0021 [11-24 22:06:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 135/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.564 (6.530) Lt: 5.775 (5.774) Accm: 3.31 (3.31) Acct: 5.17 (5.18) proj_loss: -0.6042 (-0.5997) time: 0.6777 data: 0.0016 [11-24 22:06:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 135/350] Total time: 0:19:17 (0.693 s / it) [11-24 22:06:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 135/350] Total time: 0:19:17 (0.693 s / it) [11-24 22:06:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 135/350] Total time: 0:19:17 (0.693 s / it) [11-24 22:06:30] (/home/user/VAR/train.py , line 276)=> [ep135] (training ) Lm: 6.559 (6.559), Lt: 5.803 (5.803), Acc m&t: 3.23 5.10, Remain: 2 days, 19:31:13, Finish: 2024-11-27 01:37 [11-24 22:06:30] (/home/user/VAR/train.py , line 276)=> [ep135] (training ) Lm: 6.559 (6.559), Lt: 5.803 (5.803), Acc m&t: 3.23 5.10, Remain: 2 days, 19:30:40, Finish: 2024-11-27 01:37 [11-24 22:06:30] (/home/user/VAR/train.py , line 276)=> [ep135] (training ) Lm: 6.559 (6.559), Lt: 5.803 (5.803), Acc m&t: 3.23 5.10, Remain: 2 days, 19:29:46, Finish: 2024-11-27 01:36 [11-24 22:06:30] (/home/user/VAR/train.py , line 276)=> [ep135] (training ) Lm: 6.559 (6.559), Lt: 5.803 (5.803), Acc m&t: 3.23 5.10, Remain: 2 days, 19:30:47, Finish: 2024-11-27 01:37 [11-24 22:06:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [ 0/1669] eta: 0:18:21 tlr: 0.00016 tnm: 0.32 Lm: 6.593 (6.593) Lt: 5.843 (5.843) Accm: 3.12 (3.12) Acct: 5.01 (5.01) proj_loss: -0.5864 (-0.5864) time: 0.6599 data: 0.0004 [11-24 22:06:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [ 0/1669] eta: 0:18:21 tlr: 0.00016 tnm: 0.32 Lm: 6.581 (6.581) Lt: 5.853 (5.853) Accm: 2.86 (2.86) Acct: 4.39 (4.39) proj_loss: -0.5893 (-0.5893) time: 0.6601 data: 0.0004 [11-24 22:06:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [ 0/1669] eta: 0:18:16 tlr: 0.00016 tnm: 0.32 Lm: 6.658 (6.658) Lt: 5.894 (5.894) Accm: 2.91 (2.91) Acct: 4.73 (4.73) proj_loss: -0.5836 (-0.5836) time: 0.6569 data: 0.0004 [11-24 22:06:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [ 0/1669] eta: 0:18:17 tlr: 0.00016 tnm: 0.32 Lm: 6.498 (6.498) Lt: 5.747 (5.747) Accm: 3.26 (3.26) Acct: 4.89 (4.89) proj_loss: -0.6060 (-0.6060) time: 0.6574 data: 0.0004 [11-24 22:11:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.30 Lm: 6.469 (6.469) Lt: 5.722 (5.722) Accm: 3.41 (3.41) Acct: 5.23 (5.23) proj_loss: -0.6065 (-0.6065) time: 0.6777 data: 0.0003 [11-24 22:11:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.30 Lm: 6.490 (6.490) Lt: 5.705 (5.705) Accm: 3.45 (3.45) Acct: 5.60 (5.60) proj_loss: -0.5803 (-0.5803) time: 0.6777 data: 0.0003 [11-24 22:11:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.30 Lm: 6.579 (6.579) Lt: 5.853 (5.853) Accm: 3.03 (3.03) Acct: 4.85 (4.85) proj_loss: -0.5963 (-0.5963) time: 0.6777 data: 0.0003 [11-24 22:11:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.30 Lm: 6.601 (6.601) Lt: 5.866 (5.866) Accm: 2.91 (2.91) Acct: 4.48 (4.48) proj_loss: -0.5909 (-0.5909) time: 0.6777 data: 0.0003 [11-24 22:16:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.33 Lm: 6.615 (6.606) Lt: 5.853 (5.861) Accm: 2.96 (3.00) Acct: 4.56 (4.67) proj_loss: -0.5893 (-0.5881) time: 0.6781 data: 0.0003 [11-24 22:16:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.33 Lm: 6.498 (6.523) Lt: 5.747 (5.788) Accm: 3.26 (3.32) Acct: 4.91 (5.13) proj_loss: -0.6070 (-0.6084) time: 0.6781 data: 0.0003 [11-24 22:16:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.33 Lm: 6.566 (6.553) Lt: 5.843 (5.803) Accm: 3.12 (3.20) Acct: 5.01 (5.16) proj_loss: -0.5864 (-0.5923) time: 0.6781 data: 0.0003 [11-24 22:16:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.33 Lm: 6.537 (6.506) Lt: 5.718 (5.709) Accm: 3.32 (3.40) Acct: 5.66 (5.62) proj_loss: -0.5770 (-0.5733) time: 0.6781 data: 0.0003 [11-24 22:21:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.29 Lm: 6.588 (6.539) Lt: 5.796 (5.751) Accm: 3.15 (3.30) Acct: 5.29 (5.45) proj_loss: -0.5775 (-0.5745) time: 0.7382 data: 0.0003 [11-24 22:21:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.29 Lm: 6.564 (6.576) Lt: 5.833 (5.844) Accm: 3.20 (3.15) Acct: 4.90 (4.89) proj_loss: -0.6065 (-0.6027) time: 0.7382 data: 0.0003 [11-24 22:21:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.29 Lm: 6.533 (6.536) Lt: 5.786 (5.785) Accm: 3.33 (3.32) Acct: 5.33 (5.28) proj_loss: -0.5885 (-0.5919) time: 0.7382 data: 0.0003 [11-24 22:21:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.29 Lm: 6.598 (6.597) Lt: 5.851 (5.838) Accm: 3.04 (3.03) Acct: 4.73 (4.73) proj_loss: -0.5884 (-0.5879) time: 0.7382 data: 0.0003 [11-24 22:25:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.29 Lm: 6.500 (6.561) Lt: 5.765 (5.828) Accm: 3.23 (3.16) Acct: 4.91 (4.92) proj_loss: -0.6060 (-0.5998) time: 0.6770 data: 0.0015 [11-24 22:25:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.29 Lm: 6.537 (6.499) Lt: 5.718 (5.699) Accm: 3.32 (3.42) Acct: 5.66 (5.60) proj_loss: -0.5779 (-0.5786) time: 0.6770 data: 0.0020 [11-24 22:25:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.29 Lm: 6.500 (6.512) Lt: 5.730 (5.759) Accm: 3.54 (3.39) Acct: 5.58 (5.34) proj_loss: -0.5905 (-0.5916) time: 0.6770 data: 0.0021 [11-24 22:25:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 136/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.29 Lm: 6.615 (6.614) Lt: 5.853 (5.871) Accm: 2.99 (3.03) Acct: 4.63 (4.71) proj_loss: -0.5893 (-0.5910) time: 0.6771 data: 0.0019 [11-24 22:25:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 136/350] Total time: 0:19:16 (0.693 s / it) [11-24 22:25:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 136/350] Total time: 0:19:16 (0.693 s / it) [11-24 22:25:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 136/350] Total time: 0:19:16 (0.693 s / it) [11-24 22:25:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 136/350] Total time: 0:19:16 (0.693 s / it) [11-24 22:25:47] (/home/user/VAR/train.py , line 276)=> [ep136] (training ) Lm: 6.557 (6.557), Lt: 5.803 (5.805), Acc m&t: 3.24 5.10, Remain: 2 days, 19:18:29, Finish: 2024-11-27 01:44 [11-24 22:25:47] (/home/user/VAR/train.py , line 276)=> [ep136] (training ) Lm: 6.557 (6.557), Lt: 5.803 (5.805), Acc m&t: 3.24 5.10, Remain: 2 days, 19:16:53, Finish: 2024-11-27 01:42 [11-24 22:25:47] (/home/user/VAR/train.py , line 276)=> [ep136] (training ) Lm: 6.557 (6.557), Lt: 5.803 (5.805), Acc m&t: 3.24 5.10, Remain: 2 days, 19:18:06, Finish: 2024-11-27 01:43 [11-24 22:25:47] (/home/user/VAR/train.py , line 276)=> [ep136] (training ) Lm: 6.557 (6.557), Lt: 5.803 (5.805), Acc m&t: 3.24 5.10, Remain: 2 days, 19:17:37, Finish: 2024-11-27 01:43 [11-24 22:25:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [ 0/1669] eta: 0:18:21 tlr: 0.00016 tnm: 0.30 Lm: 6.532 (6.532) Lt: 5.832 (5.832) Accm: 3.23 (3.23) Acct: 5.27 (5.27) proj_loss: -0.6270 (-0.6270) time: 0.6598 data: 0.0003 [11-24 22:25:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [ 0/1669] eta: 0:18:22 tlr: 0.00016 tnm: 0.30 Lm: 6.581 (6.581) Lt: 5.795 (5.795) Accm: 3.40 (3.40) Acct: 5.49 (5.49) proj_loss: -0.5981 (-0.5981) time: 0.6604 data: 0.0004 [11-24 22:25:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [ 0/1669] eta: 0:18:23 tlr: 0.00016 tnm: 0.30 Lm: 6.516 (6.516) Lt: 5.684 (5.684) Accm: 3.43 (3.43) Acct: 5.35 (5.35) proj_loss: -0.5759 (-0.5759) time: 0.6610 data: 0.0003 [11-24 22:25:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [ 0/1669] eta: 0:18:23 tlr: 0.00016 tnm: 0.30 Lm: 6.661 (6.661) Lt: 5.953 (5.953) Accm: 2.93 (2.93) Acct: 4.55 (4.55) proj_loss: -0.5812 (-0.5812) time: 0.6610 data: 0.0004 [11-24 22:30:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [ 417/1669] eta: 0:14:07 tlr: 0.00016 tnm: 0.31 Lm: 6.518 (6.518) Lt: 5.811 (5.811) Accm: 3.26 (3.26) Acct: 5.00 (5.00) proj_loss: -0.5989 (-0.5989) time: 0.6768 data: 0.0003 [11-24 22:30:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [ 417/1669] eta: 0:14:07 tlr: 0.00016 tnm: 0.31 Lm: 6.514 (6.514) Lt: 5.797 (5.797) Accm: 3.20 (3.20) Acct: 5.07 (5.07) proj_loss: -0.6151 (-0.6151) time: 0.6768 data: 0.0003 [11-24 22:30:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [ 417/1669] eta: 0:14:07 tlr: 0.00016 tnm: 0.31 Lm: 6.502 (6.502) Lt: 5.707 (5.707) Accm: 3.50 (3.50) Acct: 5.38 (5.38) proj_loss: -0.5906 (-0.5906) time: 0.6768 data: 0.0003 [11-24 22:30:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [ 417/1669] eta: 0:14:07 tlr: 0.00016 tnm: 0.31 Lm: 6.609 (6.609) Lt: 5.854 (5.854) Accm: 3.18 (3.18) Acct: 5.06 (5.06) proj_loss: -0.5961 (-0.5961) time: 0.6768 data: 0.0003 [11-24 22:35:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [ 834/1669] eta: 0:09:27 tlr: 0.00016 tnm: 0.31 Lm: 6.581 (6.595) Lt: 5.795 (5.820) Accm: 3.40 (3.29) Acct: 5.49 (5.31) proj_loss: -0.5941 (-0.5907) time: 0.6779 data: 0.0002 [11-24 22:35:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [ 834/1669] eta: 0:09:27 tlr: 0.00016 tnm: 0.31 Lm: 6.532 (6.569) Lt: 5.832 (5.852) Accm: 3.17 (3.10) Acct: 4.91 (5.02) proj_loss: -0.6033 (-0.6095) time: 0.6778 data: 0.0003 [11-24 22:35:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [ 834/1669] eta: 0:09:27 tlr: 0.00016 tnm: 0.31 Lm: 6.516 (6.526) Lt: 5.730 (5.725) Accm: 3.43 (3.46) Acct: 5.41 (5.46) proj_loss: -0.5943 (-0.5918) time: 0.6778 data: 0.0003 [11-24 22:35:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [ 834/1669] eta: 0:09:27 tlr: 0.00016 tnm: 0.31 Lm: 6.559 (6.532) Lt: 5.797 (5.806) Accm: 2.98 (3.16) Acct: 4.84 (4.95) proj_loss: -0.5812 (-0.5921) time: 0.6779 data: 0.0003 [11-24 22:39:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.30 Lm: 6.562 (6.575) Lt: 5.850 (5.856) Accm: 3.17 (3.12) Acct: 4.93 (5.00) proj_loss: -0.6007 (-0.6061) time: 0.6743 data: 0.0003 [11-24 22:39:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.30 Lm: 6.502 (6.505) Lt: 5.707 (5.703) Accm: 3.50 (3.53) Acct: 5.51 (5.53) proj_loss: -0.5998 (-0.5959) time: 0.6743 data: 0.0003 [11-24 22:39:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.30 Lm: 6.590 (6.596) Lt: 5.815 (5.824) Accm: 3.21 (3.22) Acct: 5.18 (5.20) proj_loss: -0.5871 (-0.5862) time: 0.6743 data: 0.0003 [11-24 22:39:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [1251/1669] eta: 0:04:43 tlr: 0.00016 tnm: 0.30 Lm: 6.506 (6.512) Lt: 5.776 (5.793) Accm: 3.25 (3.25) Acct: 5.00 (5.00) proj_loss: -0.5963 (-0.5969) time: 0.6743 data: 0.0003 [11-24 22:44:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.34 Lm: 6.559 (6.543) Lt: 5.797 (5.808) Accm: 3.10 (3.22) Acct: 5.15 (5.03) proj_loss: -0.5812 (-0.5933) time: 0.6804 data: 0.0020 [11-24 22:44:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 137/350] Total time: 0:18:53 (0.679 s / it) [11-24 22:44:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.34 Lm: 6.532 (6.563) Lt: 5.832 (5.840) Accm: 3.17 (3.17) Acct: 4.96 (5.06) proj_loss: -0.6033 (-0.6078) time: 0.6804 data: 0.0015 [11-24 22:44:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.34 Lm: 6.588 (6.595) Lt: 5.828 (5.825) Accm: 3.37 (3.25) Acct: 5.44 (5.25) proj_loss: -0.5801 (-0.5815) time: 0.6804 data: 0.0016 [11-24 22:44:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 137/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.34 Lm: 6.488 (6.485) Lt: 5.684 (5.684) Accm: 3.56 (3.61) Acct: 5.61 (5.66) proj_loss: -0.5985 (-0.5964) time: 0.6804 data: 0.0016 [11-24 22:44:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 137/350] Total time: 0:18:53 (0.679 s / it) [11-24 22:44:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 137/350] Total time: 0:18:53 (0.679 s / it) [11-24 22:44:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 137/350] Total time: 0:18:53 (0.679 s / it) [11-24 22:44:40] (/home/user/VAR/train.py , line 276)=> [ep137] (training ) Lm: 6.557 (6.559), Lt: 5.802 (5.802), Acc m&t: 3.26 5.15, Remain: 2 days, 19:22:36, Finish: 2024-11-27 02:07 [11-24 22:44:40] (/home/user/VAR/train.py , line 276)=> [ep137] (training ) Lm: 6.557 (6.559), Lt: 5.802 (5.802), Acc m&t: 3.26 5.15, Remain: 2 days, 19:21:19, Finish: 2024-11-27 02:05 [11-24 22:44:40] (/home/user/VAR/train.py , line 276)=> [ep137] (training ) Lm: 6.557 (6.559), Lt: 5.802 (5.802), Acc m&t: 3.26 5.15, Remain: 2 days, 19:21:37, Finish: 2024-11-27 02:06 [11-24 22:44:40] (/home/user/VAR/train.py , line 276)=> [ep137] (training ) Lm: 6.557 (6.559), Lt: 5.802 (5.802), Acc m&t: 3.26 5.15, Remain: 2 days, 19:21:47, Finish: 2024-11-27 02:06 [11-24 22:44:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [ 0/1669] eta: 0:18:08 tlr: 0.00016 tnm: 0.31 Lm: 6.631 (6.631) Lt: 5.926 (5.926) Accm: 2.91 (2.91) Acct: 4.41 (4.41) proj_loss: -0.6182 (-0.6182) time: 0.6519 data: 0.0003 [11-24 22:44:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [ 0/1669] eta: 0:18:10 tlr: 0.00016 tnm: 0.31 Lm: 6.632 (6.632) Lt: 5.892 (5.892) Accm: 2.99 (2.99) Acct: 4.79 (4.79) proj_loss: -0.5715 (-0.5715) time: 0.6534 data: 0.0004 [11-24 22:44:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [ 0/1669] eta: 0:18:44 tlr: 0.00016 tnm: 0.31 Lm: 6.538 (6.538) Lt: 5.736 (5.736) Accm: 3.10 (3.10) Acct: 4.91 (4.91) proj_loss: -0.5876 (-0.5876) time: 0.6738 data: 0.0004 [11-24 22:44:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [ 0/1669] eta: 0:18:50 tlr: 0.00016 tnm: 0.31 Lm: 6.541 (6.541) Lt: 5.815 (5.815) Accm: 3.33 (3.33) Acct: 5.44 (5.44) proj_loss: -0.5929 (-0.5929) time: 0.6774 data: 0.0004 [11-24 22:49:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [ 417/1669] eta: 0:15:22 tlr: 0.00016 tnm: 0.33 Lm: 6.590 (6.590) Lt: 5.875 (5.875) Accm: 3.11 (3.11) Acct: 5.05 (5.05) proj_loss: -0.5955 (-0.5955) time: 0.6745 data: 0.0003 [11-24 22:49:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [ 417/1669] eta: 0:15:22 tlr: 0.00016 tnm: 0.33 Lm: 6.608 (6.608) Lt: 5.846 (5.846) Accm: 3.00 (3.00) Acct: 4.85 (4.85) proj_loss: -0.5825 (-0.5825) time: 0.6745 data: 0.0003 [11-24 22:49:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [ 417/1669] eta: 0:15:22 tlr: 0.00016 tnm: 0.33 Lm: 6.657 (6.657) Lt: 5.933 (5.933) Accm: 2.93 (2.93) Acct: 4.42 (4.42) proj_loss: -0.5991 (-0.5991) time: 0.6745 data: 0.0003 [11-24 22:49:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [ 417/1669] eta: 0:15:22 tlr: 0.00016 tnm: 0.33 Lm: 6.612 (6.612) Lt: 5.830 (5.830) Accm: 2.94 (2.94) Acct: 4.58 (4.58) proj_loss: -0.5984 (-0.5984) time: 0.6745 data: 0.0003 [11-24 22:54:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [ 834/1669] eta: 0:09:49 tlr: 0.00016 tnm: 0.30 Lm: 6.686 (6.649) Lt: 5.924 (5.883) Accm: 2.79 (2.84) Acct: 4.25 (4.44) proj_loss: -0.5876 (-0.5936) time: 0.6749 data: 0.0003 [11-24 22:54:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [ 834/1669] eta: 0:09:49 tlr: 0.00016 tnm: 0.30 Lm: 6.631 (6.648) Lt: 5.926 (5.917) Accm: 2.91 (2.91) Acct: 4.44 (4.55) proj_loss: -0.6079 (-0.6020) time: 0.6749 data: 0.0003 [11-24 22:54:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [ 834/1669] eta: 0:09:49 tlr: 0.00016 tnm: 0.30 Lm: 6.541 (6.564) Lt: 5.815 (5.841) Accm: 3.30 (3.17) Acct: 5.06 (5.06) proj_loss: -0.5929 (-0.5935) time: 0.6749 data: 0.0003 [11-24 22:54:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [ 834/1669] eta: 0:09:49 tlr: 0.00016 tnm: 0.30 Lm: 6.604 (6.607) Lt: 5.850 (5.847) Accm: 3.02 (3.02) Acct: 4.79 (4.74) proj_loss: -0.5935 (-0.5889) time: 0.6749 data: 0.0004 [11-24 22:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.618 (6.620) Lt: 5.871 (5.864) Accm: 3.03 (3.04) Acct: 4.82 (4.77) proj_loss: -0.5962 (-0.5914) time: 0.6756 data: 0.0003 [11-24 22:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.645 (6.638) Lt: 5.872 (5.868) Accm: 2.92 (2.89) Acct: 4.55 (4.54) proj_loss: -0.5858 (-0.5905) time: 0.6756 data: 0.0003 [11-24 22:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.560 (6.568) Lt: 5.832 (5.843) Accm: 3.16 (3.13) Acct: 4.99 (5.02) proj_loss: -0.5955 (-0.5953) time: 0.6756 data: 0.0003 [11-24 22:59:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [1251/1669] eta: 0:04:51 tlr: 0.00016 tnm: 0.31 Lm: 6.631 (6.629) Lt: 5.905 (5.896) Accm: 2.93 (3.00) Acct: 4.62 (4.76) proj_loss: -0.5949 (-0.5970) time: 0.6756 data: 0.0003 [11-24 23:03:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.631 (6.608) Lt: 5.885 (5.871) Accm: 2.95 (3.09) Acct: 4.80 (4.90) proj_loss: -0.6013 (-0.5979) time: 0.6784 data: 0.0015 [11-24 23:03:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 138/350] Total time: 0:19:15 (0.692 s / it) [11-24 23:03:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.578 (6.573) Lt: 5.824 (5.839) Accm: 3.02 (3.07) Acct: 4.92 (4.84) proj_loss: -0.5974 (-0.5957) time: 0.6784 data: 0.0018 [11-24 23:03:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.604 (6.606) Lt: 5.850 (5.841) Accm: 3.04 (3.04) Acct: 4.79 (4.77) proj_loss: -0.5989 (-0.5950) time: 0.6784 data: 0.0026 [11-24 23:03:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 138/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.603 (6.612) Lt: 5.820 (5.854) Accm: 3.04 (2.97) Acct: 4.86 (4.68) proj_loss: -0.5876 (-0.5919) time: 0.6784 data: 0.0016 [11-24 23:03:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 138/350] Total time: 0:19:15 (0.692 s / it) [11-24 23:03:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 138/350] Total time: 0:19:15 (0.692 s / it) [11-24 23:03:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 138/350] Total time: 0:19:15 (0.692 s / it) [11-24 23:03:56] (/home/user/VAR/train.py , line 276)=> [ep138] (training ) Lm: 6.557 (6.567), Lt: 5.802 (5.813), Acc m&t: 3.26 5.15, Remain: 2 days, 18:50:04, Finish: 2024-11-27 01:54 [11-24 23:03:56] (/home/user/VAR/train.py , line 276)=> [ep138] (training ) Lm: 6.557 (6.567), Lt: 5.802 (5.813), Acc m&t: 3.26 5.15, Remain: 2 days, 18:50:00, Finish: 2024-11-27 01:53 [11-24 23:03:56] (/home/user/VAR/train.py , line 276)=> [ep138] (training ) Lm: 6.557 (6.567), Lt: 5.802 (5.813), Acc m&t: 3.26 5.15, Remain: 2 days, 18:50:01, Finish: 2024-11-27 01:53 [11-24 23:03:56] (/home/user/VAR/train.py , line 276)=> [ep138] (training ) Lm: 6.557 (6.567), Lt: 5.802 (5.813), Acc m&t: 3.26 5.15, Remain: 2 days, 18:51:01, Finish: 2024-11-27 01:54 [11-24 23:03:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [ 0/1669] eta: 0:18:44 tlr: 0.00016 tnm: 0.32 Lm: 6.444 (6.444) Lt: 5.711 (5.711) Accm: 3.16 (3.16) Acct: 5.06 (5.06) proj_loss: -0.5879 (-0.5879) time: 0.6739 data: 0.0004 [11-24 23:03:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [ 0/1669] eta: 0:18:39 tlr: 0.00016 tnm: 0.32 Lm: 6.352 (6.352) Lt: 5.631 (5.631) Accm: 3.66 (3.66) Acct: 5.65 (5.65) proj_loss: -0.5942 (-0.5942) time: 0.6710 data: 0.0004 [11-24 23:03:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [ 0/1669] eta: 0:18:45 tlr: 0.00016 tnm: 0.32 Lm: 6.631 (6.631) Lt: 5.842 (5.842) Accm: 3.29 (3.29) Acct: 5.18 (5.18) proj_loss: -0.6011 (-0.6011) time: 0.6742 data: 0.0004 [11-24 23:03:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [ 0/1669] eta: 0:18:45 tlr: 0.00016 tnm: 0.32 Lm: 6.722 (6.722) Lt: 6.046 (6.046) Accm: 2.72 (2.72) Acct: 4.32 (4.32) proj_loss: -0.5897 (-0.5897) time: 0.6742 data: 0.0004 [11-24 23:08:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.33 Lm: 6.614 (6.614) Lt: 5.888 (5.888) Accm: 3.06 (3.06) Acct: 4.72 (4.72) proj_loss: -0.5917 (-0.5917) time: 0.6775 data: 0.0003 [11-24 23:08:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.33 Lm: 6.498 (6.498) Lt: 5.762 (5.762) Accm: 3.25 (3.25) Acct: 5.09 (5.09) proj_loss: -0.5900 (-0.5900) time: 0.6775 data: 0.0003 [11-24 23:08:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.33 Lm: 6.542 (6.542) Lt: 5.817 (5.817) Accm: 3.25 (3.25) Acct: 4.97 (4.97) proj_loss: -0.5897 (-0.5897) time: 0.6775 data: 0.0003 [11-24 23:08:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.33 Lm: 6.628 (6.628) Lt: 5.851 (5.851) Accm: 3.07 (3.07) Acct: 4.86 (4.86) proj_loss: -0.6027 (-0.6027) time: 0.6775 data: 0.0003 [11-24 23:13:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.31 Lm: 6.625 (6.577) Lt: 5.842 (5.808) Accm: 3.29 (3.25) Acct: 5.18 (5.08) proj_loss: -0.6043 (-0.6044) time: 0.6774 data: 0.0003 [11-24 23:13:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.31 Lm: 6.505 (6.552) Lt: 5.730 (5.809) Accm: 3.39 (3.24) Acct: 5.11 (5.08) proj_loss: -0.5897 (-0.5868) time: 0.6774 data: 0.0003 [11-24 23:13:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.31 Lm: 6.517 (6.504) Lt: 5.719 (5.748) Accm: 3.34 (3.33) Acct: 5.11 (5.21) proj_loss: -0.5921 (-0.5932) time: 0.6774 data: 0.0003 [11-24 23:13:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [ 834/1669] eta: 0:09:50 tlr: 0.00016 tnm: 0.31 Lm: 6.548 (6.544) Lt: 5.835 (5.823) Accm: 3.29 (3.26) Acct: 4.89 (4.94) proj_loss: -0.5940 (-0.5912) time: 0.6774 data: 0.0003 [11-24 23:18:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [1251/1669] eta: 0:04:52 tlr: 0.00016 tnm: 0.31 Lm: 6.568 (6.555) Lt: 5.822 (5.820) Accm: 3.23 (3.24) Acct: 5.06 (5.01) proj_loss: -0.5897 (-0.5895) time: 0.7368 data: 0.0003 [11-24 23:18:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [1251/1669] eta: 0:04:52 tlr: 0.00016 tnm: 0.31 Lm: 6.558 (6.556) Lt: 5.782 (5.784) Accm: 3.45 (3.35) Acct: 5.35 (5.23) proj_loss: -0.6027 (-0.5995) time: 0.7368 data: 0.0003 [11-24 23:18:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [1251/1669] eta: 0:04:52 tlr: 0.00016 tnm: 0.31 Lm: 6.507 (6.503) Lt: 5.715 (5.730) Accm: 3.39 (3.36) Acct: 5.29 (5.28) proj_loss: -0.5900 (-0.5914) time: 0.7368 data: 0.0003 [11-24 23:18:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [1251/1669] eta: 0:04:52 tlr: 0.00016 tnm: 0.31 Lm: 6.498 (6.537) Lt: 5.714 (5.781) Accm: 3.36 (3.26) Acct: 5.14 (5.11) proj_loss: -0.5853 (-0.5853) time: 0.7368 data: 0.0003 [11-24 23:23:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.490 (6.499) Lt: 5.698 (5.741) Accm: 3.39 (3.39) Acct: 5.17 (5.29) proj_loss: -0.5897 (-0.5884) time: 0.6783 data: 0.0019 [11-24 23:23:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 139/350] Total time: 0:19:17 (0.694 s / it) [11-24 23:23:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.517 (6.518) Lt: 5.719 (5.753) Accm: 3.43 (3.43) Acct: 5.46 (5.34) proj_loss: -0.5879 (-0.5907) time: 0.6783 data: 0.0021 [11-24 23:23:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.569 (6.558) Lt: 5.808 (5.817) Accm: 3.24 (3.24) Acct: 5.08 (5.03) proj_loss: -0.5940 (-0.5905) time: 0.6783 data: 0.0019 [11-24 23:23:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 139/350] [1668/1669] eta: 0:00:00 tlr: 0.00016 tnm: 0.31 Lm: 6.491 (6.522) Lt: 5.721 (5.750) Accm: 3.61 (3.51) Acct: 5.53 (5.54) proj_loss: -0.6043 (-0.6009) time: 0.6784 data: 0.0015 [11-24 23:23:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 139/350] Total time: 0:19:17 (0.694 s / it) [11-24 23:23:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 139/350] Total time: 0:19:17 (0.694 s / it) [11-24 23:23:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 139/350] Total time: 0:19:17 (0.694 s / it) [11-24 23:25:34] (home/user/VAR/trainer.py, line 114)=> FID: 3.9343692941769177 [11-24 23:25:35] (/home/user/VAR/train.py , line 259)=> [*] [ep139] (val 50000) Lm: 6.5591, Lt: 5.8025, Acc m&t: 3.23 5.10, Val cost: 141.16s [11-24 23:25:35] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-24 23:25:55] (/home/user/VAR/train.py , line 276)=> [ep139] (training ) Lm: 6.557 (6.559), Lt: 5.802 (5.802), Acc m&t: 3.26 5.15, Remain: 2 days, 18:33:45, Finish: 2024-11-27 01:56 [11-24 23:25:55] (/home/user/VAR/train.py , line 276)=> [ep139] (training ) Lm: 6.557 (6.559), Lt: 5.802 (5.802), Acc m&t: 3.26 5.15, Remain: 2 days, 18:33:37, Finish: 2024-11-27 01:56 [11-24 23:25:55] (/home/user/VAR/train.py , line 276)=> [ep139] (training ) Lm: 6.557 (6.559), Lt: 5.802 (5.802), Acc m&t: 3.26 5.15, Remain: 2 days, 18:34:13, Finish: 2024-11-27 01:57 [11-24 23:25:55] (/home/user/VAR/train.py , line 276)=> [ep139] (training ) Lm: 6.557 (6.559), Lt: 5.802 (5.802), Acc m&t: 3.26 5.15, Remain: 2 days, 18:34:06, Finish: 2024-11-27 01:57 [11-24 23:25:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [ 0/1669] eta: 0:18:40 tlr: 0.00016 tnm: 0.31 Lm: 6.610 (6.610) Lt: 5.912 (5.912) Accm: 3.14 (3.14) Acct: 4.82 (4.82) proj_loss: -0.6203 (-0.6203) time: 0.6716 data: 0.0004 [11-24 23:25:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [ 0/1669] eta: 0:18:40 tlr: 0.00016 tnm: 0.31 Lm: 6.683 (6.683) Lt: 5.923 (5.923) Accm: 3.03 (3.03) Acct: 4.80 (4.80) proj_loss: -0.5712 (-0.5712) time: 0.6713 data: 0.0004 [11-24 23:25:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [ 0/1669] eta: 0:18:40 tlr: 0.00016 tnm: 0.31 Lm: 6.565 (6.565) Lt: 5.787 (5.787) Accm: 3.26 (3.26) Acct: 5.08 (5.08) proj_loss: -0.5749 (-0.5749) time: 0.6712 data: 0.0003 [11-24 23:25:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [ 0/1669] eta: 0:18:40 tlr: 0.00016 tnm: 0.31 Lm: 6.366 (6.366) Lt: 5.543 (5.543) Accm: 3.59 (3.59) Acct: 5.65 (5.65) proj_loss: -0.5966 (-0.5966) time: 0.6713 data: 0.0004 [11-24 23:30:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.30 Lm: 6.499 (6.499) Lt: 5.698 (5.698) Accm: 3.29 (3.29) Acct: 5.29 (5.29) proj_loss: -0.5904 (-0.5904) time: 0.6768 data: 0.0003 [11-24 23:30:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.30 Lm: 6.576 (6.576) Lt: 5.820 (5.820) Accm: 3.26 (3.26) Acct: 5.13 (5.13) proj_loss: -0.5945 (-0.5945) time: 0.6768 data: 0.0003 [11-24 23:30:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.30 Lm: 6.592 (6.592) Lt: 5.875 (5.875) Accm: 3.01 (3.01) Acct: 4.74 (4.74) proj_loss: -0.6009 (-0.6009) time: 0.6768 data: 0.0003 [11-24 23:30:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [ 417/1669] eta: 0:14:06 tlr: 0.00016 tnm: 0.30 Lm: 6.631 (6.631) Lt: 5.903 (5.903) Accm: 3.01 (3.01) Acct: 4.65 (4.65) proj_loss: -0.5817 (-0.5817) time: 0.6768 data: 0.0003 [11-24 23:35:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [ 834/1669] eta: 0:09:26 tlr: 0.00016 tnm: 0.33 Lm: 6.579 (6.597) Lt: 5.883 (5.863) Accm: 3.03 (3.07) Acct: 4.80 (4.86) proj_loss: -0.5923 (-0.5865) time: 0.6752 data: 0.0003 [11-24 23:35:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [ 834/1669] eta: 0:09:26 tlr: 0.00016 tnm: 0.33 Lm: 6.586 (6.590) Lt: 5.867 (5.872) Accm: 2.89 (2.97) Acct: 4.67 (4.61) proj_loss: -0.5986 (-0.6001) time: 0.6752 data: 0.0003 [11-24 23:35:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [ 834/1669] eta: 0:09:26 tlr: 0.00016 tnm: 0.33 Lm: 6.484 (6.494) Lt: 5.680 (5.692) Accm: 3.53 (3.37) Acct: 5.65 (5.50) proj_loss: -0.5841 (-0.5865) time: 0.6751 data: 0.0003 [11-24 23:35:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [ 834/1669] eta: 0:09:26 tlr: 0.00016 tnm: 0.33 Lm: 6.581 (6.578) Lt: 5.853 (5.837) Accm: 3.26 (3.31) Acct: 5.15 (5.14) proj_loss: -0.6142 (-0.6058) time: 0.6752 data: 0.0003 [11-24 23:40:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.584 (6.589) Lt: 5.861 (5.849) Accm: 3.26 (3.25) Acct: 5.11 (5.10) proj_loss: -0.6106 (-0.6061) time: 0.6771 data: 0.0003 [11-24 23:40:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.600 (6.603) Lt: 5.875 (5.864) Accm: 3.06 (3.08) Acct: 4.86 (4.87) proj_loss: -0.5941 (-0.5903) time: 0.6771 data: 0.0003 [11-24 23:40:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.598 (6.595) Lt: 5.853 (5.864) Accm: 3.02 (3.02) Acct: 4.74 (4.71) proj_loss: -0.6017 (-0.6013) time: 0.6771 data: 0.0003 [11-24 23:40:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.537 (6.518) Lt: 5.766 (5.739) Accm: 3.41 (3.35) Acct: 5.39 (5.41) proj_loss: -0.5904 (-0.5906) time: 0.6771 data: 0.0003 [11-24 23:44:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.33 Lm: 6.535 (6.522) Lt: 5.733 (5.738) Accm: 3.53 (3.44) Acct: 5.65 (5.58) proj_loss: -0.5888 (-0.5902) time: 0.6808 data: 0.0021 [11-24 23:44:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 140/350] Total time: 0:18:52 (0.678 s / it) [11-24 23:44:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.33 Lm: 6.581 (6.581) Lt: 5.853 (5.839) Accm: 3.26 (3.25) Acct: 5.15 (5.15) proj_loss: -0.6070 (-0.6045) time: 0.6808 data: 0.0013 [11-24 23:44:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.33 Lm: 6.586 (6.576) Lt: 5.840 (5.830) Accm: 3.14 (3.10) Acct: 4.82 (4.86) proj_loss: -0.5986 (-0.5962) time: 0.6808 data: 0.0019 [11-24 23:44:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 140/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.33 Lm: 6.579 (6.594) Lt: 5.866 (5.852) Accm: 3.09 (3.09) Acct: 4.89 (4.88) proj_loss: -0.5960 (-0.5931) time: 0.6808 data: 0.0020 [11-24 23:44:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 140/350] Total time: 0:18:52 (0.678 s / it) [11-24 23:44:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 140/350] Total time: 0:18:52 (0.678 s / it) [11-24 23:44:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 140/350] Total time: 0:18:52 (0.678 s / it) [11-24 23:44:47] (/home/user/VAR/train.py , line 276)=> [ep140] (training ) Lm: 6.557 (6.568), Lt: 5.802 (5.812), Acc m&t: 3.26 5.15, Remain: 2 days, 18:23:18, Finish: 2024-11-27 02:08 [11-24 23:44:47] (/home/user/VAR/train.py , line 276)=> [ep140] (training ) Lm: 6.557 (6.568), Lt: 5.802 (5.812), Acc m&t: 3.26 5.15, Remain: 2 days, 18:23:52, Finish: 2024-11-27 02:08 [11-24 23:44:47] (/home/user/VAR/train.py , line 276)=> [ep140] (training ) Lm: 6.557 (6.568), Lt: 5.802 (5.812), Acc m&t: 3.26 5.15, Remain: 2 days, 18:22:49, Finish: 2024-11-27 02:07 [11-24 23:44:47] (/home/user/VAR/train.py , line 276)=> [ep140] (training ) Lm: 6.557 (6.568), Lt: 5.802 (5.812), Acc m&t: 3.26 5.15, Remain: 2 days, 18:22:59, Finish: 2024-11-27 02:07 [11-24 23:44:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [ 0/1669] eta: 0:18:10 tlr: 0.00015 tnm: 0.33 Lm: 6.515 (6.515) Lt: 5.706 (5.706) Accm: 3.27 (3.27) Acct: 5.11 (5.11) proj_loss: -0.6123 (-0.6123) time: 0.6535 data: 0.0004 [11-24 23:44:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [ 0/1669] eta: 0:18:23 tlr: 0.00015 tnm: 0.33 Lm: 6.601 (6.601) Lt: 5.822 (5.822) Accm: 3.30 (3.30) Acct: 5.10 (5.10) proj_loss: -0.5717 (-0.5717) time: 0.6614 data: 0.0004 [11-24 23:44:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [ 0/1669] eta: 0:18:11 tlr: 0.00015 tnm: 0.33 Lm: 6.475 (6.475) Lt: 5.734 (5.734) Accm: 3.13 (3.13) Acct: 4.77 (4.77) proj_loss: -0.5945 (-0.5945) time: 0.6542 data: 0.0004 [11-24 23:44:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [ 0/1669] eta: 0:18:13 tlr: 0.00015 tnm: 0.33 Lm: 6.600 (6.600) Lt: 5.887 (5.887) Accm: 3.29 (3.29) Acct: 5.10 (5.10) proj_loss: -0.5887 (-0.5887) time: 0.6551 data: 0.0003 [11-24 23:49:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [ 417/1669] eta: 0:15:22 tlr: 0.00015 tnm: 0.31 Lm: 6.607 (6.607) Lt: 5.879 (5.879) Accm: 3.03 (3.03) Acct: 4.79 (4.79) proj_loss: -0.5894 (-0.5894) time: 0.6764 data: 0.0003 [11-24 23:49:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [ 417/1669] eta: 0:15:22 tlr: 0.00015 tnm: 0.31 Lm: 6.561 (6.561) Lt: 5.776 (5.776) Accm: 3.22 (3.22) Acct: 5.15 (5.15) proj_loss: -0.6034 (-0.6034) time: 0.6764 data: 0.0003 [11-24 23:49:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [ 417/1669] eta: 0:15:22 tlr: 0.00015 tnm: 0.31 Lm: 6.604 (6.604) Lt: 5.827 (5.827) Accm: 3.14 (3.14) Acct: 5.00 (5.00) proj_loss: -0.5764 (-0.5764) time: 0.6764 data: 0.0003 [11-24 23:49:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [ 417/1669] eta: 0:15:22 tlr: 0.00015 tnm: 0.31 Lm: 6.550 (6.550) Lt: 5.796 (5.796) Accm: 3.13 (3.13) Acct: 4.98 (4.98) proj_loss: -0.5833 (-0.5833) time: 0.6764 data: 0.0003 [11-24 23:54:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.33 Lm: 6.607 (6.615) Lt: 5.831 (5.828) Accm: 3.10 (3.12) Acct: 5.10 (5.04) proj_loss: -0.5717 (-0.5718) time: 0.6778 data: 0.0003 [11-24 23:54:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.33 Lm: 6.607 (6.596) Lt: 5.847 (5.832) Accm: 3.17 (3.11) Acct: 5.11 (4.92) proj_loss: -0.5945 (-0.5968) time: 0.6778 data: 0.0003 [11-24 23:54:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.33 Lm: 6.510 (6.537) Lt: 5.734 (5.760) Accm: 3.13 (3.20) Acct: 5.18 (5.10) proj_loss: -0.5723 (-0.5797) time: 0.6778 data: 0.0003 [11-24 23:54:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.33 Lm: 6.600 (6.577) Lt: 5.871 (5.828) Accm: 3.29 (3.15) Acct: 5.10 (5.03) proj_loss: -0.5887 (-0.5859) time: 0.6778 data: 0.0003 [11-24 23:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.33 Lm: 6.607 (6.603) Lt: 5.879 (5.846) Accm: 3.09 (3.09) Acct: 4.98 (4.99) proj_loss: -0.5869 (-0.5857) time: 0.6764 data: 0.0003 [11-24 23:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.33 Lm: 6.604 (6.559) Lt: 5.827 (5.781) Accm: 3.20 (3.26) Acct: 5.11 (5.27) proj_loss: -0.5764 (-0.5756) time: 0.6764 data: 0.0003 [11-24 23:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.33 Lm: 6.561 (6.547) Lt: 5.776 (5.777) Accm: 3.22 (3.31) Acct: 5.15 (5.21) proj_loss: -0.5975 (-0.5977) time: 0.6764 data: 0.0003 [11-24 23:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.33 Lm: 6.567 (6.566) Lt: 5.796 (5.799) Accm: 3.13 (3.17) Acct: 5.09 (5.07) proj_loss: -0.5834 (-0.5884) time: 0.6764 data: 0.0003 [11-25 00:04:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.596 (6.572) Lt: 5.825 (5.804) Accm: 3.13 (3.18) Acct: 4.99 (5.04) proj_loss: -0.5813 (-0.5870) time: 0.6771 data: 0.0017 [11-25 00:04:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 141/350] Total time: 0:19:16 (0.693 s / it) [11-25 00:04:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.515 (6.534) Lt: 5.706 (5.761) Accm: 3.27 (3.31) Acct: 5.18 (5.22) proj_loss: -0.5945 (-0.5928) time: 0.6771 data: 0.0013 [11-25 00:04:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.601 (6.549) Lt: 5.822 (5.769) Accm: 3.30 (3.27) Acct: 5.11 (5.29) proj_loss: -0.5726 (-0.5750) time: 0.6771 data: 0.0016 [11-25 00:04:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 141/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.614 (6.628) Lt: 5.887 (5.889) Accm: 2.89 (3.00) Acct: 4.87 (4.82) proj_loss: -0.5887 (-0.5890) time: 0.6771 data: 0.0017 [11-25 00:04:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 141/350] Total time: 0:19:16 (0.693 s / it) [11-25 00:04:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 141/350] Total time: 0:19:16 (0.693 s / it) [11-25 00:04:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 141/350] Total time: 0:19:16 (0.693 s / it) [11-25 00:04:04] (/home/user/VAR/train.py , line 276)=> [ep141] (training ) Lm: 6.557 (6.562), Lt: 5.802 (5.809), Acc m&t: 3.26 5.15, Remain: 2 days, 17:48:35, Finish: 2024-11-27 01:52 [11-25 00:04:04] (/home/user/VAR/train.py , line 276)=> [ep141] (training ) Lm: 6.557 (6.562), Lt: 5.802 (5.809), Acc m&t: 3.26 5.15, Remain: 2 days, 17:48:12, Finish: 2024-11-27 01:52 [11-25 00:04:04] (/home/user/VAR/train.py , line 276)=> [ep141] (training ) Lm: 6.557 (6.562), Lt: 5.802 (5.809), Acc m&t: 3.26 5.15, Remain: 2 days, 17:48:04, Finish: 2024-11-27 01:52 [11-25 00:04:04] (/home/user/VAR/train.py , line 276)=> [ep141] (training ) Lm: 6.557 (6.562), Lt: 5.802 (5.809), Acc m&t: 3.26 5.15, Remain: 2 days, 17:48:45, Finish: 2024-11-27 01:52 [11-25 00:04:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [ 0/1669] eta: 0:18:32 tlr: 0.00015 tnm: 0.32 Lm: 6.506 (6.506) Lt: 5.771 (5.771) Accm: 3.39 (3.39) Acct: 5.18 (5.18) proj_loss: -0.6036 (-0.6036) time: 0.6667 data: 0.0004 [11-25 00:04:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [ 0/1669] eta: 0:18:33 tlr: 0.00015 tnm: 0.32 Lm: 6.582 (6.582) Lt: 5.778 (5.778) Accm: 3.22 (3.22) Acct: 5.20 (5.20) proj_loss: -0.5792 (-0.5792) time: 0.6672 data: 0.0004 [11-25 00:04:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [ 0/1669] eta: 0:18:33 tlr: 0.00015 tnm: 0.32 Lm: 6.511 (6.511) Lt: 5.729 (5.729) Accm: 3.33 (3.33) Acct: 4.82 (4.82) proj_loss: -0.6157 (-0.6157) time: 0.6673 data: 0.0003 [11-25 00:04:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [ 0/1669] eta: 0:18:34 tlr: 0.00015 tnm: 0.32 Lm: 6.392 (6.392) Lt: 5.651 (5.651) Accm: 3.90 (3.90) Acct: 6.10 (6.10) proj_loss: -0.5835 (-0.5835) time: 0.6678 data: 0.0004 [11-25 00:08:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [ 417/1669] eta: 0:14:07 tlr: 0.00015 tnm: 0.31 Lm: 6.460 (6.460) Lt: 5.693 (5.693) Accm: 3.67 (3.67) Acct: 5.79 (5.79) proj_loss: -0.5860 (-0.5860) time: 0.6763 data: 0.0003 [11-25 00:08:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [ 417/1669] eta: 0:14:07 tlr: 0.00015 tnm: 0.31 Lm: 6.553 (6.553) Lt: 5.744 (5.744) Accm: 3.25 (3.25) Acct: 5.22 (5.22) proj_loss: -0.5740 (-0.5740) time: 0.6763 data: 0.0003 [11-25 00:08:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [ 417/1669] eta: 0:14:07 tlr: 0.00015 tnm: 0.31 Lm: 6.527 (6.527) Lt: 5.779 (5.779) Accm: 3.30 (3.30) Acct: 5.25 (5.25) proj_loss: -0.6082 (-0.6082) time: 0.6763 data: 0.0003 [11-25 00:08:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [ 417/1669] eta: 0:14:07 tlr: 0.00015 tnm: 0.31 Lm: 6.585 (6.585) Lt: 5.837 (5.837) Accm: 3.17 (3.17) Acct: 4.72 (4.72) proj_loss: -0.5989 (-0.5989) time: 0.6763 data: 0.0003 [11-25 00:13:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.31 Lm: 6.629 (6.600) Lt: 5.877 (5.850) Accm: 3.00 (3.04) Acct: 4.61 (4.61) proj_loss: -0.5851 (-0.5943) time: 0.6759 data: 0.0003 [11-25 00:13:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.31 Lm: 6.549 (6.595) Lt: 5.787 (5.853) Accm: 3.20 (3.09) Acct: 5.18 (5.00) proj_loss: -0.6036 (-0.6009) time: 0.6759 data: 0.0003 [11-25 00:13:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.31 Lm: 6.582 (6.572) Lt: 5.778 (5.779) Accm: 3.22 (3.15) Acct: 5.20 (5.06) proj_loss: -0.5740 (-0.5740) time: 0.6759 data: 0.0003 [11-25 00:13:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.31 Lm: 6.528 (6.503) Lt: 5.736 (5.758) Accm: 3.44 (3.58) Acct: 5.49 (5.55) proj_loss: -0.5884 (-0.5928) time: 0.6759 data: 0.0003 [11-25 00:18:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.30 Lm: 6.525 (6.508) Lt: 5.732 (5.750) Accm: 3.43 (3.52) Acct: 5.53 (5.55) proj_loss: -0.5938 (-0.5944) time: 0.6769 data: 0.0003 [11-25 00:18:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.30 Lm: 6.588 (6.603) Lt: 5.824 (5.855) Accm: 3.21 (3.13) Acct: 5.09 (5.00) proj_loss: -0.5951 (-0.5958) time: 0.6769 data: 0.0003 [11-25 00:18:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.30 Lm: 6.644 (6.617) Lt: 5.877 (5.857) Accm: 3.03 (3.04) Acct: 4.72 (4.66) proj_loss: -0.5837 (-0.5888) time: 0.6769 data: 0.0003 [11-25 00:18:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.30 Lm: 6.553 (6.550) Lt: 5.744 (5.759) Accm: 3.25 (3.18) Acct: 5.19 (5.09) proj_loss: -0.5766 (-0.5810) time: 0.6769 data: 0.0003 [11-25 00:23:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.582 (6.561) Lt: 5.778 (5.776) Accm: 3.22 (3.15) Acct: 5.18 (5.05) proj_loss: -0.5792 (-0.5833) time: 0.6781 data: 0.0016 [11-25 00:23:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 142/350] Total time: 0:19:16 (0.693 s / it) [11-25 00:23:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.523 (6.496) Lt: 5.728 (5.745) Accm: 3.42 (3.49) Acct: 5.49 (5.48) proj_loss: -0.5992 (-0.5954) time: 0.6781 data: 0.0017 [11-25 00:23:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.629 (6.591) Lt: 5.877 (5.829) Accm: 3.07 (3.16) Acct: 4.82 (4.82) proj_loss: -0.5851 (-0.5916) time: 0.6781 data: 0.0016 [11-25 00:23:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 142/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.603 (6.603) Lt: 5.856 (5.855) Accm: 3.21 (3.14) Acct: 5.04 (5.01) proj_loss: -0.5971 (-0.5960) time: 0.6781 data: 0.0015 [11-25 00:23:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 142/350] Total time: 0:19:16 (0.693 s / it) [11-25 00:23:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 142/350] Total time: 0:19:16 (0.693 s / it) [11-25 00:23:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 142/350] Total time: 0:19:16 (0.693 s / it) [11-25 00:23:21] (/home/user/VAR/train.py , line 276)=> [ep142] (training ) Lm: 6.557 (6.573), Lt: 5.802 (5.821), Acc m&t: 3.26 5.15, Remain: 2 days, 17:37:02, Finish: 2024-11-27 02:00 [11-25 00:23:21] (/home/user/VAR/train.py , line 276)=> [ep142] (training ) Lm: 6.557 (6.573), Lt: 5.802 (5.821), Acc m&t: 3.26 5.15, Remain: 2 days, 17:36:47, Finish: 2024-11-27 02:00 [11-25 00:23:21] (/home/user/VAR/train.py , line 276)=> [ep142] (training ) Lm: 6.557 (6.573), Lt: 5.802 (5.821), Acc m&t: 3.26 5.15, Remain: 2 days, 17:36:32, Finish: 2024-11-27 01:59 [11-25 00:23:21] (/home/user/VAR/train.py , line 276)=> [ep142] (training ) Lm: 6.557 (6.573), Lt: 5.802 (5.821), Acc m&t: 3.26 5.15, Remain: 2 days, 17:36:12, Finish: 2024-11-27 01:59 [11-25 00:23:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [ 0/1669] eta: 0:18:16 tlr: 0.00015 tnm: 0.33 Lm: 6.532 (6.532) Lt: 5.777 (5.777) Accm: 3.35 (3.35) Acct: 4.98 (4.98) proj_loss: -0.5860 (-0.5860) time: 0.6568 data: 0.0003 [11-25 00:23:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [ 0/1669] eta: 0:18:15 tlr: 0.00015 tnm: 0.33 Lm: 6.640 (6.640) Lt: 5.841 (5.841) Accm: 3.01 (3.01) Acct: 4.77 (4.77) proj_loss: -0.5790 (-0.5790) time: 0.6565 data: 0.0004 [11-25 00:23:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [ 0/1669] eta: 0:18:17 tlr: 0.00015 tnm: 0.33 Lm: 6.751 (6.751) Lt: 6.037 (6.037) Accm: 2.65 (2.65) Acct: 4.27 (4.27) proj_loss: -0.5882 (-0.5882) time: 0.6574 data: 0.0003 [11-25 00:23:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [ 0/1669] eta: 0:18:17 tlr: 0.00015 tnm: 0.33 Lm: 6.618 (6.618) Lt: 5.810 (5.810) Accm: 3.32 (3.32) Acct: 5.60 (5.60) proj_loss: -0.5943 (-0.5943) time: 0.6573 data: 0.0004 [11-25 00:28:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.32 Lm: 6.650 (6.650) Lt: 5.873 (5.873) Accm: 3.15 (3.15) Acct: 5.23 (5.23) proj_loss: -0.6029 (-0.6029) time: 0.6790 data: 0.0003 [11-25 00:28:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.32 Lm: 6.556 (6.556) Lt: 5.809 (5.809) Accm: 3.26 (3.26) Acct: 4.98 (4.98) proj_loss: -0.5916 (-0.5916) time: 0.6790 data: 0.0003 [11-25 00:28:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.32 Lm: 6.609 (6.609) Lt: 5.841 (5.841) Accm: 3.11 (3.11) Acct: 4.93 (4.93) proj_loss: -0.5892 (-0.5892) time: 0.6790 data: 0.0003 [11-25 00:28:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.32 Lm: 6.672 (6.672) Lt: 5.935 (5.935) Accm: 2.80 (2.80) Acct: 4.57 (4.57) proj_loss: -0.5977 (-0.5977) time: 0.6790 data: 0.0003 [11-25 00:32:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [ 834/1669] eta: 0:09:26 tlr: 0.00015 tnm: 0.31 Lm: 6.593 (6.626) Lt: 5.834 (5.892) Accm: 2.95 (2.87) Acct: 4.84 (4.66) proj_loss: -0.5882 (-0.5944) time: 0.6768 data: 0.0003 [11-25 00:32:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [ 834/1669] eta: 0:09:26 tlr: 0.00015 tnm: 0.31 Lm: 6.574 (6.562) Lt: 5.831 (5.817) Accm: 3.16 (3.19) Acct: 4.98 (5.04) proj_loss: -0.5971 (-0.5962) time: 0.6768 data: 0.0003 [11-25 00:32:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [ 834/1669] eta: 0:09:26 tlr: 0.00015 tnm: 0.31 Lm: 6.618 (6.582) Lt: 5.810 (5.811) Accm: 3.31 (3.21) Acct: 5.13 (5.19) proj_loss: -0.5946 (-0.6001) time: 0.6768 data: 0.0003 [11-25 00:32:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [ 834/1669] eta: 0:09:26 tlr: 0.00015 tnm: 0.31 Lm: 6.578 (6.494) Lt: 5.841 (5.729) Accm: 3.21 (3.41) Acct: 5.10 (5.35) proj_loss: -0.5964 (-0.5916) time: 0.6769 data: 0.0003 [11-25 00:37:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.31 Lm: 6.609 (6.537) Lt: 5.841 (5.773) Accm: 3.11 (3.30) Acct: 5.00 (5.24) proj_loss: -0.5946 (-0.5919) time: 0.6759 data: 0.0003 [11-25 00:37:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.31 Lm: 6.553 (6.512) Lt: 5.804 (5.754) Accm: 3.26 (3.38) Acct: 5.07 (5.39) proj_loss: -0.5916 (-0.5931) time: 0.6759 data: 0.0003 [11-25 00:37:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.31 Lm: 6.599 (6.581) Lt: 5.789 (5.800) Accm: 3.15 (3.14) Acct: 4.99 (5.08) proj_loss: -0.5945 (-0.5961) time: 0.6759 data: 0.0003 [11-25 00:37:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.31 Lm: 6.672 (6.666) Lt: 5.935 (5.945) Accm: 2.80 (2.80) Acct: 4.55 (4.48) proj_loss: -0.5887 (-0.5931) time: 0.6759 data: 0.0003 [11-25 00:42:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.36 Lm: 6.593 (6.616) Lt: 5.834 (5.888) Accm: 2.95 (3.02) Acct: 4.84 (4.81) proj_loss: -0.5891 (-0.5963) time: 0.6753 data: 0.0020 [11-25 00:42:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 143/350] Total time: 0:18:53 (0.679 s / it) [11-25 00:42:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.36 Lm: 6.579 (6.564) Lt: 5.769 (5.780) Accm: 3.30 (3.17) Acct: 5.13 (5.13) proj_loss: -0.5943 (-0.5941) time: 0.6753 data: 0.0018 [11-25 00:42:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.36 Lm: 6.532 (6.503) Lt: 5.777 (5.740) Accm: 3.35 (3.39) Acct: 5.17 (5.40) proj_loss: -0.5971 (-0.5973) time: 0.6753 data: 0.0013 [11-25 00:42:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 143/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.36 Lm: 6.584 (6.546) Lt: 5.841 (5.785) Accm: 3.11 (3.26) Acct: 4.96 (5.19) proj_loss: -0.5964 (-0.5946) time: 0.6753 data: 0.0020 [11-25 00:42:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 143/350] Total time: 0:18:53 (0.679 s / it) [11-25 00:42:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 143/350] Total time: 0:18:53 (0.679 s / it) [11-25 00:42:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 143/350] Total time: 0:18:53 (0.679 s / it) [11-25 00:42:14] (/home/user/VAR/train.py , line 276)=> [ep143] (training ) Lm: 6.557 (6.570), Lt: 5.802 (5.815), Acc m&t: 3.26 5.15, Remain: 2 days, 16:56:34, Finish: 2024-11-27 01:38 [11-25 00:42:14] (/home/user/VAR/train.py , line 276)=> [ep143] (training ) Lm: 6.557 (6.570), Lt: 5.802 (5.815), Acc m&t: 3.26 5.15, Remain: 2 days, 16:57:30, Finish: 2024-11-27 01:39 [11-25 00:42:14] (/home/user/VAR/train.py , line 276)=> [ep143] (training ) Lm: 6.557 (6.570), Lt: 5.802 (5.815), Acc m&t: 3.26 5.15, Remain: 2 days, 16:57:17, Finish: 2024-11-27 01:39 [11-25 00:42:14] (/home/user/VAR/train.py , line 276)=> [ep143] (training ) Lm: 6.557 (6.570), Lt: 5.802 (5.815), Acc m&t: 3.26 5.15, Remain: 2 days, 16:58:32, Finish: 2024-11-27 01:40 [11-25 00:42:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [ 0/1669] eta: 0:18:21 tlr: 0.00015 tnm: 0.32 Lm: 6.520 (6.520) Lt: 5.748 (5.748) Accm: 3.39 (3.39) Acct: 5.75 (5.75) proj_loss: -0.6052 (-0.6052) time: 0.6601 data: 0.0004 [11-25 00:42:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [ 0/1669] eta: 0:18:22 tlr: 0.00015 tnm: 0.32 Lm: 6.585 (6.585) Lt: 5.772 (5.772) Accm: 3.17 (3.17) Acct: 5.10 (5.10) proj_loss: -0.5805 (-0.5805) time: 0.6606 data: 0.0004 [11-25 00:42:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [ 0/1669] eta: 0:18:22 tlr: 0.00015 tnm: 0.32 Lm: 6.691 (6.691) Lt: 6.039 (6.039) Accm: 2.75 (2.75) Acct: 4.18 (4.18) proj_loss: -0.5978 (-0.5978) time: 0.6604 data: 0.0004 [11-25 00:42:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [ 0/1669] eta: 0:18:22 tlr: 0.00015 tnm: 0.32 Lm: 6.517 (6.517) Lt: 5.726 (5.726) Accm: 3.47 (3.47) Acct: 5.41 (5.41) proj_loss: -0.5968 (-0.5968) time: 0.6604 data: 0.0004 [11-25 00:47:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [ 417/1669] eta: 0:15:23 tlr: 0.00015 tnm: 0.33 Lm: 6.485 (6.485) Lt: 5.666 (5.666) Accm: 3.54 (3.54) Acct: 5.60 (5.60) proj_loss: -0.5933 (-0.5933) time: 0.6734 data: 0.0003 [11-25 00:47:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [ 417/1669] eta: 0:15:23 tlr: 0.00015 tnm: 0.33 Lm: 6.550 (6.550) Lt: 5.829 (5.829) Accm: 3.22 (3.22) Acct: 4.98 (4.98) proj_loss: -0.6069 (-0.6069) time: 0.6734 data: 0.0003 [11-25 00:47:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [ 417/1669] eta: 0:15:23 tlr: 0.00015 tnm: 0.33 Lm: 6.552 (6.552) Lt: 5.766 (5.766) Accm: 3.19 (3.19) Acct: 5.10 (5.10) proj_loss: -0.5928 (-0.5928) time: 0.6734 data: 0.0003 [11-25 00:47:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [ 417/1669] eta: 0:15:23 tlr: 0.00015 tnm: 0.33 Lm: 6.473 (6.473) Lt: 5.677 (5.677) Accm: 3.51 (3.51) Acct: 5.71 (5.71) proj_loss: -0.5968 (-0.5968) time: 0.6734 data: 0.0003 [11-25 00:52:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.31 Lm: 6.520 (6.516) Lt: 5.748 (5.737) Accm: 3.39 (3.47) Acct: 5.66 (5.58) proj_loss: -0.6052 (-0.5999) time: 0.6746 data: 0.0003 [11-25 00:52:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.31 Lm: 6.517 (6.507) Lt: 5.726 (5.712) Accm: 3.47 (3.45) Acct: 5.41 (5.49) proj_loss: -0.5968 (-0.5984) time: 0.6745 data: 0.0003 [11-25 00:52:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.31 Lm: 6.546 (6.549) Lt: 5.848 (5.836) Accm: 3.26 (3.23) Acct: 4.84 (4.93) proj_loss: -0.6017 (-0.6051) time: 0.6746 data: 0.0003 [11-25 00:52:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.31 Lm: 6.519 (6.528) Lt: 5.761 (5.739) Accm: 3.21 (3.31) Acct: 5.10 (5.33) proj_loss: -0.5917 (-0.5925) time: 0.6746 data: 0.0003 [11-25 00:56:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.32 Lm: 6.540 (6.537) Lt: 5.766 (5.772) Accm: 3.19 (3.26) Acct: 5.10 (5.16) proj_loss: -0.5949 (-0.5939) time: 0.6769 data: 0.0003 [11-25 00:56:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.32 Lm: 6.521 (6.518) Lt: 5.758 (5.744) Accm: 3.40 (3.45) Acct: 5.50 (5.50) proj_loss: -0.6035 (-0.6004) time: 0.6769 data: 0.0003 [11-25 00:56:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.32 Lm: 6.503 (6.502) Lt: 5.743 (5.724) Accm: 3.54 (3.58) Acct: 5.60 (5.68) proj_loss: -0.5933 (-0.5907) time: 0.6769 data: 0.0003 [11-25 00:56:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.32 Lm: 6.520 (6.535) Lt: 5.777 (5.803) Accm: 3.20 (3.21) Acct: 4.96 (4.97) proj_loss: -0.5997 (-0.6009) time: 0.6770 data: 0.0003 [11-25 01:01:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.546 (6.553) Lt: 5.803 (5.803) Accm: 3.24 (3.21) Acct: 5.08 (5.04) proj_loss: -0.5992 (-0.6006) time: 0.6780 data: 0.0017 [11-25 01:01:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 144/350] Total time: 0:19:16 (0.693 s / it) [11-25 01:01:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.561 (6.563) Lt: 5.772 (5.799) Accm: 3.17 (3.16) Acct: 5.10 (4.99) proj_loss: -0.5917 (-0.5902) time: 0.6780 data: 0.0017 [11-25 01:01:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.523 (6.546) Lt: 5.767 (5.788) Accm: 3.39 (3.39) Acct: 5.34 (5.33) proj_loss: -0.6052 (-0.6036) time: 0.6780 data: 0.0020 [11-25 01:01:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 144/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.517 (6.520) Lt: 5.759 (5.740) Accm: 3.47 (3.50) Acct: 5.41 (5.56) proj_loss: -0.5897 (-0.5902) time: 0.6780 data: 0.0020 [11-25 01:01:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 144/350] Total time: 0:19:16 (0.693 s / it) [11-25 01:01:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 144/350] Total time: 0:19:16 (0.693 s / it) [11-25 01:01:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 144/350] Total time: 0:19:16 (0.693 s / it) [11-25 01:01:30] (/home/user/VAR/train.py , line 276)=> [ep144] (training ) Lm: 6.557 (6.562), Lt: 5.802 (5.805), Acc m&t: 3.26 5.15, Remain: 2 days, 16:51:10, Finish: 2024-11-27 01:52 [11-25 01:01:30] (/home/user/VAR/train.py , line 276)=> [ep144] (training ) Lm: 6.557 (6.562), Lt: 5.802 (5.805), Acc m&t: 3.26 5.15, Remain: 2 days, 16:51:11, Finish: 2024-11-27 01:52 [11-25 01:01:30] (/home/user/VAR/train.py , line 276)=> [ep144] (training ) Lm: 6.557 (6.562), Lt: 5.802 (5.805), Acc m&t: 3.26 5.15, Remain: 2 days, 16:51:17, Finish: 2024-11-27 01:52 [11-25 01:01:30] (/home/user/VAR/train.py , line 276)=> [ep144] (training ) Lm: 6.557 (6.562), Lt: 5.802 (5.805), Acc m&t: 3.26 5.15, Remain: 2 days, 16:50:52, Finish: 2024-11-27 01:52 [11-25 01:01:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [ 0/1669] eta: 0:18:14 tlr: 0.00015 tnm: 0.33 Lm: 6.773 (6.773) Lt: 6.091 (6.091) Accm: 2.53 (2.53) Acct: 3.46 (3.46) proj_loss: -0.5865 (-0.5865) time: 0.6559 data: 0.0004 [11-25 01:01:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [ 0/1669] eta: 0:18:13 tlr: 0.00015 tnm: 0.33 Lm: 6.451 (6.451) Lt: 5.641 (5.641) Accm: 3.40 (3.40) Acct: 5.46 (5.46) proj_loss: -0.5899 (-0.5899) time: 0.6551 data: 0.0004 [11-25 01:01:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [ 0/1669] eta: 0:18:13 tlr: 0.00015 tnm: 0.33 Lm: 6.570 (6.570) Lt: 5.879 (5.879) Accm: 3.13 (3.13) Acct: 5.08 (5.08) proj_loss: -0.6006 (-0.6006) time: 0.6551 data: 0.0004 [11-25 01:01:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [ 0/1669] eta: 0:18:13 tlr: 0.00015 tnm: 0.33 Lm: 6.425 (6.425) Lt: 5.631 (5.631) Accm: 3.47 (3.47) Acct: 5.65 (5.65) proj_loss: -0.5901 (-0.5901) time: 0.6552 data: 0.0004 [11-25 01:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [ 417/1669] eta: 0:14:07 tlr: 0.00015 tnm: 0.33 Lm: 6.553 (6.553) Lt: 5.782 (5.782) Accm: 3.18 (3.18) Acct: 4.96 (4.96) proj_loss: -0.6008 (-0.6008) time: 0.6781 data: 0.0003 [11-25 01:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [ 417/1669] eta: 0:14:07 tlr: 0.00015 tnm: 0.33 Lm: 6.726 (6.726) Lt: 6.020 (6.020) Accm: 2.74 (2.74) Acct: 4.00 (4.00) proj_loss: -0.5892 (-0.5892) time: 0.6781 data: 0.0003 [11-25 01:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [ 417/1669] eta: 0:14:07 tlr: 0.00015 tnm: 0.33 Lm: 6.602 (6.602) Lt: 5.872 (5.872) Accm: 3.10 (3.10) Acct: 5.01 (5.01) proj_loss: -0.5972 (-0.5972) time: 0.6781 data: 0.0003 [11-25 01:06:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [ 417/1669] eta: 0:14:07 tlr: 0.00015 tnm: 0.33 Lm: 6.614 (6.614) Lt: 5.866 (5.866) Accm: 2.97 (2.97) Acct: 4.74 (4.74) proj_loss: -0.5884 (-0.5884) time: 0.6781 data: 0.0003 [11-25 01:11:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [ 834/1669] eta: 0:09:51 tlr: 0.00015 tnm: 0.33 Lm: 6.674 (6.634) Lt: 5.909 (5.880) Accm: 2.85 (2.93) Acct: 4.42 (4.64) proj_loss: -0.5868 (-0.5874) time: 0.6784 data: 0.0003 [11-25 01:11:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [ 834/1669] eta: 0:09:51 tlr: 0.00015 tnm: 0.33 Lm: 6.637 (6.581) Lt: 5.904 (5.823) Accm: 3.13 (3.16) Acct: 4.89 (4.94) proj_loss: -0.5901 (-0.5957) time: 0.6784 data: 0.0003 [11-25 01:11:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [ 834/1669] eta: 0:09:51 tlr: 0.00015 tnm: 0.33 Lm: 6.570 (6.566) Lt: 5.864 (5.836) Accm: 3.13 (3.16) Acct: 5.08 (5.06) proj_loss: -0.5938 (-0.5932) time: 0.6784 data: 0.0003 [11-25 01:11:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [ 834/1669] eta: 0:09:51 tlr: 0.00015 tnm: 0.33 Lm: 6.679 (6.678) Lt: 5.949 (5.950) Accm: 2.95 (2.93) Acct: 4.55 (4.40) proj_loss: -0.5887 (-0.5890) time: 0.6784 data: 0.0003 [11-25 01:16:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.33 Lm: 6.691 (6.684) Lt: 5.962 (5.957) Accm: 2.95 (2.94) Acct: 4.58 (4.46) proj_loss: -0.5876 (-0.5841) time: 0.6778 data: 0.0003 [11-25 01:16:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.33 Lm: 6.531 (6.511) Lt: 5.768 (5.741) Accm: 3.30 (3.35) Acct: 5.27 (5.25) proj_loss: -0.5978 (-0.5981) time: 0.6778 data: 0.0003 [11-25 01:16:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.33 Lm: 6.563 (6.563) Lt: 5.814 (5.816) Accm: 3.20 (3.27) Acct: 5.12 (5.27) proj_loss: -0.5915 (-0.5922) time: 0.6778 data: 0.0003 [11-25 01:16:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.33 Lm: 6.654 (6.634) Lt: 5.876 (5.871) Accm: 2.92 (2.95) Acct: 4.75 (4.75) proj_loss: -0.5861 (-0.5854) time: 0.6778 data: 0.0003 [11-25 01:20:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.634 (6.589) Lt: 5.844 (5.829) Accm: 2.99 (3.05) Acct: 5.08 (4.92) proj_loss: -0.5868 (-0.5859) time: 0.6787 data: 0.0017 [11-25 01:20:48] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 145/350] Total time: 0:19:17 (0.693 s / it) [11-25 01:20:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.637 (6.537) Lt: 5.902 (5.773) Accm: 3.13 (3.26) Acct: 4.89 (5.06) proj_loss: -0.6055 (-0.6016) time: 0.6787 data: 0.0013 [11-25 01:20:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.702 (6.705) Lt: 5.975 (5.980) Accm: 2.94 (2.84) Acct: 4.55 (4.34) proj_loss: -0.5887 (-0.5864) time: 0.6787 data: 0.0020 [11-25 01:20:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 145/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.556 (6.547) Lt: 5.764 (5.788) Accm: 3.26 (3.27) Acct: 5.08 (5.19) proj_loss: -0.5938 (-0.5928) time: 0.6787 data: 0.0016 [11-25 01:20:48] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 145/350] Total time: 0:19:17 (0.693 s / it) [11-25 01:20:48] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 145/350] Total time: 0:19:17 (0.693 s / it) [11-25 01:20:48] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 145/350] Total time: 0:19:17 (0.693 s / it) [11-25 01:20:48] (/home/user/VAR/train.py , line 276)=> [ep145] (training ) Lm: 6.557 (6.579), Lt: 5.802 (5.829), Acc m&t: 3.26 5.15, Remain: 2 days, 16:33:40, Finish: 2024-11-27 01:54 [11-25 01:20:48] (/home/user/VAR/train.py , line 276)=> [ep145] (training ) Lm: 6.557 (6.579), Lt: 5.802 (5.829), Acc m&t: 3.26 5.15, Remain: 2 days, 16:33:51, Finish: 2024-11-27 01:54 [11-25 01:20:48] (/home/user/VAR/train.py , line 276)=> [ep145] (training ) Lm: 6.557 (6.579), Lt: 5.802 (5.829), Acc m&t: 3.26 5.15, Remain: 2 days, 16:34:53, Finish: 2024-11-27 01:55 [11-25 01:20:48] (/home/user/VAR/train.py , line 276)=> [ep145] (training ) Lm: 6.557 (6.579), Lt: 5.802 (5.829), Acc m&t: 3.26 5.15, Remain: 2 days, 16:34:12, Finish: 2024-11-27 01:55 [11-25 01:20:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [ 0/1669] eta: 0:18:33 tlr: 0.00015 tnm: 0.33 Lm: 6.610 (6.610) Lt: 5.902 (5.902) Accm: 3.04 (3.04) Acct: 4.65 (4.65) proj_loss: -0.5921 (-0.5921) time: 0.6674 data: 0.0003 [11-25 01:20:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [ 0/1669] eta: 0:18:35 tlr: 0.00015 tnm: 0.33 Lm: 6.476 (6.476) Lt: 5.684 (5.684) Accm: 3.65 (3.65) Acct: 5.89 (5.89) proj_loss: -0.6032 (-0.6032) time: 0.6682 data: 0.0004 [11-25 01:20:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [ 0/1669] eta: 0:18:35 tlr: 0.00015 tnm: 0.33 Lm: 6.609 (6.609) Lt: 5.881 (5.881) Accm: 2.99 (2.99) Acct: 4.63 (4.63) proj_loss: -0.5922 (-0.5922) time: 0.6681 data: 0.0004 [11-25 01:20:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [ 0/1669] eta: 0:18:35 tlr: 0.00015 tnm: 0.33 Lm: 6.652 (6.652) Lt: 5.917 (5.917) Accm: 3.04 (3.04) Acct: 5.17 (5.17) proj_loss: -0.5813 (-0.5813) time: 0.6684 data: 0.0004 [11-25 01:25:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.33 Lm: 6.604 (6.604) Lt: 5.861 (5.861) Accm: 3.15 (3.15) Acct: 5.17 (5.17) proj_loss: -0.5874 (-0.5874) time: 0.6769 data: 0.0003 [11-25 01:25:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.33 Lm: 6.529 (6.529) Lt: 5.755 (5.755) Accm: 3.55 (3.55) Acct: 5.64 (5.64) proj_loss: -0.6062 (-0.6062) time: 0.6769 data: 0.0003 [11-25 01:25:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.33 Lm: 6.490 (6.490) Lt: 5.739 (5.739) Accm: 3.29 (3.29) Acct: 5.09 (5.09) proj_loss: -0.5905 (-0.5905) time: 0.6769 data: 0.0003 [11-25 01:25:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.33 Lm: 6.585 (6.585) Lt: 5.807 (5.807) Accm: 3.16 (3.16) Acct: 5.11 (5.11) proj_loss: -0.5777 (-0.5777) time: 0.6769 data: 0.0003 [11-25 01:30:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [ 834/1669] eta: 0:09:24 tlr: 0.00015 tnm: 0.33 Lm: 6.561 (6.540) Lt: 5.733 (5.759) Accm: 3.32 (3.31) Acct: 5.58 (5.39) proj_loss: -0.5922 (-0.5844) time: 0.6792 data: 0.0003 [11-25 01:30:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [ 834/1669] eta: 0:09:24 tlr: 0.00015 tnm: 0.33 Lm: 6.492 (6.491) Lt: 5.765 (5.748) Accm: 3.55 (3.41) Acct: 5.53 (5.34) proj_loss: -0.5889 (-0.5870) time: 0.6792 data: 0.0003 [11-25 01:30:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [ 834/1669] eta: 0:09:24 tlr: 0.00015 tnm: 0.33 Lm: 6.583 (6.564) Lt: 5.827 (5.790) Accm: 3.45 (3.29) Acct: 5.39 (5.27) proj_loss: -0.6032 (-0.5984) time: 0.6792 data: 0.0003 [11-25 01:30:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [ 834/1669] eta: 0:09:24 tlr: 0.00015 tnm: 0.33 Lm: 6.652 (6.627) Lt: 5.917 (5.884) Accm: 3.09 (3.13) Acct: 5.17 (5.06) proj_loss: -0.5923 (-0.5890) time: 0.6792 data: 0.0003 [11-25 01:34:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.644 (6.629) Lt: 5.919 (5.893) Accm: 3.17 (3.16) Acct: 5.11 (5.06) proj_loss: -0.5868 (-0.5855) time: 0.6744 data: 0.0003 [11-25 01:34:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.585 (6.588) Lt: 5.807 (5.830) Accm: 3.16 (3.22) Acct: 5.21 (5.26) proj_loss: -0.5951 (-0.5908) time: 0.6744 data: 0.0003 [11-25 01:34:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.521 (6.506) Lt: 5.796 (5.768) Accm: 3.49 (3.42) Acct: 5.32 (5.28) proj_loss: -0.5905 (-0.5914) time: 0.6744 data: 0.0003 [11-25 01:34:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.531 (6.543) Lt: 5.755 (5.763) Accm: 3.35 (3.27) Acct: 5.29 (5.25) proj_loss: -0.6016 (-0.5988) time: 0.6744 data: 0.0003 [11-25 01:39:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.30 Lm: 6.568 (6.548) Lt: 5.762 (5.763) Accm: 3.45 (3.32) Acct: 5.39 (5.34) proj_loss: -0.6023 (-0.5995) time: 0.6797 data: 0.0020 [11-25 01:39:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 146/350] Total time: 0:18:52 (0.678 s / it) [11-25 01:39:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.30 Lm: 6.636 (6.590) Lt: 5.917 (5.850) Accm: 3.24 (3.21) Acct: 5.17 (5.14) proj_loss: -0.5923 (-0.5905) time: 0.6797 data: 0.0017 [11-25 01:39:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.30 Lm: 6.550 (6.519) Lt: 5.824 (5.779) Accm: 3.43 (3.36) Acct: 5.11 (5.17) proj_loss: -0.5921 (-0.5945) time: 0.6797 data: 0.0014 [11-25 01:39:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 146/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.30 Lm: 6.607 (6.592) Lt: 5.881 (5.851) Accm: 3.09 (3.19) Acct: 4.84 (5.12) proj_loss: -0.5980 (-0.5966) time: 0.6797 data: 0.0021 [11-25 01:39:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 146/350] Total time: 0:18:52 (0.678 s / it) [11-25 01:39:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 146/350] Total time: 0:18:52 (0.678 s / it) [11-25 01:39:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 146/350] Total time: 0:18:52 (0.678 s / it) [11-25 01:39:40] (/home/user/VAR/train.py , line 276)=> [ep146] (training ) Lm: 6.550 (6.550), Lt: 5.794 (5.794), Acc m&t: 3.28 5.17, Remain: 2 days, 16:22:10, Finish: 2024-11-27 02:01 [11-25 01:39:40] (/home/user/VAR/train.py , line 276)=> [ep146] (training ) Lm: 6.550 (6.550), Lt: 5.794 (5.794), Acc m&t: 3.28 5.17, Remain: 2 days, 16:21:43, Finish: 2024-11-27 02:01 [11-25 01:39:40] (/home/user/VAR/train.py , line 276)=> [ep146] (training ) Lm: 6.550 (6.550), Lt: 5.794 (5.794), Acc m&t: 3.28 5.17, Remain: 2 days, 16:16:26, Finish: 2024-11-27 01:56 [11-25 01:39:40] (/home/user/VAR/train.py , line 276)=> [ep146] (training ) Lm: 6.550 (6.550), Lt: 5.794 (5.794), Acc m&t: 3.28 5.17, Remain: 2 days, 16:21:49, Finish: 2024-11-27 02:01 [11-25 01:39:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [ 0/1669] eta: 0:18:18 tlr: 0.00015 tnm: 0.32 Lm: 6.673 (6.673) Lt: 5.878 (5.878) Accm: 3.13 (3.13) Acct: 4.98 (4.98) proj_loss: -0.5800 (-0.5800) time: 0.6581 data: 0.0004 [11-25 01:39:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [ 0/1669] eta: 0:18:18 tlr: 0.00015 tnm: 0.32 Lm: 6.610 (6.610) Lt: 5.872 (5.872) Accm: 3.02 (3.02) Acct: 4.77 (4.77) proj_loss: -0.5839 (-0.5839) time: 0.6584 data: 0.0004 [11-25 01:39:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [ 0/1669] eta: 0:18:19 tlr: 0.00015 tnm: 0.32 Lm: 6.523 (6.523) Lt: 5.770 (5.770) Accm: 3.55 (3.55) Acct: 5.48 (5.48) proj_loss: -0.5695 (-0.5695) time: 0.6588 data: 0.0004 [11-25 01:39:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [ 0/1669] eta: 0:18:19 tlr: 0.00015 tnm: 0.32 Lm: 6.598 (6.598) Lt: 5.851 (5.851) Accm: 3.16 (3.16) Acct: 4.82 (4.82) proj_loss: -0.6008 (-0.6008) time: 0.6588 data: 0.0003 [11-25 01:44:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [ 417/1669] eta: 0:15:22 tlr: 0.00015 tnm: 0.31 Lm: 6.588 (6.588) Lt: 5.824 (5.824) Accm: 3.08 (3.08) Acct: 4.78 (4.78) proj_loss: -0.5901 (-0.5901) time: 0.6758 data: 0.0003 [11-25 01:44:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [ 417/1669] eta: 0:15:22 tlr: 0.00015 tnm: 0.31 Lm: 6.484 (6.484) Lt: 5.721 (5.721) Accm: 3.47 (3.47) Acct: 5.31 (5.31) proj_loss: -0.5774 (-0.5774) time: 0.6758 data: 0.0003 [11-25 01:44:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [ 417/1669] eta: 0:15:22 tlr: 0.00015 tnm: 0.31 Lm: 6.616 (6.616) Lt: 5.827 (5.827) Accm: 3.07 (3.07) Acct: 4.98 (4.98) proj_loss: -0.5864 (-0.5864) time: 0.6758 data: 0.0003 [11-25 01:44:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [ 417/1669] eta: 0:15:22 tlr: 0.00015 tnm: 0.31 Lm: 6.612 (6.612) Lt: 5.867 (5.867) Accm: 3.02 (3.02) Acct: 4.78 (4.78) proj_loss: -0.5936 (-0.5936) time: 0.6758 data: 0.0003 [11-25 01:49:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.32 Lm: 6.610 (6.609) Lt: 5.862 (5.859) Accm: 3.02 (3.10) Acct: 4.79 (4.89) proj_loss: -0.5861 (-0.5911) time: 0.6752 data: 0.0003 [11-25 01:49:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.32 Lm: 6.523 (6.506) Lt: 5.770 (5.745) Accm: 3.40 (3.35) Acct: 5.15 (5.18) proj_loss: -0.5852 (-0.5849) time: 0.6752 data: 0.0003 [11-25 01:49:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.32 Lm: 6.566 (6.599) Lt: 5.775 (5.803) Accm: 3.13 (3.10) Acct: 4.98 (4.98) proj_loss: -0.5800 (-0.5772) time: 0.6752 data: 0.0003 [11-25 01:49:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.32 Lm: 6.598 (6.592) Lt: 5.811 (5.819) Accm: 3.15 (3.11) Acct: 4.73 (4.76) proj_loss: -0.5848 (-0.5883) time: 0.6753 data: 0.0003 [11-25 01:54:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.35 Lm: 6.588 (6.551) Lt: 5.804 (5.778) Accm: 3.16 (3.19) Acct: 4.78 (4.90) proj_loss: -0.5919 (-0.5910) time: 0.6778 data: 0.0003 [11-25 01:54:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.35 Lm: 6.612 (6.626) Lt: 5.867 (5.894) Accm: 3.02 (3.07) Acct: 4.78 (4.82) proj_loss: -0.5947 (-0.5944) time: 0.6778 data: 0.0003 [11-25 01:54:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.35 Lm: 6.619 (6.645) Lt: 5.827 (5.857) Accm: 3.07 (3.01) Acct: 4.98 (4.86) proj_loss: -0.5864 (-0.5815) time: 0.6778 data: 0.0003 [11-25 01:54:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.35 Lm: 6.498 (6.498) Lt: 5.773 (5.753) Accm: 3.35 (3.34) Acct: 5.12 (5.16) proj_loss: -0.5926 (-0.5899) time: 0.6778 data: 0.0003 [11-25 01:58:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.32 Lm: 6.598 (6.566) Lt: 5.811 (5.795) Accm: 3.15 (3.16) Acct: 4.75 (4.87) proj_loss: -0.5906 (-0.5909) time: 0.6774 data: 0.0017 [11-25 01:58:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.32 Lm: 6.673 (6.652) Lt: 5.878 (5.873) Accm: 3.02 (3.00) Acct: 4.98 (4.81) proj_loss: -0.5841 (-0.5820) time: 0.6774 data: 0.0014 [11-25 01:58:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.32 Lm: 6.610 (6.610) Lt: 5.862 (5.882) Accm: 3.02 (3.15) Acct: 4.79 (4.96) proj_loss: -0.6032 (-0.5962) time: 0.6774 data: 0.0019 [11-25 01:58:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 147/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.32 Lm: 6.523 (6.524) Lt: 5.775 (5.777) Accm: 3.29 (3.31) Acct: 5.10 (5.11) proj_loss: -0.6000 (-0.5923) time: 0.6774 data: 0.0019 [11-25 01:58:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 147/350] Total time: 0:19:15 (0.692 s / it) [11-25 01:58:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 147/350] Total time: 0:19:15 (0.692 s / it) [11-25 01:58:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 147/350] Total time: 0:19:15 (0.692 s / it) [11-25 01:58:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 147/350] Total time: 0:19:15 (0.692 s / it) [11-25 01:58:56] (/home/user/VAR/train.py , line 276)=> [ep147] (training ) Lm: 6.550 (6.557), Lt: 5.794 (5.799), Acc m&t: 3.28 5.17, Remain: 2 days, 15:53:25, Finish: 2024-11-27 01:52 [11-25 01:58:56] (/home/user/VAR/train.py , line 276)=> [ep147] (training ) Lm: 6.550 (6.557), Lt: 5.794 (5.799), Acc m&t: 3.28 5.17, Remain: 2 days, 15:53:33, Finish: 2024-11-27 01:52 [11-25 01:58:56] (/home/user/VAR/train.py , line 276)=> [ep147] (training ) Lm: 6.550 (6.557), Lt: 5.794 (5.799), Acc m&t: 3.28 5.17, Remain: 2 days, 15:52:34, Finish: 2024-11-27 01:51 [11-25 01:58:56] (/home/user/VAR/train.py , line 276)=> [ep147] (training ) Lm: 6.550 (6.557), Lt: 5.794 (5.799), Acc m&t: 3.28 5.17, Remain: 2 days, 15:53:05, Finish: 2024-11-27 01:52 [11-25 01:58:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [ 0/1669] eta: 0:18:38 tlr: 0.00015 tnm: 0.32 Lm: 6.592 (6.592) Lt: 5.853 (5.853) Accm: 2.94 (2.94) Acct: 4.68 (4.68) proj_loss: -0.5864 (-0.5864) time: 0.6703 data: 0.0004 [11-25 01:58:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [ 0/1669] eta: 0:19:33 tlr: 0.00015 tnm: 0.32 Lm: 6.560 (6.560) Lt: 5.774 (5.774) Accm: 3.21 (3.21) Acct: 5.35 (5.35) proj_loss: -0.5753 (-0.5753) time: 0.7034 data: 0.0004 [11-25 01:58:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [ 0/1669] eta: 0:19:34 tlr: 0.00015 tnm: 0.32 Lm: 6.387 (6.387) Lt: 5.562 (5.562) Accm: 3.55 (3.55) Acct: 5.91 (5.91) proj_loss: -0.6091 (-0.6091) time: 0.7039 data: 0.0003 [11-25 01:58:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [ 0/1669] eta: 0:19:34 tlr: 0.00015 tnm: 0.32 Lm: 6.565 (6.565) Lt: 5.819 (5.819) Accm: 3.33 (3.33) Acct: 5.25 (5.25) proj_loss: -0.6074 (-0.6074) time: 0.7039 data: 0.0003 [11-25 02:03:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [ 417/1669] eta: 0:14:07 tlr: 0.00015 tnm: 0.31 Lm: 6.478 (6.478) Lt: 5.726 (5.726) Accm: 3.50 (3.50) Acct: 5.44 (5.44) proj_loss: -0.6104 (-0.6104) time: 0.6762 data: 0.0003 [11-25 02:03:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [ 417/1669] eta: 0:14:07 tlr: 0.00015 tnm: 0.31 Lm: 6.610 (6.610) Lt: 5.859 (5.859) Accm: 2.98 (2.98) Acct: 4.77 (4.77) proj_loss: -0.5817 (-0.5817) time: 0.6762 data: 0.0003 [11-25 02:03:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [ 417/1669] eta: 0:14:07 tlr: 0.00015 tnm: 0.31 Lm: 6.413 (6.413) Lt: 5.624 (5.624) Accm: 3.45 (3.45) Acct: 5.58 (5.58) proj_loss: -0.5979 (-0.5979) time: 0.6762 data: 0.0003 [11-25 02:03:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [ 417/1669] eta: 0:14:07 tlr: 0.00015 tnm: 0.31 Lm: 6.586 (6.586) Lt: 5.848 (5.848) Accm: 3.13 (3.13) Acct: 4.98 (4.98) proj_loss: -0.5788 (-0.5788) time: 0.6762 data: 0.0003 [11-25 02:08:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [ 834/1669] eta: 0:09:51 tlr: 0.00015 tnm: 0.32 Lm: 6.587 (6.587) Lt: 5.798 (5.831) Accm: 3.21 (3.16) Acct: 5.06 (5.00) proj_loss: -0.5808 (-0.5794) time: 0.8039 data: 0.0003 [11-25 02:08:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [ 834/1669] eta: 0:09:51 tlr: 0.00015 tnm: 0.32 Lm: 6.629 (6.637) Lt: 5.864 (5.907) Accm: 2.98 (2.98) Acct: 4.68 (4.69) proj_loss: -0.5864 (-0.5853) time: 0.8039 data: 0.0003 [11-25 02:08:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [ 834/1669] eta: 0:09:51 tlr: 0.00015 tnm: 0.32 Lm: 6.439 (6.482) Lt: 5.686 (5.732) Accm: 3.36 (3.34) Acct: 5.25 (5.23) proj_loss: -0.6033 (-0.5997) time: 0.8039 data: 0.0003 [11-25 02:08:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [ 834/1669] eta: 0:09:51 tlr: 0.00015 tnm: 0.32 Lm: 6.494 (6.484) Lt: 5.719 (5.723) Accm: 3.65 (3.55) Acct: 5.63 (5.60) proj_loss: -0.6074 (-0.6008) time: 0.8039 data: 0.0003 [11-25 02:13:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.31 Lm: 6.483 (6.481) Lt: 5.687 (5.706) Accm: 3.66 (3.60) Acct: 5.73 (5.66) proj_loss: -0.6000 (-0.5988) time: 0.6763 data: 0.0003 [11-25 02:13:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.31 Lm: 6.628 (6.634) Lt: 5.859 (5.892) Accm: 2.96 (2.95) Acct: 4.61 (4.65) proj_loss: -0.5873 (-0.5860) time: 0.6763 data: 0.0003 [11-25 02:13:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.31 Lm: 6.592 (6.589) Lt: 5.821 (5.834) Accm: 3.21 (3.17) Acct: 5.09 (5.03) proj_loss: -0.5815 (-0.5847) time: 0.6763 data: 0.0003 [11-25 02:13:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.31 Lm: 6.529 (6.540) Lt: 5.809 (5.782) Accm: 3.24 (3.17) Acct: 4.89 (5.04) proj_loss: -0.5950 (-0.5943) time: 0.6763 data: 0.0003 [11-25 02:18:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.35 Lm: 6.526 (6.537) Lt: 5.762 (5.778) Accm: 3.35 (3.20) Acct: 5.11 (5.05) proj_loss: -0.6016 (-0.5957) time: 0.6791 data: 0.0016 [11-25 02:18:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 148/350] Total time: 0:19:17 (0.693 s / it) [11-25 02:18:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.35 Lm: 6.479 (6.480) Lt: 5.719 (5.714) Accm: 3.67 (3.66) Acct: 5.84 (5.79) proj_loss: -0.5925 (-0.5941) time: 0.6791 data: 0.0020 [11-25 02:18:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.35 Lm: 6.597 (6.596) Lt: 5.844 (5.843) Accm: 3.21 (3.18) Acct: 5.06 (4.97) proj_loss: -0.5822 (-0.5849) time: 0.6791 data: 0.0019 [11-25 02:18:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 148/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.35 Lm: 6.629 (6.647) Lt: 5.864 (5.904) Accm: 2.95 (2.95) Acct: 4.56 (4.63) proj_loss: -0.5881 (-0.5932) time: 0.6791 data: 0.0013 [11-25 02:18:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 148/350] Total time: 0:19:17 (0.693 s / it) [11-25 02:18:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 148/350] Total time: 0:19:17 (0.693 s / it) [11-25 02:18:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 148/350] Total time: 0:19:17 (0.693 s / it) [11-25 02:18:13] (/home/user/VAR/train.py , line 276)=> [ep148] (training ) Lm: 6.550 (6.550), Lt: 5.794 (5.794), Acc m&t: 3.28 5.17, Remain: 2 days, 15:39:04, Finish: 2024-11-27 01:57 [11-25 02:18:13] (/home/user/VAR/train.py , line 276)=> [ep148] (training ) Lm: 6.550 (6.550), Lt: 5.794 (5.794), Acc m&t: 3.28 5.17, Remain: 2 days, 15:40:32, Finish: 2024-11-27 01:58 [11-25 02:18:13] (/home/user/VAR/train.py , line 276)=> [ep148] (training ) Lm: 6.550 (6.550), Lt: 5.794 (5.794), Acc m&t: 3.28 5.17, Remain: 2 days, 15:39:39, Finish: 2024-11-27 01:57 [11-25 02:18:13] (/home/user/VAR/train.py , line 276)=> [ep148] (training ) Lm: 6.550 (6.550), Lt: 5.794 (5.794), Acc m&t: 3.28 5.17, Remain: 2 days, 15:41:39, Finish: 2024-11-27 01:59 [11-25 02:18:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [ 0/1669] eta: 0:18:34 tlr: 0.00015 tnm: 0.32 Lm: 6.526 (6.526) Lt: 5.841 (5.841) Accm: 3.29 (3.29) Acct: 5.10 (5.10) proj_loss: -0.6203 (-0.6203) time: 0.6676 data: 0.0004 [11-25 02:18:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [ 0/1669] eta: 0:18:43 tlr: 0.00015 tnm: 0.32 Lm: 6.621 (6.621) Lt: 5.920 (5.920) Accm: 2.81 (2.81) Acct: 4.32 (4.32) proj_loss: -0.5679 (-0.5679) time: 0.6730 data: 0.0004 [11-25 02:18:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [ 0/1669] eta: 0:18:24 tlr: 0.00015 tnm: 0.32 Lm: 6.561 (6.561) Lt: 5.815 (5.815) Accm: 3.20 (3.20) Acct: 5.01 (5.01) proj_loss: -0.6072 (-0.6072) time: 0.6617 data: 0.0003 [11-25 02:18:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [ 0/1669] eta: 0:18:38 tlr: 0.00015 tnm: 0.32 Lm: 6.570 (6.570) Lt: 5.791 (5.791) Accm: 3.08 (3.08) Acct: 5.01 (5.01) proj_loss: -0.5967 (-0.5967) time: 0.6700 data: 0.0004 [11-25 02:22:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.33 Lm: 6.577 (6.577) Lt: 5.803 (5.803) Accm: 3.08 (3.08) Acct: 4.94 (4.94) proj_loss: -0.6081 (-0.6081) time: 0.6756 data: 0.0003 [11-25 02:22:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.33 Lm: 6.644 (6.644) Lt: 5.911 (5.911) Accm: 2.94 (2.94) Acct: 4.55 (4.55) proj_loss: -0.5735 (-0.5735) time: 0.6756 data: 0.0005 [11-25 02:22:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.33 Lm: 6.487 (6.487) Lt: 5.775 (5.775) Accm: 3.44 (3.44) Acct: 5.43 (5.43) proj_loss: -0.6086 (-0.6086) time: 0.6756 data: 0.0003 [11-25 02:22:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.33 Lm: 6.620 (6.620) Lt: 5.899 (5.899) Accm: 3.04 (3.04) Acct: 4.86 (4.86) proj_loss: -0.6194 (-0.6194) time: 0.6756 data: 0.0003 [11-25 02:27:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [ 834/1669] eta: 0:09:24 tlr: 0.00015 tnm: 0.32 Lm: 6.561 (6.597) Lt: 5.815 (5.855) Accm: 3.17 (3.08) Acct: 5.01 (4.91) proj_loss: -0.6072 (-0.6150) time: 0.6758 data: 0.0003 [11-25 02:27:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [ 834/1669] eta: 0:09:24 tlr: 0.00015 tnm: 0.32 Lm: 6.621 (6.623) Lt: 5.901 (5.890) Accm: 2.88 (2.92) Acct: 4.51 (4.54) proj_loss: -0.5791 (-0.5774) time: 0.6758 data: 0.0003 [11-25 02:27:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [ 834/1669] eta: 0:09:24 tlr: 0.00015 tnm: 0.32 Lm: 6.570 (6.524) Lt: 5.791 (5.753) Accm: 3.08 (3.31) Acct: 5.01 (5.24) proj_loss: -0.5967 (-0.6035) time: 0.6758 data: 0.0003 [11-25 02:27:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [ 834/1669] eta: 0:09:24 tlr: 0.00015 tnm: 0.32 Lm: 6.507 (6.494) Lt: 5.717 (5.756) Accm: 3.59 (3.52) Acct: 5.77 (5.65) proj_loss: -0.5969 (-0.6044) time: 0.6758 data: 0.0003 [11-25 02:32:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.507 (6.497) Lt: 5.713 (5.741) Accm: 3.52 (3.50) Acct: 5.58 (5.59) proj_loss: -0.5965 (-0.6023) time: 0.6772 data: 0.0003 [11-25 02:32:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.515 (6.508) Lt: 5.721 (5.719) Accm: 3.33 (3.38) Acct: 5.33 (5.34) proj_loss: -0.5956 (-0.5993) time: 0.6772 data: 0.0003 [11-25 02:32:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.601 (6.586) Lt: 5.874 (5.835) Accm: 2.97 (3.06) Acct: 4.65 (4.84) proj_loss: -0.5821 (-0.5825) time: 0.6772 data: 0.0003 [11-25 02:32:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.576 (6.596) Lt: 5.821 (5.848) Accm: 3.03 (3.00) Acct: 4.86 (4.80) proj_loss: -0.6068 (-0.6090) time: 0.6772 data: 0.0003 [11-25 02:37:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.34 Lm: 6.561 (6.580) Lt: 5.815 (5.821) Accm: 3.17 (3.10) Acct: 5.01 (4.99) proj_loss: -0.6063 (-0.6053) time: 0.6787 data: 0.0015 [11-25 02:37:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 149/350] Total time: 0:18:52 (0.679 s / it) [11-25 02:37:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.34 Lm: 6.559 (6.518) Lt: 5.784 (5.732) Accm: 3.19 (3.34) Acct: 5.01 (5.26) proj_loss: -0.5944 (-0.5976) time: 0.6787 data: 0.0017 [11-25 02:37:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.34 Lm: 6.507 (6.489) Lt: 5.709 (5.708) Accm: 3.45 (3.47) Acct: 5.39 (5.54) proj_loss: -0.5961 (-0.6010) time: 0.6787 data: 0.0017 [11-25 02:37:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 149/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.34 Lm: 6.581 (6.564) Lt: 5.848 (5.797) Accm: 3.06 (3.16) Acct: 4.79 (5.02) proj_loss: -0.5851 (-0.5847) time: 0.6787 data: 0.0019 [11-25 02:37:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 149/350] Total time: 0:18:52 (0.679 s / it) [11-25 02:37:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 149/350] Total time: 0:18:52 (0.679 s / it) [11-25 02:37:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 149/350] Total time: 0:18:52 (0.679 s / it) [11-25 02:39:30] (home/user/VAR/trainer.py, line 114)=> FID: 3.684973436701341 [11-25 02:39:31] (/home/user/VAR/train.py , line 259)=> [*] [ep149] (val 50000) Lm: 6.5438, Lt: 5.7829, Acc m&t: 3.27 5.18, Val cost: 144.40s [11-25 02:39:31] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-25 02:40:06] (/home/user/VAR/train.py , line 276)=> [ep149] (training ) Lm: 6.544 (6.544), Lt: 5.783 (5.783), Acc m&t: 3.28 5.18, Remain: 2 days, 15:11:57, Finish: 2024-11-27 01:49 [11-25 02:40:06] (/home/user/VAR/train.py , line 276)=> [ep149] (training ) Lm: 6.544 (6.544), Lt: 5.783 (5.783), Acc m&t: 3.28 5.18, Remain: 2 days, 15:11:36, Finish: 2024-11-27 01:48 [11-25 02:40:06] (/home/user/VAR/train.py , line 276)=> [ep149] (training ) Lm: 6.544 (6.544), Lt: 5.783 (5.783), Acc m&t: 3.28 5.18, Remain: 2 days, 15:12:05, Finish: 2024-11-27 01:49 [11-25 02:40:06] (/home/user/VAR/train.py , line 276)=> [ep149] (training ) Lm: 6.544 (6.544), Lt: 5.783 (5.783), Acc m&t: 3.28 5.18, Remain: 2 days, 15:12:11, Finish: 2024-11-27 01:49 [11-25 02:40:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [ 0/1669] eta: 0:18:50 tlr: 0.00015 tnm: 0.34 Lm: 6.528 (6.528) Lt: 5.755 (5.755) Accm: 3.35 (3.35) Acct: 5.41 (5.41) proj_loss: -0.5947 (-0.5947) time: 0.6772 data: 0.0004 [11-25 02:40:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [ 0/1669] eta: 0:18:50 tlr: 0.00015 tnm: 0.34 Lm: 6.482 (6.482) Lt: 5.813 (5.813) Accm: 3.48 (3.48) Acct: 5.27 (5.27) proj_loss: -0.6148 (-0.6148) time: 0.6775 data: 0.0004 [11-25 02:40:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [ 0/1669] eta: 0:18:49 tlr: 0.00015 tnm: 0.34 Lm: 6.492 (6.492) Lt: 5.762 (5.762) Accm: 3.14 (3.14) Acct: 5.03 (5.03) proj_loss: -0.6060 (-0.6060) time: 0.6766 data: 0.0004 [11-25 02:40:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [ 0/1669] eta: 0:18:49 tlr: 0.00015 tnm: 0.34 Lm: 6.621 (6.621) Lt: 5.850 (5.850) Accm: 3.01 (3.01) Acct: 4.65 (4.65) proj_loss: -0.5907 (-0.5907) time: 0.6770 data: 0.0004 [11-25 02:45:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [ 417/1669] eta: 0:15:22 tlr: 0.00015 tnm: 0.33 Lm: 6.600 (6.600) Lt: 5.847 (5.847) Accm: 3.11 (3.11) Acct: 4.78 (4.78) proj_loss: -0.5932 (-0.5932) time: 0.6761 data: 0.0003 [11-25 02:45:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [ 417/1669] eta: 0:15:23 tlr: 0.00015 tnm: 0.33 Lm: 6.510 (6.510) Lt: 5.795 (5.795) Accm: 3.38 (3.38) Acct: 5.17 (5.17) proj_loss: -0.6013 (-0.6013) time: 0.6761 data: 0.0003 [11-25 02:45:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [ 417/1669] eta: 0:15:22 tlr: 0.00015 tnm: 0.33 Lm: 6.522 (6.522) Lt: 5.784 (5.784) Accm: 3.29 (3.29) Acct: 5.29 (5.29) proj_loss: -0.6101 (-0.6101) time: 0.6761 data: 0.0003 [11-25 02:45:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [ 417/1669] eta: 0:15:23 tlr: 0.00015 tnm: 0.33 Lm: 6.568 (6.568) Lt: 5.829 (5.829) Accm: 3.22 (3.22) Acct: 5.07 (5.07) proj_loss: -0.5999 (-0.5999) time: 0.6761 data: 0.0003 [11-25 02:49:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.34 Lm: 6.608 (6.610) Lt: 5.902 (5.882) Accm: 3.08 (3.03) Acct: 4.73 (4.81) proj_loss: -0.5947 (-0.5900) time: 0.6776 data: 0.0003 [11-25 02:49:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.34 Lm: 6.492 (6.449) Lt: 5.762 (5.692) Accm: 3.44 (3.62) Acct: 5.54 (5.76) proj_loss: -0.6060 (-0.6033) time: 0.6776 data: 0.0003 [11-25 02:49:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.34 Lm: 6.539 (6.542) Lt: 5.813 (5.812) Accm: 3.31 (3.36) Acct: 5.08 (5.14) proj_loss: -0.6082 (-0.6036) time: 0.6776 data: 0.0003 [11-25 02:49:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.34 Lm: 6.621 (6.607) Lt: 5.850 (5.874) Accm: 3.09 (3.11) Acct: 4.73 (4.76) proj_loss: -0.5907 (-0.5923) time: 0.6776 data: 0.0003 [11-25 02:54:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.33 Lm: 6.600 (6.577) Lt: 5.847 (5.828) Accm: 3.15 (3.17) Acct: 4.82 (4.95) proj_loss: -0.5906 (-0.5896) time: 0.6786 data: 0.0003 [11-25 02:54:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.33 Lm: 6.517 (6.472) Lt: 5.742 (5.699) Accm: 3.30 (3.51) Acct: 5.41 (5.64) proj_loss: -0.5979 (-0.5979) time: 0.6786 data: 0.0003 [11-25 02:54:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.33 Lm: 6.572 (6.558) Lt: 5.823 (5.817) Accm: 3.30 (3.29) Acct: 5.07 (5.08) proj_loss: -0.6081 (-0.6047) time: 0.6786 data: 0.0003 [11-25 02:54:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.33 Lm: 6.568 (6.586) Lt: 5.829 (5.830) Accm: 3.19 (3.10) Acct: 5.02 (4.93) proj_loss: -0.5894 (-0.5885) time: 0.6786 data: 0.0003 [11-25 02:59:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.34 Lm: 6.528 (6.574) Lt: 5.783 (5.820) Accm: 3.27 (3.13) Acct: 4.96 (4.94) proj_loss: -0.5947 (-0.5914) time: 0.6776 data: 0.0016 [11-25 02:59:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 150/350] Total time: 0:19:14 (0.692 s / it) [11-25 02:59:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.34 Lm: 6.605 (6.596) Lt: 5.833 (5.850) Accm: 3.29 (3.13) Acct: 5.06 (4.83) proj_loss: -0.6080 (-0.6007) time: 0.6776 data: 0.0016 [11-25 02:59:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.34 Lm: 6.579 (6.571) Lt: 5.850 (5.834) Accm: 3.22 (3.18) Acct: 4.84 (4.93) proj_loss: -0.5907 (-0.5939) time: 0.6776 data: 0.0014 [11-25 02:59:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 150/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.34 Lm: 6.541 (6.493) Lt: 5.762 (5.718) Accm: 3.17 (3.43) Acct: 5.27 (5.52) proj_loss: -0.5898 (-0.5951) time: 0.6776 data: 0.0016 [11-25 02:59:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 150/350] Total time: 0:19:14 (0.692 s / it) [11-25 02:59:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 150/350] Total time: 0:19:14 (0.692 s / it) [11-25 02:59:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 150/350] Total time: 0:19:14 (0.692 s / it) [11-25 02:59:21] (/home/user/VAR/train.py , line 276)=> [ep150] (training ) Lm: 6.544 (6.558), Lt: 5.783 (5.800), Acc m&t: 3.28 5.18, Remain: 2 days, 14:59:44, Finish: 2024-11-27 01:59 [11-25 02:59:21] (/home/user/VAR/train.py , line 276)=> [ep150] (training ) Lm: 6.544 (6.558), Lt: 5.783 (5.800), Acc m&t: 3.28 5.18, Remain: 2 days, 14:59:17, Finish: 2024-11-27 01:58 [11-25 02:59:21] (/home/user/VAR/train.py , line 276)=> [ep150] (training ) Lm: 6.544 (6.558), Lt: 5.783 (5.800), Acc m&t: 3.28 5.18, Remain: 2 days, 14:59:50, Finish: 2024-11-27 01:59 [11-25 02:59:21] (/home/user/VAR/train.py , line 276)=> [ep150] (training ) Lm: 6.544 (6.558), Lt: 5.783 (5.800), Acc m&t: 3.28 5.18, Remain: 2 days, 14:59:01, Finish: 2024-11-27 01:58 [11-25 02:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [ 0/1669] eta: 0:18:06 tlr: 0.00015 tnm: 0.34 Lm: 6.445 (6.445) Lt: 5.631 (5.631) Accm: 3.50 (3.50) Acct: 5.87 (5.87) proj_loss: -0.5858 (-0.5858) time: 0.6512 data: 0.0004 [11-25 02:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [ 0/1669] eta: 0:18:32 tlr: 0.00015 tnm: 0.34 Lm: 6.715 (6.715) Lt: 5.964 (5.964) Accm: 2.78 (2.78) Acct: 4.29 (4.29) proj_loss: -0.5814 (-0.5814) time: 0.6665 data: 0.0003 [11-25 02:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [ 0/1669] eta: 0:18:09 tlr: 0.00015 tnm: 0.34 Lm: 6.483 (6.483) Lt: 5.725 (5.725) Accm: 3.35 (3.35) Acct: 5.25 (5.25) proj_loss: -0.5980 (-0.5980) time: 0.6525 data: 0.0004 [11-25 02:59:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [ 0/1669] eta: 0:18:08 tlr: 0.00015 tnm: 0.34 Lm: 6.429 (6.429) Lt: 5.681 (5.681) Accm: 3.53 (3.53) Acct: 5.61 (5.61) proj_loss: -0.5819 (-0.5819) time: 0.6520 data: 0.0004 [11-25 03:04:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [ 417/1669] eta: 0:14:12 tlr: 0.00015 tnm: 0.33 Lm: 6.513 (6.513) Lt: 5.774 (5.774) Accm: 3.33 (3.33) Acct: 5.23 (5.23) proj_loss: -0.5987 (-0.5987) time: 0.7421 data: 0.0003 [11-25 03:04:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [ 417/1669] eta: 0:14:12 tlr: 0.00015 tnm: 0.33 Lm: 6.431 (6.431) Lt: 5.627 (5.627) Accm: 3.68 (3.68) Acct: 6.00 (6.00) proj_loss: -0.5855 (-0.5855) time: 0.7421 data: 0.0003 [11-25 03:04:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [ 417/1669] eta: 0:14:12 tlr: 0.00015 tnm: 0.33 Lm: 6.526 (6.526) Lt: 5.772 (5.772) Accm: 3.48 (3.48) Acct: 5.35 (5.35) proj_loss: -0.5806 (-0.5806) time: 0.7421 data: 0.0003 [11-25 03:04:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [ 417/1669] eta: 0:14:12 tlr: 0.00015 tnm: 0.33 Lm: 6.491 (6.491) Lt: 5.718 (5.718) Accm: 3.49 (3.49) Acct: 5.49 (5.49) proj_loss: -0.5999 (-0.5999) time: 0.7421 data: 0.0002 [11-25 03:09:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.34 Lm: 6.483 (6.483) Lt: 5.712 (5.712) Accm: 3.50 (3.49) Acct: 5.41 (5.46) proj_loss: -0.6018 (-0.6014) time: 0.8007 data: 0.0002 [11-25 03:09:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.34 Lm: 6.429 (6.438) Lt: 5.681 (5.684) Accm: 3.53 (3.55) Acct: 5.61 (5.61) proj_loss: -0.5894 (-0.5956) time: 0.8007 data: 0.0003 [11-25 03:09:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.34 Lm: 6.445 (6.452) Lt: 5.631 (5.661) Accm: 3.52 (3.63) Acct: 5.87 (5.84) proj_loss: -0.5858 (-0.6020) time: 0.8007 data: 0.0003 [11-25 03:09:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [ 834/1669] eta: 0:09:50 tlr: 0.00015 tnm: 0.34 Lm: 6.715 (6.602) Lt: 5.964 (5.844) Accm: 2.78 (3.18) Acct: 4.29 (4.94) proj_loss: -0.5798 (-0.5793) time: 0.8006 data: 0.0003 [11-25 03:13:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [1251/1669] eta: 0:04:52 tlr: 0.00015 tnm: 0.32 Lm: 6.612 (6.579) Lt: 5.826 (5.805) Accm: 3.03 (3.21) Acct: 4.83 (5.05) proj_loss: -0.5806 (-0.5842) time: 0.6776 data: 0.0003 [11-25 03:13:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [1251/1669] eta: 0:04:52 tlr: 0.00015 tnm: 0.32 Lm: 6.473 (6.458) Lt: 5.728 (5.707) Accm: 3.47 (3.52) Acct: 5.47 (5.54) proj_loss: -0.5856 (-0.5915) time: 0.6776 data: 0.0003 [11-25 03:13:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [1251/1669] eta: 0:04:52 tlr: 0.00015 tnm: 0.32 Lm: 6.471 (6.468) Lt: 5.679 (5.678) Accm: 3.51 (3.55) Acct: 5.70 (5.67) proj_loss: -0.5855 (-0.5943) time: 0.6776 data: 0.0003 [11-25 03:13:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [1251/1669] eta: 0:04:52 tlr: 0.00015 tnm: 0.32 Lm: 6.491 (6.530) Lt: 5.718 (5.770) Accm: 3.42 (3.38) Acct: 5.33 (5.31) proj_loss: -0.6023 (-0.6017) time: 0.6776 data: 0.0003 [11-25 03:18:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.33 Lm: 6.499 (6.537) Lt: 5.725 (5.778) Accm: 3.35 (3.37) Acct: 5.25 (5.23) proj_loss: -0.6018 (-0.6001) time: 0.6775 data: 0.0018 [11-25 03:18:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 151/350] Total time: 0:19:18 (0.694 s / it) [11-25 03:18:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.33 Lm: 6.510 (6.553) Lt: 5.687 (5.763) Accm: 3.29 (3.27) Acct: 5.37 (5.17) proj_loss: -0.5814 (-0.5858) time: 0.6775 data: 0.0016 [11-25 03:18:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.33 Lm: 6.473 (6.469) Lt: 5.697 (5.682) Accm: 3.51 (3.54) Acct: 5.66 (5.67) proj_loss: -0.5858 (-0.5956) time: 0.6775 data: 0.0018 [11-25 03:18:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 151/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.33 Lm: 6.518 (6.472) Lt: 5.717 (5.709) Accm: 3.42 (3.50) Acct: 5.61 (5.56) proj_loss: -0.5894 (-0.5920) time: 0.6775 data: 0.0021 [11-25 03:18:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 151/350] Total time: 0:19:18 (0.694 s / it) [11-25 03:18:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 151/350] Total time: 0:19:18 (0.694 s / it) [11-25 03:18:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 151/350] Total time: 0:19:18 (0.694 s / it) [11-25 03:18:40] (/home/user/VAR/train.py , line 276)=> [ep151] (training ) Lm: 6.544 (6.544), Lt: 5.783 (5.788), Acc m&t: 3.29 5.19, Remain: 2 days, 14:39:26, Finish: 2024-11-27 01:58 [11-25 03:18:40] (/home/user/VAR/train.py , line 276)=> [ep151] (training ) Lm: 6.544 (6.544), Lt: 5.783 (5.788), Acc m&t: 3.29 5.19, Remain: 2 days, 14:40:06, Finish: 2024-11-27 01:58 [11-25 03:18:40] (/home/user/VAR/train.py , line 276)=> [ep151] (training ) Lm: 6.544 (6.544), Lt: 5.783 (5.788), Acc m&t: 3.29 5.19, Remain: 2 days, 14:40:19, Finish: 2024-11-27 01:58 [11-25 03:18:40] (/home/user/VAR/train.py , line 276)=> [ep151] (training ) Lm: 6.544 (6.544), Lt: 5.783 (5.788), Acc m&t: 3.29 5.19, Remain: 2 days, 14:39:17, Finish: 2024-11-27 01:57 [11-25 03:18:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [ 0/1669] eta: 0:18:27 tlr: 0.00015 tnm: 0.33 Lm: 6.349 (6.349) Lt: 5.566 (5.566) Accm: 3.96 (3.96) Acct: 6.27 (6.27) proj_loss: -0.5948 (-0.5948) time: 0.6635 data: 0.0004 [11-25 03:18:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [ 0/1669] eta: 0:18:27 tlr: 0.00015 tnm: 0.33 Lm: 6.446 (6.446) Lt: 5.712 (5.712) Accm: 3.35 (3.35) Acct: 5.34 (5.34) proj_loss: -0.6027 (-0.6027) time: 0.6636 data: 0.0003 [11-25 03:18:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [ 0/1669] eta: 0:18:24 tlr: 0.00015 tnm: 0.33 Lm: 6.706 (6.706) Lt: 5.989 (5.989) Accm: 2.70 (2.70) Acct: 4.01 (4.01) proj_loss: -0.5964 (-0.5964) time: 0.6617 data: 0.0004 [11-25 03:18:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [ 0/1669] eta: 0:18:27 tlr: 0.00015 tnm: 0.33 Lm: 6.490 (6.490) Lt: 5.786 (5.786) Accm: 3.22 (3.22) Acct: 4.98 (4.98) proj_loss: -0.6063 (-0.6063) time: 0.6633 data: 0.0003 [11-25 03:23:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [ 417/1669] eta: 0:14:08 tlr: 0.00015 tnm: 0.34 Lm: 6.559 (6.559) Lt: 5.846 (5.846) Accm: 3.22 (3.22) Acct: 4.98 (4.98) proj_loss: -0.5988 (-0.5988) time: 0.6782 data: 0.0003 [11-25 03:23:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [ 417/1669] eta: 0:14:08 tlr: 0.00015 tnm: 0.34 Lm: 6.415 (6.415) Lt: 5.648 (5.648) Accm: 3.83 (3.83) Acct: 6.19 (6.19) proj_loss: -0.6011 (-0.6011) time: 0.6781 data: 0.0003 [11-25 03:23:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [ 417/1669] eta: 0:14:08 tlr: 0.00015 tnm: 0.34 Lm: 6.610 (6.610) Lt: 5.874 (5.874) Accm: 2.99 (2.99) Acct: 4.61 (4.61) proj_loss: -0.6056 (-0.6056) time: 0.6781 data: 0.0003 [11-25 03:23:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [ 417/1669] eta: 0:14:08 tlr: 0.00015 tnm: 0.34 Lm: 6.445 (6.445) Lt: 5.677 (5.677) Accm: 3.51 (3.51) Acct: 5.56 (5.56) proj_loss: -0.5955 (-0.5955) time: 0.6781 data: 0.0003 [11-25 03:28:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [ 834/1669] eta: 0:09:25 tlr: 0.00015 tnm: 0.34 Lm: 6.446 (6.468) Lt: 5.712 (5.725) Accm: 3.35 (3.37) Acct: 5.34 (5.26) proj_loss: -0.5895 (-0.5935) time: 0.6790 data: 0.0003 [11-25 03:28:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [ 834/1669] eta: 0:09:25 tlr: 0.00015 tnm: 0.34 Lm: 6.627 (6.582) Lt: 5.889 (5.860) Accm: 3.22 (3.21) Acct: 4.98 (5.02) proj_loss: -0.6063 (-0.6061) time: 0.6790 data: 0.0003 [11-25 03:28:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [ 834/1669] eta: 0:09:25 tlr: 0.00015 tnm: 0.34 Lm: 6.482 (6.477) Lt: 5.729 (5.721) Accm: 3.71 (3.53) Acct: 6.11 (5.55) proj_loss: -0.5948 (-0.5967) time: 0.6790 data: 0.0003 [11-25 03:28:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [ 834/1669] eta: 0:09:25 tlr: 0.00015 tnm: 0.34 Lm: 6.665 (6.628) Lt: 5.928 (5.892) Accm: 2.75 (2.91) Acct: 4.32 (4.52) proj_loss: -0.5964 (-0.6019) time: 0.6791 data: 0.0003 [11-25 03:32:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.589 (6.600) Lt: 5.844 (5.856) Accm: 2.94 (2.97) Acct: 4.69 (4.65) proj_loss: -0.5976 (-0.6011) time: 0.6788 data: 0.0003 [11-25 03:32:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.541 (6.552) Lt: 5.799 (5.803) Accm: 3.32 (3.31) Acct: 5.19 (5.17) proj_loss: -0.5914 (-0.5910) time: 0.6788 data: 0.0003 [11-25 03:32:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.457 (6.468) Lt: 5.723 (5.727) Accm: 3.49 (3.43) Acct: 5.54 (5.38) proj_loss: -0.5889 (-0.5901) time: 0.6788 data: 0.0003 [11-25 03:32:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [1251/1669] eta: 0:04:43 tlr: 0.00015 tnm: 0.32 Lm: 6.620 (6.590) Lt: 5.885 (5.865) Accm: 3.21 (3.17) Acct: 4.98 (4.96) proj_loss: -0.6022 (-0.6041) time: 0.6787 data: 0.0003 [11-25 03:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.627 (6.598) Lt: 5.889 (5.870) Accm: 3.19 (3.13) Acct: 4.98 (4.93) proj_loss: -0.5991 (-0.6031) time: 0.6776 data: 0.0016 [11-25 03:37:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 152/350] Total time: 0:18:53 (0.679 s / it) [11-25 03:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.591 (6.560) Lt: 5.818 (5.806) Accm: 3.45 (3.34) Acct: 5.18 (5.18) proj_loss: -0.5948 (-0.5921) time: 0.6776 data: 0.0020 [11-25 03:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.601 (6.600) Lt: 5.853 (5.855) Accm: 3.13 (3.02) Acct: 5.01 (4.72) proj_loss: -0.5988 (-0.6020) time: 0.6776 data: 0.0017 [11-25 03:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 152/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.31 Lm: 6.469 (6.508) Lt: 5.733 (5.771) Accm: 3.35 (3.30) Acct: 5.34 (5.11) proj_loss: -0.5895 (-0.5931) time: 0.6776 data: 0.0020 [11-25 03:37:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 152/350] Total time: 0:18:53 (0.679 s / it) [11-25 03:37:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 152/350] Total time: 0:18:53 (0.679 s / it) [11-25 03:37:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 152/350] Total time: 0:18:53 (0.679 s / it) [11-25 03:37:34] (/home/user/VAR/train.py , line 276)=> [ep152] (training ) Lm: 6.544 (6.550), Lt: 5.783 (5.795), Acc m&t: 3.29 5.19, Remain: 2 days, 14:14:00, Finish: 2024-11-27 01:51 [11-25 03:37:34] (/home/user/VAR/train.py , line 276)=> [ep152] (training ) Lm: 6.544 (6.550), Lt: 5.783 (5.795), Acc m&t: 3.29 5.19, Remain: 2 days, 14:14:04, Finish: 2024-11-27 01:51 [11-25 03:37:34] (/home/user/VAR/train.py , line 276)=> [ep152] (training ) Lm: 6.544 (6.550), Lt: 5.783 (5.795), Acc m&t: 3.29 5.19, Remain: 2 days, 14:13:49, Finish: 2024-11-27 01:51 [11-25 03:37:34] (/home/user/VAR/train.py , line 276)=> [ep152] (training ) Lm: 6.544 (6.550), Lt: 5.783 (5.795), Acc m&t: 3.29 5.19, Remain: 2 days, 14:13:48, Finish: 2024-11-27 01:51 [11-25 03:37:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [ 0/1669] eta: 0:18:27 tlr: 0.00015 tnm: 0.34 Lm: 6.672 (6.672) Lt: 5.993 (5.993) Accm: 2.80 (2.80) Acct: 4.15 (4.15) proj_loss: -0.6035 (-0.6035) time: 0.6636 data: 0.0004 [11-25 03:37:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [ 0/1669] eta: 0:18:27 tlr: 0.00015 tnm: 0.34 Lm: 6.632 (6.632) Lt: 5.905 (5.905) Accm: 2.89 (2.89) Acct: 4.61 (4.61) proj_loss: -0.6023 (-0.6023) time: 0.6638 data: 0.0004 [11-25 03:37:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [ 0/1669] eta: 0:18:28 tlr: 0.00015 tnm: 0.34 Lm: 6.680 (6.680) Lt: 5.902 (5.902) Accm: 2.93 (2.93) Acct: 4.58 (4.58) proj_loss: -0.5700 (-0.5700) time: 0.6639 data: 0.0004 [11-25 03:37:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [ 0/1669] eta: 0:18:28 tlr: 0.00015 tnm: 0.34 Lm: 6.553 (6.553) Lt: 5.805 (5.805) Accm: 3.24 (3.24) Acct: 4.99 (4.99) proj_loss: -0.6020 (-0.6020) time: 0.6644 data: 0.0004 [11-25 03:42:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [ 417/1669] eta: 0:15:24 tlr: 0.00015 tnm: 0.34 Lm: 6.548 (6.548) Lt: 5.801 (5.801) Accm: 3.24 (3.24) Acct: 4.98 (4.98) proj_loss: -0.5993 (-0.5993) time: 0.6790 data: 0.0003 [11-25 03:42:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [ 417/1669] eta: 0:15:24 tlr: 0.00015 tnm: 0.34 Lm: 6.540 (6.540) Lt: 5.848 (5.848) Accm: 3.17 (3.17) Acct: 4.81 (4.81) proj_loss: -0.5946 (-0.5946) time: 0.6790 data: 0.0003 [11-25 03:42:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [ 417/1669] eta: 0:15:24 tlr: 0.00015 tnm: 0.34 Lm: 6.649 (6.649) Lt: 5.915 (5.915) Accm: 2.99 (2.99) Acct: 4.72 (4.72) proj_loss: -0.6095 (-0.6095) time: 0.6790 data: 0.0003 [11-25 03:42:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [ 417/1669] eta: 0:15:24 tlr: 0.00015 tnm: 0.34 Lm: 6.595 (6.595) Lt: 5.831 (5.831) Accm: 3.07 (3.07) Acct: 4.88 (4.88) proj_loss: -0.5772 (-0.5772) time: 0.6790 data: 0.0003 [11-25 03:47:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [ 834/1669] eta: 0:09:51 tlr: 0.00015 tnm: 0.33 Lm: 6.535 (6.575) Lt: 5.760 (5.801) Accm: 3.21 (3.16) Acct: 5.18 (5.06) proj_loss: -0.5843 (-0.5885) time: 0.6790 data: 0.0003 [11-25 03:47:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [ 834/1669] eta: 0:09:51 tlr: 0.00015 tnm: 0.33 Lm: 6.512 (6.530) Lt: 5.724 (5.807) Accm: 3.43 (3.26) Acct: 5.48 (5.08) proj_loss: -0.6035 (-0.5996) time: 0.6790 data: 0.0003 [11-25 03:47:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [ 834/1669] eta: 0:09:51 tlr: 0.00015 tnm: 0.33 Lm: 6.632 (6.611) Lt: 5.905 (5.887) Accm: 3.08 (3.08) Acct: 4.82 (4.81) proj_loss: -0.6023 (-0.6004) time: 0.6790 data: 0.0003 [11-25 03:47:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [ 834/1669] eta: 0:09:51 tlr: 0.00015 tnm: 0.33 Lm: 6.553 (6.595) Lt: 5.805 (5.860) Accm: 3.23 (3.12) Acct: 4.98 (4.85) proj_loss: -0.5965 (-0.5910) time: 0.6790 data: 0.0003 [11-25 03:52:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.32 Lm: 6.548 (6.571) Lt: 5.801 (5.834) Accm: 3.24 (3.19) Acct: 4.98 (4.95) proj_loss: -0.5937 (-0.5909) time: 0.6785 data: 0.0003 [11-25 03:52:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.32 Lm: 6.599 (6.600) Lt: 5.868 (5.866) Accm: 3.04 (3.06) Acct: 4.80 (4.80) proj_loss: -0.5941 (-0.5967) time: 0.6785 data: 0.0003 [11-25 03:52:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.32 Lm: 6.531 (6.563) Lt: 5.750 (5.782) Accm: 3.19 (3.17) Acct: 5.12 (5.06) proj_loss: -0.5900 (-0.5903) time: 0.6785 data: 0.0003 [11-25 03:52:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [1251/1669] eta: 0:04:51 tlr: 0.00015 tnm: 0.32 Lm: 6.551 (6.545) Lt: 5.790 (5.819) Accm: 3.27 (3.22) Acct: 4.99 (4.94) proj_loss: -0.5958 (-0.5968) time: 0.6785 data: 0.0003 [11-25 03:56:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.36 Lm: 6.590 (6.554) Lt: 5.856 (5.833) Accm: 3.24 (3.23) Acct: 5.08 (4.97) proj_loss: -0.5984 (-0.5971) time: 0.6786 data: 0.0013 [11-25 03:56:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.36 Lm: 6.543 (6.541) Lt: 5.798 (5.803) Accm: 3.24 (3.24) Acct: 4.99 (5.04) proj_loss: -0.5965 (-0.5990) time: 0.6786 data: 0.0016 [11-25 03:56:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.36 Lm: 6.527 (6.553) Lt: 5.753 (5.776) Accm: 3.21 (3.23) Acct: 5.18 (5.19) proj_loss: -0.5944 (-0.5911) time: 0.6786 data: 0.0016 [11-25 03:56:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 153/350] Total time: 0:19:15 (0.692 s / it) [11-25 03:56:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 153/350] [1668/1669] eta: 0:00:00 tlr: 0.00015 tnm: 0.36 Lm: 6.565 (6.571) Lt: 5.831 (5.837) Accm: 3.08 (3.14) Acct: 4.82 (4.91) proj_loss: -0.5994 (-0.5973) time: 0.6786 data: 0.0021 [11-25 03:56:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 153/350] Total time: 0:19:15 (0.692 s / it) [11-25 03:56:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 153/350] Total time: 0:19:15 (0.692 s / it) [11-25 03:56:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 153/350] Total time: 0:19:15 (0.693 s / it) [11-25 03:56:50] (/home/user/VAR/train.py , line 276)=> [ep153] (training ) Lm: 6.544 (6.556), Lt: 5.783 (5.802), Acc m&t: 3.29 5.19, Remain: 2 days, 14:10:57, Finish: 2024-11-27 02:07 [11-25 03:56:50] (/home/user/VAR/train.py , line 276)=> [ep153] (training ) Lm: 6.544 (6.556), Lt: 5.783 (5.802), Acc m&t: 3.29 5.19, Remain: 2 days, 14:10:46, Finish: 2024-11-27 02:07 [11-25 03:56:50] (/home/user/VAR/train.py , line 276)=> [ep153] (training ) Lm: 6.544 (6.556), Lt: 5.783 (5.802), Acc m&t: 3.29 5.19, Remain: 2 days, 14:10:54, Finish: 2024-11-27 02:07 [11-25 03:56:50] (/home/user/VAR/train.py , line 276)=> [ep153] (training ) Lm: 6.544 (6.556), Lt: 5.783 (5.802), Acc m&t: 3.29 5.19, Remain: 2 days, 14:10:25, Finish: 2024-11-27 02:07 [11-25 03:56:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [ 0/1669] eta: 0:18:42 tlr: 0.00015 tnm: 0.33 Lm: 6.419 (6.419) Lt: 5.727 (5.727) Accm: 3.69 (3.69) Acct: 5.87 (5.87) proj_loss: -0.5995 (-0.5995) time: 0.6726 data: 0.0004 [11-25 03:56:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [ 0/1669] eta: 0:18:50 tlr: 0.00015 tnm: 0.33 Lm: 6.419 (6.419) Lt: 5.602 (5.602) Accm: 3.94 (3.94) Acct: 6.40 (6.40) proj_loss: -0.6059 (-0.6059) time: 0.6772 data: 0.0003 [11-25 03:56:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [ 0/1669] eta: 0:18:42 tlr: 0.00015 tnm: 0.33 Lm: 6.623 (6.623) Lt: 5.907 (5.907) Accm: 2.83 (2.83) Acct: 4.34 (4.34) proj_loss: -0.6016 (-0.6016) time: 0.6725 data: 0.0003 [11-25 03:56:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [ 0/1669] eta: 0:18:43 tlr: 0.00015 tnm: 0.33 Lm: 6.477 (6.477) Lt: 5.729 (5.729) Accm: 3.52 (3.52) Acct: 5.63 (5.63) proj_loss: -0.6049 (-0.6049) time: 0.6729 data: 0.0004 [11-25 04:01:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.35 Lm: 6.502 (6.502) Lt: 5.781 (5.781) Accm: 3.31 (3.31) Acct: 5.26 (5.26) proj_loss: -0.6043 (-0.6043) time: 0.6777 data: 0.0003 [11-25 04:01:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.35 Lm: 6.481 (6.481) Lt: 5.690 (5.690) Accm: 3.73 (3.73) Acct: 6.02 (6.02) proj_loss: -0.6004 (-0.6004) time: 0.6777 data: 0.0003 [11-25 04:01:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.35 Lm: 6.414 (6.414) Lt: 5.672 (5.672) Accm: 3.70 (3.70) Acct: 5.79 (5.79) proj_loss: -0.6062 (-0.6062) time: 0.6777 data: 0.0003 [11-25 04:01:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [ 417/1669] eta: 0:14:06 tlr: 0.00015 tnm: 0.35 Lm: 6.601 (6.601) Lt: 5.851 (5.851) Accm: 2.93 (2.93) Acct: 4.68 (4.68) proj_loss: -0.5941 (-0.5941) time: 0.6777 data: 0.0003 [11-25 04:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [ 834/1669] eta: 0:09:48 tlr: 0.00014 tnm: 0.33 Lm: 6.597 (6.600) Lt: 5.907 (5.871) Accm: 3.04 (3.03) Acct: 4.80 (4.72) proj_loss: -0.5895 (-0.5925) time: 0.7410 data: 0.0003 [11-25 04:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [ 834/1669] eta: 0:09:48 tlr: 0.00014 tnm: 0.33 Lm: 6.419 (6.455) Lt: 5.727 (5.729) Accm: 3.69 (3.64) Acct: 5.70 (5.59) proj_loss: -0.6105 (-0.6077) time: 0.7410 data: 0.0003 [11-25 04:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [ 834/1669] eta: 0:09:48 tlr: 0.00014 tnm: 0.33 Lm: 6.543 (6.504) Lt: 5.756 (5.712) Accm: 3.51 (3.63) Acct: 5.63 (5.80) proj_loss: -0.5960 (-0.5989) time: 0.7410 data: 0.0003 [11-25 04:06:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [ 834/1669] eta: 0:09:48 tlr: 0.00014 tnm: 0.33 Lm: 6.477 (6.490) Lt: 5.729 (5.745) Accm: 3.52 (3.45) Acct: 5.63 (5.48) proj_loss: -0.6037 (-0.6025) time: 0.7410 data: 0.0003 [11-25 04:11:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [1251/1669] eta: 0:04:51 tlr: 0.00014 tnm: 0.32 Lm: 6.502 (6.521) Lt: 5.781 (5.790) Accm: 3.31 (3.31) Acct: 5.26 (5.21) proj_loss: -0.6043 (-0.6047) time: 0.6775 data: 0.0003 [11-25 04:11:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [1251/1669] eta: 0:04:51 tlr: 0.00014 tnm: 0.32 Lm: 6.502 (6.494) Lt: 5.729 (5.709) Accm: 3.47 (3.56) Acct: 5.50 (5.66) proj_loss: -0.5996 (-0.6000) time: 0.6775 data: 0.0003 [11-25 04:11:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [1251/1669] eta: 0:04:51 tlr: 0.00014 tnm: 0.32 Lm: 6.588 (6.563) Lt: 5.851 (5.825) Accm: 3.13 (3.13) Acct: 4.92 (4.92) proj_loss: -0.5933 (-0.5937) time: 0.6775 data: 0.0003 [11-25 04:11:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [1251/1669] eta: 0:04:51 tlr: 0.00014 tnm: 0.32 Lm: 6.477 (6.487) Lt: 5.776 (5.753) Accm: 3.60 (3.52) Acct: 5.45 (5.48) proj_loss: -0.6118 (-0.6096) time: 0.6775 data: 0.0003 [11-25 04:16:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.32 Lm: 6.536 (6.543) Lt: 5.825 (5.810) Accm: 3.51 (3.33) Acct: 5.20 (5.23) proj_loss: -0.6109 (-0.6099) time: 0.6761 data: 0.0020 [11-25 04:16:08] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 154/350] Total time: 0:19:18 (0.694 s / it) [11-25 04:16:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.32 Lm: 6.527 (6.556) Lt: 5.832 (5.816) Accm: 3.11 (3.15) Acct: 4.89 (4.93) proj_loss: -0.6037 (-0.5991) time: 0.6761 data: 0.0016 [11-25 04:16:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.32 Lm: 6.578 (6.555) Lt: 5.796 (5.802) Accm: 3.17 (3.14) Acct: 5.03 (4.94) proj_loss: -0.5972 (-0.5998) time: 0.6761 data: 0.0016 [11-25 04:16:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 154/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.32 Lm: 6.512 (6.497) Lt: 5.732 (5.714) Accm: 3.42 (3.53) Acct: 5.58 (5.64) proj_loss: -0.5960 (-0.5981) time: 0.6761 data: 0.0019 [11-25 04:16:08] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 154/350] Total time: 0:19:18 (0.694 s / it) [11-25 04:16:08] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 154/350] Total time: 0:19:18 (0.694 s / it) [11-25 04:16:08] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 154/350] Total time: 0:19:18 (0.694 s / it) [11-25 04:16:08] (/home/user/VAR/train.py , line 276)=> [ep154] (training ) Lm: 6.544 (6.557), Lt: 5.783 (5.807), Acc m&t: 3.29 5.19, Remain: 2 days, 13:42:03, Finish: 2024-11-27 01:58 [11-25 04:16:08] (/home/user/VAR/train.py , line 276)=> [ep154] (training ) Lm: 6.544 (6.557), Lt: 5.783 (5.807), Acc m&t: 3.29 5.19, Remain: 2 days, 13:41:41, Finish: 2024-11-27 01:57 [11-25 04:16:08] (/home/user/VAR/train.py , line 276)=> [ep154] (training ) Lm: 6.544 (6.557), Lt: 5.783 (5.807), Acc m&t: 3.29 5.19, Remain: 2 days, 13:42:09, Finish: 2024-11-27 01:58 [11-25 04:16:08] (/home/user/VAR/train.py , line 276)=> [ep154] (training ) Lm: 6.544 (6.557), Lt: 5.783 (5.807), Acc m&t: 3.29 5.19, Remain: 2 days, 13:39:49, Finish: 2024-11-27 01:55 [11-25 04:16:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [ 0/1669] eta: 0:18:18 tlr: 0.00014 tnm: 0.30 Lm: 6.556 (6.556) Lt: 5.709 (5.709) Accm: 3.26 (3.26) Acct: 5.17 (5.17) proj_loss: -0.5894 (-0.5894) time: 0.6584 data: 0.0004 [11-25 04:16:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [ 0/1669] eta: 0:18:18 tlr: 0.00014 tnm: 0.30 Lm: 6.628 (6.628) Lt: 5.888 (5.888) Accm: 2.88 (2.88) Acct: 4.65 (4.65) proj_loss: -0.6027 (-0.6027) time: 0.6581 data: 0.0003 [11-25 04:16:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [ 0/1669] eta: 0:18:19 tlr: 0.00014 tnm: 0.30 Lm: 6.327 (6.327) Lt: 5.568 (5.568) Accm: 4.14 (4.14) Acct: 6.44 (6.44) proj_loss: -0.6087 (-0.6087) time: 0.6585 data: 0.0004 [11-25 04:16:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [ 0/1669] eta: 0:18:20 tlr: 0.00014 tnm: 0.30 Lm: 6.518 (6.518) Lt: 5.751 (5.751) Accm: 3.49 (3.49) Acct: 5.63 (5.63) proj_loss: -0.5960 (-0.5960) time: 0.6596 data: 0.0003 [11-25 04:20:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [ 417/1669] eta: 0:14:06 tlr: 0.00014 tnm: 0.32 Lm: 6.593 (6.593) Lt: 5.849 (5.849) Accm: 3.16 (3.16) Acct: 4.98 (4.98) proj_loss: -0.5990 (-0.5990) time: 0.6752 data: 0.0002 [11-25 04:20:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [ 417/1669] eta: 0:14:06 tlr: 0.00014 tnm: 0.32 Lm: 6.631 (6.631) Lt: 5.895 (5.895) Accm: 2.87 (2.87) Acct: 4.41 (4.41) proj_loss: -0.5897 (-0.5897) time: 0.6752 data: 0.0003 [11-25 04:20:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [ 417/1669] eta: 0:14:06 tlr: 0.00014 tnm: 0.32 Lm: 6.497 (6.497) Lt: 5.759 (5.759) Accm: 3.46 (3.46) Acct: 5.35 (5.35) proj_loss: -0.6092 (-0.6092) time: 0.6752 data: 0.0003 [11-25 04:20:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [ 417/1669] eta: 0:14:06 tlr: 0.00014 tnm: 0.32 Lm: 6.620 (6.620) Lt: 5.836 (5.836) Accm: 3.16 (3.16) Acct: 4.94 (4.94) proj_loss: -0.5982 (-0.5982) time: 0.6752 data: 0.0002 [11-25 04:25:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [ 834/1669] eta: 0:09:24 tlr: 0.00014 tnm: 0.31 Lm: 6.556 (6.573) Lt: 5.709 (5.768) Accm: 3.26 (3.31) Acct: 5.17 (5.23) proj_loss: -0.6070 (-0.6014) time: 0.6777 data: 0.0003 [11-25 04:25:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [ 834/1669] eta: 0:09:24 tlr: 0.00014 tnm: 0.31 Lm: 6.653 (6.549) Lt: 5.926 (5.815) Accm: 2.80 (3.24) Acct: 4.41 (5.04) proj_loss: -0.6087 (-0.6050) time: 0.6777 data: 0.0003 [11-25 04:25:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [ 834/1669] eta: 0:09:24 tlr: 0.00014 tnm: 0.31 Lm: 6.518 (6.523) Lt: 5.751 (5.773) Accm: 3.49 (3.44) Acct: 5.63 (5.25) proj_loss: -0.6020 (-0.6036) time: 0.6777 data: 0.0003 [11-25 04:25:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [ 834/1669] eta: 0:09:24 tlr: 0.00014 tnm: 0.31 Lm: 6.635 (6.646) Lt: 5.901 (5.918) Accm: 2.88 (2.87) Acct: 4.60 (4.47) proj_loss: -0.5767 (-0.5848) time: 0.6776 data: 0.0003 [11-25 04:30:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [1251/1669] eta: 0:04:43 tlr: 0.00014 tnm: 0.34 Lm: 6.631 (6.604) Lt: 5.895 (5.881) Accm: 2.88 (3.09) Acct: 4.62 (4.76) proj_loss: -0.5879 (-0.5884) time: 0.6745 data: 0.0003 [11-25 04:30:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [1251/1669] eta: 0:04:43 tlr: 0.00014 tnm: 0.34 Lm: 6.615 (6.556) Lt: 5.850 (5.805) Accm: 3.04 (3.25) Acct: 4.97 (5.16) proj_loss: -0.6027 (-0.6002) time: 0.6745 data: 0.0003 [11-25 04:30:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [1251/1669] eta: 0:04:43 tlr: 0.00014 tnm: 0.34 Lm: 6.620 (6.600) Lt: 5.836 (5.820) Accm: 3.16 (3.21) Acct: 4.94 (5.07) proj_loss: -0.6073 (-0.6031) time: 0.6745 data: 0.0003 [11-25 04:30:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [1251/1669] eta: 0:04:43 tlr: 0.00014 tnm: 0.34 Lm: 6.593 (6.570) Lt: 5.849 (5.827) Accm: 3.30 (3.36) Acct: 5.36 (5.21) proj_loss: -0.5990 (-0.5981) time: 0.6745 data: 0.0003 [11-25 04:35:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.522 (6.561) Lt: 5.769 (5.816) Accm: 3.36 (3.36) Acct: 5.11 (5.19) proj_loss: -0.6020 (-0.5989) time: 0.6772 data: 0.0019 [11-25 04:35:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 155/350] Total time: 0:18:52 (0.679 s / it) [11-25 04:35:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.635 (6.612) Lt: 5.888 (5.875) Accm: 2.88 (3.03) Acct: 4.60 (4.73) proj_loss: -0.5918 (-0.5891) time: 0.6772 data: 0.0013 [11-25 04:35:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.577 (6.540) Lt: 5.774 (5.788) Accm: 3.28 (3.32) Acct: 5.53 (5.25) proj_loss: -0.6072 (-0.6016) time: 0.6772 data: 0.0020 [11-25 04:35:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 155/350] Total time: 0:18:52 (0.679 s / it) [11-25 04:35:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 155/350] Total time: 0:18:52 (0.679 s / it) [11-25 04:35:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 155/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.556 (6.575) Lt: 5.742 (5.804) Accm: 3.26 (3.28) Acct: 5.17 (5.12) proj_loss: -0.6070 (-0.6025) time: 0.6773 data: 0.0022 [11-25 04:35:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 155/350] Total time: 0:18:52 (0.679 s / it) [11-25 04:35:01] (/home/user/VAR/train.py , line 276)=> [ep155] (training ) Lm: 6.544 (6.555), Lt: 5.783 (5.806), Acc m&t: 3.29 5.19, Remain: 2 days, 13:17:13, Finish: 2024-11-27 01:52 [11-25 04:35:01] (/home/user/VAR/train.py , line 276)=> [ep155] (training ) Lm: 6.544 (6.555), Lt: 5.783 (5.806), Acc m&t: 3.29 5.19, Remain: 2 days, 13:18:13, Finish: 2024-11-27 01:53 [11-25 04:35:01] (/home/user/VAR/train.py , line 276)=> [ep155] (training ) Lm: 6.544 (6.555), Lt: 5.783 (5.806), Acc m&t: 3.29 5.19, Remain: 2 days, 13:18:21, Finish: 2024-11-27 01:53 [11-25 04:35:01] (/home/user/VAR/train.py , line 276)=> [ep155] (training ) Lm: 6.544 (6.555), Lt: 5.783 (5.806), Acc m&t: 3.29 5.19, Remain: 2 days, 13:16:48, Finish: 2024-11-27 01:51 [11-25 04:35:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [ 0/1669] eta: 0:18:28 tlr: 0.00014 tnm: 0.34 Lm: 6.764 (6.764) Lt: 6.054 (6.054) Accm: 2.53 (2.53) Acct: 3.82 (3.82) proj_loss: -0.5903 (-0.5903) time: 0.6644 data: 0.0004 [11-25 04:35:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [ 0/1669] eta: 0:18:22 tlr: 0.00014 tnm: 0.34 Lm: 6.315 (6.315) Lt: 5.545 (5.545) Accm: 3.97 (3.97) Acct: 6.16 (6.16) proj_loss: -0.5923 (-0.5923) time: 0.6608 data: 0.0004 [11-25 04:35:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [ 0/1669] eta: 0:18:22 tlr: 0.00014 tnm: 0.34 Lm: 6.474 (6.474) Lt: 5.725 (5.725) Accm: 3.62 (3.62) Acct: 6.10 (6.10) proj_loss: -0.5822 (-0.5822) time: 0.6608 data: 0.0003 [11-25 04:35:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [ 0/1669] eta: 0:18:29 tlr: 0.00014 tnm: 0.34 Lm: 6.452 (6.452) Lt: 5.734 (5.734) Accm: 3.40 (3.40) Acct: 5.10 (5.10) proj_loss: -0.5925 (-0.5925) time: 0.6646 data: 0.0004 [11-25 04:40:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [ 417/1669] eta: 0:15:29 tlr: 0.00014 tnm: 0.34 Lm: 6.455 (6.455) Lt: 5.716 (5.716) Accm: 3.39 (3.39) Acct: 5.28 (5.28) proj_loss: -0.6038 (-0.6038) time: 0.6771 data: 0.0003 [11-25 04:40:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [ 417/1669] eta: 0:15:29 tlr: 0.00014 tnm: 0.34 Lm: 6.441 (6.441) Lt: 5.699 (5.699) Accm: 3.66 (3.66) Acct: 5.66 (5.66) proj_loss: -0.5968 (-0.5968) time: 0.6771 data: 0.0003 [11-25 04:40:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [ 417/1669] eta: 0:15:29 tlr: 0.00014 tnm: 0.34 Lm: 6.656 (6.656) Lt: 5.933 (5.933) Accm: 2.91 (2.91) Acct: 4.54 (4.54) proj_loss: -0.5918 (-0.5918) time: 0.6771 data: 0.0003 [11-25 04:40:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [ 417/1669] eta: 0:15:29 tlr: 0.00014 tnm: 0.34 Lm: 6.431 (6.431) Lt: 5.643 (5.643) Accm: 3.74 (3.74) Acct: 6.06 (6.06) proj_loss: -0.5928 (-0.5928) time: 0.6771 data: 0.0003 [11-25 04:44:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [ 834/1669] eta: 0:09:52 tlr: 0.00014 tnm: 0.36 Lm: 6.474 (6.487) Lt: 5.725 (5.701) Accm: 3.62 (3.53) Acct: 6.03 (5.69) proj_loss: -0.6033 (-0.5973) time: 0.6782 data: 0.0003 [11-25 04:44:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [ 834/1669] eta: 0:09:52 tlr: 0.00014 tnm: 0.36 Lm: 6.547 (6.601) Lt: 5.813 (5.870) Accm: 3.28 (3.10) Acct: 5.25 (4.78) proj_loss: -0.5934 (-0.5930) time: 0.6782 data: 0.0003 [11-25 04:44:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [ 834/1669] eta: 0:09:52 tlr: 0.00014 tnm: 0.36 Lm: 6.458 (6.477) Lt: 5.734 (5.747) Accm: 3.39 (3.38) Acct: 5.10 (5.19) proj_loss: -0.6001 (-0.6025) time: 0.6782 data: 0.0003 [11-25 04:44:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [ 834/1669] eta: 0:09:52 tlr: 0.00014 tnm: 0.36 Lm: 6.528 (6.470) Lt: 5.773 (5.724) Accm: 3.34 (3.51) Acct: 5.34 (5.56) proj_loss: -0.5974 (-0.5970) time: 0.6782 data: 0.0003 [11-25 04:49:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.544 (6.492) Lt: 5.790 (5.745) Accm: 3.28 (3.42) Acct: 5.25 (5.40) proj_loss: -0.5961 (-0.5964) time: 0.6805 data: 0.0003 [11-25 04:49:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.568 (6.597) Lt: 5.860 (5.879) Accm: 3.19 (3.10) Acct: 4.98 (4.76) proj_loss: -0.5943 (-0.5955) time: 0.6805 data: 0.0003 [11-25 04:49:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.507 (6.500) Lt: 5.768 (5.728) Accm: 3.56 (3.52) Acct: 5.78 (5.65) proj_loss: -0.6008 (-0.5975) time: 0.6805 data: 0.0003 [11-25 04:49:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.489 (6.498) Lt: 5.771 (5.762) Accm: 3.39 (3.39) Acct: 5.23 (5.23) proj_loss: -0.5963 (-0.6000) time: 0.6805 data: 0.0002 [11-25 04:54:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.32 Lm: 6.458 (6.488) Lt: 5.734 (5.750) Accm: 3.40 (3.40) Acct: 5.35 (5.34) proj_loss: -0.6001 (-0.6018) time: 0.6789 data: 0.0019 [11-25 04:54:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 156/350] Total time: 0:19:17 (0.693 s / it) [11-25 04:54:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.32 Lm: 6.559 (6.516) Lt: 5.807 (5.775) Accm: 3.21 (3.33) Acct: 5.17 (5.26) proj_loss: -0.5974 (-0.5969) time: 0.6789 data: 0.0020 [11-25 04:54:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.32 Lm: 6.474 (6.488) Lt: 5.725 (5.719) Accm: 3.50 (3.52) Acct: 5.53 (5.61) proj_loss: -0.6033 (-0.6016) time: 0.6789 data: 0.0016 [11-25 04:54:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 156/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.32 Lm: 6.588 (6.607) Lt: 5.889 (5.881) Accm: 3.10 (3.09) Acct: 4.92 (4.79) proj_loss: -0.5953 (-0.5959) time: 0.6789 data: 0.0015 [11-25 04:54:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 156/350] Total time: 0:19:17 (0.693 s / it) [11-25 04:54:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 156/350] Total time: 0:19:17 (0.693 s / it) [11-25 04:54:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 156/350] Total time: 0:19:17 (0.693 s / it) [11-25 04:54:19] (/home/user/VAR/train.py , line 276)=> [ep156] (training ) Lm: 6.544 (6.546), Lt: 5.783 (5.792), Acc m&t: 3.29 5.19, Remain: 2 days, 13:13:56, Finish: 2024-11-27 02:08 [11-25 04:54:19] (/home/user/VAR/train.py , line 276)=> [ep156] (training ) Lm: 6.544 (6.546), Lt: 5.783 (5.792), Acc m&t: 3.29 5.19, Remain: 2 days, 13:14:07, Finish: 2024-11-27 02:08 [11-25 04:54:19] (/home/user/VAR/train.py , line 276)=> [ep156] (training ) Lm: 6.544 (6.546), Lt: 5.783 (5.792), Acc m&t: 3.29 5.19, Remain: 2 days, 13:14:40, Finish: 2024-11-27 02:08 [11-25 04:54:19] (/home/user/VAR/train.py , line 276)=> [ep156] (training ) Lm: 6.544 (6.546), Lt: 5.783 (5.792), Acc m&t: 3.29 5.19, Remain: 2 days, 13:13:11, Finish: 2024-11-27 02:07 [11-25 04:54:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [ 0/1669] eta: 0:18:23 tlr: 0.00014 tnm: 0.34 Lm: 6.645 (6.645) Lt: 5.917 (5.917) Accm: 3.07 (3.07) Acct: 4.96 (4.96) proj_loss: -0.6118 (-0.6118) time: 0.6612 data: 0.0004 [11-25 04:54:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [ 0/1669] eta: 0:18:22 tlr: 0.00014 tnm: 0.34 Lm: 6.576 (6.576) Lt: 5.815 (5.815) Accm: 3.18 (3.18) Acct: 4.84 (4.84) proj_loss: -0.5954 (-0.5954) time: 0.6608 data: 0.0004 [11-25 04:54:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [ 0/1669] eta: 0:18:18 tlr: 0.00014 tnm: 0.34 Lm: 6.447 (6.447) Lt: 5.694 (5.694) Accm: 3.35 (3.35) Acct: 5.46 (5.46) proj_loss: -0.5952 (-0.5952) time: 0.6580 data: 0.0004 [11-25 04:54:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [ 0/1669] eta: 0:18:24 tlr: 0.00014 tnm: 0.34 Lm: 6.406 (6.406) Lt: 5.623 (5.623) Accm: 3.80 (3.80) Acct: 6.04 (6.04) proj_loss: -0.6103 (-0.6103) time: 0.6615 data: 0.0005 [11-25 04:59:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [ 417/1669] eta: 0:14:06 tlr: 0.00014 tnm: 0.33 Lm: 6.419 (6.419) Lt: 5.645 (5.645) Accm: 3.71 (3.71) Acct: 5.79 (5.79) proj_loss: -0.6016 (-0.6016) time: 0.6762 data: 0.0003 [11-25 04:59:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [ 417/1669] eta: 0:14:06 tlr: 0.00014 tnm: 0.33 Lm: 6.650 (6.650) Lt: 5.904 (5.904) Accm: 2.92 (2.92) Acct: 4.59 (4.59) proj_loss: -0.6087 (-0.6087) time: 0.6762 data: 0.0003 [11-25 04:59:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [ 417/1669] eta: 0:14:06 tlr: 0.00014 tnm: 0.33 Lm: 6.512 (6.512) Lt: 5.747 (5.747) Accm: 3.27 (3.27) Acct: 5.17 (5.17) proj_loss: -0.5947 (-0.5947) time: 0.6762 data: 0.0003 [11-25 04:59:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [ 417/1669] eta: 0:14:06 tlr: 0.00014 tnm: 0.33 Lm: 6.481 (6.481) Lt: 5.720 (5.720) Accm: 3.56 (3.56) Acct: 5.72 (5.72) proj_loss: -0.5950 (-0.5950) time: 0.6762 data: 0.0003 [11-25 05:04:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [ 834/1669] eta: 0:09:46 tlr: 0.00014 tnm: 0.33 Lm: 6.576 (6.619) Lt: 5.815 (5.860) Accm: 3.18 (3.09) Acct: 4.84 (4.93) proj_loss: -0.5986 (-0.6053) time: 0.6769 data: 0.0003 [11-25 05:04:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [ 834/1669] eta: 0:09:46 tlr: 0.00014 tnm: 0.33 Lm: 6.413 (6.417) Lt: 5.623 (5.634) Accm: 3.63 (3.68) Acct: 5.72 (5.76) proj_loss: -0.6023 (-0.6018) time: 0.6769 data: 0.0003 [11-25 05:04:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [ 834/1669] eta: 0:09:46 tlr: 0.00014 tnm: 0.33 Lm: 6.644 (6.535) Lt: 5.917 (5.786) Accm: 3.12 (3.41) Acct: 4.96 (5.42) proj_loss: -0.5956 (-0.5952) time: 0.6769 data: 0.0006 [11-25 05:04:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [ 834/1669] eta: 0:09:46 tlr: 0.00014 tnm: 0.33 Lm: 6.560 (6.528) Lt: 5.787 (5.761) Accm: 3.25 (3.26) Acct: 5.15 (5.16) proj_loss: -0.5942 (-0.5920) time: 0.6769 data: 0.0003 [11-25 05:08:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.32 Lm: 6.568 (6.545) Lt: 5.794 (5.780) Accm: 3.22 (3.23) Acct: 5.13 (5.15) proj_loss: -0.5904 (-0.5889) time: 0.6759 data: 0.0003 [11-25 05:08:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.32 Lm: 6.644 (6.562) Lt: 5.918 (5.824) Accm: 3.10 (3.28) Acct: 4.89 (5.17) proj_loss: -0.6037 (-0.6012) time: 0.6759 data: 0.0003 [11-25 05:08:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.32 Lm: 6.423 (6.492) Lt: 5.645 (5.717) Accm: 3.63 (3.43) Acct: 5.62 (5.40) proj_loss: -0.5976 (-0.5972) time: 0.6759 data: 0.0003 [11-25 05:08:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.32 Lm: 6.567 (6.560) Lt: 5.793 (5.795) Accm: 3.31 (3.25) Acct: 5.23 (5.22) proj_loss: -0.5970 (-0.6025) time: 0.6759 data: 0.0003 [11-25 05:13:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.33 Lm: 6.558 (6.557) Lt: 5.800 (5.796) Accm: 3.18 (3.22) Acct: 4.96 (5.17) proj_loss: -0.5986 (-0.6032) time: 0.6787 data: 0.0013 [11-25 05:13:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 157/350] Total time: 0:19:20 (0.695 s / it) [11-25 05:13:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.33 Lm: 6.560 (6.544) Lt: 5.800 (5.784) Accm: 3.25 (3.28) Acct: 5.15 (5.19) proj_loss: -0.5865 (-0.5864) time: 0.6787 data: 0.0017 [11-25 05:13:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.33 Lm: 6.644 (6.574) Lt: 5.917 (5.837) Accm: 3.07 (3.22) Acct: 4.96 (5.15) proj_loss: -0.6104 (-0.6030) time: 0.6787 data: 0.0020 [11-25 05:13:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 157/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.33 Lm: 6.432 (6.521) Lt: 5.668 (5.756) Accm: 3.62 (3.31) Acct: 5.53 (5.21) proj_loss: -0.6016 (-0.5981) time: 0.6787 data: 0.0017 [11-25 05:13:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 157/350] Total time: 0:19:20 (0.695 s / it) [11-25 05:13:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 157/350] Total time: 0:19:20 (0.695 s / it) [11-25 05:13:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 157/350] Total time: 0:19:20 (0.695 s / it) [11-25 05:13:39] (/home/user/VAR/train.py , line 276)=> [ep157] (training ) Lm: 6.544 (6.548), Lt: 5.783 (5.792), Acc m&t: 3.29 5.19, Remain: 2 days, 12:46:50, Finish: 2024-11-27 02:00 [11-25 05:13:39] (/home/user/VAR/train.py , line 276)=> [ep157] (training ) Lm: 6.544 (6.548), Lt: 5.783 (5.792), Acc m&t: 3.29 5.19, Remain: 2 days, 12:46:53, Finish: 2024-11-27 02:00 [11-25 05:13:39] (/home/user/VAR/train.py , line 276)=> [ep157] (training ) Lm: 6.544 (6.548), Lt: 5.783 (5.792), Acc m&t: 3.29 5.19, Remain: 2 days, 12:44:39, Finish: 2024-11-27 01:58 [11-25 05:13:39] (/home/user/VAR/train.py , line 276)=> [ep157] (training ) Lm: 6.544 (6.548), Lt: 5.783 (5.792), Acc m&t: 3.29 5.19, Remain: 2 days, 12:46:44, Finish: 2024-11-27 02:00 [11-25 05:13:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [ 0/1669] eta: 0:18:51 tlr: 0.00014 tnm: 0.33 Lm: 6.489 (6.489) Lt: 5.679 (5.679) Accm: 3.72 (3.72) Acct: 5.82 (5.82) proj_loss: -0.6099 (-0.6099) time: 0.6780 data: 0.0004 [11-25 05:13:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [ 0/1669] eta: 0:18:51 tlr: 0.00014 tnm: 0.33 Lm: 6.584 (6.584) Lt: 5.849 (5.849) Accm: 2.87 (2.87) Acct: 4.13 (4.13) proj_loss: -0.6030 (-0.6030) time: 0.6782 data: 0.0004 [11-25 05:13:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [ 0/1669] eta: 0:18:51 tlr: 0.00014 tnm: 0.33 Lm: 6.405 (6.405) Lt: 5.622 (5.622) Accm: 3.34 (3.34) Acct: 5.46 (5.46) proj_loss: -0.6094 (-0.6094) time: 0.6781 data: 0.0004 [11-25 05:13:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [ 0/1669] eta: 0:18:52 tlr: 0.00014 tnm: 0.33 Lm: 6.622 (6.622) Lt: 5.867 (5.867) Accm: 3.04 (3.04) Acct: 4.67 (4.67) proj_loss: -0.6109 (-0.6109) time: 0.6785 data: 0.0004 [11-25 05:18:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [ 417/1669] eta: 0:14:08 tlr: 0.00014 tnm: 0.33 Lm: 6.643 (6.643) Lt: 5.882 (5.882) Accm: 3.03 (3.03) Acct: 4.81 (4.81) proj_loss: -0.6002 (-0.6002) time: 0.6794 data: 0.0003 [11-25 05:18:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [ 417/1669] eta: 0:14:08 tlr: 0.00014 tnm: 0.33 Lm: 6.519 (6.519) Lt: 5.714 (5.714) Accm: 3.50 (3.50) Acct: 5.55 (5.55) proj_loss: -0.5957 (-0.5957) time: 0.6794 data: 0.0003 [11-25 05:18:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [ 417/1669] eta: 0:14:08 tlr: 0.00014 tnm: 0.33 Lm: 6.544 (6.544) Lt: 5.774 (5.774) Accm: 3.06 (3.06) Acct: 4.80 (4.80) proj_loss: -0.6021 (-0.6021) time: 0.6794 data: 0.0003 [11-25 05:18:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [ 417/1669] eta: 0:14:08 tlr: 0.00014 tnm: 0.33 Lm: 6.599 (6.599) Lt: 5.842 (5.842) Accm: 2.89 (2.89) Acct: 4.72 (4.72) proj_loss: -0.6042 (-0.6042) time: 0.6794 data: 0.0003 [11-25 05:23:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [ 834/1669] eta: 0:09:25 tlr: 0.00014 tnm: 0.33 Lm: 6.751 (6.650) Lt: 5.965 (5.883) Accm: 2.70 (2.83) Acct: 4.32 (4.59) proj_loss: -0.5991 (-0.5945) time: 0.6779 data: 0.0003 [11-25 05:23:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [ 834/1669] eta: 0:09:25 tlr: 0.00014 tnm: 0.33 Lm: 6.664 (6.655) Lt: 5.896 (5.900) Accm: 3.01 (2.91) Acct: 4.67 (4.56) proj_loss: -0.5895 (-0.5957) time: 0.6779 data: 0.0003 [11-25 05:23:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [ 834/1669] eta: 0:09:25 tlr: 0.00014 tnm: 0.33 Lm: 6.549 (6.532) Lt: 5.749 (5.748) Accm: 3.37 (3.46) Acct: 5.32 (5.48) proj_loss: -0.6099 (-0.6028) time: 0.6779 data: 0.0003 [11-25 05:23:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [ 834/1669] eta: 0:09:25 tlr: 0.00014 tnm: 0.33 Lm: 6.584 (6.562) Lt: 5.802 (5.784) Accm: 3.15 (3.09) Acct: 5.15 (4.91) proj_loss: -0.6012 (-0.5964) time: 0.6779 data: 0.0003 [11-25 05:27:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [1251/1669] eta: 0:04:43 tlr: 0.00014 tnm: 0.33 Lm: 6.591 (6.581) Lt: 5.826 (5.809) Accm: 3.01 (3.02) Acct: 4.83 (4.81) proj_loss: -0.5999 (-0.5970) time: 0.6736 data: 0.0003 [11-25 05:27:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [1251/1669] eta: 0:04:43 tlr: 0.00014 tnm: 0.33 Lm: 6.643 (6.625) Lt: 5.882 (5.866) Accm: 3.03 (2.99) Acct: 4.81 (4.69) proj_loss: -0.5917 (-0.5952) time: 0.6736 data: 0.0003 [11-25 05:27:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [1251/1669] eta: 0:04:43 tlr: 0.00014 tnm: 0.33 Lm: 6.624 (6.612) Lt: 5.850 (5.846) Accm: 3.00 (2.94) Acct: 4.78 (4.75) proj_loss: -0.5996 (-0.5959) time: 0.6736 data: 0.0003 [11-25 05:27:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [1251/1669] eta: 0:04:43 tlr: 0.00014 tnm: 0.33 Lm: 6.519 (6.508) Lt: 5.714 (5.720) Accm: 3.37 (3.43) Acct: 5.30 (5.37) proj_loss: -0.6068 (-0.6030) time: 0.6736 data: 0.0003 [11-25 05:32:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.35 Lm: 6.549 (6.519) Lt: 5.749 (5.746) Accm: 3.37 (3.42) Acct: 5.29 (5.29) proj_loss: -0.6036 (-0.6020) time: 0.6803 data: 0.0014 [11-25 05:32:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 158/350] Total time: 0:18:54 (0.679 s / it) [11-25 05:32:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.35 Lm: 6.497 (6.581) Lt: 5.735 (5.815) Accm: 3.29 (3.07) Acct: 5.23 (4.89) proj_loss: -0.5991 (-0.5936) time: 0.6803 data: 0.0016 [11-25 05:32:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.35 Lm: 6.599 (6.588) Lt: 5.838 (5.815) Accm: 3.00 (3.02) Acct: 4.73 (4.80) proj_loss: -0.5987 (-0.5936) time: 0.6803 data: 0.0017 [11-25 05:32:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 158/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.35 Lm: 6.622 (6.619) Lt: 5.896 (5.876) Accm: 3.04 (3.02) Acct: 4.68 (4.69) proj_loss: -0.5939 (-0.6025) time: 0.6803 data: 0.0023 [11-25 05:32:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 158/350] Total time: 0:18:54 (0.679 s / it) [11-25 05:32:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 158/350] Total time: 0:18:54 (0.679 s / it) [11-25 05:32:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 158/350] Total time: 0:18:54 (0.679 s / it) [11-25 05:32:33] (/home/user/VAR/train.py , line 276)=> [ep158] (training ) Lm: 6.544 (6.552), Lt: 5.783 (5.795), Acc m&t: 3.29 5.19, Remain: 2 days, 12:38:31, Finish: 2024-11-27 02:11 [11-25 05:32:33] (/home/user/VAR/train.py , line 276)=> [ep158] (training ) Lm: 6.544 (6.552), Lt: 5.783 (5.795), Acc m&t: 3.29 5.19, Remain: 2 days, 12:38:29, Finish: 2024-11-27 02:11 [11-25 05:32:33] (/home/user/VAR/train.py , line 276)=> [ep158] (training ) Lm: 6.544 (6.552), Lt: 5.783 (5.795), Acc m&t: 3.29 5.19, Remain: 2 days, 12:37:51, Finish: 2024-11-27 02:10 [11-25 05:32:33] (/home/user/VAR/train.py , line 276)=> [ep158] (training ) Lm: 6.544 (6.552), Lt: 5.783 (5.795), Acc m&t: 3.29 5.19, Remain: 2 days, 12:38:32, Finish: 2024-11-27 02:11 [11-25 05:32:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [ 0/1669] eta: 0:18:38 tlr: 0.00014 tnm: 0.32 Lm: 6.516 (6.516) Lt: 5.794 (5.794) Accm: 3.45 (3.45) Acct: 5.13 (5.13) proj_loss: -0.5963 (-0.5963) time: 0.6699 data: 0.0003 [11-25 05:32:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [ 0/1669] eta: 0:18:36 tlr: 0.00014 tnm: 0.32 Lm: 6.646 (6.646) Lt: 5.912 (5.912) Accm: 3.27 (3.27) Acct: 5.15 (5.15) proj_loss: -0.5763 (-0.5763) time: 0.6691 data: 0.0004 [11-25 05:32:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [ 0/1669] eta: 0:18:38 tlr: 0.00014 tnm: 0.32 Lm: 6.655 (6.655) Lt: 5.871 (5.871) Accm: 3.07 (3.07) Acct: 4.63 (4.63) proj_loss: -0.5875 (-0.5875) time: 0.6699 data: 0.0004 [11-25 05:32:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [ 0/1669] eta: 0:18:38 tlr: 0.00014 tnm: 0.32 Lm: 6.701 (6.701) Lt: 5.977 (5.977) Accm: 2.98 (2.98) Acct: 4.86 (4.86) proj_loss: -0.5900 (-0.5900) time: 0.6704 data: 0.0004 [11-25 05:37:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [ 417/1669] eta: 0:15:28 tlr: 0.00014 tnm: 0.33 Lm: 6.618 (6.618) Lt: 5.864 (5.864) Accm: 3.12 (3.12) Acct: 4.92 (4.92) proj_loss: -0.5853 (-0.5853) time: 0.7332 data: 0.0003 [11-25 05:37:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [ 417/1669] eta: 0:15:28 tlr: 0.00014 tnm: 0.33 Lm: 6.515 (6.515) Lt: 5.775 (5.775) Accm: 3.41 (3.41) Acct: 5.28 (5.28) proj_loss: -0.5929 (-0.5929) time: 0.7332 data: 0.0003 [11-25 05:37:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [ 417/1669] eta: 0:15:28 tlr: 0.00014 tnm: 0.33 Lm: 6.607 (6.607) Lt: 5.870 (5.870) Accm: 3.15 (3.15) Acct: 4.77 (4.77) proj_loss: -0.5921 (-0.5921) time: 0.7332 data: 0.0003 [11-25 05:37:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [ 417/1669] eta: 0:15:28 tlr: 0.00014 tnm: 0.33 Lm: 6.472 (6.472) Lt: 5.715 (5.715) Accm: 3.46 (3.46) Acct: 5.18 (5.18) proj_loss: -0.5951 (-0.5951) time: 0.7332 data: 0.0003 [11-25 05:42:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [ 834/1669] eta: 0:09:52 tlr: 0.00014 tnm: 0.35 Lm: 6.431 (6.458) Lt: 5.656 (5.696) Accm: 3.48 (3.49) Acct: 5.23 (5.31) proj_loss: -0.5939 (-0.5912) time: 0.6777 data: 0.0003 [11-25 05:42:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [ 834/1669] eta: 0:09:52 tlr: 0.00014 tnm: 0.35 Lm: 6.534 (6.522) Lt: 5.786 (5.779) Accm: 3.27 (3.36) Acct: 5.15 (5.14) proj_loss: -0.5969 (-0.5942) time: 0.6777 data: 0.0003 [11-25 05:42:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [ 834/1669] eta: 0:09:52 tlr: 0.00014 tnm: 0.35 Lm: 6.534 (6.581) Lt: 5.786 (5.838) Accm: 3.22 (3.15) Acct: 4.94 (4.93) proj_loss: -0.5814 (-0.5840) time: 0.6778 data: 0.0003 [11-25 05:42:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [ 834/1669] eta: 0:09:52 tlr: 0.00014 tnm: 0.35 Lm: 6.559 (6.554) Lt: 5.868 (5.826) Accm: 3.23 (3.39) Acct: 4.91 (5.21) proj_loss: -0.5968 (-0.5990) time: 0.6777 data: 0.0002 [11-25 05:47:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.523 (6.537) Lt: 5.803 (5.798) Accm: 3.50 (3.48) Acct: 5.49 (5.43) proj_loss: -0.6034 (-0.6018) time: 0.6788 data: 0.0002 [11-25 05:47:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.521 (6.526) Lt: 5.769 (5.774) Accm: 3.24 (3.35) Acct: 4.97 (5.32) proj_loss: -0.5850 (-0.5852) time: 0.6788 data: 0.0003 [11-25 05:47:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.473 (6.503) Lt: 5.725 (5.743) Accm: 3.46 (3.34) Acct: 5.18 (5.14) proj_loss: -0.5933 (-0.5916) time: 0.6788 data: 0.0003 [11-25 05:47:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.582 (6.549) Lt: 5.835 (5.805) Accm: 3.26 (3.23) Acct: 5.00 (4.97) proj_loss: -0.5949 (-0.5939) time: 0.6787 data: 0.0003 [11-25 05:51:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.630 (6.580) Lt: 5.883 (5.844) Accm: 3.26 (3.18) Acct: 4.86 (4.85) proj_loss: -0.5929 (-0.5919) time: 0.6765 data: 0.0019 [11-25 05:51:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 159/350] Total time: 0:19:16 (0.693 s / it) [11-25 05:51:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.508 (6.501) Lt: 5.751 (5.751) Accm: 3.26 (3.37) Acct: 4.99 (5.30) proj_loss: -0.5887 (-0.5911) time: 0.6765 data: 0.0016 [11-25 05:51:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.486 (6.505) Lt: 5.738 (5.759) Accm: 3.50 (3.49) Acct: 5.77 (5.50) proj_loss: -0.5968 (-0.5995) time: 0.6765 data: 0.0016 [11-25 05:51:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 159/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.504 (6.503) Lt: 5.674 (5.729) Accm: 3.45 (3.30) Acct: 5.22 (5.15) proj_loss: -0.5927 (-0.5912) time: 0.6765 data: 0.0020 [11-25 05:51:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 159/350] Total time: 0:19:16 (0.693 s / it) [11-25 05:51:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 159/350] Total time: 0:19:16 (0.693 s / it) [11-25 05:51:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 159/350] Total time: 0:19:16 (0.693 s / it) [11-25 05:54:14] (home/user/VAR/trainer.py, line 114)=> FID: 3.6787792496190264 [11-25 05:54:15] (/home/user/VAR/train.py , line 259)=> [*] [ep159] (val 50000) Lm: 6.5454, Lt: 5.7931, Acc m&t: 3.27 5.15, Val cost: 144.94s [11-25 05:54:15] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-25 05:54:50] (/home/user/VAR/train.py , line 276)=> [ep159] (training ) Lm: 6.544 (6.545), Lt: 5.783 (5.793), Acc m&t: 3.29 5.19, Remain: 2 days, 12:06:25, Finish: 2024-11-27 01:58 [11-25 05:54:50] (/home/user/VAR/train.py , line 276)=> [ep159] (training ) Lm: 6.544 (6.545), Lt: 5.783 (5.793), Acc m&t: 3.29 5.19, Remain: 2 days, 12:05:03, Finish: 2024-11-27 01:56 [11-25 05:54:50] (/home/user/VAR/train.py , line 276)=> [ep159] (training ) Lm: 6.544 (6.545), Lt: 5.783 (5.793), Acc m&t: 3.29 5.19, Remain: 2 days, 12:03:39, Finish: 2024-11-27 01:55 [11-25 05:54:50] (/home/user/VAR/train.py , line 276)=> [ep159] (training ) Lm: 6.544 (6.545), Lt: 5.783 (5.793), Acc m&t: 3.29 5.19, Remain: 2 days, 12:04:11, Finish: 2024-11-27 01:56 [11-25 05:54:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [ 0/1669] eta: 0:18:37 tlr: 0.00014 tnm: 0.33 Lm: 6.558 (6.558) Lt: 5.731 (5.731) Accm: 3.19 (3.19) Acct: 5.37 (5.37) proj_loss: -0.6057 (-0.6057) time: 0.6693 data: 0.0004 [11-25 05:54:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [ 0/1669] eta: 0:18:35 tlr: 0.00014 tnm: 0.33 Lm: 6.452 (6.452) Lt: 5.674 (5.674) Accm: 3.25 (3.25) Acct: 5.53 (5.53) proj_loss: -0.6292 (-0.6292) time: 0.6683 data: 0.0004 [11-25 05:54:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [ 0/1669] eta: 0:18:35 tlr: 0.00014 tnm: 0.33 Lm: 6.503 (6.503) Lt: 5.728 (5.728) Accm: 3.75 (3.75) Acct: 5.77 (5.77) proj_loss: -0.5736 (-0.5736) time: 0.6684 data: 0.0003 [11-25 05:54:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [ 0/1669] eta: 0:18:36 tlr: 0.00014 tnm: 0.33 Lm: 6.477 (6.477) Lt: 5.710 (5.710) Accm: 3.79 (3.79) Acct: 5.94 (5.94) proj_loss: -0.5932 (-0.5932) time: 0.6688 data: 0.0003 [11-25 05:59:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [ 417/1669] eta: 0:14:05 tlr: 0.00014 tnm: 0.33 Lm: 6.559 (6.559) Lt: 5.814 (5.814) Accm: 3.44 (3.44) Acct: 5.44 (5.44) proj_loss: -0.6010 (-0.6010) time: 0.6762 data: 0.0003 [11-25 05:59:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [ 417/1669] eta: 0:14:05 tlr: 0.00014 tnm: 0.33 Lm: 6.546 (6.546) Lt: 5.785 (5.785) Accm: 3.13 (3.13) Acct: 5.06 (5.06) proj_loss: -0.6045 (-0.6045) time: 0.6762 data: 0.0003 [11-25 05:59:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [ 417/1669] eta: 0:14:05 tlr: 0.00014 tnm: 0.33 Lm: 6.601 (6.601) Lt: 5.801 (5.801) Accm: 3.13 (3.13) Acct: 4.93 (4.93) proj_loss: -0.5928 (-0.5928) time: 0.6762 data: 0.0003 [11-25 05:59:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [ 417/1669] eta: 0:14:05 tlr: 0.00014 tnm: 0.33 Lm: 6.407 (6.407) Lt: 5.624 (5.624) Accm: 3.77 (3.77) Acct: 5.87 (5.87) proj_loss: -0.5857 (-0.5857) time: 0.6762 data: 0.0003 [11-25 06:04:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [ 834/1669] eta: 0:09:45 tlr: 0.00014 tnm: 0.33 Lm: 6.503 (6.442) Lt: 5.699 (5.649) Accm: 3.75 (3.64) Acct: 5.77 (5.67) proj_loss: -0.5854 (-0.5856) time: 0.6778 data: 0.0003 [11-25 06:04:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [ 834/1669] eta: 0:09:45 tlr: 0.00014 tnm: 0.33 Lm: 6.607 (6.575) Lt: 5.831 (5.820) Accm: 3.29 (3.39) Acct: 5.20 (5.36) proj_loss: -0.5969 (-0.5997) time: 0.6779 data: 0.0003 [11-25 06:04:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [ 834/1669] eta: 0:09:45 tlr: 0.00014 tnm: 0.33 Lm: 6.644 (6.627) Lt: 5.872 (5.857) Accm: 3.06 (2.92) Acct: 4.49 (4.61) proj_loss: -0.5920 (-0.5925) time: 0.6779 data: 0.0003 [11-25 06:04:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [ 834/1669] eta: 0:09:45 tlr: 0.00014 tnm: 0.33 Lm: 6.452 (6.499) Lt: 5.674 (5.743) Accm: 3.25 (3.28) Acct: 5.53 (5.27) proj_loss: -0.6292 (-0.6169) time: 0.6778 data: 0.0003 [11-25 06:09:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.32 Lm: 6.472 (6.497) Lt: 5.672 (5.725) Accm: 3.38 (3.34) Acct: 5.60 (5.43) proj_loss: -0.6051 (-0.6079) time: 0.6763 data: 0.0002 [11-25 06:09:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.32 Lm: 6.661 (6.656) Lt: 5.919 (5.895) Accm: 3.04 (2.95) Acct: 4.59 (4.63) proj_loss: -0.5981 (-0.5954) time: 0.6763 data: 0.0003 [11-25 06:09:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.32 Lm: 6.507 (6.485) Lt: 5.714 (5.707) Accm: 3.57 (3.53) Acct: 5.52 (5.50) proj_loss: -0.5916 (-0.5900) time: 0.6763 data: 0.0003 [11-25 06:09:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.32 Lm: 6.625 (6.592) Lt: 5.862 (5.838) Accm: 3.19 (3.31) Acct: 5.17 (5.31) proj_loss: -0.5993 (-0.6002) time: 0.6763 data: 0.0003 [11-25 06:14:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.607 (6.538) Lt: 5.831 (5.794) Accm: 3.29 (3.41) Acct: 5.20 (5.40) proj_loss: -0.6017 (-0.6010) time: 0.6800 data: 0.0018 [11-25 06:14:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 160/350] Total time: 0:19:19 (0.695 s / it) [11-25 06:14:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.511 (6.492) Lt: 5.728 (5.721) Accm: 3.38 (3.48) Acct: 5.27 (5.39) proj_loss: -0.5978 (-0.5957) time: 0.6799 data: 0.0013 [11-25 06:14:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.644 (6.645) Lt: 5.872 (5.887) Accm: 3.06 (3.05) Acct: 4.68 (4.79) proj_loss: -0.5966 (-0.5957) time: 0.6800 data: 0.0016 [11-25 06:14:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 160/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.492 (6.523) Lt: 5.674 (5.749) Accm: 3.25 (3.27) Acct: 5.53 (5.33) proj_loss: -0.5829 (-0.6029) time: 0.6800 data: 0.0018 [11-25 06:14:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 160/350] Total time: 0:19:19 (0.695 s / it) [11-25 06:14:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 160/350] Total time: 0:19:19 (0.695 s / it) [11-25 06:14:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 160/350] Total time: 0:19:19 (0.695 s / it) [11-25 06:14:10] (/home/user/VAR/train.py , line 276)=> [ep160] (training ) Lm: 6.543 (6.543), Lt: 5.783 (5.783), Acc m&t: 3.30 5.21, Remain: 2 days, 11:57:31, Finish: 2024-11-27 02:11 [11-25 06:14:10] (/home/user/VAR/train.py , line 276)=> [ep160] (training ) Lm: 6.543 (6.543), Lt: 5.783 (5.783), Acc m&t: 3.30 5.21, Remain: 2 days, 11:56:38, Finish: 2024-11-27 02:10 [11-25 06:14:10] (/home/user/VAR/train.py , line 276)=> [ep160] (training ) Lm: 6.543 (6.543), Lt: 5.783 (5.783), Acc m&t: 3.30 5.21, Remain: 2 days, 11:56:12, Finish: 2024-11-27 02:10 [11-25 06:14:10] (/home/user/VAR/train.py , line 276)=> [ep160] (training ) Lm: 6.543 (6.543), Lt: 5.783 (5.783), Acc m&t: 3.30 5.21, Remain: 2 days, 11:56:06, Finish: 2024-11-27 02:10 [11-25 06:14:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [ 0/1669] eta: 0:18:38 tlr: 0.00014 tnm: 0.35 Lm: 6.585 (6.585) Lt: 5.839 (5.839) Accm: 2.90 (2.90) Acct: 4.30 (4.30) proj_loss: -0.5940 (-0.5940) time: 0.6699 data: 0.0004 [11-25 06:14:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [ 0/1669] eta: 0:18:38 tlr: 0.00014 tnm: 0.35 Lm: 6.650 (6.650) Lt: 5.868 (5.868) Accm: 2.80 (2.80) Acct: 4.68 (4.68) proj_loss: -0.5700 (-0.5700) time: 0.6703 data: 0.0004 [11-25 06:14:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [ 0/1669] eta: 0:18:39 tlr: 0.00014 tnm: 0.35 Lm: 6.491 (6.491) Lt: 5.725 (5.725) Accm: 3.60 (3.60) Acct: 6.04 (6.04) proj_loss: -0.5933 (-0.5933) time: 0.6705 data: 0.0003 [11-25 06:14:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [ 0/1669] eta: 0:18:39 tlr: 0.00014 tnm: 0.35 Lm: 6.512 (6.512) Lt: 5.731 (5.731) Accm: 3.57 (3.57) Acct: 6.03 (6.03) proj_loss: -0.5827 (-0.5827) time: 0.6705 data: 0.0004 [11-25 06:18:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.37 Lm: 6.446 (6.446) Lt: 5.656 (5.656) Accm: 3.62 (3.62) Acct: 5.89 (5.89) proj_loss: -0.5825 (-0.5825) time: 0.6802 data: 0.0003 [11-25 06:18:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.37 Lm: 6.598 (6.598) Lt: 5.832 (5.832) Accm: 2.87 (2.87) Acct: 4.70 (4.70) proj_loss: -0.5787 (-0.5787) time: 0.6802 data: 0.0003 [11-25 06:18:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.37 Lm: 6.591 (6.591) Lt: 5.828 (5.828) Accm: 2.94 (2.94) Acct: 4.61 (4.61) proj_loss: -0.5892 (-0.5892) time: 0.6802 data: 0.0003 [11-25 06:18:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.37 Lm: 6.501 (6.501) Lt: 5.741 (5.741) Accm: 3.59 (3.59) Acct: 5.73 (5.73) proj_loss: -0.5988 (-0.5988) time: 0.6802 data: 0.0003 [11-25 06:23:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [ 834/1669] eta: 0:09:25 tlr: 0.00014 tnm: 0.34 Lm: 6.512 (6.515) Lt: 5.756 (5.746) Accm: 3.58 (3.55) Acct: 5.80 (5.76) proj_loss: -0.5933 (-0.5959) time: 0.6753 data: 0.0003 [11-25 06:23:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [ 834/1669] eta: 0:09:25 tlr: 0.00014 tnm: 0.34 Lm: 6.585 (6.547) Lt: 5.816 (5.757) Accm: 2.97 (3.10) Acct: 4.91 (5.07) proj_loss: -0.5843 (-0.5767) time: 0.6753 data: 0.0003 [11-25 06:23:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [ 834/1669] eta: 0:09:25 tlr: 0.00014 tnm: 0.34 Lm: 6.512 (6.475) Lt: 5.731 (5.683) Accm: 3.57 (3.53) Acct: 5.75 (5.73) proj_loss: -0.5827 (-0.5847) time: 0.6753 data: 0.0003 [11-25 06:23:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [ 834/1669] eta: 0:09:25 tlr: 0.00014 tnm: 0.34 Lm: 6.546 (6.569) Lt: 5.796 (5.797) Accm: 2.94 (2.95) Acct: 4.72 (4.80) proj_loss: -0.5874 (-0.5852) time: 0.6753 data: 0.0003 [11-25 06:28:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [1251/1669] eta: 0:04:42 tlr: 0.00014 tnm: 0.34 Lm: 6.556 (6.568) Lt: 5.788 (5.793) Accm: 3.03 (3.05) Acct: 4.86 (4.89) proj_loss: -0.5883 (-0.5862) time: 0.6764 data: 0.0003 [11-25 06:28:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [1251/1669] eta: 0:04:42 tlr: 0.00014 tnm: 0.34 Lm: 6.523 (6.526) Lt: 5.734 (5.729) Accm: 3.45 (3.35) Acct: 5.58 (5.47) proj_loss: -0.5825 (-0.5821) time: 0.6764 data: 0.0003 [11-25 06:28:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [1251/1669] eta: 0:04:42 tlr: 0.00014 tnm: 0.34 Lm: 6.522 (6.525) Lt: 5.742 (5.735) Accm: 3.19 (3.17) Acct: 5.04 (5.10) proj_loss: -0.5892 (-0.5832) time: 0.6764 data: 0.0003 [11-25 06:28:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [1251/1669] eta: 0:04:42 tlr: 0.00014 tnm: 0.34 Lm: 6.526 (6.530) Lt: 5.756 (5.776) Accm: 3.53 (3.51) Acct: 5.61 (5.61) proj_loss: -0.5988 (-0.5984) time: 0.6764 data: 0.0003 [11-25 06:33:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.541 (6.539) Lt: 5.757 (5.789) Accm: 3.47 (3.43) Acct: 5.42 (5.44) proj_loss: -0.6029 (-0.5993) time: 0.6773 data: 0.0016 [11-25 06:33:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.533 (6.544) Lt: 5.737 (5.755) Accm: 3.34 (3.23) Acct: 5.41 (5.29) proj_loss: -0.5827 (-0.5835) time: 0.6773 data: 0.0016 [11-25 06:33:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 161/350] Total time: 0:18:53 (0.679 s / it) [11-25 06:33:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.566 (6.571) Lt: 5.796 (5.794) Accm: 3.13 (3.09) Acct: 5.01 (4.94) proj_loss: -0.5879 (-0.5865) time: 0.6773 data: 0.0019 [11-25 06:33:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 161/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.585 (6.558) Lt: 5.816 (5.776) Accm: 3.05 (3.15) Acct: 5.17 (5.11) proj_loss: -0.5940 (-0.5876) time: 0.6773 data: 0.0015 [11-25 06:33:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 161/350] Total time: 0:18:53 (0.679 s / it) [11-25 06:33:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 161/350] Total time: 0:18:53 (0.679 s / it) [11-25 06:33:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 161/350] Total time: 0:18:53 (0.679 s / it) [11-25 06:33:04] (/home/user/VAR/train.py , line 276)=> [ep161] (training ) Lm: 6.543 (6.557), Lt: 5.783 (5.804), Acc m&t: 3.30 5.21, Remain: 2 days, 11:29:51, Finish: 2024-11-27 02:02 [11-25 06:33:04] (/home/user/VAR/train.py , line 276)=> [ep161] (training ) Lm: 6.543 (6.557), Lt: 5.783 (5.804), Acc m&t: 3.30 5.21, Remain: 2 days, 11:29:23, Finish: 2024-11-27 02:02 [11-25 06:33:04] (/home/user/VAR/train.py , line 276)=> [ep161] (training ) Lm: 6.543 (6.557), Lt: 5.783 (5.804), Acc m&t: 3.30 5.21, Remain: 2 days, 11:29:19, Finish: 2024-11-27 02:02 [11-25 06:33:04] (/home/user/VAR/train.py , line 276)=> [ep161] (training ) Lm: 6.543 (6.557), Lt: 5.783 (5.804), Acc m&t: 3.30 5.21, Remain: 2 days, 11:28:50, Finish: 2024-11-27 02:01 [11-25 06:33:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [ 0/1669] eta: 0:18:35 tlr: 0.00014 tnm: 0.34 Lm: 6.760 (6.760) Lt: 6.037 (6.037) Accm: 2.47 (2.47) Acct: 4.05 (4.05) proj_loss: -0.5998 (-0.5998) time: 0.6682 data: 0.0003 [11-25 06:33:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [ 0/1669] eta: 0:18:34 tlr: 0.00014 tnm: 0.34 Lm: 6.440 (6.440) Lt: 5.687 (5.687) Accm: 3.56 (3.56) Acct: 5.46 (5.46) proj_loss: -0.5912 (-0.5912) time: 0.6675 data: 0.0004 [11-25 06:33:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [ 0/1669] eta: 0:18:26 tlr: 0.00014 tnm: 0.34 Lm: 6.695 (6.695) Lt: 6.002 (6.002) Accm: 2.84 (2.84) Acct: 4.15 (4.15) proj_loss: -0.6006 (-0.6006) time: 0.6633 data: 0.0004 [11-25 06:33:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [ 0/1669] eta: 0:18:34 tlr: 0.00014 tnm: 0.34 Lm: 6.491 (6.491) Lt: 5.752 (5.752) Accm: 3.30 (3.30) Acct: 5.42 (5.42) proj_loss: -0.6224 (-0.6224) time: 0.6678 data: 0.0004 [11-25 06:38:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [ 417/1669] eta: 0:15:29 tlr: 0.00014 tnm: 0.32 Lm: 6.443 (6.443) Lt: 5.691 (5.691) Accm: 3.51 (3.51) Acct: 5.67 (5.67) proj_loss: -0.6246 (-0.6246) time: 0.7978 data: 0.0003 [11-25 06:38:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [ 417/1669] eta: 0:15:29 tlr: 0.00014 tnm: 0.32 Lm: 6.649 (6.649) Lt: 5.952 (5.952) Accm: 2.94 (2.94) Acct: 4.72 (4.72) proj_loss: -0.6020 (-0.6020) time: 0.7978 data: 0.0003 [11-25 06:38:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [ 417/1669] eta: 0:15:29 tlr: 0.00014 tnm: 0.32 Lm: 6.622 (6.622) Lt: 5.915 (5.915) Accm: 3.03 (3.03) Acct: 4.61 (4.61) proj_loss: -0.6042 (-0.6042) time: 0.7978 data: 0.0003 [11-25 06:38:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [ 417/1669] eta: 0:15:29 tlr: 0.00014 tnm: 0.32 Lm: 6.459 (6.459) Lt: 5.705 (5.705) Accm: 3.54 (3.54) Acct: 5.60 (5.60) proj_loss: -0.5930 (-0.5930) time: 0.7978 data: 0.0003 [11-25 06:42:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [ 834/1669] eta: 0:09:52 tlr: 0.00014 tnm: 0.32 Lm: 6.440 (6.432) Lt: 5.687 (5.662) Accm: 3.56 (3.58) Acct: 5.75 (5.74) proj_loss: -0.5949 (-0.5950) time: 0.6787 data: 0.0003 [11-25 06:42:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [ 834/1669] eta: 0:09:52 tlr: 0.00014 tnm: 0.32 Lm: 6.626 (6.641) Lt: 5.867 (5.914) Accm: 3.06 (2.98) Acct: 4.91 (4.78) proj_loss: -0.5998 (-0.5969) time: 0.6787 data: 0.0003 [11-25 06:42:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [ 834/1669] eta: 0:09:52 tlr: 0.00014 tnm: 0.32 Lm: 6.478 (6.455) Lt: 5.668 (5.683) Accm: 3.64 (3.55) Acct: 5.72 (5.69) proj_loss: -0.6224 (-0.6174) time: 0.6787 data: 0.0003 [11-25 06:42:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [ 834/1669] eta: 0:09:52 tlr: 0.00014 tnm: 0.32 Lm: 6.550 (6.580) Lt: 5.829 (5.842) Accm: 3.23 (3.18) Acct: 5.08 (5.07) proj_loss: -0.6006 (-0.5976) time: 0.6787 data: 0.0002 [11-25 06:47:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [1251/1669] eta: 0:04:51 tlr: 0.00014 tnm: 0.34 Lm: 6.580 (6.588) Lt: 5.849 (5.849) Accm: 3.06 (3.10) Acct: 4.80 (4.93) proj_loss: -0.5924 (-0.5932) time: 0.6775 data: 0.0003 [11-25 06:47:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [1251/1669] eta: 0:04:51 tlr: 0.00014 tnm: 0.34 Lm: 6.459 (6.473) Lt: 5.705 (5.702) Accm: 3.54 (3.47) Acct: 5.60 (5.60) proj_loss: -0.5930 (-0.5936) time: 0.6775 data: 0.0003 [11-25 06:47:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [1251/1669] eta: 0:04:51 tlr: 0.00014 tnm: 0.34 Lm: 6.466 (6.455) Lt: 5.674 (5.682) Accm: 3.47 (3.48) Acct: 5.57 (5.60) proj_loss: -0.6127 (-0.6108) time: 0.6775 data: 0.0003 [11-25 06:47:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [1251/1669] eta: 0:04:51 tlr: 0.00014 tnm: 0.34 Lm: 6.659 (6.654) Lt: 5.899 (5.919) Accm: 2.89 (2.92) Acct: 4.53 (4.62) proj_loss: -0.5992 (-0.5973) time: 0.6775 data: 0.0003 [11-25 06:52:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.626 (6.641) Lt: 5.867 (5.889) Accm: 3.02 (2.94) Acct: 4.91 (4.68) proj_loss: -0.5986 (-0.5958) time: 0.6792 data: 0.0019 [11-25 06:52:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 162/350] Total time: 0:19:16 (0.693 s / it) [11-25 06:52:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.477 (6.459) Lt: 5.680 (5.695) Accm: 3.31 (3.45) Acct: 5.42 (5.46) proj_loss: -0.6106 (-0.6108) time: 0.6792 data: 0.0020 [11-25 06:52:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.478 (6.482) Lt: 5.711 (5.704) Accm: 3.56 (3.50) Acct: 5.75 (5.70) proj_loss: -0.5912 (-0.5921) time: 0.6792 data: 0.0020 [11-25 06:52:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 162/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.600 (6.590) Lt: 5.868 (5.857) Accm: 3.02 (3.09) Acct: 4.84 (4.91) proj_loss: -0.6006 (-0.5950) time: 0.6792 data: 0.0017 [11-25 06:52:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 162/350] Total time: 0:19:16 (0.693 s / it) [11-25 06:52:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 162/350] Total time: 0:19:16 (0.693 s / it) [11-25 06:52:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 162/350] Total time: 0:19:16 (0.693 s / it) [11-25 06:52:20] (/home/user/VAR/train.py , line 276)=> [ep162] (training ) Lm: 6.531 (6.531), Lt: 5.772 (5.772), Acc m&t: 3.33 5.28, Remain: 2 days, 11:18:37, Finish: 2024-11-27 02:10 [11-25 06:52:20] (/home/user/VAR/train.py , line 276)=> [ep162] (training ) Lm: 6.531 (6.531), Lt: 5.772 (5.772), Acc m&t: 3.33 5.28, Remain: 2 days, 11:18:48, Finish: 2024-11-27 02:11 [11-25 06:52:20] (/home/user/VAR/train.py , line 276)=> [ep162] (training ) Lm: 6.531 (6.531), Lt: 5.772 (5.772), Acc m&t: 3.33 5.28, Remain: 2 days, 11:18:57, Finish: 2024-11-27 02:11 [11-25 06:52:20] (/home/user/VAR/train.py , line 276)=> [ep162] (training ) Lm: 6.531 (6.531), Lt: 5.772 (5.772), Acc m&t: 3.33 5.28, Remain: 2 days, 11:18:53, Finish: 2024-11-27 02:11 [11-25 06:52:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [ 0/1669] eta: 0:18:24 tlr: 0.00014 tnm: 0.32 Lm: 6.531 (6.531) Lt: 5.758 (5.758) Accm: 3.02 (3.02) Acct: 4.60 (4.60) proj_loss: -0.5818 (-0.5818) time: 0.6616 data: 0.0004 [11-25 06:52:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [ 0/1669] eta: 0:18:24 tlr: 0.00014 tnm: 0.32 Lm: 6.430 (6.430) Lt: 5.696 (5.696) Accm: 3.56 (3.56) Acct: 5.65 (5.65) proj_loss: -0.6262 (-0.6262) time: 0.6617 data: 0.0004 [11-25 06:52:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [ 0/1669] eta: 0:18:21 tlr: 0.00014 tnm: 0.32 Lm: 6.542 (6.542) Lt: 5.778 (5.778) Accm: 3.18 (3.18) Acct: 5.08 (5.08) proj_loss: -0.5860 (-0.5860) time: 0.6602 data: 0.0004 [11-25 06:52:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [ 0/1669] eta: 0:18:25 tlr: 0.00014 tnm: 0.32 Lm: 6.352 (6.352) Lt: 5.516 (5.516) Accm: 3.70 (3.70) Acct: 6.15 (6.15) proj_loss: -0.5887 (-0.5887) time: 0.6624 data: 0.0003 [11-25 06:57:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.33 Lm: 6.435 (6.435) Lt: 5.617 (5.617) Accm: 3.51 (3.51) Acct: 5.80 (5.80) proj_loss: -0.5988 (-0.5988) time: 0.6747 data: 0.0003 [11-25 06:57:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.33 Lm: 6.576 (6.576) Lt: 5.787 (5.787) Accm: 3.06 (3.06) Acct: 4.82 (4.82) proj_loss: -0.5862 (-0.5862) time: 0.6747 data: 0.0003 [11-25 06:57:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.33 Lm: 6.559 (6.559) Lt: 5.818 (5.818) Accm: 3.33 (3.33) Acct: 5.26 (5.26) proj_loss: -0.6210 (-0.6210) time: 0.6747 data: 0.0003 [11-25 06:57:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.33 Lm: 6.618 (6.618) Lt: 5.887 (5.887) Accm: 3.15 (3.15) Acct: 5.11 (5.11) proj_loss: -0.5965 (-0.5965) time: 0.6747 data: 0.0003 [11-25 07:02:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [ 834/1669] eta: 0:09:46 tlr: 0.00014 tnm: 0.33 Lm: 6.484 (6.534) Lt: 5.696 (5.771) Accm: 3.51 (3.39) Acct: 5.65 (5.40) proj_loss: -0.6158 (-0.6139) time: 0.6739 data: 0.0003 [11-25 07:02:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [ 834/1669] eta: 0:09:46 tlr: 0.00014 tnm: 0.33 Lm: 6.542 (6.533) Lt: 5.778 (5.790) Accm: 3.18 (3.34) Acct: 5.15 (5.33) proj_loss: -0.5982 (-0.5970) time: 0.6739 data: 0.0003 [11-25 07:02:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [ 834/1669] eta: 0:09:47 tlr: 0.00014 tnm: 0.33 Lm: 6.431 (6.434) Lt: 5.657 (5.630) Accm: 3.62 (3.55) Acct: 5.68 (5.76) proj_loss: -0.6010 (-0.5996) time: 0.6739 data: 0.0003 [11-25 07:02:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [ 834/1669] eta: 0:09:47 tlr: 0.00014 tnm: 0.33 Lm: 6.621 (6.591) Lt: 5.816 (5.832) Accm: 3.02 (3.02) Acct: 4.60 (4.69) proj_loss: -0.5907 (-0.5951) time: 0.6739 data: 0.0003 [11-25 07:06:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.576 (6.566) Lt: 5.813 (5.826) Accm: 3.06 (3.08) Acct: 4.81 (4.78) proj_loss: -0.5876 (-0.5925) time: 0.7377 data: 0.0003 [11-25 07:06:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.475 (6.478) Lt: 5.688 (5.681) Accm: 3.47 (3.43) Acct: 5.57 (5.48) proj_loss: -0.5949 (-0.5946) time: 0.7377 data: 0.0003 [11-25 07:06:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.485 (6.522) Lt: 5.698 (5.754) Accm: 3.49 (3.41) Acct: 5.51 (5.39) proj_loss: -0.6077 (-0.6046) time: 0.7377 data: 0.0003 [11-25 07:06:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.548 (6.538) Lt: 5.808 (5.802) Accm: 3.25 (3.33) Acct: 5.15 (5.28) proj_loss: -0.5939 (-0.5952) time: 0.7377 data: 0.0003 [11-25 07:11:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.35 Lm: 6.553 (6.551) Lt: 5.818 (5.805) Accm: 3.18 (3.28) Acct: 5.15 (5.21) proj_loss: -0.5924 (-0.5946) time: 0.6781 data: 0.0016 [11-25 07:11:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 163/350] Total time: 0:19:20 (0.695 s / it) [11-25 07:11:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.35 Lm: 6.518 (6.514) Lt: 5.719 (5.727) Accm: 3.33 (3.33) Acct: 5.46 (5.35) proj_loss: -0.6010 (-0.5967) time: 0.6781 data: 0.0016 [11-25 07:11:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.35 Lm: 6.531 (6.497) Lt: 5.810 (5.750) Accm: 3.10 (3.31) Acct: 5.03 (5.09) proj_loss: -0.5856 (-0.5911) time: 0.6781 data: 0.0014 [11-25 07:11:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 163/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.35 Lm: 6.486 (6.519) Lt: 5.701 (5.748) Accm: 3.51 (3.44) Acct: 5.65 (5.48) proj_loss: -0.6158 (-0.6068) time: 0.6781 data: 0.0015 [11-25 07:11:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 163/350] Total time: 0:19:20 (0.695 s / it) [11-25 07:11:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 163/350] Total time: 0:19:20 (0.695 s / it) [11-25 07:11:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 163/350] Total time: 0:19:20 (0.695 s / it) [11-25 07:11:41] (/home/user/VAR/train.py , line 276)=> [ep163] (training ) Lm: 6.531 (6.550), Lt: 5.772 (5.801), Acc m&t: 3.33 5.28, Remain: 2 days, 10:59:23, Finish: 2024-11-27 02:11 [11-25 07:11:41] (/home/user/VAR/train.py , line 276)=> [ep163] (training ) Lm: 6.531 (6.550), Lt: 5.772 (5.801), Acc m&t: 3.33 5.28, Remain: 2 days, 10:59:12, Finish: 2024-11-27 02:10 [11-25 07:11:41] (/home/user/VAR/train.py , line 276)=> [ep163] (training ) Lm: 6.531 (6.550), Lt: 5.772 (5.801), Acc m&t: 3.33 5.28, Remain: 2 days, 10:59:08, Finish: 2024-11-27 02:10 [11-25 07:11:41] (/home/user/VAR/train.py , line 276)=> [ep163] (training ) Lm: 6.531 (6.550), Lt: 5.772 (5.801), Acc m&t: 3.33 5.28, Remain: 2 days, 10:59:09, Finish: 2024-11-27 02:10 [11-25 07:11:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [ 0/1669] eta: 0:17:58 tlr: 0.00014 tnm: 0.33 Lm: 6.709 (6.709) Lt: 5.996 (5.996) Accm: 2.57 (2.57) Acct: 3.74 (3.74) proj_loss: -0.5933 (-0.5933) time: 0.6462 data: 0.0003 [11-25 07:11:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [ 0/1669] eta: 0:18:36 tlr: 0.00014 tnm: 0.33 Lm: 6.591 (6.591) Lt: 5.839 (5.839) Accm: 2.98 (2.98) Acct: 4.44 (4.44) proj_loss: -0.5862 (-0.5862) time: 0.6691 data: 0.0003 [11-25 07:11:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [ 0/1669] eta: 0:18:36 tlr: 0.00014 tnm: 0.33 Lm: 6.464 (6.464) Lt: 5.739 (5.739) Accm: 3.61 (3.61) Acct: 5.65 (5.65) proj_loss: -0.6089 (-0.6089) time: 0.6689 data: 0.0004 [11-25 07:11:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [ 0/1669] eta: 0:18:35 tlr: 0.00014 tnm: 0.33 Lm: 6.537 (6.537) Lt: 5.750 (5.750) Accm: 2.91 (2.91) Acct: 4.82 (4.82) proj_loss: -0.5832 (-0.5832) time: 0.6683 data: 0.0003 [11-25 07:16:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [ 417/1669] eta: 0:14:06 tlr: 0.00014 tnm: 0.34 Lm: 6.553 (6.553) Lt: 5.786 (5.786) Accm: 3.15 (3.15) Acct: 5.09 (5.09) proj_loss: -0.5884 (-0.5884) time: 0.6768 data: 0.0003 [11-25 07:16:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [ 417/1669] eta: 0:14:06 tlr: 0.00014 tnm: 0.34 Lm: 6.526 (6.526) Lt: 5.763 (5.763) Accm: 3.07 (3.07) Acct: 4.80 (4.80) proj_loss: -0.6005 (-0.6005) time: 0.6768 data: 0.0003 [11-25 07:16:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [ 417/1669] eta: 0:14:06 tlr: 0.00014 tnm: 0.34 Lm: 6.616 (6.616) Lt: 5.886 (5.886) Accm: 2.89 (2.89) Acct: 4.42 (4.42) proj_loss: -0.5976 (-0.5976) time: 0.6767 data: 0.0003 [11-25 07:16:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [ 417/1669] eta: 0:14:06 tlr: 0.00014 tnm: 0.34 Lm: 6.514 (6.514) Lt: 5.775 (5.775) Accm: 3.43 (3.43) Acct: 5.36 (5.36) proj_loss: -0.5998 (-0.5998) time: 0.6768 data: 0.0003 [11-25 07:21:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [ 834/1669] eta: 0:09:24 tlr: 0.00014 tnm: 0.33 Lm: 6.564 (6.552) Lt: 5.811 (5.828) Accm: 3.26 (3.23) Acct: 5.08 (4.97) proj_loss: -0.6089 (-0.6039) time: 0.6765 data: 0.0003 [11-25 07:21:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [ 834/1669] eta: 0:09:24 tlr: 0.00014 tnm: 0.33 Lm: 6.522 (6.553) Lt: 5.776 (5.822) Accm: 3.21 (3.05) Acct: 5.01 (4.62) proj_loss: -0.6018 (-0.6011) time: 0.6765 data: 0.0003 [11-25 07:21:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [ 834/1669] eta: 0:09:24 tlr: 0.00014 tnm: 0.33 Lm: 6.537 (6.537) Lt: 5.750 (5.750) Accm: 3.24 (3.18) Acct: 5.35 (5.18) proj_loss: -0.5869 (-0.5879) time: 0.6765 data: 0.0003 [11-25 07:21:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [ 834/1669] eta: 0:09:24 tlr: 0.00014 tnm: 0.33 Lm: 6.461 (6.497) Lt: 5.727 (5.751) Accm: 3.16 (3.19) Acct: 5.15 (4.99) proj_loss: -0.6087 (-0.6032) time: 0.6765 data: 0.0003 [11-25 07:25:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [1251/1669] eta: 0:04:42 tlr: 0.00014 tnm: 0.33 Lm: 6.616 (6.593) Lt: 5.881 (5.863) Accm: 3.01 (2.99) Acct: 4.59 (4.51) proj_loss: -0.5976 (-0.5988) time: 0.6748 data: 0.0003 [11-25 07:25:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [1251/1669] eta: 0:04:42 tlr: 0.00014 tnm: 0.33 Lm: 6.514 (6.484) Lt: 5.775 (5.750) Accm: 3.43 (3.41) Acct: 5.36 (5.34) proj_loss: -0.6074 (-0.6044) time: 0.6748 data: 0.0003 [11-25 07:25:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [1251/1669] eta: 0:04:42 tlr: 0.00014 tnm: 0.33 Lm: 6.504 (6.509) Lt: 5.776 (5.769) Accm: 3.26 (3.23) Acct: 5.10 (5.01) proj_loss: -0.6044 (-0.6024) time: 0.6748 data: 0.0003 [11-25 07:25:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [1251/1669] eta: 0:04:42 tlr: 0.00014 tnm: 0.33 Lm: 6.520 (6.524) Lt: 5.715 (5.732) Accm: 3.31 (3.24) Acct: 5.36 (5.23) proj_loss: -0.5884 (-0.5884) time: 0.6748 data: 0.0003 [11-25 07:30:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.35 Lm: 6.461 (6.495) Lt: 5.727 (5.742) Accm: 3.35 (3.28) Acct: 5.15 (5.12) proj_loss: -0.6087 (-0.6042) time: 0.6797 data: 0.0021 [11-25 07:30:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.35 Lm: 6.482 (6.483) Lt: 5.754 (5.751) Accm: 3.57 (3.45) Acct: 5.65 (5.45) proj_loss: -0.6089 (-0.6076) time: 0.6797 data: 0.0021 [11-25 07:30:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.35 Lm: 6.709 (6.632) Lt: 5.986 (5.907) Accm: 2.81 (2.92) Acct: 4.17 (4.41) proj_loss: -0.5945 (-0.5980) time: 0.6797 data: 0.0019 [11-25 07:30:32] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 164/350] Total time: 0:18:51 (0.678 s / it) [11-25 07:30:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 164/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.35 Lm: 6.537 (6.549) Lt: 5.750 (5.770) Accm: 3.24 (3.20) Acct: 5.35 (5.14) proj_loss: -0.5869 (-0.5868) time: 0.6798 data: 0.0019 [11-25 07:30:32] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 164/350] Total time: 0:18:51 (0.678 s / it) [11-25 07:30:32] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 164/350] Total time: 0:18:51 (0.678 s / it) [11-25 07:30:32] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 164/350] Total time: 0:18:51 (0.678 s / it) [11-25 07:30:32] (/home/user/VAR/train.py , line 276)=> [ep164] (training ) Lm: 6.531 (6.552), Lt: 5.772 (5.797), Acc m&t: 3.33 5.28, Remain: 2 days, 10:28:39, Finish: 2024-11-27 01:59 [11-25 07:30:32] (/home/user/VAR/train.py , line 276)=> [ep164] (training ) Lm: 6.531 (6.552), Lt: 5.772 (5.797), Acc m&t: 3.33 5.28, Remain: 2 days, 10:28:55, Finish: 2024-11-27 01:59 [11-25 07:30:32] (/home/user/VAR/train.py , line 276)=> [ep164] (training ) Lm: 6.531 (6.552), Lt: 5.772 (5.797), Acc m&t: 3.33 5.28, Remain: 2 days, 10:29:11, Finish: 2024-11-27 01:59 [11-25 07:30:32] (/home/user/VAR/train.py , line 276)=> [ep164] (training ) Lm: 6.531 (6.552), Lt: 5.772 (5.797), Acc m&t: 3.33 5.28, Remain: 2 days, 10:29:22, Finish: 2024-11-27 01:59 [11-25 07:30:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [ 0/1669] eta: 0:18:20 tlr: 0.00014 tnm: 0.35 Lm: 6.563 (6.563) Lt: 5.783 (5.783) Accm: 3.19 (3.19) Acct: 5.01 (5.01) proj_loss: -0.5883 (-0.5883) time: 0.6591 data: 0.0004 [11-25 07:30:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [ 0/1669] eta: 0:18:20 tlr: 0.00014 tnm: 0.35 Lm: 6.497 (6.497) Lt: 5.808 (5.808) Accm: 3.46 (3.46) Acct: 5.23 (5.23) proj_loss: -0.6206 (-0.6206) time: 0.6596 data: 0.0003 [11-25 07:30:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [ 0/1669] eta: 0:18:20 tlr: 0.00014 tnm: 0.35 Lm: 6.594 (6.594) Lt: 5.871 (5.871) Accm: 3.34 (3.34) Acct: 5.20 (5.20) proj_loss: -0.5956 (-0.5956) time: 0.6596 data: 0.0004 [11-25 07:30:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [ 0/1669] eta: 0:18:22 tlr: 0.00014 tnm: 0.35 Lm: 6.358 (6.358) Lt: 5.610 (5.610) Accm: 3.63 (3.63) Acct: 5.77 (5.77) proj_loss: -0.6046 (-0.6046) time: 0.6605 data: 0.0004 [11-25 07:35:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [ 417/1669] eta: 0:15:24 tlr: 0.00014 tnm: 0.37 Lm: 6.446 (6.446) Lt: 5.714 (5.714) Accm: 3.57 (3.57) Acct: 5.64 (5.64) proj_loss: -0.6035 (-0.6035) time: 0.7398 data: 0.0003 [11-25 07:35:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [ 417/1669] eta: 0:15:24 tlr: 0.00014 tnm: 0.37 Lm: 6.476 (6.476) Lt: 5.752 (5.752) Accm: 3.59 (3.59) Acct: 5.52 (5.52) proj_loss: -0.6041 (-0.6041) time: 0.7398 data: 0.0003 [11-25 07:35:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [ 417/1669] eta: 0:15:24 tlr: 0.00014 tnm: 0.37 Lm: 6.624 (6.624) Lt: 5.882 (5.882) Accm: 3.14 (3.14) Acct: 4.84 (4.84) proj_loss: -0.5920 (-0.5920) time: 0.7398 data: 0.0002 [11-25 07:35:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [ 417/1669] eta: 0:15:24 tlr: 0.00014 tnm: 0.37 Lm: 6.551 (6.551) Lt: 5.768 (5.768) Accm: 3.23 (3.23) Acct: 5.06 (5.06) proj_loss: -0.5946 (-0.5946) time: 0.7398 data: 0.0003 [11-25 07:40:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [ 834/1669] eta: 0:09:54 tlr: 0.00014 tnm: 0.34 Lm: 6.539 (6.496) Lt: 5.754 (5.740) Accm: 3.26 (3.38) Acct: 5.11 (5.22) proj_loss: -0.5951 (-0.5948) time: 0.6763 data: 0.0003 [11-25 07:40:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [ 834/1669] eta: 0:09:54 tlr: 0.00014 tnm: 0.34 Lm: 6.535 (6.499) Lt: 5.819 (5.776) Accm: 3.50 (3.38) Acct: 5.51 (5.29) proj_loss: -0.6046 (-0.6043) time: 0.6763 data: 0.0003 [11-25 07:40:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [ 834/1669] eta: 0:09:54 tlr: 0.00014 tnm: 0.34 Lm: 6.594 (6.586) Lt: 5.871 (5.813) Accm: 3.24 (3.17) Acct: 5.20 (5.04) proj_loss: -0.5884 (-0.5905) time: 0.6763 data: 0.0003 [11-25 07:40:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [ 834/1669] eta: 0:09:54 tlr: 0.00014 tnm: 0.34 Lm: 6.497 (6.556) Lt: 5.808 (5.839) Accm: 3.46 (3.31) Acct: 5.23 (5.19) proj_loss: -0.6052 (-0.6045) time: 0.6763 data: 0.0005 [11-25 07:45:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.579 (6.582) Lt: 5.860 (5.858) Accm: 3.16 (3.20) Acct: 4.96 (5.07) proj_loss: -0.5981 (-0.6011) time: 0.6779 data: 0.0003 [11-25 07:45:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.624 (6.603) Lt: 5.882 (5.836) Accm: 3.19 (3.16) Acct: 4.98 (4.98) proj_loss: -0.5920 (-0.5923) time: 0.6779 data: 0.0003 [11-25 07:45:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.551 (6.548) Lt: 5.768 (5.796) Accm: 3.23 (3.33) Acct: 5.13 (5.20) proj_loss: -0.5979 (-0.5963) time: 0.6779 data: 0.0003 [11-25 07:45:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.34 Lm: 6.570 (6.552) Lt: 5.860 (5.837) Accm: 3.26 (3.24) Acct: 5.05 (5.02) proj_loss: -0.6053 (-0.6084) time: 0.6779 data: 0.0003 [11-25 07:49:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.33 Lm: 6.535 (6.545) Lt: 5.819 (5.826) Accm: 3.50 (3.37) Acct: 5.51 (5.23) proj_loss: -0.6046 (-0.6055) time: 0.6804 data: 0.0013 [11-25 07:49:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 165/350] Total time: 0:19:19 (0.695 s / it) [11-25 07:49:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.33 Lm: 6.627 (6.608) Lt: 5.892 (5.850) Accm: 3.13 (3.15) Acct: 5.08 (5.00) proj_loss: -0.5884 (-0.5911) time: 0.6804 data: 0.0015 [11-25 07:49:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.33 Lm: 6.497 (6.548) Lt: 5.808 (5.815) Accm: 3.46 (3.28) Acct: 5.23 (5.19) proj_loss: -0.6052 (-0.6024) time: 0.6804 data: 0.0016 [11-25 07:49:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 165/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.33 Lm: 6.563 (6.555) Lt: 5.783 (5.799) Accm: 3.19 (3.29) Acct: 5.11 (5.14) proj_loss: -0.6007 (-0.5999) time: 0.6804 data: 0.0019 [11-25 07:49:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 165/350] Total time: 0:19:19 (0.695 s / it) [11-25 07:49:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 165/350] Total time: 0:19:19 (0.695 s / it) [11-25 07:49:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 165/350] Total time: 0:19:19 (0.695 s / it) [11-25 07:49:52] (/home/user/VAR/train.py , line 276)=> [ep165] (training ) Lm: 6.531 (6.532), Lt: 5.772 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 10:21:39, Finish: 2024-11-27 02:11 [11-25 07:49:52] (/home/user/VAR/train.py , line 276)=> [ep165] (training ) Lm: 6.531 (6.532), Lt: 5.772 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 10:22:15, Finish: 2024-11-27 02:12 [11-25 07:49:52] (/home/user/VAR/train.py , line 276)=> [ep165] (training ) Lm: 6.531 (6.532), Lt: 5.772 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 10:21:53, Finish: 2024-11-27 02:11 [11-25 07:49:52] (/home/user/VAR/train.py , line 276)=> [ep165] (training ) Lm: 6.531 (6.532), Lt: 5.772 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 10:22:41, Finish: 2024-11-27 02:12 [11-25 07:49:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [ 0/1669] eta: 0:18:17 tlr: 0.00014 tnm: 0.33 Lm: 6.463 (6.463) Lt: 5.669 (5.669) Accm: 3.58 (3.58) Acct: 5.58 (5.58) proj_loss: -0.6024 (-0.6024) time: 0.6575 data: 0.0004 [11-25 07:49:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [ 0/1669] eta: 0:18:18 tlr: 0.00014 tnm: 0.33 Lm: 6.364 (6.364) Lt: 5.607 (5.607) Accm: 3.77 (3.77) Acct: 5.65 (5.65) proj_loss: -0.6144 (-0.6144) time: 0.6580 data: 0.0003 [11-25 07:49:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [ 0/1669] eta: 0:18:18 tlr: 0.00014 tnm: 0.33 Lm: 6.651 (6.651) Lt: 5.920 (5.920) Accm: 3.01 (3.01) Acct: 4.63 (4.63) proj_loss: -0.6156 (-0.6156) time: 0.6581 data: 0.0004 [11-25 07:49:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [ 0/1669] eta: 0:18:18 tlr: 0.00014 tnm: 0.33 Lm: 6.515 (6.515) Lt: 5.755 (5.755) Accm: 3.53 (3.53) Acct: 5.63 (5.63) proj_loss: -0.6045 (-0.6045) time: 0.6584 data: 0.0004 [11-25 07:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.35 Lm: 6.572 (6.572) Lt: 5.822 (5.822) Accm: 3.29 (3.29) Acct: 5.29 (5.29) proj_loss: -0.6088 (-0.6088) time: 0.6770 data: 0.0003 [11-25 07:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.35 Lm: 6.472 (6.472) Lt: 5.750 (5.750) Accm: 3.50 (3.50) Acct: 5.27 (5.27) proj_loss: -0.6158 (-0.6158) time: 0.6769 data: 0.0003 [11-25 07:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.35 Lm: 6.690 (6.690) Lt: 5.943 (5.943) Accm: 2.88 (2.88) Acct: 4.60 (4.60) proj_loss: -0.6023 (-0.6023) time: 0.6770 data: 0.0003 [11-25 07:54:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.35 Lm: 6.423 (6.423) Lt: 5.642 (5.642) Accm: 3.76 (3.76) Acct: 5.92 (5.92) proj_loss: -0.6024 (-0.6024) time: 0.6770 data: 0.0003 [11-25 07:59:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [ 834/1669] eta: 0:09:47 tlr: 0.00014 tnm: 0.33 Lm: 6.543 (6.496) Lt: 5.778 (5.760) Accm: 3.23 (3.41) Acct: 5.13 (5.22) proj_loss: -0.6144 (-0.6074) time: 0.7404 data: 0.0003 [11-25 07:59:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [ 834/1669] eta: 0:09:47 tlr: 0.00014 tnm: 0.33 Lm: 6.463 (6.443) Lt: 5.669 (5.652) Accm: 3.58 (3.67) Acct: 5.60 (5.81) proj_loss: -0.6024 (-0.5976) time: 0.7404 data: 0.0003 [11-25 07:59:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [ 834/1669] eta: 0:09:47 tlr: 0.00014 tnm: 0.33 Lm: 6.651 (6.576) Lt: 5.920 (5.830) Accm: 3.01 (3.20) Acct: 4.63 (5.07) proj_loss: -0.6156 (-0.6073) time: 0.7404 data: 0.0003 [11-25 07:59:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [ 834/1669] eta: 0:09:47 tlr: 0.00014 tnm: 0.33 Lm: 6.611 (6.585) Lt: 5.889 (5.856) Accm: 3.08 (3.22) Acct: 4.96 (5.04) proj_loss: -0.6045 (-0.6040) time: 0.7404 data: 0.0003 [11-25 08:04:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.35 Lm: 6.615 (6.594) Lt: 5.874 (5.857) Accm: 3.06 (3.17) Acct: 4.76 (4.92) proj_loss: -0.6088 (-0.6068) time: 0.6775 data: 0.0003 [11-25 08:04:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.35 Lm: 6.552 (6.512) Lt: 5.804 (5.777) Accm: 3.23 (3.30) Acct: 5.01 (5.03) proj_loss: -0.6042 (-0.6040) time: 0.6775 data: 0.0003 [11-25 08:04:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.35 Lm: 6.571 (6.555) Lt: 5.820 (5.802) Accm: 3.33 (3.31) Acct: 5.11 (5.20) proj_loss: -0.6023 (-0.6021) time: 0.6775 data: 0.0003 [11-25 08:04:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [1251/1669] eta: 0:04:52 tlr: 0.00014 tnm: 0.35 Lm: 6.473 (6.479) Lt: 5.671 (5.692) Accm: 3.54 (3.55) Acct: 5.59 (5.69) proj_loss: -0.5968 (-0.5960) time: 0.6775 data: 0.0003 [11-25 08:09:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.32 Lm: 6.483 (6.502) Lt: 5.672 (5.727) Accm: 3.50 (3.42) Acct: 5.58 (5.45) proj_loss: -0.6024 (-0.5979) time: 0.6795 data: 0.0023 [11-25 08:09:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 166/350] Total time: 0:19:21 (0.696 s / it) [11-25 08:09:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.32 Lm: 6.641 (6.572) Lt: 5.875 (5.817) Accm: 3.18 (3.28) Acct: 4.92 (5.14) proj_loss: -0.5905 (-0.5998) time: 0.6795 data: 0.0016 [11-25 08:09:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.32 Lm: 6.618 (6.643) Lt: 5.889 (5.900) Accm: 3.04 (3.01) Acct: 4.56 (4.76) proj_loss: -0.6045 (-0.6013) time: 0.6795 data: 0.0020 [11-25 08:09:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 166/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.32 Lm: 6.560 (6.544) Lt: 5.830 (5.799) Accm: 3.22 (3.17) Acct: 4.89 (4.90) proj_loss: -0.6027 (-0.6038) time: 0.6795 data: 0.0020 [11-25 08:09:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 166/350] Total time: 0:19:21 (0.696 s / it) [11-25 08:09:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 166/350] Total time: 0:19:21 (0.696 s / it) [11-25 08:09:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 166/350] Total time: 0:19:21 (0.696 s / it) [11-25 08:09:13] (/home/user/VAR/train.py , line 276)=> [ep166] (training ) Lm: 6.529 (6.529), Lt: 5.772 (5.779), Acc m&t: 3.33 5.28, Remain: 2 days, 10:05:35, Finish: 2024-11-27 02:14 [11-25 08:09:13] (/home/user/VAR/train.py , line 276)=> [ep166] (training ) Lm: 6.529 (6.529), Lt: 5.772 (5.779), Acc m&t: 3.33 5.28, Remain: 2 days, 10:05:49, Finish: 2024-11-27 02:15 [11-25 08:09:13] (/home/user/VAR/train.py , line 276)=> [ep166] (training ) Lm: 6.529 (6.529), Lt: 5.772 (5.779), Acc m&t: 3.33 5.28, Remain: 2 days, 10:05:23, Finish: 2024-11-27 02:14 [11-25 08:09:13] (/home/user/VAR/train.py , line 276)=> [ep166] (training ) Lm: 6.529 (6.529), Lt: 5.772 (5.779), Acc m&t: 3.33 5.28, Remain: 2 days, 10:05:37, Finish: 2024-11-27 02:14 [11-25 08:09:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [ 0/1669] eta: 0:18:24 tlr: 0.00014 tnm: 0.34 Lm: 6.629 (6.629) Lt: 5.840 (5.840) Accm: 3.00 (3.00) Acct: 4.80 (4.80) proj_loss: -0.5948 (-0.5948) time: 0.6619 data: 0.0004 [11-25 08:09:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [ 0/1669] eta: 0:18:24 tlr: 0.00014 tnm: 0.34 Lm: 6.485 (6.485) Lt: 5.742 (5.742) Accm: 3.38 (3.38) Acct: 5.35 (5.35) proj_loss: -0.6055 (-0.6055) time: 0.6618 data: 0.0004 [11-25 08:09:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [ 0/1669] eta: 0:18:24 tlr: 0.00014 tnm: 0.34 Lm: 6.612 (6.612) Lt: 5.884 (5.884) Accm: 3.16 (3.16) Acct: 4.99 (4.99) proj_loss: -0.6059 (-0.6059) time: 0.6621 data: 0.0004 [11-25 08:09:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [ 0/1669] eta: 0:18:25 tlr: 0.00014 tnm: 0.34 Lm: 6.495 (6.495) Lt: 5.735 (5.735) Accm: 3.22 (3.22) Acct: 5.42 (5.42) proj_loss: -0.6065 (-0.6065) time: 0.6625 data: 0.0004 [11-25 08:13:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.32 Lm: 6.534 (6.534) Lt: 5.777 (5.777) Accm: 3.28 (3.28) Acct: 5.23 (5.23) proj_loss: -0.5925 (-0.5925) time: 0.6744 data: 0.0003 [11-25 08:13:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.32 Lm: 6.581 (6.581) Lt: 5.860 (5.860) Accm: 3.04 (3.04) Acct: 4.80 (4.80) proj_loss: -0.6089 (-0.6089) time: 0.6745 data: 0.0003 [11-25 08:13:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.32 Lm: 6.588 (6.588) Lt: 5.855 (5.855) Accm: 3.13 (3.13) Acct: 4.91 (4.91) proj_loss: -0.5935 (-0.5935) time: 0.6744 data: 0.0003 [11-25 08:13:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [ 417/1669] eta: 0:14:07 tlr: 0.00014 tnm: 0.32 Lm: 6.687 (6.687) Lt: 5.886 (5.886) Accm: 2.73 (2.73) Acct: 4.49 (4.49) proj_loss: -0.5861 (-0.5861) time: 0.6745 data: 0.0004 [11-25 08:18:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [ 834/1669] eta: 0:09:25 tlr: 0.00014 tnm: 0.33 Lm: 6.629 (6.635) Lt: 5.840 (5.847) Accm: 3.00 (3.09) Acct: 4.80 (4.96) proj_loss: -0.5914 (-0.5879) time: 0.6752 data: 0.0003 [11-25 08:18:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [ 834/1669] eta: 0:09:25 tlr: 0.00014 tnm: 0.33 Lm: 6.563 (6.541) Lt: 5.826 (5.804) Accm: 3.16 (3.21) Acct: 4.99 (4.97) proj_loss: -0.6059 (-0.6012) time: 0.6752 data: 0.0003 [11-25 08:18:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [ 834/1669] eta: 0:09:25 tlr: 0.00014 tnm: 0.33 Lm: 6.581 (6.581) Lt: 5.830 (5.850) Accm: 2.93 (3.00) Acct: 4.42 (4.67) proj_loss: -0.6055 (-0.6043) time: 0.6752 data: 0.0003 [11-25 08:18:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [ 834/1669] eta: 0:09:25 tlr: 0.00014 tnm: 0.33 Lm: 6.573 (6.589) Lt: 5.819 (5.840) Accm: 3.22 (3.12) Acct: 5.03 (4.94) proj_loss: -0.5832 (-0.5894) time: 0.6752 data: 0.0003 [11-25 08:23:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [1251/1669] eta: 0:04:42 tlr: 0.00014 tnm: 0.34 Lm: 6.554 (6.576) Lt: 5.791 (5.821) Accm: 3.17 (3.12) Acct: 5.07 (4.98) proj_loss: -0.5875 (-0.5900) time: 0.6754 data: 0.0003 [11-25 08:23:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [1251/1669] eta: 0:04:42 tlr: 0.00014 tnm: 0.34 Lm: 6.580 (6.587) Lt: 5.805 (5.801) Accm: 3.19 (3.16) Acct: 5.08 (5.06) proj_loss: -0.5931 (-0.5920) time: 0.6754 data: 0.0003 [11-25 08:23:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [1251/1669] eta: 0:04:42 tlr: 0.00014 tnm: 0.34 Lm: 6.578 (6.580) Lt: 5.828 (5.844) Accm: 3.02 (3.03) Acct: 4.65 (4.72) proj_loss: -0.6030 (-0.6033) time: 0.6754 data: 0.0003 [11-25 08:23:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [1251/1669] eta: 0:04:42 tlr: 0.00014 tnm: 0.34 Lm: 6.506 (6.510) Lt: 5.764 (5.772) Accm: 3.26 (3.30) Acct: 5.04 (5.05) proj_loss: -0.5980 (-0.5984) time: 0.6754 data: 0.0003 [11-25 08:28:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.448 (6.484) Lt: 5.703 (5.748) Accm: 3.35 (3.39) Acct: 5.10 (5.20) proj_loss: -0.6037 (-0.5995) time: 0.6776 data: 0.0019 [11-25 08:28:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 167/350] Total time: 0:18:51 (0.678 s / it) [11-25 08:28:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.536 (6.538) Lt: 5.764 (5.766) Accm: 3.22 (3.23) Acct: 5.11 (5.14) proj_loss: -0.5917 (-0.5942) time: 0.6776 data: 0.0015 [11-25 08:28:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.540 (6.577) Lt: 5.770 (5.791) Accm: 3.33 (3.19) Acct: 5.32 (5.11) proj_loss: -0.5914 (-0.5910) time: 0.6776 data: 0.0014 [11-25 08:28:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 167/350] [1668/1669] eta: 0:00:00 tlr: 0.00014 tnm: 0.34 Lm: 6.575 (6.568) Lt: 5.826 (5.832) Accm: 3.11 (3.07) Acct: 4.87 (4.83) proj_loss: -0.6055 (-0.6069) time: 0.6776 data: 0.0019 [11-25 08:28:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 167/350] Total time: 0:18:51 (0.678 s / it) [11-25 08:28:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 167/350] Total time: 0:18:51 (0.678 s / it) [11-25 08:28:04] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 167/350] Total time: 0:18:51 (0.678 s / it) [11-25 08:28:04] (/home/user/VAR/train.py , line 276)=> [ep167] (training ) Lm: 6.529 (6.542), Lt: 5.772 (5.788), Acc m&t: 3.33 5.28, Remain: 2 days, 9:37:30, Finish: 2024-11-27 02:05 [11-25 08:28:04] (/home/user/VAR/train.py , line 276)=> [ep167] (training ) Lm: 6.529 (6.542), Lt: 5.772 (5.788), Acc m&t: 3.33 5.28, Remain: 2 days, 9:38:00, Finish: 2024-11-27 02:06 [11-25 08:28:04] (/home/user/VAR/train.py , line 276)=> [ep167] (training ) Lm: 6.529 (6.542), Lt: 5.772 (5.788), Acc m&t: 3.33 5.28, Remain: 2 days, 9:37:30, Finish: 2024-11-27 02:05 [11-25 08:28:04] (/home/user/VAR/train.py , line 276)=> [ep167] (training ) Lm: 6.529 (6.542), Lt: 5.772 (5.788), Acc m&t: 3.33 5.28, Remain: 2 days, 9:37:38, Finish: 2024-11-27 02:05 [11-25 08:28:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [ 0/1669] eta: 0:18:16 tlr: 0.00014 tnm: 0.34 Lm: 6.494 (6.494) Lt: 5.726 (5.726) Accm: 3.37 (3.37) Acct: 5.25 (5.25) proj_loss: -0.5945 (-0.5945) time: 0.6570 data: 0.0004 [11-25 08:28:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [ 0/1669] eta: 0:18:17 tlr: 0.00014 tnm: 0.34 Lm: 6.500 (6.500) Lt: 5.732 (5.732) Accm: 3.28 (3.28) Acct: 4.96 (4.96) proj_loss: -0.5992 (-0.5992) time: 0.6574 data: 0.0004 [11-25 08:28:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [ 0/1669] eta: 0:18:17 tlr: 0.00014 tnm: 0.34 Lm: 6.411 (6.411) Lt: 5.716 (5.716) Accm: 3.68 (3.68) Acct: 5.54 (5.54) proj_loss: -0.6002 (-0.6002) time: 0.6573 data: 0.0004 [11-25 08:28:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [ 0/1669] eta: 0:18:13 tlr: 0.00014 tnm: 0.34 Lm: 6.584 (6.584) Lt: 5.779 (5.779) Accm: 3.32 (3.32) Acct: 5.65 (5.65) proj_loss: -0.5817 (-0.5817) time: 0.6552 data: 0.0004 [11-25 08:33:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [ 417/1669] eta: 0:15:17 tlr: 0.00013 tnm: 0.34 Lm: 6.608 (6.608) Lt: 5.825 (5.825) Accm: 3.32 (3.32) Acct: 5.51 (5.51) proj_loss: -0.5912 (-0.5912) time: 0.7394 data: 0.0003 [11-25 08:33:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [ 417/1669] eta: 0:15:17 tlr: 0.00013 tnm: 0.34 Lm: 6.455 (6.455) Lt: 5.655 (5.655) Accm: 3.31 (3.31) Acct: 5.20 (5.20) proj_loss: -0.5881 (-0.5881) time: 0.7394 data: 0.0003 [11-25 08:33:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [ 417/1669] eta: 0:15:17 tlr: 0.00013 tnm: 0.34 Lm: 6.445 (6.445) Lt: 5.730 (5.730) Accm: 3.47 (3.47) Acct: 5.43 (5.43) proj_loss: -0.5908 (-0.5908) time: 0.7394 data: 0.0003 [11-25 08:33:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [ 417/1669] eta: 0:15:17 tlr: 0.00013 tnm: 0.34 Lm: 6.463 (6.463) Lt: 5.750 (5.750) Accm: 3.36 (3.36) Acct: 4.86 (4.86) proj_loss: -0.5951 (-0.5951) time: 0.7394 data: 0.0003 [11-25 08:37:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [ 834/1669] eta: 0:09:54 tlr: 0.00013 tnm: 0.33 Lm: 6.494 (6.480) Lt: 5.726 (5.741) Accm: 3.37 (3.42) Acct: 5.25 (5.04) proj_loss: -0.5945 (-0.5910) time: 0.6772 data: 0.0003 [11-25 08:37:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [ 834/1669] eta: 0:09:54 tlr: 0.00013 tnm: 0.33 Lm: 6.500 (6.475) Lt: 5.732 (5.706) Accm: 3.34 (3.34) Acct: 5.23 (5.21) proj_loss: -0.5992 (-0.5984) time: 0.6772 data: 0.0003 [11-25 08:37:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [ 834/1669] eta: 0:09:54 tlr: 0.00013 tnm: 0.33 Lm: 6.584 (6.565) Lt: 5.779 (5.804) Accm: 3.32 (3.35) Acct: 5.37 (5.44) proj_loss: -0.6007 (-0.6003) time: 0.6772 data: 0.0003 [11-25 08:37:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [ 834/1669] eta: 0:09:54 tlr: 0.00013 tnm: 0.33 Lm: 6.478 (6.466) Lt: 5.743 (5.736) Accm: 3.27 (3.31) Acct: 5.32 (5.22) proj_loss: -0.5813 (-0.5829) time: 0.6772 data: 0.0003 [11-25 08:42:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.34 Lm: 6.493 (6.515) Lt: 5.747 (5.772) Accm: 3.14 (3.24) Acct: 5.10 (5.14) proj_loss: -0.5834 (-0.5836) time: 0.6777 data: 0.0003 [11-25 08:42:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.34 Lm: 6.463 (6.453) Lt: 5.724 (5.699) Accm: 3.45 (3.54) Acct: 5.32 (5.36) proj_loss: -0.5948 (-0.5921) time: 0.6777 data: 0.0003 [11-25 08:42:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.34 Lm: 6.508 (6.500) Lt: 5.770 (5.742) Accm: 3.31 (3.32) Acct: 5.26 (5.23) proj_loss: -0.5967 (-0.5974) time: 0.6777 data: 0.0003 [11-25 08:42:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.34 Lm: 6.546 (6.551) Lt: 5.773 (5.795) Accm: 3.37 (3.39) Acct: 5.41 (5.45) proj_loss: -0.6046 (-0.6023) time: 0.6777 data: 0.0003 [11-25 08:47:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.34 Lm: 6.552 (6.551) Lt: 5.779 (5.805) Accm: 3.32 (3.33) Acct: 5.37 (5.33) proj_loss: -0.6085 (-0.6044) time: 0.6799 data: 0.0025 [11-25 08:47:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 168/350] Total time: 0:19:18 (0.694 s / it) [11-25 08:47:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.34 Lm: 6.508 (6.548) Lt: 5.750 (5.821) Accm: 3.01 (3.14) Acct: 4.87 (4.95) proj_loss: -0.5855 (-0.5910) time: 0.6799 data: 0.0017 [11-25 08:47:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.34 Lm: 6.516 (6.516) Lt: 5.807 (5.760) Accm: 3.28 (3.23) Acct: 5.23 (5.09) proj_loss: -0.5942 (-0.5945) time: 0.6799 data: 0.0017 [11-25 08:47:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 168/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.34 Lm: 6.494 (6.471) Lt: 5.726 (5.716) Accm: 3.37 (3.49) Acct: 5.35 (5.36) proj_loss: -0.5945 (-0.5891) time: 0.6799 data: 0.0013 [11-25 08:47:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 168/350] Total time: 0:19:18 (0.694 s / it) [11-25 08:47:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 168/350] Total time: 0:19:18 (0.694 s / it) [11-25 08:47:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 168/350] Total time: 0:19:18 (0.694 s / it) [11-25 08:47:23] (/home/user/VAR/train.py , line 276)=> [ep168] (training ) Lm: 6.529 (6.529), Lt: 5.772 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 9:26:31, Finish: 2024-11-27 02:13 [11-25 08:47:23] (/home/user/VAR/train.py , line 276)=> [ep168] (training ) Lm: 6.529 (6.529), Lt: 5.772 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 9:26:30, Finish: 2024-11-27 02:13 [11-25 08:47:23] (/home/user/VAR/train.py , line 276)=> [ep168] (training ) Lm: 6.529 (6.529), Lt: 5.772 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 9:26:17, Finish: 2024-11-27 02:13 [11-25 08:47:23] (/home/user/VAR/train.py , line 276)=> [ep168] (training ) Lm: 6.529 (6.529), Lt: 5.772 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 9:26:18, Finish: 2024-11-27 02:13 [11-25 08:47:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [ 0/1669] eta: 0:18:23 tlr: 0.00013 tnm: 0.36 Lm: 6.571 (6.571) Lt: 5.827 (5.827) Accm: 3.26 (3.26) Acct: 5.22 (5.22) proj_loss: -0.6169 (-0.6169) time: 0.6609 data: 0.0004 [11-25 08:47:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [ 0/1669] eta: 0:18:22 tlr: 0.00013 tnm: 0.36 Lm: 6.680 (6.680) Lt: 5.899 (5.899) Accm: 2.94 (2.94) Acct: 4.68 (4.68) proj_loss: -0.5956 (-0.5956) time: 0.6609 data: 0.0004 [11-25 08:47:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [ 0/1669] eta: 0:18:23 tlr: 0.00013 tnm: 0.36 Lm: 6.580 (6.580) Lt: 5.797 (5.797) Accm: 3.10 (3.10) Acct: 4.73 (4.73) proj_loss: -0.5872 (-0.5872) time: 0.6612 data: 0.0004 [11-25 08:47:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [ 0/1669] eta: 0:18:24 tlr: 0.00013 tnm: 0.36 Lm: 6.706 (6.706) Lt: 5.964 (5.964) Accm: 2.83 (2.83) Acct: 4.24 (4.24) proj_loss: -0.6205 (-0.6205) time: 0.6615 data: 0.0004 [11-25 08:52:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [ 417/1669] eta: 0:14:06 tlr: 0.00013 tnm: 0.36 Lm: 6.641 (6.641) Lt: 5.896 (5.896) Accm: 2.99 (2.99) Acct: 4.54 (4.54) proj_loss: -0.6142 (-0.6142) time: 0.6764 data: 0.0003 [11-25 08:52:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [ 417/1669] eta: 0:14:06 tlr: 0.00013 tnm: 0.36 Lm: 6.611 (6.611) Lt: 5.841 (5.841) Accm: 3.09 (3.09) Acct: 4.77 (4.77) proj_loss: -0.6062 (-0.6062) time: 0.6764 data: 0.0003 [11-25 08:52:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [ 417/1669] eta: 0:14:06 tlr: 0.00013 tnm: 0.36 Lm: 6.491 (6.491) Lt: 5.726 (5.726) Accm: 3.20 (3.20) Acct: 4.80 (4.80) proj_loss: -0.5969 (-0.5969) time: 0.6764 data: 0.0003 [11-25 08:52:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [ 417/1669] eta: 0:14:06 tlr: 0.00013 tnm: 0.36 Lm: 6.677 (6.677) Lt: 5.929 (5.929) Accm: 2.84 (2.84) Acct: 4.57 (4.57) proj_loss: -0.5882 (-0.5882) time: 0.6764 data: 0.0003 [11-25 08:57:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [ 834/1669] eta: 0:09:45 tlr: 0.00013 tnm: 0.35 Lm: 6.673 (6.606) Lt: 5.899 (5.852) Accm: 2.94 (3.11) Acct: 4.68 (4.96) proj_loss: -0.5853 (-0.5873) time: 0.7374 data: 0.0003 [11-25 08:57:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [ 834/1669] eta: 0:09:45 tlr: 0.00013 tnm: 0.35 Lm: 6.577 (6.598) Lt: 5.829 (5.842) Accm: 3.15 (3.07) Acct: 4.84 (4.75) proj_loss: -0.6080 (-0.6115) time: 0.7374 data: 0.0003 [11-25 08:57:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [ 834/1669] eta: 0:09:45 tlr: 0.00013 tnm: 0.35 Lm: 6.571 (6.579) Lt: 5.827 (5.831) Accm: 3.26 (3.19) Acct: 5.04 (4.86) proj_loss: -0.6169 (-0.6140) time: 0.7374 data: 0.0003 [11-25 08:57:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [ 834/1669] eta: 0:09:45 tlr: 0.00013 tnm: 0.35 Lm: 6.404 (6.462) Lt: 5.674 (5.709) Accm: 3.29 (3.31) Acct: 4.86 (4.97) proj_loss: -0.5991 (-0.5976) time: 0.7374 data: 0.0003 [11-25 09:01:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [1251/1669] eta: 0:04:51 tlr: 0.00013 tnm: 0.36 Lm: 6.442 (6.467) Lt: 5.688 (5.707) Accm: 3.41 (3.39) Acct: 5.09 (5.11) proj_loss: -0.6013 (-0.5991) time: 0.6780 data: 0.0003 [11-25 09:01:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [1251/1669] eta: 0:04:51 tlr: 0.00013 tnm: 0.36 Lm: 6.575 (6.574) Lt: 5.819 (5.824) Accm: 3.28 (3.23) Acct: 5.22 (5.16) proj_loss: -0.5905 (-0.5904) time: 0.6780 data: 0.0003 [11-25 09:01:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [1251/1669] eta: 0:04:51 tlr: 0.00013 tnm: 0.36 Lm: 6.589 (6.586) Lt: 5.841 (5.847) Accm: 3.09 (3.08) Acct: 4.68 (4.69) proj_loss: -0.6161 (-0.6143) time: 0.6780 data: 0.0003 [11-25 09:01:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [1251/1669] eta: 0:04:51 tlr: 0.00013 tnm: 0.36 Lm: 6.626 (6.618) Lt: 5.874 (5.861) Accm: 3.10 (3.07) Acct: 4.86 (4.78) proj_loss: -0.6070 (-0.6043) time: 0.6780 data: 0.0003 [11-25 09:06:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.577 (6.557) Lt: 5.829 (5.787) Accm: 3.15 (3.16) Acct: 4.87 (4.91) proj_loss: -0.6080 (-0.6079) time: 0.6774 data: 0.0021 [11-25 09:06:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 169/350] Total time: 0:19:17 (0.694 s / it) [11-25 09:06:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.404 (6.453) Lt: 5.674 (5.682) Accm: 3.53 (3.44) Acct: 5.32 (5.28) proj_loss: -0.5991 (-0.5943) time: 0.6774 data: 0.0016 [11-25 09:06:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.618 (6.583) Lt: 5.898 (5.839) Accm: 3.07 (3.20) Acct: 5.01 (5.13) proj_loss: -0.5956 (-0.5930) time: 0.6774 data: 0.0021 [11-25 09:06:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 169/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.571 (6.574) Lt: 5.827 (5.834) Accm: 3.26 (3.13) Acct: 5.04 (4.79) proj_loss: -0.6153 (-0.6099) time: 0.6774 data: 0.0018 [11-25 09:06:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 169/350] Total time: 0:19:17 (0.694 s / it) [11-25 09:06:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 169/350] Total time: 0:19:17 (0.694 s / it) [11-25 09:06:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 169/350] Total time: 0:19:17 (0.694 s / it) [11-25 09:09:07] (home/user/VAR/trainer.py, line 114)=> FID: 3.6171989805843623 [11-25 09:09:08] (/home/user/VAR/train.py , line 259)=> [*] [ep169] (val 50000) Lm: 6.5242, Lt: 5.7653, Acc m&t: 3.32 5.23, Val cost: 146.49s [11-25 09:09:08] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-25 09:09:42] (/home/user/VAR/train.py , line 276)=> [ep169] (training ) Lm: 6.524 (6.524), Lt: 5.765 (5.765), Acc m&t: 3.33 5.28, Remain: 2 days, 8:55:10, Finish: 2024-11-27 02:01 [11-25 09:09:42] (/home/user/VAR/train.py , line 276)=> [ep169] (training ) Lm: 6.524 (6.524), Lt: 5.765 (5.765), Acc m&t: 3.33 5.28, Remain: 2 days, 8:55:21, Finish: 2024-11-27 02:02 [11-25 09:09:42] (/home/user/VAR/train.py , line 276)=> [ep169] (training ) Lm: 6.524 (6.524), Lt: 5.765 (5.765), Acc m&t: 3.33 5.28, Remain: 2 days, 8:55:52, Finish: 2024-11-27 02:02 [11-25 09:09:42] (/home/user/VAR/train.py , line 276)=> [ep169] (training ) Lm: 6.524 (6.524), Lt: 5.765 (5.765), Acc m&t: 3.33 5.28, Remain: 2 days, 8:55:22, Finish: 2024-11-27 02:02 [11-25 09:09:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [ 0/1669] eta: 0:19:03 tlr: 0.00013 tnm: 0.33 Lm: 6.412 (6.412) Lt: 5.608 (5.608) Accm: 3.80 (3.80) Acct: 5.65 (5.65) proj_loss: -0.5748 (-0.5748) time: 0.6849 data: 0.0004 [11-25 09:09:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [ 0/1669] eta: 0:19:04 tlr: 0.00013 tnm: 0.33 Lm: 6.620 (6.620) Lt: 5.802 (5.802) Accm: 3.16 (3.16) Acct: 5.10 (5.10) proj_loss: -0.5956 (-0.5956) time: 0.6860 data: 0.0003 [11-25 09:09:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [ 0/1669] eta: 0:19:02 tlr: 0.00013 tnm: 0.33 Lm: 6.542 (6.542) Lt: 5.803 (5.803) Accm: 2.91 (2.91) Acct: 4.51 (4.51) proj_loss: -0.6015 (-0.6015) time: 0.6846 data: 0.0003 [11-25 09:09:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [ 0/1669] eta: 0:19:04 tlr: 0.00013 tnm: 0.33 Lm: 6.644 (6.644) Lt: 5.875 (5.875) Accm: 2.94 (2.94) Acct: 4.72 (4.72) proj_loss: -0.5745 (-0.5745) time: 0.6856 data: 0.0004 [11-25 09:14:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.35 Lm: 6.571 (6.571) Lt: 5.810 (5.810) Accm: 3.04 (3.04) Acct: 4.77 (4.77) proj_loss: -0.5797 (-0.5797) time: 0.6751 data: 0.0003 [11-25 09:14:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.35 Lm: 6.533 (6.533) Lt: 5.764 (5.764) Accm: 3.25 (3.25) Acct: 5.22 (5.22) proj_loss: -0.5963 (-0.5963) time: 0.6751 data: 0.0003 [11-25 09:14:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.35 Lm: 6.546 (6.546) Lt: 5.815 (5.815) Accm: 3.09 (3.09) Acct: 4.88 (4.88) proj_loss: -0.5911 (-0.5911) time: 0.6751 data: 0.0003 [11-25 09:14:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.35 Lm: 6.415 (6.415) Lt: 5.615 (5.615) Accm: 3.69 (3.69) Acct: 5.56 (5.56) proj_loss: -0.5853 (-0.5853) time: 0.6751 data: 0.0003 [11-25 09:19:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [ 834/1669] eta: 0:09:25 tlr: 0.00013 tnm: 0.34 Lm: 6.418 (6.467) Lt: 5.622 (5.682) Accm: 3.58 (3.51) Acct: 5.48 (5.41) proj_loss: -0.5959 (-0.5949) time: 0.6748 data: 0.0003 [11-25 09:19:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [ 834/1669] eta: 0:09:25 tlr: 0.00013 tnm: 0.34 Lm: 6.550 (6.554) Lt: 5.803 (5.810) Accm: 2.91 (3.03) Acct: 4.61 (4.79) proj_loss: -0.5888 (-0.5904) time: 0.6748 data: 0.0003 [11-25 09:19:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [ 834/1669] eta: 0:09:25 tlr: 0.00013 tnm: 0.34 Lm: 6.498 (6.483) Lt: 5.745 (5.728) Accm: 3.13 (3.35) Acct: 4.82 (5.17) proj_loss: -0.5848 (-0.5903) time: 0.6748 data: 0.0003 [11-25 09:19:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [ 834/1669] eta: 0:09:25 tlr: 0.00013 tnm: 0.34 Lm: 6.495 (6.520) Lt: 5.725 (5.723) Accm: 3.34 (3.36) Acct: 5.34 (5.39) proj_loss: -0.5956 (-0.5893) time: 0.6748 data: 0.0003 [11-25 09:23:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.35 Lm: 6.557 (6.549) Lt: 5.764 (5.762) Accm: 3.25 (3.24) Acct: 5.22 (5.19) proj_loss: -0.5928 (-0.5895) time: 0.6767 data: 0.0003 [11-25 09:23:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.35 Lm: 6.421 (6.456) Lt: 5.645 (5.679) Accm: 3.64 (3.56) Acct: 5.56 (5.63) proj_loss: -0.6050 (-0.6033) time: 0.6767 data: 0.0003 [11-25 09:23:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.35 Lm: 6.546 (6.503) Lt: 5.801 (5.747) Accm: 3.09 (3.19) Acct: 4.93 (5.13) proj_loss: -0.5924 (-0.5917) time: 0.6767 data: 0.0003 [11-25 09:23:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.35 Lm: 6.489 (6.482) Lt: 5.725 (5.722) Accm: 3.26 (3.36) Acct: 4.96 (5.15) proj_loss: -0.5908 (-0.5919) time: 0.6767 data: 0.0003 [11-25 09:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.34 Lm: 6.497 (6.485) Lt: 5.743 (5.726) Accm: 3.13 (3.30) Acct: 4.82 (5.05) proj_loss: -0.5968 (-0.5972) time: 0.6778 data: 0.0016 [11-25 09:28:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 170/350] Total time: 0:18:51 (0.678 s / it) [11-25 09:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.34 Lm: 6.542 (6.465) Lt: 5.800 (5.702) Accm: 3.26 (3.33) Acct: 5.25 (5.29) proj_loss: -0.5959 (-0.5970) time: 0.6778 data: 0.0013 [11-25 09:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.34 Lm: 6.495 (6.527) Lt: 5.757 (5.761) Accm: 3.34 (3.34) Acct: 5.34 (5.31) proj_loss: -0.5956 (-0.5926) time: 0.6778 data: 0.0017 [11-25 09:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 170/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.34 Lm: 6.424 (6.467) Lt: 5.669 (5.684) Accm: 3.58 (3.47) Acct: 5.48 (5.51) proj_loss: -0.5959 (-0.6007) time: 0.6778 data: 0.0016 [11-25 09:28:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 170/350] Total time: 0:18:51 (0.678 s / it) [11-25 09:28:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 170/350] Total time: 0:18:51 (0.678 s / it) [11-25 09:28:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 170/350] Total time: 0:18:51 (0.678 s / it) [11-25 09:28:34] (/home/user/VAR/train.py , line 276)=> [ep170] (training ) Lm: 6.524 (6.537), Lt: 5.765 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 8:36:33, Finish: 2024-11-27 02:05 [11-25 09:28:34] (/home/user/VAR/train.py , line 276)=> [ep170] (training ) Lm: 6.524 (6.537), Lt: 5.765 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 8:32:55, Finish: 2024-11-27 02:01 [11-25 09:28:34] (/home/user/VAR/train.py , line 276)=> [ep170] (training ) Lm: 6.524 (6.537), Lt: 5.765 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 8:31:00, Finish: 2024-11-27 01:59 [11-25 09:28:34] (/home/user/VAR/train.py , line 276)=> [ep170] (training ) Lm: 6.524 (6.537), Lt: 5.765 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 8:32:00, Finish: 2024-11-27 02:00 [11-25 09:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [ 0/1669] eta: 0:18:17 tlr: 0.00013 tnm: 0.33 Lm: 6.646 (6.646) Lt: 5.929 (5.929) Accm: 3.09 (3.09) Acct: 4.63 (4.63) proj_loss: -0.6104 (-0.6104) time: 0.6577 data: 0.0004 [11-25 09:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [ 0/1669] eta: 0:18:17 tlr: 0.00013 tnm: 0.33 Lm: 6.557 (6.557) Lt: 5.807 (5.807) Accm: 3.49 (3.49) Acct: 5.70 (5.70) proj_loss: -0.5836 (-0.5836) time: 0.6579 data: 0.0004 [11-25 09:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [ 0/1669] eta: 0:18:18 tlr: 0.00013 tnm: 0.33 Lm: 6.556 (6.556) Lt: 5.833 (5.833) Accm: 3.17 (3.17) Acct: 4.99 (4.99) proj_loss: -0.6305 (-0.6305) time: 0.6580 data: 0.0004 [11-25 09:28:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [ 0/1669] eta: 0:18:19 tlr: 0.00013 tnm: 0.33 Lm: 6.530 (6.530) Lt: 5.776 (5.776) Accm: 3.27 (3.27) Acct: 5.27 (5.27) proj_loss: -0.6152 (-0.6152) time: 0.6587 data: 0.0004 [11-25 09:33:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [ 417/1669] eta: 0:15:16 tlr: 0.00013 tnm: 0.35 Lm: 6.617 (6.617) Lt: 5.897 (5.897) Accm: 2.91 (2.91) Acct: 4.59 (4.59) proj_loss: -0.6133 (-0.6133) time: 0.6746 data: 0.0003 [11-25 09:33:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [ 417/1669] eta: 0:15:16 tlr: 0.00013 tnm: 0.35 Lm: 6.602 (6.602) Lt: 5.857 (5.857) Accm: 3.23 (3.23) Acct: 4.96 (4.96) proj_loss: -0.5877 (-0.5877) time: 0.6746 data: 0.0003 [11-25 09:33:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [ 417/1669] eta: 0:15:16 tlr: 0.00013 tnm: 0.35 Lm: 6.510 (6.510) Lt: 5.762 (5.762) Accm: 3.59 (3.59) Acct: 5.65 (5.65) proj_loss: -0.5875 (-0.5875) time: 0.6746 data: 0.0003 [11-25 09:33:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [ 417/1669] eta: 0:15:16 tlr: 0.00013 tnm: 0.35 Lm: 6.547 (6.547) Lt: 5.807 (5.807) Accm: 3.35 (3.35) Acct: 5.30 (5.30) proj_loss: -0.6124 (-0.6124) time: 0.6746 data: 0.0003 [11-25 09:38:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [ 834/1669] eta: 0:09:55 tlr: 0.00013 tnm: 0.38 Lm: 6.539 (6.522) Lt: 5.782 (5.791) Accm: 3.17 (3.27) Acct: 4.99 (5.10) proj_loss: -0.5945 (-0.6064) time: 0.6757 data: 0.0003 [11-25 09:38:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [ 834/1669] eta: 0:09:55 tlr: 0.00013 tnm: 0.38 Lm: 6.559 (6.579) Lt: 5.791 (5.835) Accm: 3.25 (3.24) Acct: 5.03 (4.98) proj_loss: -0.6084 (-0.5946) time: 0.6757 data: 0.0003 [11-25 09:38:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [ 834/1669] eta: 0:09:55 tlr: 0.00013 tnm: 0.38 Lm: 6.530 (6.572) Lt: 5.776 (5.834) Accm: 3.27 (3.17) Acct: 5.27 (5.13) proj_loss: -0.6152 (-0.6189) time: 0.6757 data: 0.0003 [11-25 09:38:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [ 834/1669] eta: 0:09:55 tlr: 0.00013 tnm: 0.38 Lm: 6.464 (6.491) Lt: 5.718 (5.733) Accm: 3.49 (3.55) Acct: 5.60 (5.56) proj_loss: -0.5915 (-0.5936) time: 0.6757 data: 0.0003 [11-25 09:43:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.35 Lm: 6.494 (6.500) Lt: 5.762 (5.760) Accm: 3.53 (3.55) Acct: 5.50 (5.52) proj_loss: -0.5985 (-0.5996) time: 0.6772 data: 0.0003 [11-25 09:43:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.35 Lm: 6.505 (6.507) Lt: 5.771 (5.764) Accm: 3.24 (3.28) Acct: 5.04 (5.10) proj_loss: -0.5965 (-0.6044) time: 0.6772 data: 0.0003 [11-25 09:43:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.35 Lm: 6.560 (6.576) Lt: 5.831 (5.847) Accm: 3.34 (3.23) Acct: 5.27 (5.17) proj_loss: -0.6133 (-0.6161) time: 0.6772 data: 0.0003 [11-25 09:43:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.35 Lm: 6.546 (6.560) Lt: 5.788 (5.802) Accm: 3.31 (3.29) Acct: 5.16 (5.12) proj_loss: -0.5981 (-0.5929) time: 0.6772 data: 0.0003 [11-25 09:47:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.38 Lm: 6.534 (6.507) Lt: 5.785 (5.731) Accm: 3.37 (3.44) Acct: 5.29 (5.37) proj_loss: -0.6049 (-0.5953) time: 0.6770 data: 0.0014 [11-25 09:47:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 171/350] Total time: 0:19:19 (0.695 s / it) [11-25 09:47:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.38 Lm: 6.561 (6.573) Lt: 5.776 (5.832) Accm: 3.27 (3.21) Acct: 5.27 (5.12) proj_loss: -0.6114 (-0.6108) time: 0.6770 data: 0.0016 [11-25 09:47:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.38 Lm: 6.525 (6.535) Lt: 5.807 (5.785) Accm: 3.49 (3.42) Acct: 5.41 (5.37) proj_loss: -0.5993 (-0.5996) time: 0.6770 data: 0.0021 [11-25 09:47:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 171/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.38 Lm: 6.471 (6.493) Lt: 5.781 (5.768) Accm: 3.31 (3.35) Acct: 5.08 (5.15) proj_loss: -0.5985 (-0.6052) time: 0.6770 data: 0.0016 [11-25 09:47:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 171/350] Total time: 0:19:19 (0.695 s / it) [11-25 09:47:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 171/350] Total time: 0:19:19 (0.695 s / it) [11-25 09:47:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 171/350] Total time: 0:19:19 (0.695 s / it) [11-25 09:47:53] (/home/user/VAR/train.py , line 276)=> [ep171] (training ) Lm: 6.524 (6.553), Lt: 5.765 (5.804), Acc m&t: 3.33 5.28, Remain: 2 days, 8:11:32, Finish: 2024-11-27 01:59 [11-25 09:47:53] (/home/user/VAR/train.py , line 276)=> [ep171] (training ) Lm: 6.524 (6.553), Lt: 5.765 (5.804), Acc m&t: 3.33 5.28, Remain: 2 days, 8:11:13, Finish: 2024-11-27 01:59 [11-25 09:47:53] (/home/user/VAR/train.py , line 276)=> [ep171] (training ) Lm: 6.524 (6.553), Lt: 5.765 (5.804), Acc m&t: 3.33 5.28, Remain: 2 days, 8:11:06, Finish: 2024-11-27 01:58 [11-25 09:47:53] (/home/user/VAR/train.py , line 276)=> [ep171] (training ) Lm: 6.524 (6.553), Lt: 5.765 (5.804), Acc m&t: 3.33 5.28, Remain: 2 days, 8:10:59, Finish: 2024-11-27 01:58 [11-25 09:47:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [ 0/1669] eta: 0:18:16 tlr: 0.00013 tnm: 0.34 Lm: 6.639 (6.639) Lt: 5.874 (5.874) Accm: 3.10 (3.10) Acct: 5.23 (5.23) proj_loss: -0.6015 (-0.6015) time: 0.6567 data: 0.0004 [11-25 09:47:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [ 0/1669] eta: 0:18:17 tlr: 0.00013 tnm: 0.34 Lm: 6.441 (6.441) Lt: 5.679 (5.679) Accm: 3.53 (3.53) Acct: 5.89 (5.89) proj_loss: -0.5861 (-0.5861) time: 0.6574 data: 0.0004 [11-25 09:47:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [ 0/1669] eta: 0:18:17 tlr: 0.00013 tnm: 0.34 Lm: 6.551 (6.551) Lt: 5.793 (5.793) Accm: 3.18 (3.18) Acct: 5.04 (5.04) proj_loss: -0.5853 (-0.5853) time: 0.6575 data: 0.0003 [11-25 09:47:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [ 0/1669] eta: 0:18:17 tlr: 0.00013 tnm: 0.34 Lm: 6.555 (6.555) Lt: 5.810 (5.810) Accm: 2.88 (2.88) Acct: 4.55 (4.55) proj_loss: -0.6250 (-0.6250) time: 0.6577 data: 0.0003 [11-25 09:52:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [ 417/1669] eta: 0:14:06 tlr: 0.00013 tnm: 0.35 Lm: 6.500 (6.500) Lt: 5.753 (5.753) Accm: 3.16 (3.16) Acct: 4.90 (4.90) proj_loss: -0.6230 (-0.6230) time: 0.6742 data: 0.0003 [11-25 09:52:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [ 417/1669] eta: 0:14:06 tlr: 0.00013 tnm: 0.35 Lm: 6.607 (6.607) Lt: 5.842 (5.842) Accm: 3.04 (3.04) Acct: 4.81 (4.81) proj_loss: -0.5941 (-0.5941) time: 0.6742 data: 0.0003 [11-25 09:52:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [ 417/1669] eta: 0:14:06 tlr: 0.00013 tnm: 0.35 Lm: 6.490 (6.490) Lt: 5.752 (5.752) Accm: 3.57 (3.57) Acct: 5.79 (5.79) proj_loss: -0.6002 (-0.6002) time: 0.6742 data: 0.0003 [11-25 09:52:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [ 417/1669] eta: 0:14:06 tlr: 0.00013 tnm: 0.35 Lm: 6.539 (6.539) Lt: 5.799 (5.799) Accm: 3.27 (3.27) Acct: 5.13 (5.13) proj_loss: -0.5917 (-0.5917) time: 0.6742 data: 0.0003 [11-25 09:57:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [ 834/1669] eta: 0:09:46 tlr: 0.00013 tnm: 0.35 Lm: 6.527 (6.471) Lt: 5.793 (5.718) Accm: 3.37 (3.41) Acct: 5.22 (5.34) proj_loss: -0.5981 (-0.5967) time: 0.6752 data: 0.0003 [11-25 09:57:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [ 834/1669] eta: 0:09:46 tlr: 0.00013 tnm: 0.35 Lm: 6.575 (6.557) Lt: 5.811 (5.803) Accm: 3.10 (3.21) Acct: 5.23 (5.11) proj_loss: -0.6000 (-0.5961) time: 0.6752 data: 0.0003 [11-25 09:57:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [ 834/1669] eta: 0:09:46 tlr: 0.00013 tnm: 0.35 Lm: 6.470 (6.490) Lt: 5.696 (5.717) Accm: 3.44 (3.27) Acct: 5.25 (5.18) proj_loss: -0.6211 (-0.6165) time: 0.6752 data: 0.0003 [11-25 09:57:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [ 834/1669] eta: 0:09:46 tlr: 0.00013 tnm: 0.35 Lm: 6.539 (6.540) Lt: 5.824 (5.790) Accm: 3.53 (3.36) Acct: 5.68 (5.47) proj_loss: -0.5861 (-0.5935) time: 0.6752 data: 0.0003 [11-25 10:02:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.32 Lm: 6.490 (6.481) Lt: 5.752 (5.741) Accm: 3.57 (3.45) Acct: 5.73 (5.55) proj_loss: -0.5986 (-0.5979) time: 0.6766 data: 0.0004 [11-25 10:02:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.32 Lm: 6.480 (6.490) Lt: 5.715 (5.721) Accm: 3.46 (3.38) Acct: 5.46 (5.30) proj_loss: -0.6122 (-0.6112) time: 0.6765 data: 0.0003 [11-25 10:02:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.32 Lm: 6.607 (6.581) Lt: 5.842 (5.824) Accm: 3.13 (3.19) Acct: 5.28 (5.17) proj_loss: -0.5934 (-0.5912) time: 0.6765 data: 0.0003 [11-25 10:02:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.32 Lm: 6.539 (6.519) Lt: 5.799 (5.786) Accm: 3.27 (3.28) Acct: 5.13 (5.10) proj_loss: -0.6024 (-0.6046) time: 0.6765 data: 0.0003 [11-25 10:07:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.37 Lm: 6.527 (6.515) Lt: 5.793 (5.774) Accm: 3.37 (3.32) Acct: 5.22 (5.23) proj_loss: -0.5981 (-0.6027) time: 0.6785 data: 0.0016 [11-25 10:07:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 172/350] Total time: 0:19:19 (0.695 s / it) [11-25 10:07:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.37 Lm: 6.441 (6.461) Lt: 5.679 (5.716) Accm: 3.61 (3.54) Acct: 5.79 (5.65) proj_loss: -0.6110 (-0.6052) time: 0.6785 data: 0.0016 [11-25 10:07:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.37 Lm: 6.490 (6.510) Lt: 5.735 (5.744) Accm: 3.44 (3.27) Acct: 5.25 (5.14) proj_loss: -0.6033 (-0.6069) time: 0.6785 data: 0.0019 [11-25 10:07:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 172/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.37 Lm: 6.578 (6.580) Lt: 5.845 (5.828) Accm: 3.10 (3.13) Acct: 5.23 (5.00) proj_loss: -0.5911 (-0.5912) time: 0.6785 data: 0.0015 [11-25 10:07:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 172/350] Total time: 0:19:19 (0.695 s / it) [11-25 10:07:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 172/350] Total time: 0:19:19 (0.695 s / it) [11-25 10:07:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 172/350] Total time: 0:19:19 (0.695 s / it) [11-25 10:07:13] (/home/user/VAR/train.py , line 276)=> [ep172] (training ) Lm: 6.524 (6.525), Lt: 5.765 (5.766), Acc m&t: 3.33 5.28, Remain: 2 days, 8:05:54, Finish: 2024-11-27 02:13 [11-25 10:07:13] (/home/user/VAR/train.py , line 276)=> [ep172] (training ) Lm: 6.524 (6.525), Lt: 5.765 (5.766), Acc m&t: 3.33 5.28, Remain: 2 days, 8:06:19, Finish: 2024-11-27 02:13 [11-25 10:07:13] (/home/user/VAR/train.py , line 276)=> [ep172] (training ) Lm: 6.524 (6.525), Lt: 5.765 (5.766), Acc m&t: 3.33 5.28, Remain: 2 days, 8:05:56, Finish: 2024-11-27 02:13 [11-25 10:07:13] (/home/user/VAR/train.py , line 276)=> [ep172] (training ) Lm: 6.524 (6.525), Lt: 5.765 (5.766), Acc m&t: 3.33 5.28, Remain: 2 days, 8:05:55, Finish: 2024-11-27 02:13 [11-25 10:07:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [ 0/1669] eta: 0:18:21 tlr: 0.00013 tnm: 0.35 Lm: 6.568 (6.568) Lt: 5.789 (5.789) Accm: 3.20 (3.20) Acct: 4.94 (4.94) proj_loss: -0.5826 (-0.5826) time: 0.6600 data: 0.0004 [11-25 10:07:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [ 0/1669] eta: 0:18:21 tlr: 0.00013 tnm: 0.35 Lm: 6.391 (6.391) Lt: 5.618 (5.618) Accm: 3.73 (3.73) Acct: 5.75 (5.75) proj_loss: -0.5804 (-0.5804) time: 0.6602 data: 0.0003 [11-25 10:07:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [ 0/1669] eta: 0:18:20 tlr: 0.00013 tnm: 0.35 Lm: 6.440 (6.440) Lt: 5.704 (5.704) Accm: 3.96 (3.96) Acct: 6.01 (6.01) proj_loss: -0.5935 (-0.5935) time: 0.6596 data: 0.0004 [11-25 10:07:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [ 0/1669] eta: 0:18:20 tlr: 0.00013 tnm: 0.35 Lm: 6.525 (6.525) Lt: 5.756 (5.756) Accm: 3.29 (3.29) Acct: 5.11 (5.11) proj_loss: -0.5795 (-0.5795) time: 0.6594 data: 0.0003 [11-25 10:11:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [ 417/1669] eta: 0:14:13 tlr: 0.00013 tnm: 0.35 Lm: 6.515 (6.515) Lt: 5.748 (5.748) Accm: 3.29 (3.29) Acct: 5.22 (5.22) proj_loss: -0.5874 (-0.5874) time: 0.6787 data: 0.0003 [11-25 10:11:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [ 417/1669] eta: 0:14:13 tlr: 0.00013 tnm: 0.35 Lm: 6.546 (6.546) Lt: 5.797 (5.797) Accm: 3.24 (3.24) Acct: 4.99 (4.99) proj_loss: -0.6037 (-0.6037) time: 0.6787 data: 0.0003 [11-25 10:11:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [ 417/1669] eta: 0:14:13 tlr: 0.00013 tnm: 0.35 Lm: 6.514 (6.514) Lt: 5.784 (5.784) Accm: 3.42 (3.42) Acct: 5.30 (5.30) proj_loss: -0.5984 (-0.5984) time: 0.6787 data: 0.0003 [11-25 10:11:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [ 417/1669] eta: 0:14:13 tlr: 0.00013 tnm: 0.35 Lm: 6.334 (6.334) Lt: 5.548 (5.548) Accm: 4.00 (4.00) Acct: 6.19 (6.19) proj_loss: -0.5884 (-0.5884) time: 0.6787 data: 0.0003 [11-25 10:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [ 834/1669] eta: 0:09:27 tlr: 0.00013 tnm: 0.37 Lm: 6.550 (6.547) Lt: 5.795 (5.796) Accm: 3.29 (3.26) Acct: 4.94 (4.96) proj_loss: -0.5973 (-0.6016) time: 0.6768 data: 0.0003 [11-25 10:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [ 834/1669] eta: 0:09:27 tlr: 0.00013 tnm: 0.37 Lm: 6.561 (6.530) Lt: 5.861 (5.810) Accm: 3.21 (3.35) Acct: 4.65 (5.08) proj_loss: -0.6032 (-0.6023) time: 0.6768 data: 0.0003 [11-25 10:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [ 834/1669] eta: 0:09:27 tlr: 0.00013 tnm: 0.37 Lm: 6.525 (6.519) Lt: 5.756 (5.755) Accm: 3.29 (3.26) Acct: 5.17 (5.20) proj_loss: -0.5795 (-0.5843) time: 0.6768 data: 0.0003 [11-25 10:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [ 834/1669] eta: 0:09:27 tlr: 0.00013 tnm: 0.37 Lm: 6.391 (6.412) Lt: 5.618 (5.634) Accm: 3.73 (3.76) Acct: 5.75 (5.94) proj_loss: -0.5804 (-0.5838) time: 0.6768 data: 0.0003 [11-25 10:21:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.34 Lm: 6.394 (6.408) Lt: 5.620 (5.631) Accm: 3.64 (3.71) Acct: 5.69 (5.86) proj_loss: -0.5884 (-0.5894) time: 0.6757 data: 0.0003 [11-25 10:21:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.34 Lm: 6.575 (6.558) Lt: 5.863 (5.825) Accm: 3.13 (3.27) Acct: 4.62 (4.96) proj_loss: -0.5993 (-0.6005) time: 0.6757 data: 0.0003 [11-25 10:21:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.34 Lm: 6.537 (6.518) Lt: 5.792 (5.773) Accm: 3.29 (3.32) Acct: 4.99 (5.11) proj_loss: -0.6044 (-0.6041) time: 0.6757 data: 0.0003 [11-25 10:21:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.34 Lm: 6.526 (6.549) Lt: 5.762 (5.791) Accm: 3.24 (3.14) Acct: 5.14 (5.01) proj_loss: -0.5874 (-0.5939) time: 0.6757 data: 0.0002 [11-25 10:26:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.528 (6.553) Lt: 5.769 (5.788) Accm: 3.18 (3.14) Acct: 5.11 (4.96) proj_loss: -0.5795 (-0.5883) time: 0.6779 data: 0.0020 [11-25 10:26:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 173/350] Total time: 0:18:53 (0.679 s / it) [11-25 10:26:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.397 (6.440) Lt: 5.623 (5.670) Accm: 3.55 (3.64) Acct: 5.63 (5.76) proj_loss: -0.5897 (-0.5895) time: 0.6779 data: 0.0015 [11-25 10:26:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.545 (6.523) Lt: 5.793 (5.777) Accm: 3.29 (3.32) Acct: 5.04 (5.15) proj_loss: -0.5987 (-0.6030) time: 0.6779 data: 0.0013 [11-25 10:26:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 173/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.561 (6.528) Lt: 5.861 (5.778) Accm: 3.21 (3.33) Acct: 4.65 (5.12) proj_loss: -0.6032 (-0.6042) time: 0.6779 data: 0.0020 [11-25 10:26:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 173/350] Total time: 0:18:53 (0.679 s / it) [11-25 10:26:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 173/350] Total time: 0:18:53 (0.679 s / it) [11-25 10:26:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 173/350] Total time: 0:18:53 (0.679 s / it) [11-25 10:26:06] (/home/user/VAR/train.py , line 276)=> [ep173] (training ) Lm: 6.524 (6.535), Lt: 5.765 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 7:42:29, Finish: 2024-11-27 02:08 [11-25 10:26:06] (/home/user/VAR/train.py , line 276)=> [ep173] (training ) Lm: 6.524 (6.535), Lt: 5.765 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 7:43:21, Finish: 2024-11-27 02:09 [11-25 10:26:06] (/home/user/VAR/train.py , line 276)=> [ep173] (training ) Lm: 6.524 (6.535), Lt: 5.765 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 7:42:44, Finish: 2024-11-27 02:08 [11-25 10:26:06] (/home/user/VAR/train.py , line 276)=> [ep173] (training ) Lm: 6.524 (6.535), Lt: 5.765 (5.778), Acc m&t: 3.33 5.28, Remain: 2 days, 7:43:37, Finish: 2024-11-27 02:09 [11-25 10:26:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [ 0/1669] eta: 0:18:51 tlr: 0.00013 tnm: 0.34 Lm: 6.667 (6.667) Lt: 5.931 (5.931) Accm: 2.96 (2.96) Acct: 5.01 (5.01) proj_loss: -0.5956 (-0.5956) time: 0.6779 data: 0.0004 [11-25 10:26:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [ 0/1669] eta: 0:18:45 tlr: 0.00013 tnm: 0.34 Lm: 6.522 (6.522) Lt: 5.746 (5.746) Accm: 3.58 (3.58) Acct: 5.70 (5.70) proj_loss: -0.6015 (-0.6015) time: 0.6743 data: 0.0003 [11-25 10:26:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [ 0/1669] eta: 0:18:52 tlr: 0.00013 tnm: 0.34 Lm: 6.515 (6.515) Lt: 5.761 (5.761) Accm: 3.08 (3.08) Acct: 4.87 (4.87) proj_loss: -0.6066 (-0.6066) time: 0.6784 data: 0.0004 [11-25 10:26:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [ 0/1669] eta: 0:18:52 tlr: 0.00013 tnm: 0.34 Lm: 6.707 (6.707) Lt: 6.031 (6.031) Accm: 2.93 (2.93) Acct: 4.36 (4.36) proj_loss: -0.6261 (-0.6261) time: 0.6786 data: 0.0004 [11-25 10:31:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [ 417/1669] eta: 0:15:16 tlr: 0.00013 tnm: 0.36 Lm: 6.677 (6.677) Lt: 5.954 (5.954) Accm: 3.06 (3.06) Acct: 4.74 (4.74) proj_loss: -0.6127 (-0.6127) time: 0.6788 data: 0.0003 [11-25 10:31:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [ 417/1669] eta: 0:15:16 tlr: 0.00013 tnm: 0.36 Lm: 6.530 (6.530) Lt: 5.762 (5.762) Accm: 3.26 (3.26) Acct: 5.29 (5.29) proj_loss: -0.6006 (-0.6006) time: 0.6788 data: 0.0003 [11-25 10:31:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [ 417/1669] eta: 0:15:16 tlr: 0.00013 tnm: 0.36 Lm: 6.553 (6.553) Lt: 5.765 (5.765) Accm: 3.42 (3.42) Acct: 5.78 (5.78) proj_loss: -0.6007 (-0.6007) time: 0.6788 data: 0.0003 [11-25 10:31:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [ 417/1669] eta: 0:15:16 tlr: 0.00013 tnm: 0.36 Lm: 6.531 (6.531) Lt: 5.748 (5.748) Accm: 3.44 (3.44) Acct: 5.47 (5.47) proj_loss: -0.6085 (-0.6085) time: 0.6788 data: 0.0003 [11-25 10:36:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [ 834/1669] eta: 0:09:53 tlr: 0.00013 tnm: 0.35 Lm: 6.541 (6.545) Lt: 5.750 (5.759) Accm: 3.31 (3.37) Acct: 5.23 (5.32) proj_loss: -0.6015 (-0.6017) time: 0.6788 data: 0.0003 [11-25 10:36:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [ 834/1669] eta: 0:09:53 tlr: 0.00013 tnm: 0.35 Lm: 6.667 (6.591) Lt: 5.931 (5.823) Accm: 2.96 (3.18) Acct: 5.01 (5.21) proj_loss: -0.5975 (-0.5996) time: 0.6788 data: 0.0003 [11-25 10:36:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [ 834/1669] eta: 0:09:53 tlr: 0.00013 tnm: 0.35 Lm: 6.545 (6.558) Lt: 5.763 (5.778) Accm: 3.13 (3.21) Acct: 5.06 (5.21) proj_loss: -0.5946 (-0.5973) time: 0.6788 data: 0.0002 [11-25 10:36:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [ 834/1669] eta: 0:09:53 tlr: 0.00013 tnm: 0.35 Lm: 6.683 (6.679) Lt: 5.972 (5.960) Accm: 2.93 (3.02) Acct: 4.49 (4.66) proj_loss: -0.6026 (-0.6093) time: 0.6788 data: 0.0003 [11-25 10:40:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [1251/1669] eta: 0:04:53 tlr: 0.00013 tnm: 0.32 Lm: 6.666 (6.672) Lt: 5.925 (5.938) Accm: 2.93 (2.96) Acct: 4.42 (4.57) proj_loss: -0.6009 (-0.6021) time: 0.6788 data: 0.0002 [11-25 10:40:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [1251/1669] eta: 0:04:53 tlr: 0.00013 tnm: 0.32 Lm: 6.580 (6.595) Lt: 5.787 (5.832) Accm: 3.10 (3.09) Acct: 4.97 (4.93) proj_loss: -0.6006 (-0.6023) time: 0.6788 data: 0.0003 [11-25 10:40:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [1251/1669] eta: 0:04:53 tlr: 0.00013 tnm: 0.32 Lm: 6.632 (6.593) Lt: 5.905 (5.837) Accm: 3.12 (3.20) Acct: 5.07 (5.19) proj_loss: -0.5965 (-0.5968) time: 0.6788 data: 0.0003 [11-25 10:40:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [1251/1669] eta: 0:04:53 tlr: 0.00013 tnm: 0.32 Lm: 6.556 (6.574) Lt: 5.766 (5.811) Accm: 3.27 (3.27) Acct: 5.13 (5.16) proj_loss: -0.5995 (-0.6006) time: 0.6788 data: 0.0003 [11-25 10:45:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.34 Lm: 6.571 (6.587) Lt: 5.781 (5.827) Accm: 3.23 (3.23) Acct: 5.03 (5.08) proj_loss: -0.6015 (-0.6022) time: 0.6792 data: 0.0015 [11-25 10:45:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 174/350] Total time: 0:19:20 (0.695 s / it) [11-25 10:45:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.34 Lm: 6.615 (6.597) Lt: 5.923 (5.854) Accm: 3.08 (3.18) Acct: 5.01 (5.10) proj_loss: -0.5968 (-0.5968) time: 0.6792 data: 0.0020 [11-25 10:45:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.34 Lm: 6.545 (6.572) Lt: 5.810 (5.831) Accm: 3.13 (3.11) Acct: 5.06 (4.97) proj_loss: -0.6066 (-0.6039) time: 0.6792 data: 0.0019 [11-25 10:45:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 174/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.34 Lm: 6.650 (6.634) Lt: 5.878 (5.893) Accm: 2.93 (3.09) Acct: 4.49 (4.83) proj_loss: -0.5992 (-0.6004) time: 0.6792 data: 0.0019 [11-25 10:45:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 174/350] Total time: 0:19:20 (0.695 s / it) [11-25 10:45:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 174/350] Total time: 0:19:20 (0.695 s / it) [11-25 10:45:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 174/350] Total time: 0:19:20 (0.695 s / it) [11-25 10:45:27] (/home/user/VAR/train.py , line 276)=> [ep174] (training ) Lm: 6.524 (6.542), Lt: 5.765 (5.784), Acc m&t: 3.33 5.28, Remain: 2 days, 7:25:27, Finish: 2024-11-27 02:10 [11-25 10:45:27] (/home/user/VAR/train.py , line 276)=> [ep174] (training ) Lm: 6.524 (6.542), Lt: 5.765 (5.784), Acc m&t: 3.33 5.28, Remain: 2 days, 7:25:12, Finish: 2024-11-27 02:10 [11-25 10:45:27] (/home/user/VAR/train.py , line 276)=> [ep174] (training ) Lm: 6.524 (6.542), Lt: 5.765 (5.784), Acc m&t: 3.33 5.28, Remain: 2 days, 7:25:36, Finish: 2024-11-27 02:11 [11-25 10:45:27] (/home/user/VAR/train.py , line 276)=> [ep174] (training ) Lm: 6.524 (6.542), Lt: 5.765 (5.784), Acc m&t: 3.33 5.28, Remain: 2 days, 7:24:42, Finish: 2024-11-27 02:10 [11-25 10:45:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [ 0/1669] eta: 0:17:53 tlr: 0.00013 tnm: 0.35 Lm: 6.659 (6.659) Lt: 5.940 (5.940) Accm: 2.78 (2.78) Acct: 4.37 (4.37) proj_loss: -0.5998 (-0.5998) time: 0.6435 data: 0.0003 [11-25 10:45:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [ 0/1669] eta: 0:17:54 tlr: 0.00013 tnm: 0.35 Lm: 6.649 (6.649) Lt: 5.909 (5.909) Accm: 2.60 (2.60) Acct: 3.98 (3.98) proj_loss: -0.5926 (-0.5926) time: 0.6441 data: 0.0004 [11-25 10:45:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [ 0/1669] eta: 0:17:55 tlr: 0.00013 tnm: 0.35 Lm: 6.550 (6.550) Lt: 5.859 (5.859) Accm: 2.99 (2.99) Acct: 4.65 (4.65) proj_loss: -0.5986 (-0.5986) time: 0.6444 data: 0.0004 [11-25 10:45:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [ 0/1669] eta: 0:17:55 tlr: 0.00013 tnm: 0.35 Lm: 6.518 (6.518) Lt: 5.760 (5.760) Accm: 3.57 (3.57) Acct: 5.48 (5.48) proj_loss: -0.6096 (-0.6096) time: 0.6444 data: 0.0005 [11-25 10:50:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [ 417/1669] eta: 0:14:05 tlr: 0.00013 tnm: 0.34 Lm: 6.532 (6.532) Lt: 5.816 (5.816) Accm: 3.18 (3.18) Acct: 4.92 (4.92) proj_loss: -0.5868 (-0.5868) time: 0.6771 data: 0.0003 [11-25 10:50:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [ 417/1669] eta: 0:14:05 tlr: 0.00013 tnm: 0.34 Lm: 6.603 (6.603) Lt: 5.901 (5.901) Accm: 3.19 (3.19) Acct: 4.87 (4.87) proj_loss: -0.6028 (-0.6028) time: 0.6771 data: 0.0002 [11-25 10:50:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [ 417/1669] eta: 0:14:05 tlr: 0.00013 tnm: 0.34 Lm: 6.666 (6.666) Lt: 5.945 (5.945) Accm: 2.62 (2.62) Acct: 4.01 (4.01) proj_loss: -0.5963 (-0.5963) time: 0.6771 data: 0.0003 [11-25 10:50:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [ 417/1669] eta: 0:14:05 tlr: 0.00013 tnm: 0.34 Lm: 6.632 (6.632) Lt: 5.915 (5.915) Accm: 2.88 (2.88) Acct: 4.42 (4.42) proj_loss: -0.5985 (-0.5985) time: 0.6771 data: 0.0003 [11-25 10:55:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [ 834/1669] eta: 0:09:45 tlr: 0.00013 tnm: 0.34 Lm: 6.604 (6.558) Lt: 5.889 (5.839) Accm: 2.99 (3.02) Acct: 4.46 (4.61) proj_loss: -0.5978 (-0.5983) time: 0.6743 data: 0.0003 [11-25 10:55:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [ 834/1669] eta: 0:09:45 tlr: 0.00013 tnm: 0.34 Lm: 6.529 (6.531) Lt: 5.773 (5.794) Accm: 3.23 (3.20) Acct: 5.18 (5.04) proj_loss: -0.5986 (-0.5948) time: 0.6743 data: 0.0003 [11-25 10:55:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [ 834/1669] eta: 0:09:45 tlr: 0.00013 tnm: 0.34 Lm: 6.610 (6.605) Lt: 5.850 (5.884) Accm: 3.05 (3.15) Acct: 5.04 (4.93) proj_loss: -0.5961 (-0.6001) time: 0.6743 data: 0.0003 [11-25 10:55:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [ 834/1669] eta: 0:09:45 tlr: 0.00013 tnm: 0.34 Lm: 6.649 (6.612) Lt: 5.909 (5.924) Accm: 2.63 (2.97) Acct: 4.05 (4.42) proj_loss: -0.6001 (-0.5992) time: 0.6743 data: 0.0003 [11-25 11:00:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.37 Lm: 6.623 (6.608) Lt: 5.895 (5.911) Accm: 2.95 (3.04) Acct: 4.59 (4.60) proj_loss: -0.5963 (-0.5931) time: 0.6763 data: 0.0003 [11-25 11:00:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.37 Lm: 6.540 (6.552) Lt: 5.816 (5.811) Accm: 3.26 (3.22) Acct: 4.99 (4.98) proj_loss: -0.5986 (-0.5957) time: 0.6763 data: 0.0003 [11-25 11:00:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.37 Lm: 6.564 (6.571) Lt: 5.805 (5.841) Accm: 3.13 (3.16) Acct: 5.07 (4.97) proj_loss: -0.5954 (-0.5984) time: 0.6763 data: 0.0003 [11-25 11:00:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.37 Lm: 6.580 (6.558) Lt: 5.883 (5.849) Accm: 3.03 (3.04) Acct: 4.53 (4.61) proj_loss: -0.5988 (-0.5987) time: 0.6763 data: 0.0003 [11-25 11:04:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.36 Lm: 6.557 (6.547) Lt: 5.876 (5.823) Accm: 3.08 (3.11) Acct: 4.60 (4.79) proj_loss: -0.5998 (-0.6008) time: 0.6800 data: 0.0013 [11-25 11:04:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 175/350] Total time: 0:19:19 (0.694 s / it) [11-25 11:04:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.36 Lm: 6.597 (6.602) Lt: 5.881 (5.902) Accm: 3.23 (3.08) Acct: 4.99 (4.68) proj_loss: -0.6001 (-0.5947) time: 0.6800 data: 0.0017 [11-25 11:04:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.36 Lm: 6.518 (6.520) Lt: 5.760 (5.762) Accm: 3.21 (3.32) Acct: 5.10 (5.29) proj_loss: -0.5947 (-0.5949) time: 0.6800 data: 0.0021 [11-25 11:04:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 175/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.36 Lm: 6.529 (6.525) Lt: 5.773 (5.766) Accm: 3.29 (3.30) Acct: 5.18 (5.09) proj_loss: -0.5986 (-0.5926) time: 0.6800 data: 0.0015 [11-25 11:04:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 175/350] Total time: 0:19:19 (0.694 s / it) [11-25 11:04:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 175/350] Total time: 0:19:19 (0.694 s / it) [11-25 11:04:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 175/350] Total time: 0:19:19 (0.694 s / it) [11-25 11:04:46] (/home/user/VAR/train.py , line 276)=> [ep175] (training ) Lm: 6.516 (6.516), Lt: 5.760 (5.760), Acc m&t: 3.33 5.28, Remain: 2 days, 7:05:10, Finish: 2024-11-27 02:09 [11-25 11:04:46] (/home/user/VAR/train.py , line 276)=> [ep175] (training ) Lm: 6.516 (6.516), Lt: 5.760 (5.760), Acc m&t: 3.33 5.28, Remain: 2 days, 7:05:37, Finish: 2024-11-27 02:10 [11-25 11:04:46] (/home/user/VAR/train.py , line 276)=> [ep175] (training ) Lm: 6.516 (6.516), Lt: 5.760 (5.760), Acc m&t: 3.33 5.28, Remain: 2 days, 7:04:52, Finish: 2024-11-27 02:09 [11-25 11:04:46] (/home/user/VAR/train.py , line 276)=> [ep175] (training ) Lm: 6.516 (6.516), Lt: 5.760 (5.760), Acc m&t: 3.33 5.28, Remain: 2 days, 7:05:40, Finish: 2024-11-27 02:10 [11-25 11:04:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [ 0/1669] eta: 0:18:15 tlr: 0.00013 tnm: 0.34 Lm: 6.675 (6.675) Lt: 5.978 (5.978) Accm: 2.89 (2.89) Acct: 4.49 (4.49) proj_loss: -0.6089 (-0.6089) time: 0.6566 data: 0.0003 [11-25 11:04:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [ 0/1669] eta: 0:18:16 tlr: 0.00013 tnm: 0.34 Lm: 6.455 (6.455) Lt: 5.679 (5.679) Accm: 2.99 (2.99) Acct: 4.94 (4.94) proj_loss: -0.5771 (-0.5771) time: 0.6570 data: 0.0003 [11-25 11:04:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [ 0/1669] eta: 0:18:16 tlr: 0.00013 tnm: 0.34 Lm: 6.411 (6.411) Lt: 5.617 (5.617) Accm: 3.68 (3.68) Acct: 5.80 (5.80) proj_loss: -0.5952 (-0.5952) time: 0.6569 data: 0.0004 [11-25 11:04:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [ 0/1669] eta: 0:18:17 tlr: 0.00013 tnm: 0.34 Lm: 6.413 (6.413) Lt: 5.679 (5.679) Accm: 3.69 (3.69) Acct: 5.49 (5.49) proj_loss: -0.6274 (-0.6274) time: 0.6574 data: 0.0004 [11-25 11:09:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.35 Lm: 6.432 (6.432) Lt: 5.694 (5.694) Accm: 3.67 (3.67) Acct: 5.67 (5.67) proj_loss: -0.6120 (-0.6120) time: 0.6775 data: 0.0003 [11-25 11:09:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.35 Lm: 6.542 (6.542) Lt: 5.793 (5.793) Accm: 2.98 (2.98) Acct: 4.79 (4.79) proj_loss: -0.5843 (-0.5843) time: 0.6776 data: 0.0003 [11-25 11:09:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.35 Lm: 6.592 (6.592) Lt: 5.849 (5.849) Accm: 3.08 (3.08) Acct: 4.93 (4.93) proj_loss: -0.6010 (-0.6010) time: 0.6776 data: 0.0003 [11-25 11:09:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.35 Lm: 6.412 (6.412) Lt: 5.670 (5.670) Accm: 3.66 (3.66) Acct: 5.73 (5.73) proj_loss: -0.5934 (-0.5934) time: 0.6775 data: 0.0003 [11-25 11:14:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [ 834/1669] eta: 0:09:26 tlr: 0.00013 tnm: 0.35 Lm: 6.412 (6.439) Lt: 5.722 (5.708) Accm: 3.63 (3.55) Acct: 5.66 (5.50) proj_loss: -0.5952 (-0.5986) time: 0.6741 data: 0.0003 [11-25 11:14:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [ 834/1669] eta: 0:09:26 tlr: 0.00013 tnm: 0.35 Lm: 6.452 (6.470) Lt: 5.709 (5.747) Accm: 3.65 (3.59) Acct: 5.49 (5.53) proj_loss: -0.5967 (-0.6013) time: 0.6741 data: 0.0003 [11-25 11:14:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [ 834/1669] eta: 0:09:26 tlr: 0.00013 tnm: 0.35 Lm: 6.625 (6.603) Lt: 5.937 (5.878) Accm: 2.89 (3.01) Acct: 4.53 (4.80) proj_loss: -0.6073 (-0.6031) time: 0.6741 data: 0.0003 [11-25 11:14:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [ 834/1669] eta: 0:09:26 tlr: 0.00013 tnm: 0.35 Lm: 6.481 (6.522) Lt: 5.705 (5.764) Accm: 2.99 (3.06) Acct: 4.94 (4.94) proj_loss: -0.5884 (-0.5857) time: 0.6741 data: 0.0003 [11-25 11:18:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.34 Lm: 6.468 (6.500) Lt: 5.692 (5.730) Accm: 3.10 (3.24) Acct: 5.10 (5.20) proj_loss: -0.5899 (-0.5897) time: 0.6747 data: 0.0003 [11-25 11:18:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.34 Lm: 6.498 (6.503) Lt: 5.775 (5.771) Accm: 3.54 (3.42) Acct: 5.37 (5.29) proj_loss: -0.5901 (-0.5968) time: 0.6747 data: 0.0003 [11-25 11:18:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.34 Lm: 6.589 (6.590) Lt: 5.838 (5.843) Accm: 2.98 (3.03) Acct: 4.90 (4.92) proj_loss: -0.6054 (-0.6032) time: 0.6747 data: 0.0003 [11-25 11:18:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.34 Lm: 6.428 (6.440) Lt: 5.700 (5.700) Accm: 3.66 (3.61) Acct: 5.73 (5.62) proj_loss: -0.6021 (-0.6013) time: 0.6747 data: 0.0003 [11-25 11:23:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.37 Lm: 6.443 (6.468) Lt: 5.722 (5.725) Accm: 3.63 (3.53) Acct: 5.66 (5.47) proj_loss: -0.6069 (-0.6025) time: 0.6780 data: 0.0018 [11-25 11:23:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 176/350] Total time: 0:18:52 (0.679 s / it) [11-25 11:23:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.37 Lm: 6.552 (6.561) Lt: 5.739 (5.804) Accm: 3.07 (3.08) Acct: 5.18 (4.97) proj_loss: -0.6050 (-0.6035) time: 0.6780 data: 0.0016 [11-25 11:23:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.37 Lm: 6.452 (6.490) Lt: 5.709 (5.745) Accm: 3.47 (3.43) Acct: 5.49 (5.35) proj_loss: -0.5967 (-0.5998) time: 0.6780 data: 0.0016 [11-25 11:23:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 176/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.37 Lm: 6.481 (6.536) Lt: 5.705 (5.773) Accm: 2.99 (3.13) Acct: 4.94 (4.96) proj_loss: -0.5914 (-0.5960) time: 0.6779 data: 0.0020 [11-25 11:23:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 176/350] Total time: 0:18:52 (0.679 s / it) [11-25 11:23:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 176/350] Total time: 0:18:52 (0.679 s / it) [11-25 11:23:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 176/350] Total time: 0:18:52 (0.679 s / it) [11-25 11:23:39] (/home/user/VAR/train.py , line 276)=> [ep176] (training ) Lm: 6.511 (6.511), Lt: 5.751 (5.751), Acc m&t: 3.34 5.28, Remain: 2 days, 6:49:29, Finish: 2024-11-27 02:13 [11-25 11:23:39] (/home/user/VAR/train.py , line 276)=> [ep176] (training ) Lm: 6.511 (6.511), Lt: 5.751 (5.751), Acc m&t: 3.34 5.28, Remain: 2 days, 6:48:21, Finish: 2024-11-27 02:12 [11-25 11:23:39] (/home/user/VAR/train.py , line 276)=> [ep176] (training ) Lm: 6.511 (6.511), Lt: 5.751 (5.751), Acc m&t: 3.34 5.28, Remain: 2 days, 6:49:07, Finish: 2024-11-27 02:12 [11-25 11:23:39] (/home/user/VAR/train.py , line 276)=> [ep176] (training ) Lm: 6.511 (6.511), Lt: 5.751 (5.751), Acc m&t: 3.34 5.28, Remain: 2 days, 6:48:53, Finish: 2024-11-27 02:12 [11-25 11:23:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [ 0/1669] eta: 0:18:02 tlr: 0.00013 tnm: 0.34 Lm: 6.587 (6.587) Lt: 5.817 (5.817) Accm: 3.40 (3.40) Acct: 5.30 (5.30) proj_loss: -0.5943 (-0.5943) time: 0.6484 data: 0.0004 [11-25 11:23:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [ 0/1669] eta: 0:18:02 tlr: 0.00013 tnm: 0.34 Lm: 6.561 (6.561) Lt: 5.859 (5.859) Accm: 3.18 (3.18) Acct: 4.70 (4.70) proj_loss: -0.6099 (-0.6099) time: 0.6485 data: 0.0004 [11-25 11:23:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [ 0/1669] eta: 0:18:02 tlr: 0.00013 tnm: 0.34 Lm: 6.593 (6.593) Lt: 5.833 (5.833) Accm: 3.07 (3.07) Acct: 4.60 (4.60) proj_loss: -0.5866 (-0.5866) time: 0.6486 data: 0.0004 [11-25 11:23:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [ 0/1669] eta: 0:18:02 tlr: 0.00013 tnm: 0.34 Lm: 6.619 (6.619) Lt: 5.859 (5.859) Accm: 2.91 (2.91) Acct: 4.63 (4.63) proj_loss: -0.5903 (-0.5903) time: 0.6485 data: 0.0004 [11-25 11:28:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [ 417/1669] eta: 0:15:13 tlr: 0.00013 tnm: 0.35 Lm: 6.584 (6.584) Lt: 5.827 (5.827) Accm: 3.07 (3.07) Acct: 4.80 (4.80) proj_loss: -0.5965 (-0.5965) time: 0.6781 data: 0.0003 [11-25 11:28:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [ 417/1669] eta: 0:15:13 tlr: 0.00013 tnm: 0.35 Lm: 6.580 (6.580) Lt: 5.822 (5.822) Accm: 3.21 (3.21) Acct: 5.04 (5.04) proj_loss: -0.5925 (-0.5925) time: 0.6781 data: 0.0003 [11-25 11:28:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [ 417/1669] eta: 0:15:13 tlr: 0.00013 tnm: 0.35 Lm: 6.538 (6.538) Lt: 5.799 (5.799) Accm: 3.16 (3.16) Acct: 4.88 (4.88) proj_loss: -0.5982 (-0.5982) time: 0.6781 data: 0.0003 [11-25 11:28:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [ 417/1669] eta: 0:15:13 tlr: 0.00013 tnm: 0.35 Lm: 6.548 (6.548) Lt: 5.797 (5.797) Accm: 3.15 (3.15) Acct: 4.67 (4.67) proj_loss: -0.5997 (-0.5997) time: 0.6781 data: 0.0003 [11-25 11:33:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [ 834/1669] eta: 0:09:52 tlr: 0.00013 tnm: 0.38 Lm: 6.502 (6.509) Lt: 5.761 (5.743) Accm: 3.23 (3.36) Acct: 4.73 (5.09) proj_loss: -0.6127 (-0.6080) time: 0.6759 data: 0.0003 [11-25 11:33:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [ 834/1669] eta: 0:09:52 tlr: 0.00013 tnm: 0.38 Lm: 6.587 (6.594) Lt: 5.826 (5.853) Accm: 3.14 (3.19) Acct: 4.86 (4.98) proj_loss: -0.5943 (-0.5937) time: 0.6759 data: 0.0003 [11-25 11:33:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [ 834/1669] eta: 0:09:52 tlr: 0.00013 tnm: 0.38 Lm: 6.561 (6.607) Lt: 5.859 (5.878) Accm: 3.13 (3.02) Acct: 4.70 (4.68) proj_loss: -0.6016 (-0.5993) time: 0.6759 data: 0.0002 [11-25 11:33:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [ 834/1669] eta: 0:09:52 tlr: 0.00013 tnm: 0.38 Lm: 6.550 (6.536) Lt: 5.796 (5.768) Accm: 3.24 (3.18) Acct: 4.98 (4.99) proj_loss: -0.6027 (-0.6007) time: 0.6759 data: 0.0003 [11-25 11:38:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.34 Lm: 6.530 (6.530) Lt: 5.775 (5.765) Accm: 3.13 (3.14) Acct: 4.80 (4.90) proj_loss: -0.6014 (-0.6005) time: 0.6760 data: 0.0003 [11-25 11:38:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.34 Lm: 6.580 (6.557) Lt: 5.822 (5.820) Accm: 3.27 (3.30) Acct: 5.08 (5.25) proj_loss: -0.5952 (-0.5983) time: 0.6760 data: 0.0003 [11-25 11:38:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.34 Lm: 6.518 (6.515) Lt: 5.777 (5.756) Accm: 3.16 (3.29) Acct: 4.77 (5.02) proj_loss: -0.6157 (-0.6107) time: 0.6760 data: 0.0004 [11-25 11:38:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.34 Lm: 6.538 (6.570) Lt: 5.799 (5.830) Accm: 3.16 (3.17) Acct: 4.88 (4.92) proj_loss: -0.5940 (-0.5931) time: 0.6760 data: 0.0003 [11-25 11:42:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.36 Lm: 6.536 (6.563) Lt: 5.791 (5.823) Accm: 3.18 (3.21) Acct: 5.06 (5.00) proj_loss: -0.5907 (-0.5926) time: 0.6802 data: 0.0018 [11-25 11:42:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 177/350] Total time: 0:19:18 (0.694 s / it) [11-25 11:42:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.36 Lm: 6.550 (6.557) Lt: 5.796 (5.787) Accm: 3.02 (3.09) Acct: 4.98 (4.91) proj_loss: -0.6001 (-0.5975) time: 0.6802 data: 0.0021 [11-25 11:42:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.36 Lm: 6.533 (6.542) Lt: 5.794 (5.787) Accm: 3.09 (3.25) Acct: 4.80 (4.98) proj_loss: -0.6127 (-0.6083) time: 0.6802 data: 0.0016 [11-25 11:42:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 177/350] Total time: 0:19:18 (0.694 s / it) [11-25 11:42:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 177/350] Total time: 0:19:18 (0.694 s / it) [11-25 11:42:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 177/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.36 Lm: 6.573 (6.517) Lt: 5.817 (5.784) Accm: 3.40 (3.46) Acct: 5.30 (5.43) proj_loss: -0.5960 (-0.6013) time: 0.6802 data: 0.0016 [11-25 11:42:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 177/350] Total time: 0:19:18 (0.694 s / it) [11-25 11:42:57] (/home/user/VAR/train.py , line 276)=> [ep177] (training ) Lm: 6.511 (6.545), Lt: 5.751 (5.794), Acc m&t: 3.34 5.28, Remain: 2 days, 6:24:10, Finish: 2024-11-27 02:07 [11-25 11:42:57] (/home/user/VAR/train.py , line 276)=> [ep177] (training ) Lm: 6.511 (6.545), Lt: 5.751 (5.794), Acc m&t: 3.34 5.28, Remain: 2 days, 6:24:36, Finish: 2024-11-27 02:07 [11-25 11:42:57] (/home/user/VAR/train.py , line 276)=> [ep177] (training ) Lm: 6.511 (6.545), Lt: 5.751 (5.794), Acc m&t: 3.34 5.28, Remain: 2 days, 6:24:13, Finish: 2024-11-27 02:07 [11-25 11:42:57] (/home/user/VAR/train.py , line 276)=> [ep177] (training ) Lm: 6.511 (6.545), Lt: 5.751 (5.794), Acc m&t: 3.34 5.28, Remain: 2 days, 6:23:49, Finish: 2024-11-27 02:06 [11-25 11:42:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [ 0/1669] eta: 0:18:36 tlr: 0.00013 tnm: 0.35 Lm: 6.449 (6.449) Lt: 5.665 (5.665) Accm: 3.82 (3.82) Acct: 6.15 (6.15) proj_loss: -0.6180 (-0.6180) time: 0.6688 data: 0.0004 [11-25 11:42:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [ 0/1669] eta: 0:18:37 tlr: 0.00013 tnm: 0.35 Lm: 6.572 (6.572) Lt: 5.799 (5.799) Accm: 3.05 (3.05) Acct: 4.96 (4.96) proj_loss: -0.5887 (-0.5887) time: 0.6693 data: 0.0004 [11-25 11:42:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [ 0/1669] eta: 0:18:37 tlr: 0.00013 tnm: 0.35 Lm: 6.758 (6.758) Lt: 6.082 (6.082) Accm: 2.68 (2.68) Acct: 4.05 (4.05) proj_loss: -0.6193 (-0.6193) time: 0.6693 data: 0.0004 [11-25 11:42:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [ 0/1669] eta: 0:18:37 tlr: 0.00013 tnm: 0.35 Lm: 6.506 (6.506) Lt: 5.767 (5.767) Accm: 3.34 (3.34) Acct: 5.30 (5.30) proj_loss: -0.5932 (-0.5932) time: 0.6696 data: 0.0004 [11-25 11:47:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.33 Lm: 6.463 (6.463) Lt: 5.712 (5.712) Accm: 3.37 (3.37) Acct: 5.28 (5.28) proj_loss: -0.6027 (-0.6027) time: 0.6765 data: 0.0003 [11-25 11:47:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.33 Lm: 6.438 (6.438) Lt: 5.720 (5.720) Accm: 3.65 (3.65) Acct: 5.76 (5.76) proj_loss: -0.6036 (-0.6036) time: 0.6765 data: 0.0002 [11-25 11:47:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.33 Lm: 6.722 (6.722) Lt: 6.022 (6.022) Accm: 2.64 (2.64) Acct: 4.19 (4.19) proj_loss: -0.6090 (-0.6090) time: 0.6765 data: 0.0003 [11-25 11:47:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.33 Lm: 6.510 (6.510) Lt: 5.739 (5.739) Accm: 3.44 (3.44) Acct: 5.47 (5.47) proj_loss: -0.5985 (-0.5985) time: 0.6765 data: 0.0003 [11-25 11:52:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [ 834/1669] eta: 0:09:47 tlr: 0.00013 tnm: 0.35 Lm: 6.491 (6.504) Lt: 5.756 (5.745) Accm: 3.45 (3.44) Acct: 5.37 (5.44) proj_loss: -0.6163 (-0.6044) time: 0.6770 data: 0.0003 [11-25 11:52:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [ 834/1669] eta: 0:09:47 tlr: 0.00013 tnm: 0.35 Lm: 6.572 (6.521) Lt: 5.799 (5.748) Accm: 3.10 (3.28) Acct: 5.29 (5.28) proj_loss: -0.5945 (-0.6000) time: 0.6770 data: 0.0003 [11-25 11:52:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [ 834/1669] eta: 0:09:47 tlr: 0.00013 tnm: 0.35 Lm: 6.686 (6.621) Lt: 5.962 (5.887) Accm: 2.68 (2.96) Acct: 4.34 (4.68) proj_loss: -0.5988 (-0.6031) time: 0.6770 data: 0.0003 [11-25 11:52:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [ 834/1669] eta: 0:09:47 tlr: 0.00013 tnm: 0.35 Lm: 6.370 (6.413) Lt: 5.672 (5.697) Accm: 3.79 (3.69) Acct: 5.80 (5.77) proj_loss: -0.6139 (-0.6116) time: 0.6770 data: 0.0003 [11-25 11:57:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.36 Lm: 6.398 (6.416) Lt: 5.664 (5.687) Accm: 3.66 (3.65) Acct: 5.64 (5.70) proj_loss: -0.6133 (-0.6119) time: 0.6756 data: 0.0003 [11-25 11:57:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.36 Lm: 6.506 (6.508) Lt: 5.745 (5.742) Accm: 3.45 (3.44) Acct: 5.23 (5.35) proj_loss: -0.6070 (-0.6027) time: 0.6756 data: 0.0003 [11-25 11:57:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.36 Lm: 6.609 (6.599) Lt: 5.852 (5.851) Accm: 2.92 (3.01) Acct: 4.80 (4.83) proj_loss: -0.6039 (-0.6046) time: 0.6756 data: 0.0003 [11-25 11:57:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.36 Lm: 6.525 (6.510) Lt: 5.737 (5.729) Accm: 3.27 (3.32) Acct: 5.31 (5.29) proj_loss: -0.5923 (-0.5975) time: 0.6756 data: 0.0003 [11-25 12:02:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.572 (6.550) Lt: 5.799 (5.772) Accm: 3.10 (3.24) Acct: 5.29 (5.24) proj_loss: -0.5945 (-0.5983) time: 0.6788 data: 0.0019 [11-25 12:02:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 178/350] Total time: 0:19:20 (0.695 s / it) [11-25 12:02:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.558 (6.591) Lt: 5.778 (5.836) Accm: 3.09 (3.03) Acct: 5.13 (4.89) proj_loss: -0.5990 (-0.6035) time: 0.6788 data: 0.0017 [11-25 12:02:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.505 (6.508) Lt: 5.734 (5.731) Accm: 3.45 (3.41) Acct: 5.22 (5.32) proj_loss: -0.5978 (-0.6015) time: 0.6788 data: 0.0015 [11-25 12:02:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 178/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.426 (6.437) Lt: 5.672 (5.697) Accm: 3.54 (3.58) Acct: 5.48 (5.58) proj_loss: -0.6127 (-0.6063) time: 0.6788 data: 0.0018 [11-25 12:02:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 178/350] Total time: 0:19:20 (0.695 s / it) [11-25 12:02:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 178/350] Total time: 0:19:20 (0.695 s / it) [11-25 12:02:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 178/350] Total time: 0:19:20 (0.695 s / it) [11-25 12:02:18] (/home/user/VAR/train.py , line 276)=> [ep178] (training ) Lm: 6.511 (6.532), Lt: 5.751 (5.773), Acc m&t: 3.34 5.28, Remain: 2 days, 6:10:15, Finish: 2024-11-27 02:12 [11-25 12:02:18] (/home/user/VAR/train.py , line 276)=> [ep178] (training ) Lm: 6.511 (6.532), Lt: 5.751 (5.773), Acc m&t: 3.34 5.28, Remain: 2 days, 6:10:09, Finish: 2024-11-27 02:12 [11-25 12:02:18] (/home/user/VAR/train.py , line 276)=> [ep178] (training ) Lm: 6.511 (6.532), Lt: 5.751 (5.773), Acc m&t: 3.34 5.28, Remain: 2 days, 6:10:39, Finish: 2024-11-27 02:12 [11-25 12:02:18] (/home/user/VAR/train.py , line 276)=> [ep178] (training ) Lm: 6.511 (6.532), Lt: 5.751 (5.773), Acc m&t: 3.34 5.28, Remain: 2 days, 6:10:29, Finish: 2024-11-27 02:12 [11-25 12:02:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [ 0/1669] eta: 0:18:34 tlr: 0.00013 tnm: 0.36 Lm: 6.562 (6.562) Lt: 5.815 (5.815) Accm: 3.47 (3.47) Acct: 5.37 (5.37) proj_loss: -0.6027 (-0.6027) time: 0.6678 data: 0.0004 [11-25 12:02:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [ 0/1669] eta: 0:18:35 tlr: 0.00013 tnm: 0.36 Lm: 6.663 (6.663) Lt: 5.964 (5.964) Accm: 2.88 (2.88) Acct: 4.44 (4.44) proj_loss: -0.5881 (-0.5881) time: 0.6681 data: 0.0004 [11-25 12:02:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [ 0/1669] eta: 0:18:35 tlr: 0.00013 tnm: 0.36 Lm: 6.452 (6.452) Lt: 5.657 (5.657) Accm: 3.63 (3.63) Acct: 5.77 (5.77) proj_loss: -0.5955 (-0.5955) time: 0.6682 data: 0.0004 [11-25 12:02:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [ 0/1669] eta: 0:18:35 tlr: 0.00013 tnm: 0.36 Lm: 6.466 (6.466) Lt: 5.711 (5.711) Accm: 3.62 (3.62) Acct: 5.63 (5.63) proj_loss: -0.5950 (-0.5950) time: 0.6683 data: 0.0004 [11-25 12:07:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.37 Lm: 6.541 (6.541) Lt: 5.802 (5.802) Accm: 3.27 (3.27) Acct: 5.12 (5.12) proj_loss: -0.5961 (-0.5961) time: 0.6767 data: 0.0003 [11-25 12:07:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.37 Lm: 6.483 (6.483) Lt: 5.704 (5.704) Accm: 3.61 (3.61) Acct: 5.61 (5.61) proj_loss: -0.5940 (-0.5940) time: 0.6767 data: 0.0003 [11-25 12:07:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.37 Lm: 6.596 (6.596) Lt: 5.852 (5.852) Accm: 3.05 (3.05) Acct: 4.76 (4.76) proj_loss: -0.6037 (-0.6037) time: 0.6767 data: 0.0003 [11-25 12:07:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [ 417/1669] eta: 0:14:07 tlr: 0.00013 tnm: 0.37 Lm: 6.583 (6.583) Lt: 5.833 (5.833) Accm: 3.28 (3.28) Acct: 5.00 (5.00) proj_loss: -0.6028 (-0.6028) time: 0.6767 data: 0.0003 [11-25 12:11:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [ 834/1669] eta: 0:09:25 tlr: 0.00013 tnm: 0.35 Lm: 6.584 (6.583) Lt: 5.832 (5.833) Accm: 3.21 (3.26) Acct: 5.17 (5.06) proj_loss: -0.6029 (-0.6049) time: 0.6773 data: 0.0003 [11-25 12:11:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [ 834/1669] eta: 0:09:25 tlr: 0.00013 tnm: 0.35 Lm: 6.466 (6.482) Lt: 5.711 (5.745) Accm: 3.62 (3.46) Acct: 5.63 (5.49) proj_loss: -0.5972 (-0.6105) time: 0.6774 data: 0.0003 [11-25 12:11:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [ 834/1669] eta: 0:09:25 tlr: 0.00013 tnm: 0.35 Lm: 6.537 (6.576) Lt: 5.834 (5.846) Accm: 3.11 (3.07) Acct: 4.61 (4.71) proj_loss: -0.6183 (-0.6086) time: 0.6773 data: 0.0003 [11-25 12:11:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [ 834/1669] eta: 0:09:25 tlr: 0.00013 tnm: 0.35 Lm: 6.514 (6.521) Lt: 5.751 (5.741) Accm: 3.58 (3.40) Acct: 5.46 (5.34) proj_loss: -0.5925 (-0.5889) time: 0.6773 data: 0.0003 [11-25 12:16:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.35 Lm: 6.519 (6.522) Lt: 5.784 (5.765) Accm: 3.50 (3.41) Acct: 5.41 (5.34) proj_loss: -0.5940 (-0.5953) time: 0.6762 data: 0.0003 [11-25 12:16:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.35 Lm: 6.458 (6.474) Lt: 5.684 (5.724) Accm: 3.72 (3.58) Acct: 5.90 (5.66) proj_loss: -0.5962 (-0.6066) time: 0.6762 data: 0.0003 [11-25 12:16:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.35 Lm: 6.594 (6.608) Lt: 5.842 (5.850) Accm: 3.15 (3.17) Acct: 4.92 (4.96) proj_loss: -0.6028 (-0.6025) time: 0.6762 data: 0.0003 [11-25 12:16:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [1251/1669] eta: 0:04:43 tlr: 0.00013 tnm: 0.35 Lm: 6.533 (6.564) Lt: 5.826 (5.839) Accm: 3.17 (3.11) Acct: 4.76 (4.76) proj_loss: -0.6109 (-0.6073) time: 0.6762 data: 0.0003 [11-25 12:21:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.36 Lm: 6.528 (6.530) Lt: 5.817 (5.793) Accm: 3.22 (3.18) Acct: 4.91 (4.90) proj_loss: -0.6034 (-0.6042) time: 0.6795 data: 0.0021 [11-25 12:21:11] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 179/350] Total time: 0:18:52 (0.679 s / it) [11-25 12:21:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.36 Lm: 6.584 (6.600) Lt: 5.832 (5.846) Accm: 3.12 (3.16) Acct: 4.91 (4.95) proj_loss: -0.6027 (-0.6019) time: 0.6795 data: 0.0016 [11-25 12:21:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.36 Lm: 6.524 (6.538) Lt: 5.816 (5.783) Accm: 3.43 (3.36) Acct: 5.35 (5.27) proj_loss: -0.5925 (-0.5926) time: 0.6795 data: 0.0018 [11-25 12:21:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 179/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.36 Lm: 6.466 (6.488) Lt: 5.711 (5.746) Accm: 3.62 (3.54) Acct: 5.63 (5.59) proj_loss: -0.5972 (-0.6069) time: 0.6795 data: 0.0016 [11-25 12:21:11] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 179/350] Total time: 0:18:52 (0.679 s / it) [11-25 12:21:11] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 179/350] Total time: 0:18:52 (0.679 s / it) [11-25 12:21:11] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 179/350] Total time: 0:18:52 (0.679 s / it) [11-25 12:23:35] (home/user/VAR/trainer.py, line 114)=> FID: 3.70654655189918 [11-25 12:23:35] (/home/user/VAR/train.py , line 259)=> [*] [ep179] (val 50000) Lm: 6.5213, Lt: 5.7614, Acc m&t: 3.34 5.25, Val cost: 144.08s [11-25 12:23:35] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-25 12:23:55] (/home/user/VAR/train.py , line 276)=> [ep179] (training ) Lm: 6.511 (6.521), Lt: 5.751 (5.761), Acc m&t: 3.34 5.28, Remain: 2 days, 5:49:52, Finish: 2024-11-27 02:11 [11-25 12:23:55] (/home/user/VAR/train.py , line 276)=> [ep179] (training ) Lm: 6.511 (6.521), Lt: 5.751 (5.761), Acc m&t: 3.34 5.28, Remain: 2 days, 5:49:47, Finish: 2024-11-27 02:10 [11-25 12:23:55] (/home/user/VAR/train.py , line 276)=> [ep179] (training ) Lm: 6.511 (6.521), Lt: 5.751 (5.761), Acc m&t: 3.34 5.28, Remain: 2 days, 5:49:19, Finish: 2024-11-27 02:10 [11-25 12:23:55] (/home/user/VAR/train.py , line 276)=> [ep179] (training ) Lm: 6.511 (6.521), Lt: 5.751 (5.761), Acc m&t: 3.34 5.28, Remain: 2 days, 5:50:19, Finish: 2024-11-27 02:11 [11-25 12:23:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [ 0/1669] eta: 0:18:45 tlr: 0.00013 tnm: 0.35 Lm: 6.486 (6.486) Lt: 5.705 (5.705) Accm: 3.55 (3.55) Acct: 5.39 (5.39) proj_loss: -0.5944 (-0.5944) time: 0.6741 data: 0.0004 [11-25 12:23:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [ 0/1669] eta: 0:18:44 tlr: 0.00013 tnm: 0.35 Lm: 6.569 (6.569) Lt: 5.813 (5.813) Accm: 3.21 (3.21) Acct: 5.18 (5.18) proj_loss: -0.5981 (-0.5981) time: 0.6739 data: 0.0004 [11-25 12:23:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [ 0/1669] eta: 0:18:43 tlr: 0.00013 tnm: 0.35 Lm: 6.562 (6.562) Lt: 5.855 (5.855) Accm: 3.22 (3.22) Acct: 4.73 (4.73) proj_loss: -0.5926 (-0.5926) time: 0.6733 data: 0.0003 [11-25 12:23:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [ 0/1669] eta: 0:18:44 tlr: 0.00013 tnm: 0.35 Lm: 6.533 (6.533) Lt: 5.810 (5.810) Accm: 3.25 (3.25) Acct: 5.17 (5.17) proj_loss: -0.5886 (-0.5886) time: 0.6740 data: 0.0004 [11-25 12:29:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [ 417/1669] eta: 0:15:16 tlr: 0.00013 tnm: 0.35 Lm: 6.544 (6.544) Lt: 5.783 (5.783) Accm: 3.20 (3.20) Acct: 5.01 (5.01) proj_loss: -0.5898 (-0.5898) time: 0.7326 data: 0.0003 [11-25 12:29:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [ 417/1669] eta: 0:15:16 tlr: 0.00013 tnm: 0.35 Lm: 6.542 (6.542) Lt: 5.831 (5.831) Accm: 2.97 (2.97) Acct: 4.47 (4.47) proj_loss: -0.5973 (-0.5973) time: 0.7326 data: 0.0003 [11-25 12:29:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [ 417/1669] eta: 0:15:16 tlr: 0.00013 tnm: 0.35 Lm: 6.487 (6.487) Lt: 5.707 (5.707) Accm: 3.45 (3.45) Acct: 5.29 (5.29) proj_loss: -0.5942 (-0.5942) time: 0.7326 data: 0.0003 [11-25 12:29:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [ 417/1669] eta: 0:15:16 tlr: 0.00013 tnm: 0.35 Lm: 6.471 (6.471) Lt: 5.742 (5.742) Accm: 3.38 (3.38) Acct: 5.29 (5.29) proj_loss: -0.6154 (-0.6154) time: 0.7327 data: 0.0003 [11-25 12:33:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [ 834/1669] eta: 0:09:53 tlr: 0.00013 tnm: 0.36 Lm: 6.569 (6.545) Lt: 5.813 (5.813) Accm: 3.21 (3.29) Acct: 5.18 (5.02) proj_loss: -0.6139 (-0.6149) time: 0.6774 data: 0.0003 [11-25 12:33:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [ 834/1669] eta: 0:09:53 tlr: 0.00013 tnm: 0.36 Lm: 6.486 (6.451) Lt: 5.705 (5.663) Accm: 3.55 (3.60) Acct: 5.39 (5.57) proj_loss: -0.5940 (-0.5853) time: 0.6774 data: 0.0003 [11-25 12:33:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [ 834/1669] eta: 0:09:53 tlr: 0.00013 tnm: 0.36 Lm: 6.522 (6.494) Lt: 5.808 (5.766) Accm: 3.22 (3.25) Acct: 4.73 (4.95) proj_loss: -0.5926 (-0.5952) time: 0.6774 data: 0.0003 [11-25 12:33:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [ 834/1669] eta: 0:09:53 tlr: 0.00013 tnm: 0.36 Lm: 6.533 (6.479) Lt: 5.756 (5.704) Accm: 3.25 (3.43) Acct: 5.17 (5.33) proj_loss: -0.5910 (-0.5966) time: 0.6773 data: 0.0003 [11-25 12:38:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.36 Lm: 6.539 (6.496) Lt: 5.783 (5.733) Accm: 3.39 (3.45) Acct: 5.22 (5.32) proj_loss: -0.5975 (-0.5984) time: 0.6757 data: 0.0003 [11-25 12:38:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.36 Lm: 6.595 (6.564) Lt: 5.876 (5.844) Accm: 3.15 (3.21) Acct: 4.83 (4.86) proj_loss: -0.6111 (-0.6132) time: 0.6757 data: 0.0003 [11-25 12:38:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.36 Lm: 6.542 (6.521) Lt: 5.831 (5.804) Accm: 3.18 (3.23) Acct: 4.94 (5.00) proj_loss: -0.5973 (-0.5983) time: 0.6757 data: 0.0003 [11-25 12:38:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [1251/1669] eta: 0:04:52 tlr: 0.00013 tnm: 0.36 Lm: 6.487 (6.478) Lt: 5.707 (5.695) Accm: 3.45 (3.49) Acct: 5.29 (5.46) proj_loss: -0.5942 (-0.5943) time: 0.6757 data: 0.0003 [11-25 12:43:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.488 (6.494) Lt: 5.708 (5.714) Accm: 3.34 (3.44) Acct: 5.18 (5.35) proj_loss: -0.5944 (-0.5962) time: 0.6770 data: 0.0019 [11-25 12:43:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 180/350] Total time: 0:19:17 (0.694 s / it) [11-25 12:43:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.533 (6.503) Lt: 5.802 (5.747) Accm: 3.25 (3.36) Acct: 5.17 (5.14) proj_loss: -0.6039 (-0.6021) time: 0.6770 data: 0.0018 [11-25 12:43:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.569 (6.554) Lt: 5.813 (5.827) Accm: 3.21 (3.31) Acct: 5.18 (5.01) proj_loss: -0.6083 (-0.6101) time: 0.6770 data: 0.0016 [11-25 12:43:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 180/350] [1668/1669] eta: 0:00:00 tlr: 0.00013 tnm: 0.35 Lm: 6.562 (6.533) Lt: 5.839 (5.811) Accm: 3.22 (3.23) Acct: 5.08 (5.02) proj_loss: -0.6019 (-0.6002) time: 0.6770 data: 0.0013 [11-25 12:43:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 180/350] Total time: 0:19:17 (0.694 s / it) [11-25 12:43:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 180/350] Total time: 0:19:17 (0.694 s / it) [11-25 12:43:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 180/350] Total time: 0:19:17 (0.694 s / it) [11-25 12:43:13] (/home/user/VAR/train.py , line 276)=> [ep180] (training ) Lm: 6.511 (6.524), Lt: 5.751 (5.765), Acc m&t: 3.35 5.28, Remain: 2 days, 5:32:40, Finish: 2024-11-27 02:15 [11-25 12:43:13] (/home/user/VAR/train.py , line 276)=> [ep180] (training ) Lm: 6.511 (6.524), Lt: 5.751 (5.765), Acc m&t: 3.35 5.28, Remain: 2 days, 5:33:14, Finish: 2024-11-27 02:16 [11-25 12:43:13] (/home/user/VAR/train.py , line 276)=> [ep180] (training ) Lm: 6.511 (6.524), Lt: 5.751 (5.765), Acc m&t: 3.35 5.28, Remain: 2 days, 5:33:03, Finish: 2024-11-27 02:16 [11-25 12:43:13] (/home/user/VAR/train.py , line 276)=> [ep180] (training ) Lm: 6.511 (6.524), Lt: 5.751 (5.765), Acc m&t: 3.35 5.28, Remain: 2 days, 5:33:27, Finish: 2024-11-27 02:16 [11-25 12:43:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [ 0/1669] eta: 0:18:24 tlr: 0.00013 tnm: 0.34 Lm: 6.396 (6.396) Lt: 5.597 (5.597) Accm: 3.55 (3.55) Acct: 6.04 (6.04) proj_loss: -0.6053 (-0.6053) time: 0.6617 data: 0.0003 [11-25 12:43:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [ 0/1669] eta: 0:18:22 tlr: 0.00013 tnm: 0.34 Lm: 6.328 (6.328) Lt: 5.559 (5.559) Accm: 3.92 (3.92) Acct: 6.30 (6.30) proj_loss: -0.6091 (-0.6091) time: 0.6606 data: 0.0004 [11-25 12:43:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [ 0/1669] eta: 0:18:25 tlr: 0.00013 tnm: 0.34 Lm: 6.459 (6.459) Lt: 5.707 (5.707) Accm: 3.45 (3.45) Acct: 5.51 (5.51) proj_loss: -0.5957 (-0.5957) time: 0.6624 data: 0.0004 [11-25 12:43:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [ 0/1669] eta: 0:18:25 tlr: 0.00013 tnm: 0.34 Lm: 6.572 (6.572) Lt: 5.815 (5.815) Accm: 2.79 (2.79) Acct: 4.49 (4.49) proj_loss: -0.6051 (-0.6051) time: 0.6625 data: 0.0004 [11-25 12:47:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [ 417/1669] eta: 0:14:12 tlr: 0.00013 tnm: 0.34 Lm: 6.569 (6.569) Lt: 5.848 (5.848) Accm: 2.99 (2.99) Acct: 4.69 (4.69) proj_loss: -0.6001 (-0.6001) time: 0.6761 data: 0.0003 [11-25 12:47:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [ 417/1669] eta: 0:14:12 tlr: 0.00013 tnm: 0.34 Lm: 6.495 (6.495) Lt: 5.733 (5.733) Accm: 3.37 (3.37) Acct: 5.32 (5.32) proj_loss: -0.5990 (-0.5990) time: 0.6761 data: 0.0003 [11-25 12:47:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [ 417/1669] eta: 0:14:12 tlr: 0.00013 tnm: 0.34 Lm: 6.390 (6.390) Lt: 5.624 (5.624) Accm: 3.77 (3.77) Acct: 6.03 (6.03) proj_loss: -0.6037 (-0.6037) time: 0.6761 data: 0.0003 [11-25 12:47:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [ 417/1669] eta: 0:14:12 tlr: 0.00013 tnm: 0.34 Lm: 6.514 (6.514) Lt: 5.729 (5.729) Accm: 3.31 (3.31) Acct: 5.52 (5.52) proj_loss: -0.5912 (-0.5912) time: 0.6761 data: 0.0003 [11-25 12:53:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [ 834/1669] eta: 0:09:48 tlr: 0.00013 tnm: 0.36 Lm: 6.587 (6.538) Lt: 5.807 (5.755) Accm: 3.12 (3.24) Acct: 4.99 (5.28) proj_loss: -0.5976 (-0.5933) time: 0.6776 data: 0.0003 [11-25 12:53:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [ 834/1669] eta: 0:09:48 tlr: 0.00013 tnm: 0.36 Lm: 6.453 (6.444) Lt: 5.688 (5.689) Accm: 3.62 (3.64) Acct: 5.75 (5.80) proj_loss: -0.5982 (-0.6017) time: 0.6776 data: 0.0003 [11-25 12:53:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [ 834/1669] eta: 0:09:48 tlr: 0.00013 tnm: 0.36 Lm: 6.532 (6.549) Lt: 5.758 (5.787) Accm: 3.28 (3.29) Acct: 5.13 (5.21) proj_loss: -0.5957 (-0.5916) time: 0.6775 data: 0.0003 [11-25 12:53:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [ 834/1669] eta: 0:09:48 tlr: 0.00013 tnm: 0.36 Lm: 6.572 (6.596) Lt: 5.880 (5.871) Accm: 2.91 (2.96) Acct: 4.61 (4.67) proj_loss: -0.5983 (-0.5995) time: 0.6776 data: 0.0003 [11-25 12:57:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [1251/1669] eta: 0:04:53 tlr: 0.00013 tnm: 0.37 Lm: 6.569 (6.561) Lt: 5.848 (5.831) Accm: 3.05 (3.04) Acct: 4.75 (4.91) proj_loss: -0.6017 (-0.6035) time: 0.6767 data: 0.0003 [11-25 12:57:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [1251/1669] eta: 0:04:53 tlr: 0.00013 tnm: 0.37 Lm: 6.515 (6.536) Lt: 5.760 (5.781) Accm: 3.37 (3.34) Acct: 5.32 (5.32) proj_loss: -0.5990 (-0.5990) time: 0.6767 data: 0.0003 [11-25 12:57:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [1251/1669] eta: 0:04:53 tlr: 0.00013 tnm: 0.37 Lm: 6.434 (6.437) Lt: 5.662 (5.676) Accm: 3.74 (3.69) Acct: 5.84 (5.83) proj_loss: -0.6037 (-0.6040) time: 0.6767 data: 0.0003 [11-25 12:57:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [1251/1669] eta: 0:04:53 tlr: 0.00013 tnm: 0.37 Lm: 6.569 (6.541) Lt: 5.794 (5.762) Accm: 3.12 (3.21) Acct: 5.04 (5.23) proj_loss: -0.6015 (-0.6005) time: 0.6767 data: 0.0003 [11-25 13:02:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.35 Lm: 6.551 (6.522) Lt: 5.781 (5.748) Accm: 3.12 (3.27) Acct: 5.10 (5.29) proj_loss: -0.6053 (-0.6019) time: 0.6774 data: 0.0015 [11-25 13:02:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 181/350] Total time: 0:19:22 (0.696 s / it) [11-25 13:02:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.35 Lm: 6.532 (6.568) Lt: 5.761 (5.806) Accm: 3.28 (3.26) Acct: 5.13 (5.25) proj_loss: -0.6024 (-0.5999) time: 0.6774 data: 0.0017 [11-25 13:02:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.35 Lm: 6.434 (6.436) Lt: 5.688 (5.679) Accm: 3.62 (3.64) Acct: 5.75 (5.71) proj_loss: -0.5982 (-0.6022) time: 0.6774 data: 0.0017 [11-25 13:02:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 181/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.35 Lm: 6.572 (6.570) Lt: 5.868 (5.839) Accm: 3.07 (3.05) Acct: 4.68 (4.87) proj_loss: -0.5999 (-0.6028) time: 0.6774 data: 0.0021 [11-25 13:02:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 181/350] Total time: 0:19:22 (0.696 s / it) [11-25 13:02:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 181/350] Total time: 0:19:22 (0.696 s / it) [11-25 13:02:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 181/350] Total time: 0:19:22 (0.696 s / it) [11-25 13:02:35] (/home/user/VAR/train.py , line 276)=> [ep181] (training ) Lm: 6.511 (6.529), Lt: 5.751 (5.778), Acc m&t: 3.35 5.28, Remain: 2 days, 5:03:25, Finish: 2024-11-27 02:06 [11-25 13:02:35] (/home/user/VAR/train.py , line 276)=> [ep181] (training ) Lm: 6.511 (6.529), Lt: 5.751 (5.778), Acc m&t: 3.35 5.28, Remain: 2 days, 5:04:05, Finish: 2024-11-27 02:06 [11-25 13:02:35] (/home/user/VAR/train.py , line 276)=> [ep181] (training ) Lm: 6.511 (6.529), Lt: 5.751 (5.778), Acc m&t: 3.35 5.28, Remain: 2 days, 5:04:11, Finish: 2024-11-27 02:06 [11-25 13:02:35] (/home/user/VAR/train.py , line 276)=> [ep181] (training ) Lm: 6.511 (6.529), Lt: 5.751 (5.778), Acc m&t: 3.35 5.28, Remain: 2 days, 5:03:48, Finish: 2024-11-27 02:06 [11-25 13:02:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [ 0/1669] eta: 0:18:26 tlr: 0.00012 tnm: 0.39 Lm: 6.525 (6.525) Lt: 5.808 (5.808) Accm: 3.47 (3.47) Acct: 5.46 (5.46) proj_loss: -0.6217 (-0.6217) time: 0.6631 data: 0.0003 [11-25 13:02:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [ 0/1669] eta: 0:18:17 tlr: 0.00012 tnm: 0.39 Lm: 6.619 (6.619) Lt: 5.907 (5.907) Accm: 3.07 (3.07) Acct: 4.70 (4.70) proj_loss: -0.6244 (-0.6244) time: 0.6574 data: 0.0004 [11-25 13:02:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [ 0/1669] eta: 0:18:17 tlr: 0.00012 tnm: 0.39 Lm: 6.511 (6.511) Lt: 5.738 (5.738) Accm: 3.61 (3.61) Acct: 5.53 (5.53) proj_loss: -0.6019 (-0.6019) time: 0.6575 data: 0.0004 [11-25 13:02:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [ 0/1669] eta: 0:18:16 tlr: 0.00012 tnm: 0.39 Lm: 6.498 (6.498) Lt: 5.757 (5.757) Accm: 3.28 (3.28) Acct: 5.08 (5.08) proj_loss: -0.5870 (-0.5870) time: 0.6572 data: 0.0003 [11-25 13:07:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [ 417/1669] eta: 0:14:07 tlr: 0.00012 tnm: 0.35 Lm: 6.655 (6.655) Lt: 5.933 (5.933) Accm: 2.91 (2.91) Acct: 4.57 (4.57) proj_loss: -0.5924 (-0.5924) time: 0.6757 data: 0.0003 [11-25 13:07:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [ 417/1669] eta: 0:14:07 tlr: 0.00012 tnm: 0.35 Lm: 6.593 (6.593) Lt: 5.875 (5.875) Accm: 3.14 (3.14) Acct: 4.87 (4.87) proj_loss: -0.6176 (-0.6176) time: 0.6757 data: 0.0003 [11-25 13:07:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [ 417/1669] eta: 0:14:07 tlr: 0.00012 tnm: 0.35 Lm: 6.491 (6.491) Lt: 5.732 (5.732) Accm: 3.53 (3.53) Acct: 5.58 (5.58) proj_loss: -0.6070 (-0.6070) time: 0.6757 data: 0.0003 [11-25 13:07:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [ 417/1669] eta: 0:14:07 tlr: 0.00012 tnm: 0.35 Lm: 6.582 (6.582) Lt: 5.816 (5.816) Accm: 3.29 (3.29) Acct: 5.18 (5.18) proj_loss: -0.6036 (-0.6036) time: 0.6757 data: 0.0003 [11-25 13:12:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [ 834/1669] eta: 0:09:26 tlr: 0.00012 tnm: 0.35 Lm: 6.577 (6.581) Lt: 5.823 (5.820) Accm: 3.13 (3.23) Acct: 4.91 (5.00) proj_loss: -0.6074 (-0.6049) time: 0.6757 data: 0.0003 [11-25 13:12:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [ 834/1669] eta: 0:09:26 tlr: 0.00012 tnm: 0.35 Lm: 6.511 (6.537) Lt: 5.738 (5.786) Accm: 3.45 (3.28) Acct: 5.53 (5.21) proj_loss: -0.6019 (-0.6037) time: 0.6757 data: 0.0003 [11-25 13:12:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [ 834/1669] eta: 0:09:26 tlr: 0.00012 tnm: 0.35 Lm: 6.567 (6.583) Lt: 5.843 (5.853) Accm: 3.19 (3.16) Acct: 4.94 (4.90) proj_loss: -0.6168 (-0.6173) time: 0.6757 data: 0.0003 [11-25 13:12:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [ 834/1669] eta: 0:09:26 tlr: 0.00012 tnm: 0.35 Lm: 6.558 (6.623) Lt: 5.790 (5.885) Accm: 3.23 (3.02) Acct: 4.82 (4.65) proj_loss: -0.5978 (-0.5955) time: 0.6758 data: 0.0003 [11-25 13:16:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [1251/1669] eta: 0:04:43 tlr: 0.00012 tnm: 0.36 Lm: 6.541 (6.598) Lt: 5.773 (5.847) Accm: 3.26 (3.18) Acct: 4.95 (5.01) proj_loss: -0.5997 (-0.6000) time: 0.6770 data: 0.0003 [11-25 13:16:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [1251/1669] eta: 0:04:43 tlr: 0.00012 tnm: 0.36 Lm: 6.551 (6.537) Lt: 5.816 (5.765) Accm: 3.30 (3.43) Acct: 5.18 (5.46) proj_loss: -0.5969 (-0.6003) time: 0.6770 data: 0.0003 [11-25 13:16:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [1251/1669] eta: 0:04:43 tlr: 0.00012 tnm: 0.36 Lm: 6.505 (6.527) Lt: 5.734 (5.772) Accm: 3.53 (3.37) Acct: 5.58 (5.32) proj_loss: -0.5994 (-0.6011) time: 0.6770 data: 0.0003 [11-25 13:16:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [1251/1669] eta: 0:04:43 tlr: 0.00012 tnm: 0.36 Lm: 6.565 (6.576) Lt: 5.830 (5.844) Accm: 3.19 (3.17) Acct: 4.95 (4.91) proj_loss: -0.6138 (-0.6076) time: 0.6770 data: 0.0003 [11-25 13:21:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.35 Lm: 6.558 (6.590) Lt: 5.778 (5.833) Accm: 3.28 (3.21) Acct: 5.08 (5.12) proj_loss: -0.6016 (-0.6012) time: 0.6790 data: 0.0021 [11-25 13:21:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.35 Lm: 6.525 (6.516) Lt: 5.808 (5.747) Accm: 3.40 (3.42) Acct: 5.11 (5.39) proj_loss: -0.6061 (-0.6014) time: 0.6790 data: 0.0015 [11-25 13:21:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.35 Lm: 6.511 (6.543) Lt: 5.738 (5.789) Accm: 3.45 (3.27) Acct: 5.53 (5.11) proj_loss: -0.5970 (-0.6002) time: 0.6790 data: 0.0023 [11-25 13:21:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 182/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.35 Lm: 6.563 (6.531) Lt: 5.817 (5.790) Accm: 3.20 (3.29) Acct: 4.96 (5.10) proj_loss: -0.6133 (-0.6088) time: 0.6790 data: 0.0016 [11-25 13:21:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 182/350] Total time: 0:18:53 (0.679 s / it) [11-25 13:21:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 182/350] Total time: 0:18:53 (0.679 s / it) [11-25 13:21:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 182/350] Total time: 0:18:53 (0.679 s / it) [11-25 13:21:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 182/350] Total time: 0:18:53 (0.679 s / it) [11-25 13:21:29] (/home/user/VAR/train.py , line 276)=> [ep182] (training ) Lm: 6.511 (6.523), Lt: 5.751 (5.770), Acc m&t: 3.35 5.28, Remain: 2 days, 4:48:20, Finish: 2024-11-27 02:09 [11-25 13:21:29] (/home/user/VAR/train.py , line 276)=> [ep182] (training ) Lm: 6.511 (6.523), Lt: 5.751 (5.770), Acc m&t: 3.35 5.28, Remain: 2 days, 4:48:12, Finish: 2024-11-27 02:09 [11-25 13:21:29] (/home/user/VAR/train.py , line 276)=> [ep182] (training ) Lm: 6.511 (6.523), Lt: 5.751 (5.770), Acc m&t: 3.35 5.28, Remain: 2 days, 4:47:38, Finish: 2024-11-27 02:09 [11-25 13:21:29] (/home/user/VAR/train.py , line 276)=> [ep182] (training ) Lm: 6.511 (6.523), Lt: 5.751 (5.770), Acc m&t: 3.35 5.28, Remain: 2 days, 4:48:17, Finish: 2024-11-27 02:09 [11-25 13:21:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [ 0/1669] eta: 0:18:16 tlr: 0.00012 tnm: 0.37 Lm: 6.515 (6.515) Lt: 5.759 (5.759) Accm: 3.31 (3.31) Acct: 5.27 (5.27) proj_loss: -0.6047 (-0.6047) time: 0.6572 data: 0.0004 [11-25 13:21:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [ 0/1669] eta: 0:19:11 tlr: 0.00012 tnm: 0.37 Lm: 6.626 (6.626) Lt: 5.909 (5.909) Accm: 3.18 (3.18) Acct: 4.75 (4.75) proj_loss: -0.6014 (-0.6014) time: 0.6899 data: 0.0003 [11-25 13:21:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [ 0/1669] eta: 0:19:11 tlr: 0.00012 tnm: 0.37 Lm: 6.426 (6.426) Lt: 5.639 (5.639) Accm: 3.39 (3.39) Acct: 5.23 (5.23) proj_loss: -0.5960 (-0.5960) time: 0.6897 data: 0.0004 [11-25 13:21:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [ 0/1669] eta: 0:19:11 tlr: 0.00012 tnm: 0.37 Lm: 6.493 (6.493) Lt: 5.719 (5.719) Accm: 3.81 (3.81) Acct: 5.44 (5.44) proj_loss: -0.6211 (-0.6211) time: 0.6897 data: 0.0004 [11-25 13:26:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [ 417/1669] eta: 0:15:18 tlr: 0.00012 tnm: 0.36 Lm: 6.517 (6.517) Lt: 5.753 (5.753) Accm: 3.44 (3.44) Acct: 5.11 (5.11) proj_loss: -0.6173 (-0.6173) time: 0.7393 data: 0.0003 [11-25 13:26:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [ 417/1669] eta: 0:15:18 tlr: 0.00012 tnm: 0.36 Lm: 6.545 (6.545) Lt: 5.806 (5.806) Accm: 3.27 (3.27) Acct: 4.88 (4.88) proj_loss: -0.5935 (-0.5935) time: 0.7393 data: 0.0003 [11-25 13:26:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [ 417/1669] eta: 0:15:18 tlr: 0.00012 tnm: 0.36 Lm: 6.492 (6.492) Lt: 5.717 (5.717) Accm: 3.32 (3.32) Acct: 5.15 (5.15) proj_loss: -0.5937 (-0.5937) time: 0.7393 data: 0.0003 [11-25 13:26:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [ 417/1669] eta: 0:15:18 tlr: 0.00012 tnm: 0.36 Lm: 6.527 (6.527) Lt: 5.775 (5.775) Accm: 3.35 (3.35) Acct: 5.36 (5.36) proj_loss: -0.5951 (-0.5951) time: 0.7393 data: 0.0003 [11-25 13:31:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [ 834/1669] eta: 0:09:54 tlr: 0.00012 tnm: 0.37 Lm: 6.515 (6.503) Lt: 5.759 (5.736) Accm: 3.39 (3.39) Acct: 5.29 (5.34) proj_loss: -0.5950 (-0.5951) time: 0.6758 data: 0.0003 [11-25 13:31:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [ 834/1669] eta: 0:09:54 tlr: 0.00012 tnm: 0.37 Lm: 6.480 (6.488) Lt: 5.723 (5.719) Accm: 3.39 (3.36) Acct: 5.23 (5.22) proj_loss: -0.5960 (-0.5957) time: 0.6758 data: 0.0003 [11-25 13:31:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [ 834/1669] eta: 0:09:54 tlr: 0.00012 tnm: 0.37 Lm: 6.463 (6.517) Lt: 5.704 (5.769) Accm: 3.32 (3.29) Acct: 5.01 (4.98) proj_loss: -0.5856 (-0.5833) time: 0.6758 data: 0.0003 [11-25 13:31:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [ 834/1669] eta: 0:09:54 tlr: 0.00012 tnm: 0.37 Lm: 6.541 (6.525) Lt: 5.778 (5.761) Accm: 3.25 (3.38) Acct: 4.84 (5.02) proj_loss: -0.6135 (-0.6084) time: 0.6758 data: 0.0003 [11-25 13:36:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.35 Lm: 6.517 (6.488) Lt: 5.749 (5.717) Accm: 3.53 (3.52) Acct: 5.14 (5.32) proj_loss: -0.6097 (-0.6078) time: 0.6774 data: 0.0003 [11-25 13:36:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.35 Lm: 6.514 (6.506) Lt: 5.726 (5.725) Accm: 3.42 (3.41) Acct: 5.37 (5.40) proj_loss: -0.5929 (-0.5940) time: 0.6774 data: 0.0003 [11-25 13:36:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.35 Lm: 6.478 (6.511) Lt: 5.700 (5.744) Accm: 3.34 (3.44) Acct: 5.09 (5.32) proj_loss: -0.5863 (-0.5842) time: 0.6774 data: 0.0003 [11-25 13:36:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.35 Lm: 6.497 (6.495) Lt: 5.736 (5.727) Accm: 3.39 (3.37) Acct: 5.25 (5.23) proj_loss: -0.5946 (-0.5951) time: 0.6774 data: 0.0002 [11-25 13:40:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.541 (6.521) Lt: 5.778 (5.771) Accm: 3.25 (3.32) Acct: 4.84 (5.03) proj_loss: -0.6115 (-0.6085) time: 0.6820 data: 0.0017 [11-25 13:40:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.513 (6.500) Lt: 5.693 (5.712) Accm: 3.39 (3.37) Acct: 5.35 (5.39) proj_loss: -0.5950 (-0.5954) time: 0.6819 data: 0.0013 [11-25 13:40:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.493 (6.518) Lt: 5.704 (5.765) Accm: 3.32 (3.35) Acct: 5.01 (5.14) proj_loss: -0.5871 (-0.5883) time: 0.6820 data: 0.0020 [11-25 13:40:48] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 183/350] Total time: 0:19:19 (0.695 s / it) [11-25 13:40:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 183/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.515 (6.517) Lt: 5.749 (5.751) Accm: 3.39 (3.32) Acct: 5.23 (5.15) proj_loss: -0.5960 (-0.5955) time: 0.6820 data: 0.0018 [11-25 13:40:48] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 183/350] Total time: 0:19:19 (0.694 s / it) [11-25 13:40:48] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 183/350] Total time: 0:19:19 (0.695 s / it) [11-25 13:40:48] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 183/350] Total time: 0:19:19 (0.695 s / it) [11-25 13:40:48] (/home/user/VAR/train.py , line 276)=> [ep183] (training ) Lm: 6.511 (6.533), Lt: 5.751 (5.777), Acc m&t: 3.35 5.28, Remain: 2 days, 4:47:02, Finish: 2024-11-27 02:27 [11-25 13:40:48] (/home/user/VAR/train.py , line 276)=> [ep183] (training ) Lm: 6.511 (6.533), Lt: 5.751 (5.777), Acc m&t: 3.35 5.28, Remain: 2 days, 4:47:23, Finish: 2024-11-27 02:28 [11-25 13:40:48] (/home/user/VAR/train.py , line 276)=> [ep183] (training ) Lm: 6.511 (6.533), Lt: 5.751 (5.777), Acc m&t: 3.35 5.28, Remain: 2 days, 4:46:23, Finish: 2024-11-27 02:27 [11-25 13:40:48] (/home/user/VAR/train.py , line 276)=> [ep183] (training ) Lm: 6.511 (6.533), Lt: 5.751 (5.777), Acc m&t: 3.35 5.28, Remain: 2 days, 4:46:22, Finish: 2024-11-27 02:27 [11-25 13:40:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [ 0/1669] eta: 0:18:25 tlr: 0.00012 tnm: 0.35 Lm: 6.403 (6.403) Lt: 5.664 (5.664) Accm: 3.42 (3.42) Acct: 5.34 (5.34) proj_loss: -0.5969 (-0.5969) time: 0.6621 data: 0.0003 [11-25 13:40:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [ 0/1669] eta: 0:18:25 tlr: 0.00012 tnm: 0.35 Lm: 6.567 (6.567) Lt: 5.793 (5.793) Accm: 2.99 (2.99) Acct: 4.92 (4.92) proj_loss: -0.6112 (-0.6112) time: 0.6625 data: 0.0004 [11-25 13:40:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [ 0/1669] eta: 0:18:25 tlr: 0.00012 tnm: 0.35 Lm: 6.435 (6.435) Lt: 5.694 (5.694) Accm: 3.53 (3.53) Acct: 5.58 (5.58) proj_loss: -0.6028 (-0.6028) time: 0.6623 data: 0.0004 [11-25 13:40:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [ 0/1669] eta: 0:18:25 tlr: 0.00012 tnm: 0.35 Lm: 6.452 (6.452) Lt: 5.665 (5.665) Accm: 3.52 (3.52) Acct: 5.61 (5.61) proj_loss: -0.6087 (-0.6087) time: 0.6625 data: 0.0004 [11-25 13:45:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [ 417/1669] eta: 0:14:17 tlr: 0.00012 tnm: 0.37 Lm: 6.468 (6.468) Lt: 5.686 (5.686) Accm: 3.65 (3.65) Acct: 5.67 (5.67) proj_loss: -0.6033 (-0.6033) time: 0.8005 data: 0.0003 [11-25 13:45:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [ 417/1669] eta: 0:14:17 tlr: 0.00012 tnm: 0.37 Lm: 6.475 (6.475) Lt: 5.768 (5.768) Accm: 3.41 (3.41) Acct: 5.29 (5.29) proj_loss: -0.6008 (-0.6008) time: 0.8005 data: 0.0003 [11-25 13:45:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [ 417/1669] eta: 0:14:17 tlr: 0.00012 tnm: 0.37 Lm: 6.564 (6.564) Lt: 5.810 (5.810) Accm: 2.99 (2.99) Acct: 4.77 (4.77) proj_loss: -0.6027 (-0.6027) time: 0.8006 data: 0.0003 [11-25 13:45:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [ 417/1669] eta: 0:14:17 tlr: 0.00012 tnm: 0.37 Lm: 6.529 (6.529) Lt: 5.821 (5.821) Accm: 3.25 (3.25) Acct: 4.99 (4.99) proj_loss: -0.5898 (-0.5898) time: 0.8006 data: 0.0003 [11-25 13:50:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [ 834/1669] eta: 0:09:47 tlr: 0.00012 tnm: 0.34 Lm: 6.572 (6.543) Lt: 5.886 (5.843) Accm: 3.14 (3.21) Acct: 4.80 (4.93) proj_loss: -0.5969 (-0.6005) time: 0.7934 data: 0.0003 [11-25 13:50:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [ 834/1669] eta: 0:09:47 tlr: 0.00012 tnm: 0.34 Lm: 6.560 (6.557) Lt: 5.793 (5.800) Accm: 2.99 (3.04) Acct: 4.92 (4.88) proj_loss: -0.5942 (-0.5997) time: 0.7934 data: 0.0003 [11-25 13:50:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [ 834/1669] eta: 0:09:47 tlr: 0.00012 tnm: 0.34 Lm: 6.514 (6.499) Lt: 5.771 (5.769) Accm: 3.44 (3.42) Acct: 5.58 (5.44) proj_loss: -0.5987 (-0.5968) time: 0.7934 data: 0.0003 [11-25 13:50:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [ 834/1669] eta: 0:09:47 tlr: 0.00012 tnm: 0.34 Lm: 6.483 (6.483) Lt: 5.708 (5.693) Accm: 3.54 (3.61) Acct: 5.61 (5.64) proj_loss: -0.6029 (-0.6032) time: 0.7934 data: 0.0002 [11-25 13:55:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.36 Lm: 6.472 (6.477) Lt: 5.707 (5.697) Accm: 3.53 (3.59) Acct: 5.65 (5.65) proj_loss: -0.6019 (-0.6026) time: 0.6785 data: 0.0003 [11-25 13:55:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.36 Lm: 6.613 (6.591) Lt: 5.932 (5.886) Accm: 3.11 (3.13) Acct: 4.73 (4.75) proj_loss: -0.6005 (-0.6014) time: 0.6785 data: 0.0003 [11-25 13:55:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.36 Lm: 6.551 (6.542) Lt: 5.787 (5.784) Accm: 3.07 (3.10) Acct: 5.02 (5.00) proj_loss: -0.6027 (-0.6038) time: 0.6785 data: 0.0003 [11-25 13:55:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.36 Lm: 6.475 (6.480) Lt: 5.732 (5.726) Accm: 3.49 (3.54) Acct: 5.65 (5.61) proj_loss: -0.5965 (-0.5962) time: 0.6785 data: 0.0003 [11-25 14:00:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.514 (6.511) Lt: 5.771 (5.749) Accm: 3.44 (3.41) Acct: 5.58 (5.35) proj_loss: -0.5943 (-0.5937) time: 0.6790 data: 0.0015 [11-25 14:00:09] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 184/350] Total time: 0:19:20 (0.695 s / it) [11-25 14:00:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.572 (6.535) Lt: 5.886 (5.822) Accm: 3.14 (3.32) Acct: 4.80 (5.04) proj_loss: -0.5969 (-0.5997) time: 0.6790 data: 0.0015 [11-25 14:00:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.543 (6.537) Lt: 5.780 (5.781) Accm: 3.15 (3.14) Acct: 5.11 (5.06) proj_loss: -0.6112 (-0.6055) time: 0.6790 data: 0.0019 [11-25 14:00:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 184/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.467 (6.475) Lt: 5.708 (5.702) Accm: 3.54 (3.64) Acct: 5.68 (5.76) proj_loss: -0.6029 (-0.6046) time: 0.6790 data: 0.0020 [11-25 14:00:09] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 184/350] Total time: 0:19:20 (0.695 s / it) [11-25 14:00:09] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 184/350] Total time: 0:19:20 (0.695 s / it) [11-25 14:00:09] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 184/350] Total time: 0:19:20 (0.695 s / it) [11-25 14:00:09] (/home/user/VAR/train.py , line 276)=> [ep184] (training ) Lm: 6.511 (6.522), Lt: 5.751 (5.763), Acc m&t: 3.35 5.28, Remain: 2 days, 4:21:02, Finish: 2024-11-27 02:21 [11-25 14:00:09] (/home/user/VAR/train.py , line 276)=> [ep184] (training ) Lm: 6.511 (6.522), Lt: 5.751 (5.763), Acc m&t: 3.35 5.28, Remain: 2 days, 4:21:38, Finish: 2024-11-27 02:21 [11-25 14:00:09] (/home/user/VAR/train.py , line 276)=> [ep184] (training ) Lm: 6.511 (6.522), Lt: 5.751 (5.763), Acc m&t: 3.35 5.28, Remain: 2 days, 4:21:16, Finish: 2024-11-27 02:21 [11-25 14:00:09] (/home/user/VAR/train.py , line 276)=> [ep184] (training ) Lm: 6.511 (6.522), Lt: 5.751 (5.763), Acc m&t: 3.35 5.28, Remain: 2 days, 4:21:05, Finish: 2024-11-27 02:21 [11-25 14:00:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [ 0/1669] eta: 0:18:14 tlr: 0.00012 tnm: 0.37 Lm: 6.632 (6.632) Lt: 5.938 (5.938) Accm: 3.18 (3.18) Acct: 5.06 (5.06) proj_loss: -0.5917 (-0.5917) time: 0.6560 data: 0.0003 [11-25 14:00:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [ 0/1669] eta: 0:18:14 tlr: 0.00012 tnm: 0.37 Lm: 6.639 (6.639) Lt: 5.844 (5.844) Accm: 3.21 (3.21) Acct: 4.98 (4.98) proj_loss: -0.5856 (-0.5856) time: 0.6559 data: 0.0004 [11-25 14:00:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [ 0/1669] eta: 0:18:14 tlr: 0.00012 tnm: 0.37 Lm: 6.505 (6.505) Lt: 5.776 (5.776) Accm: 3.07 (3.07) Acct: 4.51 (4.51) proj_loss: -0.5969 (-0.5969) time: 0.6558 data: 0.0004 [11-25 14:00:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [ 0/1669] eta: 0:18:14 tlr: 0.00012 tnm: 0.37 Lm: 6.520 (6.520) Lt: 5.734 (5.734) Accm: 3.26 (3.26) Acct: 5.46 (5.46) proj_loss: -0.6023 (-0.6023) time: 0.6560 data: 0.0004 [11-25 14:04:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [ 417/1669] eta: 0:14:06 tlr: 0.00012 tnm: 0.36 Lm: 6.462 (6.462) Lt: 5.688 (5.688) Accm: 3.74 (3.74) Acct: 5.99 (5.99) proj_loss: -0.6088 (-0.6088) time: 0.6746 data: 0.0003 [11-25 14:04:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [ 417/1669] eta: 0:14:06 tlr: 0.00012 tnm: 0.36 Lm: 6.577 (6.577) Lt: 5.870 (5.870) Accm: 3.07 (3.07) Acct: 4.59 (4.59) proj_loss: -0.6012 (-0.6012) time: 0.6746 data: 0.0003 [11-25 14:04:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [ 417/1669] eta: 0:14:06 tlr: 0.00012 tnm: 0.36 Lm: 6.610 (6.610) Lt: 5.856 (5.856) Accm: 3.20 (3.20) Acct: 4.98 (4.98) proj_loss: -0.5938 (-0.5938) time: 0.6746 data: 0.0003 [11-25 14:04:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [ 417/1669] eta: 0:14:06 tlr: 0.00012 tnm: 0.36 Lm: 6.618 (6.618) Lt: 5.891 (5.891) Accm: 3.18 (3.18) Acct: 4.95 (4.95) proj_loss: -0.5861 (-0.5861) time: 0.6746 data: 0.0003 [11-25 14:09:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [ 834/1669] eta: 0:09:24 tlr: 0.00012 tnm: 0.35 Lm: 6.632 (6.655) Lt: 5.938 (5.923) Accm: 3.18 (3.11) Acct: 4.84 (4.84) proj_loss: -0.5821 (-0.5848) time: 0.6764 data: 0.0003 [11-25 14:09:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [ 834/1669] eta: 0:09:24 tlr: 0.00012 tnm: 0.35 Lm: 6.508 (6.477) Lt: 5.734 (5.721) Accm: 3.39 (3.62) Acct: 5.46 (5.74) proj_loss: -0.6023 (-0.6047) time: 0.6764 data: 0.0003 [11-25 14:09:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [ 834/1669] eta: 0:09:24 tlr: 0.00012 tnm: 0.35 Lm: 6.639 (6.632) Lt: 5.867 (5.878) Accm: 3.18 (3.12) Acct: 4.98 (4.92) proj_loss: -0.5856 (-0.5892) time: 0.6764 data: 0.0003 [11-25 14:09:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [ 834/1669] eta: 0:09:24 tlr: 0.00012 tnm: 0.35 Lm: 6.623 (6.593) Lt: 5.913 (5.884) Accm: 3.07 (3.10) Acct: 4.67 (4.73) proj_loss: -0.6055 (-0.6068) time: 0.6764 data: 0.0003 [11-25 14:14:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [1251/1669] eta: 0:04:43 tlr: 0.00012 tnm: 0.36 Lm: 6.564 (6.544) Lt: 5.844 (5.828) Accm: 3.12 (3.23) Acct: 4.84 (5.01) proj_loss: -0.6082 (-0.6078) time: 0.6774 data: 0.0003 [11-25 14:14:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [1251/1669] eta: 0:04:43 tlr: 0.00012 tnm: 0.36 Lm: 6.514 (6.490) Lt: 5.744 (5.729) Accm: 3.41 (3.57) Acct: 5.48 (5.68) proj_loss: -0.5993 (-0.6010) time: 0.6774 data: 0.0003 [11-25 14:14:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [1251/1669] eta: 0:04:43 tlr: 0.00012 tnm: 0.36 Lm: 6.610 (6.589) Lt: 5.856 (5.840) Accm: 3.20 (3.22) Acct: 4.98 (5.04) proj_loss: -0.5938 (-0.5927) time: 0.6774 data: 0.0003 [11-25 14:14:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [1251/1669] eta: 0:04:43 tlr: 0.00012 tnm: 0.36 Lm: 6.618 (6.579) Lt: 5.891 (5.850) Accm: 3.18 (3.33) Acct: 4.95 (5.19) proj_loss: -0.5869 (-0.5893) time: 0.6774 data: 0.0003 [11-25 14:19:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.37 Lm: 6.605 (6.585) Lt: 5.875 (5.855) Accm: 3.18 (3.27) Acct: 4.84 (5.11) proj_loss: -0.5917 (-0.5913) time: 0.6815 data: 0.0022 [11-25 14:19:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 185/350] Total time: 0:18:53 (0.679 s / it) [11-25 14:19:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.37 Lm: 6.582 (6.580) Lt: 5.844 (5.836) Accm: 3.18 (3.20) Acct: 4.98 (4.94) proj_loss: -0.6019 (-0.5948) time: 0.6815 data: 0.0016 [11-25 14:19:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.37 Lm: 6.508 (6.473) Lt: 5.734 (5.696) Accm: 3.43 (3.61) Acct: 5.51 (5.75) proj_loss: -0.6023 (-0.6014) time: 0.6815 data: 0.0013 [11-25 14:19:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 185/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.37 Lm: 6.505 (6.523) Lt: 5.776 (5.790) Accm: 3.18 (3.33) Acct: 5.01 (5.22) proj_loss: -0.6055 (-0.6046) time: 0.6815 data: 0.0020 [11-25 14:19:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 185/350] Total time: 0:18:53 (0.679 s / it) [11-25 14:19:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 185/350] Total time: 0:18:53 (0.679 s / it) [11-25 14:19:02] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 185/350] Total time: 0:18:53 (0.679 s / it) [11-25 14:19:02] (/home/user/VAR/train.py , line 276)=> [ep185] (training ) Lm: 6.511 (6.516), Lt: 5.751 (5.761), Acc m&t: 3.35 5.28, Remain: 2 days, 4:06:22, Finish: 2024-11-27 02:25 [11-25 14:19:02] (/home/user/VAR/train.py , line 276)=> [ep185] (training ) Lm: 6.511 (6.516), Lt: 5.751 (5.761), Acc m&t: 3.35 5.28, Remain: 2 days, 4:05:34, Finish: 2024-11-27 02:24 [11-25 14:19:02] (/home/user/VAR/train.py , line 276)=> [ep185] (training ) Lm: 6.511 (6.516), Lt: 5.751 (5.761), Acc m&t: 3.35 5.28, Remain: 2 days, 4:06:28, Finish: 2024-11-27 02:25 [11-25 14:19:02] (/home/user/VAR/train.py , line 276)=> [ep185] (training ) Lm: 6.511 (6.516), Lt: 5.751 (5.761), Acc m&t: 3.35 5.28, Remain: 2 days, 4:06:06, Finish: 2024-11-27 02:25 [11-25 14:19:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [ 0/1669] eta: 0:18:29 tlr: 0.00012 tnm: 0.36 Lm: 6.558 (6.558) Lt: 5.770 (5.770) Accm: 3.40 (3.40) Acct: 5.34 (5.34) proj_loss: -0.6073 (-0.6073) time: 0.6649 data: 0.0003 [11-25 14:19:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [ 0/1669] eta: 0:18:30 tlr: 0.00012 tnm: 0.36 Lm: 6.464 (6.464) Lt: 5.675 (5.675) Accm: 3.81 (3.81) Acct: 5.94 (5.94) proj_loss: -0.6065 (-0.6065) time: 0.6653 data: 0.0004 [11-25 14:19:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [ 0/1669] eta: 0:18:30 tlr: 0.00012 tnm: 0.36 Lm: 6.631 (6.631) Lt: 5.804 (5.804) Accm: 2.91 (2.91) Acct: 4.67 (4.67) proj_loss: -0.5978 (-0.5978) time: 0.6652 data: 0.0004 [11-25 14:19:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [ 0/1669] eta: 0:18:29 tlr: 0.00012 tnm: 0.36 Lm: 6.627 (6.627) Lt: 5.909 (5.909) Accm: 3.12 (3.12) Acct: 4.98 (4.98) proj_loss: -0.6174 (-0.6174) time: 0.6648 data: 0.0004 [11-25 14:24:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [ 417/1669] eta: 0:15:09 tlr: 0.00012 tnm: 0.38 Lm: 6.648 (6.648) Lt: 5.932 (5.932) Accm: 2.95 (2.95) Acct: 4.64 (4.64) proj_loss: -0.6019 (-0.6019) time: 0.6764 data: 0.0003 [11-25 14:24:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [ 417/1669] eta: 0:15:09 tlr: 0.00012 tnm: 0.38 Lm: 6.550 (6.550) Lt: 5.773 (5.773) Accm: 3.42 (3.42) Acct: 5.26 (5.26) proj_loss: -0.5984 (-0.5984) time: 0.6765 data: 0.0003 [11-25 14:24:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [ 417/1669] eta: 0:15:09 tlr: 0.00012 tnm: 0.38 Lm: 6.545 (6.545) Lt: 5.755 (5.755) Accm: 3.34 (3.34) Acct: 5.23 (5.23) proj_loss: -0.6149 (-0.6149) time: 0.6764 data: 0.0003 [11-25 14:24:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [ 417/1669] eta: 0:15:09 tlr: 0.00012 tnm: 0.38 Lm: 6.544 (6.544) Lt: 5.788 (5.788) Accm: 3.06 (3.06) Acct: 4.59 (4.59) proj_loss: -0.6058 (-0.6058) time: 0.6764 data: 0.0003 [11-25 14:28:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [ 834/1669] eta: 0:09:52 tlr: 0.00012 tnm: 0.35 Lm: 6.537 (6.542) Lt: 5.804 (5.797) Accm: 3.21 (3.19) Acct: 4.67 (4.79) proj_loss: -0.6138 (-0.6104) time: 0.6780 data: 0.0003 [11-25 14:28:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [ 834/1669] eta: 0:09:52 tlr: 0.00012 tnm: 0.35 Lm: 6.464 (6.499) Lt: 5.675 (5.697) Accm: 3.81 (3.57) Acct: 5.94 (5.56) proj_loss: -0.5902 (-0.5942) time: 0.6780 data: 0.0003 [11-25 14:28:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [ 834/1669] eta: 0:09:52 tlr: 0.00012 tnm: 0.35 Lm: 6.627 (6.549) Lt: 5.909 (5.813) Accm: 3.12 (3.41) Acct: 4.98 (5.27) proj_loss: -0.6161 (-0.6066) time: 0.6780 data: 0.0003 [11-25 14:28:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [ 834/1669] eta: 0:09:52 tlr: 0.00012 tnm: 0.35 Lm: 6.558 (6.553) Lt: 5.770 (5.774) Accm: 3.27 (3.30) Acct: 5.22 (5.22) proj_loss: -0.6182 (-0.6160) time: 0.6780 data: 0.0003 [11-25 14:33:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.36 Lm: 6.563 (6.557) Lt: 5.791 (5.784) Accm: 3.25 (3.26) Acct: 5.17 (5.12) proj_loss: -0.6155 (-0.6152) time: 0.6780 data: 0.0003 [11-25 14:33:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.36 Lm: 6.508 (6.526) Lt: 5.788 (5.783) Accm: 3.31 (3.24) Acct: 4.90 (4.87) proj_loss: -0.6141 (-0.6114) time: 0.6780 data: 0.0002 [11-25 14:33:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.36 Lm: 6.479 (6.498) Lt: 5.726 (5.717) Accm: 3.54 (3.49) Acct: 5.41 (5.38) proj_loss: -0.5984 (-0.5981) time: 0.6780 data: 0.0003 [11-25 14:33:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.36 Lm: 6.573 (6.541) Lt: 5.855 (5.810) Accm: 3.15 (3.35) Acct: 4.94 (5.18) proj_loss: -0.6167 (-0.6098) time: 0.6780 data: 0.0003 [11-25 14:38:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.518 (6.532) Lt: 5.801 (5.794) Accm: 3.13 (3.31) Acct: 4.91 (5.08) proj_loss: -0.6161 (-0.6045) time: 0.6789 data: 0.0020 [11-25 14:38:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 186/350] Total time: 0:19:17 (0.694 s / it) [11-25 14:38:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.484 (6.495) Lt: 5.721 (5.718) Accm: 3.36 (3.47) Acct: 5.06 (5.32) proj_loss: -0.6065 (-0.6028) time: 0.6789 data: 0.0017 [11-25 14:38:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.478 (6.498) Lt: 5.771 (5.735) Accm: 3.42 (3.35) Acct: 5.13 (5.15) proj_loss: -0.6138 (-0.6046) time: 0.6789 data: 0.0019 [11-25 14:38:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 186/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.558 (6.515) Lt: 5.770 (5.745) Accm: 3.27 (3.39) Acct: 5.22 (5.33) proj_loss: -0.6127 (-0.6146) time: 0.6789 data: 0.0019 [11-25 14:38:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 186/350] Total time: 0:19:17 (0.694 s / it) [11-25 14:38:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 186/350] Total time: 0:19:17 (0.694 s / it) [11-25 14:38:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 186/350] Total time: 0:19:17 (0.694 s / it) [11-25 14:38:20] (/home/user/VAR/train.py , line 276)=> [ep186] (training ) Lm: 6.511 (6.526), Lt: 5.751 (5.768), Acc m&t: 3.35 5.28, Remain: 2 days, 3:46:33, Finish: 2024-11-27 02:24 [11-25 14:38:20] (/home/user/VAR/train.py , line 276)=> [ep186] (training ) Lm: 6.511 (6.526), Lt: 5.751 (5.768), Acc m&t: 3.35 5.28, Remain: 2 days, 3:44:47, Finish: 2024-11-27 02:23 [11-25 14:38:20] (/home/user/VAR/train.py , line 276)=> [ep186] (training ) Lm: 6.511 (6.526), Lt: 5.751 (5.768), Acc m&t: 3.35 5.28, Remain: 2 days, 3:46:04, Finish: 2024-11-27 02:24 [11-25 14:38:20] (/home/user/VAR/train.py , line 276)=> [ep186] (training ) Lm: 6.511 (6.526), Lt: 5.751 (5.768), Acc m&t: 3.35 5.28, Remain: 2 days, 3:45:38, Finish: 2024-11-27 02:23 [11-25 14:38:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [ 0/1669] eta: 0:18:21 tlr: 0.00012 tnm: 0.35 Lm: 6.522 (6.522) Lt: 5.776 (5.776) Accm: 3.23 (3.23) Acct: 5.10 (5.10) proj_loss: -0.6171 (-0.6171) time: 0.6601 data: 0.0004 [11-25 14:38:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [ 0/1669] eta: 0:18:21 tlr: 0.00012 tnm: 0.35 Lm: 6.670 (6.670) Lt: 5.953 (5.953) Accm: 3.07 (3.07) Acct: 4.98 (4.98) proj_loss: -0.6086 (-0.6086) time: 0.6600 data: 0.0004 [11-25 14:38:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [ 0/1669] eta: 0:18:13 tlr: 0.00012 tnm: 0.35 Lm: 6.545 (6.545) Lt: 5.784 (5.784) Accm: 3.03 (3.03) Acct: 4.73 (4.73) proj_loss: -0.5932 (-0.5932) time: 0.6552 data: 0.0004 [11-25 14:38:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [ 0/1669] eta: 0:18:21 tlr: 0.00012 tnm: 0.35 Lm: 6.438 (6.438) Lt: 5.627 (5.627) Accm: 3.43 (3.43) Acct: 5.49 (5.49) proj_loss: -0.6002 (-0.6002) time: 0.6603 data: 0.0003 [11-25 14:43:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [ 417/1669] eta: 0:14:11 tlr: 0.00012 tnm: 0.36 Lm: 6.457 (6.457) Lt: 5.644 (5.644) Accm: 3.58 (3.58) Acct: 5.66 (5.66) proj_loss: -0.6113 (-0.6113) time: 0.7392 data: 0.0004 [11-25 14:43:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [ 417/1669] eta: 0:14:11 tlr: 0.00012 tnm: 0.36 Lm: 6.564 (6.564) Lt: 5.814 (5.814) Accm: 3.15 (3.15) Acct: 4.91 (4.91) proj_loss: -0.5925 (-0.5925) time: 0.7392 data: 0.0003 [11-25 14:43:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [ 417/1669] eta: 0:14:11 tlr: 0.00012 tnm: 0.36 Lm: 6.488 (6.488) Lt: 5.741 (5.741) Accm: 3.27 (3.27) Acct: 5.04 (5.04) proj_loss: -0.5995 (-0.5995) time: 0.7392 data: 0.0003 [11-25 14:43:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [ 417/1669] eta: 0:14:11 tlr: 0.00012 tnm: 0.36 Lm: 6.537 (6.537) Lt: 5.800 (5.800) Accm: 3.27 (3.27) Acct: 5.16 (5.16) proj_loss: -0.5939 (-0.5939) time: 0.7392 data: 0.0003 [11-25 14:48:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [ 834/1669] eta: 0:09:46 tlr: 0.00012 tnm: 0.37 Lm: 6.490 (6.521) Lt: 5.709 (5.770) Accm: 3.37 (3.30) Acct: 5.34 (5.27) proj_loss: -0.5940 (-0.5939) time: 0.8538 data: 0.0003 [11-25 14:48:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [ 834/1669] eta: 0:09:46 tlr: 0.00012 tnm: 0.37 Lm: 6.430 (6.462) Lt: 5.698 (5.691) Accm: 3.52 (3.42) Acct: 5.34 (5.28) proj_loss: -0.5932 (-0.5934) time: 0.8538 data: 0.0003 [11-25 14:48:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [ 834/1669] eta: 0:09:46 tlr: 0.00012 tnm: 0.37 Lm: 6.607 (6.587) Lt: 5.853 (5.844) Accm: 3.07 (3.13) Acct: 4.96 (4.92) proj_loss: -0.5908 (-0.5919) time: 0.8538 data: 0.0003 [11-25 14:48:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [ 834/1669] eta: 0:09:46 tlr: 0.00012 tnm: 0.37 Lm: 6.475 (6.476) Lt: 5.662 (5.683) Accm: 3.43 (3.41) Acct: 5.49 (5.41) proj_loss: -0.6002 (-0.6048) time: 0.8538 data: 0.0003 [11-25 14:52:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.39 Lm: 6.467 (6.472) Lt: 5.684 (5.688) Accm: 3.42 (3.41) Acct: 5.44 (5.41) proj_loss: -0.6013 (-0.6042) time: 0.6759 data: 0.0003 [11-25 14:52:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.39 Lm: 6.488 (6.489) Lt: 5.741 (5.729) Accm: 3.37 (3.37) Acct: 5.04 (5.14) proj_loss: -0.5995 (-0.5985) time: 0.6760 data: 0.0003 [11-25 14:52:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.39 Lm: 6.565 (6.571) Lt: 5.836 (5.838) Accm: 3.08 (3.12) Acct: 4.84 (4.84) proj_loss: -0.6004 (-0.5965) time: 0.6760 data: 0.0003 [11-25 14:52:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.39 Lm: 6.531 (6.534) Lt: 5.741 (5.771) Accm: 3.22 (3.23) Acct: 5.16 (5.17) proj_loss: -0.5896 (-0.5918) time: 0.6760 data: 0.0003 [11-25 14:57:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.37 Lm: 6.573 (6.580) Lt: 5.772 (5.827) Accm: 3.07 (3.10) Acct: 4.98 (4.96) proj_loss: -0.5940 (-0.5950) time: 0.6770 data: 0.0019 [11-25 14:57:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 187/350] Total time: 0:19:19 (0.695 s / it) [11-25 14:57:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.37 Lm: 6.459 (6.443) Lt: 5.662 (5.664) Accm: 3.43 (3.45) Acct: 5.39 (5.40) proj_loss: -0.6025 (-0.6055) time: 0.6770 data: 0.0016 [11-25 14:57:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.37 Lm: 6.607 (6.593) Lt: 5.853 (5.850) Accm: 3.07 (3.06) Acct: 4.72 (4.80) proj_loss: -0.5908 (-0.5932) time: 0.6770 data: 0.0027 [11-25 14:57:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 187/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.37 Lm: 6.474 (6.486) Lt: 5.704 (5.724) Accm: 3.34 (3.36) Acct: 5.18 (5.15) proj_loss: -0.6059 (-0.6043) time: 0.6770 data: 0.0014 [11-25 14:57:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 187/350] Total time: 0:19:19 (0.695 s / it) [11-25 14:57:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 187/350] Total time: 0:19:19 (0.695 s / it) [11-25 14:57:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 187/350] Total time: 0:19:19 (0.695 s / it) [11-25 14:57:40] (/home/user/VAR/train.py , line 276)=> [ep187] (training ) Lm: 6.502 (6.502), Lt: 5.742 (5.742), Acc m&t: 3.39 5.35, Remain: 2 days, 3:08:09, Finish: 2024-11-27 02:05 [11-25 14:57:40] (/home/user/VAR/train.py , line 276)=> [ep187] (training ) Lm: 6.502 (6.502), Lt: 5.742 (5.742), Acc m&t: 3.39 5.35, Remain: 2 days, 3:08:14, Finish: 2024-11-27 02:05 [11-25 14:57:40] (/home/user/VAR/train.py , line 276)=> [ep187] (training ) Lm: 6.502 (6.502), Lt: 5.742 (5.742), Acc m&t: 3.39 5.35, Remain: 2 days, 3:08:37, Finish: 2024-11-27 02:06 [11-25 14:57:40] (/home/user/VAR/train.py , line 276)=> [ep187] (training ) Lm: 6.502 (6.502), Lt: 5.742 (5.742), Acc m&t: 3.39 5.35, Remain: 2 days, 3:08:18, Finish: 2024-11-27 02:05 [11-25 14:57:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [ 0/1669] eta: 0:18:31 tlr: 0.00012 tnm: 0.36 Lm: 6.478 (6.478) Lt: 5.724 (5.724) Accm: 3.29 (3.29) Acct: 5.39 (5.39) proj_loss: -0.6058 (-0.6058) time: 0.6660 data: 0.0003 [11-25 14:57:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [ 0/1669] eta: 0:18:25 tlr: 0.00012 tnm: 0.36 Lm: 6.519 (6.519) Lt: 5.719 (5.719) Accm: 3.35 (3.35) Acct: 5.42 (5.42) proj_loss: -0.6073 (-0.6073) time: 0.6621 data: 0.0003 [11-25 14:57:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [ 0/1669] eta: 0:18:26 tlr: 0.00012 tnm: 0.36 Lm: 6.509 (6.509) Lt: 5.702 (5.702) Accm: 3.53 (3.53) Acct: 5.60 (5.60) proj_loss: -0.6079 (-0.6079) time: 0.6631 data: 0.0003 [11-25 14:57:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [ 0/1669] eta: 0:18:27 tlr: 0.00012 tnm: 0.36 Lm: 6.471 (6.471) Lt: 5.670 (5.670) Accm: 3.66 (3.66) Acct: 5.82 (5.82) proj_loss: -0.5934 (-0.5934) time: 0.6633 data: 0.0004 [11-25 15:02:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [ 417/1669] eta: 0:14:08 tlr: 0.00012 tnm: 0.37 Lm: 6.516 (6.516) Lt: 5.733 (5.733) Accm: 3.55 (3.55) Acct: 5.46 (5.46) proj_loss: -0.6063 (-0.6063) time: 0.6777 data: 0.0003 [11-25 15:02:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [ 417/1669] eta: 0:14:08 tlr: 0.00012 tnm: 0.37 Lm: 6.481 (6.481) Lt: 5.710 (5.710) Accm: 3.29 (3.29) Acct: 5.23 (5.23) proj_loss: -0.6138 (-0.6138) time: 0.6777 data: 0.0003 [11-25 15:02:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [ 417/1669] eta: 0:14:08 tlr: 0.00012 tnm: 0.37 Lm: 6.535 (6.535) Lt: 5.816 (5.816) Accm: 3.23 (3.23) Acct: 5.12 (5.12) proj_loss: -0.6109 (-0.6109) time: 0.6777 data: 0.0003 [11-25 15:02:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [ 417/1669] eta: 0:14:08 tlr: 0.00012 tnm: 0.37 Lm: 6.448 (6.448) Lt: 5.667 (5.667) Accm: 3.49 (3.49) Acct: 5.49 (5.49) proj_loss: -0.5998 (-0.5998) time: 0.6777 data: 0.0003 [11-25 15:07:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [ 834/1669] eta: 0:09:25 tlr: 0.00012 tnm: 0.37 Lm: 6.509 (6.520) Lt: 5.702 (5.752) Accm: 3.45 (3.36) Acct: 5.39 (5.32) proj_loss: -0.5918 (-0.5962) time: 0.6798 data: 0.0003 [11-25 15:07:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [ 834/1669] eta: 0:09:25 tlr: 0.00012 tnm: 0.37 Lm: 6.519 (6.534) Lt: 5.719 (5.790) Accm: 3.23 (3.26) Acct: 5.04 (5.16) proj_loss: -0.6073 (-0.6109) time: 0.6798 data: 0.0003 [11-25 15:07:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [ 834/1669] eta: 0:09:25 tlr: 0.00012 tnm: 0.37 Lm: 6.561 (6.578) Lt: 5.797 (5.803) Accm: 3.43 (3.30) Acct: 5.10 (5.09) proj_loss: -0.5934 (-0.5991) time: 0.6798 data: 0.0003 [11-25 15:07:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [ 834/1669] eta: 0:09:25 tlr: 0.00012 tnm: 0.37 Lm: 6.591 (6.554) Lt: 5.840 (5.824) Accm: 3.23 (3.23) Acct: 4.86 (5.03) proj_loss: -0.6058 (-0.6064) time: 0.6798 data: 0.0003 [11-25 15:11:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [1251/1669] eta: 0:04:43 tlr: 0.00012 tnm: 0.36 Lm: 6.535 (6.490) Lt: 5.782 (5.751) Accm: 3.26 (3.43) Acct: 5.12 (5.32) proj_loss: -0.6050 (-0.6059) time: 0.6776 data: 0.0003 [11-25 15:11:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [1251/1669] eta: 0:04:43 tlr: 0.00012 tnm: 0.36 Lm: 6.489 (6.507) Lt: 5.710 (5.744) Accm: 3.49 (3.41) Acct: 5.49 (5.41) proj_loss: -0.5951 (-0.5968) time: 0.6776 data: 0.0003 [11-25 15:11:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [1251/1669] eta: 0:04:43 tlr: 0.00012 tnm: 0.36 Lm: 6.544 (6.542) Lt: 5.782 (5.804) Accm: 3.29 (3.28) Acct: 5.10 (5.16) proj_loss: -0.6089 (-0.6108) time: 0.6776 data: 0.0003 [11-25 15:11:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [1251/1669] eta: 0:04:43 tlr: 0.00012 tnm: 0.36 Lm: 6.570 (6.578) Lt: 5.799 (5.803) Accm: 3.39 (3.32) Acct: 5.34 (5.21) proj_loss: -0.5944 (-0.5982) time: 0.6776 data: 0.0003 [11-25 15:16:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.561 (6.551) Lt: 5.797 (5.792) Accm: 3.43 (3.38) Acct: 5.58 (5.32) proj_loss: -0.5955 (-0.5983) time: 0.6805 data: 0.0020 [11-25 15:16:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 188/350] Total time: 0:18:54 (0.680 s / it) [11-25 15:16:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.591 (6.516) Lt: 5.840 (5.774) Accm: 3.23 (3.29) Acct: 4.86 (5.07) proj_loss: -0.6043 (-0.6034) time: 0.6805 data: 0.0016 [11-25 15:16:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.519 (6.519) Lt: 5.719 (5.774) Accm: 3.34 (3.41) Acct: 5.15 (5.34) proj_loss: -0.6075 (-0.6101) time: 0.6805 data: 0.0018 [11-25 15:16:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 188/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.498 (6.505) Lt: 5.719 (5.757) Accm: 3.45 (3.38) Acct: 5.39 (5.34) proj_loss: -0.5985 (-0.5983) time: 0.6805 data: 0.0021 [11-25 15:16:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 188/350] Total time: 0:18:54 (0.680 s / it) [11-25 15:16:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 188/350] Total time: 0:18:54 (0.680 s / it) [11-25 15:16:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 188/350] Total time: 0:18:54 (0.680 s / it) [11-25 15:16:34] (/home/user/VAR/train.py , line 276)=> [ep188] (training ) Lm: 6.502 (6.516), Lt: 5.742 (5.762), Acc m&t: 3.39 5.35, Remain: 2 days, 3:03:19, Finish: 2024-11-27 02:19 [11-25 15:16:34] (/home/user/VAR/train.py , line 276)=> [ep188] (training ) Lm: 6.502 (6.516), Lt: 5.742 (5.762), Acc m&t: 3.39 5.35, Remain: 2 days, 3:03:41, Finish: 2024-11-27 02:20 [11-25 15:16:34] (/home/user/VAR/train.py , line 276)=> [ep188] (training ) Lm: 6.502 (6.516), Lt: 5.742 (5.762), Acc m&t: 3.39 5.35, Remain: 2 days, 3:03:55, Finish: 2024-11-27 02:20 [11-25 15:16:34] (/home/user/VAR/train.py , line 276)=> [ep188] (training ) Lm: 6.502 (6.516), Lt: 5.742 (5.762), Acc m&t: 3.39 5.35, Remain: 2 days, 3:03:35, Finish: 2024-11-27 02:20 [11-25 15:16:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [ 0/1669] eta: 0:18:40 tlr: 0.00012 tnm: 0.37 Lm: 6.678 (6.678) Lt: 5.983 (5.983) Accm: 2.96 (2.96) Acct: 4.51 (4.51) proj_loss: -0.6130 (-0.6130) time: 0.6713 data: 0.0004 [11-25 15:16:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [ 0/1669] eta: 0:18:40 tlr: 0.00012 tnm: 0.37 Lm: 6.537 (6.537) Lt: 5.801 (5.801) Accm: 3.13 (3.13) Acct: 5.13 (5.13) proj_loss: -0.6205 (-0.6205) time: 0.6711 data: 0.0005 [11-25 15:16:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [ 0/1669] eta: 0:18:40 tlr: 0.00012 tnm: 0.37 Lm: 6.276 (6.276) Lt: 5.474 (5.474) Accm: 3.74 (3.74) Acct: 5.82 (5.82) proj_loss: -0.6090 (-0.6090) time: 0.6716 data: 0.0004 [11-25 15:16:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [ 0/1669] eta: 0:18:41 tlr: 0.00012 tnm: 0.37 Lm: 6.569 (6.569) Lt: 5.814 (5.814) Accm: 2.96 (2.96) Acct: 4.67 (4.67) proj_loss: -0.6144 (-0.6144) time: 0.6718 data: 0.0003 [11-25 15:21:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [ 417/1669] eta: 0:15:12 tlr: 0.00012 tnm: 0.35 Lm: 6.532 (6.532) Lt: 5.773 (5.773) Accm: 3.26 (3.26) Acct: 5.14 (5.14) proj_loss: -0.6088 (-0.6088) time: 0.6784 data: 0.0003 [11-25 15:21:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [ 417/1669] eta: 0:15:12 tlr: 0.00012 tnm: 0.35 Lm: 6.387 (6.387) Lt: 5.598 (5.598) Accm: 3.57 (3.57) Acct: 5.63 (5.63) proj_loss: -0.6086 (-0.6086) time: 0.6784 data: 0.0003 [11-25 15:21:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [ 417/1669] eta: 0:15:12 tlr: 0.00012 tnm: 0.35 Lm: 6.640 (6.640) Lt: 5.919 (5.919) Accm: 3.04 (3.04) Acct: 4.71 (4.71) proj_loss: -0.6164 (-0.6164) time: 0.6784 data: 0.0003 [11-25 15:21:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [ 417/1669] eta: 0:15:12 tlr: 0.00012 tnm: 0.35 Lm: 6.639 (6.639) Lt: 5.882 (5.882) Accm: 3.03 (3.03) Acct: 5.08 (5.08) proj_loss: -0.6028 (-0.6028) time: 0.6784 data: 0.0003 [11-25 15:26:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [ 834/1669] eta: 0:09:53 tlr: 0.00012 tnm: 0.36 Lm: 6.551 (6.610) Lt: 5.801 (5.842) Accm: 3.13 (3.11) Acct: 5.11 (5.09) proj_loss: -0.6071 (-0.6043) time: 0.6777 data: 0.0003 [11-25 15:26:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [ 834/1669] eta: 0:09:53 tlr: 0.00012 tnm: 0.36 Lm: 6.569 (6.553) Lt: 5.814 (5.801) Accm: 3.18 (3.24) Acct: 4.99 (5.09) proj_loss: -0.6031 (-0.6038) time: 0.6777 data: 0.0003 [11-25 15:26:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [ 834/1669] eta: 0:09:53 tlr: 0.00012 tnm: 0.36 Lm: 6.499 (6.447) Lt: 5.722 (5.668) Accm: 3.39 (3.38) Acct: 5.44 (5.38) proj_loss: -0.6081 (-0.6020) time: 0.6777 data: 0.0003 [11-25 15:26:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [ 834/1669] eta: 0:09:53 tlr: 0.00012 tnm: 0.36 Lm: 6.602 (6.579) Lt: 5.856 (5.831) Accm: 3.12 (3.21) Acct: 4.91 (5.03) proj_loss: -0.6198 (-0.6176) time: 0.6777 data: 0.0003 [11-25 15:31:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.35 Lm: 6.616 (6.592) Lt: 5.873 (5.846) Accm: 3.04 (3.13) Acct: 4.84 (4.97) proj_loss: -0.6164 (-0.6126) time: 0.6746 data: 0.0003 [11-25 15:31:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.35 Lm: 6.582 (6.564) Lt: 5.823 (5.809) Accm: 3.07 (3.16) Acct: 4.84 (4.99) proj_loss: -0.5985 (-0.5983) time: 0.6746 data: 0.0003 [11-25 15:31:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.35 Lm: 6.497 (6.459) Lt: 5.725 (5.683) Accm: 3.36 (3.37) Acct: 5.37 (5.36) proj_loss: -0.5985 (-0.5982) time: 0.6746 data: 0.0003 [11-25 15:31:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.35 Lm: 6.626 (6.633) Lt: 5.876 (5.870) Accm: 3.03 (3.06) Acct: 5.07 (4.97) proj_loss: -0.6077 (-0.6053) time: 0.6746 data: 0.0003 [11-25 15:35:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.37 Lm: 6.601 (6.626) Lt: 5.857 (5.867) Accm: 3.07 (3.06) Acct: 5.03 (4.92) proj_loss: -0.6071 (-0.6035) time: 0.6764 data: 0.0020 [11-25 15:35:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 189/350] Total time: 0:19:18 (0.694 s / it) [11-25 15:35:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.37 Lm: 6.499 (6.474) Lt: 5.728 (5.704) Accm: 3.39 (3.40) Acct: 5.30 (5.34) proj_loss: -0.6033 (-0.5992) time: 0.6764 data: 0.0020 [11-25 15:35:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.37 Lm: 6.569 (6.541) Lt: 5.814 (5.801) Accm: 3.18 (3.21) Acct: 4.99 (5.05) proj_loss: -0.6031 (-0.6005) time: 0.6764 data: 0.0019 [11-25 15:35:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 189/350] Total time: 0:19:18 (0.694 s / it) [11-25 15:35:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 189/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.37 Lm: 6.617 (6.597) Lt: 5.890 (5.856) Accm: 3.07 (3.12) Acct: 4.79 (4.93) proj_loss: -0.6198 (-0.6186) time: 0.6764 data: 0.0013 [11-25 15:35:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 189/350] Total time: 0:19:18 (0.694 s / it) [11-25 15:35:53] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 189/350] Total time: 0:19:18 (0.694 s / it) [11-25 15:38:17] (home/user/VAR/trainer.py, line 114)=> FID: 3.4610912095276944 [11-25 15:38:17] (/home/user/VAR/train.py , line 259)=> [*] [ep189] (val 50000) Lm: 6.5117, Lt: 5.7587, Acc m&t: 3.36 5.28, Val cost: 144.18s [11-25 15:38:17] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-25 15:38:55] (/home/user/VAR/train.py , line 276)=> [ep189] (training ) Lm: 6.502 (6.512), Lt: 5.742 (5.759), Acc m&t: 3.39 5.35, Remain: 2 days, 2:28:36, Finish: 2024-11-27 02:04 [11-25 15:38:55] (/home/user/VAR/train.py , line 276)=> [ep189] (training ) Lm: 6.502 (6.512), Lt: 5.742 (5.759), Acc m&t: 3.39 5.35, Remain: 2 days, 2:29:31, Finish: 2024-11-27 02:05 [11-25 15:38:55] (/home/user/VAR/train.py , line 276)=> [ep189] (training ) Lm: 6.502 (6.512), Lt: 5.742 (5.759), Acc m&t: 3.39 5.35, Remain: 2 days, 2:29:52, Finish: 2024-11-27 02:05 [11-25 15:38:55] (/home/user/VAR/train.py , line 276)=> [ep189] (training ) Lm: 6.502 (6.512), Lt: 5.742 (5.759), Acc m&t: 3.39 5.35, Remain: 2 days, 2:29:44, Finish: 2024-11-27 02:05 [11-25 15:38:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [ 0/1669] eta: 0:18:39 tlr: 0.00012 tnm: 0.38 Lm: 6.434 (6.434) Lt: 5.667 (5.667) Accm: 3.55 (3.55) Acct: 5.41 (5.41) proj_loss: -0.6203 (-0.6203) time: 0.6708 data: 0.0003 [11-25 15:38:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [ 0/1669] eta: 0:18:38 tlr: 0.00012 tnm: 0.38 Lm: 6.470 (6.470) Lt: 5.659 (5.659) Accm: 3.71 (3.71) Acct: 5.89 (5.89) proj_loss: -0.5915 (-0.5915) time: 0.6703 data: 0.0004 [11-25 15:38:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [ 0/1669] eta: 0:18:38 tlr: 0.00012 tnm: 0.38 Lm: 6.572 (6.572) Lt: 5.822 (5.822) Accm: 3.04 (3.04) Acct: 4.82 (4.82) proj_loss: -0.6037 (-0.6037) time: 0.6703 data: 0.0004 [11-25 15:38:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [ 0/1669] eta: 0:18:39 tlr: 0.00012 tnm: 0.38 Lm: 6.403 (6.403) Lt: 5.638 (5.638) Accm: 3.43 (3.43) Acct: 5.20 (5.20) proj_loss: -0.6158 (-0.6158) time: 0.6708 data: 0.0004 [11-25 15:43:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [ 417/1669] eta: 0:14:14 tlr: 0.00012 tnm: 0.36 Lm: 6.377 (6.377) Lt: 5.595 (5.595) Accm: 3.73 (3.73) Acct: 5.72 (5.72) proj_loss: -0.6081 (-0.6081) time: 0.7522 data: 0.0003 [11-25 15:43:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [ 417/1669] eta: 0:14:14 tlr: 0.00012 tnm: 0.36 Lm: 6.610 (6.610) Lt: 5.851 (5.851) Accm: 3.03 (3.03) Acct: 4.67 (4.67) proj_loss: -0.6105 (-0.6105) time: 0.7521 data: 0.0003 [11-25 15:43:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [ 417/1669] eta: 0:14:14 tlr: 0.00012 tnm: 0.36 Lm: 6.497 (6.497) Lt: 5.702 (5.702) Accm: 3.49 (3.49) Acct: 5.55 (5.55) proj_loss: -0.6022 (-0.6022) time: 0.7522 data: 0.0003 [11-25 15:43:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [ 417/1669] eta: 0:14:14 tlr: 0.00012 tnm: 0.36 Lm: 6.434 (6.434) Lt: 5.692 (5.692) Accm: 3.57 (3.57) Acct: 5.48 (5.48) proj_loss: -0.6176 (-0.6176) time: 0.7522 data: 0.0003 [11-25 15:48:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [ 834/1669] eta: 0:09:47 tlr: 0.00012 tnm: 0.37 Lm: 6.435 (6.486) Lt: 5.717 (5.758) Accm: 3.55 (3.41) Acct: 5.41 (5.21) proj_loss: -0.6149 (-0.6130) time: 0.9191 data: 0.0003 [11-25 15:48:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [ 834/1669] eta: 0:09:47 tlr: 0.00012 tnm: 0.37 Lm: 6.588 (6.602) Lt: 5.822 (5.836) Accm: 3.01 (3.01) Acct: 4.63 (4.66) proj_loss: -0.6037 (-0.6076) time: 0.9191 data: 0.0003 [11-25 15:48:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [ 834/1669] eta: 0:09:47 tlr: 0.00012 tnm: 0.37 Lm: 6.403 (6.456) Lt: 5.638 (5.693) Accm: 3.43 (3.48) Acct: 5.20 (5.37) proj_loss: -0.6003 (-0.6048) time: 0.9191 data: 0.0003 [11-25 15:48:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [ 834/1669] eta: 0:09:47 tlr: 0.00012 tnm: 0.37 Lm: 6.525 (6.546) Lt: 5.745 (5.763) Accm: 3.28 (3.31) Acct: 5.22 (5.13) proj_loss: -0.5941 (-0.5995) time: 0.9191 data: 0.0003 [11-25 15:53:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [1251/1669] eta: 0:04:53 tlr: 0.00012 tnm: 0.36 Lm: 6.516 (6.536) Lt: 5.803 (5.788) Accm: 3.35 (3.34) Acct: 5.18 (5.14) proj_loss: -0.6035 (-0.6064) time: 0.6761 data: 0.0003 [11-25 15:53:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [1251/1669] eta: 0:04:53 tlr: 0.00012 tnm: 0.36 Lm: 6.454 (6.483) Lt: 5.721 (5.750) Accm: 3.57 (3.46) Acct: 5.48 (5.32) proj_loss: -0.6176 (-0.6153) time: 0.6761 data: 0.0003 [11-25 15:53:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [1251/1669] eta: 0:04:53 tlr: 0.00012 tnm: 0.36 Lm: 6.580 (6.592) Lt: 5.814 (5.824) Accm: 3.03 (3.06) Acct: 4.73 (4.80) proj_loss: -0.6027 (-0.6022) time: 0.6761 data: 0.0003 [11-25 15:53:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [1251/1669] eta: 0:04:53 tlr: 0.00012 tnm: 0.36 Lm: 6.476 (6.480) Lt: 5.738 (5.729) Accm: 3.31 (3.41) Acct: 4.96 (5.20) proj_loss: -0.6081 (-0.6078) time: 0.6761 data: 0.0003 [11-25 15:58:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.458 (6.475) Lt: 5.642 (5.712) Accm: 3.43 (3.41) Acct: 5.20 (5.27) proj_loss: -0.6003 (-0.6037) time: 0.6758 data: 0.0016 [11-25 15:58:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 190/350] Total time: 0:19:22 (0.696 s / it) [11-25 15:58:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.507 (6.516) Lt: 5.745 (5.755) Accm: 3.29 (3.33) Acct: 5.17 (5.14) proj_loss: -0.6020 (-0.6055) time: 0.6758 data: 0.0017 [11-25 15:58:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.572 (6.581) Lt: 5.807 (5.814) Accm: 3.04 (3.08) Acct: 4.82 (4.82) proj_loss: -0.6018 (-0.6016) time: 0.6758 data: 0.0014 [11-25 15:58:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 190/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.36 Lm: 6.474 (6.482) Lt: 5.726 (5.749) Accm: 3.55 (3.47) Acct: 5.41 (5.34) proj_loss: -0.6149 (-0.6124) time: 0.6758 data: 0.0019 [11-25 15:58:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 190/350] Total time: 0:19:22 (0.696 s / it) [11-25 15:58:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 190/350] Total time: 0:19:22 (0.696 s / it) [11-25 15:58:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 190/350] Total time: 0:19:22 (0.696 s / it) [11-25 15:58:18] (/home/user/VAR/train.py , line 276)=> [ep190] (training ) Lm: 6.502 (6.506), Lt: 5.742 (5.747), Acc m&t: 3.39 5.35, Remain: 2 days, 2:13:58, Finish: 2024-11-27 02:12 [11-25 15:58:18] (/home/user/VAR/train.py , line 276)=> [ep190] (training ) Lm: 6.502 (6.506), Lt: 5.742 (5.747), Acc m&t: 3.39 5.35, Remain: 2 days, 2:14:05, Finish: 2024-11-27 02:12 [11-25 15:58:18] (/home/user/VAR/train.py , line 276)=> [ep190] (training ) Lm: 6.502 (6.506), Lt: 5.742 (5.747), Acc m&t: 3.39 5.35, Remain: 2 days, 2:13:38, Finish: 2024-11-27 02:11 [11-25 15:58:18] (/home/user/VAR/train.py , line 276)=> [ep190] (training ) Lm: 6.502 (6.506), Lt: 5.742 (5.747), Acc m&t: 3.39 5.35, Remain: 2 days, 2:13:30, Finish: 2024-11-27 02:11 [11-25 15:58:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [ 0/1669] eta: 0:18:26 tlr: 0.00012 tnm: 0.36 Lm: 6.372 (6.372) Lt: 5.574 (5.574) Accm: 3.76 (3.76) Acct: 6.13 (6.13) proj_loss: -0.6209 (-0.6209) time: 0.6632 data: 0.0004 [11-25 15:58:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [ 0/1669] eta: 0:18:26 tlr: 0.00012 tnm: 0.36 Lm: 6.583 (6.583) Lt: 5.773 (5.773) Accm: 3.38 (3.38) Acct: 5.54 (5.54) proj_loss: -0.6001 (-0.6001) time: 0.6633 data: 0.0004 [11-25 15:58:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [ 0/1669] eta: 0:18:21 tlr: 0.00012 tnm: 0.36 Lm: 6.340 (6.340) Lt: 5.604 (5.604) Accm: 3.59 (3.59) Acct: 5.48 (5.48) proj_loss: -0.6026 (-0.6026) time: 0.6601 data: 0.0004 [11-25 15:58:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [ 0/1669] eta: 0:18:22 tlr: 0.00012 tnm: 0.36 Lm: 6.633 (6.633) Lt: 5.842 (5.842) Accm: 2.89 (2.89) Acct: 4.86 (4.86) proj_loss: -0.5914 (-0.5914) time: 0.6608 data: 0.0004 [11-25 16:03:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [ 417/1669] eta: 0:14:06 tlr: 0.00012 tnm: 0.35 Lm: 6.529 (6.529) Lt: 5.750 (5.750) Accm: 3.16 (3.16) Acct: 4.99 (4.99) proj_loss: -0.5989 (-0.5989) time: 0.6760 data: 0.0003 [11-25 16:03:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [ 417/1669] eta: 0:14:06 tlr: 0.00012 tnm: 0.35 Lm: 6.437 (6.437) Lt: 5.721 (5.721) Accm: 3.42 (3.42) Acct: 5.28 (5.28) proj_loss: -0.6031 (-0.6031) time: 0.6760 data: 0.0003 [11-25 16:03:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [ 417/1669] eta: 0:14:06 tlr: 0.00012 tnm: 0.35 Lm: 6.525 (6.525) Lt: 5.754 (5.754) Accm: 3.40 (3.40) Acct: 5.40 (5.40) proj_loss: -0.6039 (-0.6039) time: 0.6760 data: 0.0003 [11-25 16:03:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [ 417/1669] eta: 0:14:06 tlr: 0.00012 tnm: 0.35 Lm: 6.442 (6.442) Lt: 5.643 (5.643) Accm: 3.57 (3.57) Acct: 5.85 (5.85) proj_loss: -0.6098 (-0.6098) time: 0.6760 data: 0.0003 [11-25 16:07:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [ 834/1669] eta: 0:09:24 tlr: 0.00012 tnm: 0.37 Lm: 6.500 (6.462) Lt: 5.713 (5.673) Accm: 3.39 (3.46) Acct: 5.58 (5.57) proj_loss: -0.6004 (-0.6067) time: 0.6759 data: 0.0003 [11-25 16:07:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [ 834/1669] eta: 0:09:24 tlr: 0.00012 tnm: 0.37 Lm: 6.466 (6.468) Lt: 5.735 (5.696) Accm: 3.42 (3.47) Acct: 5.54 (5.54) proj_loss: -0.6071 (-0.6050) time: 0.6759 data: 0.0003 [11-25 16:07:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [ 834/1669] eta: 0:09:24 tlr: 0.00012 tnm: 0.37 Lm: 6.519 (6.464) Lt: 5.721 (5.721) Accm: 3.41 (3.42) Acct: 5.48 (5.36) proj_loss: -0.6026 (-0.5942) time: 0.6759 data: 0.0003 [11-25 16:07:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [ 834/1669] eta: 0:09:24 tlr: 0.00012 tnm: 0.37 Lm: 6.552 (6.537) Lt: 5.766 (5.755) Accm: 3.33 (3.21) Acct: 5.11 (5.03) proj_loss: -0.5914 (-0.5943) time: 0.6759 data: 0.0003 [11-25 16:12:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [1251/1669] eta: 0:04:42 tlr: 0.00012 tnm: 0.39 Lm: 6.538 (6.534) Lt: 5.790 (5.770) Accm: 3.30 (3.23) Acct: 4.98 (4.97) proj_loss: -0.5928 (-0.5943) time: 0.6748 data: 0.0003 [11-25 16:12:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [1251/1669] eta: 0:04:42 tlr: 0.00012 tnm: 0.39 Lm: 6.469 (6.469) Lt: 5.693 (5.685) Accm: 3.50 (3.50) Acct: 5.66 (5.60) proj_loss: -0.6036 (-0.5973) time: 0.6748 data: 0.0003 [11-25 16:12:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [1251/1669] eta: 0:04:42 tlr: 0.00012 tnm: 0.39 Lm: 6.495 (6.469) Lt: 5.695 (5.674) Accm: 3.38 (3.44) Acct: 5.48 (5.52) proj_loss: -0.5996 (-0.6010) time: 0.6748 data: 0.0003 [11-25 16:12:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [1251/1669] eta: 0:04:42 tlr: 0.00012 tnm: 0.39 Lm: 6.527 (6.510) Lt: 5.779 (5.767) Accm: 3.33 (3.30) Acct: 5.28 (5.22) proj_loss: -0.5924 (-0.5912) time: 0.6748 data: 0.0003 [11-25 16:17:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.39 Lm: 6.519 (6.491) Lt: 5.721 (5.743) Accm: 3.41 (3.40) Acct: 5.48 (5.36) proj_loss: -0.6026 (-0.5946) time: 0.6783 data: 0.0018 [11-25 16:17:11] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 191/350] Total time: 0:18:53 (0.679 s / it) [11-25 16:17:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.39 Lm: 6.525 (6.511) Lt: 5.766 (5.756) Accm: 3.33 (3.27) Acct: 5.11 (5.01) proj_loss: -0.5942 (-0.5965) time: 0.6783 data: 0.0022 [11-25 16:17:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.39 Lm: 6.491 (6.430) Lt: 5.677 (5.637) Accm: 3.39 (3.57) Acct: 5.58 (5.70) proj_loss: -0.6004 (-0.6022) time: 0.6783 data: 0.0021 [11-25 16:17:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 191/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.39 Lm: 6.472 (6.484) Lt: 5.735 (5.712) Accm: 3.42 (3.41) Acct: 5.54 (5.47) proj_loss: -0.6071 (-0.5993) time: 0.6783 data: 0.0018 [11-25 16:17:11] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 191/350] Total time: 0:18:53 (0.679 s / it) [11-25 16:17:11] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 191/350] Total time: 0:18:53 (0.679 s / it) [11-25 16:17:11] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 191/350] Total time: 0:18:53 (0.679 s / it) [11-25 16:17:11] (/home/user/VAR/train.py , line 276)=> [ep191] (training ) Lm: 6.502 (6.510), Lt: 5.742 (5.754), Acc m&t: 3.39 5.35, Remain: 2 days, 2:08:48, Finish: 2024-11-27 02:25 [11-25 16:17:11] (/home/user/VAR/train.py , line 276)=> [ep191] (training ) Lm: 6.502 (6.510), Lt: 5.742 (5.754), Acc m&t: 3.39 5.35, Remain: 2 days, 2:08:46, Finish: 2024-11-27 02:25 [11-25 16:17:11] (/home/user/VAR/train.py , line 276)=> [ep191] (training ) Lm: 6.502 (6.510), Lt: 5.742 (5.754), Acc m&t: 3.39 5.35, Remain: 2 days, 2:09:31, Finish: 2024-11-27 02:26 [11-25 16:17:11] (/home/user/VAR/train.py , line 276)=> [ep191] (training ) Lm: 6.502 (6.510), Lt: 5.742 (5.754), Acc m&t: 3.39 5.35, Remain: 2 days, 2:11:01, Finish: 2024-11-27 02:28 [11-25 16:17:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [ 0/1669] eta: 0:18:34 tlr: 0.00012 tnm: 0.35 Lm: 6.432 (6.432) Lt: 5.677 (5.677) Accm: 3.47 (3.47) Acct: 5.46 (5.46) proj_loss: -0.6164 (-0.6164) time: 0.6676 data: 0.0005 [11-25 16:17:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [ 0/1669] eta: 0:18:25 tlr: 0.00012 tnm: 0.35 Lm: 6.580 (6.580) Lt: 5.906 (5.906) Accm: 2.92 (2.92) Acct: 4.79 (4.79) proj_loss: -0.6016 (-0.6016) time: 0.6624 data: 0.0003 [11-25 16:17:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [ 0/1669] eta: 0:18:36 tlr: 0.00012 tnm: 0.35 Lm: 6.659 (6.659) Lt: 5.926 (5.926) Accm: 2.97 (2.97) Acct: 4.42 (4.42) proj_loss: -0.6122 (-0.6122) time: 0.6690 data: 0.0005 [11-25 16:17:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [ 0/1669] eta: 0:18:30 tlr: 0.00012 tnm: 0.35 Lm: 6.525 (6.525) Lt: 5.757 (5.757) Accm: 3.50 (3.50) Acct: 5.46 (5.46) proj_loss: -0.6026 (-0.6026) time: 0.6651 data: 0.0004 [11-25 16:22:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [ 417/1669] eta: 0:15:10 tlr: 0.00012 tnm: 0.37 Lm: 6.482 (6.482) Lt: 5.716 (5.716) Accm: 3.46 (3.46) Acct: 5.37 (5.37) proj_loss: -0.6000 (-0.6000) time: 0.6764 data: 0.0003 [11-25 16:22:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [ 417/1669] eta: 0:15:10 tlr: 0.00012 tnm: 0.37 Lm: 6.581 (6.581) Lt: 5.853 (5.853) Accm: 3.12 (3.12) Acct: 5.13 (5.13) proj_loss: -0.6039 (-0.6039) time: 0.6764 data: 0.0003 [11-25 16:22:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [ 417/1669] eta: 0:15:10 tlr: 0.00012 tnm: 0.37 Lm: 6.413 (6.413) Lt: 5.645 (5.645) Accm: 3.47 (3.47) Acct: 5.37 (5.37) proj_loss: -0.6143 (-0.6143) time: 0.6764 data: 0.0003 [11-25 16:22:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [ 417/1669] eta: 0:15:10 tlr: 0.00012 tnm: 0.37 Lm: 6.514 (6.514) Lt: 5.774 (5.774) Accm: 3.30 (3.30) Acct: 5.17 (5.17) proj_loss: -0.6046 (-0.6046) time: 0.6764 data: 0.0003 [11-25 16:27:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [ 834/1669] eta: 0:09:53 tlr: 0.00012 tnm: 0.35 Lm: 6.498 (6.509) Lt: 5.757 (5.768) Accm: 3.40 (3.33) Acct: 5.32 (5.22) proj_loss: -0.6028 (-0.6040) time: 0.6779 data: 0.0003 [11-25 16:27:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [ 834/1669] eta: 0:09:53 tlr: 0.00012 tnm: 0.35 Lm: 6.580 (6.518) Lt: 5.800 (5.795) Accm: 3.31 (3.26) Acct: 5.48 (5.30) proj_loss: -0.6063 (-0.6086) time: 0.6779 data: 0.0003 [11-25 16:27:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [ 834/1669] eta: 0:09:53 tlr: 0.00012 tnm: 0.35 Lm: 6.420 (6.415) Lt: 5.677 (5.664) Accm: 3.47 (3.55) Acct: 5.46 (5.41) proj_loss: -0.6164 (-0.6176) time: 0.6779 data: 0.0003 [11-25 16:27:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [ 834/1669] eta: 0:09:53 tlr: 0.00012 tnm: 0.35 Lm: 6.446 (6.470) Lt: 5.695 (5.709) Accm: 3.50 (3.52) Acct: 5.46 (5.42) proj_loss: -0.6026 (-0.6040) time: 0.6779 data: 0.0003 [11-25 16:31:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.38 Lm: 6.540 (6.513) Lt: 5.750 (5.771) Accm: 3.43 (3.33) Acct: 5.42 (5.32) proj_loss: -0.6039 (-0.6067) time: 0.6772 data: 0.0003 [11-25 16:31:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.38 Lm: 6.462 (6.472) Lt: 5.686 (5.701) Accm: 3.46 (3.43) Acct: 5.37 (5.38) proj_loss: -0.6000 (-0.5989) time: 0.6772 data: 0.0003 [11-25 16:31:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.38 Lm: 6.426 (6.421) Lt: 5.689 (5.674) Accm: 3.47 (3.53) Acct: 5.46 (5.42) proj_loss: -0.6143 (-0.6156) time: 0.6772 data: 0.0003 [11-25 16:31:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.38 Lm: 6.486 (6.500) Lt: 5.744 (5.759) Accm: 3.51 (3.40) Acct: 5.48 (5.32) proj_loss: -0.6060 (-0.6053) time: 0.6773 data: 0.0003 [11-25 16:36:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.38 Lm: 6.473 (6.481) Lt: 5.730 (5.735) Accm: 3.61 (3.49) Acct: 5.65 (5.44) proj_loss: -0.6034 (-0.6049) time: 0.6784 data: 0.0021 [11-25 16:36:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 192/350] Total time: 0:19:18 (0.694 s / it) [11-25 16:36:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.38 Lm: 6.420 (6.402) Lt: 5.677 (5.641) Accm: 3.47 (3.60) Acct: 5.46 (5.57) proj_loss: -0.6122 (-0.6096) time: 0.6784 data: 0.0013 [11-25 16:36:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.38 Lm: 6.479 (6.502) Lt: 5.695 (5.736) Accm: 3.42 (3.33) Acct: 5.29 (5.28) proj_loss: -0.5974 (-0.5973) time: 0.6784 data: 0.0020 [11-25 16:36:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 192/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.38 Lm: 6.500 (6.502) Lt: 5.701 (5.752) Accm: 3.49 (3.36) Acct: 5.37 (5.29) proj_loss: -0.6063 (-0.6066) time: 0.6784 data: 0.0019 [11-25 16:36:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 192/350] Total time: 0:19:18 (0.694 s / it) [11-25 16:36:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 192/350] Total time: 0:19:18 (0.694 s / it) [11-25 16:36:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 192/350] Total time: 0:19:18 (0.694 s / it) [11-25 16:36:29] (/home/user/VAR/train.py , line 276)=> [ep192] (training ) Lm: 6.502 (6.516), Lt: 5.742 (5.763), Acc m&t: 3.39 5.35, Remain: 2 days, 1:48:11, Finish: 2024-11-27 02:24 [11-25 16:36:29] (/home/user/VAR/train.py , line 276)=> [ep192] (training ) Lm: 6.502 (6.516), Lt: 5.742 (5.763), Acc m&t: 3.39 5.35, Remain: 2 days, 1:47:09, Finish: 2024-11-27 02:23 [11-25 16:36:29] (/home/user/VAR/train.py , line 276)=> [ep192] (training ) Lm: 6.502 (6.516), Lt: 5.742 (5.763), Acc m&t: 3.39 5.35, Remain: 2 days, 1:47:07, Finish: 2024-11-27 02:23 [11-25 16:36:29] (/home/user/VAR/train.py , line 276)=> [ep192] (training ) Lm: 6.502 (6.516), Lt: 5.742 (5.763), Acc m&t: 3.39 5.35, Remain: 2 days, 1:47:16, Finish: 2024-11-27 02:23 [11-25 16:36:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [ 0/1669] eta: 0:18:22 tlr: 0.00012 tnm: 0.37 Lm: 6.635 (6.635) Lt: 5.835 (5.835) Accm: 3.16 (3.16) Acct: 4.94 (4.94) proj_loss: -0.5893 (-0.5893) time: 0.6604 data: 0.0004 [11-25 16:36:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [ 0/1669] eta: 0:18:23 tlr: 0.00012 tnm: 0.37 Lm: 6.598 (6.598) Lt: 5.799 (5.799) Accm: 3.13 (3.13) Acct: 5.06 (5.06) proj_loss: -0.5785 (-0.5785) time: 0.6609 data: 0.0004 [11-25 16:36:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [ 0/1669] eta: 0:18:22 tlr: 0.00012 tnm: 0.37 Lm: 6.637 (6.637) Lt: 5.938 (5.938) Accm: 2.85 (2.85) Acct: 4.39 (4.39) proj_loss: -0.6088 (-0.6088) time: 0.6603 data: 0.0005 [11-25 16:36:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [ 0/1669] eta: 0:18:23 tlr: 0.00012 tnm: 0.37 Lm: 6.321 (6.321) Lt: 5.555 (5.555) Accm: 3.98 (3.98) Acct: 6.16 (6.16) proj_loss: -0.5982 (-0.5982) time: 0.6611 data: 0.0004 [11-25 16:41:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [ 417/1669] eta: 0:14:13 tlr: 0.00012 tnm: 0.37 Lm: 6.395 (6.395) Lt: 5.611 (5.611) Accm: 3.84 (3.84) Acct: 6.21 (6.21) proj_loss: -0.6006 (-0.6006) time: 0.7424 data: 0.0003 [11-25 16:41:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [ 417/1669] eta: 0:14:13 tlr: 0.00012 tnm: 0.37 Lm: 6.495 (6.495) Lt: 5.731 (5.731) Accm: 3.61 (3.61) Acct: 5.55 (5.55) proj_loss: -0.6031 (-0.6031) time: 0.7424 data: 0.0003 [11-25 16:41:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [ 417/1669] eta: 0:14:13 tlr: 0.00012 tnm: 0.37 Lm: 6.568 (6.568) Lt: 5.814 (5.814) Accm: 2.97 (2.97) Acct: 4.77 (4.77) proj_loss: -0.6112 (-0.6112) time: 0.7424 data: 0.0003 [11-25 16:41:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [ 417/1669] eta: 0:14:13 tlr: 0.00012 tnm: 0.37 Lm: 6.623 (6.623) Lt: 5.841 (5.841) Accm: 3.04 (3.04) Acct: 4.77 (4.77) proj_loss: -0.5938 (-0.5938) time: 0.7424 data: 0.0003 [11-25 16:46:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [ 834/1669] eta: 0:09:42 tlr: 0.00012 tnm: 0.37 Lm: 6.647 (6.674) Lt: 5.883 (5.913) Accm: 2.96 (2.94) Acct: 4.48 (4.60) proj_loss: -0.6091 (-0.6019) time: 0.7971 data: 0.0003 [11-25 16:46:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [ 834/1669] eta: 0:09:42 tlr: 0.00012 tnm: 0.37 Lm: 6.547 (6.512) Lt: 5.835 (5.780) Accm: 3.42 (3.54) Acct: 5.11 (5.41) proj_loss: -0.6168 (-0.6107) time: 0.7971 data: 0.0003 [11-25 16:46:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [ 834/1669] eta: 0:09:42 tlr: 0.00012 tnm: 0.37 Lm: 6.469 (6.484) Lt: 5.666 (5.724) Accm: 3.70 (3.45) Acct: 6.16 (5.57) proj_loss: -0.5982 (-0.5987) time: 0.7971 data: 0.0003 [11-25 16:46:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [ 834/1669] eta: 0:09:42 tlr: 0.00012 tnm: 0.37 Lm: 6.631 (6.589) Lt: 5.894 (5.841) Accm: 2.90 (2.95) Acct: 4.63 (4.72) proj_loss: -0.6088 (-0.6059) time: 0.7971 data: 0.0003 [11-25 16:51:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.37 Lm: 6.565 (6.531) Lt: 5.792 (5.778) Accm: 2.99 (3.16) Acct: 4.89 (5.11) proj_loss: -0.6084 (-0.6064) time: 0.7428 data: 0.0003 [11-25 16:51:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.37 Lm: 6.468 (6.482) Lt: 5.731 (5.736) Accm: 3.64 (3.62) Acct: 5.57 (5.56) proj_loss: -0.6111 (-0.6094) time: 0.7428 data: 0.0003 [11-25 16:51:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.37 Lm: 6.456 (6.474) Lt: 5.701 (5.727) Accm: 3.49 (3.41) Acct: 5.63 (5.45) proj_loss: -0.6006 (-0.6070) time: 0.7428 data: 0.0002 [11-25 16:51:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [1251/1669] eta: 0:04:52 tlr: 0.00012 tnm: 0.37 Lm: 6.623 (6.618) Lt: 5.841 (5.848) Accm: 3.04 (3.06) Acct: 4.77 (4.81) proj_loss: -0.5970 (-0.5977) time: 0.7428 data: 0.0003 [11-25 16:55:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.38 Lm: 6.607 (6.616) Lt: 5.865 (5.852) Accm: 3.06 (3.06) Acct: 4.77 (4.80) proj_loss: -0.5984 (-0.5978) time: 0.6807 data: 0.0020 [11-25 16:55:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.38 Lm: 6.547 (6.507) Lt: 5.782 (5.745) Accm: 3.42 (3.51) Acct: 5.11 (5.42) proj_loss: -0.6111 (-0.6097) time: 0.6807 data: 0.0018 [11-25 16:55:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.38 Lm: 6.561 (6.537) Lt: 5.797 (5.782) Accm: 3.09 (3.18) Acct: 4.84 (5.06) proj_loss: -0.6080 (-0.6047) time: 0.6807 data: 0.0019 [11-25 16:55:51] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 193/350] Total time: 0:19:21 (0.696 s / it) [11-25 16:55:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 193/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.38 Lm: 6.469 (6.482) Lt: 5.735 (5.733) Accm: 3.31 (3.39) Acct: 5.15 (5.39) proj_loss: -0.6022 (-0.6061) time: 0.6807 data: 0.0021 [11-25 16:55:51] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 193/350] Total time: 0:19:21 (0.696 s / it) [11-25 16:55:51] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 193/350] Total time: 0:19:21 (0.696 s / it) [11-25 16:55:51] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 193/350] Total time: 0:19:21 (0.696 s / it) [11-25 16:55:51] (/home/user/VAR/train.py , line 276)=> [ep193] (training ) Lm: 6.502 (6.515), Lt: 5.742 (5.758), Acc m&t: 3.39 5.35, Remain: 2 days, 1:35:26, Finish: 2024-11-27 02:31 [11-25 16:55:51] (/home/user/VAR/train.py , line 276)=> [ep193] (training ) Lm: 6.502 (6.515), Lt: 5.742 (5.758), Acc m&t: 3.39 5.35, Remain: 2 days, 1:35:12, Finish: 2024-11-27 02:31 [11-25 16:55:51] (/home/user/VAR/train.py , line 276)=> [ep193] (training ) Lm: 6.502 (6.515), Lt: 5.742 (5.758), Acc m&t: 3.39 5.35, Remain: 2 days, 1:34:59, Finish: 2024-11-27 02:30 [11-25 16:55:51] (/home/user/VAR/train.py , line 276)=> [ep193] (training ) Lm: 6.502 (6.515), Lt: 5.742 (5.758), Acc m&t: 3.39 5.35, Remain: 2 days, 1:34:46, Finish: 2024-11-27 02:30 [11-25 16:55:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [ 0/1669] eta: 0:18:20 tlr: 0.00012 tnm: 0.36 Lm: 6.502 (6.502) Lt: 5.744 (5.744) Accm: 3.39 (3.39) Acct: 4.99 (4.99) proj_loss: -0.6211 (-0.6211) time: 0.6595 data: 0.0003 [11-25 16:55:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [ 0/1669] eta: 0:18:21 tlr: 0.00012 tnm: 0.36 Lm: 6.375 (6.375) Lt: 5.637 (5.637) Accm: 4.08 (4.08) Acct: 6.25 (6.25) proj_loss: -0.6083 (-0.6083) time: 0.6603 data: 0.0003 [11-25 16:55:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [ 0/1669] eta: 0:18:22 tlr: 0.00012 tnm: 0.36 Lm: 6.388 (6.388) Lt: 5.593 (5.593) Accm: 3.49 (3.49) Acct: 5.48 (5.48) proj_loss: -0.6286 (-0.6286) time: 0.6605 data: 0.0004 [11-25 16:55:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [ 0/1669] eta: 0:18:29 tlr: 0.00012 tnm: 0.36 Lm: 6.495 (6.495) Lt: 5.733 (5.733) Accm: 3.66 (3.66) Acct: 5.97 (5.97) proj_loss: -0.6208 (-0.6208) time: 0.6648 data: 0.0004 [11-25 17:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [ 417/1669] eta: 0:14:07 tlr: 0.00012 tnm: 0.39 Lm: 6.477 (6.477) Lt: 5.707 (5.707) Accm: 3.67 (3.67) Acct: 5.96 (5.96) proj_loss: -0.6074 (-0.6074) time: 0.6768 data: 0.0003 [11-25 17:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [ 417/1669] eta: 0:14:07 tlr: 0.00012 tnm: 0.39 Lm: 6.565 (6.565) Lt: 5.797 (5.797) Accm: 3.25 (3.25) Acct: 5.13 (5.13) proj_loss: -0.6090 (-0.6090) time: 0.6768 data: 0.0003 [11-25 17:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [ 417/1669] eta: 0:14:07 tlr: 0.00012 tnm: 0.39 Lm: 6.460 (6.460) Lt: 5.724 (5.724) Accm: 3.71 (3.71) Acct: 5.79 (5.79) proj_loss: -0.6091 (-0.6091) time: 0.6768 data: 0.0003 [11-25 17:00:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [ 417/1669] eta: 0:14:07 tlr: 0.00012 tnm: 0.39 Lm: 6.502 (6.502) Lt: 5.720 (5.720) Accm: 3.20 (3.20) Acct: 5.11 (5.11) proj_loss: -0.6127 (-0.6127) time: 0.6768 data: 0.0003 [11-25 17:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [ 834/1669] eta: 0:09:24 tlr: 0.00012 tnm: 0.38 Lm: 6.615 (6.548) Lt: 5.847 (5.771) Accm: 2.91 (3.09) Acct: 4.75 (4.93) proj_loss: -0.5967 (-0.6041) time: 0.6751 data: 0.0003 [11-25 17:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [ 834/1669] eta: 0:09:24 tlr: 0.00012 tnm: 0.38 Lm: 6.502 (6.508) Lt: 5.744 (5.741) Accm: 3.39 (3.41) Acct: 5.27 (5.33) proj_loss: -0.6046 (-0.6075) time: 0.6751 data: 0.0003 [11-25 17:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [ 834/1669] eta: 0:09:24 tlr: 0.00012 tnm: 0.38 Lm: 6.545 (6.523) Lt: 5.811 (5.782) Accm: 3.34 (3.45) Acct: 5.32 (5.39) proj_loss: -0.6083 (-0.6019) time: 0.6751 data: 0.0003 [11-25 17:05:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [ 834/1669] eta: 0:09:24 tlr: 0.00012 tnm: 0.38 Lm: 6.459 (6.434) Lt: 5.681 (5.661) Accm: 3.66 (3.65) Acct: 5.94 (5.90) proj_loss: -0.5961 (-0.6036) time: 0.6751 data: 0.0003 [11-25 17:09:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [1251/1669] eta: 0:04:42 tlr: 0.00012 tnm: 0.36 Lm: 6.477 (6.480) Lt: 5.707 (5.704) Accm: 3.64 (3.57) Acct: 5.86 (5.75) proj_loss: -0.5951 (-0.5991) time: 0.6781 data: 0.0003 [11-25 17:09:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [1251/1669] eta: 0:04:42 tlr: 0.00012 tnm: 0.36 Lm: 6.504 (6.507) Lt: 5.739 (5.739) Accm: 3.27 (3.35) Acct: 5.13 (5.22) proj_loss: -0.6007 (-0.6035) time: 0.6781 data: 0.0003 [11-25 17:09:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [1251/1669] eta: 0:04:42 tlr: 0.00012 tnm: 0.36 Lm: 6.502 (6.497) Lt: 5.720 (5.723) Accm: 3.20 (3.31) Acct: 5.11 (5.20) proj_loss: -0.5954 (-0.6016) time: 0.6781 data: 0.0003 [11-25 17:09:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [1251/1669] eta: 0:04:42 tlr: 0.00012 tnm: 0.36 Lm: 6.597 (6.565) Lt: 5.854 (5.821) Accm: 3.13 (3.30) Acct: 5.10 (5.26) proj_loss: -0.6019 (-0.6003) time: 0.6781 data: 0.0003 [11-25 17:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.38 Lm: 6.586 (6.570) Lt: 5.894 (5.836) Accm: 3.34 (3.33) Acct: 5.32 (5.28) proj_loss: -0.5988 (-0.6000) time: 0.6787 data: 0.0016 [11-25 17:14:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 194/350] Total time: 0:18:50 (0.678 s / it) [11-25 17:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.38 Lm: 6.480 (6.480) Lt: 5.733 (5.716) Accm: 3.62 (3.50) Acct: 5.79 (5.61) proj_loss: -0.5961 (-0.6031) time: 0.6788 data: 0.0017 [11-25 17:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.38 Lm: 6.573 (6.512) Lt: 5.847 (5.751) Accm: 3.23 (3.29) Acct: 4.94 (5.15) proj_loss: -0.5949 (-0.6003) time: 0.6788 data: 0.0017 [11-25 17:14:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 194/350] Total time: 0:18:50 (0.678 s / it) [11-25 17:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 194/350] [1668/1669] eta: 0:00:00 tlr: 0.00012 tnm: 0.38 Lm: 6.502 (6.499) Lt: 5.733 (5.728) Accm: 3.39 (3.39) Acct: 5.27 (5.34) proj_loss: -0.5969 (-0.6004) time: 0.6788 data: 0.0013 [11-25 17:14:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 194/350] Total time: 0:18:50 (0.678 s / it) [11-25 17:14:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 194/350] Total time: 0:18:50 (0.678 s / it) [11-25 17:14:42] (/home/user/VAR/train.py , line 276)=> [ep194] (training ) Lm: 6.502 (6.523), Lt: 5.742 (5.771), Acc m&t: 3.39 5.35, Remain: 2 days, 1:11:37, Finish: 2024-11-27 02:26 [11-25 17:14:42] (/home/user/VAR/train.py , line 276)=> [ep194] (training ) Lm: 6.502 (6.523), Lt: 5.742 (5.771), Acc m&t: 3.39 5.35, Remain: 2 days, 1:11:55, Finish: 2024-11-27 02:26 [11-25 17:14:42] (/home/user/VAR/train.py , line 276)=> [ep194] (training ) Lm: 6.502 (6.523), Lt: 5.742 (5.771), Acc m&t: 3.39 5.35, Remain: 2 days, 1:11:48, Finish: 2024-11-27 02:26 [11-25 17:14:42] (/home/user/VAR/train.py , line 276)=> [ep194] (training ) Lm: 6.502 (6.523), Lt: 5.742 (5.771), Acc m&t: 3.39 5.35, Remain: 2 days, 1:11:53, Finish: 2024-11-27 02:26 [11-25 17:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [ 0/1669] eta: 0:18:10 tlr: 0.00012 tnm: 0.36 Lm: 6.623 (6.623) Lt: 5.884 (5.884) Accm: 3.04 (3.04) Acct: 5.13 (5.13) proj_loss: -0.6141 (-0.6141) time: 0.6531 data: 0.0003 [11-25 17:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [ 0/1669] eta: 0:18:10 tlr: 0.00012 tnm: 0.36 Lm: 6.650 (6.650) Lt: 5.892 (5.892) Accm: 3.24 (3.24) Acct: 5.39 (5.39) proj_loss: -0.6312 (-0.6312) time: 0.6532 data: 0.0003 [11-25 17:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [ 0/1669] eta: 0:18:18 tlr: 0.00012 tnm: 0.36 Lm: 6.461 (6.461) Lt: 5.677 (5.677) Accm: 3.19 (3.19) Acct: 5.22 (5.22) proj_loss: -0.5727 (-0.5727) time: 0.6583 data: 0.0004 [11-25 17:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [ 0/1669] eta: 0:18:19 tlr: 0.00012 tnm: 0.36 Lm: 6.493 (6.493) Lt: 5.750 (5.750) Accm: 3.19 (3.19) Acct: 4.94 (4.94) proj_loss: -0.5780 (-0.5780) time: 0.6585 data: 0.0005 [11-25 17:19:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [ 417/1669] eta: 0:15:14 tlr: 0.00012 tnm: 0.38 Lm: 6.488 (6.488) Lt: 5.713 (5.713) Accm: 3.51 (3.51) Acct: 5.47 (5.47) proj_loss: -0.5890 (-0.5890) time: 0.6763 data: 0.0003 [11-25 17:19:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [ 417/1669] eta: 0:15:14 tlr: 0.00012 tnm: 0.38 Lm: 6.449 (6.449) Lt: 5.660 (5.660) Accm: 3.30 (3.30) Acct: 5.39 (5.39) proj_loss: -0.5810 (-0.5810) time: 0.6763 data: 0.0003 [11-25 17:19:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [ 417/1669] eta: 0:15:14 tlr: 0.00012 tnm: 0.38 Lm: 6.588 (6.588) Lt: 5.892 (5.892) Accm: 3.09 (3.09) Acct: 4.84 (4.84) proj_loss: -0.6186 (-0.6186) time: 0.6763 data: 0.0003 [11-25 17:19:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [ 417/1669] eta: 0:15:14 tlr: 0.00012 tnm: 0.38 Lm: 6.430 (6.430) Lt: 5.666 (5.666) Accm: 3.66 (3.66) Acct: 5.89 (5.89) proj_loss: -0.6217 (-0.6217) time: 0.6763 data: 0.0003 [11-25 17:24:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [ 834/1669] eta: 0:09:54 tlr: 0.00012 tnm: 0.36 Lm: 6.524 (6.461) Lt: 5.788 (5.706) Accm: 3.26 (3.53) Acct: 5.49 (5.76) proj_loss: -0.6122 (-0.6148) time: 0.6761 data: 0.0003 [11-25 17:24:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [ 834/1669] eta: 0:09:55 tlr: 0.00012 tnm: 0.36 Lm: 6.554 (6.561) Lt: 5.884 (5.837) Accm: 3.14 (3.21) Acct: 5.13 (5.13) proj_loss: -0.6141 (-0.6123) time: 0.6761 data: 0.0003 [11-25 17:24:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [ 834/1669] eta: 0:09:55 tlr: 0.00012 tnm: 0.36 Lm: 6.493 (6.532) Lt: 5.750 (5.771) Accm: 3.19 (3.23) Acct: 4.94 (5.11) proj_loss: -0.5780 (-0.5832) time: 0.6761 data: 0.0003 [11-25 17:24:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [ 834/1669] eta: 0:09:55 tlr: 0.00012 tnm: 0.36 Lm: 6.438 (6.434) Lt: 5.644 (5.636) Accm: 3.40 (3.46) Acct: 5.56 (5.60) proj_loss: -0.5893 (-0.5883) time: 0.6761 data: 0.0003 [11-25 17:29:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.37 Lm: 6.449 (6.445) Lt: 5.660 (5.657) Accm: 3.53 (3.51) Acct: 5.71 (5.66) proj_loss: -0.5962 (-0.5945) time: 0.6753 data: 0.0002 [11-25 17:29:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.37 Lm: 6.580 (6.505) Lt: 5.838 (5.752) Accm: 3.25 (3.35) Acct: 5.44 (5.49) proj_loss: -0.6082 (-0.6121) time: 0.6753 data: 0.0003 [11-25 17:29:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.37 Lm: 6.488 (6.466) Lt: 5.713 (5.697) Accm: 3.51 (3.46) Acct: 5.47 (5.51) proj_loss: -0.5890 (-0.5925) time: 0.6753 data: 0.0003 [11-25 17:29:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.37 Lm: 6.531 (6.471) Lt: 5.804 (5.737) Accm: 3.30 (3.49) Acct: 5.41 (5.48) proj_loss: -0.6070 (-0.6080) time: 0.6753 data: 0.0003 [11-25 17:34:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.36 Lm: 6.515 (6.480) Lt: 5.769 (5.744) Accm: 3.34 (3.46) Acct: 5.17 (5.41) proj_loss: -0.5998 (-0.6036) time: 0.6779 data: 0.0019 [11-25 17:34:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 195/350] Total time: 0:19:18 (0.694 s / it) [11-25 17:34:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.36 Lm: 6.524 (6.490) Lt: 5.788 (5.725) Accm: 3.26 (3.40) Acct: 5.49 (5.58) proj_loss: -0.6116 (-0.6120) time: 0.6779 data: 0.0019 [11-25 17:34:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.36 Lm: 6.484 (6.467) Lt: 5.677 (5.686) Accm: 3.83 (3.54) Acct: 5.99 (5.61) proj_loss: -0.5808 (-0.5901) time: 0.6779 data: 0.0018 [11-25 17:34:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 195/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.36 Lm: 6.461 (6.454) Lt: 5.677 (5.670) Accm: 3.42 (3.49) Acct: 5.56 (5.61) proj_loss: -0.5939 (-0.5944) time: 0.6779 data: 0.0020 [11-25 17:34:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 195/350] Total time: 0:19:18 (0.694 s / it) [11-25 17:34:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 195/350] Total time: 0:19:18 (0.694 s / it) [11-25 17:34:01] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 195/350] Total time: 0:19:18 (0.694 s / it) [11-25 17:34:01] (/home/user/VAR/train.py , line 276)=> [ep195] (training ) Lm: 6.502 (6.507), Lt: 5.742 (5.753), Acc m&t: 3.39 5.35, Remain: 2 days, 0:42:13, Finish: 2024-11-27 02:16 [11-25 17:34:01] (/home/user/VAR/train.py , line 276)=> [ep195] (training ) Lm: 6.502 (6.507), Lt: 5.742 (5.753), Acc m&t: 3.39 5.35, Remain: 2 days, 0:42:08, Finish: 2024-11-27 02:16 [11-25 17:34:01] (/home/user/VAR/train.py , line 276)=> [ep195] (training ) Lm: 6.502 (6.507), Lt: 5.742 (5.753), Acc m&t: 3.39 5.35, Remain: 2 days, 0:42:40, Finish: 2024-11-27 02:16 [11-25 17:34:01] (/home/user/VAR/train.py , line 276)=> [ep195] (training ) Lm: 6.502 (6.507), Lt: 5.742 (5.753), Acc m&t: 3.39 5.35, Remain: 2 days, 0:42:37, Finish: 2024-11-27 02:16 [11-25 17:34:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [ 0/1669] eta: 0:18:33 tlr: 0.00011 tnm: 0.35 Lm: 6.561 (6.561) Lt: 5.808 (5.808) Accm: 3.24 (3.24) Acct: 4.94 (4.94) proj_loss: -0.6060 (-0.6060) time: 0.6673 data: 0.0003 [11-25 17:34:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [ 0/1669] eta: 0:18:38 tlr: 0.00011 tnm: 0.35 Lm: 6.519 (6.519) Lt: 5.743 (5.743) Accm: 3.28 (3.28) Acct: 5.08 (5.08) proj_loss: -0.5799 (-0.5799) time: 0.6703 data: 0.0004 [11-25 17:34:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [ 0/1669] eta: 0:18:41 tlr: 0.00011 tnm: 0.35 Lm: 6.497 (6.497) Lt: 5.680 (5.680) Accm: 3.39 (3.39) Acct: 5.41 (5.41) proj_loss: -0.6054 (-0.6054) time: 0.6719 data: 0.0004 [11-25 17:34:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [ 0/1669] eta: 0:18:45 tlr: 0.00011 tnm: 0.35 Lm: 6.550 (6.550) Lt: 5.835 (5.835) Accm: 3.16 (3.16) Acct: 5.18 (5.18) proj_loss: -0.6013 (-0.6013) time: 0.6746 data: 0.0004 [11-25 17:38:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [ 417/1669] eta: 0:14:13 tlr: 0.00011 tnm: 0.37 Lm: 6.491 (6.491) Lt: 5.731 (5.731) Accm: 3.38 (3.38) Acct: 5.63 (5.63) proj_loss: -0.6069 (-0.6069) time: 0.7436 data: 0.0003 [11-25 17:38:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [ 417/1669] eta: 0:14:13 tlr: 0.00011 tnm: 0.37 Lm: 6.509 (6.509) Lt: 5.715 (5.715) Accm: 3.19 (3.19) Acct: 5.23 (5.23) proj_loss: -0.6076 (-0.6076) time: 0.7436 data: 0.0003 [11-25 17:38:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [ 417/1669] eta: 0:14:13 tlr: 0.00011 tnm: 0.37 Lm: 6.591 (6.591) Lt: 5.841 (5.841) Accm: 3.09 (3.09) Acct: 4.77 (4.77) proj_loss: -0.5893 (-0.5893) time: 0.7436 data: 0.0003 [11-25 17:38:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [ 417/1669] eta: 0:14:13 tlr: 0.00011 tnm: 0.37 Lm: 6.573 (6.573) Lt: 5.831 (5.831) Accm: 3.11 (3.11) Acct: 4.85 (4.85) proj_loss: -0.6029 (-0.6029) time: 0.7436 data: 0.0003 [11-25 17:43:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [ 834/1669] eta: 0:09:41 tlr: 0.00011 tnm: 0.37 Lm: 6.585 (6.582) Lt: 5.854 (5.839) Accm: 2.99 (3.06) Acct: 4.75 (4.80) proj_loss: -0.5997 (-0.5995) time: 0.7381 data: 0.0003 [11-25 17:43:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [ 834/1669] eta: 0:09:41 tlr: 0.00011 tnm: 0.37 Lm: 6.497 (6.470) Lt: 5.680 (5.658) Accm: 3.39 (3.38) Acct: 5.41 (5.46) proj_loss: -0.6054 (-0.5976) time: 0.7381 data: 0.0003 [11-25 17:43:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [ 834/1669] eta: 0:09:41 tlr: 0.00011 tnm: 0.37 Lm: 6.541 (6.574) Lt: 5.743 (5.807) Accm: 3.28 (3.17) Acct: 5.08 (4.94) proj_loss: -0.5975 (-0.5920) time: 0.7381 data: 0.0003 [11-25 17:43:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [ 834/1669] eta: 0:09:41 tlr: 0.00011 tnm: 0.37 Lm: 6.432 (6.441) Lt: 5.626 (5.662) Accm: 3.59 (3.61) Acct: 6.08 (5.95) proj_loss: -0.6064 (-0.6067) time: 0.7381 data: 0.0003 [11-25 17:48:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.39 Lm: 6.491 (6.471) Lt: 5.731 (5.717) Accm: 3.38 (3.50) Acct: 5.63 (5.64) proj_loss: -0.6064 (-0.6067) time: 0.6790 data: 0.0003 [11-25 17:48:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.39 Lm: 6.573 (6.548) Lt: 5.831 (5.792) Accm: 3.11 (3.13) Acct: 4.85 (4.92) proj_loss: -0.5962 (-0.5940) time: 0.6790 data: 0.0003 [11-25 17:48:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.39 Lm: 6.530 (6.528) Lt: 5.740 (5.760) Accm: 3.31 (3.33) Acct: 5.18 (5.14) proj_loss: -0.5966 (-0.5930) time: 0.6790 data: 0.0003 [11-25 17:48:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.39 Lm: 6.509 (6.524) Lt: 5.715 (5.731) Accm: 3.19 (3.20) Acct: 5.23 (5.18) proj_loss: -0.5938 (-0.5938) time: 0.6790 data: 0.0003 [11-25 17:53:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.497 (6.508) Lt: 5.690 (5.723) Accm: 3.39 (3.25) Acct: 5.29 (5.20) proj_loss: -0.6054 (-0.5962) time: 0.6787 data: 0.0024 [11-25 17:53:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 196/350] Total time: 0:19:22 (0.697 s / it) [11-25 17:53:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.432 (6.438) Lt: 5.626 (5.670) Accm: 3.59 (3.63) Acct: 6.08 (5.86) proj_loss: -0.6064 (-0.6056) time: 0.6787 data: 0.0016 [11-25 17:53:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.561 (6.527) Lt: 5.808 (5.767) Accm: 3.24 (3.24) Acct: 4.94 (5.04) proj_loss: -0.5949 (-0.5942) time: 0.6787 data: 0.0013 [11-25 17:53:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 196/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.519 (6.511) Lt: 5.737 (5.753) Accm: 3.34 (3.34) Acct: 5.17 (5.15) proj_loss: -0.5975 (-0.5972) time: 0.6787 data: 0.0016 [11-25 17:53:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 196/350] Total time: 0:19:22 (0.697 s / it) [11-25 17:53:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 196/350] Total time: 0:19:22 (0.697 s / it) [11-25 17:53:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 196/350] Total time: 0:19:22 (0.697 s / it) [11-25 17:53:24] (/home/user/VAR/train.py , line 276)=> [ep196] (training ) Lm: 6.502 (6.512), Lt: 5.742 (5.757), Acc m&t: 3.39 5.35, Remain: 2 days, 0:28:14, Finish: 2024-11-27 02:21 [11-25 17:53:24] (/home/user/VAR/train.py , line 276)=> [ep196] (training ) Lm: 6.502 (6.512), Lt: 5.742 (5.757), Acc m&t: 3.39 5.35, Remain: 2 days, 0:27:36, Finish: 2024-11-27 02:21 [11-25 17:53:24] (/home/user/VAR/train.py , line 276)=> [ep196] (training ) Lm: 6.502 (6.512), Lt: 5.742 (5.757), Acc m&t: 3.39 5.35, Remain: 2 days, 0:28:11, Finish: 2024-11-27 02:21 [11-25 17:53:24] (/home/user/VAR/train.py , line 276)=> [ep196] (training ) Lm: 6.502 (6.512), Lt: 5.742 (5.757), Acc m&t: 3.39 5.35, Remain: 2 days, 0:27:16, Finish: 2024-11-27 02:20 [11-25 17:53:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [ 0/1669] eta: 0:18:29 tlr: 0.00011 tnm: 0.40 Lm: 6.550 (6.550) Lt: 5.809 (5.809) Accm: 3.19 (3.19) Acct: 4.79 (4.79) proj_loss: -0.5813 (-0.5813) time: 0.6649 data: 0.0004 [11-25 17:53:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [ 0/1669] eta: 0:18:30 tlr: 0.00011 tnm: 0.40 Lm: 6.404 (6.404) Lt: 5.562 (5.562) Accm: 3.98 (3.98) Acct: 6.30 (6.30) proj_loss: -0.5732 (-0.5732) time: 0.6651 data: 0.0003 [11-25 17:53:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [ 0/1669] eta: 0:18:30 tlr: 0.00011 tnm: 0.40 Lm: 6.446 (6.446) Lt: 5.638 (5.638) Accm: 3.61 (3.61) Acct: 5.72 (5.72) proj_loss: -0.5912 (-0.5912) time: 0.6651 data: 0.0004 [11-25 17:53:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [ 0/1669] eta: 0:18:30 tlr: 0.00011 tnm: 0.40 Lm: 6.389 (6.389) Lt: 5.628 (5.628) Accm: 3.43 (3.43) Acct: 4.99 (4.99) proj_loss: -0.5896 (-0.5896) time: 0.6652 data: 0.0004 [11-25 17:58:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [ 417/1669] eta: 0:14:07 tlr: 0.00011 tnm: 0.36 Lm: 6.546 (6.546) Lt: 5.825 (5.825) Accm: 3.06 (3.06) Acct: 4.56 (4.56) proj_loss: -0.5908 (-0.5908) time: 0.6768 data: 0.0003 [11-25 17:58:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [ 417/1669] eta: 0:14:07 tlr: 0.00011 tnm: 0.36 Lm: 6.475 (6.475) Lt: 5.697 (5.697) Accm: 3.52 (3.52) Acct: 5.46 (5.46) proj_loss: -0.5831 (-0.5831) time: 0.6768 data: 0.0003 [11-25 17:58:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [ 417/1669] eta: 0:14:07 tlr: 0.00011 tnm: 0.36 Lm: 6.542 (6.542) Lt: 5.784 (5.784) Accm: 3.32 (3.32) Acct: 5.20 (5.20) proj_loss: -0.6026 (-0.6026) time: 0.6768 data: 0.0003 [11-25 17:58:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [ 417/1669] eta: 0:14:07 tlr: 0.00011 tnm: 0.36 Lm: 6.497 (6.497) Lt: 5.712 (5.712) Accm: 3.44 (3.44) Acct: 5.28 (5.28) proj_loss: -0.5820 (-0.5820) time: 0.6768 data: 0.0003 [11-25 18:02:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [ 834/1669] eta: 0:09:25 tlr: 0.00011 tnm: 0.37 Lm: 6.522 (6.505) Lt: 5.734 (5.719) Accm: 3.28 (3.38) Acct: 5.29 (5.28) proj_loss: -0.5828 (-0.5832) time: 0.6759 data: 0.0003 [11-25 18:02:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [ 834/1669] eta: 0:09:25 tlr: 0.00011 tnm: 0.37 Lm: 6.568 (6.553) Lt: 5.798 (5.816) Accm: 3.11 (3.08) Acct: 4.99 (4.73) proj_loss: -0.5896 (-0.5894) time: 0.6758 data: 0.0003 [11-25 18:02:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [ 834/1669] eta: 0:09:25 tlr: 0.00011 tnm: 0.37 Lm: 6.561 (6.549) Lt: 5.815 (5.794) Accm: 3.39 (3.34) Acct: 5.37 (5.26) proj_loss: -0.6121 (-0.6058) time: 0.6759 data: 0.0003 [11-25 18:02:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [ 834/1669] eta: 0:09:25 tlr: 0.00011 tnm: 0.37 Lm: 6.542 (6.497) Lt: 5.770 (5.721) Accm: 3.57 (3.54) Acct: 5.70 (5.54) proj_loss: -0.5930 (-0.5891) time: 0.6758 data: 0.0003 [11-25 18:07:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [1251/1669] eta: 0:04:42 tlr: 0.00011 tnm: 0.37 Lm: 6.544 (6.526) Lt: 5.801 (5.767) Accm: 3.32 (3.36) Acct: 5.16 (5.26) proj_loss: -0.5970 (-0.5954) time: 0.6766 data: 0.0003 [11-25 18:07:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [1251/1669] eta: 0:04:42 tlr: 0.00011 tnm: 0.37 Lm: 6.560 (6.553) Lt: 5.759 (5.792) Accm: 3.27 (3.17) Acct: 5.03 (5.01) proj_loss: -0.5908 (-0.5907) time: 0.6766 data: 0.0003 [11-25 18:07:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [1251/1669] eta: 0:04:42 tlr: 0.00011 tnm: 0.37 Lm: 6.536 (6.525) Lt: 5.771 (5.752) Accm: 3.23 (3.30) Acct: 5.15 (5.21) proj_loss: -0.5820 (-0.5814) time: 0.6766 data: 0.0003 [11-25 18:07:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [1251/1669] eta: 0:04:42 tlr: 0.00011 tnm: 0.37 Lm: 6.573 (6.558) Lt: 5.818 (5.801) Accm: 3.29 (3.30) Acct: 5.23 (5.21) proj_loss: -0.6070 (-0.6048) time: 0.6766 data: 0.0002 [11-25 18:12:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.36 Lm: 6.585 (6.565) Lt: 5.821 (5.805) Accm: 3.18 (3.27) Acct: 5.08 (5.17) proj_loss: -0.6026 (-0.6044) time: 0.6791 data: 0.0018 [11-25 18:12:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.36 Lm: 6.542 (6.521) Lt: 5.770 (5.762) Accm: 3.17 (3.32) Acct: 5.25 (5.26) proj_loss: -0.5946 (-0.5953) time: 0.6791 data: 0.0017 [11-25 18:12:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 197/350] Total time: 0:18:50 (0.678 s / it) [11-25 18:12:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.36 Lm: 6.551 (6.531) Lt: 5.719 (5.769) Accm: 3.43 (3.27) Acct: 5.06 (5.18) proj_loss: -0.5921 (-0.5960) time: 0.6791 data: 0.0021 [11-25 18:12:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 197/350] Total time: 0:18:50 (0.678 s / it) [11-25 18:12:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 197/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.36 Lm: 6.522 (6.513) Lt: 5.734 (5.737) Accm: 3.28 (3.34) Acct: 5.29 (5.28) proj_loss: -0.5828 (-0.5860) time: 0.6792 data: 0.0020 [11-25 18:12:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 197/350] Total time: 0:18:50 (0.678 s / it) [11-25 18:12:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 197/350] Total time: 0:18:50 (0.678 s / it) [11-25 18:12:15] (/home/user/VAR/train.py , line 276)=> [ep197] (training ) Lm: 6.502 (6.511), Lt: 5.742 (5.755), Acc m&t: 3.39 5.35, Remain: 2 days, 0:12:04, Finish: 2024-11-27 02:24 [11-25 18:12:15] (/home/user/VAR/train.py , line 276)=> [ep197] (training ) Lm: 6.502 (6.511), Lt: 5.742 (5.755), Acc m&t: 3.39 5.35, Remain: 2 days, 0:11:48, Finish: 2024-11-27 02:24 [11-25 18:12:15] (/home/user/VAR/train.py , line 276)=> [ep197] (training ) Lm: 6.502 (6.511), Lt: 5.742 (5.755), Acc m&t: 3.39 5.35, Remain: 2 days, 0:11:14, Finish: 2024-11-27 02:23 [11-25 18:12:15] (/home/user/VAR/train.py , line 276)=> [ep197] (training ) Lm: 6.502 (6.511), Lt: 5.742 (5.755), Acc m&t: 3.39 5.35, Remain: 2 days, 0:11:32, Finish: 2024-11-27 02:23 [11-25 18:12:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [ 0/1669] eta: 0:18:28 tlr: 0.00011 tnm: 0.38 Lm: 6.539 (6.539) Lt: 5.817 (5.817) Accm: 3.18 (3.18) Acct: 4.80 (4.80) proj_loss: -0.6061 (-0.6061) time: 0.6642 data: 0.0004 [11-25 18:12:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [ 0/1669] eta: 0:18:28 tlr: 0.00011 tnm: 0.38 Lm: 6.513 (6.513) Lt: 5.748 (5.748) Accm: 3.22 (3.22) Acct: 5.37 (5.37) proj_loss: -0.5872 (-0.5872) time: 0.6640 data: 0.0004 [11-25 18:12:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [ 0/1669] eta: 0:18:29 tlr: 0.00011 tnm: 0.38 Lm: 6.418 (6.418) Lt: 5.641 (5.641) Accm: 3.65 (3.65) Acct: 5.54 (5.54) proj_loss: -0.6171 (-0.6171) time: 0.6645 data: 0.0003 [11-25 18:12:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [ 0/1669] eta: 0:18:29 tlr: 0.00011 tnm: 0.38 Lm: 6.449 (6.449) Lt: 5.661 (5.661) Accm: 3.74 (3.74) Acct: 5.82 (5.82) proj_loss: -0.5906 (-0.5906) time: 0.6646 data: 0.0003 [11-25 18:17:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [ 417/1669] eta: 0:15:11 tlr: 0.00011 tnm: 0.38 Lm: 6.423 (6.423) Lt: 5.675 (5.675) Accm: 3.64 (3.64) Acct: 5.74 (5.74) proj_loss: -0.6020 (-0.6020) time: 0.6770 data: 0.0003 [11-25 18:17:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [ 417/1669] eta: 0:15:11 tlr: 0.00011 tnm: 0.38 Lm: 6.486 (6.486) Lt: 5.747 (5.747) Accm: 3.41 (3.41) Acct: 5.23 (5.23) proj_loss: -0.6056 (-0.6056) time: 0.6770 data: 0.0003 [11-25 18:17:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [ 417/1669] eta: 0:15:11 tlr: 0.00011 tnm: 0.38 Lm: 6.505 (6.505) Lt: 5.754 (5.754) Accm: 3.30 (3.30) Acct: 5.10 (5.10) proj_loss: -0.6096 (-0.6096) time: 0.6770 data: 0.0003 [11-25 18:17:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [ 417/1669] eta: 0:15:11 tlr: 0.00011 tnm: 0.38 Lm: 6.467 (6.467) Lt: 5.710 (5.710) Accm: 3.47 (3.47) Acct: 5.64 (5.64) proj_loss: -0.5977 (-0.5977) time: 0.6770 data: 0.0003 [11-25 18:22:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [ 834/1669] eta: 0:09:55 tlr: 0.00011 tnm: 0.38 Lm: 6.421 (6.431) Lt: 5.672 (5.677) Accm: 3.72 (3.66) Acct: 5.91 (5.88) proj_loss: -0.6083 (-0.6039) time: 0.6798 data: 0.0003 [11-25 18:22:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [ 834/1669] eta: 0:09:55 tlr: 0.00011 tnm: 0.38 Lm: 6.539 (6.512) Lt: 5.817 (5.772) Accm: 3.18 (3.29) Acct: 4.84 (5.10) proj_loss: -0.6061 (-0.6090) time: 0.6798 data: 0.0003 [11-25 18:22:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [ 834/1669] eta: 0:09:55 tlr: 0.00011 tnm: 0.38 Lm: 6.418 (6.466) Lt: 5.641 (5.706) Accm: 3.65 (3.49) Acct: 5.54 (5.53) proj_loss: -0.6171 (-0.6181) time: 0.6798 data: 0.0003 [11-25 18:22:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [ 834/1669] eta: 0:09:55 tlr: 0.00011 tnm: 0.38 Lm: 6.432 (6.426) Lt: 5.661 (5.657) Accm: 3.54 (3.56) Acct: 5.66 (5.66) proj_loss: -0.6014 (-0.6018) time: 0.6798 data: 0.0002 [11-25 18:26:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [1251/1669] eta: 0:04:53 tlr: 0.00011 tnm: 0.37 Lm: 6.552 (6.552) Lt: 5.819 (5.817) Accm: 3.11 (3.18) Acct: 4.82 (4.97) proj_loss: -0.6056 (-0.6053) time: 0.6772 data: 0.0003 [11-25 18:26:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [1251/1669] eta: 0:04:53 tlr: 0.00011 tnm: 0.37 Lm: 6.440 (6.451) Lt: 5.675 (5.692) Accm: 3.53 (3.55) Acct: 5.58 (5.57) proj_loss: -0.6060 (-0.6040) time: 0.6772 data: 0.0003 [11-25 18:26:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [1251/1669] eta: 0:04:53 tlr: 0.00011 tnm: 0.37 Lm: 6.403 (6.436) Lt: 5.625 (5.653) Accm: 3.76 (3.59) Acct: 5.73 (5.63) proj_loss: -0.6205 (-0.6196) time: 0.6772 data: 0.0003 [11-25 18:26:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [1251/1669] eta: 0:04:53 tlr: 0.00011 tnm: 0.37 Lm: 6.467 (6.474) Lt: 5.710 (5.712) Accm: 3.47 (3.50) Acct: 5.64 (5.68) proj_loss: -0.6102 (-0.6059) time: 0.6772 data: 0.0003 [11-25 18:31:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.40 Lm: 6.444 (6.468) Lt: 5.672 (5.702) Accm: 3.54 (3.51) Acct: 5.58 (5.66) proj_loss: -0.6083 (-0.6052) time: 0.6825 data: 0.0015 [11-25 18:31:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 198/350] Total time: 0:19:20 (0.696 s / it) [11-25 18:31:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.40 Lm: 6.551 (6.552) Lt: 5.817 (5.803) Accm: 3.18 (3.21) Acct: 4.84 (5.05) proj_loss: -0.6051 (-0.5972) time: 0.6825 data: 0.0017 [11-25 18:31:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.40 Lm: 6.418 (6.444) Lt: 5.641 (5.678) Accm: 3.65 (3.59) Acct: 5.54 (5.56) proj_loss: -0.6239 (-0.6212) time: 0.6825 data: 0.0026 [11-25 18:31:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 198/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.40 Lm: 6.449 (6.482) Lt: 5.690 (5.728) Accm: 3.52 (3.50) Acct: 5.49 (5.55) proj_loss: -0.6014 (-0.6004) time: 0.6825 data: 0.0016 [11-25 18:31:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 198/350] Total time: 0:19:20 (0.696 s / it) [11-25 18:31:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 198/350] Total time: 0:19:20 (0.696 s / it) [11-25 18:31:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 198/350] Total time: 0:19:20 (0.696 s / it) [11-25 18:31:36] (/home/user/VAR/train.py , line 276)=> [ep198] (training ) Lm: 6.502 (6.507), Lt: 5.742 (5.749), Acc m&t: 3.39 5.35, Remain: 2 days, 0:04:48, Finish: 2024-11-27 02:36 [11-25 18:31:36] (/home/user/VAR/train.py , line 276)=> [ep198] (training ) Lm: 6.502 (6.507), Lt: 5.742 (5.749), Acc m&t: 3.39 5.35, Remain: 2 days, 0:05:05, Finish: 2024-11-27 02:36 [11-25 18:31:36] (/home/user/VAR/train.py , line 276)=> [ep198] (training ) Lm: 6.502 (6.507), Lt: 5.742 (5.749), Acc m&t: 3.39 5.35, Remain: 2 days, 0:05:24, Finish: 2024-11-27 02:37 [11-25 18:31:36] (/home/user/VAR/train.py , line 276)=> [ep198] (training ) Lm: 6.502 (6.507), Lt: 5.742 (5.749), Acc m&t: 3.39 5.35, Remain: 2 days, 0:05:20, Finish: 2024-11-27 02:36 [11-25 18:31:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [ 0/1669] eta: 0:18:39 tlr: 0.00011 tnm: 0.37 Lm: 6.529 (6.529) Lt: 5.778 (5.778) Accm: 3.50 (3.50) Acct: 5.77 (5.77) proj_loss: -0.5891 (-0.5891) time: 0.6707 data: 0.0004 [11-25 18:31:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [ 0/1669] eta: 0:18:40 tlr: 0.00011 tnm: 0.37 Lm: 6.180 (6.180) Lt: 5.354 (5.354) Accm: 4.36 (4.36) Acct: 6.87 (6.87) proj_loss: -0.5986 (-0.5986) time: 0.6711 data: 0.0003 [11-25 18:31:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [ 0/1669] eta: 0:18:40 tlr: 0.00011 tnm: 0.37 Lm: 6.457 (6.457) Lt: 5.621 (5.621) Accm: 3.61 (3.61) Acct: 5.72 (5.72) proj_loss: -0.5996 (-0.5996) time: 0.6715 data: 0.0004 [11-25 18:31:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [ 0/1669] eta: 0:18:41 tlr: 0.00011 tnm: 0.37 Lm: 6.323 (6.323) Lt: 5.582 (5.582) Accm: 3.93 (3.93) Acct: 5.87 (5.87) proj_loss: -0.6073 (-0.6073) time: 0.6717 data: 0.0004 [11-25 18:36:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [ 417/1669] eta: 0:14:12 tlr: 0.00011 tnm: 0.40 Lm: 6.476 (6.476) Lt: 5.734 (5.734) Accm: 3.36 (3.36) Acct: 5.01 (5.01) proj_loss: -0.6035 (-0.6035) time: 0.7426 data: 0.0003 [11-25 18:36:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [ 417/1669] eta: 0:14:12 tlr: 0.00011 tnm: 0.40 Lm: 6.469 (6.469) Lt: 5.674 (5.674) Accm: 3.43 (3.43) Acct: 5.35 (5.35) proj_loss: -0.5965 (-0.5965) time: 0.7426 data: 0.0003 [11-25 18:36:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [ 417/1669] eta: 0:14:12 tlr: 0.00011 tnm: 0.40 Lm: 6.559 (6.559) Lt: 5.781 (5.781) Accm: 3.29 (3.29) Acct: 5.30 (5.30) proj_loss: -0.5927 (-0.5927) time: 0.7426 data: 0.0003 [11-25 18:36:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [ 417/1669] eta: 0:14:12 tlr: 0.00011 tnm: 0.40 Lm: 6.375 (6.375) Lt: 5.592 (5.592) Accm: 3.77 (3.77) Acct: 6.00 (6.00) proj_loss: -0.5834 (-0.5834) time: 0.7426 data: 0.0003 [11-25 18:41:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [ 834/1669] eta: 0:09:39 tlr: 0.00011 tnm: 0.38 Lm: 6.355 (6.369) Lt: 5.464 (5.549) Accm: 3.81 (3.79) Acct: 5.92 (5.97) proj_loss: -0.5966 (-0.5878) time: 0.6755 data: 0.0003 [11-25 18:41:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [ 834/1669] eta: 0:09:39 tlr: 0.00011 tnm: 0.38 Lm: 6.589 (6.573) Lt: 5.783 (5.795) Accm: 3.21 (3.26) Acct: 5.17 (5.26) proj_loss: -0.5949 (-0.5934) time: 0.6755 data: 0.0003 [11-25 18:41:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [ 834/1669] eta: 0:09:39 tlr: 0.00011 tnm: 0.38 Lm: 6.481 (6.505) Lt: 5.728 (5.723) Accm: 3.25 (3.34) Acct: 4.98 (5.17) proj_loss: -0.5996 (-0.6028) time: 0.6755 data: 0.0003 [11-25 18:41:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [ 834/1669] eta: 0:09:39 tlr: 0.00011 tnm: 0.38 Lm: 6.506 (6.486) Lt: 5.777 (5.748) Accm: 3.38 (3.37) Acct: 5.37 (5.13) proj_loss: -0.6034 (-0.6035) time: 0.6755 data: 0.0003 [11-25 18:46:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.40 Lm: 6.567 (6.524) Lt: 5.832 (5.792) Accm: 3.11 (3.23) Acct: 4.98 (5.00) proj_loss: -0.6016 (-0.5995) time: 0.6768 data: 0.0003 [11-25 18:46:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.40 Lm: 6.530 (6.549) Lt: 5.774 (5.786) Accm: 3.21 (3.22) Acct: 4.90 (4.98) proj_loss: -0.6075 (-0.6098) time: 0.6768 data: 0.0003 [11-25 18:46:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.40 Lm: 6.559 (6.539) Lt: 5.781 (5.758) Accm: 3.35 (3.35) Acct: 5.46 (5.38) proj_loss: -0.5956 (-0.5970) time: 0.6768 data: 0.0003 [11-25 18:46:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.40 Lm: 6.420 (6.398) Lt: 5.609 (5.600) Accm: 3.55 (3.66) Acct: 5.53 (5.70) proj_loss: -0.5976 (-0.5915) time: 0.6768 data: 0.0003 [11-25 18:50:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.37 Lm: 6.485 (6.422) Lt: 5.754 (5.648) Accm: 3.42 (3.61) Acct: 5.13 (5.51) proj_loss: -0.5986 (-0.5987) time: 0.6772 data: 0.0020 [11-25 18:50:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 199/350] Total time: 0:19:19 (0.695 s / it) [11-25 18:50:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.37 Lm: 6.529 (6.513) Lt: 5.778 (5.722) Accm: 3.50 (3.41) Acct: 5.75 (5.47) proj_loss: -0.5949 (-0.5936) time: 0.6772 data: 0.0013 [11-25 18:50:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.37 Lm: 6.506 (6.512) Lt: 5.777 (5.771) Accm: 3.38 (3.32) Acct: 5.37 (5.17) proj_loss: -0.5998 (-0.5951) time: 0.6772 data: 0.0017 [11-25 18:50:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 199/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.37 Lm: 6.481 (6.511) Lt: 5.728 (5.747) Accm: 3.25 (3.35) Acct: 4.98 (5.19) proj_loss: -0.5996 (-0.6070) time: 0.6772 data: 0.0016 [11-25 18:50:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 199/350] Total time: 0:19:19 (0.695 s / it) [11-25 18:50:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 199/350] Total time: 0:19:19 (0.695 s / it) [11-25 18:50:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 199/350] Total time: 0:19:19 (0.695 s / it) [11-25 18:53:18] (home/user/VAR/trainer.py, line 114)=> FID: 3.5862756536782854 [11-25 18:53:19] (/home/user/VAR/train.py , line 259)=> [*] [ep199] (val 50000) Lm: 6.5078, Lt: 5.7530, Acc m&t: 3.36 5.29, Val cost: 142.85s [11-25 18:53:19] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-25 18:53:42] (/home/user/VAR/train.py , line 276)=> [ep199] (training ) Lm: 6.502 (6.508), Lt: 5.742 (5.753), Acc m&t: 3.39 5.35, Remain: 1 day, 23:35:32, Finish: 2024-11-27 02:26 [11-25 18:53:42] (/home/user/VAR/train.py , line 276)=> [ep199] (training ) Lm: 6.502 (6.508), Lt: 5.742 (5.753), Acc m&t: 3.39 5.35, Remain: 1 day, 23:35:30, Finish: 2024-11-27 02:26 [11-25 18:53:42] (/home/user/VAR/train.py , line 276)=> [ep199] (training ) Lm: 6.502 (6.508), Lt: 5.742 (5.753), Acc m&t: 3.39 5.35, Remain: 1 day, 23:35:01, Finish: 2024-11-27 02:25 [11-25 18:53:42] (/home/user/VAR/train.py , line 276)=> [ep199] (training ) Lm: 6.502 (6.508), Lt: 5.742 (5.753), Acc m&t: 3.39 5.35, Remain: 1 day, 23:35:35, Finish: 2024-11-27 02:26 [11-25 18:53:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [ 0/1669] eta: 0:19:14 tlr: 0.00011 tnm: 0.37 Lm: 6.507 (6.507) Lt: 5.775 (5.775) Accm: 3.75 (3.75) Acct: 5.77 (5.77) proj_loss: -0.6092 (-0.6092) time: 0.6918 data: 0.0004 [11-25 18:53:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [ 0/1669] eta: 0:19:14 tlr: 0.00011 tnm: 0.37 Lm: 6.621 (6.621) Lt: 5.866 (5.866) Accm: 3.01 (3.01) Acct: 4.94 (4.94) proj_loss: -0.5939 (-0.5939) time: 0.6918 data: 0.0004 [11-25 18:53:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [ 0/1669] eta: 0:19:14 tlr: 0.00011 tnm: 0.37 Lm: 6.493 (6.493) Lt: 5.713 (5.713) Accm: 3.44 (3.44) Acct: 5.35 (5.35) proj_loss: -0.5953 (-0.5953) time: 0.6917 data: 0.0004 [11-25 18:53:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [ 0/1669] eta: 0:19:13 tlr: 0.00011 tnm: 0.37 Lm: 6.434 (6.434) Lt: 5.630 (5.630) Accm: 3.51 (3.51) Acct: 5.73 (5.73) proj_loss: -0.6003 (-0.6003) time: 0.6912 data: 0.0004 [11-25 18:58:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [ 417/1669] eta: 0:14:12 tlr: 0.00011 tnm: 0.37 Lm: 6.522 (6.522) Lt: 5.753 (5.753) Accm: 3.20 (3.20) Acct: 4.92 (4.92) proj_loss: -0.6042 (-0.6042) time: 0.6754 data: 0.0003 [11-25 18:58:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [ 417/1669] eta: 0:14:12 tlr: 0.00011 tnm: 0.37 Lm: 6.509 (6.509) Lt: 5.769 (5.769) Accm: 3.67 (3.67) Acct: 5.62 (5.62) proj_loss: -0.6045 (-0.6045) time: 0.6754 data: 0.0003 [11-25 18:58:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [ 417/1669] eta: 0:14:12 tlr: 0.00011 tnm: 0.37 Lm: 6.599 (6.599) Lt: 5.830 (5.830) Accm: 3.21 (3.21) Acct: 5.17 (5.17) proj_loss: -0.5871 (-0.5871) time: 0.6754 data: 0.0003 [11-25 18:58:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [ 417/1669] eta: 0:14:12 tlr: 0.00011 tnm: 0.37 Lm: 6.509 (6.509) Lt: 5.748 (5.748) Accm: 3.33 (3.33) Acct: 5.40 (5.40) proj_loss: -0.6052 (-0.6052) time: 0.6754 data: 0.0003 [11-25 19:03:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [ 834/1669] eta: 0:09:26 tlr: 0.00011 tnm: 0.37 Lm: 6.465 (6.494) Lt: 5.664 (5.720) Accm: 3.43 (3.36) Acct: 5.68 (5.49) proj_loss: -0.5990 (-0.6032) time: 0.6757 data: 0.0003 [11-25 19:03:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [ 834/1669] eta: 0:09:26 tlr: 0.00011 tnm: 0.37 Lm: 6.511 (6.537) Lt: 5.775 (5.794) Accm: 3.58 (3.48) Acct: 5.48 (5.43) proj_loss: -0.5998 (-0.6016) time: 0.6757 data: 0.0003 [11-25 19:03:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [ 834/1669] eta: 0:09:26 tlr: 0.00011 tnm: 0.37 Lm: 6.594 (6.546) Lt: 5.872 (5.793) Accm: 3.07 (3.16) Acct: 4.94 (4.93) proj_loss: -0.6003 (-0.6028) time: 0.6757 data: 0.0003 [11-25 19:03:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [ 834/1669] eta: 0:09:26 tlr: 0.00011 tnm: 0.37 Lm: 6.528 (6.575) Lt: 5.828 (5.829) Accm: 3.12 (3.18) Acct: 4.98 (5.10) proj_loss: -0.5953 (-0.5913) time: 0.6757 data: 0.0003 [11-25 19:07:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [1251/1669] eta: 0:04:43 tlr: 0.00011 tnm: 0.39 Lm: 6.512 (6.556) Lt: 5.775 (5.803) Accm: 3.28 (3.26) Acct: 5.14 (5.15) proj_loss: -0.5974 (-0.5937) time: 0.6748 data: 0.0003 [11-25 19:07:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [1251/1669] eta: 0:04:43 tlr: 0.00011 tnm: 0.39 Lm: 6.430 (6.447) Lt: 5.647 (5.672) Accm: 3.54 (3.57) Acct: 5.77 (5.78) proj_loss: -0.6062 (-0.6057) time: 0.6748 data: 0.0003 [11-25 19:07:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [1251/1669] eta: 0:04:43 tlr: 0.00011 tnm: 0.39 Lm: 6.595 (6.558) Lt: 5.874 (5.815) Accm: 3.15 (3.17) Acct: 5.01 (4.97) proj_loss: -0.6001 (-0.5985) time: 0.6748 data: 0.0003 [11-25 19:07:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [1251/1669] eta: 0:04:43 tlr: 0.00011 tnm: 0.39 Lm: 6.509 (6.512) Lt: 5.769 (5.762) Accm: 3.58 (3.50) Acct: 5.61 (5.51) proj_loss: -0.6045 (-0.6067) time: 0.6748 data: 0.0003 [11-25 19:12:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.40 Lm: 6.511 (6.519) Lt: 5.764 (5.760) Accm: 3.58 (3.50) Acct: 5.54 (5.52) proj_loss: -0.6044 (-0.6062) time: 0.6785 data: 0.0017 [11-25 19:12:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 200/350] Total time: 0:18:52 (0.678 s / it) [11-25 19:12:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.40 Lm: 6.458 (6.449) Lt: 5.664 (5.683) Accm: 3.48 (3.55) Acct: 5.68 (5.74) proj_loss: -0.6061 (-0.6058) time: 0.6785 data: 0.0017 [11-25 19:12:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.40 Lm: 6.497 (6.533) Lt: 5.723 (5.783) Accm: 3.44 (3.30) Acct: 5.30 (5.20) proj_loss: -0.5953 (-0.5928) time: 0.6785 data: 0.0019 [11-25 19:12:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 200/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.40 Lm: 6.594 (6.537) Lt: 5.872 (5.788) Accm: 3.22 (3.29) Acct: 5.08 (5.17) proj_loss: -0.6003 (-0.5995) time: 0.6785 data: 0.0022 [11-25 19:12:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 200/350] Total time: 0:18:52 (0.678 s / it) [11-25 19:12:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 200/350] Total time: 0:18:52 (0.678 s / it) [11-25 19:12:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 200/350] Total time: 0:18:52 (0.678 s / it) [11-25 19:12:34] (/home/user/VAR/train.py , line 276)=> [ep200] (training ) Lm: 6.502 (6.511), Lt: 5.742 (5.755), Acc m&t: 3.39 5.35, Remain: 1 day, 23:13:43, Finish: 2024-11-27 02:26 [11-25 19:12:34] (/home/user/VAR/train.py , line 276)=> [ep200] (training ) Lm: 6.502 (6.511), Lt: 5.742 (5.755), Acc m&t: 3.39 5.35, Remain: 1 day, 23:13:41, Finish: 2024-11-27 02:26 [11-25 19:12:34] (/home/user/VAR/train.py , line 276)=> [ep200] (training ) Lm: 6.502 (6.511), Lt: 5.742 (5.755), Acc m&t: 3.39 5.35, Remain: 1 day, 23:13:53, Finish: 2024-11-27 02:26 [11-25 19:12:34] (/home/user/VAR/train.py , line 276)=> [ep200] (training ) Lm: 6.502 (6.511), Lt: 5.742 (5.755), Acc m&t: 3.39 5.35, Remain: 1 day, 23:13:21, Finish: 2024-11-27 02:25 [11-25 19:12:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [ 0/1669] eta: 0:18:26 tlr: 0.00011 tnm: 0.37 Lm: 6.507 (6.507) Lt: 5.754 (5.754) Accm: 3.16 (3.16) Acct: 4.87 (4.87) proj_loss: -0.5899 (-0.5899) time: 0.6632 data: 0.0004 [11-25 19:12:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [ 0/1669] eta: 0:18:27 tlr: 0.00011 tnm: 0.37 Lm: 6.576 (6.576) Lt: 5.776 (5.776) Accm: 2.91 (2.91) Acct: 4.73 (4.73) proj_loss: -0.5899 (-0.5899) time: 0.6633 data: 0.0003 [11-25 19:12:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [ 0/1669] eta: 0:17:58 tlr: 0.00011 tnm: 0.37 Lm: 6.483 (6.483) Lt: 5.699 (5.699) Accm: 3.42 (3.42) Acct: 5.49 (5.49) proj_loss: -0.5893 (-0.5893) time: 0.6460 data: 0.0004 [11-25 19:12:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [ 0/1669] eta: 0:17:58 tlr: 0.00011 tnm: 0.37 Lm: 6.558 (6.558) Lt: 5.839 (5.839) Accm: 3.06 (3.06) Acct: 4.77 (4.77) proj_loss: -0.6287 (-0.6287) time: 0.6464 data: 0.0003 [11-25 19:17:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [ 417/1669] eta: 0:15:11 tlr: 0.00011 tnm: 0.39 Lm: 6.540 (6.540) Lt: 5.805 (5.805) Accm: 3.18 (3.18) Acct: 4.81 (4.81) proj_loss: -0.6133 (-0.6133) time: 0.6767 data: 0.0003 [11-25 19:17:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [ 417/1669] eta: 0:15:11 tlr: 0.00011 tnm: 0.39 Lm: 6.457 (6.457) Lt: 5.690 (5.690) Accm: 3.44 (3.44) Acct: 5.45 (5.45) proj_loss: -0.5890 (-0.5890) time: 0.6766 data: 0.0003 [11-25 19:17:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [ 417/1669] eta: 0:15:11 tlr: 0.00011 tnm: 0.39 Lm: 6.478 (6.478) Lt: 5.731 (5.731) Accm: 3.57 (3.57) Acct: 5.70 (5.70) proj_loss: -0.5956 (-0.5956) time: 0.6766 data: 0.0003 [11-25 19:17:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [ 417/1669] eta: 0:15:11 tlr: 0.00011 tnm: 0.39 Lm: 6.597 (6.597) Lt: 5.803 (5.803) Accm: 3.04 (3.04) Acct: 4.89 (4.89) proj_loss: -0.5865 (-0.5865) time: 0.6766 data: 0.0003 [11-25 19:22:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [ 834/1669] eta: 0:09:55 tlr: 0.00011 tnm: 0.38 Lm: 6.619 (6.610) Lt: 5.830 (5.837) Accm: 3.10 (3.06) Acct: 5.01 (4.93) proj_loss: -0.5899 (-0.5923) time: 0.6763 data: 0.0003 [11-25 19:22:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [ 834/1669] eta: 0:09:55 tlr: 0.00011 tnm: 0.38 Lm: 6.472 (6.468) Lt: 5.699 (5.695) Accm: 3.42 (3.48) Acct: 5.49 (5.58) proj_loss: -0.6019 (-0.5987) time: 0.6763 data: 0.0003 [11-25 19:22:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [ 834/1669] eta: 0:09:55 tlr: 0.00011 tnm: 0.38 Lm: 6.450 (6.454) Lt: 5.754 (5.723) Accm: 3.72 (3.54) Acct: 5.91 (5.60) proj_loss: -0.5899 (-0.6040) time: 0.6763 data: 0.0003 [11-25 19:22:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [ 834/1669] eta: 0:09:55 tlr: 0.00011 tnm: 0.38 Lm: 6.521 (6.505) Lt: 5.772 (5.742) Accm: 3.31 (3.33) Acct: 4.86 (5.14) proj_loss: -0.6164 (-0.6143) time: 0.6763 data: 0.0005 [11-25 19:27:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [1251/1669] eta: 0:04:53 tlr: 0.00011 tnm: 0.36 Lm: 6.540 (6.542) Lt: 5.805 (5.790) Accm: 3.18 (3.19) Acct: 4.81 (4.94) proj_loss: -0.6148 (-0.6141) time: 0.6784 data: 0.0003 [11-25 19:27:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [1251/1669] eta: 0:04:53 tlr: 0.00011 tnm: 0.36 Lm: 6.478 (6.478) Lt: 5.771 (5.759) Accm: 3.57 (3.51) Acct: 5.57 (5.51) proj_loss: -0.6100 (-0.6105) time: 0.6784 data: 0.0003 [11-25 19:27:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [1251/1669] eta: 0:04:53 tlr: 0.00011 tnm: 0.36 Lm: 6.461 (6.425) Lt: 5.661 (5.653) Accm: 3.57 (3.66) Acct: 5.70 (5.78) proj_loss: -0.6021 (-0.5996) time: 0.6785 data: 0.0003 [11-25 19:27:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [1251/1669] eta: 0:04:53 tlr: 0.00011 tnm: 0.36 Lm: 6.597 (6.577) Lt: 5.809 (5.825) Accm: 3.14 (3.19) Acct: 5.03 (4.97) proj_loss: -0.5969 (-0.5989) time: 0.6785 data: 0.0003 [11-25 19:31:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.37 Lm: 6.576 (6.574) Lt: 5.830 (5.837) Accm: 3.18 (3.24) Acct: 5.04 (5.04) proj_loss: -0.6040 (-0.6008) time: 0.6767 data: 0.0019 [11-25 19:31:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 201/350] Total time: 0:19:20 (0.695 s / it) [11-25 19:31:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.37 Lm: 6.507 (6.487) Lt: 5.754 (5.745) Accm: 3.53 (3.52) Acct: 5.82 (5.57) proj_loss: -0.6124 (-0.6109) time: 0.6767 data: 0.0017 [11-25 19:31:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.37 Lm: 6.521 (6.509) Lt: 5.772 (5.747) Accm: 3.31 (3.24) Acct: 4.86 (5.02) proj_loss: -0.6132 (-0.6109) time: 0.6767 data: 0.0017 [11-25 19:31:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 201/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.37 Lm: 6.472 (6.448) Lt: 5.699 (5.672) Accm: 3.42 (3.53) Acct: 5.49 (5.58) proj_loss: -0.6019 (-0.5983) time: 0.6767 data: 0.0015 [11-25 19:31:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 201/350] Total time: 0:19:20 (0.695 s / it) [11-25 19:31:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 201/350] Total time: 0:19:20 (0.695 s / it) [11-25 19:31:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 201/350] Total time: 0:19:20 (0.695 s / it) [11-25 19:31:55] (/home/user/VAR/train.py , line 276)=> [ep201] (training ) Lm: 6.502 (6.508), Lt: 5.742 (5.755), Acc m&t: 3.39 5.35, Remain: 1 day, 22:49:03, Finish: 2024-11-27 02:20 [11-25 19:31:55] (/home/user/VAR/train.py , line 276)=> [ep201] (training ) Lm: 6.502 (6.508), Lt: 5.742 (5.755), Acc m&t: 3.39 5.35, Remain: 1 day, 22:49:47, Finish: 2024-11-27 02:21 [11-25 19:31:55] (/home/user/VAR/train.py , line 276)=> [ep201] (training ) Lm: 6.502 (6.508), Lt: 5.742 (5.755), Acc m&t: 3.39 5.35, Remain: 1 day, 22:50:08, Finish: 2024-11-27 02:22 [11-25 19:31:55] (/home/user/VAR/train.py , line 276)=> [ep201] (training ) Lm: 6.502 (6.508), Lt: 5.742 (5.755), Acc m&t: 3.39 5.35, Remain: 1 day, 22:49:53, Finish: 2024-11-27 02:21 [11-25 19:31:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [ 0/1669] eta: 0:18:21 tlr: 0.00011 tnm: 0.40 Lm: 6.535 (6.535) Lt: 5.737 (5.737) Accm: 3.39 (3.39) Acct: 5.77 (5.77) proj_loss: -0.5973 (-0.5973) time: 0.6601 data: 0.0004 [11-25 19:31:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [ 0/1669] eta: 0:18:22 tlr: 0.00011 tnm: 0.40 Lm: 6.417 (6.417) Lt: 5.638 (5.638) Accm: 3.46 (3.46) Acct: 5.32 (5.32) proj_loss: -0.5883 (-0.5883) time: 0.6604 data: 0.0004 [11-25 19:31:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [ 0/1669] eta: 0:18:21 tlr: 0.00011 tnm: 0.40 Lm: 6.523 (6.523) Lt: 5.734 (5.734) Accm: 3.36 (3.36) Acct: 5.37 (5.37) proj_loss: -0.5998 (-0.5998) time: 0.6597 data: 0.0004 [11-25 19:31:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [ 0/1669] eta: 0:18:17 tlr: 0.00011 tnm: 0.40 Lm: 6.562 (6.562) Lt: 5.819 (5.819) Accm: 3.14 (3.14) Acct: 5.06 (5.06) proj_loss: -0.6048 (-0.6048) time: 0.6577 data: 0.0004 [11-25 19:36:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [ 417/1669] eta: 0:14:13 tlr: 0.00011 tnm: 0.36 Lm: 6.590 (6.590) Lt: 5.857 (5.857) Accm: 3.16 (3.16) Acct: 4.93 (4.93) proj_loss: -0.6094 (-0.6094) time: 0.7412 data: 0.0003 [11-25 19:36:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [ 417/1669] eta: 0:14:13 tlr: 0.00011 tnm: 0.36 Lm: 6.550 (6.550) Lt: 5.809 (5.809) Accm: 3.29 (3.29) Acct: 5.22 (5.22) proj_loss: -0.6000 (-0.6000) time: 0.7412 data: 0.0003 [11-25 19:36:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [ 417/1669] eta: 0:14:13 tlr: 0.00011 tnm: 0.36 Lm: 6.506 (6.506) Lt: 5.734 (5.734) Accm: 3.13 (3.13) Acct: 4.81 (4.81) proj_loss: -0.6097 (-0.6097) time: 0.7412 data: 0.0003 [11-25 19:36:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [ 417/1669] eta: 0:14:13 tlr: 0.00011 tnm: 0.36 Lm: 6.472 (6.472) Lt: 5.668 (5.668) Accm: 3.63 (3.63) Acct: 5.85 (5.85) proj_loss: -0.5978 (-0.5978) time: 0.7412 data: 0.0003 [11-25 19:41:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [ 834/1669] eta: 0:09:39 tlr: 0.00011 tnm: 0.38 Lm: 6.535 (6.518) Lt: 5.737 (5.737) Accm: 3.39 (3.37) Acct: 5.77 (5.40) proj_loss: -0.5983 (-0.5982) time: 0.6748 data: 0.0003 [11-25 19:41:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [ 834/1669] eta: 0:09:39 tlr: 0.00011 tnm: 0.38 Lm: 6.561 (6.524) Lt: 5.831 (5.780) Accm: 3.24 (3.16) Acct: 4.96 (4.86) proj_loss: -0.6074 (-0.6090) time: 0.6748 data: 0.0002 [11-25 19:41:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [ 834/1669] eta: 0:09:39 tlr: 0.00011 tnm: 0.38 Lm: 6.562 (6.543) Lt: 5.819 (5.795) Accm: 3.18 (3.30) Acct: 5.06 (5.15) proj_loss: -0.6120 (-0.6103) time: 0.6748 data: 0.0003 [11-25 19:41:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [ 834/1669] eta: 0:09:39 tlr: 0.00011 tnm: 0.38 Lm: 6.524 (6.541) Lt: 5.824 (5.814) Accm: 3.22 (3.23) Acct: 5.13 (5.19) proj_loss: -0.5998 (-0.5987) time: 0.6748 data: 0.0003 [11-25 19:46:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.523 (6.502) Lt: 5.779 (5.768) Accm: 3.29 (3.40) Acct: 5.25 (5.38) proj_loss: -0.6000 (-0.6025) time: 0.6776 data: 0.0003 [11-25 19:46:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.540 (6.537) Lt: 5.820 (5.802) Accm: 3.16 (3.24) Acct: 4.93 (5.01) proj_loss: -0.6097 (-0.6095) time: 0.6776 data: 0.0003 [11-25 19:46:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.473 (6.492) Lt: 5.674 (5.705) Accm: 3.57 (3.46) Acct: 5.76 (5.49) proj_loss: -0.5980 (-0.5981) time: 0.6776 data: 0.0003 [11-25 19:46:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.489 (6.479) Lt: 5.734 (5.726) Accm: 3.35 (3.40) Acct: 5.14 (5.29) proj_loss: -0.6007 (-0.6052) time: 0.6776 data: 0.0003 [11-25 19:51:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.37 Lm: 6.417 (6.462) Lt: 5.638 (5.705) Accm: 3.46 (3.48) Acct: 5.32 (5.43) proj_loss: -0.6074 (-0.6082) time: 0.6769 data: 0.0019 [11-25 19:51:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 202/350] Total time: 0:19:19 (0.695 s / it) [11-25 19:51:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.37 Lm: 6.562 (6.571) Lt: 5.822 (5.840) Accm: 3.14 (3.11) Acct: 4.80 (4.83) proj_loss: -0.6073 (-0.6087) time: 0.6769 data: 0.0016 [11-25 19:51:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.37 Lm: 6.412 (6.469) Lt: 5.611 (5.685) Accm: 3.57 (3.48) Acct: 5.75 (5.52) proj_loss: -0.5983 (-0.6010) time: 0.6769 data: 0.0018 [11-25 19:51:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 202/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.37 Lm: 6.524 (6.548) Lt: 5.824 (5.819) Accm: 3.22 (3.25) Acct: 5.13 (5.09) proj_loss: -0.5998 (-0.6018) time: 0.6769 data: 0.0014 [11-25 19:51:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 202/350] Total time: 0:19:19 (0.695 s / it) [11-25 19:51:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 202/350] Total time: 0:19:19 (0.695 s / it) [11-25 19:51:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 202/350] Total time: 0:19:19 (0.695 s / it) [11-25 19:51:15] (/home/user/VAR/train.py , line 276)=> [ep202] (training ) Lm: 6.502 (6.505), Lt: 5.742 (5.749), Acc m&t: 3.39 5.35, Remain: 1 day, 22:21:48, Finish: 2024-11-27 02:13 [11-25 19:51:15] (/home/user/VAR/train.py , line 276)=> [ep202] (training ) Lm: 6.502 (6.505), Lt: 5.742 (5.749), Acc m&t: 3.39 5.35, Remain: 1 day, 22:21:55, Finish: 2024-11-27 02:13 [11-25 19:51:15] (/home/user/VAR/train.py , line 276)=> [ep202] (training ) Lm: 6.502 (6.505), Lt: 5.742 (5.749), Acc m&t: 3.39 5.35, Remain: 1 day, 22:21:51, Finish: 2024-11-27 02:13 [11-25 19:51:15] (/home/user/VAR/train.py , line 276)=> [ep202] (training ) Lm: 6.502 (6.505), Lt: 5.742 (5.749), Acc m&t: 3.39 5.35, Remain: 1 day, 22:19:53, Finish: 2024-11-27 02:11 [11-25 19:51:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [ 0/1669] eta: 0:18:18 tlr: 0.00011 tnm: 0.38 Lm: 6.526 (6.526) Lt: 5.743 (5.743) Accm: 3.35 (3.35) Acct: 5.17 (5.17) proj_loss: -0.5779 (-0.5779) time: 0.6583 data: 0.0003 [11-25 19:51:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [ 0/1669] eta: 0:18:19 tlr: 0.00011 tnm: 0.38 Lm: 6.564 (6.564) Lt: 5.848 (5.848) Accm: 3.21 (3.21) Acct: 5.29 (5.29) proj_loss: -0.6294 (-0.6294) time: 0.6587 data: 0.0003 [11-25 19:51:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [ 0/1669] eta: 0:18:19 tlr: 0.00011 tnm: 0.38 Lm: 6.335 (6.335) Lt: 5.564 (5.564) Accm: 3.92 (3.92) Acct: 5.73 (5.73) proj_loss: -0.6040 (-0.6040) time: 0.6587 data: 0.0004 [11-25 19:51:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [ 0/1669] eta: 0:18:20 tlr: 0.00011 tnm: 0.38 Lm: 6.513 (6.513) Lt: 5.759 (5.759) Accm: 3.53 (3.53) Acct: 5.68 (5.68) proj_loss: -0.5948 (-0.5948) time: 0.6591 data: 0.0003 [11-25 19:55:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [ 417/1669] eta: 0:14:06 tlr: 0.00011 tnm: 0.38 Lm: 6.473 (6.473) Lt: 5.712 (5.712) Accm: 3.58 (3.58) Acct: 5.73 (5.73) proj_loss: -0.5935 (-0.5935) time: 0.6749 data: 0.0003 [11-25 19:55:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [ 417/1669] eta: 0:14:06 tlr: 0.00011 tnm: 0.38 Lm: 6.539 (6.539) Lt: 5.820 (5.820) Accm: 3.36 (3.36) Acct: 5.42 (5.42) proj_loss: -0.6099 (-0.6099) time: 0.6748 data: 0.0003 [11-25 19:55:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [ 417/1669] eta: 0:14:06 tlr: 0.00011 tnm: 0.38 Lm: 6.439 (6.439) Lt: 5.680 (5.680) Accm: 3.60 (3.60) Acct: 5.59 (5.59) proj_loss: -0.5911 (-0.5911) time: 0.6748 data: 0.0003 [11-25 19:55:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [ 417/1669] eta: 0:14:06 tlr: 0.00011 tnm: 0.38 Lm: 6.345 (6.345) Lt: 5.568 (5.568) Accm: 3.71 (3.71) Acct: 5.47 (5.47) proj_loss: -0.5961 (-0.5961) time: 0.6748 data: 0.0003 [11-25 20:00:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [ 834/1669] eta: 0:09:26 tlr: 0.00011 tnm: 0.39 Lm: 6.355 (6.394) Lt: 5.572 (5.620) Accm: 3.50 (3.61) Acct: 5.58 (5.50) proj_loss: -0.5905 (-0.5942) time: 0.6772 data: 0.0003 [11-25 20:00:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [ 834/1669] eta: 0:09:26 tlr: 0.00011 tnm: 0.39 Lm: 6.506 (6.461) Lt: 5.725 (5.695) Accm: 3.58 (3.59) Acct: 5.75 (5.64) proj_loss: -0.6043 (-0.5955) time: 0.6772 data: 0.0003 [11-25 20:00:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [ 834/1669] eta: 0:09:26 tlr: 0.00011 tnm: 0.39 Lm: 6.433 (6.450) Lt: 5.690 (5.704) Accm: 3.53 (3.48) Acct: 5.68 (5.41) proj_loss: -0.5948 (-0.6083) time: 0.6772 data: 0.0004 [11-25 20:00:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [ 834/1669] eta: 0:09:26 tlr: 0.00011 tnm: 0.39 Lm: 6.514 (6.527) Lt: 5.792 (5.799) Accm: 3.34 (3.35) Acct: 5.29 (5.26) proj_loss: -0.6130 (-0.6110) time: 0.6772 data: 0.0003 [11-25 20:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [1251/1669] eta: 0:04:43 tlr: 0.00011 tnm: 0.39 Lm: 6.509 (6.514) Lt: 5.774 (5.773) Accm: 3.42 (3.41) Acct: 5.37 (5.31) proj_loss: -0.6045 (-0.6072) time: 0.6786 data: 0.0003 [11-25 20:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [1251/1669] eta: 0:04:43 tlr: 0.00011 tnm: 0.39 Lm: 6.370 (6.391) Lt: 5.614 (5.629) Accm: 3.54 (3.61) Acct: 5.56 (5.51) proj_loss: -0.5902 (-0.5931) time: 0.6786 data: 0.0003 [11-25 20:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [1251/1669] eta: 0:04:43 tlr: 0.00011 tnm: 0.39 Lm: 6.452 (6.446) Lt: 5.698 (5.689) Accm: 3.60 (3.60) Acct: 5.65 (5.62) proj_loss: -0.5938 (-0.5925) time: 0.6786 data: 0.0003 [11-25 20:05:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [1251/1669] eta: 0:04:43 tlr: 0.00011 tnm: 0.39 Lm: 6.473 (6.487) Lt: 5.724 (5.737) Accm: 3.39 (3.36) Acct: 5.28 (5.27) proj_loss: -0.6048 (-0.6100) time: 0.6786 data: 0.0003 [11-25 20:10:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.38 Lm: 6.513 (6.492) Lt: 5.723 (5.734) Accm: 3.31 (3.35) Acct: 5.44 (5.31) proj_loss: -0.6020 (-0.6084) time: 0.6763 data: 0.0017 [11-25 20:10:08] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 203/350] Total time: 0:18:53 (0.679 s / it) [11-25 20:10:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.38 Lm: 6.504 (6.473) Lt: 5.756 (5.719) Accm: 3.50 (3.57) Acct: 5.46 (5.57) proj_loss: -0.5960 (-0.6049) time: 0.6762 data: 0.0016 [11-25 20:10:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.38 Lm: 6.399 (6.430) Lt: 5.672 (5.661) Accm: 3.62 (3.68) Acct: 5.75 (5.75) proj_loss: -0.6034 (-0.5947) time: 0.6763 data: 0.0016 [11-25 20:10:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 203/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.38 Lm: 6.385 (6.412) Lt: 5.656 (5.645) Accm: 3.50 (3.56) Acct: 5.54 (5.44) proj_loss: -0.5905 (-0.6003) time: 0.6763 data: 0.0017 [11-25 20:10:08] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 203/350] Total time: 0:18:53 (0.679 s / it) [11-25 20:10:08] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 203/350] Total time: 0:18:53 (0.679 s / it) [11-25 20:10:08] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 203/350] Total time: 0:18:53 (0.679 s / it) [11-25 20:10:08] (/home/user/VAR/train.py , line 276)=> [ep203] (training ) Lm: 6.491 (6.491), Lt: 5.732 (5.732), Acc m&t: 3.41 5.35, Remain: 1 day, 22:05:36, Finish: 2024-11-27 02:15 [11-25 20:10:08] (/home/user/VAR/train.py , line 276)=> [ep203] (training ) Lm: 6.491 (6.491), Lt: 5.732 (5.732), Acc m&t: 3.41 5.35, Remain: 1 day, 22:04:30, Finish: 2024-11-27 02:14 [11-25 20:10:08] (/home/user/VAR/train.py , line 276)=> [ep203] (training ) Lm: 6.491 (6.491), Lt: 5.732 (5.732), Acc m&t: 3.41 5.35, Remain: 1 day, 22:05:20, Finish: 2024-11-27 02:15 [11-25 20:10:08] (/home/user/VAR/train.py , line 276)=> [ep203] (training ) Lm: 6.491 (6.491), Lt: 5.732 (5.732), Acc m&t: 3.41 5.35, Remain: 1 day, 22:04:56, Finish: 2024-11-27 02:15 [11-25 20:10:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [ 0/1669] eta: 0:18:30 tlr: 0.00011 tnm: 0.36 Lm: 6.522 (6.522) Lt: 5.755 (5.755) Accm: 3.45 (3.45) Acct: 5.41 (5.41) proj_loss: -0.5969 (-0.5969) time: 0.6656 data: 0.0004 [11-25 20:10:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [ 0/1669] eta: 0:18:31 tlr: 0.00011 tnm: 0.36 Lm: 6.525 (6.525) Lt: 5.773 (5.773) Accm: 3.50 (3.50) Acct: 5.46 (5.46) proj_loss: -0.6125 (-0.6125) time: 0.6658 data: 0.0004 [11-25 20:10:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [ 0/1669] eta: 0:18:31 tlr: 0.00011 tnm: 0.36 Lm: 6.375 (6.375) Lt: 5.577 (5.577) Accm: 3.89 (3.89) Acct: 5.99 (5.99) proj_loss: -0.6114 (-0.6114) time: 0.6660 data: 0.0004 [11-25 20:10:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [ 0/1669] eta: 0:18:31 tlr: 0.00011 tnm: 0.36 Lm: 6.586 (6.586) Lt: 5.787 (5.787) Accm: 3.13 (3.13) Acct: 5.11 (5.11) proj_loss: -0.5990 (-0.5990) time: 0.6662 data: 0.0004 [11-25 20:15:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [ 417/1669] eta: 0:15:09 tlr: 0.00011 tnm: 0.40 Lm: 6.538 (6.538) Lt: 5.741 (5.741) Accm: 3.43 (3.43) Acct: 5.65 (5.65) proj_loss: -0.6049 (-0.6049) time: 0.7897 data: 0.0003 [11-25 20:15:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [ 417/1669] eta: 0:15:09 tlr: 0.00011 tnm: 0.40 Lm: 6.447 (6.447) Lt: 5.661 (5.661) Accm: 3.60 (3.60) Acct: 5.52 (5.52) proj_loss: -0.6137 (-0.6137) time: 0.7897 data: 0.0003 [11-25 20:15:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [ 417/1669] eta: 0:15:09 tlr: 0.00011 tnm: 0.40 Lm: 6.527 (6.527) Lt: 5.786 (5.786) Accm: 3.38 (3.38) Acct: 5.25 (5.25) proj_loss: -0.6152 (-0.6152) time: 0.7897 data: 0.0003 [11-25 20:15:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [ 417/1669] eta: 0:15:09 tlr: 0.00011 tnm: 0.40 Lm: 6.484 (6.484) Lt: 5.683 (5.683) Accm: 3.61 (3.61) Acct: 5.79 (5.79) proj_loss: -0.5950 (-0.5950) time: 0.7897 data: 0.0003 [11-25 20:20:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [ 834/1669] eta: 0:09:53 tlr: 0.00011 tnm: 0.38 Lm: 6.522 (6.502) Lt: 5.755 (5.715) Accm: 3.46 (3.56) Acct: 5.61 (5.73) proj_loss: -0.5969 (-0.5983) time: 0.6787 data: 0.0003 [11-25 20:20:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [ 834/1669] eta: 0:09:53 tlr: 0.00011 tnm: 0.38 Lm: 6.512 (6.468) Lt: 5.746 (5.711) Accm: 3.31 (3.50) Acct: 5.04 (5.25) proj_loss: -0.6114 (-0.6061) time: 0.6787 data: 0.0003 [11-25 20:20:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [ 834/1669] eta: 0:09:53 tlr: 0.00011 tnm: 0.38 Lm: 6.586 (6.590) Lt: 5.787 (5.817) Accm: 3.13 (3.32) Acct: 5.11 (5.37) proj_loss: -0.6107 (-0.6087) time: 0.6787 data: 0.0003 [11-25 20:20:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [ 834/1669] eta: 0:09:53 tlr: 0.00011 tnm: 0.38 Lm: 6.529 (6.570) Lt: 5.798 (5.833) Accm: 3.26 (3.28) Acct: 5.04 (5.08) proj_loss: -0.6125 (-0.6070) time: 0.6787 data: 0.0003 [11-25 20:24:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.527 (6.547) Lt: 5.786 (5.799) Accm: 3.38 (3.45) Acct: 5.25 (5.32) proj_loss: -0.6107 (-0.6074) time: 0.6761 data: 0.0003 [11-25 20:24:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.546 (6.569) Lt: 5.770 (5.801) Accm: 3.38 (3.40) Acct: 5.41 (5.45) proj_loss: -0.6136 (-0.6123) time: 0.6761 data: 0.0003 [11-25 20:24:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.484 (6.454) Lt: 5.683 (5.664) Accm: 3.62 (3.67) Acct: 5.89 (5.85) proj_loss: -0.6009 (-0.6028) time: 0.6761 data: 0.0003 [11-25 20:24:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.473 (6.460) Lt: 5.706 (5.699) Accm: 3.56 (3.58) Acct: 5.41 (5.38) proj_loss: -0.6012 (-0.5998) time: 0.6761 data: 0.0003 [11-25 20:29:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.38 Lm: 6.459 (6.459) Lt: 5.696 (5.699) Accm: 3.55 (3.57) Acct: 5.54 (5.41) proj_loss: -0.6036 (-0.6005) time: 0.6773 data: 0.0017 [11-25 20:29:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 204/350] Total time: 0:19:18 (0.694 s / it) [11-25 20:29:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.38 Lm: 6.529 (6.552) Lt: 5.773 (5.789) Accm: 3.26 (3.37) Acct: 5.04 (5.23) proj_loss: -0.6089 (-0.6052) time: 0.6773 data: 0.0019 [11-25 20:29:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.38 Lm: 6.480 (6.460) Lt: 5.672 (5.666) Accm: 3.46 (3.62) Acct: 5.61 (5.79) proj_loss: -0.6049 (-0.6045) time: 0.6773 data: 0.0013 [11-25 20:29:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 204/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.38 Lm: 6.507 (6.536) Lt: 5.753 (5.777) Accm: 3.63 (3.48) Acct: 5.60 (5.48) proj_loss: -0.6137 (-0.6125) time: 0.6774 data: 0.0017 [11-25 20:29:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 204/350] Total time: 0:19:18 (0.694 s / it) [11-25 20:29:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 204/350] Total time: 0:19:18 (0.694 s / it) [11-25 20:29:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 204/350] Total time: 0:19:18 (0.694 s / it) [11-25 20:29:27] (/home/user/VAR/train.py , line 276)=> [ep204] (training ) Lm: 6.491 (6.500), Lt: 5.732 (5.735), Acc m&t: 3.42 5.40, Remain: 1 day, 21:53:09, Finish: 2024-11-27 02:22 [11-25 20:29:27] (/home/user/VAR/train.py , line 276)=> [ep204] (training ) Lm: 6.491 (6.500), Lt: 5.732 (5.735), Acc m&t: 3.42 5.40, Remain: 1 day, 21:51:24, Finish: 2024-11-27 02:20 [11-25 20:29:27] (/home/user/VAR/train.py , line 276)=> [ep204] (training ) Lm: 6.491 (6.500), Lt: 5.732 (5.735), Acc m&t: 3.42 5.40, Remain: 1 day, 21:52:30, Finish: 2024-11-27 02:21 [11-25 20:29:27] (/home/user/VAR/train.py , line 276)=> [ep204] (training ) Lm: 6.491 (6.500), Lt: 5.732 (5.735), Acc m&t: 3.42 5.40, Remain: 1 day, 21:52:10, Finish: 2024-11-27 02:21 [11-25 20:29:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [ 0/1669] eta: 0:18:38 tlr: 0.00011 tnm: 0.38 Lm: 6.550 (6.550) Lt: 5.870 (5.870) Accm: 3.36 (3.36) Acct: 5.30 (5.30) proj_loss: -0.6031 (-0.6031) time: 0.6700 data: 0.0004 [11-25 20:29:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [ 0/1669] eta: 0:18:38 tlr: 0.00011 tnm: 0.38 Lm: 6.417 (6.417) Lt: 5.667 (5.667) Accm: 3.52 (3.52) Acct: 5.44 (5.44) proj_loss: -0.6055 (-0.6055) time: 0.6699 data: 0.0004 [11-25 20:29:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [ 0/1669] eta: 0:18:38 tlr: 0.00011 tnm: 0.38 Lm: 6.428 (6.428) Lt: 5.617 (5.617) Accm: 3.81 (3.81) Acct: 5.85 (5.85) proj_loss: -0.5789 (-0.5789) time: 0.6703 data: 0.0004 [11-25 20:29:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [ 0/1669] eta: 0:18:38 tlr: 0.00011 tnm: 0.38 Lm: 6.527 (6.527) Lt: 5.824 (5.824) Accm: 3.26 (3.26) Acct: 5.04 (5.04) proj_loss: -0.5984 (-0.5984) time: 0.6705 data: 0.0004 [11-25 20:34:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [ 417/1669] eta: 0:14:11 tlr: 0.00011 tnm: 0.37 Lm: 6.520 (6.520) Lt: 5.785 (5.785) Accm: 3.34 (3.34) Acct: 5.27 (5.27) proj_loss: -0.5908 (-0.5908) time: 0.7383 data: 0.0003 [11-25 20:34:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [ 417/1669] eta: 0:14:11 tlr: 0.00011 tnm: 0.37 Lm: 6.609 (6.609) Lt: 5.925 (5.925) Accm: 3.07 (3.07) Acct: 4.86 (4.86) proj_loss: -0.6170 (-0.6170) time: 0.7383 data: 0.0003 [11-25 20:34:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [ 417/1669] eta: 0:14:11 tlr: 0.00011 tnm: 0.37 Lm: 6.470 (6.470) Lt: 5.735 (5.735) Accm: 3.49 (3.49) Acct: 5.56 (5.56) proj_loss: -0.6011 (-0.6011) time: 0.7383 data: 0.0003 [11-25 20:34:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [ 417/1669] eta: 0:14:11 tlr: 0.00011 tnm: 0.37 Lm: 6.418 (6.418) Lt: 5.654 (5.654) Accm: 3.68 (3.68) Acct: 5.68 (5.68) proj_loss: -0.6000 (-0.6000) time: 0.7383 data: 0.0003 [11-25 20:39:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [ 834/1669] eta: 0:09:39 tlr: 0.00011 tnm: 0.37 Lm: 6.428 (6.456) Lt: 5.691 (5.678) Accm: 3.55 (3.50) Acct: 5.51 (5.51) proj_loss: -0.6018 (-0.6006) time: 0.6780 data: 0.0003 [11-25 20:39:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [ 834/1669] eta: 0:09:39 tlr: 0.00011 tnm: 0.37 Lm: 6.570 (6.596) Lt: 5.893 (5.914) Accm: 3.09 (3.08) Acct: 4.84 (4.86) proj_loss: -0.6139 (-0.6159) time: 0.6780 data: 0.0003 [11-25 20:39:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [ 834/1669] eta: 0:09:39 tlr: 0.00011 tnm: 0.37 Lm: 6.521 (6.487) Lt: 5.788 (5.753) Accm: 3.46 (3.38) Acct: 5.44 (5.45) proj_loss: -0.5968 (-0.5994) time: 0.6780 data: 0.0003 [11-25 20:39:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [ 834/1669] eta: 0:09:39 tlr: 0.00011 tnm: 0.37 Lm: 6.514 (6.502) Lt: 5.746 (5.761) Accm: 3.42 (3.37) Acct: 5.35 (5.30) proj_loss: -0.5968 (-0.5928) time: 0.6780 data: 0.0003 [11-25 20:44:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.520 (6.540) Lt: 5.785 (5.793) Accm: 3.34 (3.19) Acct: 5.20 (5.07) proj_loss: -0.5976 (-0.5956) time: 0.6748 data: 0.0003 [11-25 20:44:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.560 (6.487) Lt: 5.881 (5.768) Accm: 3.22 (3.43) Acct: 5.07 (5.45) proj_loss: -0.6151 (-0.6160) time: 0.6748 data: 0.0003 [11-25 20:44:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.521 (6.495) Lt: 5.782 (5.758) Accm: 3.34 (3.34) Acct: 5.34 (5.38) proj_loss: -0.5964 (-0.5933) time: 0.6748 data: 0.0003 [11-25 20:44:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.466 (6.468) Lt: 5.698 (5.685) Accm: 3.38 (3.43) Acct: 5.34 (5.35) proj_loss: -0.6075 (-0.6037) time: 0.6748 data: 0.0003 [11-25 20:48:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.514 (6.526) Lt: 5.746 (5.774) Accm: 3.28 (3.21) Acct: 5.35 (5.13) proj_loss: -0.5968 (-0.5952) time: 0.6759 data: 0.0023 [11-25 20:48:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.522 (6.507) Lt: 5.776 (5.760) Accm: 3.28 (3.33) Acct: 5.23 (5.32) proj_loss: -0.5968 (-0.6011) time: 0.6759 data: 0.0017 [11-25 20:48:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.558 (6.501) Lt: 5.870 (5.778) Accm: 3.35 (3.42) Acct: 5.08 (5.38) proj_loss: -0.6139 (-0.6112) time: 0.6759 data: 0.0015 [11-25 20:48:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 205/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.436 (6.462) Lt: 5.691 (5.674) Accm: 3.43 (3.43) Acct: 5.42 (5.37) proj_loss: -0.6029 (-0.6036) time: 0.6759 data: 0.0020 [11-25 20:48:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 205/350] Total time: 0:19:20 (0.695 s / it) [11-25 20:48:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 205/350] Total time: 0:19:20 (0.695 s / it) [11-25 20:48:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 205/350] Total time: 0:19:20 (0.695 s / it) [11-25 20:48:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 205/350] Total time: 0:19:20 (0.695 s / it) [11-25 20:48:47] (/home/user/VAR/train.py , line 276)=> [ep205] (training ) Lm: 6.491 (6.504), Lt: 5.732 (5.747), Acc m&t: 3.42 5.40, Remain: 1 day, 21:26:04, Finish: 2024-11-27 02:14 [11-25 20:48:47] (/home/user/VAR/train.py , line 276)=> [ep205] (training ) Lm: 6.491 (6.504), Lt: 5.732 (5.747), Acc m&t: 3.42 5.40, Remain: 1 day, 21:25:58, Finish: 2024-11-27 02:14 [11-25 20:48:47] (/home/user/VAR/train.py , line 276)=> [ep205] (training ) Lm: 6.491 (6.504), Lt: 5.732 (5.747), Acc m&t: 3.42 5.40, Remain: 1 day, 21:25:40, Finish: 2024-11-27 02:14 [11-25 20:48:47] (/home/user/VAR/train.py , line 276)=> [ep205] (training ) Lm: 6.491 (6.504), Lt: 5.732 (5.747), Acc m&t: 3.42 5.40, Remain: 1 day, 21:25:09, Finish: 2024-11-27 02:13 [11-25 20:48:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [ 0/1669] eta: 0:18:18 tlr: 0.00011 tnm: 0.39 Lm: 6.527 (6.527) Lt: 5.798 (5.798) Accm: 2.91 (2.91) Acct: 4.55 (4.55) proj_loss: -0.6093 (-0.6093) time: 0.6579 data: 0.0004 [11-25 20:48:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [ 0/1669] eta: 0:18:18 tlr: 0.00011 tnm: 0.39 Lm: 6.582 (6.582) Lt: 5.823 (5.823) Accm: 3.15 (3.15) Acct: 4.94 (4.94) proj_loss: -0.6150 (-0.6150) time: 0.6579 data: 0.0004 [11-25 20:48:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [ 0/1669] eta: 0:18:18 tlr: 0.00011 tnm: 0.39 Lm: 6.508 (6.508) Lt: 5.763 (5.763) Accm: 3.31 (3.31) Acct: 5.17 (5.17) proj_loss: -0.6022 (-0.6022) time: 0.6580 data: 0.0004 [11-25 20:48:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [ 0/1669] eta: 0:18:18 tlr: 0.00011 tnm: 0.39 Lm: 6.674 (6.674) Lt: 5.972 (5.972) Accm: 2.76 (2.76) Acct: 4.60 (4.60) proj_loss: -0.5988 (-0.5988) time: 0.6583 data: 0.0003 [11-25 20:53:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [ 417/1669] eta: 0:14:07 tlr: 0.00011 tnm: 0.36 Lm: 6.557 (6.557) Lt: 5.806 (5.806) Accm: 3.25 (3.25) Acct: 5.38 (5.38) proj_loss: -0.6040 (-0.6040) time: 0.6776 data: 0.0003 [11-25 20:53:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [ 417/1669] eta: 0:14:07 tlr: 0.00011 tnm: 0.36 Lm: 6.562 (6.562) Lt: 5.823 (5.823) Accm: 3.00 (3.00) Acct: 4.57 (4.57) proj_loss: -0.6114 (-0.6114) time: 0.6776 data: 0.0003 [11-25 20:53:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [ 417/1669] eta: 0:14:07 tlr: 0.00011 tnm: 0.36 Lm: 6.440 (6.440) Lt: 5.674 (5.674) Accm: 3.57 (3.57) Acct: 5.67 (5.67) proj_loss: -0.6125 (-0.6125) time: 0.6776 data: 0.0003 [11-25 20:53:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [ 417/1669] eta: 0:14:07 tlr: 0.00011 tnm: 0.36 Lm: 6.505 (6.505) Lt: 5.741 (5.741) Accm: 3.45 (3.45) Acct: 5.57 (5.57) proj_loss: -0.6144 (-0.6144) time: 0.6776 data: 0.0003 [11-25 20:58:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [ 834/1669] eta: 0:09:24 tlr: 0.00011 tnm: 0.38 Lm: 6.427 (6.466) Lt: 5.659 (5.699) Accm: 3.74 (3.59) Acct: 5.99 (5.71) proj_loss: -0.6139 (-0.6032) time: 0.6745 data: 0.0003 [11-25 20:58:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [ 834/1669] eta: 0:09:24 tlr: 0.00011 tnm: 0.38 Lm: 6.527 (6.550) Lt: 5.798 (5.798) Accm: 3.10 (3.07) Acct: 4.60 (4.82) proj_loss: -0.6093 (-0.6048) time: 0.6745 data: 0.0003 [11-25 20:58:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [ 834/1669] eta: 0:09:24 tlr: 0.00011 tnm: 0.38 Lm: 6.441 (6.490) Lt: 5.641 (5.731) Accm: 3.74 (3.44) Acct: 5.87 (5.54) proj_loss: -0.5991 (-0.6024) time: 0.6745 data: 0.0003 [11-25 20:58:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [ 834/1669] eta: 0:09:24 tlr: 0.00011 tnm: 0.38 Lm: 6.435 (6.438) Lt: 5.692 (5.680) Accm: 3.32 (3.49) Acct: 5.17 (5.49) proj_loss: -0.6170 (-0.6140) time: 0.6745 data: 0.0003 [11-25 21:02:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [1251/1669] eta: 0:04:43 tlr: 0.00011 tnm: 0.36 Lm: 6.471 (6.475) Lt: 5.727 (5.717) Accm: 3.32 (3.38) Acct: 5.14 (5.29) proj_loss: -0.6123 (-0.6124) time: 0.6764 data: 0.0003 [11-25 21:02:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [1251/1669] eta: 0:04:43 tlr: 0.00011 tnm: 0.36 Lm: 6.434 (6.474) Lt: 5.678 (5.727) Accm: 3.73 (3.51) Acct: 5.93 (5.66) proj_loss: -0.6042 (-0.6059) time: 0.6764 data: 0.0003 [11-25 21:02:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [1251/1669] eta: 0:04:43 tlr: 0.00011 tnm: 0.36 Lm: 6.545 (6.553) Lt: 5.807 (5.803) Accm: 3.15 (3.14) Acct: 4.84 (4.88) proj_loss: -0.6063 (-0.6044) time: 0.6764 data: 0.0003 [11-25 21:02:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [1251/1669] eta: 0:04:43 tlr: 0.00011 tnm: 0.36 Lm: 6.438 (6.461) Lt: 5.673 (5.696) Accm: 3.59 (3.55) Acct: 5.59 (5.58) proj_loss: -0.6072 (-0.6026) time: 0.6764 data: 0.0003 [11-25 21:07:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.38 Lm: 6.448 (6.476) Lt: 5.686 (5.710) Accm: 3.43 (3.50) Acct: 5.18 (5.48) proj_loss: -0.6005 (-0.6011) time: 0.6789 data: 0.0019 [11-25 21:07:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 206/350] Total time: 0:18:52 (0.679 s / it) [11-25 21:07:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.38 Lm: 6.527 (6.540) Lt: 5.798 (5.778) Accm: 3.21 (3.25) Acct: 5.08 (5.08) proj_loss: -0.6033 (-0.6042) time: 0.6789 data: 0.0013 [11-25 21:07:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.38 Lm: 6.508 (6.502) Lt: 5.763 (5.747) Accm: 3.31 (3.33) Acct: 5.11 (5.24) proj_loss: -0.6076 (-0.6079) time: 0.6789 data: 0.0020 [11-25 21:07:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 206/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.38 Lm: 6.427 (6.425) Lt: 5.641 (5.682) Accm: 3.74 (3.67) Acct: 5.99 (5.79) proj_loss: -0.6092 (-0.6103) time: 0.6789 data: 0.0019 [11-25 21:07:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 206/350] Total time: 0:18:52 (0.679 s / it) [11-25 21:07:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 206/350] Total time: 0:18:52 (0.679 s / it) [11-25 21:07:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 206/350] Total time: 0:18:52 (0.679 s / it) [11-25 21:07:40] (/home/user/VAR/train.py , line 276)=> [ep206] (training ) Lm: 6.491 (6.500), Lt: 5.732 (5.746), Acc m&t: 3.42 5.40, Remain: 1 day, 21:23:22, Finish: 2024-11-27 02:31 [11-25 21:07:40] (/home/user/VAR/train.py , line 276)=> [ep206] (training ) Lm: 6.491 (6.500), Lt: 5.732 (5.746), Acc m&t: 3.42 5.40, Remain: 1 day, 21:24:01, Finish: 2024-11-27 02:31 [11-25 21:07:40] (/home/user/VAR/train.py , line 276)=> [ep206] (training ) Lm: 6.491 (6.500), Lt: 5.732 (5.746), Acc m&t: 3.42 5.40, Remain: 1 day, 21:23:44, Finish: 2024-11-27 02:31 [11-25 21:07:40] (/home/user/VAR/train.py , line 276)=> [ep206] (training ) Lm: 6.491 (6.500), Lt: 5.732 (5.746), Acc m&t: 3.42 5.40, Remain: 1 day, 21:24:04, Finish: 2024-11-27 02:31 [11-25 21:07:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [ 0/1669] eta: 0:18:14 tlr: 0.00011 tnm: 0.37 Lm: 6.583 (6.583) Lt: 5.903 (5.903) Accm: 3.07 (3.07) Acct: 4.79 (4.79) proj_loss: -0.6027 (-0.6027) time: 0.6560 data: 0.0004 [11-25 21:07:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [ 0/1669] eta: 0:18:14 tlr: 0.00011 tnm: 0.37 Lm: 6.479 (6.479) Lt: 5.738 (5.738) Accm: 3.26 (3.26) Acct: 4.99 (4.99) proj_loss: -0.6348 (-0.6348) time: 0.6560 data: 0.0003 [11-25 21:07:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [ 0/1669] eta: 0:18:14 tlr: 0.00011 tnm: 0.37 Lm: 6.378 (6.378) Lt: 5.587 (5.587) Accm: 3.73 (3.73) Acct: 5.80 (5.80) proj_loss: -0.6079 (-0.6079) time: 0.6560 data: 0.0004 [11-25 21:07:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [ 0/1669] eta: 0:18:15 tlr: 0.00011 tnm: 0.37 Lm: 6.448 (6.448) Lt: 5.687 (5.687) Accm: 3.52 (3.52) Acct: 5.46 (5.46) proj_loss: -0.6030 (-0.6030) time: 0.6565 data: 0.0004 [11-25 21:12:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [ 417/1669] eta: 0:15:11 tlr: 0.00011 tnm: 0.39 Lm: 6.374 (6.374) Lt: 5.592 (5.592) Accm: 3.82 (3.82) Acct: 6.08 (6.08) proj_loss: -0.6014 (-0.6014) time: 0.8671 data: 0.0003 [11-25 21:12:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [ 417/1669] eta: 0:15:11 tlr: 0.00011 tnm: 0.39 Lm: 6.518 (6.518) Lt: 5.770 (5.770) Accm: 3.09 (3.09) Acct: 4.80 (4.80) proj_loss: -0.6222 (-0.6222) time: 0.8671 data: 0.0003 [11-25 21:12:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [ 417/1669] eta: 0:15:11 tlr: 0.00011 tnm: 0.39 Lm: 6.533 (6.533) Lt: 5.848 (5.848) Accm: 3.13 (3.13) Acct: 4.85 (4.85) proj_loss: -0.6121 (-0.6121) time: 0.8671 data: 0.0003 [11-25 21:12:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [ 417/1669] eta: 0:15:11 tlr: 0.00011 tnm: 0.39 Lm: 6.412 (6.412) Lt: 5.686 (5.686) Accm: 3.64 (3.64) Acct: 5.44 (5.44) proj_loss: -0.6071 (-0.6071) time: 0.8672 data: 0.0003 [11-25 21:17:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [ 834/1669] eta: 0:09:53 tlr: 0.00011 tnm: 0.37 Lm: 6.400 (6.408) Lt: 5.686 (5.670) Accm: 3.76 (3.76) Acct: 5.46 (5.65) proj_loss: -0.6030 (-0.5996) time: 0.6757 data: 0.0003 [11-25 21:17:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [ 834/1669] eta: 0:09:53 tlr: 0.00011 tnm: 0.37 Lm: 6.583 (6.588) Lt: 5.903 (5.876) Accm: 3.07 (3.05) Acct: 4.79 (4.71) proj_loss: -0.6027 (-0.6064) time: 0.6757 data: 0.0003 [11-25 21:17:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [ 834/1669] eta: 0:09:53 tlr: 0.00011 tnm: 0.37 Lm: 6.378 (6.440) Lt: 5.598 (5.661) Accm: 3.73 (3.57) Acct: 5.80 (5.72) proj_loss: -0.5948 (-0.5990) time: 0.6757 data: 0.0003 [11-25 21:17:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [ 834/1669] eta: 0:09:53 tlr: 0.00011 tnm: 0.37 Lm: 6.558 (6.560) Lt: 5.802 (5.802) Accm: 3.04 (3.08) Acct: 4.61 (4.73) proj_loss: -0.6095 (-0.6144) time: 0.6757 data: 0.0003 [11-25 21:22:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.518 (6.527) Lt: 5.770 (5.781) Accm: 3.15 (3.27) Acct: 4.80 (4.98) proj_loss: -0.6093 (-0.6131) time: 0.6767 data: 0.0003 [11-25 21:22:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.374 (6.388) Lt: 5.592 (5.607) Accm: 3.82 (3.72) Acct: 6.08 (5.89) proj_loss: -0.5984 (-0.5997) time: 0.6767 data: 0.0003 [11-25 21:22:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.424 (6.432) Lt: 5.686 (5.680) Accm: 3.64 (3.69) Acct: 5.50 (5.62) proj_loss: -0.6055 (-0.6017) time: 0.6767 data: 0.0003 [11-25 21:22:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [1251/1669] eta: 0:04:52 tlr: 0.00011 tnm: 0.38 Lm: 6.586 (6.588) Lt: 5.887 (5.875) Accm: 2.98 (3.00) Acct: 4.69 (4.68) proj_loss: -0.6011 (-0.6047) time: 0.6767 data: 0.0003 [11-25 21:26:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.583 (6.577) Lt: 5.871 (5.847) Accm: 3.07 (3.05) Acct: 4.79 (4.74) proj_loss: -0.5996 (-0.6022) time: 0.6786 data: 0.0015 [11-25 21:26:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 207/350] Total time: 0:19:19 (0.695 s / it) [11-25 21:26:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.479 (6.508) Lt: 5.738 (5.763) Accm: 3.26 (3.38) Acct: 4.99 (5.14) proj_loss: -0.6095 (-0.6154) time: 0.6786 data: 0.0017 [11-25 21:26:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.378 (6.425) Lt: 5.598 (5.646) Accm: 3.73 (3.60) Acct: 5.80 (5.70) proj_loss: -0.5948 (-0.5976) time: 0.6786 data: 0.0020 [11-25 21:26:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 207/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.448 (6.455) Lt: 5.687 (5.706) Accm: 3.52 (3.62) Acct: 5.46 (5.57) proj_loss: -0.6081 (-0.6043) time: 0.6786 data: 0.0020 [11-25 21:26:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 207/350] Total time: 0:19:19 (0.695 s / it) [11-25 21:26:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 207/350] Total time: 0:19:19 (0.695 s / it) [11-25 21:26:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 207/350] Total time: 0:19:19 (0.695 s / it) [11-25 21:26:59] (/home/user/VAR/train.py , line 276)=> [ep207] (training ) Lm: 6.491 (6.504), Lt: 5.732 (5.750), Acc m&t: 3.42 5.40, Remain: 1 day, 21:00:52, Finish: 2024-11-27 02:27 [11-25 21:26:59] (/home/user/VAR/train.py , line 276)=> [ep207] (training ) Lm: 6.491 (6.504), Lt: 5.732 (5.750), Acc m&t: 3.42 5.40, Remain: 1 day, 20:58:22, Finish: 2024-11-27 02:25 [11-25 21:26:59] (/home/user/VAR/train.py , line 276)=> [ep207] (training ) Lm: 6.491 (6.504), Lt: 5.732 (5.750), Acc m&t: 3.42 5.40, Remain: 1 day, 20:58:25, Finish: 2024-11-27 02:25 [11-25 21:26:59] (/home/user/VAR/train.py , line 276)=> [ep207] (training ) Lm: 6.491 (6.504), Lt: 5.732 (5.750), Acc m&t: 3.42 5.40, Remain: 1 day, 20:58:27, Finish: 2024-11-27 02:25 [11-25 21:27:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [ 0/1669] eta: 0:18:21 tlr: 0.00011 tnm: 0.41 Lm: 6.536 (6.536) Lt: 5.803 (5.803) Accm: 3.34 (3.34) Acct: 4.92 (4.92) proj_loss: -0.5999 (-0.5999) time: 0.6600 data: 0.0003 [11-25 21:27:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [ 0/1669] eta: 0:18:22 tlr: 0.00011 tnm: 0.41 Lm: 6.542 (6.542) Lt: 5.773 (5.773) Accm: 3.41 (3.41) Acct: 5.30 (5.30) proj_loss: -0.6091 (-0.6091) time: 0.6603 data: 0.0004 [11-25 21:27:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [ 0/1669] eta: 0:18:22 tlr: 0.00011 tnm: 0.41 Lm: 6.563 (6.563) Lt: 5.842 (5.842) Accm: 3.36 (3.36) Acct: 5.27 (5.27) proj_loss: -0.6106 (-0.6106) time: 0.6606 data: 0.0004 [11-25 21:27:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [ 0/1669] eta: 0:18:23 tlr: 0.00011 tnm: 0.41 Lm: 6.535 (6.535) Lt: 5.825 (5.825) Accm: 3.36 (3.36) Acct: 5.17 (5.17) proj_loss: -0.6177 (-0.6177) time: 0.6610 data: 0.0004 [11-25 21:31:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [ 417/1669] eta: 0:14:11 tlr: 0.00011 tnm: 0.38 Lm: 6.497 (6.497) Lt: 5.776 (5.776) Accm: 3.34 (3.34) Acct: 5.10 (5.10) proj_loss: -0.6116 (-0.6116) time: 0.7408 data: 0.0003 [11-25 21:31:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [ 417/1669] eta: 0:14:11 tlr: 0.00011 tnm: 0.38 Lm: 6.456 (6.456) Lt: 5.715 (5.715) Accm: 3.63 (3.63) Acct: 5.56 (5.56) proj_loss: -0.6054 (-0.6054) time: 0.7408 data: 0.0003 [11-25 21:31:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [ 417/1669] eta: 0:14:11 tlr: 0.00011 tnm: 0.38 Lm: 6.484 (6.484) Lt: 5.738 (5.738) Accm: 3.47 (3.47) Acct: 5.38 (5.38) proj_loss: -0.6110 (-0.6110) time: 0.7408 data: 0.0003 [11-25 21:31:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [ 417/1669] eta: 0:14:11 tlr: 0.00011 tnm: 0.38 Lm: 6.540 (6.540) Lt: 5.767 (5.767) Accm: 3.47 (3.47) Acct: 5.41 (5.41) proj_loss: -0.5960 (-0.5960) time: 0.7408 data: 0.0003 [11-25 21:36:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [ 834/1669] eta: 0:09:38 tlr: 0.00011 tnm: 0.39 Lm: 6.542 (6.589) Lt: 5.773 (5.845) Accm: 3.41 (3.26) Acct: 5.30 (5.13) proj_loss: -0.6091 (-0.6008) time: 0.6796 data: 0.0003 [11-25 21:36:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [ 834/1669] eta: 0:09:38 tlr: 0.00011 tnm: 0.39 Lm: 6.535 (6.482) Lt: 5.803 (5.748) Accm: 3.34 (3.48) Acct: 4.92 (5.31) proj_loss: -0.6052 (-0.6053) time: 0.6796 data: 0.0003 [11-25 21:36:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [ 834/1669] eta: 0:09:38 tlr: 0.00011 tnm: 0.39 Lm: 6.406 (6.456) Lt: 5.641 (5.706) Accm: 3.58 (3.55) Acct: 5.49 (5.49) proj_loss: -0.6106 (-0.6054) time: 0.6796 data: 0.0003 [11-25 21:36:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [ 834/1669] eta: 0:09:38 tlr: 0.00011 tnm: 0.39 Lm: 6.509 (6.501) Lt: 5.728 (5.757) Accm: 3.33 (3.19) Acct: 5.03 (4.94) proj_loss: -0.6060 (-0.6097) time: 0.6796 data: 0.0003 [11-25 21:41:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [1251/1669] eta: 0:04:51 tlr: 0.00011 tnm: 0.39 Lm: 6.485 (6.491) Lt: 5.722 (5.736) Accm: 3.34 (3.23) Acct: 5.10 (5.09) proj_loss: -0.6057 (-0.6074) time: 0.6762 data: 0.0003 [11-25 21:41:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [1251/1669] eta: 0:04:51 tlr: 0.00011 tnm: 0.39 Lm: 6.484 (6.494) Lt: 5.741 (5.756) Accm: 3.47 (3.46) Acct: 5.38 (5.35) proj_loss: -0.6110 (-0.6087) time: 0.6762 data: 0.0003 [11-25 21:41:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [1251/1669] eta: 0:04:51 tlr: 0.00011 tnm: 0.39 Lm: 6.520 (6.488) Lt: 5.765 (5.743) Accm: 3.42 (3.48) Acct: 5.12 (5.31) proj_loss: -0.6026 (-0.6040) time: 0.6763 data: 0.0003 [11-25 21:41:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [1251/1669] eta: 0:04:51 tlr: 0.00011 tnm: 0.39 Lm: 6.567 (6.590) Lt: 5.799 (5.840) Accm: 3.15 (3.17) Acct: 4.92 (4.96) proj_loss: -0.6041 (-0.6004) time: 0.6763 data: 0.0003 [11-25 21:46:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.461 (6.481) Lt: 5.717 (5.730) Accm: 3.35 (3.28) Acct: 5.17 (5.15) proj_loss: -0.6060 (-0.6116) time: 0.6764 data: 0.0016 [11-25 21:46:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.591 (6.604) Lt: 5.825 (5.858) Accm: 2.88 (3.08) Acct: 4.55 (4.85) proj_loss: -0.6067 (-0.6017) time: 0.6764 data: 0.0019 [11-25 21:46:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.535 (6.514) Lt: 5.803 (5.771) Accm: 3.34 (3.41) Acct: 4.92 (5.20) proj_loss: -0.6052 (-0.6043) time: 0.6764 data: 0.0013 [11-25 21:46:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 208/350] Total time: 0:19:18 (0.694 s / it) [11-25 21:46:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 208/350] [1668/1669] eta: 0:00:00 tlr: 0.00011 tnm: 0.39 Lm: 6.527 (6.501) Lt: 5.787 (5.762) Accm: 3.42 (3.45) Acct: 5.30 (5.34) proj_loss: -0.6115 (-0.6097) time: 0.6764 data: 0.0019 [11-25 21:46:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 208/350] Total time: 0:19:18 (0.694 s / it) [11-25 21:46:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 208/350] Total time: 0:19:18 (0.694 s / it) [11-25 21:46:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 208/350] Total time: 0:19:18 (0.694 s / it) [11-25 21:46:18] (/home/user/VAR/train.py , line 276)=> [ep208] (training ) Lm: 6.491 (6.511), Lt: 5.732 (5.758), Acc m&t: 3.42 5.40, Remain: 1 day, 20:32:46, Finish: 2024-11-27 02:19 [11-25 21:46:18] (/home/user/VAR/train.py , line 276)=> [ep208] (training ) Lm: 6.491 (6.511), Lt: 5.732 (5.758), Acc m&t: 3.42 5.40, Remain: 1 day, 20:32:31, Finish: 2024-11-27 02:18 [11-25 21:46:18] (/home/user/VAR/train.py , line 276)=> [ep208] (training ) Lm: 6.491 (6.511), Lt: 5.732 (5.758), Acc m&t: 3.42 5.40, Remain: 1 day, 20:32:58, Finish: 2024-11-27 02:19 [11-25 21:46:18] (/home/user/VAR/train.py , line 276)=> [ep208] (training ) Lm: 6.491 (6.511), Lt: 5.732 (5.758), Acc m&t: 3.42 5.40, Remain: 1 day, 20:32:49, Finish: 2024-11-27 02:19 [11-25 21:46:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [ 0/1669] eta: 0:18:36 tlr: 0.00011 tnm: 0.37 Lm: 6.339 (6.339) Lt: 5.601 (5.601) Accm: 3.90 (3.90) Acct: 5.87 (5.87) proj_loss: -0.6272 (-0.6272) time: 0.6689 data: 0.0004 [11-25 21:46:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [ 0/1669] eta: 0:18:37 tlr: 0.00011 tnm: 0.37 Lm: 6.552 (6.552) Lt: 5.838 (5.838) Accm: 3.38 (3.38) Acct: 5.30 (5.30) proj_loss: -0.6188 (-0.6188) time: 0.6695 data: 0.0003 [11-25 21:46:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [ 0/1669] eta: 0:18:36 tlr: 0.00011 tnm: 0.37 Lm: 6.497 (6.497) Lt: 5.723 (5.723) Accm: 3.37 (3.37) Acct: 5.22 (5.22) proj_loss: -0.6034 (-0.6034) time: 0.6690 data: 0.0004 [11-25 21:46:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [ 0/1669] eta: 0:18:37 tlr: 0.00011 tnm: 0.37 Lm: 6.470 (6.470) Lt: 5.743 (5.743) Accm: 3.24 (3.24) Acct: 5.34 (5.34) proj_loss: -0.6031 (-0.6031) time: 0.6693 data: 0.0004 [11-25 21:51:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [ 417/1669] eta: 0:14:06 tlr: 0.00011 tnm: 0.39 Lm: 6.508 (6.508) Lt: 5.738 (5.738) Accm: 3.23 (3.23) Acct: 5.26 (5.26) proj_loss: -0.5910 (-0.5910) time: 0.6750 data: 0.0003 [11-25 21:51:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [ 417/1669] eta: 0:14:06 tlr: 0.00011 tnm: 0.39 Lm: 6.428 (6.428) Lt: 5.675 (5.675) Accm: 3.62 (3.62) Acct: 5.55 (5.55) proj_loss: -0.6179 (-0.6179) time: 0.6750 data: 0.0003 [11-25 21:51:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [ 417/1669] eta: 0:14:06 tlr: 0.00011 tnm: 0.39 Lm: 6.496 (6.496) Lt: 5.727 (5.727) Accm: 3.39 (3.39) Acct: 5.39 (5.39) proj_loss: -0.6056 (-0.6056) time: 0.6750 data: 0.0003 [11-25 21:51:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [ 417/1669] eta: 0:14:06 tlr: 0.00011 tnm: 0.39 Lm: 6.522 (6.522) Lt: 5.755 (5.755) Accm: 3.52 (3.52) Acct: 5.63 (5.63) proj_loss: -0.6166 (-0.6166) time: 0.6750 data: 0.0003 [11-25 21:55:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [ 834/1669] eta: 0:09:24 tlr: 0.0001 tnm: 0.38 Lm: 6.492 (6.430) Lt: 5.672 (5.651) Accm: 3.66 (3.82) Acct: 5.96 (6.11) proj_loss: -0.6145 (-0.6089) time: 0.6760 data: 0.0003 [11-25 21:55:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [ 834/1669] eta: 0:09:24 tlr: 0.0001 tnm: 0.38 Lm: 6.497 (6.507) Lt: 5.731 (5.752) Accm: 3.37 (3.27) Acct: 5.22 (5.13) proj_loss: -0.6078 (-0.6093) time: 0.6760 data: 0.0003 [11-25 21:55:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [ 834/1669] eta: 0:09:24 tlr: 0.0001 tnm: 0.38 Lm: 6.424 (6.427) Lt: 5.682 (5.678) Accm: 3.58 (3.61) Acct: 5.48 (5.53) proj_loss: -0.6086 (-0.6083) time: 0.6760 data: 0.0003 [11-25 21:55:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [ 834/1669] eta: 0:09:24 tlr: 0.0001 tnm: 0.38 Lm: 6.470 (6.483) Lt: 5.733 (5.724) Accm: 3.24 (3.27) Acct: 5.29 (5.27) proj_loss: -0.5971 (-0.5930) time: 0.6760 data: 0.0005 [11-25 22:00:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [1251/1669] eta: 0:04:42 tlr: 0.0001 tnm: 0.39 Lm: 6.451 (6.455) Lt: 5.714 (5.680) Accm: 3.29 (3.42) Acct: 5.31 (5.46) proj_loss: -0.6001 (-0.5974) time: 0.6749 data: 0.0002 [11-25 22:00:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [1251/1669] eta: 0:04:42 tlr: 0.0001 tnm: 0.39 Lm: 6.471 (6.476) Lt: 5.716 (5.723) Accm: 3.46 (3.49) Acct: 5.35 (5.40) proj_loss: -0.6098 (-0.6089) time: 0.6749 data: 0.0003 [11-25 22:00:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [1251/1669] eta: 0:04:42 tlr: 0.0001 tnm: 0.39 Lm: 6.522 (6.462) Lt: 5.740 (5.690) Accm: 3.52 (3.62) Acct: 5.63 (5.73) proj_loss: -0.6044 (-0.6053) time: 0.6749 data: 0.0003 [11-25 22:00:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [1251/1669] eta: 0:04:42 tlr: 0.0001 tnm: 0.39 Lm: 6.496 (6.467) Lt: 5.727 (5.691) Accm: 3.39 (3.36) Acct: 5.39 (5.38) proj_loss: -0.6056 (-0.6041) time: 0.6749 data: 0.0003 [11-25 22:05:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.39 Lm: 6.497 (6.493) Lt: 5.731 (5.712) Accm: 3.37 (3.31) Acct: 5.22 (5.28) proj_loss: -0.6034 (-0.6002) time: 0.6767 data: 0.0017 [11-25 22:05:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 209/350] Total time: 0:18:52 (0.678 s / it) [11-25 22:05:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.39 Lm: 6.424 (6.465) Lt: 5.682 (5.701) Accm: 3.58 (3.53) Acct: 5.48 (5.54) proj_loss: -0.6086 (-0.6060) time: 0.6767 data: 0.0016 [11-25 22:05:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.39 Lm: 6.470 (6.459) Lt: 5.695 (5.683) Accm: 3.33 (3.43) Acct: 5.34 (5.45) proj_loss: -0.5971 (-0.5958) time: 0.6767 data: 0.0021 [11-25 22:05:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 209/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.39 Lm: 6.492 (6.463) Lt: 5.695 (5.691) Accm: 3.38 (3.55) Acct: 5.34 (5.65) proj_loss: -0.5943 (-0.6027) time: 0.6767 data: 0.0019 [11-25 22:05:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 209/350] Total time: 0:18:52 (0.678 s / it) [11-25 22:05:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 209/350] Total time: 0:18:52 (0.678 s / it) [11-25 22:05:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 209/350] Total time: 0:18:52 (0.678 s / it) [11-25 22:07:36] (home/user/VAR/trainer.py, line 114)=> FID: 3.3002940018099025 [11-25 22:07:37] (/home/user/VAR/train.py , line 259)=> [*] [ep209] (val 50000) Lm: 6.4976, Lt: 5.7393, Acc m&t: 3.38 5.31, Val cost: 146.01s [11-25 22:07:37] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-25 22:08:22] (/home/user/VAR/train.py , line 276)=> [ep209] (training ) Lm: 6.491 (6.498), Lt: 5.732 (5.739), Acc m&t: 3.42 5.40, Remain: 1 day, 20:22:51, Finish: 2024-11-27 02:28 [11-25 22:08:22] (/home/user/VAR/train.py , line 276)=> [ep209] (training ) Lm: 6.491 (6.498), Lt: 5.732 (5.739), Acc m&t: 3.42 5.40, Remain: 1 day, 20:22:22, Finish: 2024-11-27 02:27 [11-25 22:08:22] (/home/user/VAR/train.py , line 276)=> [ep209] (training ) Lm: 6.491 (6.498), Lt: 5.732 (5.739), Acc m&t: 3.42 5.40, Remain: 1 day, 20:22:20, Finish: 2024-11-27 02:27 [11-25 22:08:22] (/home/user/VAR/train.py , line 276)=> [ep209] (training ) Lm: 6.491 (6.498), Lt: 5.732 (5.739), Acc m&t: 3.42 5.40, Remain: 1 day, 20:21:58, Finish: 2024-11-27 02:27 [11-25 22:08:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 0/1669] eta: 0:18:48 tlr: 0.0001 tnm: 0.38 Lm: 6.600 (6.600) Lt: 5.855 (5.855) Accm: 3.13 (3.13) Acct: 4.82 (4.82) proj_loss: -0.5892 (-0.5892) time: 0.6760 data: 0.0003 [11-25 22:08:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 0/1669] eta: 0:18:48 tlr: 0.0001 tnm: 0.38 Lm: 6.662 (6.662) Lt: 6.020 (6.020) Accm: 3.20 (3.20) Acct: 4.86 (4.86) proj_loss: -0.6057 (-0.6057) time: 0.6759 data: 0.0003 [11-25 22:08:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 0/1669] eta: 0:18:46 tlr: 0.0001 tnm: 0.38 Lm: 6.480 (6.480) Lt: 5.707 (5.707) Accm: 3.36 (3.36) Acct: 4.96 (4.96) proj_loss: -0.6136 (-0.6136) time: 0.6751 data: 0.0004 [11-25 22:08:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 0/1669] eta: 0:18:55 tlr: 0.0001 tnm: 0.38 Lm: 6.565 (6.565) Lt: 5.820 (5.820) Accm: 3.38 (3.38) Acct: 5.25 (5.25) proj_loss: -0.6077 (-0.6077) time: 0.6801 data: 0.0004 [11-25 22:13:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 417/1669] eta: 0:15:11 tlr: 0.0001 tnm: 0.39 Lm: 6.609 (6.609) Lt: 5.871 (5.871) Accm: 3.20 (3.20) Acct: 5.04 (5.04) proj_loss: -0.6000 (-0.6000) time: 0.9247 data: 0.0003 [11-25 22:13:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 417/1669] eta: 0:15:11 tlr: 0.0001 tnm: 0.39 Lm: 6.462 (6.462) Lt: 5.689 (5.689) Accm: 3.49 (3.49) Acct: 5.27 (5.27) proj_loss: -0.6042 (-0.6042) time: 0.9247 data: 0.0003 [11-25 22:13:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 417/1669] eta: 0:15:11 tlr: 0.0001 tnm: 0.39 Lm: 6.570 (6.570) Lt: 5.890 (5.890) Accm: 3.35 (3.35) Acct: 5.17 (5.17) proj_loss: -0.6070 (-0.6070) time: 0.9247 data: 0.0003 [11-25 22:13:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 417/1669] eta: 0:15:11 tlr: 0.0001 tnm: 0.39 Lm: 6.584 (6.584) Lt: 5.822 (5.822) Accm: 3.28 (3.28) Acct: 5.08 (5.08) proj_loss: -0.5979 (-0.5979) time: 0.9247 data: 0.0003 [11-25 22:18:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 834/1669] eta: 0:09:53 tlr: 0.0001 tnm: 0.41 Lm: 6.567 (6.569) Lt: 5.828 (5.824) Accm: 3.13 (3.19) Acct: 4.84 (5.00) proj_loss: -0.6017 (-0.5991) time: 0.6777 data: 0.0003 [11-25 22:18:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 834/1669] eta: 0:09:53 tlr: 0.0001 tnm: 0.41 Lm: 6.445 (6.443) Lt: 5.672 (5.672) Accm: 3.59 (3.52) Acct: 5.58 (5.38) proj_loss: -0.6136 (-0.6101) time: 0.6777 data: 0.0003 [11-25 22:18:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 834/1669] eta: 0:09:53 tlr: 0.0001 tnm: 0.41 Lm: 6.592 (6.604) Lt: 5.828 (5.857) Accm: 3.02 (3.13) Acct: 4.84 (4.92) proj_loss: -0.6077 (-0.6037) time: 0.6777 data: 0.0003 [11-25 22:18:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 834/1669] eta: 0:09:53 tlr: 0.0001 tnm: 0.41 Lm: 6.477 (6.533) Lt: 5.760 (5.826) Accm: 3.26 (3.32) Acct: 5.22 (5.19) proj_loss: -0.6057 (-0.6061) time: 0.6777 data: 0.0003 [11-25 22:22:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1251/1669] eta: 0:04:52 tlr: 0.0001 tnm: 0.41 Lm: 6.468 (6.491) Lt: 5.729 (5.760) Accm: 3.38 (3.47) Acct: 5.35 (5.42) proj_loss: -0.6070 (-0.6106) time: 0.6783 data: 0.0003 [11-25 22:22:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1251/1669] eta: 0:04:52 tlr: 0.0001 tnm: 0.41 Lm: 6.579 (6.586) Lt: 5.824 (5.843) Accm: 3.16 (3.17) Acct: 4.94 (4.95) proj_loss: -0.6087 (-0.6052) time: 0.6783 data: 0.0003 [11-25 22:22:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1251/1669] eta: 0:04:52 tlr: 0.0001 tnm: 0.41 Lm: 6.462 (6.458) Lt: 5.689 (5.697) Accm: 3.47 (3.43) Acct: 5.27 (5.22) proj_loss: -0.6177 (-0.6191) time: 0.6783 data: 0.0003 [11-25 22:22:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1251/1669] eta: 0:04:52 tlr: 0.0001 tnm: 0.41 Lm: 6.553 (6.526) Lt: 5.808 (5.778) Accm: 3.28 (3.42) Acct: 5.09 (5.35) proj_loss: -0.5997 (-0.5988) time: 0.6783 data: 0.0003 [11-25 22:27:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.539 (6.516) Lt: 5.789 (5.759) Accm: 3.43 (3.46) Acct: 5.34 (5.43) proj_loss: -0.5977 (-0.5962) time: 0.6783 data: 0.0018 [11-25 22:27:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 210/350] Total time: 0:19:17 (0.694 s / it) [11-25 22:27:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.477 (6.499) Lt: 5.760 (5.761) Accm: 3.32 (3.44) Acct: 5.22 (5.36) proj_loss: -0.6084 (-0.6122) time: 0.6783 data: 0.0020 [11-25 22:27:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.480 (6.485) Lt: 5.707 (5.722) Accm: 3.36 (3.34) Acct: 4.96 (5.12) proj_loss: -0.6136 (-0.6119) time: 0.6783 data: 0.0015 [11-25 22:27:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.565 (6.560) Lt: 5.820 (5.811) Accm: 3.29 (3.25) Acct: 5.04 (5.05) proj_loss: -0.6077 (-0.6033) time: 0.6783 data: 0.0016 [11-25 22:27:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 210/350] Total time: 0:19:17 (0.694 s / it) [11-25 22:27:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 210/350] Total time: 0:19:17 (0.694 s / it) [11-25 22:27:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 210/350] Total time: 0:19:17 (0.694 s / it) [11-25 22:27:40] (/home/user/VAR/train.py , line 276)=> [ep210] (training ) Lm: 6.491 (6.507), Lt: 5.732 (5.752), Acc m&t: 3.42 5.40, Remain: 1 day, 20:00:47, Finish: 2024-11-27 02:28 [11-25 22:27:40] (/home/user/VAR/train.py , line 276)=> [ep210] (training ) Lm: 6.491 (6.507), Lt: 5.732 (5.752), Acc m&t: 3.42 5.40, Remain: 1 day, 20:01:10, Finish: 2024-11-27 02:28 [11-25 22:27:40] (/home/user/VAR/train.py , line 276)=> [ep210] (training ) Lm: 6.491 (6.507), Lt: 5.732 (5.752), Acc m&t: 3.42 5.40, Remain: 1 day, 20:00:52, Finish: 2024-11-27 02:28 [11-25 22:27:40] (/home/user/VAR/train.py , line 276)=> [ep210] (training ) Lm: 6.491 (6.507), Lt: 5.732 (5.752), Acc m&t: 3.42 5.40, Remain: 1 day, 20:01:26, Finish: 2024-11-27 02:29 [11-25 22:27:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 0/1669] eta: 0:18:14 tlr: 0.0001 tnm: 0.40 Lm: 6.442 (6.442) Lt: 5.655 (5.655) Accm: 3.66 (3.66) Acct: 6.16 (6.16) proj_loss: -0.5971 (-0.5971) time: 0.6556 data: 0.0004 [11-25 22:27:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 0/1669] eta: 0:18:15 tlr: 0.0001 tnm: 0.40 Lm: 6.536 (6.536) Lt: 5.783 (5.783) Accm: 3.18 (3.18) Acct: 5.04 (5.04) proj_loss: -0.6199 (-0.6199) time: 0.6561 data: 0.0004 [11-25 22:27:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 0/1669] eta: 0:18:14 tlr: 0.0001 tnm: 0.40 Lm: 6.390 (6.390) Lt: 5.630 (5.630) Accm: 3.45 (3.45) Acct: 5.54 (5.54) proj_loss: -0.6050 (-0.6050) time: 0.6561 data: 0.0003 [11-25 22:27:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 0/1669] eta: 0:18:15 tlr: 0.0001 tnm: 0.40 Lm: 6.473 (6.473) Lt: 5.646 (5.646) Accm: 3.46 (3.46) Acct: 5.70 (5.70) proj_loss: -0.6001 (-0.6001) time: 0.6561 data: 0.0003 [11-25 22:32:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 417/1669] eta: 0:14:13 tlr: 0.0001 tnm: 0.38 Lm: 6.532 (6.532) Lt: 5.758 (5.758) Accm: 3.35 (3.35) Acct: 5.53 (5.53) proj_loss: -0.6045 (-0.6045) time: 0.7429 data: 0.0003 [11-25 22:32:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 417/1669] eta: 0:14:13 tlr: 0.0001 tnm: 0.38 Lm: 6.524 (6.524) Lt: 5.763 (5.763) Accm: 3.41 (3.41) Acct: 5.70 (5.70) proj_loss: -0.6086 (-0.6086) time: 0.7429 data: 0.0003 [11-25 22:32:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 417/1669] eta: 0:14:13 tlr: 0.0001 tnm: 0.38 Lm: 6.500 (6.500) Lt: 5.746 (5.746) Accm: 3.22 (3.22) Acct: 5.11 (5.11) proj_loss: -0.6108 (-0.6108) time: 0.7429 data: 0.0003 [11-25 22:32:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 417/1669] eta: 0:14:13 tlr: 0.0001 tnm: 0.38 Lm: 6.419 (6.419) Lt: 5.643 (5.643) Accm: 3.57 (3.57) Acct: 5.77 (5.77) proj_loss: -0.6097 (-0.6097) time: 0.7429 data: 0.0003 [11-25 22:37:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 834/1669] eta: 0:09:42 tlr: 0.0001 tnm: 0.39 Lm: 6.449 (6.461) Lt: 5.656 (5.703) Accm: 3.45 (3.42) Acct: 5.54 (5.52) proj_loss: -0.6145 (-0.6114) time: 0.6745 data: 0.0003 [11-25 22:37:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 834/1669] eta: 0:09:42 tlr: 0.0001 tnm: 0.39 Lm: 6.591 (6.591) Lt: 5.871 (5.833) Accm: 3.24 (3.14) Acct: 5.35 (5.08) proj_loss: -0.6089 (-0.6060) time: 0.6745 data: 0.0003 [11-25 22:37:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 834/1669] eta: 0:09:42 tlr: 0.0001 tnm: 0.39 Lm: 6.553 (6.534) Lt: 5.723 (5.750) Accm: 3.27 (3.36) Acct: 5.48 (5.62) proj_loss: -0.6084 (-0.6085) time: 0.6745 data: 0.0003 [11-25 22:37:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 834/1669] eta: 0:09:42 tlr: 0.0001 tnm: 0.39 Lm: 6.536 (6.537) Lt: 5.783 (5.781) Accm: 3.18 (3.11) Acct: 5.04 (4.84) proj_loss: -0.6017 (-0.6006) time: 0.6745 data: 0.0003 [11-25 22:42:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1251/1669] eta: 0:04:53 tlr: 0.0001 tnm: 0.41 Lm: 6.500 (6.508) Lt: 5.746 (5.737) Accm: 3.22 (3.18) Acct: 5.11 (4.95) proj_loss: -0.5910 (-0.5945) time: 0.6783 data: 0.0002 [11-25 22:42:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1251/1669] eta: 0:04:53 tlr: 0.0001 tnm: 0.41 Lm: 6.496 (6.486) Lt: 5.739 (5.739) Accm: 3.28 (3.32) Acct: 5.28 (5.29) proj_loss: -0.6123 (-0.6111) time: 0.6783 data: 0.0003 [11-25 22:42:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1251/1669] eta: 0:04:53 tlr: 0.0001 tnm: 0.41 Lm: 6.512 (6.518) Lt: 5.749 (5.756) Accm: 3.39 (3.40) Acct: 5.41 (5.55) proj_loss: -0.6142 (-0.6121) time: 0.6783 data: 0.0003 [11-25 22:42:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1251/1669] eta: 0:04:53 tlr: 0.0001 tnm: 0.41 Lm: 6.532 (6.515) Lt: 5.758 (5.766) Accm: 3.35 (3.38) Acct: 5.53 (5.37) proj_loss: -0.6089 (-0.6080) time: 0.6783 data: 0.0003 [11-25 22:47:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.40 Lm: 6.473 (6.498) Lt: 5.698 (5.753) Accm: 3.46 (3.42) Acct: 5.37 (5.37) proj_loss: -0.6089 (-0.6052) time: 0.6785 data: 0.0019 [11-25 22:47:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 211/350] Total time: 0:19:23 (0.697 s / it) [11-25 22:47:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.40 Lm: 6.553 (6.551) Lt: 5.774 (5.810) Accm: 3.27 (3.25) Acct: 5.34 (5.21) proj_loss: -0.6084 (-0.6097) time: 0.6785 data: 0.0013 [11-25 22:47:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.40 Lm: 6.449 (6.467) Lt: 5.668 (5.725) Accm: 3.45 (3.43) Acct: 5.54 (5.35) proj_loss: -0.6102 (-0.6099) time: 0.6785 data: 0.0018 [11-25 22:47:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.40 Lm: 6.463 (6.477) Lt: 5.709 (5.713) Accm: 3.27 (3.31) Acct: 5.18 (5.20) proj_loss: -0.6017 (-0.6015) time: 0.6785 data: 0.0016 [11-25 22:47:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 211/350] Total time: 0:19:23 (0.697 s / it) [11-25 22:47:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 211/350] Total time: 0:19:23 (0.697 s / it) [11-25 22:47:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 211/350] Total time: 0:19:23 (0.697 s / it) [11-25 22:47:03] (/home/user/VAR/train.py , line 276)=> [ep211] (training ) Lm: 6.491 (6.505), Lt: 5.732 (5.753), Acc m&t: 3.42 5.40, Remain: 1 day, 19:45:24, Finish: 2024-11-27 02:32 [11-25 22:47:03] (/home/user/VAR/train.py , line 276)=> [ep211] (training ) Lm: 6.491 (6.505), Lt: 5.732 (5.753), Acc m&t: 3.42 5.40, Remain: 1 day, 19:45:26, Finish: 2024-11-27 02:32 [11-25 22:47:03] (/home/user/VAR/train.py , line 276)=> [ep211] (training ) Lm: 6.491 (6.505), Lt: 5.732 (5.753), Acc m&t: 3.42 5.40, Remain: 1 day, 19:45:40, Finish: 2024-11-27 02:32 [11-25 22:47:03] (/home/user/VAR/train.py , line 276)=> [ep211] (training ) Lm: 6.491 (6.505), Lt: 5.732 (5.753), Acc m&t: 3.42 5.40, Remain: 1 day, 19:45:42, Finish: 2024-11-27 02:32 [11-25 22:47:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 0/1669] eta: 0:18:27 tlr: 0.0001 tnm: 0.39 Lm: 6.437 (6.437) Lt: 5.667 (5.667) Accm: 3.61 (3.61) Acct: 5.72 (5.72) proj_loss: -0.6153 (-0.6153) time: 0.6636 data: 0.0004 [11-25 22:47:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 0/1669] eta: 0:18:28 tlr: 0.0001 tnm: 0.39 Lm: 6.489 (6.489) Lt: 5.739 (5.739) Accm: 3.37 (3.37) Acct: 5.20 (5.20) proj_loss: -0.6052 (-0.6052) time: 0.6639 data: 0.0004 [11-25 22:47:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 0/1669] eta: 0:18:28 tlr: 0.0001 tnm: 0.39 Lm: 6.453 (6.453) Lt: 5.654 (5.654) Accm: 3.51 (3.51) Acct: 5.60 (5.60) proj_loss: -0.6070 (-0.6070) time: 0.6642 data: 0.0004 [11-25 22:47:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 0/1669] eta: 0:18:29 tlr: 0.0001 tnm: 0.39 Lm: 6.498 (6.498) Lt: 5.695 (5.695) Accm: 3.29 (3.29) Acct: 5.29 (5.29) proj_loss: -0.5725 (-0.5725) time: 0.6645 data: 0.0004 [11-25 22:51:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 417/1669] eta: 0:14:06 tlr: 0.0001 tnm: 0.40 Lm: 6.528 (6.528) Lt: 5.770 (5.770) Accm: 3.18 (3.18) Acct: 4.96 (4.96) proj_loss: -0.5925 (-0.5925) time: 0.6765 data: 0.0002 [11-25 22:51:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 417/1669] eta: 0:14:06 tlr: 0.0001 tnm: 0.40 Lm: 6.500 (6.500) Lt: 5.725 (5.725) Accm: 3.37 (3.37) Acct: 5.20 (5.20) proj_loss: -0.6010 (-0.6010) time: 0.6765 data: 0.0003 [11-25 22:51:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 417/1669] eta: 0:14:07 tlr: 0.0001 tnm: 0.40 Lm: 6.420 (6.420) Lt: 5.650 (5.650) Accm: 3.63 (3.63) Acct: 5.78 (5.78) proj_loss: -0.6103 (-0.6103) time: 0.6765 data: 0.0003 [11-25 22:51:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 417/1669] eta: 0:14:06 tlr: 0.0001 tnm: 0.40 Lm: 6.410 (6.410) Lt: 5.640 (5.640) Accm: 3.54 (3.54) Acct: 5.60 (5.60) proj_loss: -0.6069 (-0.6069) time: 0.6765 data: 0.0003 [11-25 22:56:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 834/1669] eta: 0:09:25 tlr: 0.0001 tnm: 0.37 Lm: 6.453 (6.475) Lt: 5.654 (5.716) Accm: 3.51 (3.43) Acct: 5.60 (5.45) proj_loss: -0.6067 (-0.6048) time: 0.6774 data: 0.0003 [11-25 22:56:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 834/1669] eta: 0:09:25 tlr: 0.0001 tnm: 0.37 Lm: 6.407 (6.415) Lt: 5.636 (5.645) Accm: 3.64 (3.71) Acct: 5.84 (5.86) proj_loss: -0.6052 (-0.6053) time: 0.6774 data: 0.0003 [11-25 22:56:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 834/1669] eta: 0:09:25 tlr: 0.0001 tnm: 0.37 Lm: 6.549 (6.535) Lt: 5.787 (5.776) Accm: 3.29 (3.21) Acct: 5.29 (5.09) proj_loss: -0.5817 (-0.5889) time: 0.6774 data: 0.0002 [11-25 22:56:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 834/1669] eta: 0:09:25 tlr: 0.0001 tnm: 0.37 Lm: 6.510 (6.515) Lt: 5.739 (5.748) Accm: 3.37 (3.33) Acct: 5.20 (5.21) proj_loss: -0.5998 (-0.6006) time: 0.6774 data: 0.0003 [11-25 23:01:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [1251/1669] eta: 0:04:42 tlr: 0.0001 tnm: 0.38 Lm: 6.500 (6.474) Lt: 5.725 (5.692) Accm: 3.37 (3.48) Acct: 5.22 (5.57) proj_loss: -0.6025 (-0.6053) time: 0.6776 data: 0.0003 [11-25 23:01:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [1251/1669] eta: 0:04:42 tlr: 0.0001 tnm: 0.38 Lm: 6.415 (6.417) Lt: 5.651 (5.652) Accm: 3.63 (3.67) Acct: 5.78 (5.76) proj_loss: -0.6058 (-0.6056) time: 0.6776 data: 0.0003 [11-25 23:01:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [1251/1669] eta: 0:04:42 tlr: 0.0001 tnm: 0.38 Lm: 6.453 (6.469) Lt: 5.671 (5.709) Accm: 3.45 (3.42) Acct: 5.47 (5.42) proj_loss: -0.6036 (-0.6032) time: 0.6776 data: 0.0003 [11-25 23:01:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [1251/1669] eta: 0:04:42 tlr: 0.0001 tnm: 0.38 Lm: 6.524 (6.521) Lt: 5.742 (5.756) Accm: 3.29 (3.28) Acct: 5.32 (5.28) proj_loss: -0.5885 (-0.5905) time: 0.6776 data: 0.0003 ======================================================= RESTART [11-26 00:02:12] ======================================================= ======================================================= RESTART [11-26 00:02:12] ======================================================= ======================================================= RESTART [11-26 00:02:12] ======================================================= ======================================================= RESTART [11-26 00:02:12] ======================================================= [11-26 00:02:12] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-26 00:02:12] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-26 00:02:12] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-26 00:03:08] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-26 00:03:08] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add branch : main commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 } [11-26 00:03:08] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-26 00:03:11] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-26 00:03:11] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> [11-26 00:03:11] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 00:03:11] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> [11-26 00:03:11] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 00:03:11] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-26 00:03:11] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep210, it0 [11-26 00:03:11] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-26 00:02:12] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-26 00:02:12] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-26 00:02:12] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-26 00:03:08] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-26 00:03:08] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 branch : main commit_msg : add } [11-26 00:03:08] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-26 00:03:11] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-26 00:03:11] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> [11-26 00:03:11] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 00:03:11] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> [11-26 00:03:11] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 00:03:11] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-26 00:03:11] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep210, it0 [11-26 00:03:11] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-26 00:02:12] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-26 00:02:12] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-26 00:02:12] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-26 00:03:08] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-26 00:03:08] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 commit_msg : add branch : main } [11-26 00:03:08] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-26 00:03:11] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-26 00:03:11] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> [11-26 00:03:11] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 00:03:11] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> [11-26 00:03:11] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 00:03:11] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-26 00:03:11] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep210, it0 [11-26 00:03:11] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-26 00:02:12] (er/VAR/utils/arg_util.py, line 227)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-26 00:02:12] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-26 00:02:12] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-26 00:03:08] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-26 00:03:08] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add commit_id : a2229298db3e1d35d196f16623f86dea25402aa2 branch : main } [11-26 00:03:08] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-26 00:03:11] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-26 00:03:11] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> [11-26 00:03:11] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 00:03:11] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 00:03:11] (e/user/VAR/utils/data.py, line 51)=> [11-26 00:03:11] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 00:03:11] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-best.pth ... [11-26 00:03:11] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep210, it0 [11-26 00:03:11] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ... [dataloader multi processing](*) finished! (47.25s) [dataloader multi processing](*) finished! (47.59s) [dataloader multi processing](*) finished! (47.76s) [dataloader multi processing](*) finished! (49.60s) [11-26 00:03:58] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-26 00:04:03] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 00:04:03] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 00:04:04] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-26 00:03:58] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-26 00:04:04] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 00:04:04] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 00:04:05] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-26 00:03:59] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-26 00:04:05] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 00:04:05] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 00:04:06] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-26 00:04:01] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-26 00:04:06] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 00:04:06] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 00:04:07] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-26 00:04:06] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-26 00:04:31] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-26 00:04:31] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-26 00:04:31] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-26 00:04:31] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-26 00:04:31] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank0] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-26 00:04:08] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-26 00:04:31] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-26 00:04:31] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-26 00:04:31] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-26 00:04:31] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-26 00:04:31] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank8] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-26 00:04:08] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-26 00:04:31] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-26 00:04:31] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-26 00:04:31] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-26 00:04:31] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-26 00:04:31] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank16] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-26 00:04:07] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-26 00:04:31] (/home/user/VAR/train.py , line 125)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-26 00:04:31] (/home/user/VAR/train.py , line 127)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-26 00:04:31] (/home/user/VAR/train.py , line 128)=> [INIT][#para] VAR=1085.77 [11-26 00:04:31] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-26 00:04:31] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank24] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-26 00:04:31] (/VAR/utils/lr_control.py, line 105)=> [11-26 00:04:31] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-26 00:04:33] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-26 00:04:33] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-26 00:04:33] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-26 00:04:33] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-26 00:19:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 0/1669] eta: 17 days, 13:51:24 tlr: 0.0001 tnm: 0.39 Lm: 6.702 (6.702) Lt: 6.034 (6.034) Accm: 2.94 (2.94) Acct: 4.46 (4.46) proj_loss: -0.6134 (-0.6134) time: 909.9370 data: 0.0006 [11-26 00:04:31] (/VAR/utils/lr_control.py, line 105)=> [11-26 00:04:31] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-26 00:04:32] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-26 00:04:32] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-26 00:04:32] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-26 00:04:32] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-26 00:19:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 0/1669] eta: 17 days, 13:48:24 tlr: 0.0001 tnm: 0.39 Lm: 6.585 (6.585) Lt: 5.847 (5.847) Accm: 3.34 (3.34) Acct: 5.04 (5.04) proj_loss: -0.5874 (-0.5874) time: 909.8290 data: 0.0006 [11-26 00:04:31] (/VAR/utils/lr_control.py, line 105)=> [11-26 00:04:31] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-26 00:04:32] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-26 00:04:32] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-26 00:04:33] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-26 00:04:33] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-26 00:19:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 0/1669] eta: 17 days, 13:47:14 tlr: 0.0001 tnm: 0.39 Lm: 6.581 (6.581) Lt: 5.840 (5.840) Accm: 3.18 (3.18) Acct: 5.03 (5.03) proj_loss: -0.6028 (-0.6028) time: 909.7868 data: 0.0007 [11-26 00:04:31] (/VAR/utils/lr_control.py, line 105)=> [11-26 00:04:31] (/home/user/VAR/train.py , line 143)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-26 00:04:32] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-26 00:04:32] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-26 00:04:32] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-26 00:04:32] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-26 00:19:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 0/1669] eta: 17 days, 13:30:40 tlr: 0.0001 tnm: 0.39 Lm: 6.481 (6.481) Lt: 5.725 (5.725) Accm: 3.51 (3.51) Acct: 5.54 (5.54) proj_loss: -0.6189 (-0.6189) time: 909.1912 data: 0.0006 [11-26 00:30:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 417/1669] eta: 1:16:15 tlr: 0.0001 tnm: 0.39 Lm: 6.422 (6.422) Lt: 5.627 (5.627) Accm: 3.66 (3.66) Acct: 5.92 (5.92) proj_loss: -0.6044 (-0.6044) time: 0.6718 data: 0.0003 [11-26 00:30:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 417/1669] eta: 1:16:17 tlr: 0.0001 tnm: 0.39 Lm: 6.570 (6.570) Lt: 5.862 (5.862) Accm: 3.15 (3.15) Acct: 4.92 (4.92) proj_loss: -0.6077 (-0.6077) time: 0.6718 data: 0.0003 [11-26 00:30:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 417/1669] eta: 1:16:17 tlr: 0.0001 tnm: 0.39 Lm: 6.583 (6.583) Lt: 5.818 (5.818) Accm: 3.30 (3.30) Acct: 5.15 (5.15) proj_loss: -0.5946 (-0.5946) time: 0.6718 data: 0.0002 [11-26 00:30:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 417/1669] eta: 1:16:17 tlr: 0.0001 tnm: 0.39 Lm: 6.636 (6.636) Lt: 5.923 (5.923) Accm: 2.95 (2.95) Acct: 4.60 (4.60) proj_loss: -0.5956 (-0.5956) time: 0.6718 data: 0.0003 [11-26 00:34:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 834/1669] eta: 0:30:07 tlr: 0.0001 tnm: 0.37 Lm: 6.581 (6.610) Lt: 5.840 (5.881) Accm: 3.18 (3.04) Acct: 4.79 (4.66) proj_loss: -0.6028 (-0.6013) time: 0.6728 data: 0.0003 [11-26 00:34:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 834/1669] eta: 0:30:08 tlr: 0.0001 tnm: 0.37 Lm: 6.582 (6.553) Lt: 5.789 (5.795) Accm: 3.34 (3.35) Acct: 5.25 (5.29) proj_loss: -0.6018 (-0.5975) time: 0.6728 data: 0.0002 [11-26 00:34:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 834/1669] eta: 0:30:08 tlr: 0.0001 tnm: 0.37 Lm: 6.553 (6.564) Lt: 5.776 (5.833) Accm: 2.98 (3.09) Acct: 4.68 (4.84) proj_loss: -0.6021 (-0.6021) time: 0.6728 data: 0.0003 [11-26 00:34:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [ 834/1669] eta: 0:30:07 tlr: 0.0001 tnm: 0.37 Lm: 6.457 (6.434) Lt: 5.725 (5.662) Accm: 3.80 (3.73) Acct: 5.96 (5.93) proj_loss: -0.6189 (-0.6102) time: 0.6728 data: 0.0003 [11-26 00:39:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1251/1669] eta: 0:11:37 tlr: 0.0001 tnm: 0.38 Lm: 6.469 (6.446) Lt: 5.729 (5.684) Accm: 3.66 (3.61) Acct: 5.75 (5.69) proj_loss: -0.6203 (-0.6156) time: 0.6736 data: 0.0003 [11-26 00:39:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1251/1669] eta: 0:11:37 tlr: 0.0001 tnm: 0.38 Lm: 6.570 (6.571) Lt: 5.818 (5.834) Accm: 3.20 (3.20) Acct: 4.91 (5.04) proj_loss: -0.6077 (-0.6048) time: 0.6736 data: 0.0003 [11-26 00:39:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1251/1669] eta: 0:11:37 tlr: 0.0001 tnm: 0.38 Lm: 6.495 (6.531) Lt: 5.733 (5.788) Accm: 3.17 (3.25) Acct: 5.03 (5.11) proj_loss: -0.6077 (-0.6089) time: 0.6736 data: 0.0003 [11-26 00:39:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1251/1669] eta: 0:11:37 tlr: 0.0001 tnm: 0.38 Lm: 6.538 (6.513) Lt: 5.769 (5.755) Accm: 3.39 (3.45) Acct: 5.41 (5.43) proj_loss: -0.6000 (-0.5977) time: 0.6736 data: 0.0003 [11-26 00:44:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1668/1669] eta: 0:00:01 tlr: 0.0001 tnm: 0.39 Lm: 6.494 (6.499) Lt: 5.748 (5.735) Accm: 3.45 (3.51) Acct: 5.58 (5.54) proj_loss: -0.5983 (-0.5970) time: 0.6772 data: 0.0014 [11-26 00:44:08] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 210/350] Total time: 0:39:28 (1.419 s / it) [11-26 00:44:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1668/1669] eta: 0:00:01 tlr: 0.0001 tnm: 0.39 Lm: 6.537 (6.532) Lt: 5.776 (5.789) Accm: 3.32 (3.27) Acct: 5.13 (5.11) proj_loss: -0.6064 (-0.6084) time: 0.6771 data: 0.0018 [11-26 00:44:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1668/1669] eta: 0:00:01 tlr: 0.0001 tnm: 0.39 Lm: 6.559 (6.541) Lt: 5.797 (5.803) Accm: 3.21 (3.30) Acct: 5.03 (5.16) proj_loss: -0.6028 (-0.6036) time: 0.6772 data: 0.0014 [11-26 00:44:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 210/350] [1668/1669] eta: 0:00:01 tlr: 0.0001 tnm: 0.39 Lm: 6.481 (6.482) Lt: 5.734 (5.721) Accm: 3.51 (3.52) Acct: 5.54 (5.55) proj_loss: -0.6189 (-0.6087) time: 0.6772 data: 0.0018 [11-26 00:44:08] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 210/350] Total time: 0:39:28 (1.419 s / it) [11-26 00:44:08] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 210/350] Total time: 0:39:28 (1.419 s / it) [11-26 00:44:08] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 210/350] Total time: 0:39:28 (1.419 s / it) [11-26 00:44:08] (/home/user/VAR/train.py , line 276)=> [ep210] (training ) Lm: 6.504 (6.504), Lt: 5.751 (5.751), Acc m&t: 3.37 5.32, Remain: 1 day, 19:55:59, Finish: 2024-11-27 04:40 [11-26 00:44:08] (/home/user/VAR/train.py , line 276)=> [ep210] (training ) Lm: 6.504 (6.504), Lt: 5.751 (5.751), Acc m&t: 3.37 5.32, Remain: 1 day, 19:56:25, Finish: 2024-11-27 04:40 [11-26 00:44:08] (/home/user/VAR/train.py , line 276)=> [ep210] (training ) Lm: 6.504 (6.504), Lt: 5.751 (5.751), Acc m&t: 3.37 5.32, Remain: 1 day, 19:55:51, Finish: 2024-11-27 04:39 [11-26 00:44:08] (/home/user/VAR/train.py , line 276)=> [ep210] (training ) Lm: 6.504 (6.504), Lt: 5.751 (5.751), Acc m&t: 3.37 5.32, Remain: 1 day, 19:55:58, Finish: 2024-11-27 04:40 [11-26 00:44:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 0/1669] eta: 0:18:20 tlr: 0.0001 tnm: 0.38 Lm: 6.530 (6.530) Lt: 5.755 (5.755) Accm: 3.21 (3.21) Acct: 5.13 (5.13) proj_loss: -0.6222 (-0.6222) time: 0.6593 data: 0.0004 [11-26 00:44:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 0/1669] eta: 0:18:04 tlr: 0.0001 tnm: 0.38 Lm: 6.441 (6.441) Lt: 5.613 (5.613) Accm: 3.47 (3.47) Acct: 5.35 (5.35) proj_loss: -0.6108 (-0.6108) time: 0.6499 data: 0.0003 [11-26 00:44:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 0/1669] eta: 0:18:05 tlr: 0.0001 tnm: 0.38 Lm: 6.405 (6.405) Lt: 5.647 (5.647) Accm: 3.64 (3.64) Acct: 5.84 (5.84) proj_loss: -0.6074 (-0.6074) time: 0.6506 data: 0.0003 [11-26 00:44:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 0/1669] eta: 0:18:05 tlr: 0.0001 tnm: 0.38 Lm: 6.394 (6.394) Lt: 5.644 (5.644) Accm: 3.76 (3.76) Acct: 5.85 (5.85) proj_loss: -0.5984 (-0.5984) time: 0.6504 data: 0.0004 [11-26 00:48:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 417/1669] eta: 0:14:02 tlr: 0.0001 tnm: 0.38 Lm: 6.514 (6.514) Lt: 5.768 (5.768) Accm: 3.46 (3.46) Acct: 5.39 (5.39) proj_loss: -0.6065 (-0.6065) time: 0.6726 data: 0.0003 [11-26 00:48:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 417/1669] eta: 0:14:02 tlr: 0.0001 tnm: 0.38 Lm: 6.495 (6.495) Lt: 5.712 (5.712) Accm: 3.37 (3.37) Acct: 5.31 (5.31) proj_loss: -0.6120 (-0.6120) time: 0.6726 data: 0.0003 [11-26 00:48:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 417/1669] eta: 0:14:02 tlr: 0.0001 tnm: 0.38 Lm: 6.442 (6.442) Lt: 5.671 (5.671) Accm: 3.45 (3.45) Acct: 5.58 (5.58) proj_loss: -0.6124 (-0.6124) time: 0.6726 data: 0.0002 [11-26 00:48:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 417/1669] eta: 0:14:02 tlr: 0.0001 tnm: 0.38 Lm: 6.512 (6.512) Lt: 5.739 (5.739) Accm: 3.20 (3.20) Acct: 4.87 (4.87) proj_loss: -0.6111 (-0.6111) time: 0.6727 data: 0.0002 [11-26 00:53:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 834/1669] eta: 0:09:27 tlr: 0.0001 tnm: 0.39 Lm: 6.583 (6.571) Lt: 5.865 (5.807) Accm: 2.94 (3.06) Acct: 4.61 (4.79) proj_loss: -0.6108 (-0.6086) time: 0.6706 data: 0.0002 [11-26 00:53:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 834/1669] eta: 0:09:27 tlr: 0.0001 tnm: 0.39 Lm: 6.530 (6.548) Lt: 5.755 (5.795) Accm: 3.21 (3.22) Acct: 5.13 (5.04) proj_loss: -0.6019 (-0.6001) time: 0.6706 data: 0.0002 [11-26 00:53:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 834/1669] eta: 0:09:27 tlr: 0.0001 tnm: 0.39 Lm: 6.551 (6.526) Lt: 5.724 (5.753) Accm: 3.23 (3.38) Acct: 5.29 (5.35) proj_loss: -0.6012 (-0.6048) time: 0.6706 data: 0.0003 [11-26 00:53:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [ 834/1669] eta: 0:09:27 tlr: 0.0001 tnm: 0.39 Lm: 6.478 (6.492) Lt: 5.695 (5.728) Accm: 3.26 (3.38) Acct: 5.32 (5.41) proj_loss: -0.6097 (-0.6115) time: 0.6706 data: 0.0002 [11-26 00:58:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1251/1669] eta: 0:04:42 tlr: 0.0001 tnm: 0.39 Lm: 6.521 (6.510) Lt: 5.769 (5.763) Accm: 3.26 (3.26) Acct: 5.20 (5.15) proj_loss: -0.6132 (-0.6128) time: 0.6730 data: 0.0002 [11-26 00:58:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1251/1669] eta: 0:04:42 tlr: 0.0001 tnm: 0.39 Lm: 6.495 (6.520) Lt: 5.712 (5.747) Accm: 3.26 (3.24) Acct: 5.26 (5.13) proj_loss: -0.5912 (-0.5952) time: 0.6730 data: 0.0002 [11-26 00:58:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1251/1669] eta: 0:04:42 tlr: 0.0001 tnm: 0.39 Lm: 6.542 (6.528) Lt: 5.756 (5.762) Accm: 3.19 (3.32) Acct: 5.29 (5.34) proj_loss: -0.6079 (-0.6095) time: 0.6730 data: 0.0003 [11-26 00:58:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1251/1669] eta: 0:04:42 tlr: 0.0001 tnm: 0.39 Lm: 6.512 (6.503) Lt: 5.739 (5.742) Accm: 3.20 (3.31) Acct: 4.98 (5.17) proj_loss: -0.6071 (-0.6059) time: 0.6730 data: 0.0002 [11-26 01:02:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.441 (6.479) Lt: 5.623 (5.718) Accm: 3.47 (3.41) Acct: 5.35 (5.37) proj_loss: -0.6035 (-0.6030) time: 0.6745 data: 0.0014 [11-26 01:02:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 211/350] Total time: 0:18:47 (0.676 s / it) [11-26 01:02:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.460 (6.488) Lt: 5.670 (5.723) Accm: 3.30 (3.35) Acct: 5.39 (5.32) proj_loss: -0.6019 (-0.6014) time: 0.6745 data: 0.0018 [11-26 01:02:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.478 (6.489) Lt: 5.695 (5.744) Accm: 3.26 (3.40) Acct: 5.32 (5.30) proj_loss: -0.6121 (-0.6127) time: 0.6745 data: 0.0019 [11-26 01:02:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 211/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.551 (6.558) Lt: 5.787 (5.810) Accm: 3.15 (3.22) Acct: 5.29 (5.11) proj_loss: -0.6012 (-0.6079) time: 0.6745 data: 0.0016 [11-26 01:02:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 211/350] Total time: 0:18:47 (0.676 s / it) [11-26 01:02:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 211/350] Total time: 0:18:47 (0.676 s / it) [11-26 01:02:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 211/350] Total time: 0:18:47 (0.676 s / it) [11-26 01:02:56] (/home/user/VAR/train.py , line 276)=> [ep211] (training ) Lm: 6.504 (6.504), Lt: 5.751 (5.751), Acc m&t: 3.39 5.32, Remain: 1 day, 19:29:07, Finish: 2024-11-27 04:32 [11-26 01:02:56] (/home/user/VAR/train.py , line 276)=> [ep211] (training ) Lm: 6.504 (6.504), Lt: 5.751 (5.751), Acc m&t: 3.39 5.32, Remain: 1 day, 19:28:49, Finish: 2024-11-27 04:31 [11-26 01:02:56] (/home/user/VAR/train.py , line 276)=> [ep211] (training ) Lm: 6.504 (6.504), Lt: 5.751 (5.751), Acc m&t: 3.39 5.32, Remain: 1 day, 19:28:24, Finish: 2024-11-27 04:31 [11-26 01:02:56] (/home/user/VAR/train.py , line 276)=> [ep211] (training ) Lm: 6.504 (6.504), Lt: 5.751 (5.751), Acc m&t: 3.39 5.32, Remain: 1 day, 19:28:37, Finish: 2024-11-27 04:31 [11-26 01:02:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 0/1669] eta: 0:18:21 tlr: 0.0001 tnm: 0.40 Lm: 6.524 (6.524) Lt: 5.794 (5.794) Accm: 3.36 (3.36) Acct: 5.15 (5.15) proj_loss: -0.5978 (-0.5978) time: 0.6602 data: 0.0003 [11-26 01:02:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 0/1669] eta: 0:18:21 tlr: 0.0001 tnm: 0.40 Lm: 6.507 (6.507) Lt: 5.780 (5.780) Accm: 3.49 (3.49) Acct: 5.37 (5.37) proj_loss: -0.5794 (-0.5794) time: 0.6598 data: 0.0003 [11-26 01:02:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 0/1669] eta: 0:18:22 tlr: 0.0001 tnm: 0.40 Lm: 6.486 (6.486) Lt: 5.700 (5.700) Accm: 3.65 (3.65) Acct: 5.85 (5.85) proj_loss: -0.6046 (-0.6046) time: 0.6605 data: 0.0003 [11-26 01:02:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 0/1669] eta: 0:18:21 tlr: 0.0001 tnm: 0.40 Lm: 6.402 (6.402) Lt: 5.611 (5.611) Accm: 3.77 (3.77) Acct: 5.96 (5.96) proj_loss: -0.6068 (-0.6068) time: 0.6600 data: 0.0003 [11-26 01:07:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 417/1669] eta: 0:14:01 tlr: 0.0001 tnm: 0.39 Lm: 6.405 (6.405) Lt: 5.623 (5.623) Accm: 3.58 (3.58) Acct: 5.63 (5.63) proj_loss: -0.6066 (-0.6066) time: 0.6732 data: 0.0003 [11-26 01:07:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 417/1669] eta: 0:14:01 tlr: 0.0001 tnm: 0.39 Lm: 6.555 (6.555) Lt: 5.827 (5.827) Accm: 3.31 (3.31) Acct: 5.09 (5.09) proj_loss: -0.5961 (-0.5961) time: 0.6732 data: 0.0003 [11-26 01:07:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 417/1669] eta: 0:14:01 tlr: 0.0001 tnm: 0.39 Lm: 6.509 (6.509) Lt: 5.763 (5.763) Accm: 3.37 (3.37) Acct: 5.17 (5.17) proj_loss: -0.5940 (-0.5940) time: 0.6732 data: 0.0002 [11-26 01:07:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 417/1669] eta: 0:14:01 tlr: 0.0001 tnm: 0.39 Lm: 6.448 (6.448) Lt: 5.685 (5.685) Accm: 3.80 (3.80) Acct: 5.98 (5.98) proj_loss: -0.6028 (-0.6028) time: 0.6732 data: 0.0003 [11-26 01:12:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 834/1669] eta: 0:09:20 tlr: 0.0001 tnm: 0.39 Lm: 6.486 (6.501) Lt: 5.700 (5.750) Accm: 3.65 (3.55) Acct: 5.85 (5.60) proj_loss: -0.6046 (-0.6037) time: 0.6724 data: 0.0003 [11-26 01:12:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 834/1669] eta: 0:09:20 tlr: 0.0001 tnm: 0.39 Lm: 6.510 (6.540) Lt: 5.780 (5.805) Accm: 3.31 (3.31) Acct: 5.27 (5.15) proj_loss: -0.5854 (-0.5925) time: 0.6724 data: 0.0002 [11-26 01:12:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 834/1669] eta: 0:09:20 tlr: 0.0001 tnm: 0.39 Lm: 6.524 (6.524) Lt: 5.794 (5.784) Accm: 3.36 (3.36) Acct: 5.15 (5.12) proj_loss: -0.5948 (-0.5943) time: 0.6724 data: 0.0002 [11-26 01:12:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [ 834/1669] eta: 0:09:20 tlr: 0.0001 tnm: 0.39 Lm: 6.408 (6.409) Lt: 5.634 (5.648) Accm: 3.72 (3.63) Acct: 5.79 (5.68) proj_loss: -0.6064 (-0.6024) time: 0.6724 data: 0.0003 [11-26 01:17:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [1251/1669] eta: 0:04:43 tlr: 0.0001 tnm: 0.38 Lm: 6.413 (6.422) Lt: 5.652 (5.654) Accm: 3.63 (3.60) Acct: 5.81 (5.72) proj_loss: -0.6066 (-0.6054) time: 0.6746 data: 0.0003 [11-26 01:17:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [1251/1669] eta: 0:04:43 tlr: 0.0001 tnm: 0.38 Lm: 6.509 (6.524) Lt: 5.771 (5.775) Accm: 3.25 (3.28) Acct: 5.22 (5.15) proj_loss: -0.5889 (-0.5925) time: 0.6746 data: 0.0002 [11-26 01:17:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [1251/1669] eta: 0:04:43 tlr: 0.0001 tnm: 0.38 Lm: 6.509 (6.490) Lt: 5.763 (5.734) Accm: 3.37 (3.49) Acct: 5.17 (5.41) proj_loss: -0.5963 (-0.6004) time: 0.6746 data: 0.0002 [11-26 01:17:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [1251/1669] eta: 0:04:43 tlr: 0.0001 tnm: 0.38 Lm: 6.494 (6.501) Lt: 5.726 (5.750) Accm: 3.44 (3.47) Acct: 5.45 (5.46) proj_loss: -0.6028 (-0.6024) time: 0.6746 data: 0.0002 [11-26 01:21:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.39 Lm: 6.502 (6.523) Lt: 5.753 (5.772) Accm: 3.23 (3.40) Acct: 5.04 (5.33) proj_loss: -0.6046 (-0.6032) time: 0.6738 data: 0.0016 [11-26 01:21:45] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 212/350] Total time: 0:18:49 (0.677 s / it) [11-26 01:21:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.39 Lm: 6.507 (6.507) Lt: 5.761 (5.744) Accm: 3.31 (3.29) Acct: 5.27 (5.26) proj_loss: -0.5921 (-0.5924) time: 0.6738 data: 0.0015 [11-26 01:21:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.39 Lm: 6.524 (6.497) Lt: 5.749 (5.737) Accm: 3.38 (3.49) Acct: 5.18 (5.45) proj_loss: -0.5978 (-0.6014) time: 0.6738 data: 0.0014 [11-26 01:21:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 212/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.39 Lm: 6.418 (6.477) Lt: 5.669 (5.710) Accm: 3.53 (3.40) Acct: 5.79 (5.37) proj_loss: -0.6064 (-0.5989) time: 0.6738 data: 0.0019 [11-26 01:21:45] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 212/350] Total time: 0:18:49 (0.677 s / it) [11-26 01:21:45] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 212/350] Total time: 0:18:49 (0.677 s / it) [11-26 01:21:45] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 212/350] Total time: 0:18:49 (0.677 s / it) [11-26 01:21:45] (/home/user/VAR/train.py , line 276)=> [ep212] (training ) Lm: 6.504 (6.505), Lt: 5.750 (5.750), Acc m&t: 3.39 5.32, Remain: 1 day, 19:05:40, Finish: 2024-11-27 04:27 [11-26 01:21:45] (/home/user/VAR/train.py , line 276)=> [ep212] (training ) Lm: 6.504 (6.505), Lt: 5.750 (5.750), Acc m&t: 3.39 5.32, Remain: 1 day, 19:05:54, Finish: 2024-11-27 04:27 [11-26 01:21:45] (/home/user/VAR/train.py , line 276)=> [ep212] (training ) Lm: 6.504 (6.505), Lt: 5.750 (5.750), Acc m&t: 3.39 5.32, Remain: 1 day, 19:05:48, Finish: 2024-11-27 04:27 [11-26 01:21:45] (/home/user/VAR/train.py , line 276)=> [ep212] (training ) Lm: 6.504 (6.505), Lt: 5.750 (5.750), Acc m&t: 3.39 5.32, Remain: 1 day, 19:06:17, Finish: 2024-11-27 04:28 [11-26 01:21:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [ 0/1669] eta: 0:18:21 tlr: 0.0001 tnm: 0.39 Lm: 6.560 (6.560) Lt: 5.754 (5.754) Accm: 3.29 (3.29) Acct: 5.25 (5.25) proj_loss: -0.5885 (-0.5885) time: 0.6598 data: 0.0003 [11-26 01:21:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [ 0/1669] eta: 0:18:22 tlr: 0.0001 tnm: 0.39 Lm: 6.333 (6.333) Lt: 5.527 (5.527) Accm: 3.84 (3.84) Acct: 5.91 (5.91) proj_loss: -0.6049 (-0.6049) time: 0.6604 data: 0.0004 [11-26 01:21:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [ 0/1669] eta: 0:18:22 tlr: 0.0001 tnm: 0.39 Lm: 6.589 (6.589) Lt: 5.835 (5.835) Accm: 3.10 (3.10) Acct: 4.94 (4.94) proj_loss: -0.6054 (-0.6054) time: 0.6605 data: 0.0003 [11-26 01:21:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [ 0/1669] eta: 0:18:23 tlr: 0.0001 tnm: 0.39 Lm: 6.536 (6.536) Lt: 5.801 (5.801) Accm: 3.10 (3.10) Acct: 4.77 (4.77) proj_loss: -0.6079 (-0.6079) time: 0.6613 data: 0.0003 [11-26 01:26:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [ 417/1669] eta: 0:14:02 tlr: 0.0001 tnm: 0.39 Lm: 6.545 (6.545) Lt: 5.810 (5.810) Accm: 3.18 (3.18) Acct: 4.92 (4.92) proj_loss: -0.5972 (-0.5972) time: 0.6737 data: 0.0003 [11-26 01:26:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [ 417/1669] eta: 0:14:02 tlr: 0.0001 tnm: 0.39 Lm: 6.492 (6.492) Lt: 5.736 (5.736) Accm: 3.32 (3.32) Acct: 5.20 (5.20) proj_loss: -0.6079 (-0.6079) time: 0.6737 data: 0.0002 [11-26 01:26:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [ 417/1669] eta: 0:14:02 tlr: 0.0001 tnm: 0.39 Lm: 6.554 (6.554) Lt: 5.828 (5.828) Accm: 3.24 (3.24) Acct: 4.99 (4.99) proj_loss: -0.6021 (-0.6021) time: 0.6737 data: 0.0002 [11-26 01:26:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [ 417/1669] eta: 0:14:02 tlr: 0.0001 tnm: 0.39 Lm: 6.518 (6.518) Lt: 5.752 (5.752) Accm: 3.39 (3.39) Acct: 5.28 (5.28) proj_loss: -0.6109 (-0.6109) time: 0.6737 data: 0.0003 [11-26 01:31:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [ 834/1669] eta: 0:09:21 tlr: 0.0001 tnm: 0.38 Lm: 6.560 (6.563) Lt: 5.754 (5.817) Accm: 3.29 (3.14) Acct: 5.25 (4.91) proj_loss: -0.5951 (-0.6056) time: 0.6747 data: 0.0002 [11-26 01:31:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [ 834/1669] eta: 0:09:21 tlr: 0.0001 tnm: 0.38 Lm: 6.542 (6.544) Lt: 5.819 (5.819) Accm: 3.25 (3.21) Acct: 5.06 (5.05) proj_loss: -0.5966 (-0.5970) time: 0.6747 data: 0.0003 [11-26 01:31:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [ 834/1669] eta: 0:09:21 tlr: 0.0001 tnm: 0.38 Lm: 6.333 (6.409) Lt: 5.527 (5.619) Accm: 3.84 (3.72) Acct: 5.91 (5.83) proj_loss: -0.6049 (-0.6025) time: 0.6747 data: 0.0002 [11-26 01:31:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [ 834/1669] eta: 0:09:21 tlr: 0.0001 tnm: 0.38 Lm: 6.518 (6.535) Lt: 5.820 (5.795) Accm: 3.23 (3.24) Acct: 5.04 (5.04) proj_loss: -0.5995 (-0.6013) time: 0.6747 data: 0.0002 [11-26 01:35:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [1251/1669] eta: 0:04:41 tlr: 0.0001 tnm: 0.40 Lm: 6.541 (6.542) Lt: 5.824 (5.803) Accm: 3.17 (3.14) Acct: 4.99 (4.92) proj_loss: -0.5992 (-0.6001) time: 0.6708 data: 0.0003 [11-26 01:35:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [1251/1669] eta: 0:04:41 tlr: 0.0001 tnm: 0.40 Lm: 6.539 (6.520) Lt: 5.810 (5.791) Accm: 3.26 (3.25) Acct: 5.14 (5.09) proj_loss: -0.6009 (-0.5991) time: 0.6708 data: 0.0003 [11-26 01:35:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [1251/1669] eta: 0:04:41 tlr: 0.0001 tnm: 0.40 Lm: 6.467 (6.457) Lt: 5.690 (5.678) Accm: 3.43 (3.54) Acct: 5.38 (5.59) proj_loss: -0.6063 (-0.6038) time: 0.6708 data: 0.0002 [11-26 01:35:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [1251/1669] eta: 0:04:41 tlr: 0.0001 tnm: 0.40 Lm: 6.541 (6.552) Lt: 5.752 (5.793) Accm: 3.30 (3.18) Acct: 5.27 (5.01) proj_loss: -0.5979 (-0.6044) time: 0.6708 data: 0.0002 [11-26 01:40:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.560 (6.586) Lt: 5.754 (5.831) Accm: 3.29 (3.07) Acct: 5.25 (4.85) proj_loss: -0.5951 (-0.6022) time: 0.7168 data: 0.0016 [11-26 01:40:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 213/350] Total time: 0:18:45 (0.674 s / it) [11-26 01:40:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.530 (6.472) Lt: 5.773 (5.697) Accm: 3.38 (3.51) Acct: 5.63 (5.60) proj_loss: -0.6077 (-0.6066) time: 0.7168 data: 0.0014 [11-26 01:40:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.536 (6.511) Lt: 5.801 (5.773) Accm: 3.27 (3.27) Acct: 5.22 (5.13) proj_loss: -0.6033 (-0.5999) time: 0.7168 data: 0.0017 [11-26 01:40:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 213/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.555 (6.545) Lt: 5.820 (5.799) Accm: 3.23 (3.22) Acct: 5.04 (5.06) proj_loss: -0.5989 (-0.5990) time: 0.7168 data: 0.0018 [11-26 01:40:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 213/350] Total time: 0:18:45 (0.674 s / it) [11-26 01:40:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 213/350] Total time: 0:18:45 (0.674 s / it) [11-26 01:40:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 213/350] Total time: 0:18:45 (0.674 s / it) [11-26 01:40:30] (/home/user/VAR/train.py , line 276)=> [ep213] (training ) Lm: 6.493 (6.493), Lt: 5.735 (5.735), Acc m&t: 3.39 5.37, Remain: 1 day, 18:54:07, Finish: 2024-11-27 04:34 [11-26 01:40:30] (/home/user/VAR/train.py , line 276)=> [ep213] (training ) Lm: 6.493 (6.493), Lt: 5.735 (5.735), Acc m&t: 3.39 5.37, Remain: 1 day, 18:53:44, Finish: 2024-11-27 04:34 [11-26 01:40:30] (/home/user/VAR/train.py , line 276)=> [ep213] (training ) Lm: 6.493 (6.493), Lt: 5.735 (5.735), Acc m&t: 3.39 5.37, Remain: 1 day, 18:54:26, Finish: 2024-11-27 04:34 [11-26 01:40:30] (/home/user/VAR/train.py , line 276)=> [ep213] (training ) Lm: 6.493 (6.493), Lt: 5.735 (5.735), Acc m&t: 3.39 5.37, Remain: 1 day, 18:54:16, Finish: 2024-11-27 04:34 [11-26 01:40:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [ 0/1669] eta: 0:18:24 tlr: 0.0001 tnm: 0.39 Lm: 6.581 (6.581) Lt: 5.812 (5.812) Accm: 2.83 (2.83) Acct: 4.61 (4.61) proj_loss: -0.5823 (-0.5823) time: 0.6616 data: 0.0003 [11-26 01:40:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [ 0/1669] eta: 0:18:24 tlr: 0.0001 tnm: 0.39 Lm: 6.520 (6.520) Lt: 5.764 (5.764) Accm: 3.18 (3.18) Acct: 5.08 (5.08) proj_loss: -0.6129 (-0.6129) time: 0.6617 data: 0.0004 [11-26 01:40:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [ 0/1669] eta: 0:18:24 tlr: 0.0001 tnm: 0.39 Lm: 6.701 (6.701) Lt: 6.057 (6.057) Accm: 2.92 (2.92) Acct: 4.55 (4.55) proj_loss: -0.6006 (-0.6006) time: 0.6618 data: 0.0004 [11-26 01:40:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [ 0/1669] eta: 0:18:26 tlr: 0.0001 tnm: 0.39 Lm: 6.478 (6.478) Lt: 5.718 (5.718) Accm: 3.51 (3.51) Acct: 5.65 (5.65) proj_loss: -0.5812 (-0.5812) time: 0.6629 data: 0.0004 [11-26 01:45:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [ 417/1669] eta: 0:14:24 tlr: 0.0001 tnm: 0.40 Lm: 6.525 (6.525) Lt: 5.785 (5.785) Accm: 3.33 (3.33) Acct: 5.21 (5.21) proj_loss: -0.5940 (-0.5940) time: 0.6704 data: 0.0003 [11-26 01:45:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [ 417/1669] eta: 0:14:24 tlr: 0.0001 tnm: 0.40 Lm: 6.621 (6.621) Lt: 5.923 (5.923) Accm: 3.15 (3.15) Acct: 4.67 (4.67) proj_loss: -0.6039 (-0.6039) time: 0.6704 data: 0.0003 [11-26 01:45:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [ 417/1669] eta: 0:14:24 tlr: 0.0001 tnm: 0.40 Lm: 6.548 (6.548) Lt: 5.789 (5.789) Accm: 3.10 (3.10) Acct: 4.98 (4.98) proj_loss: -0.6008 (-0.6008) time: 0.6704 data: 0.0003 [11-26 01:45:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [ 417/1669] eta: 0:14:24 tlr: 0.0001 tnm: 0.40 Lm: 6.536 (6.536) Lt: 5.779 (5.779) Accm: 3.25 (3.25) Acct: 5.06 (5.06) proj_loss: -0.6123 (-0.6123) time: 0.6704 data: 0.0003 [11-26 01:50:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [ 834/1669] eta: 0:09:29 tlr: 0.0001 tnm: 0.40 Lm: 6.520 (6.518) Lt: 5.764 (5.759) Accm: 3.31 (3.29) Acct: 5.08 (5.15) proj_loss: -0.6117 (-0.6090) time: 0.6729 data: 0.0002 [11-26 01:50:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [ 834/1669] eta: 0:09:29 tlr: 0.0001 tnm: 0.40 Lm: 6.540 (6.510) Lt: 5.790 (5.777) Accm: 3.38 (3.46) Acct: 4.79 (5.24) proj_loss: -0.6037 (-0.6038) time: 0.6730 data: 0.0003 [11-26 01:50:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [ 834/1669] eta: 0:09:29 tlr: 0.0001 tnm: 0.40 Lm: 6.478 (6.481) Lt: 5.718 (5.724) Accm: 3.51 (3.45) Acct: 5.65 (5.43) proj_loss: -0.6067 (-0.6015) time: 0.6729 data: 0.0003 [11-26 01:50:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [ 834/1669] eta: 0:09:29 tlr: 0.0001 tnm: 0.40 Lm: 6.561 (6.552) Lt: 5.812 (5.799) Accm: 3.36 (3.19) Acct: 5.34 (5.15) proj_loss: -0.6146 (-0.6054) time: 0.6730 data: 0.0002 [11-26 01:54:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [1251/1669] eta: 0:04:43 tlr: 0.0001 tnm: 0.41 Lm: 6.538 (6.499) Lt: 5.789 (5.750) Accm: 3.37 (3.35) Acct: 5.42 (5.26) proj_loss: -0.6065 (-0.6036) time: 0.6692 data: 0.0002 [11-26 01:54:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [1251/1669] eta: 0:04:43 tlr: 0.0001 tnm: 0.41 Lm: 6.618 (6.556) Lt: 5.876 (5.823) Accm: 3.15 (3.27) Acct: 4.67 (4.96) proj_loss: -0.6022 (-0.6030) time: 0.6692 data: 0.0002 [11-26 01:54:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [1251/1669] eta: 0:04:43 tlr: 0.0001 tnm: 0.41 Lm: 6.468 (6.475) Lt: 5.682 (5.705) Accm: 3.56 (3.49) Acct: 5.75 (5.54) proj_loss: -0.6085 (-0.6037) time: 0.6692 data: 0.0003 [11-26 01:54:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [1251/1669] eta: 0:04:43 tlr: 0.0001 tnm: 0.41 Lm: 6.500 (6.498) Lt: 5.743 (5.744) Accm: 3.31 (3.29) Acct: 5.06 (5.11) proj_loss: -0.6070 (-0.6060) time: 0.6692 data: 0.0002 [11-26 01:59:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.39 Lm: 6.520 (6.534) Lt: 5.764 (5.779) Accm: 3.31 (3.22) Acct: 5.04 (5.02) proj_loss: -0.6024 (-0.6042) time: 0.6757 data: 0.0017 [11-26 01:59:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 214/350] Total time: 0:18:50 (0.677 s / it) [11-26 01:59:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.39 Lm: 6.540 (6.539) Lt: 5.790 (5.800) Accm: 3.38 (3.35) Acct: 4.79 (5.07) proj_loss: -0.6037 (-0.6042) time: 0.6757 data: 0.0014 [11-26 01:59:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.39 Lm: 6.561 (6.516) Lt: 5.812 (5.766) Accm: 3.36 (3.29) Acct: 5.34 (5.14) proj_loss: -0.5985 (-0.6014) time: 0.6757 data: 0.0020 [11-26 01:59:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 214/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.39 Lm: 6.478 (6.497) Lt: 5.718 (5.728) Accm: 3.51 (3.39) Acct: 5.65 (5.39) proj_loss: -0.6067 (-0.6032) time: 0.6757 data: 0.0018 [11-26 01:59:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 214/350] Total time: 0:18:50 (0.677 s / it) [11-26 01:59:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 214/350] Total time: 0:18:50 (0.677 s / it) [11-26 01:59:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 214/350] Total time: 0:18:50 (0.677 s / it) [11-26 01:59:20] (/home/user/VAR/train.py , line 276)=> [ep214] (training ) Lm: 6.493 (6.498), Lt: 5.735 (5.746), Acc m&t: 3.39 5.37, Remain: 1 day, 18:37:01, Finish: 2024-11-27 04:36 [11-26 01:59:20] (/home/user/VAR/train.py , line 276)=> [ep214] (training ) Lm: 6.493 (6.498), Lt: 5.735 (5.746), Acc m&t: 3.39 5.37, Remain: 1 day, 18:37:10, Finish: 2024-11-27 04:36 [11-26 01:59:20] (/home/user/VAR/train.py , line 276)=> [ep214] (training ) Lm: 6.493 (6.498), Lt: 5.735 (5.746), Acc m&t: 3.39 5.37, Remain: 1 day, 18:37:37, Finish: 2024-11-27 04:36 [11-26 01:59:20] (/home/user/VAR/train.py , line 276)=> [ep214] (training ) Lm: 6.493 (6.498), Lt: 5.735 (5.746), Acc m&t: 3.39 5.37, Remain: 1 day, 18:37:20, Finish: 2024-11-27 04:36 [11-26 01:59:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [ 0/1669] eta: 0:18:17 tlr: 0.0001 tnm: 0.41 Lm: 6.507 (6.507) Lt: 5.802 (5.802) Accm: 3.21 (3.21) Acct: 4.87 (4.87) proj_loss: -0.6081 (-0.6081) time: 0.6577 data: 0.0003 [11-26 01:59:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [ 0/1669] eta: 0:18:18 tlr: 0.0001 tnm: 0.41 Lm: 6.706 (6.706) Lt: 6.004 (6.004) Accm: 2.89 (2.89) Acct: 4.41 (4.41) proj_loss: -0.6301 (-0.6301) time: 0.6580 data: 0.0003 [11-26 01:59:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [ 0/1669] eta: 0:18:18 tlr: 0.0001 tnm: 0.41 Lm: 6.456 (6.456) Lt: 5.691 (5.691) Accm: 3.69 (3.69) Acct: 5.65 (5.65) proj_loss: -0.6003 (-0.6003) time: 0.6583 data: 0.0003 [11-26 01:59:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [ 0/1669] eta: 0:18:25 tlr: 0.0001 tnm: 0.41 Lm: 6.456 (6.456) Lt: 5.667 (5.667) Accm: 3.84 (3.84) Acct: 6.16 (6.16) proj_loss: -0.5861 (-0.5861) time: 0.6624 data: 0.0004 [11-26 02:04:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [ 417/1669] eta: 0:14:37 tlr: 0.0001 tnm: 0.41 Lm: 6.444 (6.444) Lt: 5.659 (5.659) Accm: 3.55 (3.55) Acct: 5.65 (5.65) proj_loss: -0.5976 (-0.5976) time: 0.6722 data: 0.0003 [11-26 02:04:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [ 417/1669] eta: 0:14:37 tlr: 0.0001 tnm: 0.41 Lm: 6.602 (6.602) Lt: 5.862 (5.862) Accm: 3.31 (3.31) Acct: 5.22 (5.22) proj_loss: -0.6299 (-0.6299) time: 0.6723 data: 0.0003 [11-26 02:04:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [ 417/1669] eta: 0:14:37 tlr: 0.0001 tnm: 0.41 Lm: 6.543 (6.543) Lt: 5.824 (5.824) Accm: 3.21 (3.21) Acct: 4.94 (4.94) proj_loss: -0.6078 (-0.6078) time: 0.6723 data: 0.0002 [11-26 02:04:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [ 417/1669] eta: 0:14:37 tlr: 0.0001 tnm: 0.41 Lm: 6.522 (6.522) Lt: 5.807 (5.807) Accm: 3.19 (3.19) Acct: 4.98 (4.98) proj_loss: -0.6139 (-0.6139) time: 0.6723 data: 0.0003 [11-26 02:08:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [ 834/1669] eta: 0:09:33 tlr: 0.0001 tnm: 0.40 Lm: 6.507 (6.472) Lt: 5.802 (5.738) Accm: 3.21 (3.32) Acct: 5.08 (5.17) proj_loss: -0.6081 (-0.6058) time: 0.6743 data: 0.0003 [11-26 02:08:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [ 834/1669] eta: 0:09:33 tlr: 0.0001 tnm: 0.40 Lm: 6.497 (6.561) Lt: 5.721 (5.815) Accm: 3.37 (3.33) Acct: 5.17 (5.20) proj_loss: -0.6297 (-0.6228) time: 0.6743 data: 0.0003 [11-26 02:08:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [ 834/1669] eta: 0:09:33 tlr: 0.0001 tnm: 0.40 Lm: 6.456 (6.457) Lt: 5.667 (5.682) Accm: 3.52 (3.54) Acct: 5.87 (5.72) proj_loss: -0.5938 (-0.5963) time: 0.6743 data: 0.0003 [11-26 02:08:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [ 834/1669] eta: 0:09:33 tlr: 0.0001 tnm: 0.40 Lm: 6.503 (6.530) Lt: 5.762 (5.803) Accm: 3.47 (3.30) Acct: 5.65 (5.20) proj_loss: -0.6153 (-0.6113) time: 0.6744 data: 0.0003 [11-26 02:13:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [1251/1669] eta: 0:04:44 tlr: 0.0001 tnm: 0.40 Lm: 6.479 (6.510) Lt: 5.742 (5.783) Accm: 3.42 (3.31) Acct: 5.39 (5.18) proj_loss: -0.6114 (-0.6104) time: 0.6717 data: 0.0002 [11-26 02:13:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [1251/1669] eta: 0:04:44 tlr: 0.0001 tnm: 0.40 Lm: 6.469 (6.502) Lt: 5.698 (5.730) Accm: 3.39 (3.37) Acct: 5.50 (5.45) proj_loss: -0.6014 (-0.6026) time: 0.6717 data: 0.0002 [11-26 02:13:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [1251/1669] eta: 0:04:44 tlr: 0.0001 tnm: 0.40 Lm: 6.522 (6.529) Lt: 5.807 (5.799) Accm: 3.19 (3.18) Acct: 4.98 (4.92) proj_loss: -0.6100 (-0.6074) time: 0.6717 data: 0.0002 [11-26 02:13:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [1251/1669] eta: 0:04:44 tlr: 0.0001 tnm: 0.40 Lm: 6.488 (6.520) Lt: 5.720 (5.766) Accm: 3.54 (3.47) Acct: 5.60 (5.44) proj_loss: -0.6191 (-0.6176) time: 0.6717 data: 0.0003 [11-26 02:18:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.479 (6.499) Lt: 5.720 (5.735) Accm: 3.72 (3.54) Acct: 6.03 (5.59) proj_loss: -0.6163 (-0.6173) time: 0.6741 data: 0.0014 [11-26 02:18:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 215/350] Total time: 0:18:53 (0.679 s / it) [11-26 02:18:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.456 (6.491) Lt: 5.729 (5.732) Accm: 3.52 (3.43) Acct: 5.72 (5.50) proj_loss: -0.6091 (-0.6058) time: 0.6741 data: 0.0015 [11-26 02:18:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.518 (6.527) Lt: 5.802 (5.799) Accm: 3.21 (3.19) Acct: 5.06 (4.95) proj_loss: -0.6081 (-0.6045) time: 0.6741 data: 0.0015 [11-26 02:18:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 215/350] [1668/1669] eta: 0:00:00 tlr: 0.0001 tnm: 0.38 Lm: 6.485 (6.505) Lt: 5.762 (5.787) Accm: 3.36 (3.25) Acct: 5.13 (5.04) proj_loss: -0.6075 (-0.6089) time: 0.6740 data: 0.0021 [11-26 02:18:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 215/350] Total time: 0:18:53 (0.679 s / it) [11-26 02:18:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 215/350] Total time: 0:18:53 (0.679 s / it) [11-26 02:18:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 215/350] Total time: 0:18:53 (0.679 s / it) [11-26 02:18:14] (/home/user/VAR/train.py , line 276)=> [ep215] (training ) Lm: 6.493 (6.500), Lt: 5.735 (5.749), Acc m&t: 3.39 5.37, Remain: 1 day, 18:13:26, Finish: 2024-11-27 04:31 [11-26 02:18:14] (/home/user/VAR/train.py , line 276)=> [ep215] (training ) Lm: 6.493 (6.500), Lt: 5.735 (5.749), Acc m&t: 3.39 5.37, Remain: 1 day, 18:13:09, Finish: 2024-11-27 04:31 [11-26 02:18:14] (/home/user/VAR/train.py , line 276)=> [ep215] (training ) Lm: 6.493 (6.500), Lt: 5.735 (5.749), Acc m&t: 3.39 5.37, Remain: 1 day, 18:13:33, Finish: 2024-11-27 04:31 [11-26 02:18:14] (/home/user/VAR/train.py , line 276)=> [ep215] (training ) Lm: 6.493 (6.500), Lt: 5.735 (5.749), Acc m&t: 3.39 5.37, Remain: 1 day, 18:13:15, Finish: 2024-11-27 04:31 [11-26 02:18:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [ 0/1669] eta: 0:18:29 tlr: 0.0001 tnm: 0.40 Lm: 6.504 (6.504) Lt: 5.759 (5.759) Accm: 3.38 (3.38) Acct: 5.17 (5.17) proj_loss: -0.6072 (-0.6072) time: 0.6649 data: 0.0003 [11-26 02:18:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [ 0/1669] eta: 0:18:30 tlr: 0.0001 tnm: 0.40 Lm: 6.619 (6.619) Lt: 5.831 (5.831) Accm: 3.12 (3.12) Acct: 5.27 (5.27) proj_loss: -0.5926 (-0.5926) time: 0.6652 data: 0.0003 [11-26 02:18:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [ 0/1669] eta: 0:18:30 tlr: 0.0001 tnm: 0.40 Lm: 6.386 (6.386) Lt: 5.647 (5.647) Accm: 3.55 (3.55) Acct: 5.49 (5.49) proj_loss: -0.6137 (-0.6137) time: 0.6654 data: 0.0004 [11-26 02:18:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [ 0/1669] eta: 0:18:30 tlr: 0.0001 tnm: 0.40 Lm: 6.479 (6.479) Lt: 5.723 (5.723) Accm: 3.63 (3.63) Acct: 5.63 (5.63) proj_loss: -0.6222 (-0.6222) time: 0.6652 data: 0.0003 [11-26 02:22:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [ 417/1669] eta: 0:14:02 tlr: 0.0001 tnm: 0.41 Lm: 6.439 (6.439) Lt: 5.684 (5.684) Accm: 3.78 (3.78) Acct: 5.85 (5.85) proj_loss: -0.6254 (-0.6254) time: 0.6736 data: 0.0002 [11-26 02:22:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [ 417/1669] eta: 0:14:02 tlr: 0.0001 tnm: 0.41 Lm: 6.560 (6.560) Lt: 5.804 (5.804) Accm: 3.21 (3.21) Acct: 5.26 (5.26) proj_loss: -0.5959 (-0.5959) time: 0.6736 data: 0.0003 [11-26 02:22:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [ 417/1669] eta: 0:14:02 tlr: 0.0001 tnm: 0.41 Lm: 6.429 (6.429) Lt: 5.665 (5.665) Accm: 3.55 (3.55) Acct: 5.75 (5.75) proj_loss: -0.6105 (-0.6105) time: 0.6736 data: 0.0002 [11-26 02:22:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [ 417/1669] eta: 0:14:02 tlr: 0.0001 tnm: 0.41 Lm: 6.404 (6.404) Lt: 5.645 (5.645) Accm: 3.48 (3.48) Acct: 5.48 (5.48) proj_loss: -0.6030 (-0.6030) time: 0.6736 data: 0.0003 [11-26 02:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [ 834/1669] eta: 0:09:21 tlr: 0.0001 tnm: 0.40 Lm: 6.504 (6.443) Lt: 5.734 (5.674) Accm: 3.50 (3.49) Acct: 5.68 (5.54) proj_loss: -0.6009 (-0.6023) time: 0.6705 data: 0.0002 [11-26 02:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [ 834/1669] eta: 0:09:21 tlr: 0.0001 tnm: 0.40 Lm: 6.619 (6.581) Lt: 5.831 (5.845) Accm: 3.12 (3.12) Acct: 5.25 (5.10) proj_loss: -0.5992 (-0.6041) time: 0.6705 data: 0.0003 [11-26 02:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [ 834/1669] eta: 0:09:21 tlr: 0.0001 tnm: 0.40 Lm: 6.474 (6.451) Lt: 5.723 (5.717) Accm: 3.63 (3.60) Acct: 5.63 (5.50) proj_loss: -0.6222 (-0.6235) time: 0.6705 data: 0.0002 [11-26 02:27:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [ 834/1669] eta: 0:09:21 tlr: 0.0001 tnm: 0.40 Lm: 6.386 (6.403) Lt: 5.647 (5.625) Accm: 3.55 (3.58) Acct: 5.92 (5.81) proj_loss: -0.6137 (-0.6119) time: 0.6705 data: 0.0002 [11-26 02:32:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [1251/1669] eta: 0:04:45 tlr: 0.0001 tnm: 0.39 Lm: 6.429 (6.422) Lt: 5.662 (5.639) Accm: 3.55 (3.49) Acct: 5.71 (5.72) proj_loss: -0.6105 (-0.6028) time: 0.6732 data: 0.0003 [11-26 02:32:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [1251/1669] eta: 0:04:45 tlr: 0.0001 tnm: 0.39 Lm: 6.476 (6.478) Lt: 5.753 (5.748) Accm: 3.47 (3.53) Acct: 5.35 (5.39) proj_loss: -0.6210 (-0.6183) time: 0.6732 data: 0.0002 [11-26 02:32:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [1251/1669] eta: 0:04:45 tlr: 0.0001 tnm: 0.39 Lm: 6.560 (6.557) Lt: 5.804 (5.817) Accm: 3.21 (3.38) Acct: 5.26 (5.41) proj_loss: -0.6052 (-0.6059) time: 0.6732 data: 0.0003 [11-26 02:32:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [1251/1669] eta: 0:04:45 tlr: 0.0001 tnm: 0.39 Lm: 6.500 (6.456) Lt: 5.746 (5.696) Accm: 3.49 (3.48) Acct: 5.53 (5.50) proj_loss: -0.6041 (-0.6038) time: 0.6732 data: 0.0003 [11-26 02:37:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [1668/1669] eta: 0:00:00 tlr: 9.9e-05 tnm: 0.40 Lm: 6.504 (6.497) Lt: 5.759 (5.751) Accm: 3.47 (3.35) Acct: 5.37 (5.28) proj_loss: -0.6072 (-0.6047) time: 0.6743 data: 0.0018 [11-26 02:37:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 216/350] Total time: 0:18:55 (0.680 s / it) [11-26 02:37:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [1668/1669] eta: 0:00:00 tlr: 9.9e-05 tnm: 0.40 Lm: 6.468 (6.431) Lt: 5.652 (5.641) Accm: 3.55 (3.48) Acct: 5.66 (5.71) proj_loss: -0.6073 (-0.5997) time: 0.6743 data: 0.0019 [11-26 02:37:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [1668/1669] eta: 0:00:00 tlr: 9.9e-05 tnm: 0.40 Lm: 6.478 (6.478) Lt: 5.723 (5.738) Accm: 3.35 (3.49) Acct: 5.22 (5.36) proj_loss: -0.6197 (-0.6170) time: 0.6743 data: 0.0020 [11-26 02:37:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 216/350] [1668/1669] eta: 0:00:00 tlr: 9.9e-05 tnm: 0.40 Lm: 6.502 (6.510) Lt: 5.777 (5.773) Accm: 3.31 (3.57) Acct: 5.27 (5.58) proj_loss: -0.5992 (-0.6032) time: 0.6743 data: 0.0019 [11-26 02:37:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 216/350] Total time: 0:18:55 (0.680 s / it) [11-26 02:37:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 216/350] Total time: 0:18:55 (0.680 s / it) [11-26 02:37:10] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 216/350] Total time: 0:18:55 (0.680 s / it) [11-26 02:37:10] (/home/user/VAR/train.py , line 276)=> [ep216] (training ) Lm: 6.493 (6.495), Lt: 5.735 (5.739), Acc m&t: 3.39 5.37, Remain: 1 day, 17:54:26, Finish: 2024-11-27 04:31 [11-26 02:37:10] (/home/user/VAR/train.py , line 276)=> [ep216] (training ) Lm: 6.493 (6.495), Lt: 5.735 (5.739), Acc m&t: 3.39 5.37, Remain: 1 day, 17:54:30, Finish: 2024-11-27 04:31 [11-26 02:37:10] (/home/user/VAR/train.py , line 276)=> [ep216] (training ) Lm: 6.493 (6.495), Lt: 5.735 (5.739), Acc m&t: 3.39 5.37, Remain: 1 day, 17:54:31, Finish: 2024-11-27 04:31 [11-26 02:37:10] (/home/user/VAR/train.py , line 276)=> [ep216] (training ) Lm: 6.493 (6.495), Lt: 5.735 (5.739), Acc m&t: 3.39 5.37, Remain: 1 day, 17:54:08, Finish: 2024-11-27 04:31 [11-26 02:37:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [ 0/1669] eta: 0:18:19 tlr: 9.9e-05 tnm: 0.41 Lm: 6.442 (6.442) Lt: 5.729 (5.729) Accm: 3.15 (3.15) Acct: 4.70 (4.70) proj_loss: -0.6438 (-0.6438) time: 0.6588 data: 0.0003 [11-26 02:37:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [ 0/1669] eta: 0:18:19 tlr: 9.9e-05 tnm: 0.41 Lm: 6.412 (6.412) Lt: 5.608 (5.608) Accm: 3.55 (3.55) Acct: 5.58 (5.58) proj_loss: -0.6072 (-0.6072) time: 0.6590 data: 0.0004 [11-26 02:37:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [ 0/1669] eta: 0:18:20 tlr: 9.9e-05 tnm: 0.41 Lm: 6.734 (6.734) Lt: 5.992 (5.992) Accm: 2.82 (2.82) Acct: 4.53 (4.53) proj_loss: -0.6002 (-0.6002) time: 0.6592 data: 0.0004 [11-26 02:37:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [ 0/1669] eta: 0:18:20 tlr: 9.9e-05 tnm: 0.41 Lm: 6.435 (6.435) Lt: 5.682 (5.682) Accm: 3.60 (3.60) Acct: 5.61 (5.61) proj_loss: -0.6111 (-0.6111) time: 0.6596 data: 0.0004 [11-26 02:41:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [ 417/1669] eta: 0:14:01 tlr: 9.9e-05 tnm: 0.39 Lm: 6.450 (6.450) Lt: 5.658 (5.658) Accm: 3.52 (3.52) Acct: 5.66 (5.66) proj_loss: -0.6107 (-0.6107) time: 0.6740 data: 0.0003 [11-26 02:41:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [ 417/1669] eta: 0:14:01 tlr: 9.9e-05 tnm: 0.39 Lm: 6.390 (6.390) Lt: 5.660 (5.660) Accm: 3.70 (3.70) Acct: 5.62 (5.62) proj_loss: -0.6211 (-0.6211) time: 0.6740 data: 0.0002 [11-26 02:41:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [ 417/1669] eta: 0:14:01 tlr: 9.9e-05 tnm: 0.39 Lm: 6.439 (6.439) Lt: 5.712 (5.712) Accm: 3.40 (3.40) Acct: 5.31 (5.31) proj_loss: -0.6231 (-0.6231) time: 0.6740 data: 0.0002 [11-26 02:41:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [ 417/1669] eta: 0:14:01 tlr: 9.9e-05 tnm: 0.39 Lm: 6.548 (6.548) Lt: 5.783 (5.783) Accm: 3.16 (3.16) Acct: 4.83 (4.83) proj_loss: -0.5956 (-0.5956) time: 0.6740 data: 0.0003 [11-26 02:46:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [ 834/1669] eta: 0:09:21 tlr: 9.9e-05 tnm: 0.39 Lm: 6.466 (6.521) Lt: 5.698 (5.754) Accm: 3.41 (3.24) Acct: 5.13 (5.01) proj_loss: -0.5963 (-0.5959) time: 0.6734 data: 0.0003 [11-26 02:46:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [ 834/1669] eta: 0:09:21 tlr: 9.9e-05 tnm: 0.39 Lm: 6.412 (6.451) Lt: 5.711 (5.713) Accm: 3.55 (3.59) Acct: 5.58 (5.60) proj_loss: -0.6072 (-0.6139) time: 0.6734 data: 0.0002 [11-26 02:46:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [ 834/1669] eta: 0:09:21 tlr: 9.9e-05 tnm: 0.39 Lm: 6.466 (6.491) Lt: 5.682 (5.711) Accm: 3.60 (3.55) Acct: 5.61 (5.62) proj_loss: -0.6104 (-0.6060) time: 0.6734 data: 0.0003 [11-26 02:46:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [ 834/1669] eta: 0:09:21 tlr: 9.9e-05 tnm: 0.39 Lm: 6.442 (6.489) Lt: 5.729 (5.743) Accm: 3.15 (3.27) Acct: 4.77 (5.13) proj_loss: -0.6024 (-0.6148) time: 0.6734 data: 0.0002 [11-26 02:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [1251/1669] eta: 0:04:45 tlr: 9.9e-05 tnm: 0.40 Lm: 6.511 (6.511) Lt: 5.758 (5.754) Accm: 3.25 (3.29) Acct: 5.04 (5.18) proj_loss: -0.6003 (-0.6089) time: 0.6734 data: 0.0003 [11-26 02:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [1251/1669] eta: 0:04:45 tlr: 9.9e-05 tnm: 0.40 Lm: 6.499 (6.524) Lt: 5.762 (5.772) Accm: 3.33 (3.25) Acct: 4.91 (4.93) proj_loss: -0.5983 (-0.6000) time: 0.6734 data: 0.0003 [11-26 02:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [1251/1669] eta: 0:04:45 tlr: 9.9e-05 tnm: 0.40 Lm: 6.506 (6.504) Lt: 5.717 (5.721) Accm: 3.52 (3.47) Acct: 5.57 (5.48) proj_loss: -0.6034 (-0.6001) time: 0.6734 data: 0.0003 [11-26 02:51:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [1251/1669] eta: 0:04:45 tlr: 9.9e-05 tnm: 0.40 Lm: 6.438 (6.454) Lt: 5.672 (5.694) Accm: 3.47 (3.53) Acct: 5.57 (5.49) proj_loss: -0.6076 (-0.6124) time: 0.6734 data: 0.0003 [11-26 02:56:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [1668/1669] eta: 0:00:00 tlr: 9.9e-05 tnm: 0.41 Lm: 6.412 (6.437) Lt: 5.634 (5.667) Accm: 3.55 (3.59) Acct: 5.58 (5.60) proj_loss: -0.6072 (-0.6092) time: 0.6752 data: 0.0022 [11-26 02:56:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 217/350] Total time: 0:18:56 (0.681 s / it) [11-26 02:56:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [1668/1669] eta: 0:00:00 tlr: 9.9e-05 tnm: 0.41 Lm: 6.546 (6.513) Lt: 5.752 (5.736) Accm: 3.44 (3.40) Acct: 5.53 (5.36) proj_loss: -0.5964 (-0.5972) time: 0.6752 data: 0.0016 [11-26 02:56:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [1668/1669] eta: 0:00:00 tlr: 9.9e-05 tnm: 0.41 Lm: 6.507 (6.520) Lt: 5.796 (5.777) Accm: 3.41 (3.33) Acct: 5.13 (5.10) proj_loss: -0.6002 (-0.6051) time: 0.6752 data: 0.0015 [11-26 02:56:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 217/350] [1668/1669] eta: 0:00:00 tlr: 9.9e-05 tnm: 0.41 Lm: 6.575 (6.524) Lt: 5.787 (5.771) Accm: 3.23 (3.28) Acct: 5.06 (5.15) proj_loss: -0.5982 (-0.6028) time: 0.6752 data: 0.0015 [11-26 02:56:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 217/350] Total time: 0:18:56 (0.681 s / it) [11-26 02:56:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 217/350] Total time: 0:18:56 (0.681 s / it) [11-26 02:56:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 217/350] Total time: 0:18:56 (0.681 s / it) [11-26 02:56:06] (/home/user/VAR/train.py , line 276)=> [ep217] (training ) Lm: 6.493 (6.501), Lt: 5.735 (5.747), Acc m&t: 3.39 5.37, Remain: 1 day, 17:41:00, Finish: 2024-11-27 04:37 [11-26 02:56:06] (/home/user/VAR/train.py , line 276)=> [ep217] (training ) Lm: 6.493 (6.501), Lt: 5.735 (5.747), Acc m&t: 3.39 5.37, Remain: 1 day, 17:41:22, Finish: 2024-11-27 04:37 [11-26 02:56:06] (/home/user/VAR/train.py , line 276)=> [ep217] (training ) Lm: 6.493 (6.501), Lt: 5.735 (5.747), Acc m&t: 3.39 5.37, Remain: 1 day, 17:40:43, Finish: 2024-11-27 04:36 [11-26 02:56:06] (/home/user/VAR/train.py , line 276)=> [ep217] (training ) Lm: 6.493 (6.501), Lt: 5.735 (5.747), Acc m&t: 3.39 5.37, Remain: 1 day, 17:41:15, Finish: 2024-11-27 04:37 [11-26 02:56:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [ 0/1669] eta: 0:18:21 tlr: 9.9e-05 tnm: 0.39 Lm: 6.372 (6.372) Lt: 5.652 (5.652) Accm: 3.73 (3.73) Acct: 5.84 (5.84) proj_loss: -0.5953 (-0.5953) time: 0.6597 data: 0.0004 [11-26 02:56:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [ 0/1669] eta: 0:18:20 tlr: 9.9e-05 tnm: 0.39 Lm: 6.352 (6.352) Lt: 5.626 (5.626) Accm: 4.02 (4.02) Acct: 5.91 (5.91) proj_loss: -0.6333 (-0.6333) time: 0.6594 data: 0.0003 [11-26 02:56:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [ 0/1669] eta: 0:18:21 tlr: 9.9e-05 tnm: 0.39 Lm: 6.480 (6.480) Lt: 5.790 (5.790) Accm: 3.29 (3.29) Acct: 5.25 (5.25) proj_loss: -0.6127 (-0.6127) time: 0.6597 data: 0.0004 [11-26 02:56:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [ 0/1669] eta: 0:18:21 tlr: 9.9e-05 tnm: 0.39 Lm: 6.501 (6.501) Lt: 5.729 (5.729) Accm: 3.54 (3.54) Acct: 5.60 (5.60) proj_loss: -0.6083 (-0.6083) time: 0.6599 data: 0.0004 [11-26 03:00:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [ 417/1669] eta: 0:14:02 tlr: 9.8e-05 tnm: 0.41 Lm: 6.558 (6.558) Lt: 5.801 (5.801) Accm: 3.28 (3.28) Acct: 5.32 (5.32) proj_loss: -0.6104 (-0.6104) time: 0.6726 data: 0.0003 [11-26 03:00:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [ 417/1669] eta: 0:14:02 tlr: 9.8e-05 tnm: 0.41 Lm: 6.457 (6.457) Lt: 5.775 (5.775) Accm: 3.43 (3.43) Acct: 5.38 (5.38) proj_loss: -0.6204 (-0.6204) time: 0.6725 data: 0.0002 [11-26 03:00:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [ 417/1669] eta: 0:14:02 tlr: 9.8e-05 tnm: 0.41 Lm: 6.460 (6.460) Lt: 5.671 (5.671) Accm: 3.47 (3.47) Acct: 5.60 (5.60) proj_loss: -0.5925 (-0.5925) time: 0.6726 data: 0.0003 [11-26 03:00:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [ 417/1669] eta: 0:14:02 tlr: 9.8e-05 tnm: 0.41 Lm: 6.392 (6.392) Lt: 5.675 (5.675) Accm: 3.85 (3.85) Acct: 5.90 (5.90) proj_loss: -0.6194 (-0.6194) time: 0.6726 data: 0.0002 [11-26 03:05:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [ 834/1669] eta: 0:09:21 tlr: 9.8e-05 tnm: 0.38 Lm: 6.432 (6.445) Lt: 5.723 (5.720) Accm: 3.67 (3.66) Acct: 5.89 (5.68) proj_loss: -0.6132 (-0.6173) time: 0.6719 data: 0.0002 [11-26 03:05:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [ 834/1669] eta: 0:09:21 tlr: 9.8e-05 tnm: 0.38 Lm: 6.480 (6.511) Lt: 5.790 (5.833) Accm: 3.29 (3.35) Acct: 5.25 (5.18) proj_loss: -0.6246 (-0.6218) time: 0.6719 data: 0.0003 [11-26 03:05:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [ 834/1669] eta: 0:09:21 tlr: 9.8e-05 tnm: 0.38 Lm: 6.535 (6.485) Lt: 5.690 (5.699) Accm: 3.34 (3.43) Acct: 5.37 (5.52) proj_loss: -0.5953 (-0.5997) time: 0.6719 data: 0.0003 [11-26 03:05:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [ 834/1669] eta: 0:09:21 tlr: 9.8e-05 tnm: 0.38 Lm: 6.501 (6.513) Lt: 5.729 (5.732) Accm: 3.54 (3.39) Acct: 5.60 (5.60) proj_loss: -0.6120 (-0.6109) time: 0.6719 data: 0.0003 [11-26 03:10:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [1251/1669] eta: 0:04:41 tlr: 9.8e-05 tnm: 0.41 Lm: 6.468 (6.493) Lt: 5.708 (5.721) Accm: 3.53 (3.42) Acct: 5.60 (5.60) proj_loss: -0.6123 (-0.6115) time: 0.6717 data: 0.0003 [11-26 03:10:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [1251/1669] eta: 0:04:41 tlr: 9.8e-05 tnm: 0.41 Lm: 6.531 (6.495) Lt: 5.722 (5.723) Accm: 3.33 (3.40) Acct: 5.44 (5.52) proj_loss: -0.6001 (-0.6010) time: 0.6717 data: 0.0003 [11-26 03:10:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [1251/1669] eta: 0:04:41 tlr: 9.8e-05 tnm: 0.41 Lm: 6.475 (6.501) Lt: 5.775 (5.788) Accm: 3.35 (3.37) Acct: 5.18 (5.16) proj_loss: -0.6191 (-0.6198) time: 0.6717 data: 0.0003 [11-26 03:10:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [1251/1669] eta: 0:04:41 tlr: 9.8e-05 tnm: 0.41 Lm: 6.465 (6.459) Lt: 5.760 (5.739) Accm: 3.52 (3.59) Acct: 5.57 (5.57) proj_loss: -0.6112 (-0.6153) time: 0.6717 data: 0.0003 [11-26 03:14:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [1668/1669] eta: 0:00:00 tlr: 9.8e-05 tnm: 0.39 Lm: 6.499 (6.490) Lt: 5.797 (5.772) Accm: 3.37 (3.48) Acct: 5.25 (5.39) proj_loss: -0.6091 (-0.6079) time: 0.6704 data: 0.0017 [11-26 03:14:48] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 218/350] Total time: 0:18:41 (0.672 s / it) [11-26 03:14:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [1668/1669] eta: 0:00:00 tlr: 9.8e-05 tnm: 0.39 Lm: 6.528 (6.463) Lt: 5.690 (5.692) Accm: 3.34 (3.55) Acct: 5.51 (5.71) proj_loss: -0.5977 (-0.6003) time: 0.6704 data: 0.0016 [11-26 03:14:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [1668/1669] eta: 0:00:00 tlr: 9.8e-05 tnm: 0.39 Lm: 6.480 (6.507) Lt: 5.761 (5.781) Accm: 3.29 (3.33) Acct: 5.11 (5.12) proj_loss: -0.6137 (-0.6169) time: 0.6704 data: 0.0015 [11-26 03:14:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 218/350] [1668/1669] eta: 0:00:00 tlr: 9.8e-05 tnm: 0.39 Lm: 6.435 (6.479) Lt: 5.687 (5.701) Accm: 3.51 (3.42) Acct: 5.61 (5.62) proj_loss: -0.6120 (-0.6067) time: 0.6704 data: 0.0018 [11-26 03:14:48] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 218/350] Total time: 0:18:41 (0.672 s / it) [11-26 03:14:48] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 218/350] Total time: 0:18:41 (0.672 s / it) [11-26 03:14:48] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 218/350] Total time: 0:18:42 (0.672 s / it) [11-26 03:14:48] (/home/user/VAR/train.py , line 276)=> [ep218] (training ) Lm: 6.487 (6.487), Lt: 5.735 (5.737), Acc m&t: 3.42 5.38, Remain: 1 day, 16:58:44, Finish: 2024-11-27 04:13 [11-26 03:14:48] (/home/user/VAR/train.py , line 276)=> [ep218] (training ) Lm: 6.487 (6.487), Lt: 5.735 (5.737), Acc m&t: 3.42 5.38, Remain: 1 day, 16:58:57, Finish: 2024-11-27 04:13 [11-26 03:14:48] (/home/user/VAR/train.py , line 276)=> [ep218] (training ) Lm: 6.487 (6.487), Lt: 5.735 (5.737), Acc m&t: 3.42 5.38, Remain: 1 day, 16:59:27, Finish: 2024-11-27 04:14 [11-26 03:14:48] (/home/user/VAR/train.py , line 276)=> [ep218] (training ) Lm: 6.487 (6.487), Lt: 5.735 (5.737), Acc m&t: 3.42 5.38, Remain: 1 day, 16:59:03, Finish: 2024-11-27 04:13 [11-26 03:14:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [ 0/1669] eta: 0:18:31 tlr: 9.8e-05 tnm: 0.39 Lm: 6.462 (6.462) Lt: 5.684 (5.684) Accm: 3.32 (3.32) Acct: 5.01 (5.01) proj_loss: -0.5936 (-0.5936) time: 0.6657 data: 0.0003 [11-26 03:14:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [ 0/1669] eta: 0:18:31 tlr: 9.8e-05 tnm: 0.39 Lm: 6.415 (6.415) Lt: 5.696 (5.696) Accm: 3.69 (3.69) Acct: 5.53 (5.53) proj_loss: -0.6132 (-0.6132) time: 0.6657 data: 0.0003 [11-26 03:14:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [ 0/1669] eta: 0:18:31 tlr: 9.8e-05 tnm: 0.39 Lm: 6.333 (6.333) Lt: 5.544 (5.544) Accm: 4.06 (4.06) Acct: 6.32 (6.32) proj_loss: -0.6109 (-0.6109) time: 0.6657 data: 0.0003 [11-26 03:14:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [ 0/1669] eta: 0:18:32 tlr: 9.8e-05 tnm: 0.39 Lm: 6.576 (6.576) Lt: 5.838 (5.838) Accm: 2.94 (2.94) Acct: 4.63 (4.63) proj_loss: -0.6152 (-0.6152) time: 0.6663 data: 0.0003 [11-26 03:19:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [ 417/1669] eta: 0:14:39 tlr: 9.8e-05 tnm: 0.42 Lm: 6.561 (6.561) Lt: 5.840 (5.840) Accm: 3.08 (3.08) Acct: 4.80 (4.80) proj_loss: -0.6235 (-0.6235) time: 0.6716 data: 0.0003 [11-26 03:19:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [ 417/1669] eta: 0:14:39 tlr: 9.8e-05 tnm: 0.42 Lm: 6.455 (6.455) Lt: 5.717 (5.717) Accm: 3.67 (3.67) Acct: 5.73 (5.73) proj_loss: -0.6110 (-0.6110) time: 0.6716 data: 0.0003 [11-26 03:19:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [ 417/1669] eta: 0:14:39 tlr: 9.8e-05 tnm: 0.42 Lm: 6.528 (6.528) Lt: 5.789 (5.789) Accm: 3.38 (3.38) Acct: 5.16 (5.16) proj_loss: -0.6000 (-0.6000) time: 0.6716 data: 0.0003 [11-26 03:19:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [ 417/1669] eta: 0:14:39 tlr: 9.8e-05 tnm: 0.42 Lm: 6.413 (6.413) Lt: 5.628 (5.628) Accm: 3.52 (3.52) Acct: 5.52 (5.52) proj_loss: -0.6083 (-0.6083) time: 0.6716 data: 0.0002 [11-26 03:24:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [ 834/1669] eta: 0:09:33 tlr: 9.8e-05 tnm: 0.40 Lm: 6.462 (6.451) Lt: 5.684 (5.660) Accm: 3.37 (3.47) Acct: 5.18 (5.41) proj_loss: -0.6168 (-0.6111) time: 0.6704 data: 0.0003 [11-26 03:24:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [ 834/1669] eta: 0:09:33 tlr: 9.8e-05 tnm: 0.40 Lm: 6.578 (6.551) Lt: 5.891 (5.819) Accm: 3.28 (3.32) Acct: 5.15 (5.19) proj_loss: -0.6109 (-0.6053) time: 0.6704 data: 0.0003 [11-26 03:24:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [ 834/1669] eta: 0:09:33 tlr: 9.8e-05 tnm: 0.40 Lm: 6.415 (6.480) Lt: 5.696 (5.741) Accm: 3.69 (3.54) Acct: 5.53 (5.49) proj_loss: -0.6132 (-0.6051) time: 0.6704 data: 0.0003 [11-26 03:24:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [ 834/1669] eta: 0:09:33 tlr: 9.8e-05 tnm: 0.40 Lm: 6.545 (6.525) Lt: 5.838 (5.809) Accm: 3.22 (3.19) Acct: 4.96 (4.92) proj_loss: -0.6152 (-0.6190) time: 0.6704 data: 0.0003 [11-26 03:29:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [1251/1669] eta: 0:04:44 tlr: 9.7e-05 tnm: 0.40 Lm: 6.500 (6.466) Lt: 5.793 (5.737) Accm: 3.32 (3.39) Acct: 5.07 (5.22) proj_loss: -0.6159 (-0.6184) time: 0.6739 data: 0.0003 [11-26 03:29:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [1251/1669] eta: 0:04:44 tlr: 9.7e-05 tnm: 0.40 Lm: 6.556 (6.547) Lt: 5.831 (5.807) Accm: 3.23 (3.29) Acct: 5.04 (5.12) proj_loss: -0.6025 (-0.6016) time: 0.6739 data: 0.0003 [11-26 03:29:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [1251/1669] eta: 0:04:44 tlr: 9.7e-05 tnm: 0.40 Lm: 6.464 (6.455) Lt: 5.668 (5.658) Accm: 3.53 (3.52) Acct: 5.60 (5.58) proj_loss: -0.6073 (-0.6078) time: 0.6739 data: 0.0003 [11-26 03:29:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [1251/1669] eta: 0:04:44 tlr: 9.7e-05 tnm: 0.40 Lm: 6.466 (6.489) Lt: 5.761 (5.762) Accm: 3.47 (3.47) Acct: 5.23 (5.35) proj_loss: -0.6087 (-0.6049) time: 0.6739 data: 0.0003 [11-26 03:33:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [1668/1669] eta: 0:00:00 tlr: 9.7e-05 tnm: 0.38 Lm: 6.446 (6.480) Lt: 5.758 (5.762) Accm: 3.53 (3.48) Acct: 5.42 (5.37) proj_loss: -0.6067 (-0.6052) time: 0.6765 data: 0.0017 [11-26 03:33:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 219/350] Total time: 0:18:54 (0.680 s / it) [11-26 03:33:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [1668/1669] eta: 0:00:00 tlr: 9.7e-05 tnm: 0.38 Lm: 6.467 (6.482) Lt: 5.684 (5.703) Accm: 3.37 (3.39) Acct: 5.18 (5.38) proj_loss: -0.6066 (-0.6076) time: 0.6765 data: 0.0017 [11-26 03:33:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [1668/1669] eta: 0:00:00 tlr: 9.7e-05 tnm: 0.38 Lm: 6.455 (6.462) Lt: 5.747 (5.731) Accm: 3.42 (3.45) Acct: 5.18 (5.37) proj_loss: -0.6152 (-0.6177) time: 0.6765 data: 0.0017 [11-26 03:33:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 219/350] Total time: 0:18:54 (0.680 s / it) [11-26 03:33:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 219/350] Total time: 0:18:54 (0.680 s / it) [11-26 03:33:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 219/350] [1668/1669] eta: 0:00:00 tlr: 9.7e-05 tnm: 0.38 Lm: 6.533 (6.512) Lt: 5.771 (5.773) Accm: 3.28 (3.36) Acct: 5.15 (5.17) proj_loss: -0.6069 (-0.6026) time: 0.6766 data: 0.0015 [11-26 03:33:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 219/350] Total time: 0:18:54 (0.680 s / it) [11-26 03:38:02] (me/user/VAR/evaluator.py, line 590)=> downloading InceptionV3 model... [11-26 03:39:15] (home/user/VAR/trainer.py, line 114)=> FID: 3.340298912668004 [11-26 03:39:16] (/home/user/VAR/train.py , line 259)=> [*] [ep219] (val 50000) Lm: 6.4925, Lt: 5.7392, Acc m&t: 3.39 5.32, Val cost: 333.19s [11-26 03:39:16] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-26 03:40:08] (/home/user/VAR/train.py , line 276)=> [ep219] (training ) Lm: 6.487 (6.492), Lt: 5.735 (5.739), Acc m&t: 3.42 5.38, Remain: 1 day, 17:05:03, Finish: 2024-11-27 04:38 [11-26 03:40:08] (/home/user/VAR/train.py , line 276)=> [ep219] (training ) Lm: 6.487 (6.492), Lt: 5.735 (5.739), Acc m&t: 3.42 5.38, Remain: 1 day, 17:05:43, Finish: 2024-11-27 04:39 [11-26 03:40:08] (/home/user/VAR/train.py , line 276)=> [ep219] (training ) Lm: 6.487 (6.492), Lt: 5.735 (5.739), Acc m&t: 3.42 5.38, Remain: 1 day, 17:05:03, Finish: 2024-11-27 04:38 [11-26 03:40:08] (/home/user/VAR/train.py , line 276)=> [ep219] (training ) Lm: 6.487 (6.492), Lt: 5.735 (5.739), Acc m&t: 3.42 5.38, Remain: 1 day, 17:05:38, Finish: 2024-11-27 04:39 [11-26 03:40:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [ 0/1669] eta: 0:21:08 tlr: 9.7e-05 tnm: 0.39 Lm: 6.531 (6.531) Lt: 5.826 (5.826) Accm: 3.46 (3.46) Acct: 4.92 (4.92) proj_loss: -0.6093 (-0.6093) time: 0.7600 data: 0.0004 [11-26 03:40:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [ 0/1669] eta: 0:21:08 tlr: 9.7e-05 tnm: 0.39 Lm: 6.319 (6.319) Lt: 5.563 (5.563) Accm: 4.03 (4.03) Acct: 6.40 (6.40) proj_loss: -0.6076 (-0.6076) time: 0.7600 data: 0.0004 [11-26 03:40:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [ 0/1669] eta: 0:21:51 tlr: 9.7e-05 tnm: 0.39 Lm: 6.560 (6.560) Lt: 5.783 (5.783) Accm: 3.21 (3.21) Acct: 5.22 (5.22) proj_loss: -0.5918 (-0.5918) time: 0.7860 data: 0.0004 [11-26 03:40:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [ 0/1669] eta: 0:21:10 tlr: 9.7e-05 tnm: 0.39 Lm: 6.426 (6.426) Lt: 5.666 (5.666) Accm: 3.34 (3.34) Acct: 5.03 (5.03) proj_loss: -0.6124 (-0.6124) time: 0.7610 data: 0.0004 [11-26 03:44:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [ 417/1669] eta: 0:14:07 tlr: 9.7e-05 tnm: 0.40 Lm: 6.569 (6.569) Lt: 5.795 (5.795) Accm: 3.08 (3.08) Acct: 4.70 (4.70) proj_loss: -0.5948 (-0.5948) time: 0.6780 data: 0.0003 [11-26 03:44:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [ 417/1669] eta: 0:14:07 tlr: 9.7e-05 tnm: 0.40 Lm: 6.460 (6.460) Lt: 5.706 (5.706) Accm: 3.51 (3.51) Acct: 5.54 (5.54) proj_loss: -0.6058 (-0.6058) time: 0.6780 data: 0.0003 [11-26 03:44:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [ 417/1669] eta: 0:14:07 tlr: 9.7e-05 tnm: 0.40 Lm: 6.430 (6.430) Lt: 5.714 (5.714) Accm: 3.65 (3.65) Acct: 5.42 (5.42) proj_loss: -0.6142 (-0.6142) time: 0.6780 data: 0.0003 [11-26 03:44:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [ 417/1669] eta: 0:14:07 tlr: 9.7e-05 tnm: 0.40 Lm: 6.529 (6.529) Lt: 5.782 (5.782) Accm: 3.32 (3.32) Acct: 5.21 (5.21) proj_loss: -0.5990 (-0.5990) time: 0.6781 data: 0.0003 [11-26 03:49:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [ 834/1669] eta: 0:09:24 tlr: 9.7e-05 tnm: 0.42 Lm: 6.497 (6.484) Lt: 5.781 (5.736) Accm: 3.44 (3.47) Acct: 5.22 (5.39) proj_loss: -0.6061 (-0.6112) time: 0.6753 data: 0.0003 [11-26 03:49:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [ 834/1669] eta: 0:09:24 tlr: 9.7e-05 tnm: 0.42 Lm: 6.503 (6.474) Lt: 5.751 (5.721) Accm: 3.40 (3.48) Acct: 5.53 (5.54) proj_loss: -0.6076 (-0.6091) time: 0.6753 data: 0.0003 [11-26 03:49:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [ 834/1669] eta: 0:09:24 tlr: 9.7e-05 tnm: 0.42 Lm: 6.376 (6.412) Lt: 5.645 (5.691) Accm: 3.79 (3.70) Acct: 5.75 (5.53) proj_loss: -0.6093 (-0.6060) time: 0.6753 data: 0.0003 [11-26 03:49:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [ 834/1669] eta: 0:09:24 tlr: 9.7e-05 tnm: 0.42 Lm: 6.426 (6.489) Lt: 5.666 (5.698) Accm: 3.34 (3.43) Acct: 5.03 (5.34) proj_loss: -0.6083 (-0.5993) time: 0.6754 data: 0.0003 [11-26 03:54:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [1251/1669] eta: 0:04:42 tlr: 9.7e-05 tnm: 0.40 Lm: 6.455 (6.488) Lt: 5.718 (5.716) Accm: 3.42 (3.45) Acct: 5.11 (5.31) proj_loss: -0.6104 (-0.6038) time: 0.6721 data: 0.0003 [11-26 03:54:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [1251/1669] eta: 0:04:42 tlr: 9.7e-05 tnm: 0.40 Lm: 6.482 (6.471) Lt: 5.714 (5.710) Accm: 3.46 (3.49) Acct: 5.43 (5.49) proj_loss: -0.6108 (-0.6103) time: 0.6721 data: 0.0003 [11-26 03:54:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [1251/1669] eta: 0:04:42 tlr: 9.7e-05 tnm: 0.40 Lm: 6.452 (6.465) Lt: 5.724 (5.719) Accm: 3.39 (3.44) Acct: 5.23 (5.35) proj_loss: -0.6129 (-0.6133) time: 0.6721 data: 0.0003 [11-26 03:54:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [1251/1669] eta: 0:04:42 tlr: 9.7e-05 tnm: 0.40 Lm: 6.451 (6.440) Lt: 5.736 (5.726) Accm: 3.62 (3.59) Acct: 5.35 (5.38) proj_loss: -0.6142 (-0.6101) time: 0.6721 data: 0.0003 [11-26 03:58:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [1668/1669] eta: 0:00:00 tlr: 9.6e-05 tnm: 0.39 Lm: 6.525 (6.459) Lt: 5.781 (5.737) Accm: 3.46 (3.53) Acct: 5.30 (5.37) proj_loss: -0.6190 (-0.6135) time: 0.6786 data: 0.0020 [11-26 03:58:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 220/350] Total time: 0:18:47 (0.676 s / it) [11-26 03:58:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [1668/1669] eta: 0:00:00 tlr: 9.6e-05 tnm: 0.39 Lm: 6.503 (6.499) Lt: 5.751 (5.737) Accm: 3.40 (3.44) Acct: 5.34 (5.38) proj_loss: -0.6076 (-0.6049) time: 0.6786 data: 0.0016 [11-26 03:58:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [1668/1669] eta: 0:00:00 tlr: 9.6e-05 tnm: 0.39 Lm: 6.497 (6.480) Lt: 5.781 (5.741) Accm: 3.35 (3.40) Acct: 5.22 (5.29) proj_loss: -0.6111 (-0.6129) time: 0.6786 data: 0.0019 [11-26 03:58:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 220/350] [1668/1669] eta: 0:00:00 tlr: 9.6e-05 tnm: 0.39 Lm: 6.485 (6.508) Lt: 5.770 (5.740) Accm: 3.34 (3.41) Acct: 5.08 (5.26) proj_loss: -0.6083 (-0.6027) time: 0.6787 data: 0.0016 [11-26 03:58:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 220/350] Total time: 0:18:47 (0.676 s / it) [11-26 03:58:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 220/350] Total time: 0:18:47 (0.676 s / it) [11-26 03:58:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 220/350] Total time: 0:18:47 (0.676 s / it) [11-26 03:58:56] (/home/user/VAR/train.py , line 276)=> [ep220] (training ) Lm: 6.487 (6.507), Lt: 5.735 (5.755), Acc m&t: 3.42 5.38, Remain: 1 day, 16:52:52, Finish: 2024-11-27 04:51 [11-26 03:58:56] (/home/user/VAR/train.py , line 276)=> [ep220] (training ) Lm: 6.487 (6.507), Lt: 5.735 (5.755), Acc m&t: 3.42 5.38, Remain: 1 day, 16:53:05, Finish: 2024-11-27 04:52 [11-26 03:58:56] (/home/user/VAR/train.py , line 276)=> [ep220] (training ) Lm: 6.487 (6.507), Lt: 5.735 (5.755), Acc m&t: 3.42 5.38, Remain: 1 day, 16:53:04, Finish: 2024-11-27 04:52 [11-26 03:58:56] (/home/user/VAR/train.py , line 276)=> [ep220] (training ) Lm: 6.487 (6.507), Lt: 5.735 (5.755), Acc m&t: 3.42 5.38, Remain: 1 day, 16:53:06, Finish: 2024-11-27 04:52 [11-26 03:58:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [ 0/1669] eta: 0:18:52 tlr: 9.6e-05 tnm: 0.40 Lm: 6.406 (6.406) Lt: 5.661 (5.661) Accm: 3.61 (3.61) Acct: 5.37 (5.37) proj_loss: -0.6202 (-0.6202) time: 0.6784 data: 0.0004 [11-26 03:58:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [ 0/1669] eta: 0:18:12 tlr: 9.6e-05 tnm: 0.40 Lm: 6.430 (6.430) Lt: 5.636 (5.636) Accm: 3.53 (3.53) Acct: 5.48 (5.48) proj_loss: -0.6031 (-0.6031) time: 0.6543 data: 0.0004 [11-26 03:58:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [ 0/1669] eta: 0:18:11 tlr: 9.6e-05 tnm: 0.40 Lm: 6.623 (6.623) Lt: 5.855 (5.855) Accm: 3.11 (3.11) Acct: 5.08 (5.08) proj_loss: -0.5998 (-0.5998) time: 0.6543 data: 0.0004 [11-26 03:58:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [ 0/1669] eta: 0:18:12 tlr: 9.6e-05 tnm: 0.40 Lm: 6.502 (6.502) Lt: 5.771 (5.771) Accm: 3.47 (3.47) Acct: 5.46 (5.46) proj_loss: -0.6083 (-0.6083) time: 0.6547 data: 0.0003 [11-26 04:03:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [ 417/1669] eta: 0:14:10 tlr: 9.6e-05 tnm: 0.39 Lm: 6.511 (6.511) Lt: 5.762 (5.762) Accm: 3.36 (3.36) Acct: 5.15 (5.15) proj_loss: -0.6092 (-0.6092) time: 0.6769 data: 0.0003 [11-26 04:03:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [ 417/1669] eta: 0:14:10 tlr: 9.6e-05 tnm: 0.39 Lm: 6.568 (6.568) Lt: 5.797 (5.797) Accm: 3.38 (3.38) Acct: 5.34 (5.34) proj_loss: -0.6093 (-0.6093) time: 0.6770 data: 0.0003 [11-26 04:03:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [ 417/1669] eta: 0:14:10 tlr: 9.6e-05 tnm: 0.39 Lm: 6.420 (6.420) Lt: 5.644 (5.644) Accm: 3.68 (3.68) Acct: 5.71 (5.71) proj_loss: -0.6076 (-0.6076) time: 0.6770 data: 0.0003 [11-26 04:03:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [ 417/1669] eta: 0:14:11 tlr: 9.6e-05 tnm: 0.39 Lm: 6.435 (6.435) Lt: 5.680 (5.680) Accm: 3.56 (3.56) Acct: 5.55 (5.55) proj_loss: -0.6226 (-0.6226) time: 0.6770 data: 0.0002 [11-26 04:08:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [ 834/1669] eta: 0:09:36 tlr: 9.6e-05 tnm: 0.40 Lm: 6.464 (6.531) Lt: 5.699 (5.790) Accm: 3.50 (3.36) Acct: 5.37 (5.32) proj_loss: -0.6202 (-0.6184) time: 0.6792 data: 0.0003 [11-26 04:08:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [ 834/1669] eta: 0:09:36 tlr: 9.6e-05 tnm: 0.40 Lm: 6.623 (6.608) Lt: 5.855 (5.834) Accm: 3.11 (3.18) Acct: 5.08 (5.04) proj_loss: -0.5998 (-0.6020) time: 0.6792 data: 0.0003 [11-26 04:08:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [ 834/1669] eta: 0:09:36 tlr: 9.6e-05 tnm: 0.40 Lm: 6.411 (6.386) Lt: 5.636 (5.605) Accm: 3.84 (3.83) Acct: 5.94 (5.90) proj_loss: -0.6038 (-0.6064) time: 0.6792 data: 0.0003 [11-26 04:08:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [ 834/1669] eta: 0:09:36 tlr: 9.6e-05 tnm: 0.40 Lm: 6.520 (6.517) Lt: 5.771 (5.767) Accm: 3.42 (3.38) Acct: 5.46 (5.31) proj_loss: -0.6083 (-0.6054) time: 0.6792 data: 0.0003 [11-26 04:13:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [1251/1669] eta: 0:04:46 tlr: 9.6e-05 tnm: 0.39 Lm: 6.524 (6.542) Lt: 5.774 (5.797) Accm: 3.33 (3.30) Acct: 5.15 (5.17) proj_loss: -0.6030 (-0.6028) time: 0.6735 data: 0.0003 [11-26 04:13:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [1251/1669] eta: 0:04:46 tlr: 9.6e-05 tnm: 0.39 Lm: 6.420 (6.445) Lt: 5.644 (5.672) Accm: 3.68 (3.66) Acct: 5.71 (5.66) proj_loss: -0.6034 (-0.6025) time: 0.6735 data: 0.0003 [11-26 04:13:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [1251/1669] eta: 0:04:46 tlr: 9.6e-05 tnm: 0.39 Lm: 6.447 (6.506) Lt: 5.680 (5.747) Accm: 3.55 (3.42) Acct: 5.55 (5.42) proj_loss: -0.6155 (-0.6165) time: 0.6735 data: 0.0003 [11-26 04:13:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [1251/1669] eta: 0:04:46 tlr: 9.6e-05 tnm: 0.39 Lm: 6.568 (6.577) Lt: 5.805 (5.814) Accm: 3.20 (3.20) Acct: 5.17 (5.09) proj_loss: -0.6093 (-0.6119) time: 0.6735 data: 0.0003 [11-26 04:17:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [1668/1669] eta: 0:00:00 tlr: 9.6e-05 tnm: 0.38 Lm: 6.514 (6.556) Lt: 5.754 (5.793) Accm: 3.29 (3.30) Acct: 5.25 (5.27) proj_loss: -0.6029 (-0.6101) time: 0.6786 data: 0.0018 [11-26 04:17:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [1668/1669] eta: 0:00:00 tlr: 9.6e-05 tnm: 0.38 Lm: 6.430 (6.455) Lt: 5.653 (5.688) Accm: 3.53 (3.59) Acct: 5.48 (5.60) proj_loss: -0.6031 (-0.6023) time: 0.6786 data: 0.0019 [11-26 04:17:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [1668/1669] eta: 0:00:00 tlr: 9.6e-05 tnm: 0.38 Lm: 6.431 (6.457) Lt: 5.661 (5.688) Accm: 3.59 (3.50) Acct: 5.73 (5.54) proj_loss: -0.6109 (-0.6128) time: 0.6786 data: 0.0017 [11-26 04:17:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 221/350] [1668/1669] eta: 0:00:00 tlr: 9.6e-05 tnm: 0.38 Lm: 6.520 (6.511) Lt: 5.771 (5.755) Accm: 3.42 (3.38) Acct: 5.46 (5.24) proj_loss: -0.5977 (-0.6009) time: 0.6786 data: 0.0016 [11-26 04:17:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 221/350] Total time: 0:18:59 (0.683 s / it) [11-26 04:17:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 221/350] Total time: 0:18:59 (0.683 s / it) [11-26 04:17:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 221/350] Total time: 0:18:59 (0.683 s / it) [11-26 04:17:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 221/350] Total time: 0:18:59 (0.683 s / it) [11-26 04:17:56] (/home/user/VAR/train.py , line 276)=> [ep221] (training ) Lm: 6.487 (6.488), Lt: 5.730 (5.730), Acc m&t: 3.44 5.41, Remain: 1 day, 16:30:24, Finish: 2024-11-27 04:48 [11-26 04:17:56] (/home/user/VAR/train.py , line 276)=> [ep221] (training ) Lm: 6.487 (6.488), Lt: 5.730 (5.730), Acc m&t: 3.44 5.41, Remain: 1 day, 16:30:03, Finish: 2024-11-27 04:47 [11-26 04:17:56] (/home/user/VAR/train.py , line 276)=> [ep221] (training ) Lm: 6.487 (6.488), Lt: 5.730 (5.730), Acc m&t: 3.44 5.41, Remain: 1 day, 16:29:55, Finish: 2024-11-27 04:47 [11-26 04:17:56] (/home/user/VAR/train.py , line 276)=> [ep221] (training ) Lm: 6.487 (6.488), Lt: 5.730 (5.730), Acc m&t: 3.44 5.41, Remain: 1 day, 16:30:08, Finish: 2024-11-27 04:48 [11-26 04:17:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [ 0/1669] eta: 0:18:20 tlr: 9.6e-05 tnm: 0.39 Lm: 6.529 (6.529) Lt: 5.748 (5.748) Accm: 3.24 (3.24) Acct: 5.29 (5.29) proj_loss: -0.6119 (-0.6119) time: 0.6593 data: 0.0004 [11-26 04:17:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [ 0/1669] eta: 0:18:23 tlr: 9.6e-05 tnm: 0.39 Lm: 6.422 (6.422) Lt: 5.574 (5.574) Accm: 3.97 (3.97) Acct: 6.56 (6.56) proj_loss: -0.5931 (-0.5931) time: 0.6612 data: 0.0003 [11-26 04:17:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [ 0/1669] eta: 0:18:23 tlr: 9.6e-05 tnm: 0.39 Lm: 6.629 (6.629) Lt: 5.926 (5.926) Accm: 3.11 (3.11) Acct: 4.42 (4.42) proj_loss: -0.6026 (-0.6026) time: 0.6613 data: 0.0003 [11-26 04:17:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [ 0/1669] eta: 0:18:24 tlr: 9.6e-05 tnm: 0.39 Lm: 6.544 (6.544) Lt: 5.780 (5.780) Accm: 3.31 (3.31) Acct: 5.15 (5.15) proj_loss: -0.6121 (-0.6121) time: 0.6616 data: 0.0004 [11-26 04:22:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [ 417/1669] eta: 0:14:05 tlr: 9.6e-05 tnm: 0.39 Lm: 6.514 (6.514) Lt: 5.763 (5.763) Accm: 3.34 (3.34) Acct: 5.12 (5.12) proj_loss: -0.6111 (-0.6111) time: 0.6774 data: 0.0003 [11-26 04:22:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [ 417/1669] eta: 0:14:05 tlr: 9.6e-05 tnm: 0.39 Lm: 6.432 (6.432) Lt: 5.626 (5.626) Accm: 3.51 (3.51) Acct: 5.94 (5.94) proj_loss: -0.5923 (-0.5923) time: 0.6774 data: 0.0002 [11-26 04:22:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [ 417/1669] eta: 0:14:05 tlr: 9.6e-05 tnm: 0.39 Lm: 6.461 (6.461) Lt: 5.682 (5.682) Accm: 3.43 (3.43) Acct: 5.41 (5.41) proj_loss: -0.5984 (-0.5984) time: 0.6774 data: 0.0003 [11-26 04:22:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [ 417/1669] eta: 0:14:05 tlr: 9.6e-05 tnm: 0.39 Lm: 6.545 (6.545) Lt: 5.808 (5.808) Accm: 3.31 (3.31) Acct: 4.95 (4.95) proj_loss: -0.6001 (-0.6001) time: 0.6775 data: 0.0003 [11-26 04:27:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [ 834/1669] eta: 0:09:23 tlr: 9.5e-05 tnm: 0.40 Lm: 6.559 (6.550) Lt: 5.737 (5.784) Accm: 3.11 (3.24) Acct: 5.06 (4.99) proj_loss: -0.5976 (-0.5944) time: 0.6731 data: 0.0003 [11-26 04:27:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [ 834/1669] eta: 0:09:23 tlr: 9.5e-05 tnm: 0.40 Lm: 6.422 (6.411) Lt: 5.623 (5.625) Accm: 3.88 (3.63) Acct: 6.04 (5.97) proj_loss: -0.5931 (-0.5990) time: 0.6731 data: 0.0003 [11-26 04:27:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [ 834/1669] eta: 0:09:23 tlr: 9.5e-05 tnm: 0.40 Lm: 6.514 (6.479) Lt: 5.748 (5.717) Accm: 3.34 (3.40) Acct: 5.29 (5.31) proj_loss: -0.6119 (-0.6115) time: 0.6731 data: 0.0003 [11-26 04:27:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [ 834/1669] eta: 0:09:23 tlr: 9.5e-05 tnm: 0.40 Lm: 6.483 (6.477) Lt: 5.745 (5.717) Accm: 3.37 (3.49) Acct: 5.15 (5.45) proj_loss: -0.6100 (-0.6090) time: 0.6731 data: 0.0003 [11-26 04:32:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [1251/1669] eta: 0:04:42 tlr: 9.5e-05 tnm: 0.42 Lm: 6.443 (6.455) Lt: 5.690 (5.696) Accm: 3.52 (3.53) Acct: 5.40 (5.50) proj_loss: -0.6092 (-0.6088) time: 0.6782 data: 0.0003 [11-26 04:32:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [1251/1669] eta: 0:04:42 tlr: 9.5e-05 tnm: 0.42 Lm: 6.432 (6.432) Lt: 5.651 (5.661) Accm: 3.61 (3.56) Acct: 5.68 (5.81) proj_loss: -0.5984 (-0.6002) time: 0.6782 data: 0.0002 [11-26 04:32:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [1251/1669] eta: 0:04:42 tlr: 9.5e-05 tnm: 0.42 Lm: 6.511 (6.521) Lt: 5.713 (5.756) Accm: 3.29 (3.30) Acct: 5.27 (5.14) proj_loss: -0.6001 (-0.5979) time: 0.6782 data: 0.0003 [11-26 04:32:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [1251/1669] eta: 0:04:42 tlr: 9.5e-05 tnm: 0.42 Lm: 6.454 (6.452) Lt: 5.682 (5.681) Accm: 3.40 (3.42) Acct: 5.41 (5.38) proj_loss: -0.6072 (-0.6092) time: 0.6782 data: 0.0002 [11-26 04:37:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [1668/1669] eta: 0:00:00 tlr: 9.5e-05 tnm: 0.40 Lm: 6.480 (6.457) Lt: 5.748 (5.699) Accm: 3.38 (3.41) Acct: 5.29 (5.36) proj_loss: -0.6119 (-0.6102) time: 0.9258 data: 0.0015 [11-26 04:37:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 222/350] Total time: 0:19:09 (0.689 s / it) [11-26 04:37:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [1668/1669] eta: 0:00:00 tlr: 9.5e-05 tnm: 0.40 Lm: 6.422 (6.426) Lt: 5.675 (5.663) Accm: 3.88 (3.62) Acct: 6.04 (5.87) proj_loss: -0.6038 (-0.6045) time: 0.9257 data: 0.0017 [11-26 04:37:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [1668/1669] eta: 0:00:00 tlr: 9.5e-05 tnm: 0.40 Lm: 6.462 (6.474) Lt: 5.689 (5.711) Accm: 3.47 (3.49) Acct: 5.48 (5.44) proj_loss: -0.6026 (-0.5989) time: 0.9257 data: 0.0015 [11-26 04:37:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 222/350] [1668/1669] eta: 0:00:00 tlr: 9.5e-05 tnm: 0.40 Lm: 6.483 (6.466) Lt: 5.745 (5.707) Accm: 3.44 (3.52) Acct: 5.65 (5.56) proj_loss: -0.6083 (-0.6079) time: 0.9257 data: 0.0020 [11-26 04:37:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 222/350] Total time: 0:19:09 (0.689 s / it) [11-26 04:37:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 222/350] Total time: 0:19:09 (0.689 s / it) [11-26 04:37:06] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 222/350] Total time: 0:19:09 (0.689 s / it) [11-26 04:37:06] (/home/user/VAR/train.py , line 276)=> [ep222] (training ) Lm: 6.487 (6.490), Lt: 5.730 (5.733), Acc m&t: 3.44 5.41, Remain: 1 day, 16:04:09, Finish: 2024-11-27 04:41 [11-26 04:37:06] (/home/user/VAR/train.py , line 276)=> [ep222] (training ) Lm: 6.487 (6.490), Lt: 5.730 (5.733), Acc m&t: 3.44 5.41, Remain: 1 day, 16:04:24, Finish: 2024-11-27 04:41 [11-26 04:37:06] (/home/user/VAR/train.py , line 276)=> [ep222] (training ) Lm: 6.487 (6.490), Lt: 5.730 (5.733), Acc m&t: 3.44 5.41, Remain: 1 day, 16:04:36, Finish: 2024-11-27 04:41 [11-26 04:37:06] (/home/user/VAR/train.py , line 276)=> [ep222] (training ) Lm: 6.487 (6.490), Lt: 5.730 (5.733), Acc m&t: 3.44 5.41, Remain: 1 day, 16:04:03, Finish: 2024-11-27 04:41 [11-26 04:37:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [ 0/1669] eta: 0:18:17 tlr: 9.5e-05 tnm: 0.40 Lm: 6.439 (6.439) Lt: 5.715 (5.715) Accm: 3.53 (3.53) Acct: 5.56 (5.56) proj_loss: -0.6126 (-0.6126) time: 0.6575 data: 0.0004 [11-26 04:37:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [ 0/1669] eta: 0:18:16 tlr: 9.5e-05 tnm: 0.40 Lm: 6.448 (6.448) Lt: 5.695 (5.695) Accm: 3.77 (3.77) Acct: 5.91 (5.91) proj_loss: -0.6126 (-0.6126) time: 0.6571 data: 0.0004 [11-26 04:37:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [ 0/1669] eta: 0:18:19 tlr: 9.5e-05 tnm: 0.40 Lm: 6.564 (6.564) Lt: 5.811 (5.811) Accm: 2.85 (2.85) Acct: 4.42 (4.42) proj_loss: -0.6161 (-0.6161) time: 0.6585 data: 0.0003 [11-26 04:37:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [ 0/1669] eta: 0:18:19 tlr: 9.5e-05 tnm: 0.40 Lm: 6.543 (6.543) Lt: 5.786 (5.786) Accm: 3.53 (3.53) Acct: 5.77 (5.77) proj_loss: -0.6184 (-0.6184) time: 0.6588 data: 0.0004 [11-26 04:41:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [ 417/1669] eta: 0:14:04 tlr: 9.5e-05 tnm: 0.39 Lm: 6.539 (6.539) Lt: 5.793 (5.793) Accm: 3.39 (3.39) Acct: 5.37 (5.37) proj_loss: -0.6142 (-0.6142) time: 0.6748 data: 0.0003 [11-26 04:41:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [ 417/1669] eta: 0:14:04 tlr: 9.5e-05 tnm: 0.39 Lm: 6.385 (6.385) Lt: 5.603 (5.603) Accm: 3.80 (3.80) Acct: 6.07 (6.07) proj_loss: -0.6122 (-0.6122) time: 0.6748 data: 0.0003 [11-26 04:41:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [ 417/1669] eta: 0:14:04 tlr: 9.5e-05 tnm: 0.39 Lm: 6.377 (6.377) Lt: 5.616 (5.616) Accm: 3.71 (3.71) Acct: 5.86 (5.86) proj_loss: -0.6188 (-0.6188) time: 0.6748 data: 0.0003 [11-26 04:41:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [ 417/1669] eta: 0:14:04 tlr: 9.5e-05 tnm: 0.39 Lm: 6.594 (6.594) Lt: 5.903 (5.903) Accm: 3.15 (3.15) Acct: 4.90 (4.90) proj_loss: -0.6139 (-0.6139) time: 0.6748 data: 0.0003 [11-26 04:46:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [ 834/1669] eta: 0:09:23 tlr: 9.5e-05 tnm: 0.39 Lm: 6.692 (6.627) Lt: 5.941 (5.916) Accm: 2.87 (3.05) Acct: 4.86 (4.88) proj_loss: -0.6126 (-0.6132) time: 0.6774 data: 0.0003 [11-26 04:46:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [ 834/1669] eta: 0:09:23 tlr: 9.5e-05 tnm: 0.39 Lm: 6.564 (6.447) Lt: 5.811 (5.694) Accm: 3.20 (3.54) Acct: 5.29 (5.67) proj_loss: -0.6161 (-0.6058) time: 0.6774 data: 0.0002 [11-26 04:46:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [ 834/1669] eta: 0:09:23 tlr: 9.5e-05 tnm: 0.39 Lm: 6.535 (6.445) Lt: 5.786 (5.697) Accm: 3.53 (3.65) Acct: 5.77 (5.76) proj_loss: -0.6100 (-0.6125) time: 0.6774 data: 0.0003 [11-26 04:46:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [ 834/1669] eta: 0:09:23 tlr: 9.5e-05 tnm: 0.39 Lm: 6.322 (6.356) Lt: 5.510 (5.560) Accm: 3.77 (3.75) Acct: 6.01 (6.05) proj_loss: -0.6119 (-0.6069) time: 0.6774 data: 0.0003 [11-26 04:51:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [1251/1669] eta: 0:04:42 tlr: 9.4e-05 tnm: 0.41 Lm: 6.385 (6.421) Lt: 5.603 (5.638) Accm: 3.71 (3.57) Acct: 5.96 (5.75) proj_loss: -0.6041 (-0.6017) time: 0.6729 data: 0.0003 [11-26 04:51:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [1251/1669] eta: 0:04:42 tlr: 9.4e-05 tnm: 0.41 Lm: 6.575 (6.482) Lt: 5.830 (5.740) Accm: 3.15 (3.43) Acct: 5.08 (5.47) proj_loss: -0.6170 (-0.6089) time: 0.6729 data: 0.0002 [11-26 04:51:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [1251/1669] eta: 0:04:42 tlr: 9.4e-05 tnm: 0.41 Lm: 6.616 (6.605) Lt: 5.862 (5.882) Accm: 3.11 (3.13) Acct: 5.09 (4.99) proj_loss: -0.6123 (-0.6122) time: 0.6729 data: 0.0002 [11-26 04:51:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [1251/1669] eta: 0:04:42 tlr: 9.4e-05 tnm: 0.41 Lm: 6.466 (6.433) Lt: 5.682 (5.668) Accm: 3.67 (3.70) Acct: 5.81 (5.78) proj_loss: -0.6142 (-0.6143) time: 0.6729 data: 0.0003 [11-26 04:55:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [1668/1669] eta: 0:00:00 tlr: 9.4e-05 tnm: 0.41 Lm: 6.429 (6.432) Lt: 5.651 (5.664) Accm: 3.53 (3.62) Acct: 5.77 (5.65) proj_loss: -0.6100 (-0.6108) time: 0.6744 data: 0.0019 [11-26 04:55:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 223/350] Total time: 0:18:46 (0.675 s / it) [11-26 04:55:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [1668/1669] eta: 0:00:00 tlr: 9.4e-05 tnm: 0.41 Lm: 6.586 (6.515) Lt: 5.849 (5.772) Accm: 3.10 (3.32) Acct: 4.87 (5.30) proj_loss: -0.6161 (-0.6048) time: 0.6744 data: 0.0016 [11-26 04:55:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [1668/1669] eta: 0:00:00 tlr: 9.4e-05 tnm: 0.41 Lm: 6.540 (6.589) Lt: 5.783 (5.857) Accm: 3.21 (3.15) Acct: 5.32 (5.08) proj_loss: -0.6120 (-0.6099) time: 0.6744 data: 0.0015 [11-26 04:55:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 223/350] [1668/1669] eta: 0:00:00 tlr: 9.4e-05 tnm: 0.41 Lm: 6.448 (6.455) Lt: 5.695 (5.683) Accm: 3.66 (3.52) Acct: 5.91 (5.62) proj_loss: -0.6055 (-0.6024) time: 0.6744 data: 0.0019 [11-26 04:55:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 223/350] Total time: 0:18:46 (0.675 s / it) [11-26 04:55:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 223/350] Total time: 0:18:46 (0.675 s / it) [11-26 04:55:52] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 223/350] Total time: 0:18:46 (0.675 s / it) [11-26 04:55:52] (/home/user/VAR/train.py , line 276)=> [ep223] (training ) Lm: 6.474 (6.474), Lt: 5.716 (5.716), Acc m&t: 3.48 5.47, Remain: 1 day, 15:40:11, Finish: 2024-11-27 04:36 [11-26 04:55:52] (/home/user/VAR/train.py , line 276)=> [ep223] (training ) Lm: 6.474 (6.474), Lt: 5.716 (5.716), Acc m&t: 3.48 5.47, Remain: 1 day, 15:40:36, Finish: 2024-11-27 04:36 [11-26 04:55:52] (/home/user/VAR/train.py , line 276)=> [ep223] (training ) Lm: 6.474 (6.474), Lt: 5.716 (5.716), Acc m&t: 3.48 5.47, Remain: 1 day, 15:40:18, Finish: 2024-11-27 04:36 [11-26 04:55:52] (/home/user/VAR/train.py , line 276)=> [ep223] (training ) Lm: 6.474 (6.474), Lt: 5.716 (5.716), Acc m&t: 3.48 5.47, Remain: 1 day, 15:40:27, Finish: 2024-11-27 04:36 [11-26 04:55:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [ 0/1669] eta: 0:18:18 tlr: 9.4e-05 tnm: 0.42 Lm: 6.411 (6.411) Lt: 5.688 (5.688) Accm: 3.53 (3.53) Acct: 5.51 (5.51) proj_loss: -0.6136 (-0.6136) time: 0.6584 data: 0.0004 [11-26 04:55:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [ 0/1669] eta: 0:18:19 tlr: 9.4e-05 tnm: 0.42 Lm: 6.709 (6.709) Lt: 5.979 (5.979) Accm: 2.98 (2.98) Acct: 4.36 (4.36) proj_loss: -0.6288 (-0.6288) time: 0.6585 data: 0.0004 [11-26 04:55:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [ 0/1669] eta: 0:18:19 tlr: 9.4e-05 tnm: 0.42 Lm: 6.666 (6.666) Lt: 5.942 (5.942) Accm: 2.71 (2.71) Acct: 4.34 (4.34) proj_loss: -0.6378 (-0.6378) time: 0.6588 data: 0.0004 [11-26 04:55:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [ 0/1669] eta: 0:18:19 tlr: 9.4e-05 tnm: 0.42 Lm: 6.667 (6.667) Lt: 5.967 (5.967) Accm: 2.96 (2.96) Acct: 4.61 (4.61) proj_loss: -0.6314 (-0.6314) time: 0.6590 data: 0.0005 [11-26 05:00:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [ 417/1669] eta: 0:14:09 tlr: 9.4e-05 tnm: 0.40 Lm: 6.549 (6.549) Lt: 5.817 (5.817) Accm: 3.26 (3.26) Acct: 5.11 (5.11) proj_loss: -0.6092 (-0.6092) time: 0.6732 data: 0.0003 [11-26 05:00:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [ 417/1669] eta: 0:14:09 tlr: 9.4e-05 tnm: 0.40 Lm: 6.549 (6.549) Lt: 5.824 (5.824) Accm: 3.11 (3.11) Acct: 5.01 (5.01) proj_loss: -0.6207 (-0.6207) time: 0.6731 data: 0.0003 [11-26 05:00:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [ 417/1669] eta: 0:14:09 tlr: 9.4e-05 tnm: 0.40 Lm: 6.498 (6.498) Lt: 5.770 (5.770) Accm: 3.38 (3.38) Acct: 5.27 (5.27) proj_loss: -0.6053 (-0.6053) time: 0.6732 data: 0.0003 [11-26 05:00:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [ 417/1669] eta: 0:14:09 tlr: 9.4e-05 tnm: 0.40 Lm: 6.703 (6.703) Lt: 5.978 (5.978) Accm: 2.98 (2.98) Acct: 4.42 (4.42) proj_loss: -0.6093 (-0.6093) time: 0.6732 data: 0.0003 [11-26 05:05:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [ 834/1669] eta: 0:09:46 tlr: 9.4e-05 tnm: 0.40 Lm: 6.696 (6.629) Lt: 5.976 (5.877) Accm: 2.99 (3.13) Acct: 4.48 (4.87) proj_loss: -0.5999 (-0.6062) time: 0.6752 data: 0.0003 [11-26 05:05:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [ 834/1669] eta: 0:09:46 tlr: 9.4e-05 tnm: 0.40 Lm: 6.585 (6.557) Lt: 5.851 (5.841) Accm: 3.23 (3.13) Acct: 5.03 (4.93) proj_loss: -0.6037 (-0.6048) time: 0.6752 data: 0.0003 [11-26 05:05:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [ 834/1669] eta: 0:09:46 tlr: 9.4e-05 tnm: 0.40 Lm: 6.542 (6.547) Lt: 5.814 (5.816) Accm: 3.26 (3.26) Acct: 5.06 (5.09) proj_loss: -0.6004 (-0.6063) time: 0.6752 data: 0.0003 [11-26 05:05:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [ 834/1669] eta: 0:09:46 tlr: 9.4e-05 tnm: 0.40 Lm: 6.538 (6.545) Lt: 5.793 (5.814) Accm: 3.40 (3.21) Acct: 5.49 (5.17) proj_loss: -0.6036 (-0.6132) time: 0.6751 data: 0.0003 [11-26 05:10:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [1251/1669] eta: 0:04:49 tlr: 9.4e-05 tnm: 0.40 Lm: 6.527 (6.538) Lt: 5.764 (5.794) Accm: 3.46 (3.35) Acct: 5.59 (5.44) proj_loss: -0.6009 (-0.6056) time: 0.6745 data: 0.0003 [11-26 05:10:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [1251/1669] eta: 0:04:49 tlr: 9.4e-05 tnm: 0.40 Lm: 6.603 (6.599) Lt: 5.857 (5.842) Accm: 3.11 (3.16) Acct: 4.92 (4.99) proj_loss: -0.6029 (-0.6061) time: 0.6745 data: 0.0003 [11-26 05:10:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [1251/1669] eta: 0:04:49 tlr: 9.4e-05 tnm: 0.40 Lm: 6.541 (6.542) Lt: 5.818 (5.827) Accm: 3.31 (3.20) Acct: 5.27 (5.09) proj_loss: -0.6086 (-0.6083) time: 0.6745 data: 0.0002 [11-26 05:10:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [1251/1669] eta: 0:04:49 tlr: 9.4e-05 tnm: 0.40 Lm: 6.487 (6.497) Lt: 5.741 (5.759) Accm: 3.40 (3.40) Acct: 5.33 (5.29) proj_loss: -0.6093 (-0.6093) time: 0.6745 data: 0.0003 [11-26 05:15:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [1668/1669] eta: 0:00:00 tlr: 9.4e-05 tnm: 0.43 Lm: 6.454 (6.489) Lt: 5.703 (5.748) Accm: 3.26 (3.36) Acct: 5.13 (5.26) proj_loss: -0.6004 (-0.6068) time: 0.6784 data: 0.0021 [11-26 05:15:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 224/350] Total time: 0:19:10 (0.689 s / it) [11-26 05:15:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [1668/1669] eta: 0:00:00 tlr: 9.4e-05 tnm: 0.43 Lm: 6.498 (6.529) Lt: 5.785 (5.808) Accm: 3.39 (3.25) Acct: 5.25 (5.12) proj_loss: -0.6136 (-0.6112) time: 0.6784 data: 0.0021 [11-26 05:15:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [1668/1669] eta: 0:00:00 tlr: 9.4e-05 tnm: 0.43 Lm: 6.516 (6.504) Lt: 5.735 (5.746) Accm: 3.51 (3.44) Acct: 5.68 (5.51) proj_loss: -0.6036 (-0.6066) time: 0.6784 data: 0.0019 [11-26 05:15:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 224/350] [1668/1669] eta: 0:00:00 tlr: 9.4e-05 tnm: 0.43 Lm: 6.510 (6.562) Lt: 5.738 (5.801) Accm: 3.24 (3.27) Acct: 5.35 (5.14) proj_loss: -0.5999 (-0.6041) time: 0.6785 data: 0.0017 [11-26 05:15:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 224/350] Total time: 0:19:10 (0.689 s / it) [11-26 05:15:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 224/350] Total time: 0:19:10 (0.689 s / it) [11-26 05:15:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 224/350] Total time: 0:19:10 (0.689 s / it) [11-26 05:15:03] (/home/user/VAR/train.py , line 276)=> [ep224] (training ) Lm: 6.474 (6.495), Lt: 5.716 (5.742), Acc m&t: 3.48 5.47, Remain: 1 day, 15:30:37, Finish: 2024-11-27 04:45 [11-26 05:15:03] (/home/user/VAR/train.py , line 276)=> [ep224] (training ) Lm: 6.474 (6.495), Lt: 5.716 (5.742), Acc m&t: 3.48 5.47, Remain: 1 day, 15:31:13, Finish: 2024-11-27 04:46 [11-26 05:15:03] (/home/user/VAR/train.py , line 276)=> [ep224] (training ) Lm: 6.474 (6.495), Lt: 5.716 (5.742), Acc m&t: 3.48 5.47, Remain: 1 day, 15:31:00, Finish: 2024-11-27 04:46 [11-26 05:15:03] (/home/user/VAR/train.py , line 276)=> [ep224] (training ) Lm: 6.474 (6.495), Lt: 5.716 (5.742), Acc m&t: 3.48 5.47, Remain: 1 day, 15:29:49, Finish: 2024-11-27 04:44 [11-26 05:15:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [ 0/1669] eta: 0:18:19 tlr: 9.4e-05 tnm: 0.40 Lm: 6.498 (6.498) Lt: 5.792 (5.792) Accm: 3.45 (3.45) Acct: 5.10 (5.10) proj_loss: -0.6275 (-0.6275) time: 0.6590 data: 0.0004 [11-26 05:15:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [ 0/1669] eta: 0:18:18 tlr: 9.4e-05 tnm: 0.40 Lm: 6.540 (6.540) Lt: 5.796 (5.796) Accm: 3.33 (3.33) Acct: 5.29 (5.29) proj_loss: -0.6041 (-0.6041) time: 0.6582 data: 0.0003 [11-26 05:15:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [ 0/1669] eta: 0:18:20 tlr: 9.4e-05 tnm: 0.40 Lm: 6.558 (6.558) Lt: 5.833 (5.833) Accm: 3.14 (3.14) Acct: 4.87 (4.87) proj_loss: -0.6200 (-0.6200) time: 0.6592 data: 0.0004 [11-26 05:15:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [ 0/1669] eta: 0:18:21 tlr: 9.4e-05 tnm: 0.40 Lm: 6.369 (6.369) Lt: 5.571 (5.571) Accm: 3.82 (3.82) Acct: 6.13 (6.13) proj_loss: -0.5968 (-0.5968) time: 0.6600 data: 0.0004 [11-26 05:19:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [ 417/1669] eta: 0:14:05 tlr: 9.3e-05 tnm: 0.41 Lm: 6.468 (6.468) Lt: 5.723 (5.723) Accm: 3.42 (3.42) Acct: 5.33 (5.33) proj_loss: -0.5962 (-0.5962) time: 0.6746 data: 0.0003 [11-26 05:19:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [ 417/1669] eta: 0:14:05 tlr: 9.3e-05 tnm: 0.41 Lm: 6.521 (6.521) Lt: 5.791 (5.791) Accm: 3.36 (3.36) Acct: 5.12 (5.12) proj_loss: -0.6121 (-0.6121) time: 0.6746 data: 0.0003 [11-26 05:19:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [ 417/1669] eta: 0:14:05 tlr: 9.3e-05 tnm: 0.41 Lm: 6.432 (6.432) Lt: 5.689 (5.689) Accm: 3.70 (3.70) Acct: 5.72 (5.72) proj_loss: -0.6135 (-0.6135) time: 0.6746 data: 0.0003 [11-26 05:19:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [ 417/1669] eta: 0:14:05 tlr: 9.3e-05 tnm: 0.41 Lm: 6.487 (6.487) Lt: 5.722 (5.722) Accm: 3.26 (3.26) Acct: 5.04 (5.04) proj_loss: -0.6083 (-0.6083) time: 0.6746 data: 0.0003 [11-26 05:24:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [ 834/1669] eta: 0:09:23 tlr: 9.3e-05 tnm: 0.40 Lm: 6.515 (6.496) Lt: 5.742 (5.728) Accm: 3.38 (3.34) Acct: 5.22 (5.21) proj_loss: -0.6134 (-0.6100) time: 0.6790 data: 0.0003 [11-26 05:24:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [ 834/1669] eta: 0:09:23 tlr: 9.3e-05 tnm: 0.40 Lm: 6.498 (6.496) Lt: 5.791 (5.769) Accm: 3.45 (3.51) Acct: 5.15 (5.26) proj_loss: -0.6275 (-0.6179) time: 0.6790 data: 0.0003 [11-26 05:24:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [ 834/1669] eta: 0:09:23 tlr: 9.3e-05 tnm: 0.40 Lm: 6.555 (6.497) Lt: 5.805 (5.750) Accm: 3.16 (3.33) Acct: 4.98 (5.21) proj_loss: -0.5968 (-0.6026) time: 0.6790 data: 0.0002 [11-26 05:24:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [ 834/1669] eta: 0:09:23 tlr: 9.3e-05 tnm: 0.40 Lm: 6.540 (6.517) Lt: 5.796 (5.778) Accm: 3.33 (3.46) Acct: 5.29 (5.37) proj_loss: -0.6041 (-0.6059) time: 0.6790 data: 0.0003 [11-26 05:29:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [1251/1669] eta: 0:04:42 tlr: 9.3e-05 tnm: 0.40 Lm: 6.546 (6.526) Lt: 5.771 (5.770) Accm: 3.30 (3.41) Acct: 5.31 (5.36) proj_loss: -0.5974 (-0.5995) time: 0.6762 data: 0.0003 [11-26 05:29:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [1251/1669] eta: 0:04:42 tlr: 9.3e-05 tnm: 0.40 Lm: 6.521 (6.535) Lt: 5.791 (5.790) Accm: 3.36 (3.41) Acct: 5.12 (5.16) proj_loss: -0.6121 (-0.6080) time: 0.6762 data: 0.0002 [11-26 05:29:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [1251/1669] eta: 0:04:42 tlr: 9.3e-05 tnm: 0.40 Lm: 6.489 (6.478) Lt: 5.735 (5.729) Accm: 3.48 (3.45) Acct: 5.55 (5.48) proj_loss: -0.6062 (-0.6065) time: 0.6762 data: 0.0002 [11-26 05:29:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [1251/1669] eta: 0:04:42 tlr: 9.3e-05 tnm: 0.40 Lm: 6.466 (6.468) Lt: 5.676 (5.697) Accm: 3.44 (3.44) Acct: 5.37 (5.34) proj_loss: -0.6095 (-0.6089) time: 0.6762 data: 0.0003 [11-26 05:34:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [1668/1669] eta: 0:00:00 tlr: 9.3e-05 tnm: 0.40 Lm: 6.416 (6.445) Lt: 5.611 (5.661) Accm: 3.50 (3.51) Acct: 5.53 (5.52) proj_loss: -0.6056 (-0.6067) time: 1.0847 data: 0.0016 [11-26 05:34:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [1668/1669] eta: 0:00:00 tlr: 9.3e-05 tnm: 0.40 Lm: 6.498 (6.503) Lt: 5.791 (5.737) Accm: 3.45 (3.50) Acct: 5.15 (5.36) proj_loss: -0.5976 (-0.6059) time: 1.0847 data: 0.0016 [11-26 05:34:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [1668/1669] eta: 0:00:00 tlr: 9.3e-05 tnm: 0.40 Lm: 6.540 (6.509) Lt: 5.747 (5.755) Accm: 3.33 (3.46) Acct: 5.34 (5.40) proj_loss: -0.6041 (-0.6022) time: 1.0847 data: 0.0017 [11-26 05:34:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 225/350] [1668/1669] eta: 0:00:00 tlr: 9.3e-05 tnm: 0.40 Lm: 6.435 (6.470) Lt: 5.665 (5.714) Accm: 3.65 (3.49) Acct: 5.96 (5.57) proj_loss: -0.6156 (-0.6109) time: 1.0847 data: 0.0020 [11-26 05:34:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 225/350] Total time: 0:19:13 (0.691 s / it) [11-26 05:34:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 225/350] Total time: 0:19:13 (0.691 s / it) [11-26 05:34:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 225/350] Total time: 0:19:13 (0.691 s / it) [11-26 05:34:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 225/350] Total time: 0:19:13 (0.691 s / it) [11-26 05:34:16] (/home/user/VAR/train.py , line 276)=> [ep225] (training ) Lm: 6.474 (6.497), Lt: 5.716 (5.744), Acc m&t: 3.48 5.47, Remain: 1 day, 15:12:42, Finish: 2024-11-27 04:46 [11-26 05:34:16] (/home/user/VAR/train.py , line 276)=> [ep225] (training ) Lm: 6.474 (6.497), Lt: 5.716 (5.744), Acc m&t: 3.48 5.47, Remain: 1 day, 15:12:07, Finish: 2024-11-27 04:46 [11-26 05:34:16] (/home/user/VAR/train.py , line 276)=> [ep225] (training ) Lm: 6.474 (6.497), Lt: 5.716 (5.744), Acc m&t: 3.48 5.47, Remain: 1 day, 15:12:10, Finish: 2024-11-27 04:46 [11-26 05:34:16] (/home/user/VAR/train.py , line 276)=> [ep225] (training ) Lm: 6.474 (6.497), Lt: 5.716 (5.744), Acc m&t: 3.48 5.47, Remain: 1 day, 15:12:38, Finish: 2024-11-27 04:46 [11-26 05:34:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [ 0/1669] eta: 0:18:17 tlr: 9.3e-05 tnm: 0.40 Lm: 6.684 (6.684) Lt: 5.930 (5.930) Accm: 2.80 (2.80) Acct: 4.22 (4.22) proj_loss: -0.6067 (-0.6067) time: 0.6574 data: 0.0004 [11-26 05:34:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [ 0/1669] eta: 0:18:17 tlr: 9.3e-05 tnm: 0.40 Lm: 6.221 (6.221) Lt: 5.441 (5.441) Accm: 4.14 (4.14) Acct: 6.71 (6.71) proj_loss: -0.6150 (-0.6150) time: 0.6575 data: 0.0004 [11-26 05:34:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [ 0/1669] eta: 0:18:18 tlr: 9.3e-05 tnm: 0.40 Lm: 6.461 (6.461) Lt: 5.641 (5.641) Accm: 3.00 (3.00) Acct: 4.60 (4.60) proj_loss: -0.5806 (-0.5806) time: 0.6579 data: 0.0004 [11-26 05:34:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [ 0/1669] eta: 0:18:18 tlr: 9.3e-05 tnm: 0.40 Lm: 6.444 (6.444) Lt: 5.680 (5.680) Accm: 3.34 (3.34) Acct: 5.20 (5.20) proj_loss: -0.5940 (-0.5940) time: 0.6583 data: 0.0003 [11-26 05:38:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [ 417/1669] eta: 0:14:04 tlr: 9.3e-05 tnm: 0.40 Lm: 6.509 (6.509) Lt: 5.737 (5.737) Accm: 3.32 (3.32) Acct: 5.19 (5.19) proj_loss: -0.5999 (-0.5999) time: 0.6751 data: 0.0003 [11-26 05:38:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [ 417/1669] eta: 0:14:04 tlr: 9.3e-05 tnm: 0.40 Lm: 6.539 (6.539) Lt: 5.790 (5.790) Accm: 3.34 (3.34) Acct: 5.11 (5.11) proj_loss: -0.5985 (-0.5985) time: 0.6751 data: 0.0003 [11-26 05:38:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [ 417/1669] eta: 0:14:04 tlr: 9.3e-05 tnm: 0.40 Lm: 6.344 (6.344) Lt: 5.585 (5.585) Accm: 3.87 (3.87) Acct: 6.25 (6.25) proj_loss: -0.6083 (-0.6083) time: 0.6751 data: 0.0003 [11-26 05:38:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [ 417/1669] eta: 0:14:04 tlr: 9.3e-05 tnm: 0.40 Lm: 6.466 (6.466) Lt: 5.714 (5.714) Accm: 3.22 (3.22) Acct: 4.88 (4.88) proj_loss: -0.5959 (-0.5959) time: 0.6751 data: 0.0003 [11-26 05:43:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [ 834/1669] eta: 0:09:23 tlr: 9.2e-05 tnm: 0.40 Lm: 6.472 (6.479) Lt: 5.713 (5.714) Accm: 3.29 (3.24) Acct: 5.17 (5.07) proj_loss: -0.5978 (-0.5966) time: 0.6753 data: 0.0003 [11-26 05:43:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [ 834/1669] eta: 0:09:23 tlr: 9.2e-05 tnm: 0.40 Lm: 6.574 (6.562) Lt: 5.794 (5.778) Accm: 3.30 (3.18) Acct: 5.18 (5.08) proj_loss: -0.5940 (-0.5955) time: 0.6753 data: 0.0002 [11-26 05:43:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [ 834/1669] eta: 0:09:23 tlr: 9.2e-05 tnm: 0.40 Lm: 6.603 (6.560) Lt: 5.864 (5.815) Accm: 3.17 (3.28) Acct: 5.01 (5.08) proj_loss: -0.6067 (-0.6023) time: 0.6753 data: 0.0003 [11-26 05:43:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [ 834/1669] eta: 0:09:23 tlr: 9.2e-05 tnm: 0.40 Lm: 6.380 (6.356) Lt: 5.671 (5.614) Accm: 3.92 (3.89) Acct: 6.27 (6.26) proj_loss: -0.6150 (-0.6129) time: 0.6753 data: 0.0003 [11-26 05:48:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [1251/1669] eta: 0:04:42 tlr: 9.2e-05 tnm: 0.42 Lm: 6.423 (6.427) Lt: 5.700 (5.695) Accm: 3.76 (3.64) Acct: 6.03 (5.85) proj_loss: -0.6161 (-0.6140) time: 0.6739 data: 0.0003 [11-26 05:48:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [1251/1669] eta: 0:04:42 tlr: 9.2e-05 tnm: 0.42 Lm: 6.498 (6.492) Lt: 5.757 (5.741) Accm: 3.52 (3.52) Acct: 5.51 (5.52) proj_loss: -0.6083 (-0.6045) time: 0.6739 data: 0.0002 [11-26 05:48:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [1251/1669] eta: 0:04:42 tlr: 9.2e-05 tnm: 0.42 Lm: 6.488 (6.503) Lt: 5.750 (5.739) Accm: 3.36 (3.29) Acct: 5.31 (5.18) proj_loss: -0.5945 (-0.5952) time: 0.6739 data: 0.0003 [11-26 05:48:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [1251/1669] eta: 0:04:42 tlr: 9.2e-05 tnm: 0.42 Lm: 6.509 (6.533) Lt: 5.740 (5.755) Accm: 3.32 (3.30) Acct: 5.19 (5.24) proj_loss: -0.5950 (-0.5957) time: 0.6739 data: 0.0002 [11-26 05:53:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [1668/1669] eta: 0:00:00 tlr: 9.2e-05 tnm: 0.42 Lm: 6.444 (6.504) Lt: 5.686 (5.725) Accm: 3.34 (3.43) Acct: 5.20 (5.48) proj_loss: -0.5961 (-0.5958) time: 0.6787 data: 0.0017 [11-26 05:53:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 226/350] Total time: 0:18:46 (0.675 s / it) [11-26 05:53:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [1668/1669] eta: 0:00:00 tlr: 9.2e-05 tnm: 0.42 Lm: 6.467 (6.454) Lt: 5.729 (5.728) Accm: 3.60 (3.57) Acct: 5.79 (5.70) proj_loss: -0.6172 (-0.6151) time: 0.6787 data: 0.0016 [11-26 05:53:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [1668/1669] eta: 0:00:00 tlr: 9.2e-05 tnm: 0.42 Lm: 6.603 (6.520) Lt: 5.864 (5.771) Accm: 3.17 (3.41) Acct: 5.01 (5.34) proj_loss: -0.6098 (-0.6057) time: 0.6787 data: 0.0016 [11-26 05:53:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 226/350] [1668/1669] eta: 0:00:00 tlr: 9.2e-05 tnm: 0.42 Lm: 6.492 (6.501) Lt: 5.769 (5.745) Accm: 3.42 (3.32) Acct: 5.46 (5.25) proj_loss: -0.5978 (-0.5988) time: 0.6787 data: 0.0022 [11-26 05:53:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 226/350] Total time: 0:18:46 (0.675 s / it) [11-26 05:53:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 226/350] Total time: 0:18:46 (0.675 s / it) [11-26 05:53:03] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 226/350] Total time: 0:18:46 (0.675 s / it) [11-26 05:53:03] (/home/user/VAR/train.py , line 276)=> [ep226] (training ) Lm: 6.474 (6.495), Lt: 5.716 (5.745), Acc m&t: 3.48 5.47, Remain: 1 day, 15:02:23, Finish: 2024-11-27 04:55 [11-26 05:53:03] (/home/user/VAR/train.py , line 276)=> [ep226] (training ) Lm: 6.474 (6.495), Lt: 5.716 (5.745), Acc m&t: 3.48 5.47, Remain: 1 day, 15:02:56, Finish: 2024-11-27 04:55 [11-26 05:53:03] (/home/user/VAR/train.py , line 276)=> [ep226] (training ) Lm: 6.474 (6.495), Lt: 5.716 (5.745), Acc m&t: 3.48 5.47, Remain: 1 day, 15:01:36, Finish: 2024-11-27 04:54 [11-26 05:53:03] (/home/user/VAR/train.py , line 276)=> [ep226] (training ) Lm: 6.474 (6.495), Lt: 5.716 (5.745), Acc m&t: 3.48 5.47, Remain: 1 day, 15:02:46, Finish: 2024-11-27 04:55 [11-26 05:53:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [ 0/1669] eta: 0:18:51 tlr: 9.2e-05 tnm: 0.39 Lm: 6.528 (6.528) Lt: 5.798 (5.798) Accm: 3.23 (3.23) Acct: 5.10 (5.10) proj_loss: -0.6373 (-0.6373) time: 0.6779 data: 0.0004 [11-26 05:53:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [ 0/1669] eta: 0:18:51 tlr: 9.2e-05 tnm: 0.39 Lm: 6.590 (6.590) Lt: 5.782 (5.782) Accm: 3.15 (3.15) Acct: 4.89 (4.89) proj_loss: -0.5842 (-0.5842) time: 0.6782 data: 0.0003 [11-26 05:53:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [ 0/1669] eta: 0:18:52 tlr: 9.2e-05 tnm: 0.39 Lm: 6.631 (6.631) Lt: 5.835 (5.835) Accm: 3.02 (3.02) Acct: 4.84 (4.84) proj_loss: -0.5880 (-0.5880) time: 0.6784 data: 0.0004 [11-26 05:53:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [ 0/1669] eta: 0:18:52 tlr: 9.2e-05 tnm: 0.39 Lm: 6.459 (6.459) Lt: 5.761 (5.761) Accm: 3.46 (3.46) Acct: 5.37 (5.37) proj_loss: -0.6156 (-0.6156) time: 0.6787 data: 0.0004 [11-26 05:57:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [ 417/1669] eta: 0:14:10 tlr: 9.2e-05 tnm: 0.40 Lm: 6.577 (6.577) Lt: 5.793 (5.793) Accm: 3.21 (3.21) Acct: 5.19 (5.19) proj_loss: -0.5876 (-0.5876) time: 0.6744 data: 0.0003 [11-26 05:57:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [ 417/1669] eta: 0:14:10 tlr: 9.2e-05 tnm: 0.40 Lm: 6.470 (6.470) Lt: 5.737 (5.737) Accm: 3.46 (3.46) Acct: 5.35 (5.35) proj_loss: -0.6374 (-0.6374) time: 0.6744 data: 0.0003 [11-26 05:57:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [ 417/1669] eta: 0:14:10 tlr: 9.2e-05 tnm: 0.40 Lm: 6.432 (6.432) Lt: 5.650 (5.650) Accm: 3.61 (3.61) Acct: 5.60 (5.60) proj_loss: -0.5947 (-0.5947) time: 0.6744 data: 0.0003 [11-26 05:57:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [ 417/1669] eta: 0:14:10 tlr: 9.2e-05 tnm: 0.40 Lm: 6.442 (6.442) Lt: 5.719 (5.719) Accm: 3.62 (3.62) Acct: 5.58 (5.58) proj_loss: -0.6127 (-0.6127) time: 0.6745 data: 0.0003 [11-26 06:02:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [ 834/1669] eta: 0:09:52 tlr: 9.2e-05 tnm: 0.41 Lm: 6.631 (6.620) Lt: 5.835 (5.840) Accm: 3.02 (3.06) Acct: 4.84 (4.94) proj_loss: -0.5880 (-0.5940) time: 0.6752 data: 0.0002 [11-26 06:02:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [ 834/1669] eta: 0:09:52 tlr: 9.2e-05 tnm: 0.41 Lm: 6.439 (6.441) Lt: 5.685 (5.708) Accm: 3.77 (3.69) Acct: 5.66 (5.61) proj_loss: -0.6097 (-0.6027) time: 0.6752 data: 0.0003 [11-26 06:02:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [ 834/1669] eta: 0:09:52 tlr: 9.2e-05 tnm: 0.41 Lm: 6.411 (6.441) Lt: 5.676 (5.672) Accm: 3.69 (3.58) Acct: 5.61 (5.70) proj_loss: -0.6373 (-0.6174) time: 0.6752 data: 0.0003 [11-26 06:02:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [ 834/1669] eta: 0:09:52 tlr: 9.2e-05 tnm: 0.41 Lm: 6.496 (6.453) Lt: 5.758 (5.686) Accm: 3.28 (3.50) Acct: 5.29 (5.50) proj_loss: -0.6012 (-0.5969) time: 0.6752 data: 0.0002 [11-26 06:07:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [1251/1669] eta: 0:04:51 tlr: 9.2e-05 tnm: 0.41 Lm: 6.420 (6.426) Lt: 5.679 (5.664) Accm: 3.49 (3.55) Acct: 5.53 (5.57) proj_loss: -0.6032 (-0.6019) time: 0.6757 data: 0.0002 [11-26 06:07:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [1251/1669] eta: 0:04:51 tlr: 9.2e-05 tnm: 0.41 Lm: 6.651 (6.633) Lt: 5.885 (5.870) Accm: 2.93 (3.00) Acct: 4.68 (4.84) proj_loss: -0.5948 (-0.5959) time: 0.6757 data: 0.0003 [11-26 06:07:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [1251/1669] eta: 0:04:51 tlr: 9.2e-05 tnm: 0.41 Lm: 6.397 (6.422) Lt: 5.662 (5.666) Accm: 3.75 (3.65) Acct: 5.75 (5.75) proj_loss: -0.6205 (-0.6140) time: 0.6757 data: 0.0003 [11-26 06:07:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [1251/1669] eta: 0:04:51 tlr: 9.2e-05 tnm: 0.41 Lm: 6.432 (6.431) Lt: 5.681 (5.691) Accm: 3.73 (3.69) Acct: 5.72 (5.69) proj_loss: -0.6127 (-0.6075) time: 0.6757 data: 0.0003 [11-26 06:12:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [1668/1669] eta: 0:00:00 tlr: 9.1e-05 tnm: 0.43 Lm: 6.439 (6.441) Lt: 5.685 (5.702) Accm: 3.69 (3.64) Acct: 5.66 (5.64) proj_loss: -0.6156 (-0.6103) time: 0.6771 data: 0.0019 [11-26 06:12:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 227/350] Total time: 0:19:15 (0.692 s / it) [11-26 06:12:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [1668/1669] eta: 0:00:00 tlr: 9.1e-05 tnm: 0.43 Lm: 6.631 (6.600) Lt: 5.835 (5.844) Accm: 3.02 (3.10) Acct: 4.84 (4.96) proj_loss: -0.6016 (-0.5975) time: 0.6771 data: 0.0015 [11-26 06:12:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [1668/1669] eta: 0:00:00 tlr: 9.1e-05 tnm: 0.43 Lm: 6.411 (6.428) Lt: 5.676 (5.676) Accm: 3.69 (3.60) Acct: 5.61 (5.66) proj_loss: -0.6037 (-0.6100) time: 0.6771 data: 0.0021 [11-26 06:12:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 227/350] [1668/1669] eta: 0:00:00 tlr: 9.1e-05 tnm: 0.43 Lm: 6.496 (6.457) Lt: 5.758 (5.688) Accm: 3.43 (3.53) Acct: 5.37 (5.53) proj_loss: -0.6012 (-0.6001) time: 0.6771 data: 0.0019 [11-26 06:12:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 227/350] Total time: 0:19:15 (0.692 s / it) [11-26 06:12:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 227/350] Total time: 0:19:15 (0.692 s / it) [11-26 06:12:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 227/350] Total time: 0:19:15 (0.692 s / it) [11-26 06:12:18] (/home/user/VAR/train.py , line 276)=> [ep227] (training ) Lm: 6.474 (6.493), Lt: 5.716 (5.741), Acc m&t: 3.48 5.47, Remain: 1 day, 14:31:08, Finish: 2024-11-27 04:43 [11-26 06:12:18] (/home/user/VAR/train.py , line 276)=> [ep227] (training ) Lm: 6.474 (6.493), Lt: 5.716 (5.741), Acc m&t: 3.48 5.47, Remain: 1 day, 14:31:00, Finish: 2024-11-27 04:43 [11-26 06:12:18] (/home/user/VAR/train.py , line 276)=> [ep227] (training ) Lm: 6.474 (6.493), Lt: 5.716 (5.741), Acc m&t: 3.48 5.47, Remain: 1 day, 14:30:26, Finish: 2024-11-27 04:42 [11-26 06:12:18] (/home/user/VAR/train.py , line 276)=> [ep227] (training ) Lm: 6.474 (6.493), Lt: 5.716 (5.741), Acc m&t: 3.48 5.47, Remain: 1 day, 14:30:56, Finish: 2024-11-27 04:43 [11-26 06:12:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [ 0/1669] eta: 0:18:34 tlr: 9.1e-05 tnm: 0.41 Lm: 6.378 (6.378) Lt: 5.587 (5.587) Accm: 3.66 (3.66) Acct: 5.61 (5.61) proj_loss: -0.5974 (-0.5974) time: 0.6679 data: 0.0003 [11-26 06:12:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [ 0/1669] eta: 0:18:36 tlr: 9.1e-05 tnm: 0.41 Lm: 6.642 (6.642) Lt: 5.931 (5.931) Accm: 2.88 (2.88) Acct: 4.42 (4.42) proj_loss: -0.6098 (-0.6098) time: 0.6690 data: 0.0003 [11-26 06:12:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [ 0/1669] eta: 0:18:36 tlr: 9.1e-05 tnm: 0.41 Lm: 6.442 (6.442) Lt: 5.682 (5.682) Accm: 3.21 (3.21) Acct: 4.99 (4.99) proj_loss: -0.6000 (-0.6000) time: 0.6692 data: 0.0004 [11-26 06:12:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [ 0/1669] eta: 0:18:38 tlr: 9.1e-05 tnm: 0.41 Lm: 6.588 (6.588) Lt: 5.855 (5.855) Accm: 3.28 (3.28) Acct: 5.22 (5.22) proj_loss: -0.6114 (-0.6114) time: 0.6703 data: 0.0003 [11-26 06:17:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [ 417/1669] eta: 0:14:03 tlr: 9.1e-05 tnm: 0.43 Lm: 6.533 (6.533) Lt: 5.773 (5.773) Accm: 3.47 (3.47) Acct: 5.54 (5.54) proj_loss: -0.6008 (-0.6008) time: 0.6737 data: 0.0003 [11-26 06:17:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [ 417/1669] eta: 0:14:03 tlr: 9.1e-05 tnm: 0.43 Lm: 6.500 (6.500) Lt: 5.736 (5.736) Accm: 3.35 (3.35) Acct: 5.23 (5.23) proj_loss: -0.6031 (-0.6031) time: 0.6737 data: 0.0003 [11-26 06:17:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [ 417/1669] eta: 0:14:03 tlr: 9.1e-05 tnm: 0.43 Lm: 6.548 (6.548) Lt: 5.837 (5.837) Accm: 3.17 (3.17) Acct: 4.81 (4.81) proj_loss: -0.5935 (-0.5935) time: 0.6737 data: 0.0002 [11-26 06:17:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [ 417/1669] eta: 0:14:03 tlr: 9.1e-05 tnm: 0.43 Lm: 6.556 (6.556) Lt: 5.792 (5.792) Accm: 3.13 (3.13) Acct: 5.04 (5.04) proj_loss: -0.5893 (-0.5893) time: 0.6737 data: 0.0003 [11-26 06:21:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [ 834/1669] eta: 0:09:22 tlr: 9.1e-05 tnm: 0.41 Lm: 6.442 (6.508) Lt: 5.682 (5.732) Accm: 3.21 (3.32) Acct: 5.08 (5.21) proj_loss: -0.5815 (-0.5867) time: 0.6723 data: 0.0003 [11-26 06:21:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [ 834/1669] eta: 0:09:22 tlr: 9.1e-05 tnm: 0.41 Lm: 6.534 (6.511) Lt: 5.773 (5.748) Accm: 3.04 (3.23) Acct: 4.84 (5.00) proj_loss: -0.5974 (-0.6002) time: 0.6723 data: 0.0002 [11-26 06:21:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [ 834/1669] eta: 0:09:22 tlr: 9.1e-05 tnm: 0.41 Lm: 6.504 (6.533) Lt: 5.757 (5.810) Accm: 3.46 (3.28) Acct: 5.20 (5.03) proj_loss: -0.6098 (-0.5996) time: 0.6723 data: 0.0002 [11-26 06:21:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [ 834/1669] eta: 0:09:22 tlr: 9.1e-05 tnm: 0.41 Lm: 6.588 (6.571) Lt: 5.855 (5.821) Accm: 3.28 (3.30) Acct: 5.22 (5.28) proj_loss: -0.6114 (-0.6066) time: 0.6723 data: 0.0003 [11-26 06:26:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [1251/1669] eta: 0:04:41 tlr: 9.1e-05 tnm: 0.42 Lm: 6.533 (6.536) Lt: 5.773 (5.774) Accm: 3.41 (3.36) Acct: 5.35 (5.33) proj_loss: -0.6012 (-0.6027) time: 0.6715 data: 0.0003 [11-26 06:26:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [1251/1669] eta: 0:04:41 tlr: 9.1e-05 tnm: 0.42 Lm: 6.478 (6.508) Lt: 5.750 (5.785) Accm: 3.49 (3.41) Acct: 5.33 (5.17) proj_loss: -0.6109 (-0.6042) time: 0.6716 data: 0.0002 [11-26 06:26:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [1251/1669] eta: 0:04:41 tlr: 9.1e-05 tnm: 0.42 Lm: 6.578 (6.552) Lt: 5.829 (5.804) Accm: 3.02 (3.17) Acct: 4.72 (4.90) proj_loss: -0.6031 (-0.6030) time: 0.6715 data: 0.0003 [11-26 06:26:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [1251/1669] eta: 0:04:41 tlr: 9.1e-05 tnm: 0.42 Lm: 6.474 (6.507) Lt: 5.731 (5.744) Accm: 3.29 (3.34) Acct: 5.22 (5.25) proj_loss: -0.5908 (-0.5907) time: 0.6716 data: 0.0003 [11-26 06:31:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [1668/1669] eta: 0:00:00 tlr: 9.1e-05 tnm: 0.43 Lm: 6.480 (6.502) Lt: 5.753 (5.746) Accm: 3.34 (3.34) Acct: 5.08 (5.21) proj_loss: -0.6000 (-0.5962) time: 1.0882 data: 0.0020 [11-26 06:31:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 228/350] Total time: 0:19:12 (0.691 s / it) [11-26 06:31:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [1668/1669] eta: 0:00:00 tlr: 9.1e-05 tnm: 0.43 Lm: 6.534 (6.537) Lt: 5.773 (5.783) Accm: 3.04 (3.24) Acct: 4.84 (5.06) proj_loss: -0.6013 (-0.6026) time: 1.0882 data: 0.0017 [11-26 06:31:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [1668/1669] eta: 0:00:00 tlr: 9.1e-05 tnm: 0.43 Lm: 6.453 (6.479) Lt: 5.743 (5.735) Accm: 3.51 (3.52) Acct: 5.46 (5.36) proj_loss: -0.6098 (-0.6032) time: 1.0882 data: 0.0018 [11-26 06:31:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 228/350] [1668/1669] eta: 0:00:00 tlr: 9.1e-05 tnm: 0.43 Lm: 6.477 (6.523) Lt: 5.708 (5.761) Accm: 3.51 (3.39) Acct: 5.48 (5.39) proj_loss: -0.6086 (-0.6039) time: 1.0883 data: 0.0016 [11-26 06:31:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 228/350] Total time: 0:19:12 (0.691 s / it) [11-26 06:31:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 228/350] Total time: 0:19:12 (0.691 s / it) [11-26 06:31:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 228/350] Total time: 0:19:12 (0.691 s / it) [11-26 06:31:31] (/home/user/VAR/train.py , line 276)=> [ep228] (training ) Lm: 6.474 (6.489), Lt: 5.716 (5.734), Acc m&t: 3.48 5.47, Remain: 1 day, 14:28:53, Finish: 2024-11-27 05:00 [11-26 06:31:31] (/home/user/VAR/train.py , line 276)=> [ep228] (training ) Lm: 6.474 (6.489), Lt: 5.716 (5.734), Acc m&t: 3.48 5.47, Remain: 1 day, 14:28:34, Finish: 2024-11-27 05:00 [11-26 06:31:31] (/home/user/VAR/train.py , line 276)=> [ep228] (training ) Lm: 6.474 (6.489), Lt: 5.716 (5.734), Acc m&t: 3.48 5.47, Remain: 1 day, 14:28:46, Finish: 2024-11-27 05:00 [11-26 06:31:31] (/home/user/VAR/train.py , line 276)=> [ep228] (training ) Lm: 6.474 (6.489), Lt: 5.716 (5.734), Acc m&t: 3.48 5.47, Remain: 1 day, 14:29:01, Finish: 2024-11-27 05:00 [11-26 06:31:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [ 0/1669] eta: 0:18:59 tlr: 9.1e-05 tnm: 0.40 Lm: 6.412 (6.412) Lt: 5.685 (5.685) Accm: 3.57 (3.57) Acct: 5.56 (5.56) proj_loss: -0.6292 (-0.6292) time: 0.6827 data: 0.0003 [11-26 06:31:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [ 0/1669] eta: 0:18:59 tlr: 9.1e-05 tnm: 0.40 Lm: 6.319 (6.319) Lt: 5.564 (5.564) Accm: 4.07 (4.07) Acct: 6.22 (6.22) proj_loss: -0.6090 (-0.6090) time: 0.6830 data: 0.0003 [11-26 06:31:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [ 0/1669] eta: 0:19:02 tlr: 9.1e-05 tnm: 0.40 Lm: 6.716 (6.716) Lt: 5.971 (5.971) Accm: 2.67 (2.67) Acct: 4.24 (4.24) proj_loss: -0.6202 (-0.6202) time: 0.6844 data: 0.0003 [11-26 06:31:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [ 0/1669] eta: 0:18:55 tlr: 9.1e-05 tnm: 0.40 Lm: 6.689 (6.689) Lt: 5.954 (5.954) Accm: 2.94 (2.94) Acct: 4.61 (4.61) proj_loss: -0.5924 (-0.5924) time: 0.6803 data: 0.0005 [11-26 06:36:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [ 417/1669] eta: 0:14:15 tlr: 9e-05 tnm: 0.41 Lm: 6.463 (6.463) Lt: 5.732 (5.732) Accm: 3.47 (3.47) Acct: 5.41 (5.41) proj_loss: -0.6087 (-0.6087) time: 0.6754 data: 0.0003 [11-26 06:36:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [ 417/1669] eta: 0:14:15 tlr: 9e-05 tnm: 0.41 Lm: 6.563 (6.563) Lt: 5.778 (5.778) Accm: 3.15 (3.15) Acct: 4.92 (4.92) proj_loss: -0.6028 (-0.6028) time: 0.6754 data: 0.0002 [11-26 06:36:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [ 417/1669] eta: 0:14:15 tlr: 9e-05 tnm: 0.41 Lm: 6.445 (6.445) Lt: 5.685 (5.685) Accm: 3.61 (3.61) Acct: 5.48 (5.48) proj_loss: -0.5931 (-0.5931) time: 0.6754 data: 0.0003 [11-26 06:36:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [ 417/1669] eta: 0:14:15 tlr: 9e-05 tnm: 0.41 Lm: 6.465 (6.465) Lt: 5.726 (5.726) Accm: 3.53 (3.53) Acct: 5.51 (5.51) proj_loss: -0.6170 (-0.6170) time: 0.6754 data: 0.0003 [11-26 06:40:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [ 834/1669] eta: 0:09:27 tlr: 9e-05 tnm: 0.39 Lm: 6.518 (6.536) Lt: 5.767 (5.805) Accm: 3.48 (3.32) Acct: 5.46 (5.18) proj_loss: -0.6049 (-0.6123) time: 0.6766 data: 0.0003 [11-26 06:40:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [ 834/1669] eta: 0:09:27 tlr: 9e-05 tnm: 0.39 Lm: 6.319 (6.384) Lt: 5.564 (5.637) Accm: 3.98 (3.74) Acct: 6.06 (5.68) proj_loss: -0.6090 (-0.6073) time: 0.6766 data: 0.0003 [11-26 06:40:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [ 834/1669] eta: 0:09:27 tlr: 9e-05 tnm: 0.39 Lm: 6.409 (6.457) Lt: 5.585 (5.652) Accm: 3.63 (3.45) Acct: 5.61 (5.37) proj_loss: -0.6032 (-0.6029) time: 0.6766 data: 0.0002 [11-26 06:40:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [ 834/1669] eta: 0:09:27 tlr: 9e-05 tnm: 0.39 Lm: 6.574 (6.500) Lt: 5.786 (5.750) Accm: 3.30 (3.42) Acct: 5.25 (5.36) proj_loss: -0.5924 (-0.6033) time: 0.6766 data: 0.0003 [11-26 06:45:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [1251/1669] eta: 0:04:43 tlr: 9e-05 tnm: 0.40 Lm: 6.546 (6.505) Lt: 5.732 (5.732) Accm: 3.33 (3.40) Acct: 5.29 (5.35) proj_loss: -0.5924 (-0.5981) time: 0.6775 data: 0.0003 [11-26 06:45:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [1251/1669] eta: 0:04:43 tlr: 9e-05 tnm: 0.40 Lm: 6.526 (6.535) Lt: 5.795 (5.810) Accm: 3.22 (3.23) Acct: 5.08 (5.06) proj_loss: -0.6082 (-0.6121) time: 0.6775 data: 0.0003 [11-26 06:45:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [1251/1669] eta: 0:04:43 tlr: 9e-05 tnm: 0.40 Lm: 6.406 (6.411) Lt: 5.624 (5.648) Accm: 3.82 (3.72) Acct: 5.97 (5.72) proj_loss: -0.5947 (-0.6005) time: 0.6775 data: 0.0003 [11-26 06:45:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [1251/1669] eta: 0:04:43 tlr: 9e-05 tnm: 0.40 Lm: 6.348 (6.414) Lt: 5.560 (5.623) Accm: 3.84 (3.63) Acct: 5.93 (5.60) proj_loss: -0.5998 (-0.6013) time: 0.6775 data: 0.0002 [11-26 06:50:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [1668/1669] eta: 0:00:00 tlr: 9e-05 tnm: 0.42 Lm: 6.409 (6.426) Lt: 5.585 (5.638) Accm: 3.69 (3.64) Acct: 5.99 (5.68) proj_loss: -0.6032 (-0.6049) time: 0.6788 data: 0.0018 [11-26 06:50:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 229/350] Total time: 0:18:51 (0.678 s / it) [11-26 06:50:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [1668/1669] eta: 0:00:00 tlr: 9e-05 tnm: 0.42 Lm: 6.482 (6.426) Lt: 5.683 (5.675) Accm: 3.65 (3.61) Acct: 5.87 (5.55) proj_loss: -0.6090 (-0.6024) time: 0.6788 data: 0.0017 [11-26 06:50:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [1668/1669] eta: 0:00:00 tlr: 9e-05 tnm: 0.42 Lm: 6.518 (6.524) Lt: 5.767 (5.794) Accm: 3.39 (3.26) Acct: 5.06 (5.06) proj_loss: -0.6097 (-0.6117) time: 0.6788 data: 0.0016 [11-26 06:50:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 229/350] [1668/1669] eta: 0:00:00 tlr: 9e-05 tnm: 0.42 Lm: 6.574 (6.522) Lt: 5.786 (5.754) Accm: 3.30 (3.35) Acct: 5.30 (5.34) proj_loss: -0.5924 (-0.6000) time: 0.6788 data: 0.0017 [11-26 06:50:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 229/350] Total time: 0:18:51 (0.678 s / it) [11-26 06:50:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 229/350] Total time: 0:18:51 (0.678 s / it) [11-26 06:50:22] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 229/350] Total time: 0:18:51 (0.678 s / it) [11-26 06:52:41] (home/user/VAR/trainer.py, line 114)=> FID: 3.4398229655987507 [11-26 06:52:41] (/home/user/VAR/train.py , line 259)=> [*] [ep229] (val 50000) Lm: 6.4929, Lt: 5.7429, Acc m&t: 3.41 5.37, Val cost: 138.84s [11-26 06:52:41] (/home/user/VAR/train.py , line 264)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-26 06:53:03] (/home/user/VAR/train.py , line 276)=> [ep229] (training ) Lm: 6.474 (6.493), Lt: 5.716 (5.743), Acc m&t: 3.48 5.47, Remain: 1 day, 14:02:44, Finish: 2024-11-27 04:53 [11-26 06:53:03] (/home/user/VAR/train.py , line 276)=> [ep229] (training ) Lm: 6.474 (6.493), Lt: 5.716 (5.743), Acc m&t: 3.48 5.47, Remain: 1 day, 14:02:46, Finish: 2024-11-27 04:53 [11-26 06:53:03] (/home/user/VAR/train.py , line 276)=> [ep229] (training ) Lm: 6.474 (6.493), Lt: 5.716 (5.743), Acc m&t: 3.48 5.47, Remain: 1 day, 14:03:06, Finish: 2024-11-27 04:53 [11-26 06:53:03] (/home/user/VAR/train.py , line 276)=> [ep229] (training ) Lm: 6.474 (6.493), Lt: 5.716 (5.743), Acc m&t: 3.48 5.47, Remain: 1 day, 14:03:50, Finish: 2024-11-27 04:54 [11-26 06:53:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 0/1669] eta: 0:18:41 tlr: 9e-05 tnm: 0.42 Lm: 6.454 (6.454) Lt: 5.667 (5.667) Accm: 3.47 (3.47) Acct: 5.32 (5.32) proj_loss: -0.5995 (-0.5995) time: 0.6718 data: 0.0003 [11-26 06:53:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 0/1669] eta: 0:18:41 tlr: 9e-05 tnm: 0.42 Lm: 6.400 (6.400) Lt: 5.666 (5.666) Accm: 3.87 (3.87) Acct: 5.89 (5.89) proj_loss: -0.5888 (-0.5888) time: 0.6720 data: 0.0003 [11-26 06:53:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 0/1669] eta: 0:18:41 tlr: 9e-05 tnm: 0.42 Lm: 6.543 (6.543) Lt: 5.796 (5.796) Accm: 3.42 (3.42) Acct: 5.35 (5.35) proj_loss: -0.6140 (-0.6140) time: 0.6722 data: 0.0004 [11-26 06:53:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 0/1669] eta: 0:18:41 tlr: 9e-05 tnm: 0.42 Lm: 6.321 (6.321) Lt: 5.523 (5.523) Accm: 4.06 (4.06) Acct: 6.59 (6.59) proj_loss: -0.6078 (-0.6078) time: 0.6717 data: 0.0004 [11-26 06:57:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 417/1669] eta: 0:14:04 tlr: 9e-05 tnm: 0.40 Lm: 6.348 (6.348) Lt: 5.534 (5.534) Accm: 3.88 (3.88) Acct: 6.10 (6.10) proj_loss: -0.6000 (-0.6000) time: 0.6746 data: 0.0002 [11-26 06:57:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 417/1669] eta: 0:14:04 tlr: 9e-05 tnm: 0.40 Lm: 6.487 (6.487) Lt: 5.744 (5.744) Accm: 3.47 (3.47) Acct: 5.28 (5.28) proj_loss: -0.6048 (-0.6048) time: 0.6746 data: 0.0002 [11-26 06:57:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 417/1669] eta: 0:14:04 tlr: 9e-05 tnm: 0.40 Lm: 6.383 (6.383) Lt: 5.591 (5.591) Accm: 3.80 (3.80) Acct: 5.94 (5.94) proj_loss: -0.6047 (-0.6047) time: 0.6746 data: 0.0003 [11-26 06:57:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 417/1669] eta: 0:14:04 tlr: 9e-05 tnm: 0.40 Lm: 6.576 (6.576) Lt: 5.828 (5.828) Accm: 3.28 (3.28) Acct: 5.04 (5.04) proj_loss: -0.6050 (-0.6050) time: 0.6746 data: 0.0003 [11-26 07:02:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 834/1669] eta: 0:09:23 tlr: 9e-05 tnm: 0.40 Lm: 6.561 (6.571) Lt: 5.820 (5.825) Accm: 3.42 (3.33) Acct: 5.34 (5.14) proj_loss: -0.6140 (-0.6096) time: 0.6750 data: 0.0003 [11-26 07:02:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 834/1669] eta: 0:09:23 tlr: 9e-05 tnm: 0.40 Lm: 6.454 (6.434) Lt: 5.667 (5.675) Accm: 3.47 (3.55) Acct: 5.32 (5.50) proj_loss: -0.6099 (-0.6143) time: 0.6750 data: 0.0002 [11-26 07:02:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 834/1669] eta: 0:09:23 tlr: 9e-05 tnm: 0.40 Lm: 6.573 (6.546) Lt: 5.822 (5.802) Accm: 3.07 (3.33) Acct: 4.67 (5.06) proj_loss: -0.6207 (-0.6105) time: 0.6750 data: 0.0002 [11-26 07:02:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 834/1669] eta: 0:09:23 tlr: 9e-05 tnm: 0.40 Lm: 6.376 (6.405) Lt: 5.546 (5.620) Accm: 3.69 (3.74) Acct: 5.60 (5.87) proj_loss: -0.6078 (-0.6081) time: 0.6750 data: 0.0002 [11-26 07:07:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1251/1669] eta: 0:04:42 tlr: 8.9e-05 tnm: 0.40 Lm: 6.446 (6.467) Lt: 5.669 (5.690) Accm: 3.58 (3.53) Acct: 5.50 (5.55) proj_loss: -0.6093 (-0.6088) time: 0.6777 data: 0.0002 [11-26 07:07:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1251/1669] eta: 0:04:42 tlr: 8.9e-05 tnm: 0.40 Lm: 6.495 (6.473) Lt: 5.756 (5.720) Accm: 3.42 (3.51) Acct: 5.30 (5.45) proj_loss: -0.6116 (-0.6140) time: 0.6776 data: 0.0002 [11-26 07:07:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1251/1669] eta: 0:04:42 tlr: 8.9e-05 tnm: 0.40 Lm: 6.597 (6.565) Lt: 5.862 (5.827) Accm: 3.13 (3.30) Acct: 4.96 (5.11) proj_loss: -0.6172 (-0.6113) time: 0.6777 data: 0.0002 [11-26 07:07:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1251/1669] eta: 0:04:42 tlr: 8.9e-05 tnm: 0.40 Lm: 6.552 (6.555) Lt: 5.808 (5.806) Accm: 3.37 (3.33) Acct: 5.32 (5.18) proj_loss: -0.6070 (-0.6072) time: 0.6777 data: 0.0003 [11-26 07:11:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1668/1669] eta: 0:00:00 tlr: 8.9e-05 tnm: 0.41 Lm: 6.543 (6.522) Lt: 5.796 (5.766) Accm: 3.42 (3.40) Acct: 5.34 (5.27) proj_loss: -0.6047 (-0.6067) time: 0.6784 data: 0.0017 [11-26 07:11:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 230/350] Total time: 0:18:47 (0.675 s / it) [11-26 07:11:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1668/1669] eta: 0:00:00 tlr: 8.9e-05 tnm: 0.41 Lm: 6.454 (6.463) Lt: 5.686 (5.713) Accm: 3.47 (3.52) Acct: 5.32 (5.52) proj_loss: -0.6100 (-0.6132) time: 0.6784 data: 0.0015 [11-26 07:11:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1668/1669] eta: 0:00:00 tlr: 8.9e-05 tnm: 0.41 Lm: 6.578 (6.567) Lt: 5.883 (5.838) Accm: 3.20 (3.28) Acct: 5.10 (5.10) proj_loss: -0.6137 (-0.6069) time: 0.6784 data: 0.0018 [11-26 07:11:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1668/1669] eta: 0:00:00 tlr: 8.9e-05 tnm: 0.41 Lm: 6.517 (6.496) Lt: 5.793 (5.725) Accm: 3.47 (3.42) Acct: 5.41 (5.39) proj_loss: -0.6108 (-0.6104) time: 0.6784 data: 0.0017 [11-26 07:11:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 230/350] Total time: 0:18:47 (0.675 s / it) [11-26 07:11:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 230/350] Total time: 0:18:47 (0.675 s / it) [11-26 07:11:50] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 230/350] Total time: 0:18:47 (0.675 s / it) [11-26 07:11:50] (/home/user/VAR/train.py , line 276)=> [ep230] (training ) Lm: 6.474 (6.478), Lt: 5.716 (5.723), Acc m&t: 3.48 5.47, Remain: 1 day, 13:41:18, Finish: 2024-11-27 04:53 [11-26 07:11:50] (/home/user/VAR/train.py , line 276)=> [ep230] (training ) Lm: 6.474 (6.478), Lt: 5.716 (5.723), Acc m&t: 3.48 5.47, Remain: 1 day, 13:41:33, Finish: 2024-11-27 04:53 [11-26 07:11:50] (/home/user/VAR/train.py , line 276)=> [ep230] (training ) Lm: 6.474 (6.478), Lt: 5.716 (5.723), Acc m&t: 3.48 5.47, Remain: 1 day, 13:40:42, Finish: 2024-11-27 04:52 [11-26 07:11:50] (/home/user/VAR/train.py , line 276)=> [ep230] (training ) Lm: 6.474 (6.478), Lt: 5.716 (5.723), Acc m&t: 3.48 5.47, Remain: 1 day, 13:41:09, Finish: 2024-11-27 04:53 [11-26 07:11:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 0/1669] eta: 0:18:11 tlr: 8.9e-05 tnm: 0.41 Lm: 6.744 (6.744) Lt: 6.038 (6.038) Accm: 2.85 (2.85) Acct: 4.41 (4.41) proj_loss: -0.6018 (-0.6018) time: 0.6542 data: 0.0004 [11-26 07:11:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 0/1669] eta: 0:18:13 tlr: 8.9e-05 tnm: 0.41 Lm: 6.396 (6.396) Lt: 5.628 (5.628) Accm: 3.75 (3.75) Acct: 6.01 (6.01) proj_loss: -0.5890 (-0.5890) time: 0.6551 data: 0.0003 [11-26 07:11:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 0/1669] eta: 0:18:24 tlr: 8.9e-05 tnm: 0.41 Lm: 6.380 (6.380) Lt: 5.651 (5.651) Accm: 3.67 (3.67) Acct: 5.85 (5.85) proj_loss: -0.6086 (-0.6086) time: 0.6617 data: 0.0004 [11-26 07:11:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 0/1669] eta: 0:18:27 tlr: 8.9e-05 tnm: 0.41 Lm: 6.383 (6.383) Lt: 5.594 (5.594) Accm: 3.61 (3.61) Acct: 5.89 (5.89) proj_loss: -0.6321 (-0.6321) time: 0.6633 data: 0.0004 [11-26 07:16:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 417/1669] eta: 0:14:04 tlr: 8.9e-05 tnm: 0.41 Lm: 6.501 (6.501) Lt: 5.704 (5.704) Accm: 3.42 (3.42) Acct: 5.57 (5.57) proj_loss: -0.6213 (-0.6213) time: 0.6750 data: 0.0003 [11-26 07:16:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 417/1669] eta: 0:14:04 tlr: 8.9e-05 tnm: 0.41 Lm: 6.604 (6.604) Lt: 5.868 (5.868) Accm: 3.25 (3.25) Acct: 5.17 (5.17) proj_loss: -0.5958 (-0.5958) time: 0.6750 data: 0.0002 [11-26 07:16:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 417/1669] eta: 0:14:04 tlr: 8.9e-05 tnm: 0.41 Lm: 6.437 (6.437) Lt: 5.670 (5.670) Accm: 3.54 (3.54) Acct: 5.70 (5.70) proj_loss: -0.6017 (-0.6017) time: 0.6751 data: 0.0002 [11-26 07:16:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 417/1669] eta: 0:14:04 tlr: 8.9e-05 tnm: 0.41 Lm: 6.455 (6.455) Lt: 5.703 (5.703) Accm: 3.39 (3.39) Acct: 5.54 (5.54) proj_loss: -0.6013 (-0.6013) time: 0.6751 data: 0.0003 [11-26 07:21:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 834/1669] eta: 0:09:29 tlr: 8.9e-05 tnm: 0.41 Lm: 6.388 (6.433) Lt: 5.651 (5.676) Accm: 3.60 (3.46) Acct: 5.61 (5.56) proj_loss: -0.5940 (-0.5959) time: 0.6721 data: 0.0003 [11-26 07:21:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 834/1669] eta: 0:09:29 tlr: 8.9e-05 tnm: 0.41 Lm: 6.463 (6.555) Lt: 5.697 (5.799) Accm: 3.64 (3.38) Acct: 5.92 (5.48) proj_loss: -0.6018 (-0.6019) time: 0.6721 data: 0.0003 [11-26 07:21:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 834/1669] eta: 0:09:29 tlr: 8.9e-05 tnm: 0.41 Lm: 6.479 (6.478) Lt: 5.711 (5.726) Accm: 3.33 (3.36) Acct: 5.39 (5.26) proj_loss: -0.6123 (-0.6052) time: 0.6721 data: 0.0003 [11-26 07:21:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 834/1669] eta: 0:09:29 tlr: 8.9e-05 tnm: 0.41 Lm: 6.595 (6.532) Lt: 5.813 (5.764) Accm: 3.22 (3.27) Acct: 5.25 (5.25) proj_loss: -0.6104 (-0.6133) time: 0.6721 data: 0.0003 [11-26 07:26:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1251/1669] eta: 0:04:44 tlr: 8.9e-05 tnm: 0.42 Lm: 6.489 (6.488) Lt: 5.704 (5.722) Accm: 3.42 (3.43) Acct: 5.57 (5.57) proj_loss: -0.6078 (-0.6112) time: 0.6734 data: 0.0003 [11-26 07:26:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1251/1669] eta: 0:04:44 tlr: 8.9e-05 tnm: 0.42 Lm: 6.493 (6.547) Lt: 5.708 (5.779) Accm: 3.40 (3.33) Acct: 5.34 (5.30) proj_loss: -0.6055 (-0.6037) time: 0.6734 data: 0.0002 [11-26 07:26:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1251/1669] eta: 0:04:44 tlr: 8.9e-05 tnm: 0.42 Lm: 6.519 (6.524) Lt: 5.775 (5.779) Accm: 3.20 (3.29) Acct: 5.05 (5.13) proj_loss: -0.6043 (-0.6030) time: 0.6734 data: 0.0003 [11-26 07:26:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1251/1669] eta: 0:04:44 tlr: 8.9e-05 tnm: 0.42 Lm: 6.459 (6.464) Lt: 5.703 (5.724) Accm: 3.35 (3.35) Acct: 5.41 (5.39) proj_loss: -0.6001 (-0.5985) time: 0.6734 data: 0.0003 [11-26 07:30:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.41 Lm: 6.530 (6.496) Lt: 5.755 (5.743) Accm: 3.31 (3.34) Acct: 5.49 (5.41) proj_loss: -0.6062 (-0.6003) time: 0.6758 data: 0.0017 [11-26 07:30:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 231/350] Total time: 0:18:53 (0.679 s / it) [11-26 07:30:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.41 Lm: 6.523 (6.565) Lt: 5.719 (5.804) Accm: 3.15 (3.25) Acct: 4.75 (5.18) proj_loss: -0.6075 (-0.6045) time: 0.6758 data: 0.0017 [11-26 07:30:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.41 Lm: 6.558 (6.534) Lt: 5.839 (5.793) Accm: 3.33 (3.31) Acct: 5.37 (5.18) proj_loss: -0.5969 (-0.6018) time: 0.6758 data: 0.0018 [11-26 07:30:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.41 Lm: 6.557 (6.502) Lt: 5.806 (5.739) Accm: 3.53 (3.45) Acct: 5.89 (5.64) proj_loss: -0.6104 (-0.6116) time: 0.6758 data: 0.0018 [11-26 07:30:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 231/350] Total time: 0:18:53 (0.679 s / it) [11-26 07:30:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 231/350] Total time: 0:18:53 (0.679 s / it) [11-26 07:30:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 231/350] Total time: 0:18:53 (0.679 s / it) [11-26 07:30:44] (/home/user/VAR/train.py , line 276)=> [ep231] (training ) Lm: 6.474 (6.486), Lt: 5.716 (5.734), Acc m&t: 3.48 5.47, Remain: 1 day, 13:11:38, Finish: 2024-11-27 04:42 [11-26 07:30:44] (/home/user/VAR/train.py , line 276)=> [ep231] (training ) Lm: 6.474 (6.486), Lt: 5.716 (5.734), Acc m&t: 3.48 5.47, Remain: 1 day, 13:12:21, Finish: 2024-11-27 04:43 [11-26 07:30:44] (/home/user/VAR/train.py , line 276)=> [ep231] (training ) Lm: 6.474 (6.486), Lt: 5.716 (5.734), Acc m&t: 3.48 5.47, Remain: 1 day, 13:12:25, Finish: 2024-11-27 04:43 [11-26 07:30:44] (/home/user/VAR/train.py , line 276)=> [ep231] (training ) Lm: 6.474 (6.486), Lt: 5.716 (5.734), Acc m&t: 3.48 5.47, Remain: 1 day, 13:11:58, Finish: 2024-11-27 04:42 [11-26 07:30:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 0/1669] eta: 0:18:30 tlr: 8.8e-05 tnm: 0.41 Lm: 6.495 (6.495) Lt: 5.731 (5.731) Accm: 3.37 (3.37) Acct: 5.39 (5.39) proj_loss: -0.5997 (-0.5997) time: 0.6652 data: 0.0003 [11-26 07:30:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 0/1669] eta: 0:18:30 tlr: 8.8e-05 tnm: 0.41 Lm: 6.507 (6.507) Lt: 5.736 (5.736) Accm: 3.26 (3.26) Acct: 5.20 (5.20) proj_loss: -0.5818 (-0.5818) time: 0.6656 data: 0.0003 [11-26 07:30:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 0/1669] eta: 0:18:29 tlr: 8.8e-05 tnm: 0.41 Lm: 6.443 (6.443) Lt: 5.681 (5.681) Accm: 3.80 (3.80) Acct: 6.03 (6.03) proj_loss: -0.6125 (-0.6125) time: 0.6648 data: 0.0003 [11-26 07:30:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 0/1669] eta: 0:18:31 tlr: 8.8e-05 tnm: 0.41 Lm: 6.587 (6.587) Lt: 5.898 (5.898) Accm: 3.09 (3.09) Acct: 4.53 (4.53) proj_loss: -0.6169 (-0.6169) time: 0.6661 data: 0.0004 [11-26 07:35:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 417/1669] eta: 0:14:05 tlr: 8.8e-05 tnm: 0.42 Lm: 6.571 (6.571) Lt: 5.826 (5.826) Accm: 3.16 (3.16) Acct: 4.87 (4.87) proj_loss: -0.6006 (-0.6006) time: 0.6751 data: 0.0002 [11-26 07:35:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 417/1669] eta: 0:14:05 tlr: 8.8e-05 tnm: 0.42 Lm: 6.477 (6.477) Lt: 5.721 (5.721) Accm: 3.55 (3.55) Acct: 5.51 (5.51) proj_loss: -0.5915 (-0.5915) time: 0.6751 data: 0.0002 [11-26 07:35:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 417/1669] eta: 0:14:05 tlr: 8.8e-05 tnm: 0.42 Lm: 6.480 (6.480) Lt: 5.745 (5.745) Accm: 3.59 (3.59) Acct: 5.66 (5.66) proj_loss: -0.6105 (-0.6105) time: 0.6751 data: 0.0003 [11-26 07:35:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 417/1669] eta: 0:14:05 tlr: 8.8e-05 tnm: 0.42 Lm: 6.489 (6.489) Lt: 5.766 (5.766) Accm: 3.53 (3.53) Acct: 5.40 (5.40) proj_loss: -0.6069 (-0.6069) time: 0.6751 data: 0.0003 [11-26 07:40:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 834/1669] eta: 0:09:23 tlr: 8.8e-05 tnm: 0.41 Lm: 6.564 (6.514) Lt: 5.829 (5.787) Accm: 3.33 (3.46) Acct: 5.48 (5.42) proj_loss: -0.6104 (-0.6080) time: 0.6734 data: 0.0003 [11-26 07:40:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 834/1669] eta: 0:09:23 tlr: 8.8e-05 tnm: 0.41 Lm: 6.495 (6.509) Lt: 5.731 (5.755) Accm: 3.37 (3.37) Acct: 5.39 (5.26) proj_loss: -0.6014 (-0.6081) time: 0.6734 data: 0.0002 [11-26 07:40:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 834/1669] eta: 0:09:23 tlr: 8.8e-05 tnm: 0.41 Lm: 6.443 (6.391) Lt: 5.681 (5.640) Accm: 3.80 (3.84) Acct: 6.03 (5.99) proj_loss: -0.6086 (-0.6016) time: 0.6734 data: 0.0003 [11-26 07:40:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 834/1669] eta: 0:09:23 tlr: 8.8e-05 tnm: 0.41 Lm: 6.507 (6.518) Lt: 5.736 (5.789) Accm: 3.26 (3.41) Acct: 5.20 (5.17) proj_loss: -0.6012 (-0.5989) time: 0.6734 data: 0.0003 [11-26 07:44:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1251/1669] eta: 0:04:42 tlr: 8.8e-05 tnm: 0.41 Lm: 6.554 (6.546) Lt: 5.787 (5.802) Accm: 3.41 (3.45) Acct: 5.43 (5.29) proj_loss: -0.5956 (-0.5966) time: 0.6749 data: 0.0003 [11-26 07:44:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1251/1669] eta: 0:04:42 tlr: 8.8e-05 tnm: 0.41 Lm: 6.480 (6.437) Lt: 5.742 (5.681) Accm: 3.59 (3.71) Acct: 5.72 (5.85) proj_loss: -0.6014 (-0.5997) time: 0.6749 data: 0.0003 [11-26 07:44:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1251/1669] eta: 0:04:42 tlr: 8.8e-05 tnm: 0.41 Lm: 6.482 (6.499) Lt: 5.757 (5.762) Accm: 3.31 (3.34) Acct: 5.33 (5.26) proj_loss: -0.6123 (-0.6123) time: 0.6749 data: 0.0003 [11-26 07:44:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1251/1669] eta: 0:04:42 tlr: 8.8e-05 tnm: 0.41 Lm: 6.558 (6.524) Lt: 5.777 (5.772) Accm: 3.45 (3.49) Acct: 5.72 (5.56) proj_loss: -0.6036 (-0.6049) time: 0.6749 data: 0.0003 [11-26 07:49:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.44 Lm: 6.552 (6.503) Lt: 5.726 (5.745) Accm: 3.50 (3.49) Acct: 5.54 (5.55) proj_loss: -0.5989 (-0.6037) time: 0.7469 data: 0.0019 [11-26 07:49:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 232/350] Total time: 0:18:51 (0.678 s / it) [11-26 07:49:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.44 Lm: 6.495 (6.521) Lt: 5.783 (5.784) Accm: 3.25 (3.30) Acct: 5.27 (5.21) proj_loss: -0.6014 (-0.6055) time: 0.7469 data: 0.0015 [11-26 07:49:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.44 Lm: 6.507 (6.508) Lt: 5.736 (5.753) Accm: 3.55 (3.58) Acct: 5.66 (5.48) proj_loss: -0.5974 (-0.5968) time: 0.7469 data: 0.0019 [11-26 07:49:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.44 Lm: 6.506 (6.451) Lt: 5.716 (5.688) Accm: 3.54 (3.68) Acct: 5.80 (5.84) proj_loss: -0.6060 (-0.6010) time: 0.7469 data: 0.0017 [11-26 07:49:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 232/350] Total time: 0:18:51 (0.678 s / it) [11-26 07:49:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 232/350] Total time: 0:18:51 (0.678 s / it) [11-26 07:49:35] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 232/350] Total time: 0:18:51 (0.678 s / it) [11-26 07:49:35] (/home/user/VAR/train.py , line 276)=> [ep232] (training ) Lm: 6.474 (6.480), Lt: 5.716 (5.727), Acc m&t: 3.48 5.47, Remain: 1 day, 13:05:29, Finish: 2024-11-27 04:55 [11-26 07:49:35] (/home/user/VAR/train.py , line 276)=> [ep232] (training ) Lm: 6.474 (6.480), Lt: 5.716 (5.727), Acc m&t: 3.48 5.47, Remain: 1 day, 13:04:58, Finish: 2024-11-27 04:54 [11-26 07:49:35] (/home/user/VAR/train.py , line 276)=> [ep232] (training ) Lm: 6.474 (6.480), Lt: 5.716 (5.727), Acc m&t: 3.48 5.47, Remain: 1 day, 13:05:31, Finish: 2024-11-27 04:55 [11-26 07:49:35] (/home/user/VAR/train.py , line 276)=> [ep232] (training ) Lm: 6.474 (6.480), Lt: 5.716 (5.727), Acc m&t: 3.48 5.47, Remain: 1 day, 13:04:57, Finish: 2024-11-27 04:54 [11-26 07:49:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 0/1669] eta: 0:18:22 tlr: 8.8e-05 tnm: 0.43 Lm: 6.586 (6.586) Lt: 5.817 (5.817) Accm: 3.10 (3.10) Acct: 4.80 (4.80) proj_loss: -0.6240 (-0.6240) time: 0.6605 data: 0.0004 [11-26 07:49:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 0/1669] eta: 0:18:23 tlr: 8.8e-05 tnm: 0.43 Lm: 6.761 (6.761) Lt: 6.018 (6.018) Accm: 2.73 (2.73) Acct: 4.65 (4.65) proj_loss: -0.5887 (-0.5887) time: 0.6612 data: 0.0004 [11-26 07:49:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 0/1669] eta: 0:18:23 tlr: 8.8e-05 tnm: 0.43 Lm: 6.596 (6.596) Lt: 5.845 (5.845) Accm: 3.18 (3.18) Acct: 5.27 (5.27) proj_loss: -0.6050 (-0.6050) time: 0.6613 data: 0.0004 [11-26 07:49:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 0/1669] eta: 0:18:25 tlr: 8.8e-05 tnm: 0.43 Lm: 6.501 (6.501) Lt: 5.795 (5.795) Accm: 3.31 (3.31) Acct: 4.92 (4.92) proj_loss: -0.6171 (-0.6171) time: 0.6625 data: 0.0004 [11-26 07:54:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 417/1669] eta: 0:14:21 tlr: 8.8e-05 tnm: 0.42 Lm: 6.560 (6.560) Lt: 5.870 (5.870) Accm: 3.15 (3.15) Acct: 4.67 (4.67) proj_loss: -0.6195 (-0.6195) time: 0.6754 data: 0.0003 [11-26 07:54:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 417/1669] eta: 0:14:21 tlr: 8.8e-05 tnm: 0.42 Lm: 6.397 (6.397) Lt: 5.633 (5.633) Accm: 3.70 (3.70) Acct: 5.89 (5.89) proj_loss: -0.6097 (-0.6097) time: 0.6754 data: 0.0003 [11-26 07:54:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 417/1669] eta: 0:14:21 tlr: 8.8e-05 tnm: 0.42 Lm: 6.437 (6.437) Lt: 5.651 (5.651) Accm: 3.61 (3.61) Acct: 5.66 (5.66) proj_loss: -0.6166 (-0.6166) time: 0.6754 data: 0.0003 [11-26 07:54:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 417/1669] eta: 0:14:21 tlr: 8.8e-05 tnm: 0.42 Lm: 6.501 (6.501) Lt: 5.699 (5.699) Accm: 3.61 (3.61) Acct: 5.98 (5.98) proj_loss: -0.5959 (-0.5959) time: 0.6754 data: 0.0003 [11-26 07:59:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 834/1669] eta: 0:09:29 tlr: 8.7e-05 tnm: 0.42 Lm: 6.562 (6.521) Lt: 5.837 (5.745) Accm: 3.45 (3.56) Acct: 5.18 (5.72) proj_loss: -0.6031 (-0.6039) time: 0.6765 data: 0.0003 [11-26 07:59:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 834/1669] eta: 0:09:29 tlr: 8.7e-05 tnm: 0.42 Lm: 6.501 (6.477) Lt: 5.795 (5.766) Accm: 3.31 (3.38) Acct: 4.92 (5.08) proj_loss: -0.6177 (-0.6189) time: 0.6765 data: 0.0003 [11-26 07:59:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 834/1669] eta: 0:09:29 tlr: 8.7e-05 tnm: 0.42 Lm: 6.332 (6.375) Lt: 5.535 (5.600) Accm: 3.87 (3.76) Acct: 6.27 (6.01) proj_loss: -0.6145 (-0.6127) time: 0.6765 data: 0.0003 [11-26 07:59:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 834/1669] eta: 0:09:29 tlr: 8.7e-05 tnm: 0.42 Lm: 6.501 (6.458) Lt: 5.793 (5.698) Accm: 3.13 (3.45) Acct: 4.87 (5.40) proj_loss: -0.6092 (-0.6066) time: 0.6765 data: 0.0003 [11-26 08:03:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1251/1669] eta: 0:04:44 tlr: 8.7e-05 tnm: 0.43 Lm: 6.495 (6.466) Lt: 5.761 (5.706) Accm: 3.45 (3.53) Acct: 5.41 (5.54) proj_loss: -0.6166 (-0.6130) time: 0.6772 data: 0.0002 [11-26 08:03:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1251/1669] eta: 0:04:44 tlr: 8.7e-05 tnm: 0.43 Lm: 6.531 (6.498) Lt: 5.832 (5.792) Accm: 3.15 (3.27) Acct: 4.71 (4.94) proj_loss: -0.6198 (-0.6199) time: 0.6772 data: 0.0003 [11-26 08:03:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1251/1669] eta: 0:04:44 tlr: 8.7e-05 tnm: 0.43 Lm: 6.575 (6.538) Lt: 5.831 (5.765) Accm: 3.26 (3.44) Acct: 5.03 (5.51) proj_loss: -0.5960 (-0.6001) time: 0.6772 data: 0.0003 [11-26 08:03:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1251/1669] eta: 0:04:44 tlr: 8.7e-05 tnm: 0.43 Lm: 6.368 (6.382) Lt: 5.625 (5.629) Accm: 3.87 (3.78) Acct: 6.16 (6.02) proj_loss: -0.6165 (-0.6176) time: 0.6772 data: 0.0003 [11-26 08:08:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1668/1669] eta: 0:00:00 tlr: 8.7e-05 tnm: 0.40 Lm: 6.404 (6.434) Lt: 5.714 (5.678) Accm: 3.87 (3.56) Acct: 6.04 (5.67) proj_loss: -0.6185 (-0.6181) time: 0.6766 data: 0.0018 [11-26 08:08:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 233/350] Total time: 0:18:53 (0.679 s / it) [11-26 08:08:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1668/1669] eta: 0:00:00 tlr: 8.7e-05 tnm: 0.40 Lm: 6.501 (6.483) Lt: 5.795 (5.762) Accm: 3.31 (3.38) Acct: 4.92 (5.10) proj_loss: -0.6219 (-0.6214) time: 0.6766 data: 0.0015 [11-26 08:08:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1668/1669] eta: 0:00:00 tlr: 8.7e-05 tnm: 0.40 Lm: 6.501 (6.501) Lt: 5.793 (5.751) Accm: 3.13 (3.41) Acct: 4.87 (5.31) proj_loss: -0.6174 (-0.6139) time: 0.6766 data: 0.0018 [11-26 08:08:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1668/1669] eta: 0:00:00 tlr: 8.7e-05 tnm: 0.40 Lm: 6.562 (6.531) Lt: 5.826 (5.755) Accm: 3.45 (3.48) Acct: 5.18 (5.59) proj_loss: -0.6031 (-0.6018) time: 0.6766 data: 0.0020 [11-26 08:08:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 233/350] Total time: 0:18:53 (0.679 s / it) [11-26 08:08:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 233/350] Total time: 0:18:53 (0.679 s / it) [11-26 08:08:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 233/350] Total time: 0:18:53 (0.679 s / it) [11-26 08:08:29] (/home/user/VAR/train.py , line 276)=> [ep233] (training ) Lm: 6.474 (6.486), Lt: 5.716 (5.731), Acc m&t: 3.48 5.47, Remain: 1 day, 12:41:32, Finish: 2024-11-27 04:50 [11-26 08:08:29] (/home/user/VAR/train.py , line 276)=> [ep233] (training ) Lm: 6.474 (6.486), Lt: 5.716 (5.731), Acc m&t: 3.48 5.47, Remain: 1 day, 12:41:24, Finish: 2024-11-27 04:49 [11-26 08:08:29] (/home/user/VAR/train.py , line 276)=> [ep233] (training ) Lm: 6.474 (6.486), Lt: 5.716 (5.731), Acc m&t: 3.48 5.47, Remain: 1 day, 12:41:54, Finish: 2024-11-27 04:50 [11-26 08:08:29] (/home/user/VAR/train.py , line 276)=> [ep233] (training ) Lm: 6.474 (6.486), Lt: 5.716 (5.731), Acc m&t: 3.48 5.47, Remain: 1 day, 12:41:26, Finish: 2024-11-27 04:49 [11-26 08:08:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 0/1669] eta: 0:18:17 tlr: 8.7e-05 tnm: 0.41 Lm: 6.320 (6.320) Lt: 5.530 (5.530) Accm: 4.08 (4.08) Acct: 6.49 (6.49) proj_loss: -0.6098 (-0.6098) time: 0.6577 data: 0.0003 [11-26 08:08:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 0/1669] eta: 0:18:18 tlr: 8.7e-05 tnm: 0.41 Lm: 6.436 (6.436) Lt: 5.718 (5.718) Accm: 4.14 (4.14) Acct: 6.35 (6.35) proj_loss: -0.6074 (-0.6074) time: 0.6582 data: 0.0004 [11-26 08:08:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 0/1669] eta: 0:18:18 tlr: 8.7e-05 tnm: 0.41 Lm: 6.605 (6.605) Lt: 5.939 (5.939) Accm: 3.12 (3.12) Acct: 4.67 (4.67) proj_loss: -0.5996 (-0.5996) time: 0.6584 data: 0.0004 [11-26 08:08:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 0/1669] eta: 0:18:19 tlr: 8.7e-05 tnm: 0.41 Lm: 6.618 (6.618) Lt: 5.898 (5.898) Accm: 3.16 (3.16) Acct: 4.99 (4.99) proj_loss: -0.6089 (-0.6089) time: 0.6585 data: 0.0004 [11-26 08:13:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 417/1669] eta: 0:14:05 tlr: 8.7e-05 tnm: 0.41 Lm: 6.635 (6.635) Lt: 5.908 (5.908) Accm: 3.10 (3.10) Acct: 4.88 (4.88) proj_loss: -0.6032 (-0.6032) time: 0.6744 data: 0.0004 [11-26 08:13:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 417/1669] eta: 0:14:05 tlr: 8.7e-05 tnm: 0.41 Lm: 6.455 (6.455) Lt: 5.710 (5.710) Accm: 3.58 (3.58) Acct: 5.69 (5.69) proj_loss: -0.6158 (-0.6158) time: 0.6744 data: 0.0002 [11-26 08:13:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 417/1669] eta: 0:14:05 tlr: 8.7e-05 tnm: 0.41 Lm: 6.456 (6.456) Lt: 5.717 (5.717) Accm: 3.71 (3.71) Acct: 5.75 (5.75) proj_loss: -0.6068 (-0.6068) time: 0.6744 data: 0.0003 [11-26 08:13:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 417/1669] eta: 0:14:05 tlr: 8.7e-05 tnm: 0.41 Lm: 6.477 (6.477) Lt: 5.779 (5.779) Accm: 3.60 (3.60) Acct: 5.37 (5.37) proj_loss: -0.6136 (-0.6136) time: 0.6744 data: 0.0003 [11-26 08:18:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 834/1669] eta: 0:09:33 tlr: 8.7e-05 tnm: 0.43 Lm: 6.424 (6.459) Lt: 5.639 (5.732) Accm: 3.69 (3.63) Acct: 5.99 (5.58) proj_loss: -0.6053 (-0.6108) time: 0.6746 data: 0.0003 [11-26 08:18:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 834/1669] eta: 0:09:33 tlr: 8.7e-05 tnm: 0.43 Lm: 6.320 (6.376) Lt: 5.530 (5.634) Accm: 4.08 (3.87) Acct: 6.49 (6.02) proj_loss: -0.6098 (-0.6107) time: 0.6746 data: 0.0002 [11-26 08:18:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 834/1669] eta: 0:09:33 tlr: 8.7e-05 tnm: 0.43 Lm: 6.618 (6.557) Lt: 5.898 (5.815) Accm: 3.16 (3.35) Acct: 4.99 (5.28) proj_loss: -0.6089 (-0.6078) time: 0.6746 data: 0.0003 [11-26 08:18:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 834/1669] eta: 0:09:33 tlr: 8.7e-05 tnm: 0.43 Lm: 6.436 (6.414) Lt: 5.717 (5.688) Accm: 3.98 (3.80) Acct: 6.08 (5.86) proj_loss: -0.6074 (-0.6120) time: 0.6746 data: 0.0003 [11-26 08:22:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1251/1669] eta: 0:04:46 tlr: 8.6e-05 tnm: 0.41 Lm: 6.456 (6.473) Lt: 5.717 (5.754) Accm: 3.63 (3.58) Acct: 5.61 (5.53) proj_loss: -0.6068 (-0.6069) time: 0.6768 data: 0.0003 [11-26 08:22:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1251/1669] eta: 0:04:46 tlr: 8.6e-05 tnm: 0.41 Lm: 6.394 (6.399) Lt: 5.642 (5.664) Accm: 3.74 (3.75) Acct: 5.90 (5.84) proj_loss: -0.6144 (-0.6128) time: 0.6768 data: 0.0003 [11-26 08:22:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1251/1669] eta: 0:04:46 tlr: 8.6e-05 tnm: 0.41 Lm: 6.434 (6.455) Lt: 5.643 (5.711) Accm: 3.62 (3.61) Acct: 5.93 (5.65) proj_loss: -0.6057 (-0.6096) time: 0.6768 data: 0.0003 [11-26 08:22:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1251/1669] eta: 0:04:46 tlr: 8.6e-05 tnm: 0.41 Lm: 6.509 (6.504) Lt: 5.764 (5.767) Accm: 3.31 (3.38) Acct: 5.17 (5.29) proj_loss: -0.6130 (-0.6141) time: 0.6768 data: 0.0003 [11-26 08:27:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.43 Lm: 6.618 (6.544) Lt: 5.898 (5.805) Accm: 3.16 (3.25) Acct: 4.99 (5.16) proj_loss: -0.6109 (-0.6135) time: 0.6779 data: 0.0016 [11-26 08:27:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 234/350] Total time: 0:18:58 (0.682 s / it) [11-26 08:27:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.43 Lm: 6.450 (6.468) Lt: 5.717 (5.743) Accm: 3.59 (3.58) Acct: 5.54 (5.53) proj_loss: -0.6061 (-0.6067) time: 0.6779 data: 0.0016 [11-26 08:27:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.43 Lm: 6.468 (6.431) Lt: 5.753 (5.703) Accm: 3.39 (3.61) Acct: 5.30 (5.62) proj_loss: -0.6154 (-0.6133) time: 0.6779 data: 0.0017 [11-26 08:27:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.43 Lm: 6.444 (6.459) Lt: 5.647 (5.724) Accm: 3.55 (3.54) Acct: 5.87 (5.51) proj_loss: -0.6061 (-0.6113) time: 0.6779 data: 0.0016 [11-26 08:27:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 234/350] Total time: 0:18:58 (0.682 s / it) [11-26 08:27:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 234/350] Total time: 0:18:58 (0.682 s / it) [11-26 08:27:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 234/350] Total time: 0:18:58 (0.682 s / it) [11-26 08:27:28] (/home/user/VAR/train.py , line 276)=> [ep234] (training ) Lm: 6.474 (6.480), Lt: 5.716 (5.728), Acc m&t: 3.48 5.47, Remain: 1 day, 12:29:46, Finish: 2024-11-27 04:57 [11-26 08:27:28] (/home/user/VAR/train.py , line 276)=> [ep234] (training ) Lm: 6.474 (6.480), Lt: 5.716 (5.728), Acc m&t: 3.48 5.47, Remain: 1 day, 12:29:49, Finish: 2024-11-27 04:57 [11-26 08:27:28] (/home/user/VAR/train.py , line 276)=> [ep234] (training ) Lm: 6.474 (6.480), Lt: 5.716 (5.728), Acc m&t: 3.48 5.47, Remain: 1 day, 12:29:40, Finish: 2024-11-27 04:57 [11-26 08:27:28] (/home/user/VAR/train.py , line 276)=> [ep234] (training ) Lm: 6.474 (6.480), Lt: 5.716 (5.728), Acc m&t: 3.48 5.47, Remain: 1 day, 12:29:43, Finish: 2024-11-27 04:57 [11-26 08:27:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 0/1669] eta: 0:18:13 tlr: 8.6e-05 tnm: 0.42 Lm: 6.427 (6.427) Lt: 5.588 (5.588) Accm: 3.63 (3.63) Acct: 6.04 (6.04) proj_loss: -0.5721 (-0.5721) time: 0.6554 data: 0.0004 [11-26 08:27:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 0/1669] eta: 0:18:15 tlr: 8.6e-05 tnm: 0.42 Lm: 6.436 (6.436) Lt: 5.658 (5.658) Accm: 3.51 (3.51) Acct: 5.39 (5.39) proj_loss: -0.6078 (-0.6078) time: 0.6563 data: 0.0003 [11-26 08:27:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 0/1669] eta: 0:18:15 tlr: 8.6e-05 tnm: 0.42 Lm: 6.570 (6.570) Lt: 5.869 (5.869) Accm: 3.00 (3.00) Acct: 4.67 (4.67) proj_loss: -0.5953 (-0.5953) time: 0.6563 data: 0.0003 [11-26 08:27:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 0/1669] eta: 0:18:15 tlr: 8.6e-05 tnm: 0.42 Lm: 6.388 (6.388) Lt: 5.660 (5.660) Accm: 3.44 (3.44) Acct: 5.30 (5.30) proj_loss: -0.6125 (-0.6125) time: 0.6566 data: 0.0004 [11-26 08:32:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 417/1669] eta: 0:14:04 tlr: 8.6e-05 tnm: 0.43 Lm: 6.436 (6.436) Lt: 5.680 (5.680) Accm: 3.34 (3.34) Acct: 5.25 (5.25) proj_loss: -0.6117 (-0.6117) time: 0.6739 data: 0.0003 [11-26 08:32:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 417/1669] eta: 0:14:04 tlr: 8.6e-05 tnm: 0.43 Lm: 6.465 (6.465) Lt: 5.688 (5.688) Accm: 3.41 (3.41) Acct: 5.34 (5.34) proj_loss: -0.6110 (-0.6110) time: 0.6739 data: 0.0003 [11-26 08:32:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 417/1669] eta: 0:14:04 tlr: 8.6e-05 tnm: 0.43 Lm: 6.463 (6.463) Lt: 5.679 (5.679) Accm: 3.50 (3.50) Acct: 5.67 (5.67) proj_loss: -0.5979 (-0.5979) time: 0.6739 data: 0.0003 [11-26 08:32:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 417/1669] eta: 0:14:04 tlr: 8.6e-05 tnm: 0.43 Lm: 6.520 (6.520) Lt: 5.776 (5.776) Accm: 3.34 (3.34) Acct: 5.24 (5.24) proj_loss: -0.5995 (-0.5995) time: 0.6740 data: 0.0003 [11-26 08:36:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 834/1669] eta: 0:09:23 tlr: 8.6e-05 tnm: 0.42 Lm: 6.470 (6.502) Lt: 5.695 (5.749) Accm: 3.23 (3.30) Acct: 5.22 (5.23) proj_loss: -0.6023 (-0.6005) time: 0.6740 data: 0.0003 [11-26 08:36:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 834/1669] eta: 0:09:23 tlr: 8.6e-05 tnm: 0.42 Lm: 6.427 (6.430) Lt: 5.609 (5.656) Accm: 3.63 (3.65) Acct: 5.80 (5.72) proj_loss: -0.6237 (-0.6071) time: 0.6740 data: 0.0003 [11-26 08:36:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 834/1669] eta: 0:09:23 tlr: 8.6e-05 tnm: 0.42 Lm: 6.484 (6.453) Lt: 5.694 (5.685) Accm: 3.41 (3.36) Acct: 5.27 (5.26) proj_loss: -0.6109 (-0.6108) time: 0.6740 data: 0.0003 [11-26 08:36:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 834/1669] eta: 0:09:23 tlr: 8.6e-05 tnm: 0.42 Lm: 6.436 (6.455) Lt: 5.699 (5.691) Accm: 3.48 (3.43) Acct: 5.30 (5.33) proj_loss: -0.6085 (-0.6101) time: 0.6740 data: 0.0003 [11-26 08:41:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1251/1669] eta: 0:04:42 tlr: 8.6e-05 tnm: 0.42 Lm: 6.465 (6.498) Lt: 5.708 (5.744) Accm: 3.39 (3.36) Acct: 5.29 (5.21) proj_loss: -0.6113 (-0.6131) time: 0.6772 data: 0.0004 [11-26 08:41:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1251/1669] eta: 0:04:42 tlr: 8.6e-05 tnm: 0.42 Lm: 6.468 (6.493) Lt: 5.725 (5.750) Accm: 3.37 (3.35) Acct: 5.31 (5.28) proj_loss: -0.6030 (-0.6048) time: 0.6772 data: 0.0003 [11-26 08:41:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1251/1669] eta: 0:04:42 tlr: 8.6e-05 tnm: 0.42 Lm: 6.463 (6.452) Lt: 5.689 (5.684) Accm: 3.55 (3.61) Acct: 5.78 (5.72) proj_loss: -0.6081 (-0.6034) time: 0.6772 data: 0.0003 [11-26 08:41:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1251/1669] eta: 0:04:42 tlr: 8.6e-05 tnm: 0.42 Lm: 6.485 (6.486) Lt: 5.697 (5.727) Accm: 3.32 (3.23) Acct: 5.23 (5.05) proj_loss: -0.6099 (-0.6066) time: 0.6772 data: 0.0003 [11-26 08:46:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.42 Lm: 6.487 (6.487) Lt: 5.694 (5.719) Accm: 3.29 (3.24) Acct: 5.27 (5.12) proj_loss: -0.6089 (-0.6039) time: 0.7453 data: 0.0019 [11-26 08:46:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 235/350] Total time: 0:18:52 (0.678 s / it) [11-26 08:46:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.42 Lm: 6.470 (6.520) Lt: 5.754 (5.772) Accm: 3.23 (3.29) Acct: 5.22 (5.18) proj_loss: -0.6023 (-0.6019) time: 0.7453 data: 0.0016 [11-26 08:46:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.42 Lm: 6.495 (6.503) Lt: 5.718 (5.754) Accm: 3.31 (3.35) Acct: 5.29 (5.22) proj_loss: -0.6142 (-0.6150) time: 0.7453 data: 0.0019 [11-26 08:46:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.42 Lm: 6.499 (6.463) Lt: 5.769 (5.704) Accm: 3.47 (3.51) Acct: 5.75 (5.46) proj_loss: -0.6050 (-0.6037) time: 0.7453 data: 0.0018 [11-26 08:46:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 235/350] Total time: 0:18:52 (0.678 s / it) [11-26 08:46:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 235/350] Total time: 0:18:52 (0.678 s / it) [11-26 08:46:20] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 235/350] Total time: 0:18:52 (0.678 s / it) [11-26 08:46:20] (/home/user/VAR/train.py , line 276)=> [ep235] (training ) Lm: 6.474 (6.479), Lt: 5.716 (5.725), Acc m&t: 3.48 5.47, Remain: 1 day, 12:12:42, Finish: 2024-11-27 04:59 [11-26 08:46:20] (/home/user/VAR/train.py , line 276)=> [ep235] (training ) Lm: 6.474 (6.479), Lt: 5.716 (5.725), Acc m&t: 3.48 5.47, Remain: 1 day, 12:12:57, Finish: 2024-11-27 04:59 [11-26 08:46:20] (/home/user/VAR/train.py , line 276)=> [ep235] (training ) Lm: 6.474 (6.479), Lt: 5.716 (5.725), Acc m&t: 3.48 5.47, Remain: 1 day, 12:13:36, Finish: 2024-11-27 04:59 [11-26 08:46:20] (/home/user/VAR/train.py , line 276)=> [ep235] (training ) Lm: 6.474 (6.479), Lt: 5.716 (5.725), Acc m&t: 3.48 5.47, Remain: 1 day, 12:12:57, Finish: 2024-11-27 04:59 [11-26 08:46:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 0/1669] eta: 0:18:34 tlr: 8.6e-05 tnm: 0.42 Lm: 6.391 (6.391) Lt: 5.663 (5.663) Accm: 3.67 (3.67) Acct: 5.63 (5.63) proj_loss: -0.6246 (-0.6246) time: 0.6680 data: 0.0004 [11-26 08:46:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 0/1669] eta: 0:18:35 tlr: 8.6e-05 tnm: 0.42 Lm: 6.575 (6.575) Lt: 5.834 (5.834) Accm: 3.07 (3.07) Acct: 4.98 (4.98) proj_loss: -0.5924 (-0.5924) time: 0.6683 data: 0.0003 [11-26 08:46:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 0/1669] eta: 0:18:31 tlr: 8.6e-05 tnm: 0.42 Lm: 6.601 (6.601) Lt: 5.820 (5.820) Accm: 3.00 (3.00) Acct: 4.67 (4.67) proj_loss: -0.6099 (-0.6099) time: 0.6660 data: 0.0004 [11-26 08:46:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 0/1669] eta: 0:18:32 tlr: 8.6e-05 tnm: 0.42 Lm: 6.457 (6.457) Lt: 5.684 (5.684) Accm: 3.50 (3.50) Acct: 5.65 (5.65) proj_loss: -0.6159 (-0.6159) time: 0.6668 data: 0.0004 [11-26 08:51:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 417/1669] eta: 0:14:36 tlr: 8.5e-05 tnm: 0.43 Lm: 6.498 (6.498) Lt: 5.758 (5.758) Accm: 3.42 (3.42) Acct: 5.48 (5.48) proj_loss: -0.6232 (-0.6232) time: 0.6801 data: 0.0003 [11-26 08:51:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 417/1669] eta: 0:14:36 tlr: 8.5e-05 tnm: 0.43 Lm: 6.533 (6.533) Lt: 5.765 (5.765) Accm: 3.25 (3.25) Acct: 5.23 (5.23) proj_loss: -0.6043 (-0.6043) time: 0.6800 data: 0.0003 [11-26 08:51:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 417/1669] eta: 0:14:36 tlr: 8.5e-05 tnm: 0.43 Lm: 6.452 (6.452) Lt: 5.677 (5.677) Accm: 3.59 (3.59) Acct: 5.59 (5.59) proj_loss: -0.6087 (-0.6087) time: 0.6801 data: 0.0003 [11-26 08:51:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 417/1669] eta: 0:14:36 tlr: 8.5e-05 tnm: 0.43 Lm: 6.526 (6.526) Lt: 5.788 (5.788) Accm: 3.19 (3.19) Acct: 4.90 (4.90) proj_loss: -0.6180 (-0.6180) time: 0.6800 data: 0.0003 [11-26 08:55:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 834/1669] eta: 0:09:35 tlr: 8.5e-05 tnm: 0.44 Lm: 6.451 (6.494) Lt: 5.755 (5.750) Accm: 3.39 (3.35) Acct: 5.13 (5.27) proj_loss: -0.6203 (-0.6187) time: 0.6785 data: 0.0003 [11-26 08:55:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 834/1669] eta: 0:09:35 tlr: 8.5e-05 tnm: 0.44 Lm: 6.490 (6.417) Lt: 5.696 (5.657) Accm: 3.42 (3.56) Acct: 5.48 (5.59) proj_loss: -0.6163 (-0.6092) time: 0.6785 data: 0.0003 [11-26 08:55:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 834/1669] eta: 0:09:35 tlr: 8.5e-05 tnm: 0.44 Lm: 6.513 (6.536) Lt: 5.691 (5.784) Accm: 3.52 (3.36) Acct: 5.54 (5.28) proj_loss: -0.6155 (-0.6110) time: 0.6785 data: 0.0003 [11-26 08:55:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 834/1669] eta: 0:09:35 tlr: 8.5e-05 tnm: 0.44 Lm: 6.539 (6.516) Lt: 5.787 (5.768) Accm: 3.50 (3.48) Acct: 5.65 (5.54) proj_loss: -0.6159 (-0.6177) time: 0.6785 data: 0.0003 [11-26 09:00:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1251/1669] eta: 0:04:46 tlr: 8.5e-05 tnm: 0.42 Lm: 6.498 (6.494) Lt: 5.736 (5.740) Accm: 3.55 (3.59) Acct: 5.66 (5.71) proj_loss: -0.6113 (-0.6145) time: 0.6780 data: 0.0003 [11-26 09:00:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1251/1669] eta: 0:04:46 tlr: 8.5e-05 tnm: 0.42 Lm: 6.498 (6.439) Lt: 5.746 (5.691) Accm: 3.34 (3.49) Acct: 5.26 (5.45) proj_loss: -0.6176 (-0.6122) time: 0.6780 data: 0.0003 [11-26 09:00:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1251/1669] eta: 0:04:46 tlr: 8.5e-05 tnm: 0.42 Lm: 6.441 (6.471) Lt: 5.715 (5.712) Accm: 3.47 (3.40) Acct: 5.39 (5.36) proj_loss: -0.6151 (-0.6142) time: 0.6780 data: 0.0003 [11-26 09:00:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1251/1669] eta: 0:04:46 tlr: 8.5e-05 tnm: 0.42 Lm: 6.456 (6.501) Lt: 5.677 (5.751) Accm: 3.59 (3.51) Acct: 5.59 (5.54) proj_loss: -0.6128 (-0.6108) time: 0.6780 data: 0.0003 [11-26 09:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1668/1669] eta: 0:00:00 tlr: 8.5e-05 tnm: 0.43 Lm: 6.475 (6.496) Lt: 5.691 (5.753) Accm: 3.52 (3.43) Acct: 5.54 (5.38) proj_loss: -0.6101 (-0.6099) time: 0.6799 data: 0.0018 [11-26 09:05:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 236/350] Total time: 0:19:00 (0.683 s / it) [11-26 09:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1668/1669] eta: 0:00:00 tlr: 8.5e-05 tnm: 0.43 Lm: 6.490 (6.408) Lt: 5.696 (5.652) Accm: 3.42 (3.61) Acct: 5.48 (5.66) proj_loss: -0.6163 (-0.6092) time: 0.6799 data: 0.0017 [11-26 09:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1668/1669] eta: 0:00:00 tlr: 8.5e-05 tnm: 0.43 Lm: 6.457 (6.475) Lt: 5.684 (5.717) Accm: 3.53 (3.58) Acct: 5.65 (5.68) proj_loss: -0.6126 (-0.6141) time: 0.6799 data: 0.0015 [11-26 09:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1668/1669] eta: 0:00:00 tlr: 8.5e-05 tnm: 0.43 Lm: 6.442 (6.465) Lt: 5.676 (5.701) Accm: 3.39 (3.38) Acct: 5.13 (5.31) proj_loss: -0.6188 (-0.6151) time: 0.6799 data: 0.0018 [11-26 09:05:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 236/350] Total time: 0:19:00 (0.683 s / it) [11-26 09:05:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 236/350] Total time: 0:19:00 (0.683 s / it) [11-26 09:05:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 236/350] Total time: 0:19:00 (0.683 s / it) [11-26 09:05:21] (/home/user/VAR/train.py , line 276)=> [ep236] (training ) Lm: 6.473 (6.473), Lt: 5.716 (5.717), Acc m&t: 3.48 5.47, Remain: 1 day, 11:51:24, Finish: 2024-11-27 04:56 [11-26 09:05:21] (/home/user/VAR/train.py , line 276)=> [ep236] (training ) Lm: 6.473 (6.473), Lt: 5.716 (5.717), Acc m&t: 3.48 5.47, Remain: 1 day, 11:50:51, Finish: 2024-11-27 04:56 [11-26 09:05:21] (/home/user/VAR/train.py , line 276)=> [ep236] (training ) Lm: 6.473 (6.473), Lt: 5.716 (5.717), Acc m&t: 3.48 5.47, Remain: 1 day, 11:51:06, Finish: 2024-11-27 04:56 [11-26 09:05:21] (/home/user/VAR/train.py , line 276)=> [ep236] (training ) Lm: 6.473 (6.473), Lt: 5.716 (5.717), Acc m&t: 3.48 5.47, Remain: 1 day, 11:51:21, Finish: 2024-11-27 04:56 [11-26 09:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 0/1669] eta: 0:18:19 tlr: 8.5e-05 tnm: 0.43 Lm: 6.390 (6.390) Lt: 5.626 (5.626) Accm: 3.60 (3.60) Acct: 5.46 (5.46) proj_loss: -0.6099 (-0.6099) time: 0.6587 data: 0.0004 [11-26 09:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 0/1669] eta: 0:18:19 tlr: 8.5e-05 tnm: 0.43 Lm: 6.476 (6.476) Lt: 5.672 (5.672) Accm: 3.29 (3.29) Acct: 5.32 (5.32) proj_loss: -0.6250 (-0.6250) time: 0.6589 data: 0.0004 [11-26 09:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 0/1669] eta: 0:18:18 tlr: 8.5e-05 tnm: 0.43 Lm: 6.561 (6.561) Lt: 5.780 (5.780) Accm: 3.02 (3.02) Acct: 4.84 (4.84) proj_loss: -0.6008 (-0.6008) time: 0.6581 data: 0.0003 [11-26 09:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 0/1669] eta: 0:18:17 tlr: 8.5e-05 tnm: 0.43 Lm: 6.477 (6.477) Lt: 5.701 (5.701) Accm: 3.46 (3.46) Acct: 5.80 (5.80) proj_loss: -0.6111 (-0.6111) time: 0.6575 data: 0.0003 [11-26 09:10:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 417/1669] eta: 0:14:07 tlr: 8.5e-05 tnm: 0.43 Lm: 6.445 (6.445) Lt: 5.687 (5.687) Accm: 3.45 (3.45) Acct: 5.60 (5.60) proj_loss: -0.6185 (-0.6185) time: 0.6741 data: 0.0003 [11-26 09:10:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 417/1669] eta: 0:14:07 tlr: 8.5e-05 tnm: 0.43 Lm: 6.463 (6.463) Lt: 5.679 (5.679) Accm: 3.33 (3.33) Acct: 5.35 (5.35) proj_loss: -0.6266 (-0.6266) time: 0.6741 data: 0.0003 [11-26 09:10:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 417/1669] eta: 0:14:07 tlr: 8.5e-05 tnm: 0.43 Lm: 6.441 (6.441) Lt: 5.673 (5.673) Accm: 3.60 (3.60) Acct: 5.71 (5.71) proj_loss: -0.5998 (-0.5998) time: 0.6741 data: 0.0003 [11-26 09:10:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 417/1669] eta: 0:14:07 tlr: 8.5e-05 tnm: 0.43 Lm: 6.466 (6.466) Lt: 5.726 (5.726) Accm: 3.43 (3.43) Acct: 5.38 (5.38) proj_loss: -0.6076 (-0.6076) time: 0.6740 data: 0.0003 ======================================================= RESTART [11-26 09:44:41] ======================================================= ======================================================= RESTART [11-26 09:44:41] ======================================================= ======================================================= RESTART [11-26 09:44:41] ======================================================= ======================================================= RESTART [11-26 09:44:41] ======================================================= [11-26 09:44:41] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-26 09:44:41] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-26 09:44:41] (er/VAR/utils/arg_util.py, line 230)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-26 09:45:40] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-26 09:45:40] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False finetune_ckpt : None same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add push origin main commit_id : ca9b13c7b773f48247047ce959b4a6b3af1d406f branch : main } [11-26 09:45:40] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-26 09:45:42] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-26 09:45:42] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> [11-26 09:45:42] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 09:45:42] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> [11-26 09:45:42] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 09:45:42] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth ... [11-26 09:45:42] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep230, it0 [11-26 09:45:42] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-26 09:44:41] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-26 09:44:41] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-26 09:44:41] (er/VAR/utils/arg_util.py, line 230)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-26 09:45:40] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-26 09:45:40] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False finetune_ckpt : None same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_id : ca9b13c7b773f48247047ce959b4a6b3af1d406f commit_msg : add push origin main } [11-26 09:45:40] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-26 09:45:42] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-26 09:45:42] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> [11-26 09:45:42] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 09:45:42] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> [11-26 09:45:42] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 09:45:42] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth ... [11-26 09:45:42] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep230, it0 [11-26 09:45:42] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-26 09:44:41] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-26 09:44:41] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-26 09:44:41] (er/VAR/utils/arg_util.py, line 230)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-26 09:45:40] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-26 09:45:40] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False finetune_ckpt : None same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_id : ca9b13c7b773f48247047ce959b4a6b3af1d406f commit_msg : add push origin main } [11-26 09:45:40] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-26 09:45:42] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-26 09:45:42] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> [11-26 09:45:42] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 09:45:42] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> [11-26 09:45:42] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 09:45:42] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth ... [11-26 09:45:42] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep230, it0 [11-26 09:45:42] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-26 09:44:41] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-26 09:44:41] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-26 09:44:41] (er/VAR/utils/arg_util.py, line 230)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-26 09:45:40] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-26 09:45:40] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False finetune_ckpt : None same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_msg : add push origin main commit_id : ca9b13c7b773f48247047ce959b4a6b3af1d406f } [11-26 09:45:40] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-26 09:45:42] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-26 09:45:42] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> [11-26 09:45:42] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 09:45:42] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 09:45:42] (e/user/VAR/utils/data.py, line 51)=> [11-26 09:45:42] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 09:45:43] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth ... [11-26 09:45:43] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep230, it0 [11-26 09:45:43] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ... [dataloader multi processing](*) finished! (47.27s) [dataloader multi processing](*) finished! (48.37s) [dataloader multi processing](*) finished! (50.01s) [dataloader multi processing](*) finished! (50.12s) [11-26 09:46:30] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-26 09:46:35] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 09:46:35] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 09:46:36] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-26 09:46:32] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-26 09:46:35] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 09:46:35] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 09:46:37] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-26 09:46:31] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-26 09:46:36] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 09:46:36] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 09:46:37] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-26 09:46:33] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-26 09:46:37] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 09:46:37] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 09:46:39] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-26 09:46:38] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-26 09:47:01] (/home/user/VAR/train.py , line 128)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-26 09:47:01] (/home/user/VAR/train.py , line 130)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-26 09:47:01] (/home/user/VAR/train.py , line 131)=> [INIT][#para] VAR=1085.77 [11-26 09:47:01] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-26 09:47:01] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank0] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-26 09:46:40] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-26 09:47:01] (/home/user/VAR/train.py , line 128)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-26 09:47:01] (/home/user/VAR/train.py , line 130)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-26 09:47:01] (/home/user/VAR/train.py , line 131)=> [INIT][#para] VAR=1085.77 [11-26 09:47:01] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-26 09:47:01] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank8] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-26 09:46:38] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-26 09:47:01] (/home/user/VAR/train.py , line 128)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-26 09:47:01] (/home/user/VAR/train.py , line 130)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-26 09:47:01] (/home/user/VAR/train.py , line 131)=> [INIT][#para] VAR=1085.77 [11-26 09:47:01] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-26 09:47:01] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank16] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-26 09:46:39] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-26 09:47:01] (/home/user/VAR/train.py , line 128)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-26 09:47:01] (/home/user/VAR/train.py , line 130)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-26 09:47:01] (/home/user/VAR/train.py , line 131)=> [INIT][#para] VAR=1085.77 [11-26 09:47:01] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, 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'_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-26 09:47:01] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank24] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-26 09:47:02] (/VAR/utils/lr_control.py, line 105)=> [11-26 09:47:02] (/home/user/VAR/train.py , line 146)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-26 09:47:03] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-26 09:47:03] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-26 09:47:03] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-26 09:47:03] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-26 10:02:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 0/1669] eta: 17 days, 10:26:28 tlr: 9e-05 tnm: 0.43 Lm: 6.491 (6.491) Lt: 5.775 (5.775) Accm: 3.30 (3.30) Acct: 4.84 (4.84) proj_loss: -0.5953 (-0.5953) time: 902.5694 data: 0.0007 [11-26 09:47:02] (/VAR/utils/lr_control.py, line 105)=> [11-26 09:47:02] (/home/user/VAR/train.py , line 146)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-26 09:47:03] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-26 09:47:03] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-26 09:47:04] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-26 09:47:04] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-26 10:02:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 0/1669] eta: 17 days, 10:25:56 tlr: 9e-05 tnm: 0.43 Lm: 6.525 (6.525) Lt: 5.761 (5.761) Accm: 3.45 (3.45) Acct: 5.39 (5.39) proj_loss: -0.6042 (-0.6042) time: 902.5501 data: 0.0007 [11-26 09:47:02] (/VAR/utils/lr_control.py, line 105)=> [11-26 09:47:02] (/home/user/VAR/train.py , line 146)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-26 09:47:03] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-26 09:47:03] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-26 09:47:03] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-26 09:47:03] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-26 10:02:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 0/1669] eta: 17 days, 10:27:15 tlr: 9e-05 tnm: 0.43 Lm: 6.561 (6.561) Lt: 5.761 (5.761) Accm: 3.13 (3.13) Acct: 5.15 (5.15) proj_loss: -0.6021 (-0.6021) time: 902.5975 data: 0.0006 [11-26 09:47:02] (/VAR/utils/lr_control.py, line 105)=> [11-26 09:47:02] (/home/user/VAR/train.py , line 146)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-26 09:47:03] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-26 09:47:03] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-26 09:47:04] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-26 09:47:04] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-26 10:02:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 0/1669] eta: 17 days, 10:01:40 tlr: 9e-05 tnm: 0.43 Lm: 6.257 (6.257) Lt: 5.498 (5.498) Accm: 3.96 (3.96) Acct: 6.13 (6.13) proj_loss: -0.6047 (-0.6047) time: 901.6777 data: 0.0006 [11-26 10:12:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 417/1669] eta: 1:16:26 tlr: 9e-05 tnm: 0.42 Lm: 6.331 (6.331) Lt: 5.534 (5.534) Accm: 3.91 (3.91) Acct: 6.22 (6.22) proj_loss: -0.5939 (-0.5939) time: 0.6766 data: 0.0003 [11-26 10:12:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 417/1669] eta: 1:16:29 tlr: 9e-05 tnm: 0.42 Lm: 6.588 (6.588) Lt: 5.797 (5.797) Accm: 3.27 (3.27) Acct: 5.25 (5.25) proj_loss: -0.5975 (-0.5975) time: 0.6766 data: 0.0003 [11-26 10:12:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 417/1669] eta: 1:16:29 tlr: 9e-05 tnm: 0.42 Lm: 6.417 (6.417) Lt: 5.657 (5.657) Accm: 3.77 (3.77) Acct: 5.72 (5.72) proj_loss: -0.6075 (-0.6075) time: 0.6766 data: 0.0003 [11-26 10:12:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 417/1669] eta: 1:16:29 tlr: 9e-05 tnm: 0.42 Lm: 6.526 (6.526) Lt: 5.784 (5.784) Accm: 3.24 (3.24) Acct: 4.96 (4.96) proj_loss: -0.6036 (-0.6036) time: 0.6766 data: 0.0003 [11-26 10:17:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 834/1669] eta: 0:30:14 tlr: 9e-05 tnm: 0.42 Lm: 6.560 (6.571) Lt: 5.793 (5.825) Accm: 3.18 (3.11) Acct: 4.84 (4.87) proj_loss: -0.6120 (-0.6087) time: 0.6768 data: 0.0003 [11-26 10:17:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 834/1669] eta: 0:30:14 tlr: 9e-05 tnm: 0.42 Lm: 6.593 (6.589) Lt: 5.834 (5.810) Accm: 3.13 (3.20) Acct: 5.15 (5.04) proj_loss: -0.6021 (-0.6007) time: 0.6768 data: 0.0003 [11-26 10:17:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 834/1669] eta: 0:30:13 tlr: 9e-05 tnm: 0.42 Lm: 6.405 (6.391) Lt: 5.571 (5.621) Accm: 3.86 (3.73) Acct: 6.13 (5.80) proj_loss: -0.6047 (-0.6054) time: 0.6768 data: 0.0003 [11-26 10:17:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 834/1669] eta: 0:30:14 tlr: 9e-05 tnm: 0.42 Lm: 6.525 (6.469) Lt: 5.761 (5.744) Accm: 3.45 (3.63) Acct: 5.39 (5.54) proj_loss: -0.6107 (-0.6139) time: 0.6768 data: 0.0003 [11-26 10:22:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1251/1669] eta: 0:11:39 tlr: 8.9e-05 tnm: 0.42 Lm: 6.538 (6.489) Lt: 5.778 (5.757) Accm: 3.41 (3.54) Acct: 5.38 (5.50) proj_loss: -0.6094 (-0.6124) time: 0.6756 data: 0.0002 [11-26 10:22:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1251/1669] eta: 0:11:39 tlr: 8.9e-05 tnm: 0.42 Lm: 6.577 (6.579) Lt: 5.809 (5.804) Accm: 3.23 (3.24) Acct: 5.11 (5.05) proj_loss: -0.6045 (-0.6027) time: 0.6755 data: 0.0002 [11-26 10:22:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1251/1669] eta: 0:11:39 tlr: 8.9e-05 tnm: 0.42 Lm: 6.576 (6.576) Lt: 5.841 (5.841) Accm: 3.10 (3.08) Acct: 4.77 (4.83) proj_loss: -0.6154 (-0.6113) time: 0.6755 data: 0.0003 [11-26 10:22:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1251/1669] eta: 0:11:39 tlr: 8.9e-05 tnm: 0.42 Lm: 6.458 (6.458) Lt: 5.682 (5.702) Accm: 3.62 (3.54) Acct: 5.55 (5.50) proj_loss: -0.6090 (-0.6074) time: 0.6756 data: 0.0003 [11-26 10:26:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1668/1669] eta: 0:00:01 tlr: 8.9e-05 tnm: 0.41 Lm: 6.510 (6.498) Lt: 5.794 (5.744) Accm: 3.37 (3.37) Acct: 4.98 (5.29) proj_loss: -0.6133 (-0.6095) time: 0.6767 data: 0.0019 [11-26 10:26:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1668/1669] eta: 0:00:01 tlr: 8.9e-05 tnm: 0.41 Lm: 6.561 (6.537) Lt: 5.785 (5.762) Accm: 3.34 (3.41) Acct: 5.15 (5.30) proj_loss: -0.6070 (-0.6039) time: 0.6767 data: 0.0016 [11-26 10:26:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1668/1669] eta: 0:00:01 tlr: 8.9e-05 tnm: 0.41 Lm: 6.592 (6.586) Lt: 5.889 (5.855) Accm: 3.01 (3.04) Acct: 4.70 (4.75) proj_loss: -0.6120 (-0.6081) time: 0.6767 data: 0.0015 [11-26 10:26:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1668/1669] eta: 0:00:01 tlr: 8.9e-05 tnm: 0.41 Lm: 6.525 (6.476) Lt: 5.761 (5.743) Accm: 3.45 (3.56) Acct: 5.39 (5.54) proj_loss: -0.6107 (-0.6125) time: 0.6767 data: 0.0019 [11-26 10:26:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 230/350] Total time: 0:39:37 (1.424 s / it) [11-26 10:26:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 230/350] Total time: 0:39:38 (1.425 s / it) [11-26 10:26:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 230/350] Total time: 0:39:38 (1.425 s / it) [11-26 10:26:47] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 230/350] Total time: 0:39:38 (1.425 s / it) [11-26 10:26:47] (/home/user/VAR/train.py , line 279)=> [ep230] (training ) Lm: 6.479 (6.479), Lt: 5.720 (5.720), Acc m&t: 3.45 5.44, Remain: 1 day, 13:34:36, Finish: 2024-11-27 08:01 [11-26 10:26:47] (/home/user/VAR/train.py , line 279)=> [ep230] (training ) Lm: 6.479 (6.479), Lt: 5.720 (5.720), Acc m&t: 3.45 5.44, Remain: 1 day, 13:34:30, Finish: 2024-11-27 08:01 [11-26 10:26:47] (/home/user/VAR/train.py , line 279)=> [ep230] (training ) Lm: 6.479 (6.479), Lt: 5.720 (5.720), Acc m&t: 3.45 5.44, Remain: 1 day, 13:35:28, Finish: 2024-11-27 08:02 [11-26 10:26:47] (/home/user/VAR/train.py , line 279)=> [ep230] (training ) Lm: 6.479 (6.479), Lt: 5.720 (5.720), Acc m&t: 3.45 5.44, Remain: 1 day, 13:34:49, Finish: 2024-11-27 08:01 [11-26 10:26:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 0/1669] eta: 0:18:11 tlr: 8.9e-05 tnm: 0.42 Lm: 6.752 (6.752) Lt: 6.047 (6.047) Accm: 2.32 (2.32) Acct: 3.70 (3.70) proj_loss: -0.6001 (-0.6001) time: 0.6541 data: 0.0004 [11-26 10:26:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 0/1669] eta: 0:18:12 tlr: 8.9e-05 tnm: 0.42 Lm: 6.331 (6.331) Lt: 5.535 (5.535) Accm: 3.63 (3.63) Acct: 5.87 (5.87) proj_loss: -0.6323 (-0.6323) time: 0.6544 data: 0.0004 [11-26 10:26:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 0/1669] eta: 0:19:07 tlr: 8.9e-05 tnm: 0.42 Lm: 6.373 (6.373) Lt: 5.594 (5.594) Accm: 4.14 (4.14) Acct: 6.75 (6.75) proj_loss: -0.5865 (-0.5865) time: 0.6874 data: 0.0003 [11-26 10:26:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 0/1669] eta: 0:18:12 tlr: 8.9e-05 tnm: 0.42 Lm: 6.381 (6.381) Lt: 5.676 (5.676) Accm: 3.80 (3.80) Acct: 5.97 (5.97) proj_loss: -0.6143 (-0.6143) time: 0.6548 data: 0.0004 [11-26 10:31:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 417/1669] eta: 0:14:05 tlr: 8.9e-05 tnm: 0.42 Lm: 6.443 (6.443) Lt: 5.736 (5.736) Accm: 3.46 (3.46) Acct: 5.31 (5.31) proj_loss: -0.6101 (-0.6101) time: 0.6770 data: 0.0003 [11-26 10:31:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 417/1669] eta: 0:14:05 tlr: 8.9e-05 tnm: 0.42 Lm: 6.612 (6.612) Lt: 5.877 (5.877) Accm: 2.96 (2.96) Acct: 4.68 (4.68) proj_loss: -0.5934 (-0.5934) time: 0.6770 data: 0.0003 [11-26 10:31:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 417/1669] eta: 0:14:05 tlr: 8.9e-05 tnm: 0.42 Lm: 6.498 (6.498) Lt: 5.696 (5.696) Accm: 3.32 (3.32) Acct: 5.46 (5.46) proj_loss: -0.6187 (-0.6187) time: 0.6770 data: 0.0003 [11-26 10:31:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 417/1669] eta: 0:14:05 tlr: 8.9e-05 tnm: 0.42 Lm: 6.452 (6.452) Lt: 5.693 (5.693) Accm: 3.81 (3.81) Acct: 6.05 (6.05) proj_loss: -0.5977 (-0.5977) time: 0.6770 data: 0.0003 [11-26 10:36:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 834/1669] eta: 0:09:30 tlr: 8.9e-05 tnm: 0.43 Lm: 6.531 (6.483) Lt: 5.792 (5.730) Accm: 3.49 (3.59) Acct: 5.35 (5.66) proj_loss: -0.6089 (-0.6025) time: 0.6789 data: 0.0003 [11-26 10:36:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 834/1669] eta: 0:09:30 tlr: 8.9e-05 tnm: 0.43 Lm: 6.406 (6.431) Lt: 5.676 (5.715) Accm: 3.80 (3.61) Acct: 5.92 (5.52) proj_loss: -0.6060 (-0.6016) time: 0.6789 data: 0.0003 [11-26 10:36:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 834/1669] eta: 0:09:30 tlr: 8.9e-05 tnm: 0.43 Lm: 6.530 (6.585) Lt: 5.733 (5.829) Accm: 3.53 (3.15) Acct: 5.66 (5.07) proj_loss: -0.6001 (-0.5967) time: 0.6789 data: 0.0003 [11-26 10:36:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 834/1669] eta: 0:09:30 tlr: 8.9e-05 tnm: 0.43 Lm: 6.573 (6.523) Lt: 5.813 (5.735) Accm: 3.29 (3.31) Acct: 5.17 (5.36) proj_loss: -0.6052 (-0.6128) time: 0.6789 data: 0.0003 [11-26 10:41:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1251/1669] eta: 0:04:44 tlr: 8.9e-05 tnm: 0.41 Lm: 6.453 (6.476) Lt: 5.686 (5.691) Accm: 3.41 (3.37) Acct: 5.41 (5.44) proj_loss: -0.6124 (-0.6145) time: 0.6773 data: 0.0003 [11-26 10:41:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1251/1669] eta: 0:04:44 tlr: 8.9e-05 tnm: 0.41 Lm: 6.534 (6.573) Lt: 5.760 (5.819) Accm: 3.27 (3.11) Acct: 5.17 (4.98) proj_loss: -0.6017 (-0.5984) time: 0.6773 data: 0.0003 [11-26 10:41:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1251/1669] eta: 0:04:44 tlr: 8.9e-05 tnm: 0.41 Lm: 6.455 (6.459) Lt: 5.736 (5.749) Accm: 3.59 (3.55) Acct: 5.63 (5.47) proj_loss: -0.6045 (-0.6019) time: 0.6773 data: 0.0003 [11-26 10:41:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1251/1669] eta: 0:04:44 tlr: 8.9e-05 tnm: 0.41 Lm: 6.537 (6.524) Lt: 5.798 (5.778) Accm: 3.31 (3.47) Acct: 5.17 (5.49) proj_loss: -0.6014 (-0.6003) time: 0.6773 data: 0.0003 [11-26 10:45:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.43 Lm: 6.543 (6.530) Lt: 5.803 (5.792) Accm: 3.48 (3.48) Acct: 5.08 (5.41) proj_loss: -0.6043 (-0.6011) time: 0.6784 data: 0.0018 [11-26 10:45:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 231/350] Total time: 0:18:54 (0.680 s / it) [11-26 10:45:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.43 Lm: 6.548 (6.490) Lt: 5.784 (5.710) Accm: 3.29 (3.32) Acct: 5.17 (5.31) proj_loss: -0.6075 (-0.6131) time: 0.6784 data: 0.0017 [11-26 10:45:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.43 Lm: 6.537 (6.597) Lt: 5.787 (5.856) Accm: 3.02 (3.05) Acct: 4.68 (4.85) proj_loss: -0.6033 (-0.6023) time: 0.6784 data: 0.0016 [11-26 10:45:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.43 Lm: 6.505 (6.486) Lt: 5.796 (5.762) Accm: 3.39 (3.47) Acct: 5.34 (5.43) proj_loss: -0.6060 (-0.6033) time: 0.6784 data: 0.0015 [11-26 10:45:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 231/350] Total time: 0:18:54 (0.680 s / it) [11-26 10:45:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 231/350] Total time: 0:18:54 (0.680 s / it) [11-26 10:45:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 231/350] Total time: 0:18:54 (0.680 s / it) [11-26 10:45:42] (/home/user/VAR/train.py , line 279)=> [ep231] (training ) Lm: 6.479 (6.487), Lt: 5.720 (5.735), Acc m&t: 3.45 5.44, Remain: 1 day, 13:27:11, Finish: 2024-11-27 08:12 [11-26 10:45:42] (/home/user/VAR/train.py , line 279)=> [ep231] (training ) Lm: 6.479 (6.487), Lt: 5.720 (5.735), Acc m&t: 3.45 5.44, Remain: 1 day, 13:26:57, Finish: 2024-11-27 08:12 [11-26 10:45:42] (/home/user/VAR/train.py , line 279)=> [ep231] (training ) Lm: 6.479 (6.487), Lt: 5.720 (5.735), Acc m&t: 3.45 5.44, Remain: 1 day, 13:27:02, Finish: 2024-11-27 08:12 [11-26 10:45:42] (/home/user/VAR/train.py , line 279)=> [ep231] (training ) Lm: 6.479 (6.487), Lt: 5.720 (5.735), Acc m&t: 3.45 5.44, Remain: 1 day, 13:27:06, Finish: 2024-11-27 08:12 [11-26 10:45:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 0/1669] eta: 0:18:25 tlr: 8.8e-05 tnm: 0.44 Lm: 6.509 (6.509) Lt: 5.747 (5.747) Accm: 3.11 (3.11) Acct: 4.68 (4.68) proj_loss: -0.6060 (-0.6060) time: 0.6624 data: 0.0003 [11-26 10:45:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 0/1669] eta: 0:18:26 tlr: 8.8e-05 tnm: 0.44 Lm: 6.559 (6.559) Lt: 5.860 (5.860) Accm: 3.06 (3.06) Acct: 4.49 (4.49) proj_loss: -0.6110 (-0.6110) time: 0.6632 data: 0.0003 [11-26 10:45:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 0/1669] eta: 0:18:27 tlr: 8.8e-05 tnm: 0.44 Lm: 6.555 (6.555) Lt: 5.771 (5.771) Accm: 2.97 (2.97) Acct: 4.77 (4.77) proj_loss: -0.5887 (-0.5887) time: 0.6633 data: 0.0004 [11-26 10:45:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 0/1669] eta: 0:18:27 tlr: 8.8e-05 tnm: 0.44 Lm: 6.473 (6.473) Lt: 5.742 (5.742) Accm: 3.57 (3.57) Acct: 5.65 (5.65) proj_loss: -0.6161 (-0.6161) time: 0.6636 data: 0.0003 [11-26 10:50:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 417/1669] eta: 0:14:07 tlr: 8.8e-05 tnm: 0.40 Lm: 6.506 (6.506) Lt: 5.788 (5.788) Accm: 3.51 (3.51) Acct: 5.41 (5.41) proj_loss: -0.6056 (-0.6056) time: 0.6781 data: 0.0003 [11-26 10:50:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 417/1669] eta: 0:14:07 tlr: 8.8e-05 tnm: 0.40 Lm: 6.491 (6.491) Lt: 5.755 (5.755) Accm: 3.42 (3.42) Acct: 5.14 (5.14) proj_loss: -0.6056 (-0.6056) time: 0.6781 data: 0.0003 [11-26 10:50:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 417/1669] eta: 0:14:07 tlr: 8.8e-05 tnm: 0.40 Lm: 6.480 (6.480) Lt: 5.726 (5.726) Accm: 3.29 (3.29) Acct: 5.24 (5.24) proj_loss: -0.5962 (-0.5962) time: 0.6781 data: 0.0003 [11-26 10:50:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 417/1669] eta: 0:14:07 tlr: 8.8e-05 tnm: 0.40 Lm: 6.592 (6.592) Lt: 5.851 (5.851) Accm: 2.94 (2.94) Acct: 4.43 (4.43) proj_loss: -0.6015 (-0.6015) time: 0.6781 data: 0.0003 [11-26 10:55:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 834/1669] eta: 0:09:25 tlr: 8.8e-05 tnm: 0.40 Lm: 6.509 (6.521) Lt: 5.747 (5.773) Accm: 3.11 (3.20) Acct: 4.68 (4.91) proj_loss: -0.6060 (-0.6076) time: 0.6761 data: 0.0003 [11-26 10:55:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 834/1669] eta: 0:09:25 tlr: 8.8e-05 tnm: 0.40 Lm: 6.555 (6.527) Lt: 5.771 (5.783) Accm: 2.97 (3.18) Acct: 4.96 (5.15) proj_loss: -0.6038 (-0.5988) time: 0.6761 data: 0.0003 [11-26 10:55:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 834/1669] eta: 0:09:25 tlr: 8.8e-05 tnm: 0.40 Lm: 6.559 (6.523) Lt: 5.852 (5.787) Accm: 3.15 (3.33) Acct: 5.03 (5.10) proj_loss: -0.6110 (-0.6101) time: 0.6761 data: 0.0003 [11-26 10:55:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 834/1669] eta: 0:09:25 tlr: 8.8e-05 tnm: 0.40 Lm: 6.473 (6.424) Lt: 5.742 (5.674) Accm: 3.57 (3.75) Acct: 5.65 (5.85) proj_loss: -0.5951 (-0.5956) time: 0.6762 data: 0.0003 [11-26 10:59:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1251/1669] eta: 0:04:45 tlr: 8.8e-05 tnm: 0.41 Lm: 6.506 (6.467) Lt: 5.788 (5.721) Accm: 3.51 (3.59) Acct: 5.41 (5.67) proj_loss: -0.5906 (-0.5932) time: 0.6761 data: 0.0002 [11-26 10:59:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1251/1669] eta: 0:04:45 tlr: 8.8e-05 tnm: 0.41 Lm: 6.554 (6.529) Lt: 5.803 (5.779) Accm: 3.28 (3.35) Acct: 5.29 (5.21) proj_loss: -0.6056 (-0.6069) time: 0.6761 data: 0.0002 [11-26 10:59:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1251/1669] eta: 0:04:45 tlr: 8.8e-05 tnm: 0.41 Lm: 6.589 (6.554) Lt: 5.835 (5.813) Accm: 2.99 (3.14) Acct: 4.86 (5.04) proj_loss: -0.5995 (-0.5979) time: 0.6761 data: 0.0003 [11-26 10:59:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1251/1669] eta: 0:04:45 tlr: 8.8e-05 tnm: 0.41 Lm: 6.450 (6.489) Lt: 5.712 (5.748) Accm: 3.42 (3.34) Acct: 5.23 (5.13) proj_loss: -0.6129 (-0.6125) time: 0.6761 data: 0.0003 [11-26 11:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.43 Lm: 6.509 (6.512) Lt: 5.747 (5.768) Accm: 3.15 (3.31) Acct: 5.25 (5.15) proj_loss: -0.6060 (-0.6026) time: 0.6782 data: 0.0019 [11-26 11:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.43 Lm: 6.548 (6.512) Lt: 5.754 (5.760) Accm: 3.41 (3.44) Acct: 5.54 (5.37) proj_loss: -0.6002 (-0.6047) time: 0.6782 data: 0.0016 [11-26 11:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.43 Lm: 6.555 (6.513) Lt: 5.771 (5.770) Accm: 3.01 (3.35) Acct: 4.96 (5.40) proj_loss: -0.6038 (-0.5994) time: 0.6782 data: 0.0015 [11-26 11:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.43 Lm: 6.538 (6.482) Lt: 5.768 (5.730) Accm: 3.46 (3.49) Acct: 5.17 (5.55) proj_loss: -0.5951 (-0.5956) time: 0.6782 data: 0.0017 [11-26 11:04:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 232/350] Total time: 0:18:56 (0.681 s / it) [11-26 11:04:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 232/350] Total time: 0:18:56 (0.681 s / it) [11-26 11:04:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 232/350] Total time: 0:18:56 (0.681 s / it) [11-26 11:04:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 232/350] Total time: 0:18:56 (0.681 s / it) [11-26 11:04:39] (/home/user/VAR/train.py , line 279)=> [ep232] (training ) Lm: 6.479 (6.479), Lt: 5.720 (5.728), Acc m&t: 3.47 5.46, Remain: 1 day, 13:07:40, Finish: 2024-11-27 08:12 [11-26 11:04:39] (/home/user/VAR/train.py , line 279)=> [ep232] (training ) Lm: 6.479 (6.479), Lt: 5.720 (5.728), Acc m&t: 3.47 5.46, Remain: 1 day, 13:07:53, Finish: 2024-11-27 08:12 [11-26 11:04:39] (/home/user/VAR/train.py , line 279)=> [ep232] (training ) Lm: 6.479 (6.479), Lt: 5.720 (5.728), Acc m&t: 3.47 5.46, Remain: 1 day, 13:08:06, Finish: 2024-11-27 08:12 [11-26 11:04:39] (/home/user/VAR/train.py , line 279)=> [ep232] (training ) Lm: 6.479 (6.479), Lt: 5.720 (5.728), Acc m&t: 3.47 5.46, Remain: 1 day, 13:07:39, Finish: 2024-11-27 08:12 [11-26 11:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 0/1669] eta: 0:18:29 tlr: 8.8e-05 tnm: 0.43 Lm: 6.496 (6.496) Lt: 5.813 (5.813) Accm: 3.29 (3.29) Acct: 4.79 (4.79) proj_loss: -0.6129 (-0.6129) time: 0.6647 data: 0.0003 [11-26 11:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 0/1669] eta: 0:18:33 tlr: 8.8e-05 tnm: 0.43 Lm: 6.517 (6.517) Lt: 5.786 (5.786) Accm: 3.29 (3.29) Acct: 5.29 (5.29) proj_loss: -0.6004 (-0.6004) time: 0.6675 data: 0.0003 [11-26 11:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 0/1669] eta: 0:18:34 tlr: 8.8e-05 tnm: 0.43 Lm: 6.752 (6.752) Lt: 6.002 (6.002) Accm: 2.93 (2.93) Acct: 4.96 (4.96) proj_loss: -0.5969 (-0.5969) time: 0.6676 data: 0.0004 [11-26 11:04:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 0/1669] eta: 0:18:24 tlr: 8.8e-05 tnm: 0.43 Lm: 6.559 (6.559) Lt: 5.795 (5.795) Accm: 3.32 (3.32) Acct: 5.30 (5.30) proj_loss: -0.6146 (-0.6146) time: 0.6619 data: 0.0004 [11-26 11:09:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 417/1669] eta: 0:14:07 tlr: 8.8e-05 tnm: 0.42 Lm: 6.429 (6.429) Lt: 5.639 (5.639) Accm: 3.71 (3.71) Acct: 5.95 (5.95) proj_loss: -0.6115 (-0.6115) time: 0.6775 data: 0.0003 [11-26 11:09:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 417/1669] eta: 0:14:07 tlr: 8.8e-05 tnm: 0.42 Lm: 6.526 (6.526) Lt: 5.799 (5.799) Accm: 3.27 (3.27) Acct: 5.08 (5.08) proj_loss: -0.6223 (-0.6223) time: 0.6775 data: 0.0003 [11-26 11:09:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 417/1669] eta: 0:14:07 tlr: 8.8e-05 tnm: 0.42 Lm: 6.508 (6.508) Lt: 5.706 (5.706) Accm: 3.44 (3.44) Acct: 5.63 (5.63) proj_loss: -0.6017 (-0.6017) time: 0.6775 data: 0.0003 [11-26 11:09:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 417/1669] eta: 0:14:07 tlr: 8.8e-05 tnm: 0.42 Lm: 6.337 (6.337) Lt: 5.576 (5.576) Accm: 4.01 (4.01) Acct: 6.34 (6.34) proj_loss: -0.6071 (-0.6071) time: 0.6775 data: 0.0003 [11-26 11:14:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 834/1669] eta: 0:09:24 tlr: 8.7e-05 tnm: 0.41 Lm: 6.402 (6.358) Lt: 5.577 (5.576) Accm: 3.72 (3.91) Acct: 5.82 (6.16) proj_loss: -0.6131 (-0.6091) time: 0.6761 data: 0.0003 [11-26 11:14:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 834/1669] eta: 0:09:24 tlr: 8.7e-05 tnm: 0.41 Lm: 6.496 (6.455) Lt: 5.785 (5.714) Accm: 3.29 (3.45) Acct: 5.37 (5.35) proj_loss: -0.6130 (-0.6192) time: 0.6761 data: 0.0003 [11-26 11:14:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 834/1669] eta: 0:09:24 tlr: 8.7e-05 tnm: 0.41 Lm: 6.512 (6.457) Lt: 5.794 (5.691) Accm: 3.42 (3.62) Acct: 5.30 (5.68) proj_loss: -0.6083 (-0.6086) time: 0.6761 data: 0.0002 [11-26 11:14:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 834/1669] eta: 0:09:24 tlr: 8.7e-05 tnm: 0.41 Lm: 6.559 (6.525) Lt: 5.786 (5.733) Accm: 3.13 (3.34) Acct: 4.96 (5.35) proj_loss: -0.6065 (-0.6036) time: 0.6761 data: 0.0002 [11-26 11:18:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1251/1669] eta: 0:04:42 tlr: 8.7e-05 tnm: 0.42 Lm: 6.587 (6.547) Lt: 5.814 (5.760) Accm: 3.03 (3.22) Acct: 4.87 (5.11) proj_loss: -0.6017 (-0.5982) time: 0.6741 data: 0.0002 [11-26 11:18:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1251/1669] eta: 0:04:42 tlr: 8.7e-05 tnm: 0.42 Lm: 6.526 (6.484) Lt: 5.799 (5.751) Accm: 3.27 (3.34) Acct: 5.08 (5.14) proj_loss: -0.6175 (-0.6199) time: 0.6741 data: 0.0003 [11-26 11:18:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1251/1669] eta: 0:04:42 tlr: 8.7e-05 tnm: 0.42 Lm: 6.439 (6.388) Lt: 5.670 (5.623) Accm: 3.61 (3.81) Acct: 5.66 (6.00) proj_loss: -0.6135 (-0.6132) time: 0.6741 data: 0.0003 [11-26 11:18:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1251/1669] eta: 0:04:42 tlr: 8.7e-05 tnm: 0.42 Lm: 6.511 (6.470) Lt: 5.771 (5.705) Accm: 3.42 (3.57) Acct: 5.41 (5.64) proj_loss: -0.6115 (-0.6126) time: 0.6741 data: 0.0002 [11-26 11:23:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1668/1669] eta: 0:00:00 tlr: 8.7e-05 tnm: 0.41 Lm: 6.512 (6.492) Lt: 5.794 (5.733) Accm: 3.42 (3.43) Acct: 5.30 (5.44) proj_loss: -0.6146 (-0.6135) time: 0.7225 data: 0.0015 [11-26 11:23:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 233/350] Total time: 0:18:51 (0.678 s / it) [11-26 11:23:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1668/1669] eta: 0:00:00 tlr: 8.7e-05 tnm: 0.41 Lm: 6.476 (6.437) Lt: 5.763 (5.682) Accm: 3.50 (3.63) Acct: 5.51 (5.74) proj_loss: -0.6131 (-0.6118) time: 0.7225 data: 0.0018 [11-26 11:23:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1668/1669] eta: 0:00:00 tlr: 8.7e-05 tnm: 0.41 Lm: 6.559 (6.540) Lt: 5.786 (5.752) Accm: 3.13 (3.25) Acct: 4.96 (5.13) proj_loss: -0.6065 (-0.6019) time: 0.7225 data: 0.0019 [11-26 11:23:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1668/1669] eta: 0:00:00 tlr: 8.7e-05 tnm: 0.41 Lm: 6.496 (6.485) Lt: 5.796 (5.760) Accm: 3.29 (3.37) Acct: 5.11 (5.14) proj_loss: -0.6219 (-0.6223) time: 0.7225 data: 0.0018 [11-26 11:23:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 233/350] Total time: 0:18:51 (0.678 s / it) [11-26 11:23:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 233/350] Total time: 0:18:51 (0.678 s / it) [11-26 11:23:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 233/350] Total time: 0:18:51 (0.678 s / it) [11-26 11:23:30] (/home/user/VAR/train.py , line 279)=> [ep233] (training ) Lm: 6.479 (6.482), Lt: 5.720 (5.729), Acc m&t: 3.47 5.46, Remain: 1 day, 12:46:18, Finish: 2024-11-27 08:09 [11-26 11:23:30] (/home/user/VAR/train.py , line 279)=> [ep233] (training ) Lm: 6.479 (6.482), Lt: 5.720 (5.729), Acc m&t: 3.47 5.46, Remain: 1 day, 12:46:16, Finish: 2024-11-27 08:09 [11-26 11:23:30] (/home/user/VAR/train.py , line 279)=> [ep233] (training ) Lm: 6.479 (6.482), Lt: 5.720 (5.729), Acc m&t: 3.47 5.46, Remain: 1 day, 12:46:37, Finish: 2024-11-27 08:10 [11-26 11:23:30] (/home/user/VAR/train.py , line 279)=> [ep233] (training ) Lm: 6.479 (6.482), Lt: 5.720 (5.729), Acc m&t: 3.47 5.46, Remain: 1 day, 12:46:58, Finish: 2024-11-27 08:10 [11-26 11:23:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 0/1669] eta: 0:18:09 tlr: 8.7e-05 tnm: 0.45 Lm: 6.366 (6.366) Lt: 5.600 (5.600) Accm: 3.69 (3.69) Acct: 5.65 (5.65) proj_loss: -0.6099 (-0.6099) time: 0.6530 data: 0.0003 [11-26 11:23:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 0/1669] eta: 0:18:56 tlr: 8.7e-05 tnm: 0.45 Lm: 6.620 (6.620) Lt: 5.923 (5.923) Accm: 2.91 (2.91) Acct: 4.46 (4.46) proj_loss: -0.6020 (-0.6020) time: 0.6809 data: 0.0004 [11-26 11:23:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 0/1669] eta: 0:18:49 tlr: 8.7e-05 tnm: 0.45 Lm: 6.560 (6.560) Lt: 5.800 (5.800) Accm: 3.51 (3.51) Acct: 5.54 (5.54) proj_loss: -0.6090 (-0.6090) time: 0.6770 data: 0.0004 [11-26 11:23:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 0/1669] eta: 0:19:00 tlr: 8.7e-05 tnm: 0.45 Lm: 6.433 (6.433) Lt: 5.653 (5.653) Accm: 3.70 (3.70) Acct: 6.30 (6.30) proj_loss: -0.6136 (-0.6136) time: 0.6831 data: 0.0004 [11-26 11:28:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 417/1669] eta: 0:14:32 tlr: 8.7e-05 tnm: 0.43 Lm: 6.450 (6.450) Lt: 5.677 (5.677) Accm: 3.51 (3.51) Acct: 5.83 (5.83) proj_loss: -0.6064 (-0.6064) time: 0.6784 data: 0.0003 [11-26 11:28:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 417/1669] eta: 0:14:32 tlr: 8.7e-05 tnm: 0.43 Lm: 6.489 (6.489) Lt: 5.806 (5.806) Accm: 3.33 (3.33) Acct: 5.00 (5.00) proj_loss: -0.6215 (-0.6215) time: 0.6784 data: 0.0003 [11-26 11:28:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 417/1669] eta: 0:14:32 tlr: 8.7e-05 tnm: 0.43 Lm: 6.490 (6.490) Lt: 5.770 (5.770) Accm: 3.35 (3.35) Acct: 5.14 (5.14) proj_loss: -0.6160 (-0.6160) time: 0.6784 data: 0.0002 [11-26 11:28:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 417/1669] eta: 0:14:32 tlr: 8.7e-05 tnm: 0.43 Lm: 6.577 (6.577) Lt: 5.810 (5.810) Accm: 3.23 (3.23) Acct: 5.21 (5.21) proj_loss: -0.6049 (-0.6049) time: 0.6784 data: 0.0003 [11-26 11:33:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 834/1669] eta: 0:09:32 tlr: 8.7e-05 tnm: 0.43 Lm: 6.560 (6.508) Lt: 5.800 (5.756) Accm: 3.51 (3.38) Acct: 5.32 (5.25) proj_loss: -0.6090 (-0.6076) time: 0.6775 data: 0.0003 [11-26 11:33:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 834/1669] eta: 0:09:32 tlr: 8.7e-05 tnm: 0.43 Lm: 6.454 (6.477) Lt: 5.700 (5.771) Accm: 3.25 (3.30) Acct: 4.91 (4.97) proj_loss: -0.6020 (-0.6135) time: 0.6775 data: 0.0003 [11-26 11:33:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 834/1669] eta: 0:09:32 tlr: 8.7e-05 tnm: 0.43 Lm: 6.366 (6.406) Lt: 5.600 (5.665) Accm: 3.69 (3.65) Acct: 5.65 (5.57) proj_loss: -0.6099 (-0.6118) time: 0.6775 data: 0.0002 [11-26 11:33:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 834/1669] eta: 0:09:32 tlr: 8.7e-05 tnm: 0.43 Lm: 6.433 (6.400) Lt: 5.653 (5.649) Accm: 3.70 (3.73) Acct: 6.30 (6.03) proj_loss: -0.6128 (-0.6086) time: 0.6775 data: 0.0003 [11-26 11:37:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1251/1669] eta: 0:04:45 tlr: 8.6e-05 tnm: 0.42 Lm: 6.450 (6.444) Lt: 5.677 (5.691) Accm: 3.51 (3.59) Acct: 5.83 (5.70) proj_loss: -0.6060 (-0.6034) time: 0.6754 data: 0.0003 [11-26 11:37:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1251/1669] eta: 0:04:45 tlr: 8.6e-05 tnm: 0.42 Lm: 6.471 (6.480) Lt: 5.719 (5.763) Accm: 3.24 (3.28) Acct: 5.07 (5.04) proj_loss: -0.6031 (-0.6112) time: 0.6754 data: 0.0002 [11-26 11:37:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1251/1669] eta: 0:04:45 tlr: 8.6e-05 tnm: 0.42 Lm: 6.466 (6.453) Lt: 5.725 (5.707) Accm: 3.59 (3.45) Acct: 5.43 (5.32) proj_loss: -0.6111 (-0.6133) time: 0.6754 data: 0.0003 [11-26 11:37:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1251/1669] eta: 0:04:45 tlr: 8.6e-05 tnm: 0.42 Lm: 6.401 (6.414) Lt: 5.649 (5.673) Accm: 3.56 (3.60) Acct: 5.39 (5.46) proj_loss: -0.6160 (-0.6155) time: 0.6754 data: 0.0003 [11-26 11:42:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.41 Lm: 6.436 (6.443) Lt: 5.697 (5.710) Accm: 3.43 (3.46) Acct: 5.13 (5.23) proj_loss: -0.6099 (-0.6127) time: 0.6791 data: 0.0018 [11-26 11:42:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 234/350] Total time: 0:18:57 (0.681 s / it) [11-26 11:42:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.41 Lm: 6.438 (6.443) Lt: 5.701 (5.706) Accm: 3.41 (3.55) Acct: 5.35 (5.60) proj_loss: -0.5993 (-0.6014) time: 0.6791 data: 0.0016 [11-26 11:42:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.41 Lm: 6.454 (6.461) Lt: 5.700 (5.738) Accm: 3.25 (3.37) Acct: 5.23 (5.15) proj_loss: -0.6042 (-0.6128) time: 0.6791 data: 0.0017 [11-26 11:42:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.41 Lm: 6.560 (6.499) Lt: 5.800 (5.755) Accm: 3.51 (3.36) Acct: 5.32 (5.18) proj_loss: -0.6132 (-0.6135) time: 0.6791 data: 0.0017 [11-26 11:42:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 234/350] Total time: 0:18:57 (0.681 s / it) [11-26 11:42:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 234/350] Total time: 0:18:57 (0.681 s / it) [11-26 11:42:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 234/350] Total time: 0:18:57 (0.681 s / it) [11-26 11:42:27] (/home/user/VAR/train.py , line 279)=> [ep234] (training ) Lm: 6.476 (6.476), Lt: 5.720 (5.728), Acc m&t: 3.47 5.46, Remain: 1 day, 12:34:29, Finish: 2024-11-27 08:16 [11-26 11:42:27] (/home/user/VAR/train.py , line 279)=> [ep234] (training ) Lm: 6.476 (6.476), Lt: 5.720 (5.728), Acc m&t: 3.47 5.46, Remain: 1 day, 12:34:02, Finish: 2024-11-27 08:16 [11-26 11:42:27] (/home/user/VAR/train.py , line 279)=> [ep234] (training ) Lm: 6.476 (6.476), Lt: 5.720 (5.728), Acc m&t: 3.47 5.46, Remain: 1 day, 12:33:53, Finish: 2024-11-27 08:16 [11-26 11:42:27] (/home/user/VAR/train.py , line 279)=> [ep234] (training ) Lm: 6.476 (6.476), Lt: 5.720 (5.728), Acc m&t: 3.47 5.46, Remain: 1 day, 12:34:19, Finish: 2024-11-27 08:16 [11-26 11:42:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 0/1669] eta: 0:18:32 tlr: 8.6e-05 tnm: 0.42 Lm: 6.554 (6.554) Lt: 5.814 (5.814) Accm: 3.39 (3.39) Acct: 5.39 (5.39) proj_loss: -0.6008 (-0.6008) time: 0.6668 data: 0.0004 [11-26 11:42:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 0/1669] eta: 0:18:31 tlr: 8.6e-05 tnm: 0.42 Lm: 6.421 (6.421) Lt: 5.620 (5.620) Accm: 3.46 (3.46) Acct: 5.41 (5.41) proj_loss: -0.5748 (-0.5748) time: 0.6661 data: 0.0004 [11-26 11:42:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 0/1669] eta: 0:18:25 tlr: 8.6e-05 tnm: 0.42 Lm: 6.405 (6.405) Lt: 5.679 (5.679) Accm: 3.87 (3.87) Acct: 5.89 (5.89) proj_loss: -0.6116 (-0.6116) time: 0.6622 data: 0.0004 [11-26 11:42:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 0/1669] eta: 0:18:25 tlr: 8.6e-05 tnm: 0.42 Lm: 6.475 (6.475) Lt: 5.746 (5.746) Accm: 3.51 (3.51) Acct: 5.51 (5.51) proj_loss: -0.6059 (-0.6059) time: 0.6624 data: 0.0004 [11-26 11:47:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 417/1669] eta: 0:14:42 tlr: 8.6e-05 tnm: 0.42 Lm: 6.495 (6.495) Lt: 5.733 (5.733) Accm: 3.36 (3.36) Acct: 5.45 (5.45) proj_loss: -0.6065 (-0.6065) time: 0.6763 data: 0.0003 [11-26 11:47:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 417/1669] eta: 0:14:42 tlr: 8.6e-05 tnm: 0.42 Lm: 6.487 (6.487) Lt: 5.699 (5.699) Accm: 3.39 (3.39) Acct: 5.27 (5.27) proj_loss: -0.5931 (-0.5931) time: 0.6763 data: 0.0002 [11-26 11:47:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 417/1669] eta: 0:14:42 tlr: 8.6e-05 tnm: 0.42 Lm: 6.462 (6.462) Lt: 5.711 (5.711) Accm: 3.60 (3.60) Acct: 5.61 (5.61) proj_loss: -0.6151 (-0.6151) time: 0.6763 data: 0.0002 [11-26 11:47:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 417/1669] eta: 0:14:42 tlr: 8.6e-05 tnm: 0.42 Lm: 6.502 (6.502) Lt: 5.762 (5.762) Accm: 3.38 (3.38) Acct: 5.17 (5.17) proj_loss: -0.6078 (-0.6078) time: 0.6763 data: 0.0003 [11-26 11:52:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 834/1669] eta: 0:09:37 tlr: 8.6e-05 tnm: 0.41 Lm: 6.487 (6.497) Lt: 5.722 (5.749) Accm: 3.39 (3.41) Acct: 5.39 (5.27) proj_loss: -0.6008 (-0.6049) time: 0.6748 data: 0.0003 [11-26 11:52:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 834/1669] eta: 0:09:37 tlr: 8.6e-05 tnm: 0.41 Lm: 6.488 (6.471) Lt: 5.707 (5.710) Accm: 3.34 (3.47) Acct: 5.34 (5.46) proj_loss: -0.6116 (-0.6129) time: 0.6748 data: 0.0003 [11-26 11:52:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 834/1669] eta: 0:09:37 tlr: 8.6e-05 tnm: 0.41 Lm: 6.421 (6.456) Lt: 5.620 (5.668) Accm: 3.46 (3.48) Acct: 5.41 (5.38) proj_loss: -0.6114 (-0.6060) time: 0.6748 data: 0.0002 [11-26 11:52:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 834/1669] eta: 0:09:37 tlr: 8.6e-05 tnm: 0.41 Lm: 6.475 (6.486) Lt: 5.720 (5.728) Accm: 3.51 (3.42) Acct: 5.51 (5.50) proj_loss: -0.6071 (-0.6073) time: 0.6748 data: 0.0003 [11-26 11:56:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1251/1669] eta: 0:04:46 tlr: 8.6e-05 tnm: 0.42 Lm: 6.495 (6.519) Lt: 5.733 (5.773) Accm: 3.36 (3.34) Acct: 5.45 (5.34) proj_loss: -0.6079 (-0.6133) time: 0.6783 data: 0.0003 [11-26 11:56:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1251/1669] eta: 0:04:46 tlr: 8.6e-05 tnm: 0.42 Lm: 6.503 (6.487) Lt: 5.725 (5.737) Accm: 3.27 (3.41) Acct: 5.25 (5.36) proj_loss: -0.6100 (-0.6070) time: 0.6783 data: 0.0003 [11-26 11:56:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1251/1669] eta: 0:04:46 tlr: 8.6e-05 tnm: 0.42 Lm: 6.458 (6.466) Lt: 5.694 (5.693) Accm: 3.39 (3.35) Acct: 5.27 (5.21) proj_loss: -0.5995 (-0.6015) time: 0.6783 data: 0.0002 [11-26 11:56:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1251/1669] eta: 0:04:46 tlr: 8.6e-05 tnm: 0.42 Lm: 6.489 (6.495) Lt: 5.726 (5.744) Accm: 3.41 (3.41) Acct: 5.44 (5.34) proj_loss: -0.6027 (-0.6048) time: 0.6783 data: 0.0003 [11-26 12:01:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.42 Lm: 6.490 (6.528) Lt: 5.730 (5.773) Accm: 3.39 (3.32) Acct: 5.39 (5.20) proj_loss: -0.6008 (-0.6038) time: 0.6765 data: 0.0018 [11-26 12:01:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 235/350] Total time: 0:19:01 (0.684 s / it) [11-26 12:01:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.42 Lm: 6.495 (6.481) Lt: 5.768 (5.718) Accm: 3.31 (3.28) Acct: 5.13 (5.09) proj_loss: -0.6099 (-0.6031) time: 0.6765 data: 0.0015 [11-26 12:01:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.42 Lm: 6.488 (6.486) Lt: 5.744 (5.745) Accm: 3.23 (3.37) Acct: 5.17 (5.32) proj_loss: -0.6084 (-0.6043) time: 0.6764 data: 0.0019 [11-26 12:01:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.42 Lm: 6.515 (6.531) Lt: 5.746 (5.790) Accm: 3.21 (3.27) Acct: 5.39 (5.21) proj_loss: -0.6087 (-0.6124) time: 0.6765 data: 0.0015 [11-26 12:01:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 235/350] Total time: 0:19:01 (0.684 s / it) [11-26 12:01:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 235/350] Total time: 0:19:01 (0.684 s / it) [11-26 12:01:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 235/350] Total time: 0:19:01 (0.684 s / it) [11-26 12:01:29] (/home/user/VAR/train.py , line 279)=> [ep235] (training ) Lm: 6.476 (6.480), Lt: 5.720 (5.728), Acc m&t: 3.47 5.46, Remain: 1 day, 12:00:12, Finish: 2024-11-27 08:01 [11-26 12:01:29] (/home/user/VAR/train.py , line 279)=> [ep235] (training ) Lm: 6.476 (6.480), Lt: 5.720 (5.728), Acc m&t: 3.47 5.46, Remain: 1 day, 12:00:09, Finish: 2024-11-27 08:01 [11-26 12:01:29] (/home/user/VAR/train.py , line 279)=> [ep235] (training ) Lm: 6.476 (6.480), Lt: 5.720 (5.728), Acc m&t: 3.47 5.46, Remain: 1 day, 12:00:32, Finish: 2024-11-27 08:02 [11-26 12:01:29] (/home/user/VAR/train.py , line 279)=> [ep235] (training ) Lm: 6.476 (6.480), Lt: 5.720 (5.728), Acc m&t: 3.47 5.46, Remain: 1 day, 12:00:36, Finish: 2024-11-27 08:02 [11-26 12:01:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 0/1669] eta: 0:18:22 tlr: 8.6e-05 tnm: 0.43 Lm: 6.635 (6.635) Lt: 5.889 (5.889) Accm: 2.77 (2.77) Acct: 4.41 (4.41) proj_loss: -0.6087 (-0.6087) time: 0.6606 data: 0.0003 [11-26 12:01:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 0/1669] eta: 0:18:22 tlr: 8.6e-05 tnm: 0.43 Lm: 6.466 (6.466) Lt: 5.718 (5.718) Accm: 3.55 (3.55) Acct: 5.58 (5.58) proj_loss: -0.6025 (-0.6025) time: 0.6605 data: 0.0004 [11-26 12:01:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 0/1669] eta: 0:18:26 tlr: 8.6e-05 tnm: 0.43 Lm: 6.569 (6.569) Lt: 5.812 (5.812) Accm: 3.23 (3.23) Acct: 4.91 (4.91) proj_loss: -0.5966 (-0.5966) time: 0.6629 data: 0.0003 [11-26 12:01:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 0/1669] eta: 0:18:23 tlr: 8.6e-05 tnm: 0.43 Lm: 6.372 (6.372) Lt: 5.613 (5.613) Accm: 3.72 (3.72) Acct: 5.99 (5.99) proj_loss: -0.6246 (-0.6246) time: 0.6614 data: 0.0003 [11-26 12:06:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 417/1669] eta: 0:14:06 tlr: 8.5e-05 tnm: 0.44 Lm: 6.446 (6.446) Lt: 5.668 (5.668) Accm: 3.66 (3.66) Acct: 5.78 (5.78) proj_loss: -0.6123 (-0.6123) time: 0.6763 data: 0.0003 [11-26 12:06:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 417/1669] eta: 0:14:06 tlr: 8.5e-05 tnm: 0.44 Lm: 6.484 (6.484) Lt: 5.748 (5.748) Accm: 3.48 (3.48) Acct: 5.38 (5.38) proj_loss: -0.6144 (-0.6144) time: 0.6763 data: 0.0003 [11-26 12:06:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 417/1669] eta: 0:14:06 tlr: 8.5e-05 tnm: 0.44 Lm: 6.542 (6.542) Lt: 5.767 (5.767) Accm: 3.42 (3.42) Acct: 5.12 (5.12) proj_loss: -0.6007 (-0.6007) time: 0.6763 data: 0.0002 [11-26 12:06:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 417/1669] eta: 0:14:06 tlr: 8.5e-05 tnm: 0.44 Lm: 6.581 (6.581) Lt: 5.859 (5.859) Accm: 3.13 (3.13) Acct: 4.75 (4.75) proj_loss: -0.6182 (-0.6182) time: 0.6763 data: 0.0003 [11-26 12:10:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 834/1669] eta: 0:09:24 tlr: 8.5e-05 tnm: 0.42 Lm: 6.526 (6.526) Lt: 5.828 (5.788) Accm: 3.49 (3.35) Acct: 5.10 (5.13) proj_loss: -0.6276 (-0.6222) time: 0.6787 data: 0.0002 [11-26 12:10:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 834/1669] eta: 0:09:24 tlr: 8.5e-05 tnm: 0.42 Lm: 6.488 (6.485) Lt: 5.718 (5.735) Accm: 3.41 (3.44) Acct: 5.29 (5.35) proj_loss: -0.6041 (-0.6110) time: 0.6787 data: 0.0002 [11-26 12:10:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 834/1669] eta: 0:09:24 tlr: 8.5e-05 tnm: 0.42 Lm: 6.520 (6.536) Lt: 5.722 (5.797) Accm: 3.61 (3.32) Acct: 5.56 (5.19) proj_loss: -0.6137 (-0.6128) time: 0.6787 data: 0.0003 [11-26 12:10:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 834/1669] eta: 0:09:24 tlr: 8.5e-05 tnm: 0.42 Lm: 6.516 (6.433) Lt: 5.723 (5.672) Accm: 3.61 (3.71) Acct: 5.34 (5.67) proj_loss: -0.6049 (-0.6064) time: 0.6787 data: 0.0002 [11-26 12:15:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1251/1669] eta: 0:04:47 tlr: 8.5e-05 tnm: 0.42 Lm: 6.494 (6.443) Lt: 5.735 (5.690) Accm: 3.60 (3.68) Acct: 5.49 (5.66) proj_loss: -0.6061 (-0.6067) time: 0.6762 data: 0.0003 [11-26 12:15:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1251/1669] eta: 0:04:47 tlr: 8.5e-05 tnm: 0.42 Lm: 6.482 (6.504) Lt: 5.749 (5.759) Accm: 3.59 (3.43) Acct: 5.45 (5.30) proj_loss: -0.6182 (-0.6165) time: 0.6762 data: 0.0002 [11-26 12:15:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1251/1669] eta: 0:04:47 tlr: 8.5e-05 tnm: 0.42 Lm: 6.477 (6.472) Lt: 5.714 (5.716) Accm: 3.45 (3.45) Acct: 5.33 (5.35) proj_loss: -0.6074 (-0.6109) time: 0.6762 data: 0.0003 [11-26 12:15:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1251/1669] eta: 0:04:47 tlr: 8.5e-05 tnm: 0.42 Lm: 6.473 (6.508) Lt: 5.732 (5.784) Accm: 3.66 (3.44) Acct: 5.54 (5.27) proj_loss: -0.6069 (-0.6091) time: 0.6762 data: 0.0003 [11-26 12:20:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1668/1669] eta: 0:00:00 tlr: 8.5e-05 tnm: 0.41 Lm: 6.469 (6.500) Lt: 5.742 (5.777) Accm: 3.61 (3.45) Acct: 5.56 (5.34) proj_loss: -0.6047 (-0.6083) time: 0.6766 data: 0.0015 [11-26 12:20:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 236/350] Total time: 0:19:01 (0.684 s / it) [11-26 12:20:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1668/1669] eta: 0:00:00 tlr: 8.5e-05 tnm: 0.41 Lm: 6.447 (6.493) Lt: 5.670 (5.741) Accm: 3.69 (3.50) Acct: 5.80 (5.42) proj_loss: -0.6176 (-0.6167) time: 0.6766 data: 0.0018 [11-26 12:20:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1668/1669] eta: 0:00:00 tlr: 8.5e-05 tnm: 0.41 Lm: 6.466 (6.457) Lt: 5.709 (5.698) Accm: 3.41 (3.40) Acct: 5.29 (5.27) proj_loss: -0.6107 (-0.6116) time: 0.6766 data: 0.0017 [11-26 12:20:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1668/1669] eta: 0:00:00 tlr: 8.5e-05 tnm: 0.41 Lm: 6.473 (6.416) Lt: 5.723 (5.657) Accm: 3.61 (3.77) Acct: 5.65 (5.77) proj_loss: -0.6051 (-0.6063) time: 0.6766 data: 0.0017 [11-26 12:20:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 236/350] Total time: 0:19:01 (0.684 s / it) [11-26 12:20:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 236/350] Total time: 0:19:01 (0.684 s / it) [11-26 12:20:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 236/350] Total time: 0:19:01 (0.684 s / it) [11-26 12:20:31] (/home/user/VAR/train.py , line 279)=> [ep236] (training ) Lm: 6.476 (6.476), Lt: 5.720 (5.725), Acc m&t: 3.48 5.46, Remain: 1 day, 11:41:14, Finish: 2024-11-27 08:01 [11-26 12:20:31] (/home/user/VAR/train.py , line 279)=> [ep236] (training ) Lm: 6.476 (6.476), Lt: 5.720 (5.725), Acc m&t: 3.48 5.46, Remain: 1 day, 11:41:28, Finish: 2024-11-27 08:01 [11-26 12:20:31] (/home/user/VAR/train.py , line 279)=> [ep236] (training ) Lm: 6.476 (6.476), Lt: 5.720 (5.725), Acc m&t: 3.48 5.46, Remain: 1 day, 11:42:09, Finish: 2024-11-27 08:02 [11-26 12:20:31] (/home/user/VAR/train.py , line 279)=> [ep236] (training ) Lm: 6.476 (6.476), Lt: 5.720 (5.725), Acc m&t: 3.48 5.46, Remain: 1 day, 11:41:48, Finish: 2024-11-27 08:02 [11-26 12:20:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 0/1669] eta: 0:18:27 tlr: 8.5e-05 tnm: 0.42 Lm: 6.588 (6.588) Lt: 5.816 (5.816) Accm: 2.75 (2.75) Acct: 4.39 (4.39) proj_loss: -0.5980 (-0.5980) time: 0.6635 data: 0.0003 [11-26 12:20:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 0/1669] eta: 0:18:28 tlr: 8.5e-05 tnm: 0.42 Lm: 6.468 (6.468) Lt: 5.704 (5.704) Accm: 3.50 (3.50) Acct: 5.34 (5.34) proj_loss: -0.6317 (-0.6317) time: 0.6639 data: 0.0003 [11-26 12:20:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 0/1669] eta: 0:18:28 tlr: 8.5e-05 tnm: 0.42 Lm: 6.405 (6.405) Lt: 5.631 (5.631) Accm: 3.60 (3.60) Acct: 5.53 (5.53) proj_loss: -0.6089 (-0.6089) time: 0.6643 data: 0.0004 [11-26 12:20:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 0/1669] eta: 0:18:28 tlr: 8.5e-05 tnm: 0.42 Lm: 6.448 (6.448) Lt: 5.657 (5.657) Accm: 3.37 (3.37) Acct: 5.41 (5.41) proj_loss: -0.6044 (-0.6044) time: 0.6643 data: 0.0003 [11-26 12:25:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 417/1669] eta: 0:14:05 tlr: 8.5e-05 tnm: 0.44 Lm: 6.467 (6.467) Lt: 5.708 (5.708) Accm: 3.47 (3.47) Acct: 5.36 (5.36) proj_loss: -0.6145 (-0.6145) time: 0.6752 data: 0.0002 [11-26 12:25:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 417/1669] eta: 0:14:05 tlr: 8.5e-05 tnm: 0.44 Lm: 6.458 (6.458) Lt: 5.696 (5.696) Accm: 3.43 (3.43) Acct: 5.09 (5.09) proj_loss: -0.6281 (-0.6281) time: 0.6752 data: 0.0003 [11-26 12:25:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 417/1669] eta: 0:14:05 tlr: 8.5e-05 tnm: 0.44 Lm: 6.483 (6.483) Lt: 5.728 (5.728) Accm: 3.43 (3.43) Acct: 5.27 (5.27) proj_loss: -0.6078 (-0.6078) time: 0.6752 data: 0.0002 [11-26 12:25:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 417/1669] eta: 0:14:05 tlr: 8.5e-05 tnm: 0.44 Lm: 6.473 (6.473) Lt: 5.700 (5.700) Accm: 3.30 (3.30) Acct: 5.30 (5.30) proj_loss: -0.6016 (-0.6016) time: 0.6752 data: 0.0002 [11-26 12:29:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 834/1669] eta: 0:09:24 tlr: 8.4e-05 tnm: 0.42 Lm: 6.366 (6.437) Lt: 5.583 (5.657) Accm: 3.85 (3.51) Acct: 6.10 (5.57) proj_loss: -0.6034 (-0.6022) time: 0.6761 data: 0.0002 [11-26 12:29:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 834/1669] eta: 0:09:24 tlr: 8.4e-05 tnm: 0.42 Lm: 6.448 (6.450) Lt: 5.678 (5.698) Accm: 3.58 (3.54) Acct: 5.41 (5.46) proj_loss: -0.6159 (-0.6150) time: 0.6761 data: 0.0002 [11-26 12:29:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 834/1669] eta: 0:09:24 tlr: 8.4e-05 tnm: 0.42 Lm: 6.454 (6.473) Lt: 5.631 (5.694) Accm: 3.57 (3.47) Acct: 5.53 (5.50) proj_loss: -0.6066 (-0.6016) time: 0.6761 data: 0.0003 [11-26 12:29:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 834/1669] eta: 0:09:24 tlr: 8.4e-05 tnm: 0.42 Lm: 6.448 (6.403) Lt: 5.687 (5.658) Accm: 3.50 (3.67) Acct: 5.34 (5.35) proj_loss: -0.6279 (-0.6280) time: 0.6761 data: 0.0002 [11-26 12:34:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1251/1669] eta: 0:04:47 tlr: 8.4e-05 tnm: 0.42 Lm: 6.370 (6.364) Lt: 5.634 (5.614) Accm: 3.74 (3.75) Acct: 5.60 (5.50) proj_loss: -0.6262 (-0.6235) time: 0.6779 data: 0.0003 [11-26 12:34:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1251/1669] eta: 0:04:47 tlr: 8.4e-05 tnm: 0.42 Lm: 6.508 (6.521) Lt: 5.728 (5.751) Accm: 3.41 (3.31) Acct: 5.27 (5.24) proj_loss: -0.6002 (-0.5997) time: 0.6779 data: 0.0003 [11-26 12:34:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1251/1669] eta: 0:04:47 tlr: 8.4e-05 tnm: 0.42 Lm: 6.417 (6.445) Lt: 5.641 (5.667) Accm: 3.77 (3.55) Acct: 5.94 (5.62) proj_loss: -0.6043 (-0.6075) time: 0.6779 data: 0.0002 [11-26 12:34:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1251/1669] eta: 0:04:47 tlr: 8.4e-05 tnm: 0.42 Lm: 6.467 (6.462) Lt: 5.713 (5.710) Accm: 3.55 (3.53) Acct: 5.43 (5.46) proj_loss: -0.6191 (-0.6168) time: 0.6778 data: 0.0003 [11-26 12:39:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1668/1669] eta: 0:00:00 tlr: 8.4e-05 tnm: 0.44 Lm: 6.479 (6.465) Lt: 5.747 (5.720) Accm: 3.51 (3.47) Acct: 5.41 (5.34) proj_loss: -0.6202 (-0.6175) time: 0.6764 data: 0.0017 [11-26 12:39:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 237/350] Total time: 0:19:02 (0.685 s / it) [11-26 12:39:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1668/1669] eta: 0:00:00 tlr: 8.4e-05 tnm: 0.44 Lm: 6.424 (6.376) Lt: 5.670 (5.625) Accm: 3.50 (3.69) Acct: 5.44 (5.49) proj_loss: -0.6245 (-0.6212) time: 0.6764 data: 0.0020 [11-26 12:39:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1668/1669] eta: 0:00:00 tlr: 8.4e-05 tnm: 0.44 Lm: 6.561 (6.546) Lt: 5.825 (5.783) Accm: 3.27 (3.30) Acct: 5.11 (5.22) proj_loss: -0.6018 (-0.6001) time: 0.6764 data: 0.0018 [11-26 12:39:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1668/1669] eta: 0:00:00 tlr: 8.4e-05 tnm: 0.44 Lm: 6.468 (6.460) Lt: 5.699 (5.686) Accm: 3.69 (3.53) Acct: 5.79 (5.59) proj_loss: -0.6053 (-0.6100) time: 0.6764 data: 0.0017 [11-26 12:39:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 237/350] Total time: 0:19:02 (0.685 s / it) [11-26 12:39:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 237/350] Total time: 0:19:02 (0.685 s / it) [11-26 12:39:34] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 237/350] Total time: 0:19:02 (0.685 s / it) [11-26 12:39:34] (/home/user/VAR/train.py , line 279)=> [ep237] (training ) Lm: 6.476 (6.485), Lt: 5.720 (5.726), Acc m&t: 3.48 5.46, Remain: 1 day, 11:24:48, Finish: 2024-11-27 08:04 [11-26 12:39:34] (/home/user/VAR/train.py , line 279)=> [ep237] (training ) Lm: 6.476 (6.485), Lt: 5.720 (5.726), Acc m&t: 3.48 5.46, Remain: 1 day, 11:22:59, Finish: 2024-11-27 08:02 [11-26 12:39:34] (/home/user/VAR/train.py , line 279)=> [ep237] (training ) Lm: 6.476 (6.485), Lt: 5.720 (5.726), Acc m&t: 3.48 5.46, Remain: 1 day, 11:22:56, Finish: 2024-11-27 08:02 [11-26 12:39:34] (/home/user/VAR/train.py , line 279)=> [ep237] (training ) Lm: 6.476 (6.485), Lt: 5.720 (5.726), Acc m&t: 3.48 5.46, Remain: 1 day, 11:23:21, Finish: 2024-11-27 08:02 [11-26 12:39:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 0/1669] eta: 0:18:27 tlr: 8.4e-05 tnm: 0.44 Lm: 6.516 (6.516) Lt: 5.659 (5.659) Accm: 3.46 (3.46) Acct: 5.97 (5.97) proj_loss: -0.5783 (-0.5783) time: 0.6636 data: 0.0003 [11-26 12:39:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 0/1669] eta: 0:18:26 tlr: 8.4e-05 tnm: 0.44 Lm: 6.316 (6.316) Lt: 5.492 (5.492) Accm: 4.01 (4.01) Acct: 6.53 (6.53) proj_loss: -0.6172 (-0.6172) time: 0.6633 data: 0.0004 [11-26 12:39:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 0/1669] eta: 0:18:25 tlr: 8.4e-05 tnm: 0.44 Lm: 6.371 (6.371) Lt: 5.540 (5.540) Accm: 3.66 (3.66) Acct: 5.84 (5.84) proj_loss: -0.6180 (-0.6180) time: 0.6623 data: 0.0004 [11-26 12:39:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 0/1669] eta: 0:18:27 tlr: 8.4e-05 tnm: 0.44 Lm: 6.550 (6.550) Lt: 5.875 (5.875) Accm: 3.34 (3.34) Acct: 4.84 (4.84) proj_loss: -0.6176 (-0.6176) time: 0.6633 data: 0.0003 [11-26 12:44:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 417/1669] eta: 0:14:05 tlr: 8.4e-05 tnm: 0.42 Lm: 6.484 (6.484) Lt: 5.760 (5.760) Accm: 3.40 (3.40) Acct: 5.16 (5.16) proj_loss: -0.6152 (-0.6152) time: 0.6768 data: 0.0003 [11-26 12:44:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 417/1669] eta: 0:14:05 tlr: 8.4e-05 tnm: 0.42 Lm: 6.386 (6.386) Lt: 5.574 (5.574) Accm: 3.76 (3.76) Acct: 6.05 (6.05) proj_loss: -0.6074 (-0.6074) time: 0.6768 data: 0.0002 [11-26 12:44:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 417/1669] eta: 0:14:05 tlr: 8.4e-05 tnm: 0.42 Lm: 6.435 (6.435) Lt: 5.621 (5.621) Accm: 3.59 (3.59) Acct: 5.70 (5.70) proj_loss: -0.6139 (-0.6139) time: 0.6768 data: 0.0003 [11-26 12:44:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 417/1669] eta: 0:14:05 tlr: 8.4e-05 tnm: 0.42 Lm: 6.488 (6.488) Lt: 5.682 (5.682) Accm: 3.49 (3.49) Acct: 5.82 (5.82) proj_loss: -0.5970 (-0.5970) time: 0.6769 data: 0.0002 [11-26 12:48:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 834/1669] eta: 0:09:24 tlr: 8.4e-05 tnm: 0.42 Lm: 6.484 (6.487) Lt: 5.705 (5.704) Accm: 3.51 (3.51) Acct: 5.66 (5.76) proj_loss: -0.6056 (-0.5999) time: 0.6787 data: 0.0003 [11-26 12:48:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 834/1669] eta: 0:09:24 tlr: 8.4e-05 tnm: 0.42 Lm: 6.452 (6.408) Lt: 5.657 (5.607) Accm: 3.56 (3.70) Acct: 5.66 (5.92) proj_loss: -0.6172 (-0.6130) time: 0.6787 data: 0.0002 [11-26 12:48:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 834/1669] eta: 0:09:24 tlr: 8.4e-05 tnm: 0.42 Lm: 6.550 (6.507) Lt: 5.747 (5.755) Accm: 3.46 (3.46) Acct: 5.48 (5.39) proj_loss: -0.6129 (-0.6124) time: 0.6787 data: 0.0002 [11-26 12:48:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 834/1669] eta: 0:09:24 tlr: 8.4e-05 tnm: 0.42 Lm: 6.500 (6.457) Lt: 5.689 (5.644) Accm: 3.53 (3.49) Acct: 5.56 (5.57) proj_loss: -0.6098 (-0.6044) time: 0.6787 data: 0.0003 [11-26 12:53:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1251/1669] eta: 0:04:42 tlr: 8.4e-05 tnm: 0.42 Lm: 6.413 (6.400) Lt: 5.656 (5.620) Accm: 3.78 (3.77) Acct: 5.70 (5.88) proj_loss: -0.6199 (-0.6153) time: 0.6789 data: 0.0003 [11-26 12:53:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1251/1669] eta: 0:04:42 tlr: 8.4e-05 tnm: 0.42 Lm: 6.551 (6.535) Lt: 5.811 (5.804) Accm: 3.40 (3.35) Acct: 5.16 (5.13) proj_loss: -0.6152 (-0.6163) time: 0.6789 data: 0.0003 [11-26 12:53:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1251/1669] eta: 0:04:42 tlr: 8.4e-05 tnm: 0.42 Lm: 6.473 (6.481) Lt: 5.694 (5.699) Accm: 3.53 (3.52) Acct: 5.66 (5.73) proj_loss: -0.6106 (-0.6048) time: 0.6789 data: 0.0003 [11-26 12:53:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1251/1669] eta: 0:04:42 tlr: 8.4e-05 tnm: 0.42 Lm: 6.435 (6.431) Lt: 5.641 (5.631) Accm: 3.59 (3.53) Acct: 5.66 (5.62) proj_loss: -0.6075 (-0.6046) time: 0.6790 data: 0.0003 [11-26 12:58:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.43 Lm: 6.371 (6.402) Lt: 5.594 (5.597) Accm: 3.66 (3.67) Acct: 5.75 (5.90) proj_loss: -0.6051 (-0.6039) time: 0.6786 data: 0.0015 [11-26 12:58:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 238/350] Total time: 0:18:49 (0.676 s / it) [11-26 12:58:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.43 Lm: 6.550 (6.538) Lt: 5.860 (5.815) Accm: 3.34 (3.31) Acct: 5.15 (5.13) proj_loss: -0.6176 (-0.6170) time: 0.6786 data: 0.0019 [11-26 12:58:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.43 Lm: 6.484 (6.496) Lt: 5.705 (5.726) Accm: 3.51 (3.47) Acct: 5.66 (5.66) proj_loss: -0.6108 (-0.6060) time: 0.6786 data: 0.0018 [11-26 12:58:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.43 Lm: 6.409 (6.402) Lt: 5.657 (5.636) Accm: 3.80 (3.78) Acct: 5.70 (5.84) proj_loss: -0.6172 (-0.6142) time: 0.6786 data: 0.0021 [11-26 12:58:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 238/350] Total time: 0:18:49 (0.676 s / it) [11-26 12:58:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 238/350] Total time: 0:18:49 (0.676 s / it) [11-26 12:58:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 238/350] Total time: 0:18:49 (0.676 s / it) [11-26 12:58:23] (/home/user/VAR/train.py , line 279)=> [ep238] (training ) Lm: 6.463 (6.463), Lt: 5.702 (5.702), Acc m&t: 3.49 5.51, Remain: 1 day, 11:13:48, Finish: 2024-11-27 08:12 [11-26 12:58:23] (/home/user/VAR/train.py , line 279)=> [ep238] (training ) Lm: 6.463 (6.463), Lt: 5.702 (5.702), Acc m&t: 3.49 5.51, Remain: 1 day, 11:14:12, Finish: 2024-11-27 08:12 [11-26 12:58:23] (/home/user/VAR/train.py , line 279)=> [ep238] (training ) Lm: 6.463 (6.463), Lt: 5.702 (5.702), Acc m&t: 3.49 5.51, Remain: 1 day, 11:14:36, Finish: 2024-11-27 08:12 [11-26 12:58:23] (/home/user/VAR/train.py , line 279)=> [ep238] (training ) Lm: 6.463 (6.463), Lt: 5.702 (5.702), Acc m&t: 3.49 5.51, Remain: 1 day, 11:14:26, Finish: 2024-11-27 08:12 [11-26 12:58:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 0/1669] eta: 0:18:23 tlr: 8.3e-05 tnm: 0.41 Lm: 6.501 (6.501) Lt: 5.780 (5.780) Accm: 3.14 (3.14) Acct: 4.72 (4.72) proj_loss: -0.6505 (-0.6505) time: 0.6611 data: 0.0003 [11-26 12:58:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 0/1669] eta: 0:18:23 tlr: 8.3e-05 tnm: 0.41 Lm: 6.408 (6.408) Lt: 5.606 (5.606) Accm: 3.87 (3.87) Acct: 5.92 (5.92) proj_loss: -0.5963 (-0.5963) time: 0.6612 data: 0.0004 [11-26 12:58:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 0/1669] eta: 0:18:23 tlr: 8.3e-05 tnm: 0.41 Lm: 6.511 (6.511) Lt: 5.843 (5.843) Accm: 3.06 (3.06) Acct: 4.34 (4.34) proj_loss: -0.6305 (-0.6305) time: 0.6613 data: 0.0004 [11-26 12:58:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 0/1669] eta: 0:18:23 tlr: 8.3e-05 tnm: 0.41 Lm: 6.375 (6.375) Lt: 5.613 (5.613) Accm: 3.52 (3.52) Acct: 5.22 (5.22) proj_loss: -0.5816 (-0.5816) time: 0.6615 data: 0.0003 [11-26 13:03:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 417/1669] eta: 0:14:51 tlr: 8.3e-05 tnm: 0.42 Lm: 6.411 (6.411) Lt: 5.661 (5.661) Accm: 3.48 (3.48) Acct: 5.25 (5.25) proj_loss: -0.5899 (-0.5899) time: 0.6750 data: 0.0003 [11-26 13:03:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 417/1669] eta: 0:14:51 tlr: 8.3e-05 tnm: 0.42 Lm: 6.374 (6.374) Lt: 5.585 (5.585) Accm: 3.89 (3.89) Acct: 6.18 (6.18) proj_loss: -0.6161 (-0.6161) time: 0.6750 data: 0.0002 [11-26 13:03:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 417/1669] eta: 0:14:51 tlr: 8.3e-05 tnm: 0.42 Lm: 6.500 (6.500) Lt: 5.749 (5.749) Accm: 3.22 (3.22) Acct: 4.80 (4.80) proj_loss: -0.6290 (-0.6290) time: 0.6750 data: 0.0003 [11-26 13:03:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 417/1669] eta: 0:14:51 tlr: 8.3e-05 tnm: 0.42 Lm: 6.558 (6.558) Lt: 5.894 (5.894) Accm: 3.15 (3.15) Acct: 4.67 (4.67) proj_loss: -0.6194 (-0.6194) time: 0.6751 data: 0.0003 [11-26 13:08:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 834/1669] eta: 0:09:39 tlr: 8.3e-05 tnm: 0.42 Lm: 6.583 (6.566) Lt: 5.855 (5.881) Accm: 3.06 (3.11) Acct: 4.91 (4.75) proj_loss: -0.6084 (-0.6140) time: 0.6770 data: 0.0003 [11-26 13:08:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 834/1669] eta: 0:09:39 tlr: 8.3e-05 tnm: 0.42 Lm: 6.389 (6.404) Lt: 5.627 (5.650) Accm: 3.52 (3.54) Acct: 5.29 (5.39) proj_loss: -0.5982 (-0.6025) time: 0.6770 data: 0.0003 [11-26 13:08:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 834/1669] eta: 0:09:39 tlr: 8.3e-05 tnm: 0.42 Lm: 6.408 (6.412) Lt: 5.606 (5.635) Accm: 3.87 (3.80) Acct: 5.92 (5.99) proj_loss: -0.6120 (-0.6147) time: 0.6771 data: 0.0002 [11-26 13:08:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 834/1669] eta: 0:09:39 tlr: 8.3e-05 tnm: 0.42 Lm: 6.501 (6.510) Lt: 5.764 (5.754) Accm: 3.30 (3.27) Acct: 4.87 (5.02) proj_loss: -0.6136 (-0.6238) time: 0.6771 data: 0.0003 [11-26 13:12:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1251/1669] eta: 0:04:47 tlr: 8.3e-05 tnm: 0.45 Lm: 6.500 (6.477) Lt: 5.741 (5.721) Accm: 3.33 (3.33) Acct: 5.17 (5.22) proj_loss: -0.6105 (-0.6174) time: 0.6788 data: 0.0003 [11-26 13:12:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1251/1669] eta: 0:04:47 tlr: 8.3e-05 tnm: 0.45 Lm: 6.449 (6.436) Lt: 5.670 (5.669) Accm: 3.75 (3.67) Acct: 5.77 (5.78) proj_loss: -0.6041 (-0.6073) time: 0.6788 data: 0.0003 [11-26 13:12:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1251/1669] eta: 0:04:47 tlr: 8.3e-05 tnm: 0.45 Lm: 6.549 (6.554) Lt: 5.849 (5.850) Accm: 3.15 (3.22) Acct: 4.95 (4.92) proj_loss: -0.6101 (-0.6134) time: 0.6788 data: 0.0003 [11-26 13:12:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1251/1669] eta: 0:04:47 tlr: 8.3e-05 tnm: 0.45 Lm: 6.418 (6.458) Lt: 5.668 (5.714) Accm: 3.48 (3.40) Acct: 5.25 (5.20) proj_loss: -0.6007 (-0.6027) time: 0.6788 data: 0.0003 [11-26 13:17:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.44 Lm: 6.447 (6.482) Lt: 5.708 (5.734) Accm: 3.44 (3.28) Acct: 5.22 (5.02) proj_loss: -0.5982 (-0.5995) time: 0.6788 data: 0.0016 [11-26 13:17:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 239/350] Total time: 0:19:04 (0.686 s / it) [11-26 13:17:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.44 Lm: 6.499 (6.457) Lt: 5.718 (5.696) Accm: 3.37 (3.43) Acct: 5.48 (5.36) proj_loss: -0.6074 (-0.6130) time: 0.6788 data: 0.0016 [11-26 13:17:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.44 Lm: 6.490 (6.458) Lt: 5.734 (5.703) Accm: 3.63 (3.59) Acct: 5.61 (5.66) proj_loss: -0.6120 (-0.6084) time: 0.6788 data: 0.0017 [11-26 13:17:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.44 Lm: 6.516 (6.543) Lt: 5.843 (5.838) Accm: 3.25 (3.24) Acct: 4.92 (4.92) proj_loss: -0.6117 (-0.6160) time: 0.6788 data: 0.0018 [11-26 13:17:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 239/350] Total time: 0:19:04 (0.686 s / it) [11-26 13:17:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 239/350] Total time: 0:19:04 (0.686 s / it) [11-26 13:17:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 239/350] Total time: 0:19:04 (0.686 s / it) ======================================================= RESTART [11-26 14:25:55] ======================================================= ======================================================= RESTART [11-26 14:25:55] ======================================================= ======================================================= RESTART [11-26 14:25:55] ======================================================= ======================================================= RESTART [11-26 14:25:55] ======================================================= [11-26 14:25:55] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-26 14:25:55] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-26 14:25:55] (er/VAR/utils/arg_util.py, line 230)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-26 14:26:53] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-26 14:26:53] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False finetune_ckpt : None same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : ca9b13c7b773f48247047ce959b4a6b3af1d406f branch : main commit_msg : add push origin main } [11-26 14:26:53] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-26 14:26:56] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-26 14:26:56] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> [11-26 14:26:56] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 14:26:56] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> [11-26 14:26:56] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 14:26:56] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth ... [11-26 14:26:56] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep230, it0 [11-26 14:26:56] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-26 14:25:55] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-26 14:25:55] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-26 14:25:55] (er/VAR/utils/arg_util.py, line 230)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-26 14:26:53] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-26 14:26:53] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False finetune_ckpt : None same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_id : ca9b13c7b773f48247047ce959b4a6b3af1d406f branch : main commit_msg : add push origin main } [11-26 14:26:53] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-26 14:26:56] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-26 14:26:56] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> [11-26 14:26:56] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 14:26:56] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> [11-26 14:26:56] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 14:26:56] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth ... [11-26 14:26:56] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep230, it0 [11-26 14:26:56] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-26 14:25:55] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-26 14:25:55] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-26 14:25:55] (er/VAR/utils/arg_util.py, line 230)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-26 14:26:53] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-26 14:26:53] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False finetune_ckpt : None same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 branch : main commit_id : ca9b13c7b773f48247047ce959b4a6b3af1d406f commit_msg : add push origin main } [11-26 14:26:53] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-26 14:26:56] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-26 14:26:56] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> [11-26 14:26:56] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 14:26:56] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> [11-26 14:26:56] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 14:26:56] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth ... [11-26 14:26:56] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep230, it0 [11-26 14:26:56] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ...[11-26 14:25:55] (er/VAR/utils/arg_util.py, line 228)=> [tf32] [precis] torch.get_float32_matmul_precision(): high [11-26 14:25:55] (er/VAR/utils/arg_util.py, line 229)=> [tf32] [ conv ] torch.backends.cudnn.allow_tf32: True [11-26 14:25:55] (er/VAR/utils/arg_util.py, line 230)=> [tf32] [matmul] torch.backends.cuda.matmul.allow_tf32: True [11-26 14:26:53] (/home/user/VAR/train.py , line 37)=> global bs=768, local bs=24 [11-26 14:26:53] (/home/user/VAR/train.py , line 38)=> initial args: { data_path : /mnt/localssd/ImageNet2012/ exp_name : text vae_ckpt : /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt vfast : 2 tfast : 2 depth : 24 ini : -1 hd : 0.02 aln : 0.5 alng : 0.0001 fp16 : 1 tblr : 8e-05 tlr : 0.00024000000000000003 twd : 0.05 twde : 0.05 tclip : 2.0 ls : 0.0 bs : 768 batch_size : 24 glb_batch_size : 768 ac : 1 ep : 350 wp : 7.0 wp0 : 0.005 wpe : 0.01 sche : lin0 opt : adamw afuse : True saln : False anorm : True fuse : True pn : 1_1_2_3_3_4_5_6_8_11 patch_size : 11 patch_nums : (1, 1, 2, 3, 3, 4, 5, 6, 8, 11) resos : (11, 11, 22, 33, 33, 44, 55, 66, 88, 121) data_load_reso : 256 mid_reso : 1.125 hflip : False workers : 12 pg : 0.0 pg0 : 4 pgwp : 1.1666666666666667 cmd : --depth=24 --bs=768 --ep=350 --fp16=1 --alng=1e-4 --wpe=0.01 --tblr=8e-5 --data_path /mnt/localssd/ImageNet2012/ --workers 12 --vfast 2 --tfast 2 --encoder_model vit_base_patch14_dinov2.lvd142m --decoder_model vit_base_patch14_dinov2.lvd142m --product_quant 2 --semantic_guide dinov2 --num_latent_tokens 121 --codebook_embed_dim 14 --codebook_size 16384 --v_patch_nums 1 1 2 3 3 4 5 6 8 11 --pn 1_1_2_3_3_4_5_6_8_11 --patch_size 11 --local_out_dir_path /sensei-fs/users/xiangl/exp141-var-d24/ --vae_ckpt /sensei-fs/users/xiangl/output/exp141/best_ckpt.pt --p_drop 0.0 --st_ep 50 --ed_ep 150 --sem_half True --clip_norm True --scale 1.0 --encoder_depth 6 --proj_coef 0.2 --query False acc_mean : None acc_tail : None L_mean : None L_tail : None vacc_mean : None vacc_tail : None vL_mean : None vL_tail : None grad_norm : None cur_lr : None cur_wd : None cur_it : cur_ep : remain_time : finish_time : local_out_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/ tb_log_dir_path : /sensei-fs/users/xiangl/exp141-var-d24/tb-VARd24__pn1_1_2_3_3_4_5_6_8_11__b768ep350adamlr8e-05wd0.05 log_txt_path : /sensei-fs/users/xiangl/exp141-var-d24/log.txt last_ckpt_path : /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth tf32 : True seed : None codebook_size : 16384 codebook_embed_dim : 14 codebook_l2_norm : True codebook_show_usage : True commit_loss_beta : 0.25 entropy_loss_ratio : 0.0 soft_entropy : True scale : 1.0 test_model : True encoder_ch_mult : [1, 1, 2, 2, 4] decoder_ch_mult : [1, 1, 2, 2, 4] z_channels : 256 dropout_p : 0.0 clip_norm : True v_patch_nums : [1, 1, 2, 3, 3, 4, 5, 6, 8, 11] enc_type : dinov2 dec_type : dinov2 semantic_guide : dinov2 num_latent_tokens : 121 encoder_model : vit_base_patch14_dinov2.lvd142m decoder_model : vit_base_patch14_dinov2.lvd142m abs_pos_embed : True share_quant_resi : 4 product_quant : 2 half_sem : True p_drop : 0.0 joint_sample : False infer_ckpt : masking_method : uniform st_ep : 50 ed_ep : 150 p_rand : 0.0 encoder_depth : 6 projector_dim : 2048 z_dims : [768] proj_coef : 0.2 query : False finetune_ckpt : None same_seed_for_all_ranks: 0 local_debug : False dbg_nan : False cfg : [3.5, 3.5] top_k : 900 top_p : 0.95 commit_msg : add push origin main commit_id : ca9b13c7b773f48247047ce959b4a6b3af1d406f branch : main } [11-26 14:26:53] (/home/user/VAR/train.py , line 42)=> [build PT data] ... [11-26 14:26:56] (e/user/VAR/utils/data.py, line 34)=> [Dataset] len(train_set)=1281167, len(val_set)=50000, num_classes=1000 [11-26 14:26:56] (e/user/VAR/utils/data.py, line 48)=> Transform [train] = [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> RandomCrop(size=(256, 256), padding=None) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> [11-26 14:26:56] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 14:26:56] (e/user/VAR/utils/data.py, line 48)=> Transform [val] = [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> Resize(size=288, interpolation=lanczos, max_size=None, antialias=True) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> CenterCrop(size=(256, 256)) [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> ToTensor() [11-26 14:26:56] (e/user/VAR/utils/data.py, line 51)=> [11-26 14:26:56] (e/user/VAR/utils/data.py, line 54)=> --------------------------- [11-26 14:26:56] (/home/user/VAR/train.py , line 65)=> [auto_resume] load ckpt from @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth ... [11-26 14:26:56] (/home/user/VAR/train.py , line 65)=> [auto_resume success] resume from ep230, it0 [11-26 14:26:56] (/home/user/VAR/train.py , line 66)=> [dataloader multi processing] ... [dataloader multi processing](*) finished! (45.91s) [dataloader multi processing](*) finished! (46.21s) [dataloader multi processing](*) finished! (48.14s) [11-26 14:27:42] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [dataloader multi processing](*) finished! (48.83s) [11-26 14:27:42] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-26 14:27:47] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 14:27:47] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 14:27:49] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-26 14:27:49] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 14:27:49] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 14:27:50] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-26 14:27:44] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-26 14:27:49] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 14:27:49] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 14:27:50] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-26 14:27:45] (/home/user/VAR/train.py , line 72)=> [dataloader] gbs=768, lbs=24, iters_train=1669, types(tr, va)=('DatasetFolder', 'DatasetFolder') [11-26 14:27:50] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 14:27:50] (/lookup_free_quantize.py, line 128)=> scale is tensor([0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673, 0.2673]) [11-26 14:27:52] (e/user/VAR/models/var.py, line 117)=> [constructor] ==== flash_if_available=True (0/24), fused_if_available=True (fusing_add_ln=0/24, fusing_mlp=0/24) ==== [VAR config ] embed_dim=1536, num_heads=24, depth=24, mlp_ratio=4.0 [drop ratios ] drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.1 (tensor([0.0000, 0.0043, 0.0087, 0.0130, 0.0174, 0.0217, 0.0261, 0.0304, 0.0348, 0.0391, 0.0435, 0.0478, 0.0522, 0.0565, 0.0609, 0.0652, 0.0696, 0.0739, 0.0783, 0.0826, 0.0870, 0.0913, 0.0957, 0.1000])) [11-26 14:27:50] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-26 14:28:15] (/home/user/VAR/train.py , line 128)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-26 14:28:15] (/home/user/VAR/train.py , line 130)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-26 14:28:15] (/home/user/VAR/train.py , line 131)=> [INIT][#para] VAR=1085.77 [11-26 14:28:15] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-26 14:28:15] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank0] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-26 14:27:52] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-26 14:28:15] (/home/user/VAR/train.py , line 128)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-26 14:28:15] (/home/user/VAR/train.py , line 130)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-26 14:28:15] (/home/user/VAR/train.py , line 131)=> [INIT][#para] VAR=1085.77 [11-26 14:28:15] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-26 14:28:15] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank8] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-26 14:27:52] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-26 14:28:15] (/home/user/VAR/train.py , line 128)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-26 14:28:15] (/home/user/VAR/train.py , line 130)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-26 14:28:15] (/home/user/VAR/train.py , line 131)=> [INIT][#para] VAR=1085.77 [11-26 14:28:15] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-26 14:28:15] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank16] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-26 14:27:53] (e/user/VAR/models/var.py, line 425)=> [init_weights] VAR with init_std=0.0147314 [11-26 14:28:15] (/home/user/VAR/train.py , line 128)=> [INIT] VAR model = OptimizedModule( (_orig_mod): VAR( drop_path_rate=0.1 (word_embed): Linear(in_features=28, out_features=1536, bias=False) (class_emb): Embedding(1001, 1536) (lvl_embed): Embedding(10, 1536) (shared_ada_lin): Identity() (blocks): ModuleList( (0): AdaLNSelfAttn( shared_aln=False (drop_path): Identity() (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) (1-23): 23 x AdaLNSelfAttn( shared_aln=False (drop_path): DropPath((drop_prob=...)) (attn): SelfAttention( (mat_qkv): Linear(in_features=1536, out_features=4608, bias=False) (proj): Linear(in_features=1536, out_features=1536, bias=True) (proj_drop): Identity() ) (ffn): FFN( fused_mlp_func=False (fc1): Linear(in_features=1536, out_features=6144, bias=True) (act): GELU(approximate='tanh') (fc2): Linear(in_features=6144, out_features=1536, bias=True) (drop): Identity() ) (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=9216, bias=True) ) ) ) (head_nm): AdaLNBeforeHead( (ln_wo_grad): LayerNorm((1536,), eps=1e-06, elementwise_affine=False) (ada_lin): Sequential( (0): SiLU() (1): Linear(in_features=1536, out_features=3072, bias=True) ) ) (head): Linear(in_features=1536, out_features=32768, bias=True) (projectors): ModuleList( (0): Sequential( (0): Linear(in_features=1536, out_features=2048, bias=True) (1): SiLU() (2): Linear(in_features=2048, out_features=2048, bias=True) (3): SiLU() (4): Linear(in_features=2048, out_features=768, bias=True) ) ) ) ) [11-26 14:28:15] (/home/user/VAR/train.py , line 130)=> [INIT][#para] VAE=257.83, VAE.enc=86.05, VAE.dec=85.87, VAE.quant=0.01 [11-26 14:28:15] (/home/user/VAR/train.py , line 131)=> [INIT][#para] VAR=1085.77 [11-26 14:28:15] (/VAR/utils/lr_control.py, line 99)=> [get_param_groups] param_groups = { 'D': { 'lr_sc': 1.0, 'params': "('_orig_mod.word_embed.weight, _orig_mod.class_emb.weight, _orig_mod.blocks.0.attn.mat_qkv.weight, _orig_mod.blocks.0.attn.proj.weight, _orig_mod.blocks.0.ffn.fc1.weight, '\n" " '_orig_mod.blocks.0.ffn.fc2.weight, _orig_mod.blocks.0.ada_lin.1.weight, _orig_mod.blocks.1.attn.mat_qkv.weight, _orig_mod.blocks.1.attn.proj.weight, _orig_mod.blocks.1.ffn.fc1.weight, '\n" " '_orig_mod.blocks.1.ffn.fc2.weight, _orig_mod.blocks.1.ada_lin.1.weight, _orig_mod.blocks.2.attn.mat_qkv.weight, _orig_mod.blocks.2.attn.proj.weight, _orig_mod.blocks.2.ffn.fc1.weight, '\n" " '_orig_mod.blocks.2.ffn.fc2.weight, _orig_mod.blocks.2.ada_lin.1.weight, _orig_mod.blocks.3.attn.mat_qkv.weight, _orig_mod.blocks.3.attn.proj.weight, _orig_mod.blocks.3.ffn.fc1.weight, '\n" " '_orig_mod.blocks.3.ffn.fc2.weight, _orig_mod.blocks.3.ada_lin.1.weight, _orig_mod.blocks.4.attn.mat_qkv.weight, _orig_mod.blocks.4.attn.proj.weight, _orig_mod.blocks.4.ffn.fc1.weight, '\n" " '_orig_mod.blocks.4.ffn.fc2.weight, _orig_mod.blocks.4.ada_lin.1.weight, _orig_mod.blocks.5.attn.mat_qkv.weight, _orig_mod.blocks.5.attn.proj.weight, _orig_mod.blocks.5.ffn.fc1.weight, '\n" " '_orig_mod.blocks.5.ffn.fc2.weight, _orig_mod.blocks.5.ada_lin.1.weight, _orig_mod.blocks.6.attn.mat_qkv.weight, _orig_mod.blocks.6.attn.proj.weight, _orig_mod.blocks.6.ffn.fc1.weight, '\n" " '_orig_mod.blocks.6.ffn.fc2.weight, _orig_mod.blocks.6.ada_lin.1.weight, _orig_mod.blocks.7.attn.mat_qkv.weight, _orig_mod.blocks.7.attn.proj.weight, _orig_mod.blocks.7.ffn.fc1.weight, '\n" " '_orig_mod.blocks.7.ffn.fc2.weight, _orig_mod.blocks.7.ada_lin.1.weight, _orig_mod.blocks.8.attn.mat_qkv.weight, _orig_mod.blocks.8.attn.proj.weight, _orig_mod.blocks.8.ffn.fc1.weight, '\n" " '_orig_mod.blocks.8.ffn.fc2.weight, _orig_mod.blocks.8.ada_lin.1.weight, _orig_mod.blocks.9.attn.mat_qkv.weight, _orig_mod.blocks.9.attn.proj.weight, _orig_mod.blocks.9.ffn.fc1.weight, '\n" " '_orig_mod.blocks.9.ffn.fc2.weight, _orig_mod.blocks.9.ada_lin.1.weight, _orig_mod.blocks.10.attn.mat_qkv.weight, _orig_mod.blocks.10.attn.proj.weight, _orig_mod.blocks.10.ffn.fc1.weight, '\n" " '_orig_mod.blocks.10.ffn.fc2.weight, _orig_mod.blocks.10.ada_lin.1.weight, _orig_mod.blocks.11.attn.mat_qkv.weight, _orig_mod.blocks.11.attn.proj.weight, _orig_mod.blocks.11.ffn.fc1.weight, '\n" " '_orig_mod.blocks.11.ffn.fc2.weight, _orig_mod.blocks.11.ada_lin.1.weight, _orig_mod.blocks.12.attn.mat_qkv.weight, _orig_mod.blocks.12.attn.proj.weight, _orig_mod.blocks.12.ffn.fc1.weight, '\n" " '_orig_mod.blocks.12.ffn.fc2.weight, _orig_mod.blocks.12.ada_lin.1.weight, _orig_mod.blocks.13.attn.mat_qkv.weight, _orig_mod.blocks.13.attn.proj.weight, _orig_mod.blocks.13.ffn.fc1.weight, '\n" " '_orig_mod.blocks.13.ffn.fc2.weight, _orig_mod.blocks.13.ada_lin.1.weight, _orig_mod.blocks.14.attn.mat_qkv.weight, _orig_mod.blocks.14.attn.proj.weight, _orig_mod.blocks.14.ffn.fc1.weight, '\n" " '_orig_mod.blocks.14.ffn.fc2.weight, _orig_mod.blocks.14.ada_lin.1.weight, _orig_mod.blocks.15.attn.mat_qkv.weight, _orig_mod.blocks.15.attn.proj.weight, _orig_mod.blocks.15.ffn.fc1.weight, '\n" " '_orig_mod.blocks.15.ffn.fc2.weight, _orig_mod.blocks.15.ada_lin.1.weight, _orig_mod.blocks.16.attn.mat_qkv.weight, _orig_mod.blocks.16.attn.proj.weight, _orig_mod.blocks.16.ffn.fc1.weight, '\n" " '_orig_mod.blocks.16.ffn.fc2.weight, _orig_mod.blocks.16.ada_lin.1.weight, _orig_mod.blocks.17.attn.mat_qkv.weight, _orig_mod.blocks.17.attn.proj.weight, _orig_mod.blocks.17.ffn.fc1.weight, '\n" " '_orig_mod.blocks.17.ffn.fc2.weight, _orig_mod.blocks.17.ada_lin.1.weight, _orig_mod.blocks.18.attn.mat_qkv.weight, _orig_mod.blocks.18.attn.proj.weight, _orig_mod.blocks.18.ffn.fc1.weight, '\n" " '_orig_mod.blocks.18.ffn.fc2.weight, _orig_mod.blocks.18.ada_lin.1.weight, _orig_mod.blocks.19.attn.mat_qkv.weight, _orig_mod.blocks.19.attn.proj.weight, _orig_mod.blocks.19.ffn.fc1.weight, '\n" " '_orig_mod.blocks.19.ffn.fc2.weight, _orig_mod.blocks.19.ada_lin.1.weight, _orig_mod.blocks.20.attn.mat_qkv.weight, _orig_mod.blocks.20.attn.proj.weight, _orig_mod.blocks.20.ffn.fc1.weight, '\n" " '_orig_mod.blocks.20.ffn.fc2.weight, _orig_mod.blocks.20.ada_lin.1.weight, _orig_mod.blocks.21.attn.mat_qkv.weight, _orig_mod.blocks.21.attn.proj.weight, _orig_mod.blocks.21.ffn.fc1.weight, '\n" " '_orig_mod.blocks.21.ffn.fc2.weight, _orig_mod.blocks.21.ada_lin.1.weight, _orig_mod.blocks.22.attn.mat_qkv.weight, _orig_mod.blocks.22.attn.proj.weight, _orig_mod.blocks.22.ffn.fc1.weight, '\n" " '_orig_mod.blocks.22.ffn.fc2.weight, _orig_mod.blocks.22.ada_lin.1.weight, _orig_mod.blocks.23.attn.mat_qkv.weight, _orig_mod.blocks.23.attn.proj.weight, _orig_mod.blocks.23.ffn.fc1.weight, '\n" " '_orig_mod.blocks.23.ffn.fc2.weight, _orig_mod.blocks.23.ada_lin.1.weight, _orig_mod.head_nm.ada_lin.1.weight, _orig_mod.head.weight, _orig_mod.projectors.0.0.weight, '\n" " '_orig_mod.projectors.0.2.weight, _orig_mod.projectors.0.4.weight')", 'wd_sc': 1.0}, 'ND': { 'lr_sc': 1.0, 'params': "('_orig_mod.pos_start, _orig_mod.pos_1LC, _orig_mod.lvl_embed.weight, _orig_mod.blocks.0.attn.scale_mul_1H11, _orig_mod.blocks.0.attn.q_bias, _orig_mod.blocks.0.attn.v_bias, '\n" " '_orig_mod.blocks.0.attn.proj.bias, _orig_mod.blocks.0.ffn.fc1.bias, _orig_mod.blocks.0.ffn.fc2.bias, _orig_mod.blocks.0.ada_lin.1.bias, _orig_mod.blocks.1.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.1.attn.q_bias, _orig_mod.blocks.1.attn.v_bias, _orig_mod.blocks.1.attn.proj.bias, _orig_mod.blocks.1.ffn.fc1.bias, _orig_mod.blocks.1.ffn.fc2.bias, '\n" " '_orig_mod.blocks.1.ada_lin.1.bias, _orig_mod.blocks.2.attn.scale_mul_1H11, _orig_mod.blocks.2.attn.q_bias, _orig_mod.blocks.2.attn.v_bias, _orig_mod.blocks.2.attn.proj.bias, '\n" " '_orig_mod.blocks.2.ffn.fc1.bias, _orig_mod.blocks.2.ffn.fc2.bias, _orig_mod.blocks.2.ada_lin.1.bias, _orig_mod.blocks.3.attn.scale_mul_1H11, _orig_mod.blocks.3.attn.q_bias, '\n" " '_orig_mod.blocks.3.attn.v_bias, _orig_mod.blocks.3.attn.proj.bias, _orig_mod.blocks.3.ffn.fc1.bias, _orig_mod.blocks.3.ffn.fc2.bias, _orig_mod.blocks.3.ada_lin.1.bias, '\n" " '_orig_mod.blocks.4.attn.scale_mul_1H11, _orig_mod.blocks.4.attn.q_bias, _orig_mod.blocks.4.attn.v_bias, _orig_mod.blocks.4.attn.proj.bias, _orig_mod.blocks.4.ffn.fc1.bias, '\n" " '_orig_mod.blocks.4.ffn.fc2.bias, _orig_mod.blocks.4.ada_lin.1.bias, _orig_mod.blocks.5.attn.scale_mul_1H11, _orig_mod.blocks.5.attn.q_bias, _orig_mod.blocks.5.attn.v_bias, '\n" " '_orig_mod.blocks.5.attn.proj.bias, _orig_mod.blocks.5.ffn.fc1.bias, _orig_mod.blocks.5.ffn.fc2.bias, _orig_mod.blocks.5.ada_lin.1.bias, _orig_mod.blocks.6.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.6.attn.q_bias, _orig_mod.blocks.6.attn.v_bias, _orig_mod.blocks.6.attn.proj.bias, _orig_mod.blocks.6.ffn.fc1.bias, _orig_mod.blocks.6.ffn.fc2.bias, '\n" " '_orig_mod.blocks.6.ada_lin.1.bias, _orig_mod.blocks.7.attn.scale_mul_1H11, _orig_mod.blocks.7.attn.q_bias, _orig_mod.blocks.7.attn.v_bias, _orig_mod.blocks.7.attn.proj.bias, '\n" " '_orig_mod.blocks.7.ffn.fc1.bias, _orig_mod.blocks.7.ffn.fc2.bias, _orig_mod.blocks.7.ada_lin.1.bias, _orig_mod.blocks.8.attn.scale_mul_1H11, _orig_mod.blocks.8.attn.q_bias, '\n" " '_orig_mod.blocks.8.attn.v_bias, _orig_mod.blocks.8.attn.proj.bias, _orig_mod.blocks.8.ffn.fc1.bias, _orig_mod.blocks.8.ffn.fc2.bias, _orig_mod.blocks.8.ada_lin.1.bias, '\n" " '_orig_mod.blocks.9.attn.scale_mul_1H11, _orig_mod.blocks.9.attn.q_bias, _orig_mod.blocks.9.attn.v_bias, _orig_mod.blocks.9.attn.proj.bias, _orig_mod.blocks.9.ffn.fc1.bias, '\n" " '_orig_mod.blocks.9.ffn.fc2.bias, _orig_mod.blocks.9.ada_lin.1.bias, _orig_mod.blocks.10.attn.scale_mul_1H11, _orig_mod.blocks.10.attn.q_bias, _orig_mod.blocks.10.attn.v_bias, '\n" " '_orig_mod.blocks.10.attn.proj.bias, _orig_mod.blocks.10.ffn.fc1.bias, _orig_mod.blocks.10.ffn.fc2.bias, _orig_mod.blocks.10.ada_lin.1.bias, _orig_mod.blocks.11.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.11.attn.q_bias, _orig_mod.blocks.11.attn.v_bias, _orig_mod.blocks.11.attn.proj.bias, _orig_mod.blocks.11.ffn.fc1.bias, _orig_mod.blocks.11.ffn.fc2.bias, '\n" " '_orig_mod.blocks.11.ada_lin.1.bias, _orig_mod.blocks.12.attn.scale_mul_1H11, _orig_mod.blocks.12.attn.q_bias, _orig_mod.blocks.12.attn.v_bias, _orig_mod.blocks.12.attn.proj.bias, '\n" " '_orig_mod.blocks.12.ffn.fc1.bias, _orig_mod.blocks.12.ffn.fc2.bias, _orig_mod.blocks.12.ada_lin.1.bias, _orig_mod.blocks.13.attn.scale_mul_1H11, _orig_mod.blocks.13.attn.q_bias, '\n" " '_orig_mod.blocks.13.attn.v_bias, _orig_mod.blocks.13.attn.proj.bias, _orig_mod.blocks.13.ffn.fc1.bias, _orig_mod.blocks.13.ffn.fc2.bias, _orig_mod.blocks.13.ada_lin.1.bias, '\n" " '_orig_mod.blocks.14.attn.scale_mul_1H11, _orig_mod.blocks.14.attn.q_bias, _orig_mod.blocks.14.attn.v_bias, _orig_mod.blocks.14.attn.proj.bias, _orig_mod.blocks.14.ffn.fc1.bias, '\n" " '_orig_mod.blocks.14.ffn.fc2.bias, _orig_mod.blocks.14.ada_lin.1.bias, _orig_mod.blocks.15.attn.scale_mul_1H11, _orig_mod.blocks.15.attn.q_bias, _orig_mod.blocks.15.attn.v_bias, '\n" " '_orig_mod.blocks.15.attn.proj.bias, _orig_mod.blocks.15.ffn.fc1.bias, _orig_mod.blocks.15.ffn.fc2.bias, _orig_mod.blocks.15.ada_lin.1.bias, _orig_mod.blocks.16.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.16.attn.q_bias, _orig_mod.blocks.16.attn.v_bias, _orig_mod.blocks.16.attn.proj.bias, _orig_mod.blocks.16.ffn.fc1.bias, _orig_mod.blocks.16.ffn.fc2.bias, '\n" " '_orig_mod.blocks.16.ada_lin.1.bias, _orig_mod.blocks.17.attn.scale_mul_1H11, _orig_mod.blocks.17.attn.q_bias, _orig_mod.blocks.17.attn.v_bias, _orig_mod.blocks.17.attn.proj.bias, '\n" " '_orig_mod.blocks.17.ffn.fc1.bias, _orig_mod.blocks.17.ffn.fc2.bias, _orig_mod.blocks.17.ada_lin.1.bias, _orig_mod.blocks.18.attn.scale_mul_1H11, _orig_mod.blocks.18.attn.q_bias, '\n" " '_orig_mod.blocks.18.attn.v_bias, _orig_mod.blocks.18.attn.proj.bias, _orig_mod.blocks.18.ffn.fc1.bias, _orig_mod.blocks.18.ffn.fc2.bias, _orig_mod.blocks.18.ada_lin.1.bias, '\n" " '_orig_mod.blocks.19.attn.scale_mul_1H11, _orig_mod.blocks.19.attn.q_bias, _orig_mod.blocks.19.attn.v_bias, _orig_mod.blocks.19.attn.proj.bias, _orig_mod.blocks.19.ffn.fc1.bias, '\n" " '_orig_mod.blocks.19.ffn.fc2.bias, _orig_mod.blocks.19.ada_lin.1.bias, _orig_mod.blocks.20.attn.scale_mul_1H11, _orig_mod.blocks.20.attn.q_bias, _orig_mod.blocks.20.attn.v_bias, '\n" " '_orig_mod.blocks.20.attn.proj.bias, _orig_mod.blocks.20.ffn.fc1.bias, _orig_mod.blocks.20.ffn.fc2.bias, _orig_mod.blocks.20.ada_lin.1.bias, _orig_mod.blocks.21.attn.scale_mul_1H11, '\n" " '_orig_mod.blocks.21.attn.q_bias, _orig_mod.blocks.21.attn.v_bias, _orig_mod.blocks.21.attn.proj.bias, _orig_mod.blocks.21.ffn.fc1.bias, _orig_mod.blocks.21.ffn.fc2.bias, '\n" " '_orig_mod.blocks.21.ada_lin.1.bias, _orig_mod.blocks.22.attn.scale_mul_1H11, _orig_mod.blocks.22.attn.q_bias, _orig_mod.blocks.22.attn.v_bias, _orig_mod.blocks.22.attn.proj.bias, '\n" " '_orig_mod.blocks.22.ffn.fc1.bias, _orig_mod.blocks.22.ffn.fc2.bias, _orig_mod.blocks.22.ada_lin.1.bias, _orig_mod.blocks.23.attn.scale_mul_1H11, _orig_mod.blocks.23.attn.q_bias, '\n" " '_orig_mod.blocks.23.attn.v_bias, _orig_mod.blocks.23.attn.proj.bias, _orig_mod.blocks.23.ffn.fc1.bias, _orig_mod.blocks.23.ffn.fc2.bias, _orig_mod.blocks.23.ada_lin.1.bias, '\n" " '_orig_mod.head_nm.ada_lin.1.bias, _orig_mod.head.bias, _orig_mod.projectors.0.0.bias, _orig_mod.projectors.0.2.bias, _orig_mod.projectors.0.4.bias')", 'wd_sc': 0.0}} [11-26 14:28:15] (/VAR/utils/lr_control.py, line 104)=> [get_param_groups][rank24] type(model).__name__='OptimizedModule' count=303, numel=1085773120 [11-26 14:28:15] (/VAR/utils/lr_control.py, line 105)=> [11-26 14:28:15] (/home/user/VAR/train.py , line 146)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-26 14:28:17] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-26 14:28:17] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-26 14:28:17] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-26 14:28:17] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-26 14:43:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 0/1669] eta: 17 days, 15:40:04 tlr: 9e-05 tnm: 0.42 Lm: 6.569 (6.569) Lt: 5.844 (5.844) Accm: 3.35 (3.35) Acct: 5.44 (5.44) proj_loss: -0.6215 (-0.6215) time: 913.8432 data: 0.0007 [11-26 14:28:15] (/VAR/utils/lr_control.py, line 105)=> [11-26 14:28:15] (/home/user/VAR/train.py , line 146)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-26 14:28:16] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-26 14:28:16] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-26 14:28:16] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-26 14:28:16] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-26 14:43:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 0/1669] eta: 17 days, 15:35:04 tlr: 9e-05 tnm: 0.42 Lm: 6.491 (6.491) Lt: 5.732 (5.732) Accm: 3.31 (3.31) Acct: 5.53 (5.53) proj_loss: -0.5949 (-0.5949) time: 913.6638 data: 0.0007 [11-26 14:28:15] (/VAR/utils/lr_control.py, line 105)=> [11-26 14:28:15] (/home/user/VAR/train.py , line 146)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-26 14:28:17] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-26 14:28:17] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-26 14:28:17] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-26 14:28:17] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-26 14:43:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 0/1669] eta: 17 days, 15:37:53 tlr: 9e-05 tnm: 0.42 Lm: 6.494 (6.494) Lt: 5.731 (5.731) Accm: 3.22 (3.22) Acct: 4.86 (4.86) proj_loss: -0.5808 (-0.5808) time: 913.7648 data: 0.0005 [11-26 14:28:15] (/VAR/utils/lr_control.py, line 105)=> [11-26 14:28:15] (/home/user/VAR/train.py , line 146)=> [INIT] optim=functools.partial(, betas=(0.9, 0.95), fused=True), opt_kw={'lr': 0.00024000000000000003, 'weight_decay': 0} [11-26 14:28:17] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] var_wo_ddp missing: [] [11-26 14:28:17] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] var_wo_ddp unexpected: [] [11-26 14:28:17] (home/user/VAR/trainer.py, line 263)=> [VARTrainer.load_state_dict] vae_local missing: [] [11-26 14:28:17] (home/user/VAR/trainer.py, line 264)=> [VARTrainer.load_state_dict] vae_local unexpected: [] [11-26 14:43:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 0/1669] eta: 17 days, 15:02:35 tlr: 9e-05 tnm: 0.42 Lm: 6.307 (6.307) Lt: 5.498 (5.498) Accm: 3.92 (3.92) Acct: 6.39 (6.39) proj_loss: -0.6072 (-0.6072) time: 912.4960 data: 0.0006 [11-26 14:53:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 417/1669] eta: 1:16:10 tlr: 9e-05 tnm: 0.41 Lm: 6.344 (6.344) Lt: 5.518 (5.518) Accm: 3.88 (3.88) Acct: 6.30 (6.30) proj_loss: -0.5957 (-0.5957) time: 0.6667 data: 0.0002 [11-26 14:53:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 417/1669] eta: 1:16:13 tlr: 9e-05 tnm: 0.41 Lm: 6.406 (6.406) Lt: 5.638 (5.638) Accm: 3.55 (3.55) Acct: 5.71 (5.71) proj_loss: -0.6032 (-0.6032) time: 0.6667 data: 0.0003 [11-26 14:53:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 417/1669] eta: 1:16:14 tlr: 9e-05 tnm: 0.41 Lm: 6.541 (6.541) Lt: 5.790 (5.790) Accm: 3.27 (3.27) Acct: 4.80 (4.80) proj_loss: -0.5963 (-0.5963) time: 0.6667 data: 0.0003 [11-26 14:53:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 417/1669] eta: 1:16:14 tlr: 9e-05 tnm: 0.41 Lm: 6.567 (6.567) Lt: 5.826 (5.826) Accm: 3.26 (3.26) Acct: 5.35 (5.35) proj_loss: -0.6137 (-0.6137) time: 0.6667 data: 0.0003 [11-26 14:58:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 834/1669] eta: 0:30:05 tlr: 9e-05 tnm: 0.43 Lm: 6.569 (6.568) Lt: 5.808 (5.815) Accm: 3.35 (3.34) Acct: 5.44 (5.44) proj_loss: -0.6177 (-0.6151) time: 0.6662 data: 0.0003 [11-26 14:58:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 834/1669] eta: 0:30:05 tlr: 9e-05 tnm: 0.43 Lm: 6.588 (6.584) Lt: 5.850 (5.835) Accm: 3.22 (3.17) Acct: 4.82 (4.81) proj_loss: -0.6117 (-0.6026) time: 0.6662 data: 0.0002 [11-26 14:58:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 834/1669] eta: 0:30:05 tlr: 9e-05 tnm: 0.43 Lm: 6.491 (6.452) Lt: 5.732 (5.711) Accm: 3.50 (3.54) Acct: 5.53 (5.58) proj_loss: -0.6114 (-0.6157) time: 0.6662 data: 0.0003 [11-26 14:58:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [ 834/1669] eta: 0:30:04 tlr: 9e-05 tnm: 0.43 Lm: 6.382 (6.412) Lt: 5.537 (5.619) Accm: 3.84 (3.71) Acct: 6.22 (5.92) proj_loss: -0.6072 (-0.6062) time: 0.6662 data: 0.0003 [11-26 15:03:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1251/1669] eta: 0:11:35 tlr: 8.9e-05 tnm: 0.40 Lm: 6.465 (6.485) Lt: 5.679 (5.715) Accm: 3.60 (3.46) Acct: 5.69 (5.48) proj_loss: -0.6075 (-0.6066) time: 0.6679 data: 0.0003 [11-26 15:03:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1251/1669] eta: 0:11:35 tlr: 8.9e-05 tnm: 0.40 Lm: 6.598 (6.590) Lt: 5.865 (5.846) Accm: 3.14 (3.14) Acct: 4.82 (4.81) proj_loss: -0.6070 (-0.6025) time: 0.6679 data: 0.0002 [11-26 15:03:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1251/1669] eta: 0:11:35 tlr: 8.9e-05 tnm: 0.40 Lm: 6.569 (6.572) Lt: 5.826 (5.824) Accm: 3.26 (3.28) Acct: 5.35 (5.23) proj_loss: -0.6125 (-0.6131) time: 0.6679 data: 0.0002 [11-26 15:03:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1251/1669] eta: 0:11:35 tlr: 8.9e-05 tnm: 0.40 Lm: 6.517 (6.475) Lt: 5.761 (5.731) Accm: 3.46 (3.51) Acct: 5.42 (5.50) proj_loss: -0.6095 (-0.6137) time: 0.6679 data: 0.0003 [11-26 15:07:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1668/1669] eta: 0:00:01 tlr: 8.9e-05 tnm: 0.42 Lm: 6.491 (6.466) Lt: 5.732 (5.719) Accm: 3.42 (3.49) Acct: 5.53 (5.52) proj_loss: -0.6109 (-0.6131) time: 0.6701 data: 0.0015 [11-26 15:07:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 230/350] Total time: 0:39:22 (1.415 s / it) [11-26 15:07:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1668/1669] eta: 0:00:01 tlr: 8.9e-05 tnm: 0.42 Lm: 6.569 (6.537) Lt: 5.808 (5.777) Accm: 3.35 (3.38) Acct: 5.44 (5.40) proj_loss: -0.6073 (-0.6119) time: 0.6701 data: 0.0020 [11-26 15:07:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1668/1669] eta: 0:00:01 tlr: 8.9e-05 tnm: 0.42 Lm: 6.588 (6.586) Lt: 5.850 (5.844) Accm: 3.21 (3.16) Acct: 4.82 (4.87) proj_loss: -0.6024 (-0.6020) time: 0.6701 data: 0.0015 [11-26 15:07:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 230/350] [1668/1669] eta: 0:00:01 tlr: 8.9e-05 tnm: 0.42 Lm: 6.549 (6.525) Lt: 5.821 (5.765) Accm: 3.36 (3.36) Acct: 5.17 (5.36) proj_loss: -0.6077 (-0.6084) time: 0.6701 data: 0.0017 [11-26 15:07:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 230/350] Total time: 0:39:22 (1.416 s / it) [11-26 15:07:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 230/350] Total time: 0:39:22 (1.415 s / it) [11-26 15:07:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 230/350] Total time: 0:39:21 (1.415 s / it) [11-26 15:07:44] (/home/user/VAR/train.py , line 279)=> [ep230] (training ) Lm: 6.477 (6.477), Lt: 5.721 (5.721), Acc m&t: 3.46 5.47, Remain: 1 day, 13:13:15, Finish: 2024-11-27 12:21 [11-26 15:07:44] (/home/user/VAR/train.py , line 279)=> [ep230] (training ) Lm: 6.477 (6.477), Lt: 5.721 (5.721), Acc m&t: 3.46 5.47, Remain: 1 day, 13:15:17, Finish: 2024-11-27 12:23 [11-26 15:07:44] (/home/user/VAR/train.py , line 279)=> [ep230] (training ) Lm: 6.477 (6.477), Lt: 5.721 (5.721), Acc m&t: 3.46 5.47, Remain: 1 day, 13:13:33, Finish: 2024-11-27 12:21 [11-26 15:07:44] (/home/user/VAR/train.py , line 279)=> [ep230] (training ) Lm: 6.477 (6.477), Lt: 5.721 (5.721), Acc m&t: 3.46 5.47, Remain: 1 day, 13:13:02, Finish: 2024-11-27 12:20 [11-26 15:07:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 0/1669] eta: 0:18:00 tlr: 8.9e-05 tnm: 0.41 Lm: 6.447 (6.447) Lt: 5.692 (5.692) Accm: 3.37 (3.37) Acct: 5.32 (5.32) proj_loss: -0.6070 (-0.6070) time: 0.6472 data: 0.0005 [11-26 15:07:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 0/1669] eta: 0:18:00 tlr: 8.9e-05 tnm: 0.41 Lm: 6.313 (6.313) Lt: 5.518 (5.518) Accm: 3.88 (3.88) Acct: 6.23 (6.23) proj_loss: -0.6317 (-0.6317) time: 0.6471 data: 0.0003 [11-26 15:07:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 0/1669] eta: 0:18:37 tlr: 8.9e-05 tnm: 0.41 Lm: 6.756 (6.756) Lt: 6.069 (6.069) Accm: 2.56 (2.56) Acct: 3.84 (3.84) proj_loss: -0.6051 (-0.6051) time: 0.6693 data: 0.0004 [11-26 15:07:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 0/1669] eta: 0:18:00 tlr: 8.9e-05 tnm: 0.41 Lm: 6.428 (6.428) Lt: 5.642 (5.642) Accm: 3.69 (3.69) Acct: 5.96 (5.96) proj_loss: -0.5911 (-0.5911) time: 0.6475 data: 0.0004 [11-26 15:12:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 417/1669] eta: 0:13:55 tlr: 8.9e-05 tnm: 0.41 Lm: 6.473 (6.473) Lt: 5.718 (5.718) Accm: 3.55 (3.55) Acct: 5.66 (5.66) proj_loss: -0.6030 (-0.6030) time: 0.6673 data: 0.0003 [11-26 15:12:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 417/1669] eta: 0:13:55 tlr: 8.9e-05 tnm: 0.41 Lm: 6.609 (6.609) Lt: 5.864 (5.864) Accm: 3.02 (3.02) Acct: 4.64 (4.64) proj_loss: -0.5938 (-0.5938) time: 0.6673 data: 0.0003 [11-26 15:12:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 417/1669] eta: 0:13:55 tlr: 8.9e-05 tnm: 0.41 Lm: 6.453 (6.453) Lt: 5.706 (5.706) Accm: 3.42 (3.42) Acct: 5.26 (5.26) proj_loss: -0.6038 (-0.6038) time: 0.6673 data: 0.0003 [11-26 15:12:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 417/1669] eta: 0:13:55 tlr: 8.9e-05 tnm: 0.41 Lm: 6.474 (6.474) Lt: 5.685 (5.685) Accm: 3.66 (3.66) Acct: 5.88 (5.88) proj_loss: -0.6111 (-0.6111) time: 0.6673 data: 0.0003 [11-26 15:17:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 834/1669] eta: 0:09:22 tlr: 8.9e-05 tnm: 0.40 Lm: 6.582 (6.510) Lt: 5.844 (5.738) Accm: 3.43 (3.50) Acct: 5.53 (5.65) proj_loss: -0.6004 (-0.6075) time: 0.6658 data: 0.0003 [11-26 15:17:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 834/1669] eta: 0:09:22 tlr: 8.9e-05 tnm: 0.40 Lm: 6.447 (6.436) Lt: 5.692 (5.687) Accm: 3.48 (3.46) Acct: 5.32 (5.38) proj_loss: -0.6006 (-0.5983) time: 0.6658 data: 0.0003 [11-26 15:17:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 834/1669] eta: 0:09:22 tlr: 8.9e-05 tnm: 0.40 Lm: 6.518 (6.501) Lt: 5.793 (5.772) Accm: 3.42 (3.49) Acct: 5.35 (5.52) proj_loss: -0.6025 (-0.6028) time: 0.6658 data: 0.0003 [11-26 15:17:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [ 834/1669] eta: 0:09:22 tlr: 8.9e-05 tnm: 0.40 Lm: 6.529 (6.583) Lt: 5.789 (5.839) Accm: 3.26 (3.10) Acct: 5.17 (4.82) proj_loss: -0.6051 (-0.5976) time: 0.6658 data: 0.0003 [11-26 15:21:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1251/1669] eta: 0:04:40 tlr: 8.9e-05 tnm: 0.41 Lm: 6.496 (6.548) Lt: 5.727 (5.796) Accm: 3.33 (3.18) Acct: 5.25 (4.95) proj_loss: -0.6052 (-0.6024) time: 0.6676 data: 0.0003 [11-26 15:21:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1251/1669] eta: 0:04:40 tlr: 8.9e-05 tnm: 0.41 Lm: 6.538 (6.537) Lt: 5.837 (5.809) Accm: 3.38 (3.38) Acct: 5.29 (5.30) proj_loss: -0.5994 (-0.6012) time: 0.6675 data: 0.0002 [11-26 15:21:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1251/1669] eta: 0:04:40 tlr: 8.9e-05 tnm: 0.41 Lm: 6.461 (6.467) Lt: 5.695 (5.690) Accm: 3.66 (3.62) Acct: 5.88 (5.84) proj_loss: -0.6105 (-0.6108) time: 0.6676 data: 0.0003 [11-26 15:21:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1251/1669] eta: 0:04:40 tlr: 8.9e-05 tnm: 0.41 Lm: 6.453 (6.463) Lt: 5.706 (5.727) Accm: 3.42 (3.40) Acct: 5.26 (5.27) proj_loss: -0.6029 (-0.6000) time: 0.6676 data: 0.0003 [11-26 15:26:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.41 Lm: 6.459 (6.487) Lt: 5.720 (5.745) Accm: 3.37 (3.36) Acct: 5.20 (5.21) proj_loss: -0.6052 (-0.6030) time: 0.6685 data: 0.0019 [11-26 15:26:25] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 231/350] Total time: 0:18:40 (0.671 s / it) [11-26 15:26:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.41 Lm: 6.529 (6.576) Lt: 5.789 (5.832) Accm: 3.26 (3.12) Acct: 5.17 (4.91) proj_loss: -0.6053 (-0.6031) time: 0.6685 data: 0.0014 [11-26 15:26:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.41 Lm: 6.568 (6.487) Lt: 5.826 (5.717) Accm: 3.43 (3.57) Acct: 5.53 (5.74) proj_loss: -0.6122 (-0.6111) time: 0.6685 data: 0.0015 [11-26 15:26:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 231/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.41 Lm: 6.558 (6.544) Lt: 5.850 (5.817) Accm: 3.36 (3.38) Acct: 5.27 (5.30) proj_loss: -0.6014 (-0.6012) time: 0.6685 data: 0.0019 [11-26 15:26:25] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 231/350] Total time: 0:18:40 (0.671 s / it) [11-26 15:26:25] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 231/350] Total time: 0:18:40 (0.671 s / it) [11-26 15:26:25] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 231/350] Total time: 0:18:40 (0.671 s / it) [11-26 15:26:25] (/home/user/VAR/train.py , line 279)=> [ep231] (training ) Lm: 6.477 (6.484), Lt: 5.721 (5.732), Acc m&t: 3.46 5.47, Remain: 1 day, 12:49:44, Finish: 2024-11-27 12:16 [11-26 15:26:25] (/home/user/VAR/train.py , line 279)=> [ep231] (training ) Lm: 6.477 (6.484), Lt: 5.721 (5.732), Acc m&t: 3.46 5.47, Remain: 1 day, 12:49:31, Finish: 2024-11-27 12:15 [11-26 15:26:25] (/home/user/VAR/train.py , line 279)=> [ep231] (training ) Lm: 6.477 (6.484), Lt: 5.721 (5.732), Acc m&t: 3.46 5.47, Remain: 1 day, 12:48:48, Finish: 2024-11-27 12:15 [11-26 15:26:25] (/home/user/VAR/train.py , line 279)=> [ep231] (training ) Lm: 6.477 (6.484), Lt: 5.721 (5.732), Acc m&t: 3.46 5.47, Remain: 1 day, 12:48:58, Finish: 2024-11-27 12:15 [11-26 15:26:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 0/1669] eta: 0:18:10 tlr: 8.8e-05 tnm: 0.41 Lm: 6.450 (6.450) Lt: 5.706 (5.706) Accm: 3.28 (3.28) Acct: 5.27 (5.27) proj_loss: -0.6223 (-0.6223) time: 0.6536 data: 0.0004 [11-26 15:26:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 0/1669] eta: 0:18:10 tlr: 8.8e-05 tnm: 0.41 Lm: 6.562 (6.562) Lt: 5.821 (5.821) Accm: 3.37 (3.37) Acct: 5.53 (5.53) proj_loss: -0.5826 (-0.5826) time: 0.6532 data: 0.0004 [11-26 15:26:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 0/1669] eta: 0:18:10 tlr: 8.8e-05 tnm: 0.41 Lm: 6.602 (6.602) Lt: 5.943 (5.943) Accm: 2.90 (2.90) Acct: 4.48 (4.48) proj_loss: -0.6083 (-0.6083) time: 0.6535 data: 0.0003 [11-26 15:26:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 0/1669] eta: 0:18:10 tlr: 8.8e-05 tnm: 0.41 Lm: 6.498 (6.498) Lt: 5.764 (5.764) Accm: 3.27 (3.27) Acct: 5.20 (5.20) proj_loss: -0.6090 (-0.6090) time: 0.6534 data: 0.0003 [11-26 15:31:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 417/1669] eta: 0:13:57 tlr: 8.8e-05 tnm: 0.43 Lm: 6.465 (6.465) Lt: 5.701 (5.701) Accm: 3.62 (3.62) Acct: 5.68 (5.68) proj_loss: -0.5899 (-0.5899) time: 0.6696 data: 0.0002 [11-26 15:31:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 417/1669] eta: 0:13:57 tlr: 8.8e-05 tnm: 0.43 Lm: 6.493 (6.493) Lt: 5.775 (5.775) Accm: 3.46 (3.46) Acct: 5.41 (5.41) proj_loss: -0.6033 (-0.6033) time: 0.6696 data: 0.0003 [11-26 15:31:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 417/1669] eta: 0:13:57 tlr: 8.8e-05 tnm: 0.43 Lm: 6.504 (6.504) Lt: 5.783 (5.783) Accm: 3.26 (3.26) Acct: 5.19 (5.19) proj_loss: -0.6074 (-0.6074) time: 0.6696 data: 0.0003 [11-26 15:31:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 417/1669] eta: 0:13:57 tlr: 8.8e-05 tnm: 0.43 Lm: 6.582 (6.582) Lt: 5.860 (5.860) Accm: 3.14 (3.14) Acct: 5.04 (5.04) proj_loss: -0.6089 (-0.6089) time: 0.6696 data: 0.0003 [11-26 15:35:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 834/1669] eta: 0:09:18 tlr: 8.8e-05 tnm: 0.41 Lm: 6.498 (6.510) Lt: 5.764 (5.759) Accm: 3.27 (3.41) Acct: 5.20 (5.38) proj_loss: -0.6090 (-0.6117) time: 0.6715 data: 0.0003 [11-26 15:35:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 834/1669] eta: 0:09:18 tlr: 8.8e-05 tnm: 0.41 Lm: 6.545 (6.510) Lt: 5.831 (5.794) Accm: 3.02 (3.32) Acct: 4.55 (5.13) proj_loss: -0.6083 (-0.6091) time: 0.6715 data: 0.0003 [11-26 15:35:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 834/1669] eta: 0:09:18 tlr: 8.8e-05 tnm: 0.41 Lm: 6.547 (6.492) Lt: 5.790 (5.731) Accm: 3.46 (3.57) Acct: 5.53 (5.61) proj_loss: -0.5972 (-0.5979) time: 0.6715 data: 0.0002 [11-26 15:35:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [ 834/1669] eta: 0:09:18 tlr: 8.8e-05 tnm: 0.41 Lm: 6.450 (6.423) Lt: 5.706 (5.676) Accm: 3.28 (3.55) Acct: 5.27 (5.65) proj_loss: -0.5925 (-0.5969) time: 0.6715 data: 0.0003 [11-26 15:40:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1251/1669] eta: 0:04:41 tlr: 8.8e-05 tnm: 0.42 Lm: 6.504 (6.464) Lt: 5.768 (5.715) Accm: 3.26 (3.46) Acct: 5.35 (5.60) proj_loss: -0.5958 (-0.5974) time: 0.6674 data: 0.0004 [11-26 15:40:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1251/1669] eta: 0:04:41 tlr: 8.8e-05 tnm: 0.42 Lm: 6.557 (6.525) Lt: 5.797 (5.786) Accm: 3.06 (3.26) Acct: 4.72 (5.07) proj_loss: -0.6033 (-0.6042) time: 0.6675 data: 0.0003 [11-26 15:40:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1251/1669] eta: 0:04:41 tlr: 8.8e-05 tnm: 0.42 Lm: 6.555 (6.512) Lt: 5.793 (5.747) Accm: 3.42 (3.48) Acct: 5.49 (5.41) proj_loss: -0.5963 (-0.5973) time: 0.6675 data: 0.0002 [11-26 15:40:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1251/1669] eta: 0:04:41 tlr: 8.8e-05 tnm: 0.42 Lm: 6.464 (6.490) Lt: 5.748 (5.752) Accm: 3.35 (3.41) Acct: 5.39 (5.43) proj_loss: -0.6131 (-0.6173) time: 0.6675 data: 0.0002 [11-26 15:45:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.43 Lm: 6.498 (6.504) Lt: 5.764 (5.761) Accm: 3.27 (3.36) Acct: 5.27 (5.40) proj_loss: -0.6090 (-0.6098) time: 0.6670 data: 0.0019 [11-26 15:45:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 232/350] Total time: 0:18:41 (0.672 s / it) [11-26 15:45:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.43 Lm: 6.547 (6.482) Lt: 5.790 (5.708) Accm: 3.46 (3.60) Acct: 5.53 (5.61) proj_loss: -0.5958 (-0.5970) time: 0.6670 data: 0.0014 [11-26 15:45:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.43 Lm: 6.558 (6.484) Lt: 5.795 (5.731) Accm: 3.28 (3.46) Acct: 5.35 (5.55) proj_loss: -0.5991 (-0.5982) time: 0.6670 data: 0.0015 [11-26 15:45:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 232/350] [1668/1669] eta: 0:00:00 tlr: 8.8e-05 tnm: 0.43 Lm: 6.545 (6.514) Lt: 5.763 (5.769) Accm: 3.09 (3.34) Acct: 4.89 (5.22) proj_loss: -0.5983 (-0.6009) time: 0.6670 data: 0.0015 [11-26 15:45:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 232/350] Total time: 0:18:41 (0.672 s / it) [11-26 15:45:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 232/350] Total time: 0:18:41 (0.672 s / it) [11-26 15:45:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 232/350] Total time: 0:18:41 (0.672 s / it) [11-26 15:45:07] (/home/user/VAR/train.py , line 279)=> [ep232] (training ) Lm: 6.477 (6.481), Lt: 5.721 (5.729), Acc m&t: 3.46 5.47, Remain: 1 day, 12:32:16, Finish: 2024-11-27 12:17 [11-26 15:45:07] (/home/user/VAR/train.py , line 279)=> [ep232] (training ) Lm: 6.477 (6.481), Lt: 5.721 (5.729), Acc m&t: 3.46 5.47, Remain: 1 day, 12:32:02, Finish: 2024-11-27 12:17 [11-26 15:45:07] (/home/user/VAR/train.py , line 279)=> [ep232] (training ) Lm: 6.477 (6.481), Lt: 5.721 (5.729), Acc m&t: 3.46 5.47, Remain: 1 day, 12:32:24, Finish: 2024-11-27 12:17 [11-26 15:45:07] (/home/user/VAR/train.py , line 279)=> [ep232] (training ) Lm: 6.477 (6.481), Lt: 5.721 (5.729), Acc m&t: 3.46 5.47, Remain: 1 day, 12:32:06, Finish: 2024-11-27 12:17 [11-26 15:45:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 0/1669] eta: 0:18:06 tlr: 8.8e-05 tnm: 0.43 Lm: 6.561 (6.561) Lt: 5.852 (5.852) Accm: 3.12 (3.12) Acct: 4.55 (4.55) proj_loss: -0.6239 (-0.6239) time: 0.6508 data: 0.0003 [11-26 15:45:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 0/1669] eta: 0:18:06 tlr: 8.8e-05 tnm: 0.43 Lm: 6.473 (6.473) Lt: 5.770 (5.770) Accm: 3.36 (3.36) Acct: 5.27 (5.27) proj_loss: -0.6143 (-0.6143) time: 0.6511 data: 0.0004 [11-26 15:45:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 0/1669] eta: 0:18:07 tlr: 8.8e-05 tnm: 0.43 Lm: 6.539 (6.539) Lt: 5.829 (5.829) Accm: 3.18 (3.18) Acct: 4.73 (4.73) proj_loss: -0.6023 (-0.6023) time: 0.6515 data: 0.0003 [11-26 15:45:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 0/1669] eta: 0:18:07 tlr: 8.8e-05 tnm: 0.43 Lm: 6.770 (6.770) Lt: 6.016 (6.016) Accm: 2.60 (2.60) Acct: 4.20 (4.20) proj_loss: -0.6006 (-0.6006) time: 0.6516 data: 0.0004 [11-26 15:49:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 417/1669] eta: 0:13:55 tlr: 8.8e-05 tnm: 0.41 Lm: 6.525 (6.525) Lt: 5.752 (5.752) Accm: 3.25 (3.25) Acct: 5.16 (5.16) proj_loss: -0.6047 (-0.6047) time: 0.6677 data: 0.0003 [11-26 15:49:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 417/1669] eta: 0:13:55 tlr: 8.8e-05 tnm: 0.41 Lm: 6.541 (6.541) Lt: 5.827 (5.827) Accm: 3.12 (3.12) Acct: 4.87 (4.87) proj_loss: -0.6207 (-0.6207) time: 0.6677 data: 0.0002 [11-26 15:49:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 417/1669] eta: 0:13:55 tlr: 8.8e-05 tnm: 0.41 Lm: 6.382 (6.382) Lt: 5.648 (5.648) Accm: 3.53 (3.53) Acct: 5.34 (5.34) proj_loss: -0.6084 (-0.6084) time: 0.6677 data: 0.0003 [11-26 15:49:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 417/1669] eta: 0:13:55 tlr: 8.8e-05 tnm: 0.41 Lm: 6.453 (6.453) Lt: 5.670 (5.670) Accm: 3.41 (3.41) Acct: 5.28 (5.28) proj_loss: -0.6129 (-0.6129) time: 0.6677 data: 0.0003 [11-26 15:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 834/1669] eta: 0:09:17 tlr: 8.7e-05 tnm: 0.40 Lm: 6.537 (6.481) Lt: 5.797 (5.712) Accm: 3.31 (3.37) Acct: 5.22 (5.26) proj_loss: -0.6019 (-0.6081) time: 0.6692 data: 0.0002 [11-26 15:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 834/1669] eta: 0:09:17 tlr: 8.7e-05 tnm: 0.40 Lm: 6.473 (6.475) Lt: 5.770 (5.747) Accm: 3.36 (3.42) Acct: 5.27 (5.23) proj_loss: -0.6143 (-0.6175) time: 0.6692 data: 0.0002 [11-26 15:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 834/1669] eta: 0:09:17 tlr: 8.7e-05 tnm: 0.40 Lm: 6.415 (6.393) Lt: 5.554 (5.617) Accm: 3.74 (3.60) Acct: 5.87 (5.52) proj_loss: -0.6091 (-0.6086) time: 0.6692 data: 0.0002 [11-26 15:54:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [ 834/1669] eta: 0:09:17 tlr: 8.7e-05 tnm: 0.40 Lm: 6.585 (6.545) Lt: 5.876 (5.794) Accm: 3.23 (3.24) Acct: 5.37 (5.23) proj_loss: -0.6088 (-0.6066) time: 0.6692 data: 0.0003 [11-26 15:59:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1251/1669] eta: 0:04:39 tlr: 8.7e-05 tnm: 0.40 Lm: 6.606 (6.566) Lt: 5.869 (5.811) Accm: 3.11 (3.18) Acct: 5.11 (5.13) proj_loss: -0.6047 (-0.6043) time: 0.6705 data: 0.0003 [11-26 15:59:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1251/1669] eta: 0:04:39 tlr: 8.7e-05 tnm: 0.40 Lm: 6.530 (6.503) Lt: 5.827 (5.788) Accm: 3.18 (3.31) Acct: 4.87 (5.04) proj_loss: -0.6168 (-0.6179) time: 0.6705 data: 0.0002 [11-26 15:59:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1251/1669] eta: 0:04:39 tlr: 8.7e-05 tnm: 0.40 Lm: 6.439 (6.411) Lt: 5.650 (5.649) Accm: 3.81 (3.70) Acct: 5.79 (5.57) proj_loss: -0.6118 (-0.6121) time: 0.6705 data: 0.0002 [11-26 15:59:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1251/1669] eta: 0:04:39 tlr: 8.7e-05 tnm: 0.40 Lm: 6.520 (6.487) Lt: 5.779 (5.724) Accm: 3.35 (3.38) Acct: 5.21 (5.24) proj_loss: -0.6129 (-0.6138) time: 0.6705 data: 0.0003 [11-26 16:03:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1668/1669] eta: 0:00:00 tlr: 8.7e-05 tnm: 0.42 Lm: 6.537 (6.514) Lt: 5.797 (5.766) Accm: 3.34 (3.37) Acct: 5.22 (5.26) proj_loss: -0.6219 (-0.6154) time: 0.7142 data: 0.0019 [11-26 16:03:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 233/350] Total time: 0:18:37 (0.669 s / it) [11-26 16:03:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1668/1669] eta: 0:00:00 tlr: 8.7e-05 tnm: 0.42 Lm: 6.585 (6.553) Lt: 5.861 (5.778) Accm: 3.23 (3.23) Acct: 5.37 (5.24) proj_loss: -0.6074 (-0.6049) time: 0.7142 data: 0.0014 [11-26 16:03:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1668/1669] eta: 0:00:00 tlr: 8.7e-05 tnm: 0.42 Lm: 6.473 (6.496) Lt: 5.770 (5.775) Accm: 3.26 (3.30) Acct: 5.20 (5.08) proj_loss: -0.6192 (-0.6203) time: 0.7142 data: 0.0014 [11-26 16:03:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 233/350] [1668/1669] eta: 0:00:00 tlr: 8.7e-05 tnm: 0.42 Lm: 6.464 (6.448) Lt: 5.747 (5.693) Accm: 3.74 (3.61) Acct: 5.72 (5.48) proj_loss: -0.6091 (-0.6102) time: 0.7142 data: 0.0014 [11-26 16:03:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 233/350] Total time: 0:18:37 (0.669 s / it) [11-26 16:03:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 233/350] Total time: 0:18:37 (0.669 s / it) [11-26 16:03:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 233/350] Total time: 0:18:37 (0.669 s / it) [11-26 16:03:44] (/home/user/VAR/train.py , line 279)=> [ep233] (training ) Lm: 6.477 (6.481), Lt: 5.721 (5.728), Acc m&t: 3.46 5.47, Remain: 1 day, 12:24:58, Finish: 2024-11-27 12:28 [11-26 16:03:44] (/home/user/VAR/train.py , line 279)=> [ep233] (training ) Lm: 6.477 (6.481), Lt: 5.721 (5.728), Acc m&t: 3.46 5.47, Remain: 1 day, 12:24:53, Finish: 2024-11-27 12:28 [11-26 16:03:44] (/home/user/VAR/train.py , line 279)=> [ep233] (training ) Lm: 6.477 (6.481), Lt: 5.721 (5.728), Acc m&t: 3.46 5.47, Remain: 1 day, 12:24:37, Finish: 2024-11-27 12:28 [11-26 16:03:44] (/home/user/VAR/train.py , line 279)=> [ep233] (training ) Lm: 6.477 (6.481), Lt: 5.721 (5.728), Acc m&t: 3.46 5.47, Remain: 1 day, 12:24:55, Finish: 2024-11-27 12:28 [11-26 16:03:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 0/1669] eta: 0:18:07 tlr: 8.7e-05 tnm: 0.42 Lm: 6.387 (6.387) Lt: 5.641 (5.641) Accm: 3.87 (3.87) Acct: 5.96 (5.96) proj_loss: -0.6015 (-0.6015) time: 0.6517 data: 0.0003 [11-26 16:03:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 0/1669] eta: 0:18:08 tlr: 8.7e-05 tnm: 0.42 Lm: 6.451 (6.451) Lt: 5.708 (5.708) Accm: 3.86 (3.86) Acct: 6.04 (6.04) proj_loss: -0.6133 (-0.6133) time: 0.6524 data: 0.0003 [11-26 16:03:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 0/1669] eta: 0:18:09 tlr: 8.7e-05 tnm: 0.42 Lm: 6.602 (6.602) Lt: 5.891 (5.891) Accm: 3.02 (3.02) Acct: 4.46 (4.46) proj_loss: -0.5929 (-0.5929) time: 0.6528 data: 0.0004 [11-26 16:03:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 0/1669] eta: 0:18:09 tlr: 8.7e-05 tnm: 0.42 Lm: 6.571 (6.571) Lt: 5.839 (5.839) Accm: 3.16 (3.16) Acct: 4.92 (4.92) proj_loss: -0.6101 (-0.6101) time: 0.6526 data: 0.0003 [11-26 16:08:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 417/1669] eta: 0:14:20 tlr: 8.7e-05 tnm: 0.40 Lm: 6.618 (6.618) Lt: 5.906 (5.906) Accm: 3.07 (3.07) Acct: 4.91 (4.91) proj_loss: -0.6008 (-0.6008) time: 0.6715 data: 0.0003 [11-26 16:08:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 417/1669] eta: 0:14:20 tlr: 8.7e-05 tnm: 0.40 Lm: 6.451 (6.451) Lt: 5.728 (5.728) Accm: 3.64 (3.64) Acct: 5.75 (5.75) proj_loss: -0.6064 (-0.6064) time: 0.6715 data: 0.0002 [11-26 16:08:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 417/1669] eta: 0:14:20 tlr: 8.7e-05 tnm: 0.40 Lm: 6.457 (6.457) Lt: 5.724 (5.724) Accm: 3.60 (3.60) Acct: 5.58 (5.58) proj_loss: -0.6053 (-0.6053) time: 0.6715 data: 0.0003 [11-26 16:08:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 417/1669] eta: 0:14:20 tlr: 8.7e-05 tnm: 0.40 Lm: 6.450 (6.450) Lt: 5.756 (5.756) Accm: 3.58 (3.58) Acct: 5.29 (5.29) proj_loss: -0.6152 (-0.6152) time: 0.6715 data: 0.0003 [11-26 16:13:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 834/1669] eta: 0:09:26 tlr: 8.7e-05 tnm: 0.41 Lm: 6.457 (6.452) Lt: 5.711 (5.741) Accm: 3.39 (3.52) Acct: 5.44 (5.34) proj_loss: -0.6147 (-0.6150) time: 0.6701 data: 0.0002 [11-26 16:13:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 834/1669] eta: 0:09:26 tlr: 8.7e-05 tnm: 0.41 Lm: 6.451 (6.408) Lt: 5.708 (5.688) Accm: 3.86 (3.80) Acct: 6.04 (5.93) proj_loss: -0.6133 (-0.6111) time: 0.6701 data: 0.0002 [11-26 16:13:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 834/1669] eta: 0:09:26 tlr: 8.7e-05 tnm: 0.41 Lm: 6.387 (6.375) Lt: 5.641 (5.647) Accm: 3.87 (3.73) Acct: 5.96 (5.78) proj_loss: -0.6038 (-0.6048) time: 0.6701 data: 0.0003 [11-26 16:13:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [ 834/1669] eta: 0:09:26 tlr: 8.7e-05 tnm: 0.41 Lm: 6.571 (6.536) Lt: 5.839 (5.807) Accm: 3.16 (3.31) Acct: 4.92 (5.41) proj_loss: -0.6101 (-0.6044) time: 0.6701 data: 0.0003 [11-26 16:17:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1251/1669] eta: 0:04:42 tlr: 8.6e-05 tnm: 0.44 Lm: 6.471 (6.487) Lt: 5.729 (5.760) Accm: 3.48 (3.44) Acct: 5.37 (5.51) proj_loss: -0.6109 (-0.6087) time: 0.6685 data: 0.0003 [11-26 16:17:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1251/1669] eta: 0:04:42 tlr: 8.6e-05 tnm: 0.44 Lm: 6.451 (6.455) Lt: 5.728 (5.728) Accm: 3.64 (3.62) Acct: 5.75 (5.69) proj_loss: -0.6067 (-0.6084) time: 0.6685 data: 0.0003 [11-26 16:17:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1251/1669] eta: 0:04:42 tlr: 8.6e-05 tnm: 0.44 Lm: 6.410 (6.389) Lt: 5.665 (5.658) Accm: 3.77 (3.72) Acct: 5.72 (5.70) proj_loss: -0.6064 (-0.6094) time: 0.6685 data: 0.0003 [11-26 16:17:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1251/1669] eta: 0:04:42 tlr: 8.6e-05 tnm: 0.44 Lm: 6.466 (6.458) Lt: 5.707 (5.731) Accm: 3.40 (3.49) Acct: 5.33 (5.31) proj_loss: -0.6090 (-0.6121) time: 0.6685 data: 0.0003 [11-26 16:22:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.43 Lm: 6.457 (6.450) Lt: 5.705 (5.726) Accm: 3.42 (3.59) Acct: 5.44 (5.51) proj_loss: -0.6147 (-0.6142) time: 0.6720 data: 0.0015 [11-26 16:22:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 234/350] Total time: 0:18:44 (0.674 s / it) [11-26 16:22:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.43 Lm: 6.452 (6.463) Lt: 5.748 (5.742) Accm: 3.42 (3.53) Acct: 5.46 (5.49) proj_loss: -0.6001 (-0.6049) time: 0.6720 data: 0.0020 [11-26 16:22:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.43 Lm: 6.571 (6.518) Lt: 5.839 (5.777) Accm: 3.16 (3.32) Acct: 4.92 (5.36) proj_loss: -0.6116 (-0.6103) time: 0.6720 data: 0.0018 [11-26 16:22:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 234/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.43 Lm: 6.434 (6.420) Lt: 5.689 (5.673) Accm: 3.68 (3.66) Acct: 5.65 (5.69) proj_loss: -0.6090 (-0.6107) time: 0.6720 data: 0.0019 [11-26 16:22:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 234/350] Total time: 0:18:44 (0.674 s / it) [11-26 16:22:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 234/350] Total time: 0:18:44 (0.674 s / it) [11-26 16:22:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 234/350] Total time: 0:18:44 (0.674 s / it) [11-26 16:22:29] (/home/user/VAR/train.py , line 279)=> [ep234] (training ) Lm: 6.477 (6.477), Lt: 5.721 (5.728), Acc m&t: 3.46 5.47, Remain: 1 day, 12:02:02, Finish: 2024-11-27 12:24 [11-26 16:22:29] (/home/user/VAR/train.py , line 279)=> [ep234] (training ) Lm: 6.477 (6.477), Lt: 5.721 (5.728), Acc m&t: 3.46 5.47, Remain: 1 day, 12:02:30, Finish: 2024-11-27 12:24 [11-26 16:22:29] (/home/user/VAR/train.py , line 279)=> [ep234] (training ) Lm: 6.477 (6.477), Lt: 5.721 (5.728), Acc m&t: 3.46 5.47, Remain: 1 day, 12:02:11, Finish: 2024-11-27 12:24 [11-26 16:22:29] (/home/user/VAR/train.py , line 279)=> [ep234] (training ) Lm: 6.477 (6.477), Lt: 5.721 (5.728), Acc m&t: 3.46 5.47, Remain: 1 day, 12:01:37, Finish: 2024-11-27 12:24 [11-26 16:22:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 0/1669] eta: 0:18:11 tlr: 8.6e-05 tnm: 0.41 Lm: 6.478 (6.478) Lt: 5.794 (5.794) Accm: 3.50 (3.50) Acct: 5.30 (5.30) proj_loss: -0.6135 (-0.6135) time: 0.6538 data: 0.0004 [11-26 16:22:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 0/1669] eta: 0:18:10 tlr: 8.6e-05 tnm: 0.41 Lm: 6.379 (6.379) Lt: 5.653 (5.653) Accm: 3.79 (3.79) Acct: 5.89 (5.89) proj_loss: -0.6109 (-0.6109) time: 0.6533 data: 0.0003 [11-26 16:22:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 0/1669] eta: 0:18:09 tlr: 8.6e-05 tnm: 0.41 Lm: 6.399 (6.399) Lt: 5.554 (5.554) Accm: 3.75 (3.75) Acct: 6.15 (6.15) proj_loss: -0.5813 (-0.5813) time: 0.6530 data: 0.0003 [11-26 16:22:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 0/1669] eta: 0:18:12 tlr: 8.6e-05 tnm: 0.41 Lm: 6.555 (6.555) Lt: 5.831 (5.831) Accm: 3.28 (3.28) Acct: 4.94 (4.94) proj_loss: -0.6001 (-0.6001) time: 0.6546 data: 0.0004 [11-26 16:27:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 417/1669] eta: 0:14:30 tlr: 8.6e-05 tnm: 0.43 Lm: 6.494 (6.494) Lt: 5.749 (5.749) Accm: 3.27 (3.27) Acct: 4.92 (4.92) proj_loss: -0.6061 (-0.6061) time: 0.6681 data: 0.0002 [11-26 16:27:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 417/1669] eta: 0:14:30 tlr: 8.6e-05 tnm: 0.43 Lm: 6.448 (6.448) Lt: 5.722 (5.722) Accm: 3.53 (3.53) Acct: 5.53 (5.53) proj_loss: -0.6091 (-0.6091) time: 0.6681 data: 0.0003 [11-26 16:27:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 417/1669] eta: 0:14:30 tlr: 8.6e-05 tnm: 0.43 Lm: 6.450 (6.450) Lt: 5.645 (5.645) Accm: 3.54 (3.54) Acct: 5.65 (5.65) proj_loss: -0.5994 (-0.5994) time: 0.6681 data: 0.0003 [11-26 16:27:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 417/1669] eta: 0:14:30 tlr: 8.6e-05 tnm: 0.43 Lm: 6.483 (6.483) Lt: 5.751 (5.751) Accm: 3.48 (3.48) Acct: 5.35 (5.35) proj_loss: -0.6150 (-0.6150) time: 0.6681 data: 0.0003 [11-26 16:31:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 834/1669] eta: 0:09:29 tlr: 8.6e-05 tnm: 0.42 Lm: 6.478 (6.478) Lt: 5.735 (5.745) Accm: 3.45 (3.46) Acct: 5.39 (5.37) proj_loss: -0.6135 (-0.6090) time: 0.6722 data: 0.0003 [11-26 16:31:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 834/1669] eta: 0:09:29 tlr: 8.6e-05 tnm: 0.42 Lm: 6.515 (6.471) Lt: 5.737 (5.727) Accm: 3.47 (3.51) Acct: 5.72 (5.59) proj_loss: -0.6072 (-0.6062) time: 0.6722 data: 0.0002 [11-26 16:31:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 834/1669] eta: 0:09:29 tlr: 8.6e-05 tnm: 0.42 Lm: 6.399 (6.429) Lt: 5.599 (5.630) Accm: 3.74 (3.60) Acct: 5.82 (5.70) proj_loss: -0.6175 (-0.6105) time: 0.6722 data: 0.0002 [11-26 16:31:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [ 834/1669] eta: 0:09:29 tlr: 8.6e-05 tnm: 0.42 Lm: 6.497 (6.495) Lt: 5.739 (5.746) Accm: 3.28 (3.33) Acct: 4.94 (5.06) proj_loss: -0.6001 (-0.6036) time: 0.6722 data: 0.0003 [11-26 16:36:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1251/1669] eta: 0:04:43 tlr: 8.6e-05 tnm: 0.41 Lm: 6.500 (6.497) Lt: 5.771 (5.760) Accm: 3.36 (3.37) Acct: 5.15 (5.24) proj_loss: -0.6036 (-0.6045) time: 0.6702 data: 0.0003 [11-26 16:36:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1251/1669] eta: 0:04:43 tlr: 8.6e-05 tnm: 0.41 Lm: 6.450 (6.451) Lt: 5.668 (5.679) Accm: 3.53 (3.52) Acct: 5.48 (5.53) proj_loss: -0.6052 (-0.6061) time: 0.6702 data: 0.0003 [11-26 16:36:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1251/1669] eta: 0:04:43 tlr: 8.6e-05 tnm: 0.41 Lm: 6.517 (6.490) Lt: 5.764 (5.749) Accm: 3.37 (3.42) Acct: 5.49 (5.51) proj_loss: -0.6069 (-0.6063) time: 0.6702 data: 0.0002 [11-26 16:36:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1251/1669] eta: 0:04:43 tlr: 8.6e-05 tnm: 0.41 Lm: 6.483 (6.517) Lt: 5.765 (5.789) Accm: 3.43 (3.32) Acct: 5.35 (5.13) proj_loss: -0.6150 (-0.6117) time: 0.6702 data: 0.0003 [11-26 16:41:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.43 Lm: 6.489 (6.524) Lt: 5.794 (5.795) Accm: 3.42 (3.25) Acct: 5.30 (5.04) proj_loss: -0.6135 (-0.6116) time: 0.6722 data: 0.0022 [11-26 16:41:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 235/350] Total time: 0:18:47 (0.676 s / it) [11-26 16:41:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.43 Lm: 6.515 (6.493) Lt: 5.756 (5.750) Accm: 3.47 (3.43) Acct: 5.49 (5.51) proj_loss: -0.6065 (-0.6035) time: 0.6721 data: 0.0015 [11-26 16:41:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.43 Lm: 6.502 (6.535) Lt: 5.803 (5.789) Accm: 3.28 (3.29) Acct: 5.04 (5.20) proj_loss: -0.6001 (-0.6015) time: 0.6721 data: 0.0019 [11-26 16:41:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 235/350] [1668/1669] eta: 0:00:00 tlr: 8.6e-05 tnm: 0.43 Lm: 6.496 (6.460) Lt: 5.691 (5.681) Accm: 3.44 (3.51) Acct: 5.23 (5.47) proj_loss: -0.5952 (-0.6039) time: 0.6721 data: 0.0018 [11-26 16:41:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 235/350] Total time: 0:18:47 (0.676 s / it) [11-26 16:41:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 235/350] Total time: 0:18:47 (0.676 s / it) [11-26 16:41:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 235/350] Total time: 0:18:47 (0.676 s / it) [11-26 16:41:16] (/home/user/VAR/train.py , line 279)=> [ep235] (training ) Lm: 6.477 (6.482), Lt: 5.721 (5.729), Acc m&t: 3.46 5.47, Remain: 1 day, 11:44:23, Finish: 2024-11-27 12:25 [11-26 16:41:16] (/home/user/VAR/train.py , line 279)=> [ep235] (training ) Lm: 6.477 (6.482), Lt: 5.721 (5.729), Acc m&t: 3.46 5.47, Remain: 1 day, 11:45:19, Finish: 2024-11-27 12:26 [11-26 16:41:16] (/home/user/VAR/train.py , line 279)=> [ep235] (training ) Lm: 6.477 (6.482), Lt: 5.721 (5.729), Acc m&t: 3.46 5.47, Remain: 1 day, 11:44:00, Finish: 2024-11-27 12:25 [11-26 16:41:16] (/home/user/VAR/train.py , line 279)=> [ep235] (training ) Lm: 6.477 (6.482), Lt: 5.721 (5.729), Acc m&t: 3.46 5.47, Remain: 1 day, 11:44:33, Finish: 2024-11-27 12:25 [11-26 16:41:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 0/1669] eta: 0:18:37 tlr: 8.6e-05 tnm: 0.41 Lm: 6.522 (6.522) Lt: 5.722 (5.722) Accm: 3.39 (3.39) Acct: 5.51 (5.51) proj_loss: -0.5955 (-0.5955) time: 0.6696 data: 0.0003 [11-26 16:41:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 0/1669] eta: 0:18:37 tlr: 8.6e-05 tnm: 0.41 Lm: 6.597 (6.597) Lt: 5.823 (5.823) Accm: 3.18 (3.18) Acct: 4.80 (4.80) proj_loss: -0.5867 (-0.5867) time: 0.6699 data: 0.0004 [11-26 16:41:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 0/1669] eta: 0:18:38 tlr: 8.6e-05 tnm: 0.41 Lm: 6.591 (6.591) Lt: 5.839 (5.839) Accm: 2.99 (2.99) Acct: 4.92 (4.92) proj_loss: -0.6144 (-0.6144) time: 0.6699 data: 0.0003 [11-26 16:41:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 0/1669] eta: 0:18:38 tlr: 8.6e-05 tnm: 0.41 Lm: 6.368 (6.368) Lt: 5.601 (5.601) Accm: 3.49 (3.49) Acct: 5.56 (5.56) proj_loss: -0.6173 (-0.6173) time: 0.6704 data: 0.0004 [11-26 16:45:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 417/1669] eta: 0:13:58 tlr: 8.5e-05 tnm: 0.43 Lm: 6.422 (6.422) Lt: 5.632 (5.632) Accm: 3.57 (3.57) Acct: 5.69 (5.69) proj_loss: -0.6082 (-0.6082) time: 0.6693 data: 0.0003 [11-26 16:45:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 417/1669] eta: 0:13:58 tlr: 8.5e-05 tnm: 0.43 Lm: 6.530 (6.530) Lt: 5.764 (5.764) Accm: 3.26 (3.26) Acct: 5.11 (5.11) proj_loss: -0.6151 (-0.6151) time: 0.6693 data: 0.0002 [11-26 16:45:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 417/1669] eta: 0:13:58 tlr: 8.5e-05 tnm: 0.43 Lm: 6.546 (6.546) Lt: 5.780 (5.780) Accm: 3.36 (3.36) Acct: 5.10 (5.10) proj_loss: -0.5923 (-0.5923) time: 0.6693 data: 0.0003 [11-26 16:45:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 417/1669] eta: 0:13:58 tlr: 8.5e-05 tnm: 0.43 Lm: 6.554 (6.554) Lt: 5.835 (5.835) Accm: 3.19 (3.19) Acct: 5.04 (5.04) proj_loss: -0.6189 (-0.6189) time: 0.6693 data: 0.0003 [11-26 16:50:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 834/1669] eta: 0:09:18 tlr: 8.5e-05 tnm: 0.43 Lm: 6.516 (6.521) Lt: 5.830 (5.802) Accm: 3.39 (3.28) Acct: 5.15 (5.08) proj_loss: -0.6184 (-0.6187) time: 0.6681 data: 0.0002 [11-26 16:50:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 834/1669] eta: 0:09:18 tlr: 8.5e-05 tnm: 0.43 Lm: 6.495 (6.437) Lt: 5.736 (5.672) Accm: 3.55 (3.69) Acct: 5.39 (5.66) proj_loss: -0.5980 (-0.6026) time: 0.6681 data: 0.0003 [11-26 16:50:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 834/1669] eta: 0:09:18 tlr: 8.5e-05 tnm: 0.43 Lm: 6.522 (6.510) Lt: 5.722 (5.743) Accm: 3.39 (3.31) Acct: 5.46 (5.22) proj_loss: -0.6101 (-0.6134) time: 0.6681 data: 0.0003 [11-26 16:50:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [ 834/1669] eta: 0:09:18 tlr: 8.5e-05 tnm: 0.43 Lm: 6.476 (6.524) Lt: 5.662 (5.780) Accm: 3.49 (3.22) Acct: 5.56 (5.06) proj_loss: -0.6099 (-0.6088) time: 0.6681 data: 0.0003 [11-26 16:55:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1251/1669] eta: 0:04:43 tlr: 8.5e-05 tnm: 0.43 Lm: 6.436 (6.493) Lt: 5.646 (5.742) Accm: 3.57 (3.43) Acct: 5.69 (5.48) proj_loss: -0.6064 (-0.6073) time: 0.6707 data: 0.0003 [11-26 16:55:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1251/1669] eta: 0:04:43 tlr: 8.5e-05 tnm: 0.43 Lm: 6.496 (6.480) Lt: 5.711 (5.717) Accm: 3.40 (3.48) Acct: 5.48 (5.41) proj_loss: -0.6086 (-0.6119) time: 0.6707 data: 0.0002 [11-26 16:55:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1251/1669] eta: 0:04:43 tlr: 8.5e-05 tnm: 0.43 Lm: 6.487 (6.495) Lt: 5.784 (5.752) Accm: 3.35 (3.29) Acct: 5.17 (5.20) proj_loss: -0.6164 (-0.6151) time: 0.6707 data: 0.0002 [11-26 16:55:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1251/1669] eta: 0:04:43 tlr: 8.5e-05 tnm: 0.43 Lm: 6.497 (6.452) Lt: 5.760 (5.700) Accm: 3.52 (3.64) Acct: 5.46 (5.63) proj_loss: -0.6061 (-0.6055) time: 0.6707 data: 0.0003 [11-26 17:00:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1668/1669] eta: 0:00:00 tlr: 8.5e-05 tnm: 0.43 Lm: 6.495 (6.420) Lt: 5.736 (5.652) Accm: 3.55 (3.73) Acct: 5.53 (5.80) proj_loss: -0.5980 (-0.6037) time: 0.6725 data: 0.0019 [11-26 17:00:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 236/350] Total time: 0:18:48 (0.676 s / it) [11-26 17:00:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1668/1669] eta: 0:00:00 tlr: 8.5e-05 tnm: 0.43 Lm: 6.469 (6.469) Lt: 5.699 (5.698) Accm: 3.40 (3.53) Acct: 5.51 (5.45) proj_loss: -0.6085 (-0.6112) time: 0.6725 data: 0.0020 [11-26 17:00:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1668/1669] eta: 0:00:00 tlr: 8.5e-05 tnm: 0.43 Lm: 6.470 (6.490) Lt: 5.737 (5.742) Accm: 3.39 (3.34) Acct: 5.18 (5.29) proj_loss: -0.6184 (-0.6159) time: 0.6725 data: 0.0018 [11-26 17:00:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 236/350] [1668/1669] eta: 0:00:00 tlr: 8.5e-05 tnm: 0.43 Lm: 6.452 (6.484) Lt: 5.662 (5.734) Accm: 3.49 (3.43) Acct: 5.56 (5.44) proj_loss: -0.6091 (-0.6077) time: 0.6725 data: 0.0015 [11-26 17:00:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 236/350] Total time: 0:18:48 (0.676 s / it) [11-26 17:00:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 236/350] Total time: 0:18:48 (0.676 s / it) [11-26 17:00:05] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 236/350] Total time: 0:18:48 (0.676 s / it) [11-26 17:00:05] (/home/user/VAR/train.py , line 279)=> [ep236] (training ) Lm: 6.474 (6.474), Lt: 5.717 (5.717), Acc m&t: 3.46 5.47, Remain: 1 day, 11:28:13, Finish: 2024-11-27 12:28 [11-26 17:00:05] (/home/user/VAR/train.py , line 279)=> [ep236] (training ) Lm: 6.474 (6.474), Lt: 5.717 (5.717), Acc m&t: 3.46 5.47, Remain: 1 day, 11:27:49, Finish: 2024-11-27 12:27 [11-26 17:00:05] (/home/user/VAR/train.py , line 279)=> [ep236] (training ) Lm: 6.474 (6.474), Lt: 5.717 (5.717), Acc m&t: 3.46 5.47, Remain: 1 day, 11:28:13, Finish: 2024-11-27 12:28 [11-26 17:00:05] (/home/user/VAR/train.py , line 279)=> [ep236] (training ) Lm: 6.474 (6.474), Lt: 5.717 (5.717), Acc m&t: 3.46 5.47, Remain: 1 day, 11:28:07, Finish: 2024-11-27 12:28 [11-26 17:00:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 0/1669] eta: 0:18:09 tlr: 8.5e-05 tnm: 0.44 Lm: 6.578 (6.578) Lt: 5.806 (5.806) Accm: 3.41 (3.41) Acct: 5.54 (5.54) proj_loss: -0.5951 (-0.5951) time: 0.6529 data: 0.0003 [11-26 17:00:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 0/1669] eta: 0:18:09 tlr: 8.5e-05 tnm: 0.44 Lm: 6.411 (6.411) Lt: 5.683 (5.683) Accm: 3.61 (3.61) Acct: 5.27 (5.27) proj_loss: -0.6197 (-0.6197) time: 0.6528 data: 0.0004 [11-26 17:00:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 0/1669] eta: 0:18:09 tlr: 8.5e-05 tnm: 0.44 Lm: 6.458 (6.458) Lt: 5.688 (5.688) Accm: 3.37 (3.37) Acct: 5.65 (5.65) proj_loss: -0.6321 (-0.6321) time: 0.6527 data: 0.0003 [11-26 17:00:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 0/1669] eta: 0:18:11 tlr: 8.5e-05 tnm: 0.44 Lm: 6.366 (6.366) Lt: 5.612 (5.612) Accm: 3.89 (3.89) Acct: 6.23 (6.23) proj_loss: -0.6140 (-0.6140) time: 0.6539 data: 0.0004 [11-26 17:04:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 417/1669] eta: 0:13:58 tlr: 8.5e-05 tnm: 0.43 Lm: 6.421 (6.421) Lt: 5.702 (5.702) Accm: 3.67 (3.67) Acct: 5.73 (5.73) proj_loss: -0.6137 (-0.6137) time: 0.6711 data: 0.0003 [11-26 17:04:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 417/1669] eta: 0:13:58 tlr: 8.5e-05 tnm: 0.43 Lm: 6.467 (6.467) Lt: 5.702 (5.702) Accm: 3.36 (3.36) Acct: 5.35 (5.35) proj_loss: -0.6297 (-0.6297) time: 0.6711 data: 0.0003 [11-26 17:04:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 417/1669] eta: 0:13:58 tlr: 8.5e-05 tnm: 0.43 Lm: 6.453 (6.453) Lt: 5.709 (5.709) Accm: 3.57 (3.57) Acct: 5.58 (5.58) proj_loss: -0.6202 (-0.6202) time: 0.6711 data: 0.0003 [11-26 17:04:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 417/1669] eta: 0:13:58 tlr: 8.5e-05 tnm: 0.43 Lm: 6.458 (6.458) Lt: 5.686 (5.686) Accm: 3.60 (3.60) Acct: 5.75 (5.75) proj_loss: -0.5995 (-0.5995) time: 0.6711 data: 0.0002 [11-26 17:09:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 834/1669] eta: 0:09:19 tlr: 8.4e-05 tnm: 0.42 Lm: 6.337 (6.407) Lt: 5.566 (5.644) Accm: 3.72 (3.64) Acct: 5.65 (5.72) proj_loss: -0.6039 (-0.6018) time: 0.6723 data: 0.0002 [11-26 17:09:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 834/1669] eta: 0:09:19 tlr: 8.4e-05 tnm: 0.42 Lm: 6.458 (6.414) Lt: 5.688 (5.662) Accm: 3.37 (3.57) Acct: 5.65 (5.70) proj_loss: -0.6273 (-0.6287) time: 0.6723 data: 0.0003 [11-26 17:09:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 834/1669] eta: 0:09:19 tlr: 8.4e-05 tnm: 0.42 Lm: 6.438 (6.427) Lt: 5.658 (5.687) Accm: 3.66 (3.67) Acct: 5.84 (5.77) proj_loss: -0.6135 (-0.6078) time: 0.6723 data: 0.0003 [11-26 17:09:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [ 834/1669] eta: 0:09:19 tlr: 8.4e-05 tnm: 0.42 Lm: 6.411 (6.426) Lt: 5.683 (5.682) Accm: 3.61 (3.62) Acct: 5.65 (5.60) proj_loss: -0.6197 (-0.6192) time: 0.6723 data: 0.0003 [11-26 17:14:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1251/1669] eta: 0:04:44 tlr: 8.4e-05 tnm: 0.42 Lm: 6.453 (6.445) Lt: 5.709 (5.698) Accm: 3.57 (3.53) Acct: 5.60 (5.59) proj_loss: -0.6202 (-0.6197) time: 0.6701 data: 0.0003 [11-26 17:14:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1251/1669] eta: 0:04:44 tlr: 8.4e-05 tnm: 0.42 Lm: 6.384 (6.381) Lt: 5.635 (5.630) Accm: 3.56 (3.61) Acct: 5.87 (5.80) proj_loss: -0.6271 (-0.6226) time: 0.6701 data: 0.0003 [11-26 17:14:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1251/1669] eta: 0:04:44 tlr: 8.4e-05 tnm: 0.42 Lm: 6.402 (6.422) Lt: 5.648 (5.666) Accm: 3.76 (3.69) Acct: 5.80 (5.82) proj_loss: -0.6052 (-0.6045) time: 0.6701 data: 0.0002 [11-26 17:14:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1251/1669] eta: 0:04:44 tlr: 8.4e-05 tnm: 0.42 Lm: 6.458 (6.497) Lt: 5.724 (5.753) Accm: 3.55 (3.40) Acct: 5.54 (5.37) proj_loss: -0.6047 (-0.6026) time: 0.6701 data: 0.0006 [11-26 17:18:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1668/1669] eta: 0:00:00 tlr: 8.4e-05 tnm: 0.42 Lm: 6.477 (6.544) Lt: 5.791 (5.812) Accm: 3.45 (3.26) Acct: 5.23 (5.15) proj_loss: -0.6034 (-0.6027) time: 0.6724 data: 0.0025 [11-26 17:18:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 237/350] Total time: 0:18:50 (0.677 s / it) [11-26 17:18:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1668/1669] eta: 0:00:00 tlr: 8.4e-05 tnm: 0.42 Lm: 6.408 (6.386) Lt: 5.643 (5.632) Accm: 3.74 (3.65) Acct: 5.87 (5.81) proj_loss: -0.6268 (-0.6208) time: 0.6724 data: 0.0019 [11-26 17:18:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1668/1669] eta: 0:00:00 tlr: 8.4e-05 tnm: 0.42 Lm: 6.470 (6.450) Lt: 5.721 (5.703) Accm: 3.52 (3.50) Acct: 5.54 (5.47) proj_loss: -0.6197 (-0.6177) time: 0.6724 data: 0.0019 [11-26 17:18:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 237/350] [1668/1669] eta: 0:00:00 tlr: 8.4e-05 tnm: 0.42 Lm: 6.467 (6.449) Lt: 5.731 (5.693) Accm: 3.72 (3.60) Acct: 5.65 (5.70) proj_loss: -0.6065 (-0.6061) time: 0.6724 data: 0.0016 [11-26 17:18:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 237/350] Total time: 0:18:50 (0.677 s / it) [11-26 17:18:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 237/350] Total time: 0:18:50 (0.677 s / it) [11-26 17:18:55] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 237/350] Total time: 0:18:50 (0.677 s / it) [11-26 17:18:55] (/home/user/VAR/train.py , line 279)=> [ep237] (training ) Lm: 6.474 (6.479), Lt: 5.717 (5.721), Acc m&t: 3.46 5.47, Remain: 1 day, 11:13:27, Finish: 2024-11-27 12:32 [11-26 17:18:55] (/home/user/VAR/train.py , line 279)=> [ep237] (training ) Lm: 6.474 (6.479), Lt: 5.717 (5.721), Acc m&t: 3.46 5.47, Remain: 1 day, 11:13:28, Finish: 2024-11-27 12:32 [11-26 17:18:55] (/home/user/VAR/train.py , line 279)=> [ep237] (training ) Lm: 6.474 (6.479), Lt: 5.717 (5.721), Acc m&t: 3.46 5.47, Remain: 1 day, 11:13:17, Finish: 2024-11-27 12:32 [11-26 17:18:55] (/home/user/VAR/train.py , line 279)=> [ep237] (training ) Lm: 6.474 (6.479), Lt: 5.717 (5.721), Acc m&t: 3.46 5.47, Remain: 1 day, 11:13:16, Finish: 2024-11-27 12:32 [11-26 17:18:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 0/1669] eta: 0:18:11 tlr: 8.4e-05 tnm: 0.44 Lm: 6.355 (6.355) Lt: 5.574 (5.574) Accm: 3.91 (3.91) Acct: 6.10 (6.10) proj_loss: -0.6307 (-0.6307) time: 0.6540 data: 0.0004 [11-26 17:18:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 0/1669] eta: 0:18:10 tlr: 8.4e-05 tnm: 0.44 Lm: 6.341 (6.341) Lt: 5.521 (5.521) Accm: 3.73 (3.73) Acct: 5.97 (5.97) proj_loss: -0.6025 (-0.6025) time: 0.6536 data: 0.0004 [11-26 17:18:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 0/1669] eta: 0:18:09 tlr: 8.4e-05 tnm: 0.44 Lm: 6.545 (6.545) Lt: 5.735 (5.735) Accm: 3.27 (3.27) Acct: 5.58 (5.58) proj_loss: -0.5789 (-0.5789) time: 0.6529 data: 0.0004 [11-26 17:18:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 0/1669] eta: 0:18:12 tlr: 8.4e-05 tnm: 0.44 Lm: 6.550 (6.550) Lt: 5.868 (5.868) Accm: 3.17 (3.17) Acct: 5.01 (5.01) proj_loss: -0.6173 (-0.6173) time: 0.6546 data: 0.0003 [11-26 17:23:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 417/1669] eta: 0:13:58 tlr: 8.4e-05 tnm: 0.43 Lm: 6.483 (6.483) Lt: 5.742 (5.742) Accm: 3.34 (3.34) Acct: 5.35 (5.35) proj_loss: -0.6129 (-0.6129) time: 0.6699 data: 0.0002 [11-26 17:23:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 417/1669] eta: 0:13:58 tlr: 8.4e-05 tnm: 0.43 Lm: 6.387 (6.387) Lt: 5.587 (5.587) Accm: 3.78 (3.78) Acct: 5.93 (5.93) proj_loss: -0.6031 (-0.6031) time: 0.6699 data: 0.0003 [11-26 17:23:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 417/1669] eta: 0:13:58 tlr: 8.4e-05 tnm: 0.43 Lm: 6.495 (6.495) Lt: 5.716 (5.716) Accm: 3.46 (3.46) Acct: 5.71 (5.71) proj_loss: -0.5953 (-0.5953) time: 0.6699 data: 0.0003 [11-26 17:23:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 417/1669] eta: 0:13:58 tlr: 8.4e-05 tnm: 0.43 Lm: 6.441 (6.441) Lt: 5.657 (5.657) Accm: 3.56 (3.56) Acct: 5.66 (5.66) proj_loss: -0.6189 (-0.6189) time: 0.6700 data: 0.0003 [11-26 17:28:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 834/1669] eta: 0:09:19 tlr: 8.4e-05 tnm: 0.43 Lm: 6.506 (6.463) Lt: 5.700 (5.671) Accm: 3.21 (3.43) Acct: 5.22 (5.50) proj_loss: -0.6072 (-0.6067) time: 0.6688 data: 0.0003 [11-26 17:28:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 834/1669] eta: 0:09:19 tlr: 8.4e-05 tnm: 0.43 Lm: 6.528 (6.498) Lt: 5.729 (5.737) Accm: 3.27 (3.31) Acct: 5.25 (5.31) proj_loss: -0.6110 (-0.6123) time: 0.6688 data: 0.0002 [11-26 17:28:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 834/1669] eta: 0:09:19 tlr: 8.4e-05 tnm: 0.43 Lm: 6.454 (6.481) Lt: 5.735 (5.725) Accm: 3.45 (3.46) Acct: 5.58 (5.45) proj_loss: -0.6064 (-0.5990) time: 0.6688 data: 0.0003 [11-26 17:28:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [ 834/1669] eta: 0:09:19 tlr: 8.4e-05 tnm: 0.43 Lm: 6.433 (6.435) Lt: 5.653 (5.641) Accm: 3.73 (3.63) Acct: 5.89 (5.75) proj_loss: -0.6036 (-0.6062) time: 0.6688 data: 0.0002 [11-26 17:32:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1251/1669] eta: 0:04:39 tlr: 8.4e-05 tnm: 0.43 Lm: 6.401 (6.418) Lt: 5.665 (5.650) Accm: 3.77 (3.68) Acct: 5.79 (5.73) proj_loss: -0.6081 (-0.6090) time: 0.6705 data: 0.0003 [11-26 17:32:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1251/1669] eta: 0:04:39 tlr: 8.4e-05 tnm: 0.43 Lm: 6.539 (6.528) Lt: 5.799 (5.783) Accm: 3.22 (3.14) Acct: 5.13 (5.05) proj_loss: -0.6141 (-0.6177) time: 0.6705 data: 0.0003 [11-26 17:32:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1251/1669] eta: 0:04:39 tlr: 8.4e-05 tnm: 0.43 Lm: 6.431 (6.431) Lt: 5.662 (5.659) Accm: 3.56 (3.60) Acct: 5.66 (5.69) proj_loss: -0.6037 (-0.6050) time: 0.6706 data: 0.0003 [11-26 17:32:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1251/1669] eta: 0:04:39 tlr: 8.4e-05 tnm: 0.43 Lm: 6.476 (6.486) Lt: 5.733 (5.727) Accm: 3.42 (3.44) Acct: 5.41 (5.40) proj_loss: -0.6090 (-0.6033) time: 0.6706 data: 0.0003 [11-26 17:37:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.43 Lm: 6.499 (6.489) Lt: 5.735 (5.736) Accm: 3.38 (3.42) Acct: 5.25 (5.30) proj_loss: -0.6073 (-0.6041) time: 0.6735 data: 0.0020 [11-26 17:37:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 238/350] Total time: 0:18:38 (0.670 s / it) [11-26 17:37:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.43 Lm: 6.432 (6.421) Lt: 5.677 (5.667) Accm: 3.73 (3.67) Acct: 5.68 (5.72) proj_loss: -0.6095 (-0.6091) time: 0.6735 data: 0.0018 [11-26 17:37:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.43 Lm: 6.528 (6.521) Lt: 5.791 (5.784) Accm: 3.27 (3.21) Acct: 5.25 (5.16) proj_loss: -0.6173 (-0.6200) time: 0.6735 data: 0.0020 [11-26 17:37:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 238/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.43 Lm: 6.355 (6.398) Lt: 5.624 (5.626) Accm: 3.91 (3.73) Acct: 6.10 (5.79) proj_loss: -0.6001 (-0.6038) time: 0.6735 data: 0.0015 [11-26 17:37:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 238/350] Total time: 0:18:38 (0.670 s / it) [11-26 17:37:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 238/350] Total time: 0:18:38 (0.670 s / it) [11-26 17:37:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 238/350] Total time: 0:18:38 (0.670 s / it) [11-26 17:37:33] (/home/user/VAR/train.py , line 279)=> [ep238] (training ) Lm: 6.472 (6.472), Lt: 5.717 (5.722), Acc m&t: 3.46 5.47, Remain: 1 day, 10:49:50, Finish: 2024-11-27 12:27 [11-26 17:37:33] (/home/user/VAR/train.py , line 279)=> [ep238] (training ) Lm: 6.472 (6.472), Lt: 5.717 (5.722), Acc m&t: 3.46 5.47, Remain: 1 day, 10:49:51, Finish: 2024-11-27 12:27 [11-26 17:37:33] (/home/user/VAR/train.py , line 279)=> [ep238] (training ) Lm: 6.472 (6.472), Lt: 5.717 (5.722), Acc m&t: 3.46 5.47, Remain: 1 day, 10:49:31, Finish: 2024-11-27 12:27 [11-26 17:37:33] (/home/user/VAR/train.py , line 279)=> [ep238] (training ) Lm: 6.472 (6.472), Lt: 5.717 (5.722), Acc m&t: 3.46 5.47, Remain: 1 day, 10:50:12, Finish: 2024-11-27 12:27 [11-26 17:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 0/1669] eta: 0:17:58 tlr: 8.3e-05 tnm: 0.45 Lm: 6.503 (6.503) Lt: 5.800 (5.800) Accm: 3.07 (3.07) Acct: 4.79 (4.79) proj_loss: -0.6435 (-0.6435) time: 0.6462 data: 0.0003 [11-26 17:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 0/1669] eta: 0:17:59 tlr: 8.3e-05 tnm: 0.45 Lm: 6.411 (6.411) Lt: 5.688 (5.688) Accm: 3.60 (3.60) Acct: 5.30 (5.30) proj_loss: -0.5785 (-0.5785) time: 0.6468 data: 0.0004 [11-26 17:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 0/1669] eta: 0:17:59 tlr: 8.3e-05 tnm: 0.45 Lm: 6.305 (6.305) Lt: 5.501 (5.501) Accm: 4.22 (4.22) Acct: 6.51 (6.51) proj_loss: -0.5929 (-0.5929) time: 0.6467 data: 0.0004 [11-26 17:37:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 0/1669] eta: 0:17:59 tlr: 8.3e-05 tnm: 0.45 Lm: 6.501 (6.501) Lt: 5.751 (5.751) Accm: 3.37 (3.37) Acct: 4.96 (4.96) proj_loss: -0.6182 (-0.6182) time: 0.6471 data: 0.0004 [11-26 17:42:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 417/1669] eta: 0:14:33 tlr: 8.3e-05 tnm: 0.43 Lm: 6.540 (6.540) Lt: 5.819 (5.819) Accm: 3.18 (3.18) Acct: 4.66 (4.66) proj_loss: -0.6095 (-0.6095) time: 0.6678 data: 0.0003 [11-26 17:42:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 417/1669] eta: 0:14:33 tlr: 8.3e-05 tnm: 0.43 Lm: 6.308 (6.308) Lt: 5.524 (5.524) Accm: 4.14 (4.14) Acct: 6.35 (6.35) proj_loss: -0.6122 (-0.6122) time: 0.6678 data: 0.0003 [11-26 17:42:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 417/1669] eta: 0:14:33 tlr: 8.3e-05 tnm: 0.43 Lm: 6.432 (6.432) Lt: 5.701 (5.701) Accm: 3.47 (3.47) Acct: 5.32 (5.32) proj_loss: -0.5881 (-0.5881) time: 0.6678 data: 0.0003 [11-26 17:42:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 417/1669] eta: 0:14:33 tlr: 8.3e-05 tnm: 0.43 Lm: 6.486 (6.486) Lt: 5.768 (5.768) Accm: 3.19 (3.19) Acct: 4.92 (4.92) proj_loss: -0.6302 (-0.6302) time: 0.6679 data: 0.0003 [11-26 17:47:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 834/1669] eta: 0:09:30 tlr: 8.3e-05 tnm: 0.41 Lm: 6.503 (6.500) Lt: 5.735 (5.753) Accm: 3.31 (3.23) Acct: 5.04 (5.10) proj_loss: -0.6169 (-0.6229) time: 0.6694 data: 0.0003 [11-26 17:47:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 834/1669] eta: 0:09:30 tlr: 8.3e-05 tnm: 0.41 Lm: 6.561 (6.547) Lt: 5.807 (5.815) Accm: 3.18 (3.18) Acct: 4.92 (4.75) proj_loss: -0.6012 (-0.6067) time: 0.6694 data: 0.0003 [11-26 17:47:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 834/1669] eta: 0:09:30 tlr: 8.3e-05 tnm: 0.41 Lm: 6.311 (6.390) Lt: 5.548 (5.629) Accm: 4.06 (3.82) Acct: 6.20 (5.88) proj_loss: -0.6150 (-0.6131) time: 0.6694 data: 0.0003 [11-26 17:47:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [ 834/1669] eta: 0:09:30 tlr: 8.3e-05 tnm: 0.41 Lm: 6.411 (6.414) Lt: 5.688 (5.666) Accm: 3.60 (3.61) Acct: 5.34 (5.49) proj_loss: -0.5977 (-0.6012) time: 0.6694 data: 0.0003 [11-26 17:51:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1251/1669] eta: 0:04:43 tlr: 8.3e-05 tnm: 0.42 Lm: 6.432 (6.471) Lt: 5.701 (5.723) Accm: 3.47 (3.41) Acct: 5.32 (5.21) proj_loss: -0.5974 (-0.6002) time: 0.6685 data: 0.0003 [11-26 17:51:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1251/1669] eta: 0:04:43 tlr: 8.3e-05 tnm: 0.42 Lm: 6.432 (6.437) Lt: 5.667 (5.668) Accm: 3.69 (3.69) Acct: 5.90 (5.81) proj_loss: -0.6039 (-0.6064) time: 0.6685 data: 0.0003 [11-26 17:51:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1251/1669] eta: 0:04:43 tlr: 8.3e-05 tnm: 0.42 Lm: 6.486 (6.472) Lt: 5.729 (5.713) Accm: 3.31 (3.35) Acct: 5.26 (5.31) proj_loss: -0.6126 (-0.6179) time: 0.6685 data: 0.0003 [11-26 17:51:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1251/1669] eta: 0:04:43 tlr: 8.3e-05 tnm: 0.42 Lm: 6.544 (6.542) Lt: 5.807 (5.813) Accm: 3.10 (3.14) Acct: 4.94 (4.80) proj_loss: -0.6074 (-0.6085) time: 0.6685 data: 0.0003 [11-26 17:56:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.43 Lm: 6.533 (6.540) Lt: 5.807 (5.822) Accm: 3.02 (3.10) Acct: 4.92 (4.75) proj_loss: -0.6136 (-0.6108) time: 0.6710 data: 0.0021 [11-26 17:56:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 239/350] Total time: 0:18:49 (0.677 s / it) [11-26 17:56:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.43 Lm: 6.469 (6.455) Lt: 5.722 (5.697) Accm: 3.32 (3.42) Acct: 5.48 (5.44) proj_loss: -0.6083 (-0.6135) time: 0.6710 data: 0.0018 [11-26 17:56:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.43 Lm: 6.453 (6.484) Lt: 5.715 (5.737) Accm: 3.34 (3.35) Acct: 5.30 (5.16) proj_loss: -0.5971 (-0.5975) time: 0.6710 data: 0.0020 [11-26 17:56:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 239/350] [1668/1669] eta: 0:00:00 tlr: 8.3e-05 tnm: 0.43 Lm: 6.553 (6.460) Lt: 5.786 (5.701) Accm: 3.31 (3.54) Acct: 5.60 (5.61) proj_loss: -0.6150 (-0.6083) time: 0.6710 data: 0.0015 [11-26 17:56:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 239/350] Total time: 0:18:49 (0.677 s / it) [11-26 17:56:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 239/350] Total time: 0:18:49 (0.677 s / it) [11-26 17:56:23] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 239/350] Total time: 0:18:49 (0.677 s / it) [11-26 18:00:47] (me/user/VAR/evaluator.py, line 590)=> downloading InceptionV3 model... [11-26 18:02:03] (home/user/VAR/trainer.py, line 114)=> FID: 3.2454558703929592 [11-26 18:02:04] (/home/user/VAR/train.py , line 262)=> [*] [ep239] (val 50000) Lm: 6.4741, Lt: 5.7229, Acc m&t: 3.46 5.42, Val cost: 340.30s [11-26 18:02:04] (/home/user/VAR/train.py , line 267)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-26 18:02:38] (/home/user/VAR/train.py , line 279)=> [ep239] (training ) Lm: 6.472 (6.474), Lt: 5.717 (5.723), Acc m&t: 3.46 5.47, Remain: 1 day, 10:32:58, Finish: 2024-11-27 12:29 [11-26 18:02:38] (/home/user/VAR/train.py , line 279)=> [ep239] (training ) Lm: 6.472 (6.474), Lt: 5.717 (5.723), Acc m&t: 3.46 5.47, Remain: 1 day, 10:34:20, Finish: 2024-11-27 12:30 [11-26 18:02:38] (/home/user/VAR/train.py , line 279)=> [ep239] (training ) Lm: 6.472 (6.474), Lt: 5.717 (5.723), Acc m&t: 3.46 5.47, Remain: 1 day, 10:31:48, Finish: 2024-11-27 12:28 [11-26 18:02:38] (/home/user/VAR/train.py , line 279)=> [ep239] (training ) Lm: 6.472 (6.474), Lt: 5.717 (5.723), Acc m&t: 3.46 5.47, Remain: 1 day, 10:32:11, Finish: 2024-11-27 12:28 [11-26 18:02:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [ 0/1669] eta: 0:21:47 tlr: 8.3e-05 tnm: 0.42 Lm: 6.561 (6.561) Lt: 5.821 (5.821) Accm: 3.08 (3.08) Acct: 4.98 (4.98) proj_loss: -0.6128 (-0.6128) time: 0.7836 data: 0.0004 [11-26 18:02:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [ 0/1669] eta: 0:22:01 tlr: 8.3e-05 tnm: 0.42 Lm: 6.437 (6.437) Lt: 5.736 (5.736) Accm: 3.48 (3.48) Acct: 5.53 (5.53) proj_loss: -0.5967 (-0.5967) time: 0.7915 data: 0.0004 [11-26 18:02:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [ 0/1669] eta: 0:21:47 tlr: 8.3e-05 tnm: 0.42 Lm: 6.344 (6.344) Lt: 5.573 (5.573) Accm: 3.87 (3.87) Acct: 6.40 (6.40) proj_loss: -0.6130 (-0.6130) time: 0.7834 data: 0.0004 [11-26 18:02:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [ 0/1669] eta: 0:21:47 tlr: 8.3e-05 tnm: 0.42 Lm: 6.235 (6.235) Lt: 5.431 (5.431) Accm: 3.99 (3.99) Acct: 6.39 (6.39) proj_loss: -0.6192 (-0.6192) time: 0.7833 data: 0.0004 [11-26 18:07:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [ 417/1669] eta: 0:14:02 tlr: 8.2e-05 tnm: 0.43 Lm: 6.285 (6.285) Lt: 5.501 (5.501) Accm: 3.85 (3.85) Acct: 5.96 (5.96) proj_loss: -0.6163 (-0.6163) time: 0.6732 data: 0.0003 [11-26 18:07:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [ 417/1669] eta: 0:14:02 tlr: 8.2e-05 tnm: 0.43 Lm: 6.511 (6.511) Lt: 5.790 (5.790) Accm: 3.31 (3.31) Acct: 5.17 (5.17) proj_loss: -0.5998 (-0.5998) time: 0.6732 data: 0.0003 [11-26 18:07:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [ 417/1669] eta: 0:14:02 tlr: 8.2e-05 tnm: 0.43 Lm: 6.399 (6.399) Lt: 5.636 (5.636) Accm: 3.68 (3.68) Acct: 6.04 (6.04) proj_loss: -0.6082 (-0.6082) time: 0.6732 data: 0.0003 [11-26 18:07:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [ 417/1669] eta: 0:14:02 tlr: 8.2e-05 tnm: 0.43 Lm: 6.494 (6.494) Lt: 5.755 (5.755) Accm: 3.29 (3.29) Acct: 5.16 (5.16) proj_loss: -0.6026 (-0.6026) time: 0.6732 data: 0.0002 [11-26 18:11:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [ 834/1669] eta: 0:09:21 tlr: 8.2e-05 tnm: 0.42 Lm: 6.427 (6.444) Lt: 5.690 (5.700) Accm: 3.50 (3.52) Acct: 5.34 (5.48) proj_loss: -0.6128 (-0.6120) time: 0.6731 data: 0.0003 [11-26 18:11:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [ 834/1669] eta: 0:09:21 tlr: 8.2e-05 tnm: 0.42 Lm: 6.455 (6.450) Lt: 5.700 (5.694) Accm: 3.50 (3.48) Acct: 5.68 (5.62) proj_loss: -0.6033 (-0.6037) time: 0.6730 data: 0.0003 [11-26 18:11:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [ 834/1669] eta: 0:09:21 tlr: 8.2e-05 tnm: 0.42 Lm: 6.439 (6.487) Lt: 5.736 (5.752) Accm: 3.48 (3.44) Acct: 5.53 (5.41) proj_loss: -0.6030 (-0.6051) time: 0.6731 data: 0.0003 [11-26 18:11:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [ 834/1669] eta: 0:09:21 tlr: 8.2e-05 tnm: 0.42 Lm: 6.336 (6.334) Lt: 5.572 (5.548) Accm: 3.72 (3.75) Acct: 5.73 (5.88) proj_loss: -0.6134 (-0.6095) time: 0.6731 data: 0.0003 [11-26 18:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [1251/1669] eta: 0:04:41 tlr: 8.2e-05 tnm: 0.44 Lm: 6.383 (6.381) Lt: 5.606 (5.601) Accm: 3.62 (3.61) Acct: 5.63 (5.66) proj_loss: -0.6047 (-0.6039) time: 0.6735 data: 0.0003 [11-26 18:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [1251/1669] eta: 0:04:41 tlr: 8.2e-05 tnm: 0.44 Lm: 6.494 (6.477) Lt: 5.740 (5.722) Accm: 3.55 (3.54) Acct: 5.57 (5.56) proj_loss: -0.6026 (-0.6066) time: 0.6735 data: 0.0003 [11-26 18:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [1251/1669] eta: 0:04:41 tlr: 8.2e-05 tnm: 0.44 Lm: 6.438 (6.462) Lt: 5.706 (5.718) Accm: 3.56 (3.49) Acct: 5.54 (5.45) proj_loss: -0.6093 (-0.6080) time: 0.6735 data: 0.0003 [11-26 18:16:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [1251/1669] eta: 0:04:41 tlr: 8.2e-05 tnm: 0.44 Lm: 6.460 (6.454) Lt: 5.705 (5.698) Accm: 3.57 (3.52) Acct: 5.53 (5.56) proj_loss: -0.6008 (-0.6024) time: 0.6735 data: 0.0003 [11-26 18:21:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [1668/1669] eta: 0:00:00 tlr: 8.2e-05 tnm: 0.42 Lm: 6.466 (6.466) Lt: 5.711 (5.716) Accm: 3.53 (3.52) Acct: 5.68 (5.61) proj_loss: -0.5984 (-0.5999) time: 0.6777 data: 0.0020 [11-26 18:21:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 240/350] Total time: 0:18:42 (0.673 s / it) [11-26 18:21:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [1668/1669] eta: 0:00:00 tlr: 8.2e-05 tnm: 0.42 Lm: 6.427 (6.460) Lt: 5.690 (5.706) Accm: 3.60 (3.58) Acct: 5.80 (5.66) proj_loss: -0.6068 (-0.6067) time: 0.6777 data: 0.0016 [11-26 18:21:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [1668/1669] eta: 0:00:00 tlr: 8.2e-05 tnm: 0.42 Lm: 6.437 (6.456) Lt: 5.703 (5.715) Accm: 3.56 (3.51) Acct: 5.54 (5.49) proj_loss: -0.6157 (-0.6111) time: 0.6777 data: 0.0019 [11-26 18:21:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 240/350] [1668/1669] eta: 0:00:00 tlr: 8.2e-05 tnm: 0.42 Lm: 6.430 (6.404) Lt: 5.640 (5.627) Accm: 3.53 (3.54) Acct: 5.53 (5.53) proj_loss: -0.5960 (-0.6022) time: 0.6777 data: 0.0016 [11-26 18:21:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 240/350] Total time: 0:18:42 (0.673 s / it) [11-26 18:21:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 240/350] Total time: 0:18:42 (0.673 s / it) [11-26 18:21:21] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 240/350] Total time: 0:18:42 (0.673 s / it) [11-26 18:21:21] (/home/user/VAR/train.py , line 279)=> [ep240] (training ) Lm: 6.458 (6.458), Lt: 5.699 (5.699), Acc m&t: 3.49 5.52, Remain: 1 day, 10:31:41, Finish: 2024-11-27 12:53 [11-26 18:21:21] (/home/user/VAR/train.py , line 279)=> [ep240] (training ) Lm: 6.458 (6.458), Lt: 5.699 (5.699), Acc m&t: 3.49 5.52, Remain: 1 day, 10:31:44, Finish: 2024-11-27 12:53 [11-26 18:21:21] (/home/user/VAR/train.py , line 279)=> [ep240] (training ) Lm: 6.458 (6.458), Lt: 5.699 (5.699), Acc m&t: 3.49 5.52, Remain: 1 day, 10:31:26, Finish: 2024-11-27 12:52 [11-26 18:21:21] (/home/user/VAR/train.py , line 279)=> [ep240] (training ) Lm: 6.458 (6.458), Lt: 5.699 (5.699), Acc m&t: 3.49 5.52, Remain: 1 day, 10:31:36, Finish: 2024-11-27 12:52 [11-26 18:21:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [ 0/1669] eta: 0:18:01 tlr: 8.2e-05 tnm: 0.44 Lm: 6.437 (6.437) Lt: 5.637 (5.637) Accm: 3.63 (3.63) Acct: 6.23 (6.23) proj_loss: -0.6035 (-0.6035) time: 0.6478 data: 0.0004 [11-26 18:21:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [ 0/1669] eta: 0:18:01 tlr: 8.2e-05 tnm: 0.44 Lm: 6.088 (6.088) Lt: 5.290 (5.290) Accm: 4.65 (4.65) Acct: 7.21 (7.21) proj_loss: -0.6084 (-0.6084) time: 0.6482 data: 0.0003 [11-26 18:21:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [ 0/1669] eta: 0:18:02 tlr: 8.2e-05 tnm: 0.44 Lm: 6.533 (6.533) Lt: 5.752 (5.752) Accm: 3.26 (3.26) Acct: 5.22 (5.22) proj_loss: -0.5940 (-0.5940) time: 0.6484 data: 0.0003 [11-26 18:21:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [ 0/1669] eta: 0:18:02 tlr: 8.2e-05 tnm: 0.44 Lm: 6.527 (6.527) Lt: 5.757 (5.757) Accm: 3.32 (3.32) Acct: 5.11 (5.11) proj_loss: -0.5843 (-0.5843) time: 0.6487 data: 0.0004 [11-26 18:26:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [ 417/1669] eta: 0:14:07 tlr: 8.2e-05 tnm: 0.44 Lm: 6.553 (6.553) Lt: 5.826 (5.826) Accm: 3.29 (3.29) Acct: 5.11 (5.11) proj_loss: -0.5899 (-0.5899) time: 0.6714 data: 0.0003 [11-26 18:26:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [ 417/1669] eta: 0:14:07 tlr: 8.2e-05 tnm: 0.44 Lm: 6.615 (6.615) Lt: 5.875 (5.875) Accm: 2.93 (2.93) Acct: 4.73 (4.73) proj_loss: -0.5975 (-0.5975) time: 0.6714 data: 0.0003 [11-26 18:26:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [ 417/1669] eta: 0:14:07 tlr: 8.2e-05 tnm: 0.44 Lm: 6.434 (6.434) Lt: 5.692 (5.692) Accm: 3.53 (3.53) Acct: 5.67 (5.67) proj_loss: -0.6188 (-0.6188) time: 0.6714 data: 0.0003 [11-26 18:26:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [ 417/1669] eta: 0:14:07 tlr: 8.2e-05 tnm: 0.44 Lm: 6.339 (6.339) Lt: 5.573 (5.573) Accm: 3.85 (3.85) Acct: 6.10 (6.10) proj_loss: -0.6092 (-0.6092) time: 0.6714 data: 0.0003 [11-26 18:30:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [ 834/1669] eta: 0:09:34 tlr: 8.2e-05 tnm: 0.43 Lm: 6.411 (6.363) Lt: 5.619 (5.588) Accm: 3.61 (3.77) Acct: 5.79 (5.99) proj_loss: -0.6101 (-0.6120) time: 0.6728 data: 0.0003 [11-26 18:30:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [ 834/1669] eta: 0:09:34 tlr: 8.2e-05 tnm: 0.43 Lm: 6.533 (6.528) Lt: 5.752 (5.785) Accm: 3.26 (3.21) Acct: 5.22 (5.12) proj_loss: -0.6010 (-0.5998) time: 0.6727 data: 0.0003 [11-26 18:30:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [ 834/1669] eta: 0:09:34 tlr: 8.2e-05 tnm: 0.43 Lm: 6.430 (6.432) Lt: 5.703 (5.696) Accm: 3.49 (3.52) Acct: 5.56 (5.64) proj_loss: -0.6342 (-0.6254) time: 0.6728 data: 0.0003 [11-26 18:30:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [ 834/1669] eta: 0:09:34 tlr: 8.2e-05 tnm: 0.43 Lm: 6.579 (6.573) Lt: 5.896 (5.852) Accm: 3.26 (3.21) Acct: 5.10 (5.07) proj_loss: -0.5955 (-0.5998) time: 0.6728 data: 0.0003 [11-26 18:35:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [1251/1669] eta: 0:04:45 tlr: 8.1e-05 tnm: 0.43 Lm: 6.553 (6.532) Lt: 5.826 (5.793) Accm: 3.29 (3.33) Acct: 5.11 (5.20) proj_loss: -0.5952 (-0.5986) time: 0.6727 data: 0.0003 [11-26 18:35:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [1251/1669] eta: 0:04:45 tlr: 8.1e-05 tnm: 0.43 Lm: 6.512 (6.519) Lt: 5.724 (5.763) Accm: 3.27 (3.23) Acct: 5.26 (5.17) proj_loss: -0.5989 (-0.5991) time: 0.6727 data: 0.0003 [11-26 18:35:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [1251/1669] eta: 0:04:45 tlr: 8.1e-05 tnm: 0.43 Lm: 6.482 (6.410) Lt: 5.727 (5.650) Accm: 3.51 (3.68) Acct: 5.63 (5.86) proj_loss: -0.6139 (-0.6137) time: 0.6727 data: 0.0003 [11-26 18:35:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [1251/1669] eta: 0:04:45 tlr: 8.1e-05 tnm: 0.43 Lm: 6.434 (6.443) Lt: 5.714 (5.703) Accm: 3.52 (3.52) Acct: 5.59 (5.63) proj_loss: -0.6202 (-0.6206) time: 0.6727 data: 0.0003 [11-26 18:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [1668/1669] eta: 0:00:00 tlr: 8.1e-05 tnm: 0.43 Lm: 6.430 (6.433) Lt: 5.703 (5.673) Accm: 3.55 (3.59) Acct: 5.61 (5.73) proj_loss: -0.6062 (-0.6127) time: 0.6724 data: 0.0019 [11-26 18:40:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 241/350] Total time: 0:18:55 (0.681 s / it) [11-26 18:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [1668/1669] eta: 0:00:00 tlr: 8.1e-05 tnm: 0.43 Lm: 6.491 (6.506) Lt: 5.696 (5.735) Accm: 3.29 (3.28) Acct: 5.30 (5.23) proj_loss: -0.5968 (-0.5979) time: 0.6724 data: 0.0018 [11-26 18:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [1668/1669] eta: 0:00:00 tlr: 8.1e-05 tnm: 0.43 Lm: 6.411 (6.403) Lt: 5.619 (5.625) Accm: 3.61 (3.74) Acct: 5.79 (5.97) proj_loss: -0.6101 (-0.6093) time: 0.6724 data: 0.0016 [11-26 18:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 241/350] [1668/1669] eta: 0:00:00 tlr: 8.1e-05 tnm: 0.43 Lm: 6.527 (6.524) Lt: 5.757 (5.773) Accm: 3.27 (3.32) Acct: 5.11 (5.19) proj_loss: -0.5955 (-0.5998) time: 0.6724 data: 0.0021 [11-26 18:40:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 241/350] Total time: 0:18:55 (0.681 s / it) [11-26 18:40:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 241/350] Total time: 0:18:55 (0.681 s / it) [11-26 18:40:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 241/350] Total time: 0:18:55 (0.681 s / it) [11-26 18:40:17] (/home/user/VAR/train.py , line 279)=> [ep241] (training ) Lm: 6.457 (6.457), Lt: 5.699 (5.699), Acc m&t: 3.51 5.53, Remain: 1 day, 9:50:22, Finish: 2024-11-27 12:30 [11-26 18:40:17] (/home/user/VAR/train.py , line 279)=> [ep241] (training ) Lm: 6.457 (6.457), Lt: 5.699 (5.699), Acc m&t: 3.51 5.53, Remain: 1 day, 9:50:10, Finish: 2024-11-27 12:30 [11-26 18:40:17] (/home/user/VAR/train.py , line 279)=> [ep241] (training ) Lm: 6.457 (6.457), Lt: 5.699 (5.699), Acc m&t: 3.51 5.53, Remain: 1 day, 9:50:27, Finish: 2024-11-27 12:30 [11-26 18:40:17] (/home/user/VAR/train.py , line 279)=> [ep241] (training ) Lm: 6.457 (6.457), Lt: 5.699 (5.699), Acc m&t: 3.51 5.53, Remain: 1 day, 9:50:27, Finish: 2024-11-27 12:30 [11-26 18:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [ 0/1669] eta: 0:18:37 tlr: 8.1e-05 tnm: 0.43 Lm: 6.621 (6.621) Lt: 5.847 (5.847) Accm: 2.91 (2.91) Acct: 4.55 (4.55) proj_loss: -0.6070 (-0.6070) time: 0.6695 data: 0.0003 [11-26 18:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [ 0/1669] eta: 0:18:38 tlr: 8.1e-05 tnm: 0.43 Lm: 6.401 (6.401) Lt: 5.653 (5.653) Accm: 3.80 (3.80) Acct: 5.96 (5.96) proj_loss: -0.6146 (-0.6146) time: 0.6699 data: 0.0004 [11-26 18:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [ 0/1669] eta: 0:18:38 tlr: 8.1e-05 tnm: 0.43 Lm: 6.608 (6.608) Lt: 5.862 (5.862) Accm: 3.05 (3.05) Acct: 5.01 (5.01) proj_loss: -0.6233 (-0.6233) time: 0.6701 data: 0.0004 [11-26 18:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [ 0/1669] eta: 0:18:38 tlr: 8.1e-05 tnm: 0.43 Lm: 6.405 (6.405) Lt: 5.608 (5.608) Accm: 3.65 (3.65) Acct: 6.03 (6.03) proj_loss: -0.6059 (-0.6059) time: 0.6704 data: 0.0004 [11-26 18:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [ 417/1669] eta: 0:14:01 tlr: 8.1e-05 tnm: 0.43 Lm: 6.500 (6.500) Lt: 5.754 (5.754) Accm: 3.39 (3.39) Acct: 5.39 (5.39) proj_loss: -0.5996 (-0.5996) time: 0.6764 data: 0.0003 [11-26 18:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [ 417/1669] eta: 0:14:01 tlr: 8.1e-05 tnm: 0.43 Lm: 6.513 (6.513) Lt: 5.778 (5.778) Accm: 3.43 (3.43) Acct: 5.41 (5.41) proj_loss: -0.6146 (-0.6146) time: 0.6764 data: 0.0003 [11-26 18:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [ 417/1669] eta: 0:14:01 tlr: 8.1e-05 tnm: 0.43 Lm: 6.570 (6.570) Lt: 5.837 (5.837) Accm: 3.08 (3.08) Acct: 4.87 (4.87) proj_loss: -0.6082 (-0.6082) time: 0.6763 data: 0.0003 [11-26 18:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [ 417/1669] eta: 0:14:01 tlr: 8.1e-05 tnm: 0.43 Lm: 6.506 (6.506) Lt: 5.745 (5.745) Accm: 3.29 (3.29) Acct: 5.10 (5.10) proj_loss: -0.5971 (-0.5971) time: 0.6763 data: 0.0003 [11-26 18:49:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [ 834/1669] eta: 0:09:21 tlr: 8.1e-05 tnm: 0.43 Lm: 6.547 (6.520) Lt: 5.806 (5.765) Accm: 3.34 (3.31) Acct: 5.54 (5.25) proj_loss: -0.6070 (-0.6006) time: 0.6741 data: 0.0003 [11-26 18:49:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [ 834/1669] eta: 0:09:21 tlr: 8.1e-05 tnm: 0.43 Lm: 6.532 (6.510) Lt: 5.811 (5.762) Accm: 3.11 (3.33) Acct: 5.01 (5.32) proj_loss: -0.6087 (-0.6084) time: 0.6741 data: 0.0002 [11-26 18:49:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [ 834/1669] eta: 0:09:21 tlr: 8.1e-05 tnm: 0.43 Lm: 6.540 (6.522) Lt: 5.766 (5.774) Accm: 3.05 (3.29) Acct: 4.96 (5.26) proj_loss: -0.6145 (-0.6086) time: 0.6741 data: 0.0003 [11-26 18:49:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [ 834/1669] eta: 0:09:21 tlr: 8.1e-05 tnm: 0.43 Lm: 6.405 (6.431) Lt: 5.608 (5.682) Accm: 3.65 (3.55) Acct: 5.99 (5.59) proj_loss: -0.6059 (-0.6062) time: 0.6741 data: 0.0003 [11-26 18:54:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [1251/1669] eta: 0:04:41 tlr: 8.1e-05 tnm: 0.45 Lm: 6.454 (6.449) Lt: 5.681 (5.700) Accm: 3.45 (3.48) Acct: 5.59 (5.49) proj_loss: -0.6107 (-0.6085) time: 0.6738 data: 0.0003 [11-26 18:54:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [1251/1669] eta: 0:04:41 tlr: 8.1e-05 tnm: 0.45 Lm: 6.469 (6.480) Lt: 5.725 (5.724) Accm: 3.50 (3.43) Acct: 5.60 (5.42) proj_loss: -0.6048 (-0.6011) time: 0.6738 data: 0.0003 [11-26 18:54:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [1251/1669] eta: 0:04:41 tlr: 8.1e-05 tnm: 0.45 Lm: 6.470 (6.490) Lt: 5.709 (5.729) Accm: 3.31 (3.36) Acct: 5.28 (5.35) proj_loss: -0.6146 (-0.6112) time: 0.6738 data: 0.0002 [11-26 18:54:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [1251/1669] eta: 0:04:41 tlr: 8.1e-05 tnm: 0.45 Lm: 6.481 (6.490) Lt: 5.745 (5.741) Accm: 3.35 (3.39) Acct: 5.43 (5.45) proj_loss: -0.6017 (-0.6050) time: 0.6738 data: 0.0002 [11-26 18:59:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [1668/1669] eta: 0:00:00 tlr: 8e-05 tnm: 0.43 Lm: 6.532 (6.505) Lt: 5.793 (5.752) Accm: 3.23 (3.36) Acct: 5.15 (5.39) proj_loss: -0.6057 (-0.6051) time: 0.8587 data: 0.0022 [11-26 18:59:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 242/350] Total time: 0:19:00 (0.684 s / it) [11-26 18:59:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [1668/1669] eta: 0:00:00 tlr: 8e-05 tnm: 0.43 Lm: 6.401 (6.456) Lt: 5.653 (5.684) Accm: 3.57 (3.53) Acct: 5.60 (5.60) proj_loss: -0.6145 (-0.6107) time: 0.8587 data: 0.0019 [11-26 18:59:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [1668/1669] eta: 0:00:00 tlr: 8e-05 tnm: 0.43 Lm: 6.463 (6.452) Lt: 5.716 (5.703) Accm: 3.52 (3.49) Acct: 5.51 (5.49) proj_loss: -0.6064 (-0.6081) time: 0.8587 data: 0.0020 [11-26 18:59:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 242/350] [1668/1669] eta: 0:00:00 tlr: 8e-05 tnm: 0.43 Lm: 6.513 (6.487) Lt: 5.721 (5.724) Accm: 3.34 (3.40) Acct: 5.54 (5.39) proj_loss: -0.6070 (-0.6055) time: 0.8587 data: 0.0018 [11-26 18:59:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 242/350] Total time: 0:19:00 (0.684 s / it) [11-26 18:59:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 242/350] Total time: 0:19:00 (0.684 s / it) [11-26 18:59:18] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 242/350] Total time: 0:19:00 (0.684 s / it) [11-26 18:59:18] (/home/user/VAR/train.py , line 279)=> [ep242] (training ) Lm: 6.457 (6.484), Lt: 5.699 (5.732), Acc m&t: 3.51 5.53, Remain: 1 day, 9:42:06, Finish: 2024-11-27 12:41 [11-26 18:59:18] (/home/user/VAR/train.py , line 279)=> [ep242] (training ) Lm: 6.457 (6.484), Lt: 5.699 (5.732), Acc m&t: 3.51 5.53, Remain: 1 day, 9:41:53, Finish: 2024-11-27 12:41 [11-26 18:59:18] (/home/user/VAR/train.py , line 279)=> [ep242] (training ) Lm: 6.457 (6.484), Lt: 5.699 (5.732), Acc m&t: 3.51 5.53, Remain: 1 day, 9:42:44, Finish: 2024-11-27 12:42 [11-26 18:59:18] (/home/user/VAR/train.py , line 279)=> [ep242] (training ) Lm: 6.457 (6.484), Lt: 5.699 (5.732), Acc m&t: 3.51 5.53, Remain: 1 day, 9:42:19, Finish: 2024-11-27 12:41 [11-26 18:59:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [ 0/1669] eta: 0:18:08 tlr: 8e-05 tnm: 0.45 Lm: 6.485 (6.485) Lt: 5.690 (5.690) Accm: 3.45 (3.45) Acct: 5.60 (5.60) proj_loss: -0.6175 (-0.6175) time: 0.6519 data: 0.0004 [11-26 18:59:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [ 0/1669] eta: 0:18:08 tlr: 8e-05 tnm: 0.45 Lm: 6.386 (6.386) Lt: 5.569 (5.569) Accm: 3.90 (3.90) Acct: 5.84 (5.84) proj_loss: -0.6192 (-0.6192) time: 0.6520 data: 0.0004 [11-26 18:59:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [ 0/1669] eta: 0:18:16 tlr: 8e-05 tnm: 0.45 Lm: 6.378 (6.378) Lt: 5.552 (5.552) Accm: 3.90 (3.90) Acct: 6.40 (6.40) proj_loss: -0.5882 (-0.5882) time: 0.6570 data: 0.0004 [11-26 18:59:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [ 0/1669] eta: 0:18:08 tlr: 8e-05 tnm: 0.45 Lm: 6.229 (6.229) Lt: 5.443 (5.443) Accm: 4.35 (4.35) Acct: 7.04 (7.04) proj_loss: -0.6369 (-0.6369) time: 0.6520 data: 0.0004 [11-26 19:03:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [ 417/1669] eta: 0:14:01 tlr: 8e-05 tnm: 0.43 Lm: 6.339 (6.339) Lt: 5.552 (5.552) Accm: 3.93 (3.93) Acct: 6.42 (6.42) proj_loss: -0.6315 (-0.6315) time: 0.6729 data: 0.0003 [11-26 19:03:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [ 417/1669] eta: 0:14:01 tlr: 8e-05 tnm: 0.43 Lm: 6.477 (6.477) Lt: 5.725 (5.725) Accm: 3.41 (3.41) Acct: 5.05 (5.05) proj_loss: -0.6136 (-0.6136) time: 0.6729 data: 0.0003 [11-26 19:03:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [ 417/1669] eta: 0:14:01 tlr: 8e-05 tnm: 0.43 Lm: 6.376 (6.376) Lt: 5.574 (5.574) Accm: 3.96 (3.96) Acct: 6.26 (6.26) proj_loss: -0.6070 (-0.6070) time: 0.6728 data: 0.0003 [11-26 19:03:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [ 417/1669] eta: 0:14:01 tlr: 8e-05 tnm: 0.43 Lm: 6.453 (6.453) Lt: 5.697 (5.697) Accm: 3.43 (3.43) Acct: 5.51 (5.51) proj_loss: -0.5938 (-0.5938) time: 0.6728 data: 0.0003 [11-26 19:08:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [ 834/1669] eta: 0:09:21 tlr: 8e-05 tnm: 0.45 Lm: 6.411 (6.439) Lt: 5.665 (5.686) Accm: 3.75 (3.54) Acct: 5.66 (5.56) proj_loss: -0.5915 (-0.5930) time: 0.6732 data: 0.0003 [11-26 19:08:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [ 834/1669] eta: 0:09:21 tlr: 8e-05 tnm: 0.45 Lm: 6.540 (6.498) Lt: 5.810 (5.753) Accm: 3.34 (3.39) Acct: 5.20 (5.10) proj_loss: -0.6192 (-0.6201) time: 0.6733 data: 0.0003 [11-26 19:08:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [ 834/1669] eta: 0:09:21 tlr: 8e-05 tnm: 0.45 Lm: 6.421 (6.366) Lt: 5.637 (5.580) Accm: 3.66 (3.84) Acct: 5.84 (6.23) proj_loss: -0.6261 (-0.6202) time: 0.6733 data: 0.0003 [11-26 19:08:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [ 834/1669] eta: 0:09:21 tlr: 8e-05 tnm: 0.45 Lm: 6.403 (6.385) Lt: 5.590 (5.580) Accm: 3.77 (3.90) Acct: 6.06 (6.19) proj_loss: -0.6059 (-0.6066) time: 0.6732 data: 0.0003 [11-26 19:13:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [1251/1669] eta: 0:04:41 tlr: 8e-05 tnm: 0.43 Lm: 6.434 (6.405) Lt: 5.640 (5.608) Accm: 3.74 (3.85) Acct: 5.98 (6.12) proj_loss: -0.6012 (-0.6037) time: 0.6736 data: 0.0003 [11-26 19:13:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [1251/1669] eta: 0:04:41 tlr: 8e-05 tnm: 0.43 Lm: 6.404 (6.429) Lt: 5.637 (5.667) Accm: 3.70 (3.57) Acct: 5.72 (5.61) proj_loss: -0.5954 (-0.5978) time: 0.6736 data: 0.0003 [11-26 19:13:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [1251/1669] eta: 0:04:41 tlr: 8e-05 tnm: 0.43 Lm: 6.435 (6.410) Lt: 5.649 (5.640) Accm: 3.59 (3.64) Acct: 5.82 (5.86) proj_loss: -0.6192 (-0.6182) time: 0.6736 data: 0.0003 [11-26 19:13:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [1251/1669] eta: 0:04:41 tlr: 8e-05 tnm: 0.43 Lm: 6.519 (6.498) Lt: 5.784 (5.755) Accm: 3.29 (3.35) Acct: 5.05 (5.05) proj_loss: -0.6136 (-0.6139) time: 0.6736 data: 0.0002 [11-26 19:17:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [1668/1669] eta: 0:00:00 tlr: 8e-05 tnm: 0.43 Lm: 6.540 (6.527) Lt: 5.810 (5.788) Accm: 3.23 (3.30) Acct: 4.91 (5.00) proj_loss: -0.6086 (-0.6128) time: 0.6729 data: 0.0020 [11-26 19:17:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 243/350] Total time: 0:18:41 (0.672 s / it) [11-26 19:17:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [1668/1669] eta: 0:00:00 tlr: 8e-05 tnm: 0.43 Lm: 6.465 (6.427) Lt: 5.690 (5.654) Accm: 3.70 (3.69) Acct: 5.91 (5.84) proj_loss: -0.6059 (-0.6057) time: 0.6729 data: 0.0016 [11-26 19:17:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [1668/1669] eta: 0:00:00 tlr: 8e-05 tnm: 0.43 Lm: 6.411 (6.434) Lt: 5.652 (5.664) Accm: 3.71 (3.59) Acct: 5.77 (5.68) proj_loss: -0.5937 (-0.5970) time: 0.6729 data: 0.0014 [11-26 19:17:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 243/350] [1668/1669] eta: 0:00:00 tlr: 8e-05 tnm: 0.43 Lm: 6.421 (6.401) Lt: 5.637 (5.628) Accm: 3.66 (3.67) Acct: 5.84 (5.87) proj_loss: -0.6124 (-0.6157) time: 0.6729 data: 0.0015 [11-26 19:17:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 243/350] Total time: 0:18:41 (0.672 s / it) [11-26 19:17:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 243/350] Total time: 0:18:41 (0.672 s / it) [11-26 19:17:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 243/350] Total time: 0:18:41 (0.672 s / it) [11-26 19:18:00] (/home/user/VAR/train.py , line 279)=> [ep243] (training ) Lm: 6.457 (6.458), Lt: 5.699 (5.701), Acc m&t: 3.52 5.53, Remain: 1 day, 9:20:01, Finish: 2024-11-27 12:38 [11-26 19:18:00] (/home/user/VAR/train.py , line 279)=> [ep243] (training ) Lm: 6.457 (6.458), Lt: 5.699 (5.701), Acc m&t: 3.52 5.53, Remain: 1 day, 9:20:02, Finish: 2024-11-27 12:38 [11-26 19:18:00] (/home/user/VAR/train.py , line 279)=> [ep243] (training ) Lm: 6.457 (6.458), Lt: 5.699 (5.701), Acc m&t: 3.52 5.53, Remain: 1 day, 9:19:14, Finish: 2024-11-27 12:37 [11-26 19:18:00] (/home/user/VAR/train.py , line 279)=> [ep243] (training ) Lm: 6.457 (6.458), Lt: 5.699 (5.701), Acc m&t: 3.52 5.53, Remain: 1 day, 9:19:30, Finish: 2024-11-27 12:37 [11-26 19:18:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [ 0/1669] eta: 0:18:11 tlr: 8e-05 tnm: 0.42 Lm: 6.477 (6.477) Lt: 5.706 (5.706) Accm: 3.32 (3.32) Acct: 5.68 (5.68) proj_loss: -0.5957 (-0.5957) time: 0.6538 data: 0.0003 [11-26 19:18:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [ 0/1669] eta: 0:18:11 tlr: 8e-05 tnm: 0.42 Lm: 6.610 (6.610) Lt: 5.896 (5.896) Accm: 2.83 (2.83) Acct: 4.29 (4.29) proj_loss: -0.5973 (-0.5973) time: 0.6542 data: 0.0004 [11-26 19:18:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [ 0/1669] eta: 0:18:11 tlr: 8e-05 tnm: 0.42 Lm: 6.537 (6.537) Lt: 5.810 (5.810) Accm: 3.15 (3.15) Acct: 4.77 (4.77) proj_loss: -0.5922 (-0.5922) time: 0.6543 data: 0.0004 [11-26 19:18:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [ 0/1669] eta: 0:18:12 tlr: 8e-05 tnm: 0.42 Lm: 6.457 (6.457) Lt: 5.710 (5.710) Accm: 3.39 (3.39) Acct: 5.30 (5.30) proj_loss: -0.6003 (-0.6003) time: 0.6546 data: 0.0004 [11-26 19:22:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [ 417/1669] eta: 0:14:06 tlr: 8e-05 tnm: 0.43 Lm: 6.535 (6.535) Lt: 5.795 (5.795) Accm: 3.19 (3.19) Acct: 5.04 (5.04) proj_loss: -0.5920 (-0.5920) time: 0.6707 data: 0.0003 [11-26 19:22:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [ 417/1669] eta: 0:14:06 tlr: 8e-05 tnm: 0.43 Lm: 6.580 (6.580) Lt: 5.849 (5.849) Accm: 3.00 (3.00) Acct: 4.69 (4.69) proj_loss: -0.5970 (-0.5970) time: 0.6707 data: 0.0003 [11-26 19:22:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [ 417/1669] eta: 0:14:06 tlr: 8e-05 tnm: 0.43 Lm: 6.535 (6.535) Lt: 5.826 (5.826) Accm: 3.27 (3.27) Acct: 4.98 (4.98) proj_loss: -0.6088 (-0.6088) time: 0.6707 data: 0.0003 [11-26 19:22:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [ 417/1669] eta: 0:14:06 tlr: 8e-05 tnm: 0.43 Lm: 6.487 (6.487) Lt: 5.745 (5.745) Accm: 3.33 (3.33) Acct: 5.62 (5.62) proj_loss: -0.6065 (-0.6065) time: 0.6707 data: 0.0003 [11-26 19:27:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [ 834/1669] eta: 0:09:39 tlr: 7.9e-05 tnm: 0.43 Lm: 6.498 (6.550) Lt: 5.783 (5.821) Accm: 3.32 (3.24) Acct: 5.56 (5.41) proj_loss: -0.5957 (-0.5987) time: 0.6712 data: 0.0003 [11-26 19:27:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [ 834/1669] eta: 0:09:39 tlr: 7.9e-05 tnm: 0.43 Lm: 6.542 (6.538) Lt: 5.828 (5.806) Accm: 3.18 (3.19) Acct: 4.87 (4.98) proj_loss: -0.6001 (-0.5947) time: 0.6712 data: 0.0003 [11-26 19:27:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [ 834/1669] eta: 0:09:39 tlr: 7.9e-05 tnm: 0.43 Lm: 6.586 (6.582) Lt: 5.896 (5.867) Accm: 3.07 (3.02) Acct: 5.08 (4.82) proj_loss: -0.5973 (-0.6051) time: 0.6712 data: 0.0003 [11-26 19:27:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [ 834/1669] eta: 0:09:39 tlr: 7.9e-05 tnm: 0.43 Lm: 6.533 (6.528) Lt: 5.810 (5.806) Accm: 3.17 (3.23) Acct: 5.18 (5.04) proj_loss: -0.6142 (-0.6106) time: 0.6712 data: 0.0003 [11-26 19:32:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [1251/1669] eta: 0:04:47 tlr: 7.9e-05 tnm: 0.42 Lm: 6.526 (6.526) Lt: 5.793 (5.798) Accm: 3.16 (3.19) Acct: 4.98 (4.96) proj_loss: -0.6112 (-0.6100) time: 0.6717 data: 0.0003 [11-26 19:32:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [1251/1669] eta: 0:04:47 tlr: 7.9e-05 tnm: 0.42 Lm: 6.487 (6.492) Lt: 5.745 (5.739) Accm: 3.33 (3.40) Acct: 5.62 (5.59) proj_loss: -0.6022 (-0.6012) time: 0.6717 data: 0.0002 [11-26 19:32:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [1251/1669] eta: 0:04:47 tlr: 7.9e-05 tnm: 0.42 Lm: 6.499 (6.516) Lt: 5.769 (5.769) Accm: 3.29 (3.26) Acct: 5.03 (5.03) proj_loss: -0.5941 (-0.5930) time: 0.6717 data: 0.0003 [11-26 19:32:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [1251/1669] eta: 0:04:47 tlr: 7.9e-05 tnm: 0.42 Lm: 6.568 (6.526) Lt: 5.849 (5.804) Accm: 3.11 (3.29) Acct: 5.09 (5.17) proj_loss: -0.5980 (-0.6035) time: 0.6717 data: 0.0003 [11-26 19:36:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [1668/1669] eta: 0:00:00 tlr: 7.9e-05 tnm: 0.43 Lm: 6.551 (6.519) Lt: 5.802 (5.793) Accm: 3.16 (3.31) Acct: 5.10 (5.27) proj_loss: -0.5987 (-0.6042) time: 0.6736 data: 0.0018 [11-26 19:36:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 244/350] Total time: 0:18:59 (0.683 s / it) [11-26 19:36:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [1668/1669] eta: 0:00:00 tlr: 7.9e-05 tnm: 0.43 Lm: 6.457 (6.502) Lt: 5.710 (5.743) Accm: 3.39 (3.32) Acct: 5.18 (5.15) proj_loss: -0.6001 (-0.5951) time: 0.6736 data: 0.0016 [11-26 19:36:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [1668/1669] eta: 0:00:00 tlr: 7.9e-05 tnm: 0.43 Lm: 6.477 (6.488) Lt: 5.706 (5.722) Accm: 3.33 (3.39) Acct: 5.56 (5.58) proj_loss: -0.5957 (-0.5989) time: 0.6736 data: 0.0019 [11-26 19:36:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 244/350] [1668/1669] eta: 0:00:00 tlr: 7.9e-05 tnm: 0.43 Lm: 6.520 (6.514) Lt: 5.775 (5.775) Accm: 3.17 (3.25) Acct: 5.18 (5.10) proj_loss: -0.6082 (-0.6082) time: 0.6735 data: 0.0022 [11-26 19:36:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 244/350] Total time: 0:18:59 (0.683 s / it) [11-26 19:36:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 244/350] Total time: 0:18:59 (0.683 s / it) [11-26 19:36:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 244/350] Total time: 0:18:59 (0.683 s / it) [11-26 19:37:00] (/home/user/VAR/train.py , line 279)=> [ep244] (training ) Lm: 6.457 (6.477), Lt: 5.699 (5.721), Acc m&t: 3.52 5.53, Remain: 1 day, 8:59:40, Finish: 2024-11-27 12:36 [11-26 19:37:00] (/home/user/VAR/train.py , line 279)=> [ep244] (training ) Lm: 6.457 (6.477), Lt: 5.699 (5.721), Acc m&t: 3.52 5.53, Remain: 1 day, 8:59:35, Finish: 2024-11-27 12:36 [11-26 19:37:00] (/home/user/VAR/train.py , line 279)=> [ep244] (training ) Lm: 6.457 (6.477), Lt: 5.699 (5.721), Acc m&t: 3.52 5.53, Remain: 1 day, 9:00:03, Finish: 2024-11-27 12:37 [11-26 19:37:00] (/home/user/VAR/train.py , line 279)=> [ep244] (training ) Lm: 6.457 (6.477), Lt: 5.699 (5.721), Acc m&t: 3.52 5.53, Remain: 1 day, 9:00:09, Finish: 2024-11-27 12:37 [11-26 19:37:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [ 0/1669] eta: 0:18:20 tlr: 7.9e-05 tnm: 0.44 Lm: 6.456 (6.456) Lt: 5.665 (5.665) Accm: 3.45 (3.45) Acct: 5.29 (5.29) proj_loss: -0.5904 (-0.5904) time: 0.6592 data: 0.0003 [11-26 19:37:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [ 0/1669] eta: 0:18:21 tlr: 7.9e-05 tnm: 0.44 Lm: 6.578 (6.578) Lt: 5.857 (5.857) Accm: 3.19 (3.19) Acct: 4.80 (4.80) proj_loss: -0.6284 (-0.6284) time: 0.6599 data: 0.0004 [11-26 19:37:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [ 0/1669] eta: 0:18:21 tlr: 7.9e-05 tnm: 0.44 Lm: 6.557 (6.557) Lt: 5.801 (5.801) Accm: 3.14 (3.14) Acct: 5.01 (5.01) proj_loss: -0.6029 (-0.6029) time: 0.6599 data: 0.0004 [11-26 19:37:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [ 0/1669] eta: 0:18:13 tlr: 7.9e-05 tnm: 0.44 Lm: 6.677 (6.677) Lt: 5.954 (5.954) Accm: 3.02 (3.02) Acct: 4.84 (4.84) proj_loss: -0.6174 (-0.6174) time: 0.6550 data: 0.0003 [11-26 19:41:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [ 417/1669] eta: 0:14:00 tlr: 7.9e-05 tnm: 0.44 Lm: 6.534 (6.534) Lt: 5.803 (5.803) Accm: 3.45 (3.45) Acct: 5.42 (5.42) proj_loss: -0.6117 (-0.6117) time: 0.6713 data: 0.0003 [11-26 19:41:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [ 417/1669] eta: 0:14:00 tlr: 7.9e-05 tnm: 0.44 Lm: 6.483 (6.483) Lt: 5.715 (5.715) Accm: 3.49 (3.49) Acct: 5.40 (5.40) proj_loss: -0.6061 (-0.6061) time: 0.6713 data: 0.0003 [11-26 19:41:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [ 417/1669] eta: 0:14:00 tlr: 7.9e-05 tnm: 0.44 Lm: 6.528 (6.528) Lt: 5.809 (5.809) Accm: 3.26 (3.26) Acct: 4.96 (4.96) proj_loss: -0.6227 (-0.6227) time: 0.6713 data: 0.0003 [11-26 19:41:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [ 417/1669] eta: 0:14:00 tlr: 7.9e-05 tnm: 0.44 Lm: 6.399 (6.399) Lt: 5.631 (5.631) Accm: 3.54 (3.54) Acct: 5.37 (5.37) proj_loss: -0.5953 (-0.5953) time: 0.6713 data: 0.0003 [11-26 19:46:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [ 834/1669] eta: 0:09:20 tlr: 7.9e-05 tnm: 0.43 Lm: 6.341 (6.364) Lt: 5.598 (5.620) Accm: 3.62 (3.63) Acct: 5.46 (5.54) proj_loss: -0.6002 (-0.6002) time: 0.6741 data: 0.0003 [11-26 19:46:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [ 834/1669] eta: 0:09:20 tlr: 7.9e-05 tnm: 0.43 Lm: 6.409 (6.446) Lt: 5.629 (5.662) Accm: 3.83 (3.62) Acct: 5.79 (5.69) proj_loss: -0.6029 (-0.6047) time: 0.6740 data: 0.0003 [11-26 19:46:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [ 834/1669] eta: 0:09:20 tlr: 7.9e-05 tnm: 0.43 Lm: 6.520 (6.530) Lt: 5.736 (5.781) Accm: 3.39 (3.43) Acct: 5.34 (5.39) proj_loss: -0.6093 (-0.6109) time: 0.6740 data: 0.0003 [11-26 19:46:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [ 834/1669] eta: 0:09:20 tlr: 7.9e-05 tnm: 0.43 Lm: 6.478 (6.488) Lt: 5.761 (5.753) Accm: 3.33 (3.31) Acct: 5.11 (5.13) proj_loss: -0.6171 (-0.6165) time: 0.6741 data: 0.0003 [11-26 19:51:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [1251/1669] eta: 0:04:40 tlr: 7.8e-05 tnm: 0.43 Lm: 6.443 (6.441) Lt: 5.701 (5.693) Accm: 3.38 (3.56) Acct: 5.29 (5.59) proj_loss: -0.6227 (-0.6219) time: 0.6724 data: 0.0003 [11-26 19:51:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [1251/1669] eta: 0:04:40 tlr: 7.8e-05 tnm: 0.43 Lm: 6.399 (6.404) Lt: 5.631 (5.664) Accm: 3.55 (3.59) Acct: 5.60 (5.60) proj_loss: -0.6051 (-0.6058) time: 0.6723 data: 0.0003 [11-26 19:51:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [1251/1669] eta: 0:04:40 tlr: 7.8e-05 tnm: 0.43 Lm: 6.463 (6.499) Lt: 5.694 (5.729) Accm: 3.50 (3.47) Acct: 5.67 (5.56) proj_loss: -0.6077 (-0.6062) time: 0.6724 data: 0.0003 [11-26 19:51:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [1251/1669] eta: 0:04:40 tlr: 7.8e-05 tnm: 0.43 Lm: 6.390 (6.425) Lt: 5.609 (5.644) Accm: 3.86 (3.70) Acct: 6.03 (5.84) proj_loss: -0.6023 (-0.6035) time: 0.6724 data: 0.0003 [11-26 19:55:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [1668/1669] eta: 0:00:00 tlr: 7.8e-05 tnm: 0.45 Lm: 6.409 (6.448) Lt: 5.629 (5.663) Accm: 3.83 (3.57) Acct: 5.79 (5.69) proj_loss: -0.6017 (-0.6007) time: 0.9795 data: 0.0020 [11-26 19:55:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 245/350] Total time: 0:18:59 (0.683 s / it) [11-26 19:55:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [1668/1669] eta: 0:00:00 tlr: 7.8e-05 tnm: 0.45 Lm: 6.382 (6.399) Lt: 5.656 (5.662) Accm: 3.59 (3.59) Acct: 5.75 (5.65) proj_loss: -0.6100 (-0.6081) time: 0.9795 data: 0.0016 [11-26 19:55:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [1668/1669] eta: 0:00:00 tlr: 7.8e-05 tnm: 0.45 Lm: 6.478 (6.459) Lt: 5.761 (5.714) Accm: 3.34 (3.52) Acct: 5.13 (5.50) proj_loss: -0.6171 (-0.6183) time: 0.9795 data: 0.0019 [11-26 19:55:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 245/350] [1668/1669] eta: 0:00:00 tlr: 7.8e-05 tnm: 0.45 Lm: 6.520 (6.506) Lt: 5.736 (5.742) Accm: 3.39 (3.40) Acct: 5.34 (5.43) proj_loss: -0.6061 (-0.6050) time: 0.9795 data: 0.0022 [11-26 19:55:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 245/350] Total time: 0:18:59 (0.683 s / it) [11-26 19:55:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 245/350] Total time: 0:18:59 (0.683 s / it) [11-26 19:55:59] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 245/350] Total time: 0:18:59 (0.683 s / it) [11-26 19:55:59] (/home/user/VAR/train.py , line 279)=> [ep245] (training ) Lm: 6.457 (6.475), Lt: 5.699 (5.718), Acc m&t: 3.52 5.53, Remain: 1 day, 8:47:19, Finish: 2024-11-27 12:43 [11-26 19:55:59] (/home/user/VAR/train.py , line 279)=> [ep245] (training ) Lm: 6.457 (6.475), Lt: 5.699 (5.718), Acc m&t: 3.52 5.53, Remain: 1 day, 8:47:12, Finish: 2024-11-27 12:43 [11-26 19:55:59] (/home/user/VAR/train.py , line 279)=> [ep245] (training ) Lm: 6.457 (6.475), Lt: 5.699 (5.718), Acc m&t: 3.52 5.53, Remain: 1 day, 8:47:52, Finish: 2024-11-27 12:43 [11-26 19:55:59] (/home/user/VAR/train.py , line 279)=> [ep245] (training ) Lm: 6.457 (6.475), Lt: 5.699 (5.718), Acc m&t: 3.52 5.53, Remain: 1 day, 8:47:27, Finish: 2024-11-27 12:43 [11-26 19:56:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [ 0/1669] eta: 0:18:36 tlr: 7.8e-05 tnm: 0.46 Lm: 6.570 (6.570) Lt: 5.826 (5.826) Accm: 3.26 (3.26) Acct: 5.06 (5.06) proj_loss: -0.5945 (-0.5945) time: 0.6687 data: 0.0003 [11-26 19:56:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [ 0/1669] eta: 0:18:27 tlr: 7.8e-05 tnm: 0.46 Lm: 6.386 (6.386) Lt: 5.574 (5.574) Accm: 3.74 (3.74) Acct: 5.97 (5.97) proj_loss: -0.6230 (-0.6230) time: 0.6634 data: 0.0004 [11-26 19:56:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [ 0/1669] eta: 0:18:37 tlr: 7.8e-05 tnm: 0.46 Lm: 6.540 (6.540) Lt: 5.816 (5.816) Accm: 3.34 (3.34) Acct: 5.27 (5.27) proj_loss: -0.6196 (-0.6196) time: 0.6693 data: 0.0004 [11-26 19:56:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [ 0/1669] eta: 0:18:38 tlr: 7.8e-05 tnm: 0.46 Lm: 6.515 (6.515) Lt: 5.791 (5.791) Accm: 3.21 (3.21) Acct: 5.30 (5.30) proj_loss: -0.6280 (-0.6280) time: 0.6701 data: 0.0005 [11-26 20:00:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [ 417/1669] eta: 0:14:02 tlr: 7.8e-05 tnm: 0.43 Lm: 6.460 (6.460) Lt: 5.701 (5.701) Accm: 3.37 (3.37) Acct: 5.54 (5.54) proj_loss: -0.6195 (-0.6195) time: 0.6735 data: 0.0003 [11-26 20:00:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [ 417/1669] eta: 0:14:02 tlr: 7.8e-05 tnm: 0.43 Lm: 6.500 (6.500) Lt: 5.699 (5.699) Accm: 3.38 (3.38) Acct: 5.57 (5.57) proj_loss: -0.6067 (-0.6067) time: 0.6734 data: 0.0003 [11-26 20:00:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [ 417/1669] eta: 0:14:02 tlr: 7.8e-05 tnm: 0.43 Lm: 6.503 (6.503) Lt: 5.775 (5.775) Accm: 3.47 (3.47) Acct: 5.33 (5.33) proj_loss: -0.5930 (-0.5930) time: 0.6735 data: 0.0003 [11-26 20:00:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [ 417/1669] eta: 0:14:02 tlr: 7.8e-05 tnm: 0.43 Lm: 6.507 (6.507) Lt: 5.745 (5.745) Accm: 3.42 (3.42) Acct: 5.48 (5.48) proj_loss: -0.6023 (-0.6023) time: 0.6735 data: 0.0003 [11-26 20:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [ 834/1669] eta: 0:09:21 tlr: 7.8e-05 tnm: 0.44 Lm: 6.474 (6.433) Lt: 5.675 (5.682) Accm: 3.50 (3.65) Acct: 5.68 (5.83) proj_loss: -0.6121 (-0.6056) time: 0.6697 data: 0.0003 [11-26 20:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [ 834/1669] eta: 0:09:21 tlr: 7.8e-05 tnm: 0.44 Lm: 6.470 (6.490) Lt: 5.721 (5.706) Accm: 3.56 (3.44) Acct: 5.56 (5.57) proj_loss: -0.5911 (-0.6015) time: 0.6697 data: 0.0003 [11-26 20:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [ 834/1669] eta: 0:09:21 tlr: 7.8e-05 tnm: 0.44 Lm: 6.501 (6.503) Lt: 5.725 (5.724) Accm: 3.44 (3.46) Acct: 5.60 (5.53) proj_loss: -0.5945 (-0.5973) time: 0.6697 data: 0.0002 [11-26 20:05:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [ 834/1669] eta: 0:09:21 tlr: 7.8e-05 tnm: 0.44 Lm: 6.404 (6.388) Lt: 5.610 (5.623) Accm: 3.54 (3.61) Acct: 5.77 (5.83) proj_loss: -0.6175 (-0.6189) time: 0.6697 data: 0.0003 [11-26 20:10:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [1251/1669] eta: 0:04:40 tlr: 7.8e-05 tnm: 0.45 Lm: 6.460 (6.442) Lt: 5.701 (5.674) Accm: 3.39 (3.51) Acct: 5.54 (5.66) proj_loss: -0.6143 (-0.6144) time: 0.6717 data: 0.0003 [11-26 20:10:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [1251/1669] eta: 0:04:40 tlr: 7.8e-05 tnm: 0.45 Lm: 6.428 (6.434) Lt: 5.648 (5.651) Accm: 3.65 (3.64) Acct: 5.77 (5.89) proj_loss: -0.6063 (-0.6065) time: 0.6717 data: 0.0003 [11-26 20:10:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [1251/1669] eta: 0:04:40 tlr: 7.8e-05 tnm: 0.45 Lm: 6.469 (6.443) Lt: 5.674 (5.662) Accm: 3.56 (3.69) Acct: 5.76 (5.85) proj_loss: -0.5930 (-0.5954) time: 0.6717 data: 0.0003 [11-26 20:10:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [1251/1669] eta: 0:04:40 tlr: 7.8e-05 tnm: 0.45 Lm: 6.461 (6.437) Lt: 5.693 (5.689) Accm: 3.43 (3.58) Acct: 5.48 (5.69) proj_loss: -0.6136 (-0.6079) time: 0.6717 data: 0.0003 [11-26 20:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [1668/1669] eta: 0:00:00 tlr: 7.8e-05 tnm: 0.44 Lm: 6.474 (6.474) Lt: 5.711 (5.721) Accm: 3.37 (3.43) Acct: 5.29 (5.49) proj_loss: -0.6121 (-0.6060) time: 0.6736 data: 0.0016 [11-26 20:14:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 246/350] Total time: 0:18:42 (0.672 s / it) [11-26 20:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [1668/1669] eta: 0:00:00 tlr: 7.8e-05 tnm: 0.44 Lm: 6.386 (6.415) Lt: 5.574 (5.629) Accm: 3.74 (3.74) Acct: 5.97 (5.97) proj_loss: -0.6128 (-0.6078) time: 0.6736 data: 0.0018 [11-26 20:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [1668/1669] eta: 0:00:00 tlr: 7.8e-05 tnm: 0.44 Lm: 6.436 (6.422) Lt: 5.622 (5.644) Accm: 3.68 (3.73) Acct: 5.92 (5.89) proj_loss: -0.5945 (-0.5986) time: 0.6736 data: 0.0016 [11-26 20:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 246/350] [1668/1669] eta: 0:00:00 tlr: 7.8e-05 tnm: 0.44 Lm: 6.444 (6.442) Lt: 5.653 (5.670) Accm: 3.50 (3.51) Acct: 5.63 (5.65) proj_loss: -0.6111 (-0.6096) time: 0.6736 data: 0.0025 [11-26 20:14:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 246/350] Total time: 0:18:42 (0.672 s / it) [11-26 20:14:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 246/350] Total time: 0:18:42 (0.672 s / it) [11-26 20:14:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 246/350] Total time: 0:18:42 (0.672 s / it) [11-26 20:14:42] (/home/user/VAR/train.py , line 279)=> [ep246] (training ) Lm: 6.457 (6.465), Lt: 5.699 (5.704), Acc m&t: 3.52 5.54, Remain: 1 day, 8:22:09, Finish: 2024-11-27 12:36 [11-26 20:14:42] (/home/user/VAR/train.py , line 279)=> [ep246] (training ) Lm: 6.457 (6.465), Lt: 5.699 (5.704), Acc m&t: 3.52 5.54, Remain: 1 day, 8:21:18, Finish: 2024-11-27 12:36 [11-26 20:14:42] (/home/user/VAR/train.py , line 279)=> [ep246] (training ) Lm: 6.457 (6.465), Lt: 5.699 (5.704), Acc m&t: 3.52 5.54, Remain: 1 day, 8:21:25, Finish: 2024-11-27 12:36 [11-26 20:14:42] (/home/user/VAR/train.py , line 279)=> [ep246] (training ) Lm: 6.457 (6.465), Lt: 5.699 (5.704), Acc m&t: 3.52 5.54, Remain: 1 day, 8:22:04, Finish: 2024-11-27 12:36 [11-26 20:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [ 0/1669] eta: 0:18:15 tlr: 7.8e-05 tnm: 0.43 Lm: 6.466 (6.466) Lt: 5.715 (5.715) Accm: 3.51 (3.51) Acct: 5.35 (5.35) proj_loss: -0.5956 (-0.5956) time: 0.6565 data: 0.0004 [11-26 20:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [ 0/1669] eta: 0:18:15 tlr: 7.8e-05 tnm: 0.43 Lm: 6.477 (6.477) Lt: 5.654 (5.654) Accm: 3.59 (3.59) Acct: 5.97 (5.97) proj_loss: -0.6066 (-0.6066) time: 0.6566 data: 0.0004 [11-26 20:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [ 0/1669] eta: 0:18:16 tlr: 7.8e-05 tnm: 0.43 Lm: 6.470 (6.470) Lt: 5.724 (5.724) Accm: 3.47 (3.47) Acct: 5.70 (5.70) proj_loss: -0.6123 (-0.6123) time: 0.6572 data: 0.0005 [11-26 20:14:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [ 0/1669] eta: 0:18:09 tlr: 7.8e-05 tnm: 0.43 Lm: 6.324 (6.324) Lt: 5.566 (5.566) Accm: 3.92 (3.92) Acct: 5.89 (5.89) proj_loss: -0.6077 (-0.6077) time: 0.6528 data: 0.0004 [11-26 20:19:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [ 417/1669] eta: 0:14:06 tlr: 7.7e-05 tnm: 0.44 Lm: 6.403 (6.403) Lt: 5.638 (5.638) Accm: 3.74 (3.74) Acct: 5.75 (5.75) proj_loss: -0.6035 (-0.6035) time: 0.6733 data: 0.0003 [11-26 20:19:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [ 417/1669] eta: 0:14:06 tlr: 7.7e-05 tnm: 0.44 Lm: 6.439 (6.439) Lt: 5.701 (5.701) Accm: 3.70 (3.70) Acct: 5.58 (5.58) proj_loss: -0.6093 (-0.6093) time: 0.6733 data: 0.0003 [11-26 20:19:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [ 417/1669] eta: 0:14:06 tlr: 7.7e-05 tnm: 0.44 Lm: 6.419 (6.419) Lt: 5.619 (5.619) Accm: 3.74 (3.74) Acct: 6.18 (6.18) proj_loss: -0.6139 (-0.6139) time: 0.6733 data: 0.0003 [11-26 20:19:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [ 417/1669] eta: 0:14:06 tlr: 7.7e-05 tnm: 0.44 Lm: 6.383 (6.383) Lt: 5.612 (5.612) Accm: 3.62 (3.62) Acct: 5.85 (5.85) proj_loss: -0.6081 (-0.6081) time: 0.6733 data: 0.0003 [11-26 20:24:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [ 834/1669] eta: 0:09:39 tlr: 7.7e-05 tnm: 0.44 Lm: 6.450 (6.405) Lt: 5.687 (5.637) Accm: 3.62 (3.62) Acct: 5.80 (5.84) proj_loss: -0.6123 (-0.6097) time: 0.6698 data: 0.0003 [11-26 20:24:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [ 834/1669] eta: 0:09:39 tlr: 7.7e-05 tnm: 0.44 Lm: 6.416 (6.418) Lt: 5.654 (5.636) Accm: 3.75 (3.74) Acct: 5.97 (6.05) proj_loss: -0.6113 (-0.6130) time: 0.6698 data: 0.0003 [11-26 20:24:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [ 834/1669] eta: 0:09:39 tlr: 7.7e-05 tnm: 0.44 Lm: 6.411 (6.394) Lt: 5.688 (5.656) Accm: 3.88 (3.86) Acct: 5.80 (5.80) proj_loss: -0.6219 (-0.6135) time: 0.6698 data: 0.0003 [11-26 20:24:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [ 834/1669] eta: 0:09:39 tlr: 7.7e-05 tnm: 0.44 Lm: 6.343 (6.383) Lt: 5.566 (5.599) Accm: 3.92 (3.91) Acct: 5.89 (6.05) proj_loss: -0.6077 (-0.6086) time: 0.6698 data: 0.0003 [11-26 20:29:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [1251/1669] eta: 0:04:47 tlr: 7.7e-05 tnm: 0.43 Lm: 6.334 (6.365) Lt: 5.588 (5.602) Accm: 4.02 (3.96) Acct: 6.04 (6.09) proj_loss: -0.6107 (-0.6099) time: 0.6718 data: 0.0003 [11-26 20:29:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [1251/1669] eta: 0:04:47 tlr: 7.7e-05 tnm: 0.43 Lm: 6.439 (6.429) Lt: 5.701 (5.697) Accm: 3.70 (3.76) Acct: 5.60 (5.70) proj_loss: -0.6224 (-0.6168) time: 0.6718 data: 0.0003 [11-26 20:29:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [1251/1669] eta: 0:04:47 tlr: 7.7e-05 tnm: 0.43 Lm: 6.412 (6.415) Lt: 5.635 (5.632) Accm: 3.82 (3.81) Acct: 6.08 (6.09) proj_loss: -0.6155 (-0.6147) time: 0.6718 data: 0.0003 [11-26 20:29:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [1251/1669] eta: 0:04:47 tlr: 7.7e-05 tnm: 0.43 Lm: 6.460 (6.426) Lt: 5.706 (5.670) Accm: 3.55 (3.59) Acct: 5.75 (5.72) proj_loss: -0.6081 (-0.6080) time: 0.6718 data: 0.0003 [11-26 20:33:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [1668/1669] eta: 0:00:00 tlr: 7.7e-05 tnm: 0.44 Lm: 6.470 (6.473) Lt: 5.724 (5.715) Accm: 3.49 (3.53) Acct: 5.70 (5.60) proj_loss: -0.6038 (-0.6042) time: 0.6757 data: 0.0018 [11-26 20:33:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 247/350] Total time: 0:19:00 (0.683 s / it) [11-26 20:33:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [1668/1669] eta: 0:00:00 tlr: 7.7e-05 tnm: 0.44 Lm: 6.466 (6.456) Lt: 5.715 (5.732) Accm: 3.51 (3.62) Acct: 5.41 (5.48) proj_loss: -0.6219 (-0.6126) time: 0.6757 data: 0.0016 [11-26 20:33:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [1668/1669] eta: 0:00:00 tlr: 7.7e-05 tnm: 0.44 Lm: 6.407 (6.408) Lt: 5.654 (5.639) Accm: 3.75 (3.77) Acct: 5.97 (6.00) proj_loss: -0.6169 (-0.6151) time: 0.6757 data: 0.0019 [11-26 20:33:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 247/350] [1668/1669] eta: 0:00:00 tlr: 7.7e-05 tnm: 0.44 Lm: 6.343 (6.408) Lt: 5.610 (5.638) Accm: 3.92 (3.80) Acct: 5.89 (5.93) proj_loss: -0.6077 (-0.6083) time: 0.6757 data: 0.0017 [11-26 20:33:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 247/350] Total time: 0:19:00 (0.683 s / it) [11-26 20:33:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 247/350] Total time: 0:19:00 (0.683 s / it) [11-26 20:33:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 247/350] Total time: 0:19:00 (0.683 s / it) [11-26 20:33:42] (/home/user/VAR/train.py , line 279)=> [ep247] (training ) Lm: 6.457 (6.469), Lt: 5.699 (5.716), Acc m&t: 3.52 5.54, Remain: 1 day, 8:06:33, Finish: 2024-11-27 12:40 [11-26 20:33:42] (/home/user/VAR/train.py , line 279)=> [ep247] (training ) Lm: 6.457 (6.469), Lt: 5.699 (5.716), Acc m&t: 3.52 5.54, Remain: 1 day, 8:05:26, Finish: 2024-11-27 12:39 [11-26 20:33:42] (/home/user/VAR/train.py , line 279)=> [ep247] (training ) Lm: 6.457 (6.469), Lt: 5.699 (5.716), Acc m&t: 3.52 5.54, Remain: 1 day, 8:05:40, Finish: 2024-11-27 12:39 [11-26 20:33:42] (/home/user/VAR/train.py , line 279)=> [ep247] (training ) Lm: 6.457 (6.469), Lt: 5.699 (5.716), Acc m&t: 3.52 5.54, Remain: 1 day, 8:05:58, Finish: 2024-11-27 12:39 [11-26 20:33:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [ 0/1669] eta: 0:18:29 tlr: 7.7e-05 tnm: 0.43 Lm: 6.721 (6.721) Lt: 5.997 (5.997) Accm: 2.83 (2.83) Acct: 4.49 (4.49) proj_loss: -0.6057 (-0.6057) time: 0.6649 data: 0.0003 [11-26 20:33:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [ 0/1669] eta: 0:18:29 tlr: 7.7e-05 tnm: 0.43 Lm: 6.402 (6.402) Lt: 5.589 (5.589) Accm: 3.58 (3.58) Acct: 5.92 (5.92) proj_loss: -0.6083 (-0.6083) time: 0.6650 data: 0.0005 [11-26 20:33:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [ 0/1669] eta: 0:18:35 tlr: 7.7e-05 tnm: 0.43 Lm: 6.510 (6.510) Lt: 5.690 (5.690) Accm: 3.46 (3.46) Acct: 5.70 (5.70) proj_loss: -0.5994 (-0.5994) time: 0.6681 data: 0.0004 [11-26 20:33:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [ 0/1669] eta: 0:18:34 tlr: 7.7e-05 tnm: 0.43 Lm: 6.489 (6.489) Lt: 5.807 (5.807) Accm: 3.31 (3.31) Acct: 5.20 (5.20) proj_loss: -0.6267 (-0.6267) time: 0.6680 data: 0.0004 [11-26 20:38:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [ 417/1669] eta: 0:14:01 tlr: 7.7e-05 tnm: 0.45 Lm: 6.441 (6.441) Lt: 5.718 (5.718) Accm: 3.49 (3.49) Acct: 5.59 (5.59) proj_loss: -0.6141 (-0.6141) time: 0.6701 data: 0.0003 [11-26 20:38:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [ 417/1669] eta: 0:14:01 tlr: 7.7e-05 tnm: 0.45 Lm: 6.691 (6.691) Lt: 5.988 (5.988) Accm: 2.92 (2.92) Acct: 4.58 (4.58) proj_loss: -0.6113 (-0.6113) time: 0.6701 data: 0.0003 [11-26 20:38:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [ 417/1669] eta: 0:14:01 tlr: 7.7e-05 tnm: 0.45 Lm: 6.393 (6.393) Lt: 5.609 (5.609) Accm: 3.69 (3.69) Acct: 5.89 (5.89) proj_loss: -0.6109 (-0.6109) time: 0.6701 data: 0.0003 [11-26 20:38:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [ 417/1669] eta: 0:14:01 tlr: 7.7e-05 tnm: 0.45 Lm: 6.420 (6.420) Lt: 5.658 (5.658) Accm: 3.57 (3.57) Acct: 5.62 (5.62) proj_loss: -0.6057 (-0.6057) time: 0.6701 data: 0.0003 [11-26 20:43:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [ 834/1669] eta: 0:09:21 tlr: 7.6e-05 tnm: 0.44 Lm: 6.510 (6.450) Lt: 5.690 (5.689) Accm: 3.69 (3.61) Acct: 5.70 (5.66) proj_loss: -0.6011 (-0.6042) time: 0.6751 data: 0.0003 [11-26 20:43:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [ 834/1669] eta: 0:09:21 tlr: 7.6e-05 tnm: 0.44 Lm: 6.661 (6.623) Lt: 5.979 (5.914) Accm: 3.01 (3.19) Acct: 4.67 (5.02) proj_loss: -0.6169 (-0.6146) time: 0.6751 data: 0.0002 [11-26 20:43:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [ 834/1669] eta: 0:09:21 tlr: 7.6e-05 tnm: 0.44 Lm: 6.402 (6.425) Lt: 5.629 (5.656) Accm: 3.58 (3.46) Acct: 5.85 (5.45) proj_loss: -0.6083 (-0.6099) time: 0.6751 data: 0.0003 [11-26 20:43:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [ 834/1669] eta: 0:09:21 tlr: 7.6e-05 tnm: 0.44 Lm: 6.479 (6.453) Lt: 5.759 (5.732) Accm: 3.31 (3.38) Acct: 5.20 (5.35) proj_loss: -0.6175 (-0.6153) time: 0.6751 data: 0.0003 [11-26 20:47:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [1251/1669] eta: 0:04:41 tlr: 7.6e-05 tnm: 0.45 Lm: 6.484 (6.465) Lt: 5.783 (5.751) Accm: 3.49 (3.46) Acct: 5.37 (5.40) proj_loss: -0.6132 (-0.6137) time: 0.6730 data: 0.0002 [11-26 20:47:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [1251/1669] eta: 0:04:41 tlr: 7.6e-05 tnm: 0.45 Lm: 6.574 (6.565) Lt: 5.873 (5.831) Accm: 3.35 (3.32) Acct: 5.28 (5.26) proj_loss: -0.6113 (-0.6101) time: 0.6730 data: 0.0003 [11-26 20:47:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [1251/1669] eta: 0:04:41 tlr: 7.6e-05 tnm: 0.45 Lm: 6.445 (6.442) Lt: 5.661 (5.665) Accm: 3.32 (3.36) Acct: 5.47 (5.35) proj_loss: -0.6081 (-0.6094) time: 0.6730 data: 0.0003 [11-26 20:47:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [1251/1669] eta: 0:04:41 tlr: 7.6e-05 tnm: 0.45 Lm: 6.484 (6.452) Lt: 5.677 (5.682) Accm: 3.66 (3.62) Acct: 5.70 (5.67) proj_loss: -0.6020 (-0.6039) time: 0.6730 data: 0.0003 [11-26 20:52:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [1668/1669] eta: 0:00:00 tlr: 7.6e-05 tnm: 0.44 Lm: 6.510 (6.467) Lt: 5.690 (5.696) Accm: 3.63 (3.55) Acct: 5.70 (5.58) proj_loss: -0.6011 (-0.6022) time: 0.9177 data: 0.0020 [11-26 20:52:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 248/350] Total time: 0:18:58 (0.682 s / it) [11-26 20:52:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [1668/1669] eta: 0:00:00 tlr: 7.6e-05 tnm: 0.44 Lm: 6.561 (6.564) Lt: 5.796 (5.824) Accm: 3.18 (3.29) Acct: 5.11 (5.23) proj_loss: -0.6063 (-0.6094) time: 0.9176 data: 0.0017 [11-26 20:52:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [1668/1669] eta: 0:00:00 tlr: 7.6e-05 tnm: 0.44 Lm: 6.479 (6.442) Lt: 5.759 (5.726) Accm: 3.68 (3.53) Acct: 5.54 (5.52) proj_loss: -0.6139 (-0.6137) time: 0.9176 data: 0.0020 [11-26 20:52:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 248/350] [1668/1669] eta: 0:00:00 tlr: 7.6e-05 tnm: 0.44 Lm: 6.450 (6.444) Lt: 5.693 (5.674) Accm: 3.58 (3.45) Acct: 5.85 (5.46) proj_loss: -0.6083 (-0.6116) time: 0.9177 data: 0.0016 [11-26 20:52:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 248/350] Total time: 0:18:58 (0.682 s / it) [11-26 20:52:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 248/350] Total time: 0:18:58 (0.682 s / it) [11-26 20:52:41] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 248/350] Total time: 0:18:58 (0.682 s / it) [11-26 20:52:41] (/home/user/VAR/train.py , line 279)=> [ep248] (training ) Lm: 6.457 (6.482), Lt: 5.699 (5.729), Acc m&t: 3.52 5.54, Remain: 1 day, 7:48:43, Finish: 2024-11-27 12:41 [11-26 20:52:41] (/home/user/VAR/train.py , line 279)=> [ep248] (training ) Lm: 6.457 (6.482), Lt: 5.699 (5.729), Acc m&t: 3.52 5.54, Remain: 1 day, 7:49:08, Finish: 2024-11-27 12:41 [11-26 20:52:41] (/home/user/VAR/train.py , line 279)=> [ep248] (training ) Lm: 6.457 (6.482), Lt: 5.699 (5.729), Acc m&t: 3.52 5.54, Remain: 1 day, 7:49:04, Finish: 2024-11-27 12:41 [11-26 20:52:41] (/home/user/VAR/train.py , line 279)=> [ep248] (training ) Lm: 6.457 (6.482), Lt: 5.699 (5.729), Acc m&t: 3.52 5.54, Remain: 1 day, 7:51:53, Finish: 2024-11-27 12:44 [11-26 20:52:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [ 0/1669] eta: 0:18:19 tlr: 7.6e-05 tnm: 0.45 Lm: 6.575 (6.575) Lt: 5.810 (5.810) Accm: 2.99 (2.99) Acct: 4.89 (4.89) proj_loss: -0.6060 (-0.6060) time: 0.6585 data: 0.0003 [11-26 20:52:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [ 0/1669] eta: 0:18:19 tlr: 7.6e-05 tnm: 0.45 Lm: 6.402 (6.402) Lt: 5.593 (5.593) Accm: 3.77 (3.77) Acct: 5.85 (5.85) proj_loss: -0.5868 (-0.5868) time: 0.6590 data: 0.0004 [11-26 20:52:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [ 0/1669] eta: 0:18:19 tlr: 7.6e-05 tnm: 0.45 Lm: 6.516 (6.516) Lt: 5.766 (5.766) Accm: 3.50 (3.50) Acct: 5.25 (5.25) proj_loss: -0.6019 (-0.6019) time: 0.6589 data: 0.0004 [11-26 20:52:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [ 0/1669] eta: 0:18:20 tlr: 7.6e-05 tnm: 0.45 Lm: 6.296 (6.296) Lt: 5.490 (5.490) Accm: 3.98 (3.98) Acct: 6.42 (6.42) proj_loss: -0.6145 (-0.6145) time: 0.6593 data: 0.0004 [11-26 20:57:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [ 417/1669] eta: 0:14:05 tlr: 7.6e-05 tnm: 0.44 Lm: 6.413 (6.413) Lt: 5.630 (5.630) Accm: 3.54 (3.54) Acct: 5.69 (5.69) proj_loss: -0.6254 (-0.6254) time: 0.6745 data: 0.0003 [11-26 20:57:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [ 417/1669] eta: 0:14:05 tlr: 7.6e-05 tnm: 0.44 Lm: 6.503 (6.503) Lt: 5.720 (5.720) Accm: 3.43 (3.43) Acct: 5.27 (5.27) proj_loss: -0.5932 (-0.5932) time: 0.6745 data: 0.0002 [11-26 20:57:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [ 417/1669] eta: 0:14:06 tlr: 7.6e-05 tnm: 0.44 Lm: 6.406 (6.406) Lt: 5.653 (5.653) Accm: 3.54 (3.54) Acct: 5.39 (5.39) proj_loss: -0.6076 (-0.6076) time: 0.6745 data: 0.0003 [11-26 20:57:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [ 417/1669] eta: 0:14:06 tlr: 7.6e-05 tnm: 0.44 Lm: 6.458 (6.458) Lt: 5.694 (5.694) Accm: 3.43 (3.43) Acct: 5.46 (5.46) proj_loss: -0.6013 (-0.6013) time: 0.6745 data: 0.0003 [11-26 21:02:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [ 834/1669] eta: 0:09:23 tlr: 7.6e-05 tnm: 0.42 Lm: 6.342 (6.389) Lt: 5.578 (5.607) Accm: 3.86 (3.70) Acct: 6.03 (5.83) proj_loss: -0.6060 (-0.6117) time: 0.6727 data: 0.0003 [11-26 21:02:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [ 834/1669] eta: 0:09:23 tlr: 7.6e-05 tnm: 0.42 Lm: 6.409 (6.422) Lt: 5.712 (5.673) Accm: 3.37 (3.48) Acct: 5.22 (5.33) proj_loss: -0.5980 (-0.6044) time: 0.6727 data: 0.0003 [11-26 21:02:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [ 834/1669] eta: 0:09:23 tlr: 7.6e-05 tnm: 0.42 Lm: 6.490 (6.473) Lt: 5.675 (5.683) Accm: 3.50 (3.51) Acct: 5.29 (5.49) proj_loss: -0.5931 (-0.5931) time: 0.6727 data: 0.0003 [11-26 21:02:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [ 834/1669] eta: 0:09:23 tlr: 7.6e-05 tnm: 0.42 Lm: 6.312 (6.379) Lt: 5.490 (5.581) Accm: 3.98 (3.72) Acct: 6.42 (6.00) proj_loss: -0.6145 (-0.6171) time: 0.6727 data: 0.0003 [11-26 21:06:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [1251/1669] eta: 0:04:41 tlr: 7.5e-05 tnm: 0.47 Lm: 6.375 (6.394) Lt: 5.583 (5.605) Accm: 3.85 (3.72) Acct: 6.02 (5.91) proj_loss: -0.6118 (-0.6151) time: 0.6738 data: 0.0003 [11-26 21:06:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [1251/1669] eta: 0:04:41 tlr: 7.5e-05 tnm: 0.47 Lm: 6.431 (6.466) Lt: 5.713 (5.718) Accm: 3.34 (3.37) Acct: 5.07 (5.22) proj_loss: -0.6087 (-0.6081) time: 0.6738 data: 0.0003 [11-26 21:06:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [1251/1669] eta: 0:04:41 tlr: 7.5e-05 tnm: 0.47 Lm: 6.503 (6.509) Lt: 5.720 (5.737) Accm: 3.43 (3.33) Acct: 5.27 (5.20) proj_loss: -0.5975 (-0.5979) time: 0.6738 data: 0.0002 [11-26 21:06:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [1251/1669] eta: 0:04:41 tlr: 7.5e-05 tnm: 0.47 Lm: 6.435 (6.424) Lt: 5.683 (5.652) Accm: 3.50 (3.56) Acct: 5.46 (5.57) proj_loss: -0.6140 (-0.6143) time: 0.6738 data: 0.0003 [11-26 21:11:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [1668/1669] eta: 0:00:00 tlr: 7.5e-05 tnm: 0.45 Lm: 6.409 (6.426) Lt: 5.712 (5.668) Accm: 3.37 (3.53) Acct: 5.22 (5.44) proj_loss: -0.6004 (-0.6066) time: 0.6749 data: 0.0016 [11-26 21:11:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [1668/1669] eta: 0:00:00 tlr: 7.5e-05 tnm: 0.45 Lm: 6.509 (6.509) Lt: 5.711 (5.732) Accm: 3.37 (3.29) Acct: 5.25 (5.11) proj_loss: -0.5931 (-0.5969) time: 0.6749 data: 0.0017 [11-26 21:11:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [1668/1669] eta: 0:00:00 tlr: 7.5e-05 tnm: 0.45 Lm: 6.312 (6.358) Lt: 5.490 (5.564) Accm: 3.98 (3.78) Acct: 6.18 (5.96) proj_loss: -0.6145 (-0.6150) time: 0.6749 data: 0.0015 [11-26 21:11:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 249/350] [1668/1669] eta: 0:00:00 tlr: 7.5e-05 tnm: 0.45 Lm: 6.527 (6.462) Lt: 5.788 (5.700) Accm: 3.29 (3.51) Acct: 5.35 (5.52) proj_loss: -0.6060 (-0.6091) time: 0.6750 data: 0.0017 [11-26 21:11:25] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 249/350] Total time: 0:18:44 (0.674 s / it) [11-26 21:11:25] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 249/350] Total time: 0:18:44 (0.674 s / it) [11-26 21:11:25] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 249/350] Total time: 0:18:44 (0.674 s / it) [11-26 21:11:25] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 249/350] Total time: 0:18:44 (0.674 s / it) [11-26 21:13:55] (home/user/VAR/trainer.py, line 114)=> FID: 3.2243293679035787 [11-26 21:13:56] (/home/user/VAR/train.py , line 262)=> [*] [ep249] (val 50000) Lm: 6.4608, Lt: 5.6992, Acc m&t: 3.49 5.49, Val cost: 149.99s [11-26 21:13:56] (/home/user/VAR/train.py , line 267)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-26 21:14:33] (/home/user/VAR/train.py , line 279)=> [ep249] (training ) Lm: 6.457 (6.461), Lt: 5.699 (5.699), Acc m&t: 3.52 5.54, Remain: 1 day, 7:31:02, Finish: 2024-11-27 12:42 [11-26 21:14:33] (/home/user/VAR/train.py , line 279)=> [ep249] (training ) Lm: 6.457 (6.461), Lt: 5.699 (5.699), Acc m&t: 3.52 5.54, Remain: 1 day, 7:30:33, Finish: 2024-11-27 12:41 [11-26 21:14:33] (/home/user/VAR/train.py , line 279)=> [ep249] (training ) Lm: 6.457 (6.461), Lt: 5.699 (5.699), Acc m&t: 3.52 5.54, Remain: 1 day, 7:30:48, Finish: 2024-11-27 12:42 [11-26 21:14:33] (/home/user/VAR/train.py , line 279)=> [ep249] (training ) Lm: 6.457 (6.461), Lt: 5.699 (5.699), Acc m&t: 3.52 5.54, Remain: 1 day, 7:30:52, Finish: 2024-11-27 12:42 [11-26 21:14:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [ 0/1669] eta: 0:18:41 tlr: 7.5e-05 tnm: 0.44 Lm: 6.647 (6.647) Lt: 5.938 (5.938) Accm: 2.79 (2.79) Acct: 4.48 (4.48) proj_loss: -0.6139 (-0.6139) time: 0.6718 data: 0.0004 [11-26 21:14:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [ 0/1669] eta: 0:18:40 tlr: 7.5e-05 tnm: 0.44 Lm: 6.568 (6.568) Lt: 5.838 (5.838) Accm: 3.21 (3.21) Acct: 5.03 (5.03) proj_loss: -0.6185 (-0.6185) time: 0.6715 data: 0.0004 [11-26 21:14:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [ 0/1669] eta: 0:18:40 tlr: 7.5e-05 tnm: 0.44 Lm: 6.680 (6.680) Lt: 5.945 (5.945) Accm: 3.17 (3.17) Acct: 4.94 (4.94) proj_loss: -0.6058 (-0.6058) time: 0.6712 data: 0.0004 [11-26 21:14:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [ 0/1669] eta: 0:18:44 tlr: 7.5e-05 tnm: 0.44 Lm: 6.443 (6.443) Lt: 5.662 (5.662) Accm: 3.42 (3.42) Acct: 5.77 (5.77) proj_loss: -0.6083 (-0.6083) time: 0.6739 data: 0.0004 [11-26 21:19:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [ 417/1669] eta: 0:14:03 tlr: 7.5e-05 tnm: 0.44 Lm: 6.394 (6.394) Lt: 5.601 (5.601) Accm: 3.61 (3.61) Acct: 5.79 (5.79) proj_loss: -0.5984 (-0.5984) time: 0.6725 data: 0.0003 [11-26 21:19:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [ 417/1669] eta: 0:14:03 tlr: 7.5e-05 tnm: 0.44 Lm: 6.431 (6.431) Lt: 5.689 (5.689) Accm: 3.43 (3.43) Acct: 5.35 (5.35) proj_loss: -0.6140 (-0.6140) time: 0.6725 data: 0.0003 [11-26 21:19:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [ 417/1669] eta: 0:14:03 tlr: 7.5e-05 tnm: 0.44 Lm: 6.625 (6.625) Lt: 5.913 (5.913) Accm: 2.95 (2.95) Acct: 4.59 (4.59) proj_loss: -0.6066 (-0.6066) time: 0.6725 data: 0.0003 [11-26 21:19:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [ 417/1669] eta: 0:14:03 tlr: 7.5e-05 tnm: 0.44 Lm: 6.583 (6.583) Lt: 5.827 (5.827) Accm: 3.34 (3.34) Acct: 5.23 (5.23) proj_loss: -0.5987 (-0.5987) time: 0.6725 data: 0.0003 [11-26 21:23:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [ 834/1669] eta: 0:09:22 tlr: 7.5e-05 tnm: 0.44 Lm: 6.486 (6.547) Lt: 5.726 (5.793) Accm: 3.17 (3.28) Acct: 4.94 (5.12) proj_loss: -0.6058 (-0.6026) time: 0.6752 data: 0.0003 [11-26 21:23:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [ 834/1669] eta: 0:09:22 tlr: 7.5e-05 tnm: 0.44 Lm: 6.363 (6.408) Lt: 5.540 (5.630) Accm: 3.64 (3.54) Acct: 5.68 (5.60) proj_loss: -0.6095 (-0.6111) time: 0.6752 data: 0.0003 [11-26 21:23:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [ 834/1669] eta: 0:09:22 tlr: 7.5e-05 tnm: 0.44 Lm: 6.443 (6.434) Lt: 5.662 (5.649) Accm: 3.42 (3.39) Acct: 5.77 (5.48) proj_loss: -0.6067 (-0.6012) time: 0.6752 data: 0.0003 [11-26 21:23:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [ 834/1669] eta: 0:09:22 tlr: 7.5e-05 tnm: 0.44 Lm: 6.603 (6.586) Lt: 5.888 (5.856) Accm: 3.12 (3.08) Acct: 4.70 (4.80) proj_loss: -0.5994 (-0.6033) time: 0.6752 data: 0.0003 [11-26 21:28:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [1251/1669] eta: 0:04:41 tlr: 7.5e-05 tnm: 0.44 Lm: 6.555 (6.531) Lt: 5.814 (5.788) Accm: 3.22 (3.19) Acct: 4.96 (5.06) proj_loss: -0.6048 (-0.6050) time: 0.6715 data: 0.0003 [11-26 21:28:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [1251/1669] eta: 0:04:41 tlr: 7.5e-05 tnm: 0.44 Lm: 6.396 (6.414) Lt: 5.627 (5.651) Accm: 3.59 (3.54) Acct: 5.57 (5.57) proj_loss: -0.6118 (-0.6118) time: 0.6715 data: 0.0003 [11-26 21:28:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [1251/1669] eta: 0:04:41 tlr: 7.5e-05 tnm: 0.44 Lm: 6.480 (6.520) Lt: 5.717 (5.764) Accm: 3.34 (3.34) Acct: 5.19 (5.20) proj_loss: -0.5987 (-0.5993) time: 0.6715 data: 0.0003 [11-26 21:28:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [1251/1669] eta: 0:04:41 tlr: 7.5e-05 tnm: 0.44 Lm: 6.479 (6.465) Lt: 5.705 (5.693) Accm: 3.31 (3.35) Acct: 5.45 (5.39) proj_loss: -0.5977 (-0.5953) time: 0.6715 data: 0.0003 [11-26 21:33:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [1668/1669] eta: 0:00:00 tlr: 7.5e-05 tnm: 0.47 Lm: 6.443 (6.452) Lt: 5.662 (5.685) Accm: 3.42 (3.47) Acct: 5.77 (5.58) proj_loss: -0.6067 (-0.6001) time: 0.6734 data: 0.0019 [11-26 21:33:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 250/350] Total time: 0:18:43 (0.673 s / it) [11-26 21:33:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [1668/1669] eta: 0:00:00 tlr: 7.5e-05 tnm: 0.47 Lm: 6.429 (6.420) Lt: 5.684 (5.658) Accm: 3.61 (3.55) Acct: 5.68 (5.61) proj_loss: -0.6095 (-0.6099) time: 0.6734 data: 0.0017 [11-26 21:33:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [1668/1669] eta: 0:00:00 tlr: 7.5e-05 tnm: 0.47 Lm: 6.507 (6.520) Lt: 5.815 (5.793) Accm: 3.33 (3.30) Acct: 5.22 (5.19) proj_loss: -0.6102 (-0.6082) time: 0.6734 data: 0.0016 [11-26 21:33:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 250/350] [1668/1669] eta: 0:00:00 tlr: 7.5e-05 tnm: 0.47 Lm: 6.475 (6.501) Lt: 5.708 (5.744) Accm: 3.39 (3.35) Acct: 5.44 (5.25) proj_loss: -0.6058 (-0.6010) time: 0.6734 data: 0.0019 [11-26 21:33:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 250/350] Total time: 0:18:43 (0.673 s / it) [11-26 21:33:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 250/350] Total time: 0:18:43 (0.673 s / it) [11-26 21:33:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 250/350] Total time: 0:18:43 (0.673 s / it) [11-26 21:33:16] (/home/user/VAR/train.py , line 279)=> [ep250] (training ) Lm: 6.457 (6.476), Lt: 5.699 (5.729), Acc m&t: 3.52 5.54, Remain: 1 day, 7:07:56, Finish: 2024-11-27 12:41 [11-26 21:33:16] (/home/user/VAR/train.py , line 279)=> [ep250] (training ) Lm: 6.457 (6.476), Lt: 5.699 (5.729), Acc m&t: 3.52 5.54, Remain: 1 day, 7:08:08, Finish: 2024-11-27 12:41 [11-26 21:33:16] (/home/user/VAR/train.py , line 279)=> [ep250] (training ) Lm: 6.457 (6.476), Lt: 5.699 (5.729), Acc m&t: 3.52 5.54, Remain: 1 day, 7:08:22, Finish: 2024-11-27 12:41 [11-26 21:33:16] (/home/user/VAR/train.py , line 279)=> [ep250] (training ) Lm: 6.457 (6.476), Lt: 5.699 (5.729), Acc m&t: 3.52 5.54, Remain: 1 day, 7:08:59, Finish: 2024-11-27 12:42 [11-26 21:33:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [ 0/1669] eta: 0:18:09 tlr: 7.5e-05 tnm: 0.46 Lm: 6.527 (6.527) Lt: 5.755 (5.755) Accm: 3.19 (3.19) Acct: 5.20 (5.20) proj_loss: -0.5819 (-0.5819) time: 0.6526 data: 0.0004 [11-26 21:33:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [ 0/1669] eta: 0:18:54 tlr: 7.5e-05 tnm: 0.46 Lm: 6.286 (6.286) Lt: 5.564 (5.564) Accm: 4.15 (4.15) Acct: 6.10 (6.10) proj_loss: -0.6109 (-0.6109) time: 0.6796 data: 0.0004 [11-26 21:33:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [ 0/1669] eta: 0:18:09 tlr: 7.5e-05 tnm: 0.46 Lm: 6.527 (6.527) Lt: 5.760 (5.760) Accm: 3.13 (3.13) Acct: 4.96 (4.96) proj_loss: -0.6355 (-0.6355) time: 0.6528 data: 0.0003 [11-26 21:33:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [ 0/1669] eta: 0:18:10 tlr: 7.5e-05 tnm: 0.46 Lm: 6.196 (6.196) Lt: 5.444 (5.444) Accm: 4.52 (4.52) Acct: 7.30 (7.30) proj_loss: -0.6155 (-0.6155) time: 0.6531 data: 0.0004 [11-26 21:37:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [ 417/1669] eta: 0:14:02 tlr: 7.4e-05 tnm: 0.45 Lm: 6.402 (6.402) Lt: 5.654 (5.654) Accm: 3.87 (3.87) Acct: 6.14 (6.14) proj_loss: -0.6078 (-0.6078) time: 0.6746 data: 0.0003 [11-26 21:37:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [ 417/1669] eta: 0:14:02 tlr: 7.4e-05 tnm: 0.45 Lm: 6.495 (6.495) Lt: 5.741 (5.741) Accm: 3.39 (3.39) Acct: 5.60 (5.60) proj_loss: -0.6068 (-0.6068) time: 0.6746 data: 0.0003 [11-26 21:37:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [ 417/1669] eta: 0:14:02 tlr: 7.4e-05 tnm: 0.45 Lm: 6.359 (6.359) Lt: 5.610 (5.610) Accm: 3.85 (3.85) Acct: 5.79 (5.79) proj_loss: -0.6155 (-0.6155) time: 0.6746 data: 0.0002 [11-26 21:37:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [ 417/1669] eta: 0:14:02 tlr: 7.4e-05 tnm: 0.45 Lm: 6.440 (6.440) Lt: 5.673 (5.673) Accm: 3.46 (3.46) Acct: 5.41 (5.41) proj_loss: -0.6256 (-0.6256) time: 0.6746 data: 0.0003 [11-26 21:42:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [ 834/1669] eta: 0:09:27 tlr: 7.4e-05 tnm: 0.45 Lm: 6.352 (6.369) Lt: 5.586 (5.610) Accm: 3.79 (3.74) Acct: 5.85 (5.88) proj_loss: -0.6328 (-0.6280) time: 0.6736 data: 0.0003 [11-26 21:42:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [ 834/1669] eta: 0:09:27 tlr: 7.4e-05 tnm: 0.45 Lm: 6.432 (6.417) Lt: 5.656 (5.669) Accm: 3.54 (3.70) Acct: 5.49 (5.64) proj_loss: -0.6109 (-0.6101) time: 0.6736 data: 0.0003 [11-26 21:42:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [ 834/1669] eta: 0:09:27 tlr: 7.4e-05 tnm: 0.45 Lm: 6.196 (6.306) Lt: 5.444 (5.528) Accm: 4.52 (4.09) Acct: 7.25 (6.51) proj_loss: -0.6091 (-0.6082) time: 0.6736 data: 0.0003 [11-26 21:42:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [ 834/1669] eta: 0:09:27 tlr: 7.4e-05 tnm: 0.45 Lm: 6.492 (6.494) Lt: 5.727 (5.729) Accm: 3.42 (3.40) Acct: 5.34 (5.51) proj_loss: -0.6011 (-0.6049) time: 0.6736 data: 0.0003 [11-26 21:47:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [1251/1669] eta: 0:04:43 tlr: 7.4e-05 tnm: 0.46 Lm: 6.477 (6.452) Lt: 5.717 (5.678) Accm: 3.51 (3.55) Acct: 5.66 (5.70) proj_loss: -0.5967 (-0.6018) time: 0.6719 data: 0.0002 [11-26 21:47:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [1251/1669] eta: 0:04:43 tlr: 7.4e-05 tnm: 0.46 Lm: 6.440 (6.423) Lt: 5.673 (5.660) Accm: 3.46 (3.57) Acct: 5.41 (5.64) proj_loss: -0.6242 (-0.6199) time: 0.6719 data: 0.0003 [11-26 21:47:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [1251/1669] eta: 0:04:43 tlr: 7.4e-05 tnm: 0.46 Lm: 6.288 (6.324) Lt: 5.516 (5.543) Accm: 4.16 (4.02) Acct: 6.68 (6.41) proj_loss: -0.6123 (-0.6107) time: 0.6719 data: 0.0003 [11-26 21:47:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [1251/1669] eta: 0:04:43 tlr: 7.4e-05 tnm: 0.46 Lm: 6.461 (6.435) Lt: 5.678 (5.677) Accm: 3.47 (3.61) Acct: 5.59 (5.65) proj_loss: -0.6051 (-0.6068) time: 0.6719 data: 0.0003 [11-26 21:52:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [1668/1669] eta: 0:00:00 tlr: 7.4e-05 tnm: 0.44 Lm: 6.455 (6.439) Lt: 5.680 (5.678) Accm: 3.39 (3.54) Acct: 5.49 (5.54) proj_loss: -0.6109 (-0.6093) time: 0.6745 data: 0.0019 [11-26 21:52:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 251/350] Total time: 0:18:50 (0.677 s / it) [11-26 21:52:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [1668/1669] eta: 0:00:00 tlr: 7.4e-05 tnm: 0.44 Lm: 6.462 (6.423) Lt: 5.706 (5.643) Accm: 3.60 (3.64) Acct: 5.99 (5.84) proj_loss: -0.6007 (-0.6015) time: 0.6745 data: 0.0017 [11-26 21:52:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [1668/1669] eta: 0:00:00 tlr: 7.4e-05 tnm: 0.44 Lm: 6.527 (6.453) Lt: 5.760 (5.693) Accm: 3.19 (3.49) Acct: 4.96 (5.49) proj_loss: -0.6157 (-0.6190) time: 0.6745 data: 0.0017 [11-26 21:52:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 251/350] [1668/1669] eta: 0:00:00 tlr: 7.4e-05 tnm: 0.44 Lm: 6.380 (6.350) Lt: 5.588 (5.576) Accm: 3.80 (3.93) Acct: 6.11 (6.21) proj_loss: -0.6091 (-0.6102) time: 0.6745 data: 0.0016 [11-26 21:52:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 251/350] Total time: 0:18:50 (0.677 s / it) [11-26 21:52:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 251/350] Total time: 0:18:50 (0.677 s / it) [11-26 21:52:07] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 251/350] Total time: 0:18:50 (0.677 s / it) [11-26 21:52:07] (/home/user/VAR/train.py , line 279)=> [ep251] (training ) Lm: 6.445 (6.445), Lt: 5.687 (5.687), Acc m&t: 3.56 5.57, Remain: 1 day, 6:48:06, Finish: 2024-11-27 12:40 [11-26 21:52:07] (/home/user/VAR/train.py , line 279)=> [ep251] (training ) Lm: 6.445 (6.445), Lt: 5.687 (5.687), Acc m&t: 3.56 5.57, Remain: 1 day, 6:48:21, Finish: 2024-11-27 12:40 [11-26 21:52:07] (/home/user/VAR/train.py , line 279)=> [ep251] (training ) Lm: 6.445 (6.445), Lt: 5.687 (5.687), Acc m&t: 3.56 5.57, Remain: 1 day, 6:48:30, Finish: 2024-11-27 12:40 [11-26 21:52:07] (/home/user/VAR/train.py , line 279)=> [ep251] (training ) Lm: 6.445 (6.445), Lt: 5.687 (5.687), Acc m&t: 3.56 5.57, Remain: 1 day, 6:48:05, Finish: 2024-11-27 12:40 [11-26 21:52:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [ 0/1669] eta: 0:18:14 tlr: 7.4e-05 tnm: 0.43 Lm: 6.673 (6.673) Lt: 5.959 (5.959) Accm: 2.90 (2.90) Acct: 4.65 (4.65) proj_loss: -0.6227 (-0.6227) time: 0.6555 data: 0.0003 [11-26 21:52:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [ 0/1669] eta: 0:18:14 tlr: 7.4e-05 tnm: 0.43 Lm: 6.557 (6.557) Lt: 5.799 (5.799) Accm: 3.28 (3.28) Acct: 5.15 (5.15) proj_loss: -0.6209 (-0.6209) time: 0.6560 data: 0.0003 [11-26 21:52:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [ 0/1669] eta: 0:18:15 tlr: 7.4e-05 tnm: 0.43 Lm: 6.507 (6.507) Lt: 5.722 (5.722) Accm: 3.42 (3.42) Acct: 5.56 (5.56) proj_loss: -0.5792 (-0.5792) time: 0.6563 data: 0.0004 [11-26 21:52:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [ 0/1669] eta: 0:18:15 tlr: 7.4e-05 tnm: 0.43 Lm: 6.560 (6.560) Lt: 5.789 (5.789) Accm: 3.34 (3.34) Acct: 5.32 (5.32) proj_loss: -0.5846 (-0.5846) time: 0.6566 data: 0.0004 [11-26 21:56:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [ 417/1669] eta: 0:14:02 tlr: 7.4e-05 tnm: 0.45 Lm: 6.539 (6.539) Lt: 5.777 (5.777) Accm: 3.37 (3.37) Acct: 5.26 (5.26) proj_loss: -0.5979 (-0.5979) time: 0.6742 data: 0.0003 [11-26 21:56:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [ 417/1669] eta: 0:14:02 tlr: 7.4e-05 tnm: 0.45 Lm: 6.434 (6.434) Lt: 5.666 (5.666) Accm: 3.62 (3.62) Acct: 5.82 (5.82) proj_loss: -0.6143 (-0.6143) time: 0.6742 data: 0.0003 [11-26 21:56:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [ 417/1669] eta: 0:14:02 tlr: 7.4e-05 tnm: 0.45 Lm: 6.477 (6.477) Lt: 5.740 (5.740) Accm: 3.58 (3.58) Acct: 5.69 (5.69) proj_loss: -0.6267 (-0.6267) time: 0.6742 data: 0.0003 [11-26 21:56:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [ 417/1669] eta: 0:14:02 tlr: 7.4e-05 tnm: 0.45 Lm: 6.451 (6.451) Lt: 5.700 (5.700) Accm: 3.49 (3.49) Acct: 5.60 (5.60) proj_loss: -0.5959 (-0.5959) time: 0.6742 data: 0.0003 [11-26 22:01:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [ 834/1669] eta: 0:09:21 tlr: 7.3e-05 tnm: 0.45 Lm: 6.507 (6.489) Lt: 5.722 (5.777) Accm: 3.50 (3.49) Acct: 5.56 (5.51) proj_loss: -0.6126 (-0.6015) time: 0.6698 data: 0.0002 [11-26 22:01:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [ 834/1669] eta: 0:09:21 tlr: 7.3e-05 tnm: 0.45 Lm: 6.498 (6.456) Lt: 5.754 (5.696) Accm: 3.28 (3.49) Acct: 5.23 (5.62) proj_loss: -0.6143 (-0.6143) time: 0.6699 data: 0.0003 [11-26 22:01:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [ 834/1669] eta: 0:09:21 tlr: 7.3e-05 tnm: 0.45 Lm: 6.565 (6.507) Lt: 5.825 (5.768) Accm: 3.28 (3.48) Acct: 4.92 (5.44) proj_loss: -0.6227 (-0.6205) time: 0.6699 data: 0.0003 [11-26 22:01:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [ 834/1669] eta: 0:09:21 tlr: 7.3e-05 tnm: 0.45 Lm: 6.518 (6.509) Lt: 5.766 (5.746) Accm: 3.39 (3.47) Acct: 5.32 (5.52) proj_loss: -0.6091 (-0.6017) time: 0.6699 data: 0.0003 [11-26 22:06:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [1251/1669] eta: 0:04:41 tlr: 7.3e-05 tnm: 0.44 Lm: 6.539 (6.557) Lt: 5.777 (5.823) Accm: 3.37 (3.33) Acct: 5.26 (5.25) proj_loss: -0.6067 (-0.6023) time: 0.6725 data: 0.0003 [11-26 22:06:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [1251/1669] eta: 0:04:41 tlr: 7.3e-05 tnm: 0.44 Lm: 6.418 (6.426) Lt: 5.651 (5.659) Accm: 3.61 (3.60) Acct: 5.60 (5.71) proj_loss: -0.6120 (-0.6132) time: 0.6725 data: 0.0003 [11-26 22:06:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [1251/1669] eta: 0:04:41 tlr: 7.3e-05 tnm: 0.44 Lm: 6.471 (6.474) Lt: 5.707 (5.723) Accm: 3.47 (3.53) Acct: 5.38 (5.54) proj_loss: -0.6155 (-0.6171) time: 0.6725 data: 0.0003 [11-26 22:06:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [1251/1669] eta: 0:04:41 tlr: 7.3e-05 tnm: 0.44 Lm: 6.453 (6.467) Lt: 5.700 (5.748) Accm: 3.53 (3.58) Acct: 5.60 (5.59) proj_loss: -0.6127 (-0.6058) time: 0.6725 data: 0.0003 [11-26 22:10:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [1668/1669] eta: 0:00:00 tlr: 7.3e-05 tnm: 0.46 Lm: 6.507 (6.495) Lt: 5.722 (5.769) Accm: 3.50 (3.49) Acct: 5.56 (5.51) proj_loss: -0.6129 (-0.6104) time: 0.7387 data: 0.0016 [11-26 22:10:54] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 252/350] Total time: 0:18:46 (0.675 s / it) [11-26 22:10:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [1668/1669] eta: 0:00:00 tlr: 7.3e-05 tnm: 0.46 Lm: 6.484 (6.438) Lt: 5.739 (5.675) Accm: 3.28 (3.53) Acct: 5.23 (5.55) proj_loss: -0.6097 (-0.6113) time: 0.7387 data: 0.0015 [11-26 22:10:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [1668/1669] eta: 0:00:00 tlr: 7.3e-05 tnm: 0.46 Lm: 6.507 (6.481) Lt: 5.751 (5.729) Accm: 3.50 (3.52) Acct: 5.46 (5.52) proj_loss: -0.6132 (-0.6163) time: 0.7387 data: 0.0020 [11-26 22:10:54] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 252/350] [1668/1669] eta: 0:00:00 tlr: 7.3e-05 tnm: 0.46 Lm: 6.518 (6.536) Lt: 5.766 (5.800) Accm: 3.39 (3.39) Acct: 5.32 (5.30) proj_loss: -0.6049 (-0.6028) time: 0.7387 data: 0.0019 [11-26 22:10:54] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 252/350] Total time: 0:18:46 (0.675 s / it) [11-26 22:10:54] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 252/350] Total time: 0:18:46 (0.675 s / it) [11-26 22:10:54] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 252/350] Total time: 0:18:46 (0.675 s / it) [11-26 22:10:54] (/home/user/VAR/train.py , line 279)=> [ep252] (training ) Lm: 6.445 (6.465), Lt: 5.687 (5.708), Acc m&t: 3.56 5.57, Remain: 1 day, 6:28:12, Finish: 2024-11-27 12:39 [11-26 22:10:54] (/home/user/VAR/train.py , line 279)=> [ep252] (training ) Lm: 6.445 (6.465), Lt: 5.687 (5.708), Acc m&t: 3.56 5.57, Remain: 1 day, 6:29:02, Finish: 2024-11-27 12:39 [11-26 22:10:54] (/home/user/VAR/train.py , line 279)=> [ep252] (training ) Lm: 6.445 (6.465), Lt: 5.687 (5.708), Acc m&t: 3.56 5.57, Remain: 1 day, 6:28:19, Finish: 2024-11-27 12:39 [11-26 22:10:54] (/home/user/VAR/train.py , line 279)=> [ep252] (training ) Lm: 6.445 (6.465), Lt: 5.687 (5.708), Acc m&t: 3.56 5.57, Remain: 1 day, 6:28:08, Finish: 2024-11-27 12:39 [11-26 22:10:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [ 0/1669] eta: 0:18:10 tlr: 7.3e-05 tnm: 0.47 Lm: 6.576 (6.576) Lt: 5.864 (5.864) Accm: 3.26 (3.26) Acct: 5.17 (5.17) proj_loss: -0.5986 (-0.5986) time: 0.6532 data: 0.0004 [11-26 22:10:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [ 0/1669] eta: 0:18:10 tlr: 7.3e-05 tnm: 0.47 Lm: 6.401 (6.401) Lt: 5.610 (5.610) Accm: 3.74 (3.74) Acct: 6.18 (6.18) proj_loss: -0.6040 (-0.6040) time: 0.6533 data: 0.0003 [11-26 22:10:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [ 0/1669] eta: 0:18:11 tlr: 7.3e-05 tnm: 0.47 Lm: 6.291 (6.291) Lt: 5.589 (5.589) Accm: 4.36 (4.36) Acct: 6.96 (6.96) proj_loss: -0.6263 (-0.6263) time: 0.6538 data: 0.0003 [11-26 22:10:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [ 0/1669] eta: 0:18:11 tlr: 7.3e-05 tnm: 0.47 Lm: 6.407 (6.407) Lt: 5.651 (5.651) Accm: 3.69 (3.69) Acct: 5.94 (5.94) proj_loss: -0.6173 (-0.6173) time: 0.6538 data: 0.0004 [11-26 22:15:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [ 417/1669] eta: 0:14:24 tlr: 7.3e-05 tnm: 0.46 Lm: 6.485 (6.485) Lt: 5.716 (5.716) Accm: 3.43 (3.43) Acct: 5.51 (5.51) proj_loss: -0.6165 (-0.6165) time: 0.6723 data: 0.0002 [11-26 22:15:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [ 417/1669] eta: 0:14:24 tlr: 7.3e-05 tnm: 0.46 Lm: 6.382 (6.382) Lt: 5.614 (5.614) Accm: 3.73 (3.73) Acct: 6.00 (6.00) proj_loss: -0.6090 (-0.6090) time: 0.6723 data: 0.0003 [11-26 22:15:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [ 417/1669] eta: 0:14:24 tlr: 7.3e-05 tnm: 0.46 Lm: 6.370 (6.370) Lt: 5.624 (5.624) Accm: 3.96 (3.96) Acct: 6.34 (6.34) proj_loss: -0.6299 (-0.6299) time: 0.6723 data: 0.0003 [11-26 22:15:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [ 417/1669] eta: 0:14:24 tlr: 7.3e-05 tnm: 0.46 Lm: 6.601 (6.601) Lt: 5.882 (5.882) Accm: 3.09 (3.09) Acct: 4.83 (4.83) proj_loss: -0.6067 (-0.6067) time: 0.6723 data: 0.0003 [11-26 22:20:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [ 834/1669] eta: 0:09:29 tlr: 7.3e-05 tnm: 0.45 Lm: 6.576 (6.516) Lt: 5.864 (5.782) Accm: 3.26 (3.44) Acct: 5.17 (5.46) proj_loss: -0.6138 (-0.6090) time: 0.6718 data: 0.0003 [11-26 22:20:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [ 834/1669] eta: 0:09:29 tlr: 7.3e-05 tnm: 0.45 Lm: 6.401 (6.468) Lt: 5.617 (5.715) Accm: 3.71 (3.51) Acct: 5.82 (5.65) proj_loss: -0.6139 (-0.6159) time: 0.6718 data: 0.0003 [11-26 22:20:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [ 834/1669] eta: 0:09:29 tlr: 7.3e-05 tnm: 0.45 Lm: 6.562 (6.544) Lt: 5.782 (5.763) Accm: 3.18 (3.26) Acct: 5.08 (5.23) proj_loss: -0.6157 (-0.6026) time: 0.6718 data: 0.0003 [11-26 22:20:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [ 834/1669] eta: 0:09:29 tlr: 7.3e-05 tnm: 0.45 Lm: 6.379 (6.373) Lt: 5.599 (5.616) Accm: 3.91 (3.95) Acct: 6.22 (6.30) proj_loss: -0.6327 (-0.6308) time: 0.6718 data: 0.0003 [11-26 22:25:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [1251/1669] eta: 0:04:43 tlr: 7.3e-05 tnm: 0.46 Lm: 6.335 (6.353) Lt: 5.594 (5.594) Accm: 4.02 (3.99) Acct: 6.41 (6.37) proj_loss: -0.6295 (-0.6204) time: 0.6706 data: 0.0003 [11-26 22:25:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [1251/1669] eta: 0:04:43 tlr: 7.3e-05 tnm: 0.46 Lm: 6.396 (6.449) Lt: 5.614 (5.688) Accm: 3.73 (3.58) Acct: 6.00 (5.79) proj_loss: -0.6189 (-0.6179) time: 0.6706 data: 0.0003 [11-26 22:25:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [1251/1669] eta: 0:04:43 tlr: 7.3e-05 tnm: 0.46 Lm: 6.540 (6.513) Lt: 5.788 (5.764) Accm: 3.33 (3.43) Acct: 5.18 (5.40) proj_loss: -0.6062 (-0.6064) time: 0.6706 data: 0.0003 [11-26 22:25:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [1251/1669] eta: 0:04:43 tlr: 7.3e-05 tnm: 0.46 Lm: 6.498 (6.517) Lt: 5.742 (5.748) Accm: 3.38 (3.34) Acct: 5.45 (5.38) proj_loss: -0.6038 (-0.5999) time: 0.6706 data: 0.0003 [11-26 22:29:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [1668/1669] eta: 0:00:00 tlr: 7.2e-05 tnm: 0.45 Lm: 6.558 (6.525) Lt: 5.782 (5.767) Accm: 3.33 (3.34) Acct: 5.35 (5.37) proj_loss: -0.6157 (-0.6054) time: 0.6737 data: 0.0016 [11-26 22:29:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 253/350] Total time: 0:18:50 (0.677 s / it) [11-26 22:29:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [1668/1669] eta: 0:00:00 tlr: 7.2e-05 tnm: 0.45 Lm: 6.392 (6.427) Lt: 5.610 (5.669) Accm: 3.74 (3.63) Acct: 6.06 (5.84) proj_loss: -0.6139 (-0.6143) time: 0.6737 data: 0.0015 [11-26 22:29:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [1668/1669] eta: 0:00:00 tlr: 7.2e-05 tnm: 0.45 Lm: 6.503 (6.506) Lt: 5.712 (5.753) Accm: 3.40 (3.48) Acct: 5.20 (5.47) proj_loss: -0.5986 (-0.6041) time: 0.6737 data: 0.0016 [11-26 22:29:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 253/350] [1668/1669] eta: 0:00:00 tlr: 7.2e-05 tnm: 0.45 Lm: 6.379 (6.379) Lt: 5.599 (5.617) Accm: 3.91 (3.89) Acct: 6.22 (6.17) proj_loss: -0.6263 (-0.6155) time: 0.6737 data: 0.0016 [11-26 22:29:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 253/350] Total time: 0:18:50 (0.677 s / it) [11-26 22:29:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 253/350] Total time: 0:18:50 (0.677 s / it) [11-26 22:29:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 253/350] Total time: 0:18:50 (0.677 s / it) [11-26 22:29:44] (/home/user/VAR/train.py , line 279)=> [ep253] (training ) Lm: 6.445 (6.469), Lt: 5.687 (5.718), Acc m&t: 3.56 5.57, Remain: 1 day, 6:15:00, Finish: 2024-11-27 12:44 [11-26 22:29:44] (/home/user/VAR/train.py , line 279)=> [ep253] (training ) Lm: 6.445 (6.469), Lt: 5.687 (5.718), Acc m&t: 3.56 5.57, Remain: 1 day, 6:14:22, Finish: 2024-11-27 12:44 [11-26 22:29:44] (/home/user/VAR/train.py , line 279)=> [ep253] (training ) Lm: 6.445 (6.469), Lt: 5.687 (5.718), Acc m&t: 3.56 5.57, Remain: 1 day, 6:15:15, Finish: 2024-11-27 12:44 [11-26 22:29:44] (/home/user/VAR/train.py , line 279)=> [ep253] (training ) Lm: 6.445 (6.469), Lt: 5.687 (5.718), Acc m&t: 3.56 5.57, Remain: 1 day, 6:14:50, Finish: 2024-11-27 12:44 [11-26 22:29:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [ 0/1669] eta: 0:18:14 tlr: 7.2e-05 tnm: 0.44 Lm: 6.481 (6.481) Lt: 5.745 (5.745) Accm: 3.72 (3.72) Acct: 5.77 (5.77) proj_loss: -0.6008 (-0.6008) time: 0.6559 data: 0.0003 [11-26 22:29:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [ 0/1669] eta: 0:18:15 tlr: 7.2e-05 tnm: 0.44 Lm: 6.545 (6.545) Lt: 5.782 (5.782) Accm: 3.11 (3.11) Acct: 4.75 (4.75) proj_loss: -0.6055 (-0.6055) time: 0.6562 data: 0.0003 [11-26 22:29:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [ 0/1669] eta: 0:18:14 tlr: 7.2e-05 tnm: 0.44 Lm: 6.529 (6.529) Lt: 5.833 (5.833) Accm: 3.26 (3.26) Acct: 4.82 (4.82) proj_loss: -0.6134 (-0.6134) time: 0.6557 data: 0.0004 [11-26 22:29:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [ 0/1669] eta: 0:18:10 tlr: 7.2e-05 tnm: 0.44 Lm: 6.419 (6.419) Lt: 5.700 (5.700) Accm: 3.82 (3.82) Acct: 5.53 (5.53) proj_loss: -0.6118 (-0.6118) time: 0.6532 data: 0.0004 [11-26 22:34:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [ 417/1669] eta: 0:14:01 tlr: 7.2e-05 tnm: 0.45 Lm: 6.395 (6.395) Lt: 5.665 (5.665) Accm: 3.77 (3.77) Acct: 5.51 (5.51) proj_loss: -0.6138 (-0.6138) time: 0.6742 data: 0.0003 [11-26 22:34:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [ 417/1669] eta: 0:14:01 tlr: 7.2e-05 tnm: 0.45 Lm: 6.395 (6.395) Lt: 5.598 (5.598) Accm: 3.68 (3.68) Acct: 5.72 (5.72) proj_loss: -0.6058 (-0.6058) time: 0.6742 data: 0.0002 [11-26 22:34:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [ 417/1669] eta: 0:14:01 tlr: 7.2e-05 tnm: 0.45 Lm: 6.531 (6.531) Lt: 5.790 (5.790) Accm: 3.37 (3.37) Acct: 5.16 (5.16) proj_loss: -0.6015 (-0.6015) time: 0.6742 data: 0.0003 [11-26 22:34:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [ 417/1669] eta: 0:14:01 tlr: 7.2e-05 tnm: 0.45 Lm: 6.566 (6.566) Lt: 5.840 (5.840) Accm: 3.22 (3.22) Acct: 4.96 (4.96) proj_loss: -0.6255 (-0.6255) time: 0.6742 data: 0.0003 [11-26 22:39:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [ 834/1669] eta: 0:09:32 tlr: 7.2e-05 tnm: 0.46 Lm: 6.529 (6.481) Lt: 5.833 (5.729) Accm: 3.26 (3.57) Acct: 5.10 (5.58) proj_loss: -0.6221 (-0.6244) time: 0.6729 data: 0.0003 [11-26 22:39:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [ 834/1669] eta: 0:09:32 tlr: 7.2e-05 tnm: 0.46 Lm: 6.463 (6.418) Lt: 5.729 (5.642) Accm: 3.37 (3.57) Acct: 5.41 (5.61) proj_loss: -0.6055 (-0.6034) time: 0.6729 data: 0.0003 [11-26 22:39:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [ 834/1669] eta: 0:09:32 tlr: 7.2e-05 tnm: 0.46 Lm: 6.481 (6.496) Lt: 5.762 (5.781) Accm: 3.40 (3.38) Acct: 5.37 (5.23) proj_loss: -0.6022 (-0.6053) time: 0.6729 data: 0.0003 [11-26 22:39:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [ 834/1669] eta: 0:09:32 tlr: 7.2e-05 tnm: 0.46 Lm: 6.419 (6.451) Lt: 5.700 (5.701) Accm: 3.73 (3.60) Acct: 5.49 (5.45) proj_loss: -0.6118 (-0.6104) time: 0.6729 data: 0.0003 [11-26 22:43:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [1251/1669] eta: 0:04:45 tlr: 7.2e-05 tnm: 0.46 Lm: 6.395 (6.419) Lt: 5.665 (5.666) Accm: 3.77 (3.73) Acct: 5.51 (5.69) proj_loss: -0.6077 (-0.6085) time: 0.6705 data: 0.0002 [11-26 22:43:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [1251/1669] eta: 0:04:45 tlr: 7.2e-05 tnm: 0.46 Lm: 6.381 (6.388) Lt: 5.611 (5.605) Accm: 3.81 (3.76) Acct: 6.04 (5.94) proj_loss: -0.6044 (-0.6034) time: 0.6705 data: 0.0003 [11-26 22:43:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [1251/1669] eta: 0:04:45 tlr: 7.2e-05 tnm: 0.46 Lm: 6.488 (6.496) Lt: 5.754 (5.768) Accm: 3.49 (3.43) Acct: 5.57 (5.37) proj_loss: -0.6015 (-0.6030) time: 0.6705 data: 0.0003 [11-26 22:43:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [1251/1669] eta: 0:04:45 tlr: 7.2e-05 tnm: 0.46 Lm: 6.424 (6.441) Lt: 5.716 (5.697) Accm: 3.53 (3.63) Acct: 5.32 (5.57) proj_loss: -0.6280 (-0.6268) time: 0.6705 data: 0.0003 [11-26 22:48:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [1668/1669] eta: 0:00:00 tlr: 7.2e-05 tnm: 0.45 Lm: 6.503 (6.453) Lt: 5.703 (5.698) Accm: 3.26 (3.52) Acct: 5.35 (5.53) proj_loss: -0.6221 (-0.6246) time: 0.6775 data: 0.0015 [11-26 22:48:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 254/350] Total time: 0:18:55 (0.680 s / it) [11-26 22:48:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [1668/1669] eta: 0:00:00 tlr: 7.2e-05 tnm: 0.45 Lm: 6.453 (6.401) Lt: 5.710 (5.626) Accm: 3.58 (3.72) Acct: 5.79 (5.91) proj_loss: -0.6052 (-0.6038) time: 0.6775 data: 0.0021 [11-26 22:48:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [1668/1669] eta: 0:00:00 tlr: 7.2e-05 tnm: 0.45 Lm: 6.496 (6.506) Lt: 5.758 (5.766) Accm: 3.40 (3.38) Acct: 5.37 (5.31) proj_loss: -0.6022 (-0.6031) time: 0.6775 data: 0.0018 [11-26 22:48:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 254/350] [1668/1669] eta: 0:00:00 tlr: 7.2e-05 tnm: 0.45 Lm: 6.419 (6.430) Lt: 5.700 (5.685) Accm: 3.73 (3.67) Acct: 5.49 (5.63) proj_loss: -0.6118 (-0.6115) time: 0.6775 data: 0.0019 [11-26 22:48:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 254/350] Total time: 0:18:55 (0.680 s / it) [11-26 22:48:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 254/350] Total time: 0:18:55 (0.680 s / it) [11-26 22:48:40] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 254/350] Total time: 0:18:55 (0.680 s / it) [11-26 22:48:40] (/home/user/VAR/train.py , line 279)=> [ep254] (training ) Lm: 6.445 (6.451), Lt: 5.687 (5.695), Acc m&t: 3.56 5.57, Remain: 1 day, 6:04:02, Finish: 2024-11-27 12:52 [11-26 22:48:40] (/home/user/VAR/train.py , line 279)=> [ep254] (training ) Lm: 6.445 (6.451), Lt: 5.687 (5.695), Acc m&t: 3.56 5.57, Remain: 1 day, 6:04:20, Finish: 2024-11-27 12:53 [11-26 22:48:40] (/home/user/VAR/train.py , line 279)=> [ep254] (training ) Lm: 6.445 (6.451), Lt: 5.687 (5.695), Acc m&t: 3.56 5.57, Remain: 1 day, 6:04:24, Finish: 2024-11-27 12:53 [11-26 22:48:40] (/home/user/VAR/train.py , line 279)=> [ep254] (training ) Lm: 6.445 (6.451), Lt: 5.687 (5.695), Acc m&t: 3.56 5.57, Remain: 1 day, 6:04:07, Finish: 2024-11-27 12:52 [11-26 22:48:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [ 0/1669] eta: 0:18:18 tlr: 7.2e-05 tnm: 0.47 Lm: 6.583 (6.583) Lt: 5.861 (5.861) Accm: 3.06 (3.06) Acct: 4.37 (4.37) proj_loss: -0.6153 (-0.6153) time: 0.6582 data: 0.0003 [11-26 22:48:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [ 0/1669] eta: 0:18:21 tlr: 7.2e-05 tnm: 0.47 Lm: 6.628 (6.628) Lt: 5.880 (5.880) Accm: 2.85 (2.85) Acct: 4.49 (4.49) proj_loss: -0.5794 (-0.5794) time: 0.6599 data: 0.0004 [11-26 22:48:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [ 0/1669] eta: 0:18:08 tlr: 7.2e-05 tnm: 0.47 Lm: 6.352 (6.352) Lt: 5.595 (5.595) Accm: 3.83 (3.83) Acct: 5.91 (5.91) proj_loss: -0.6181 (-0.6181) time: 0.6520 data: 0.0004 [11-26 22:48:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [ 0/1669] eta: 0:18:21 tlr: 7.2e-05 tnm: 0.47 Lm: 6.212 (6.212) Lt: 5.424 (5.424) Accm: 4.34 (4.34) Acct: 6.90 (6.90) proj_loss: -0.6228 (-0.6228) time: 0.6601 data: 0.0004 [11-26 22:53:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [ 417/1669] eta: 0:14:02 tlr: 7.1e-05 tnm: 0.47 Lm: 6.388 (6.388) Lt: 5.618 (5.618) Accm: 3.79 (3.79) Acct: 5.89 (5.89) proj_loss: -0.6170 (-0.6170) time: 0.6745 data: 0.0003 [11-26 22:53:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [ 417/1669] eta: 0:14:02 tlr: 7.1e-05 tnm: 0.47 Lm: 6.450 (6.450) Lt: 5.721 (5.721) Accm: 3.38 (3.38) Acct: 5.05 (5.05) proj_loss: -0.6114 (-0.6114) time: 0.6745 data: 0.0003 [11-26 22:53:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [ 417/1669] eta: 0:14:02 tlr: 7.1e-05 tnm: 0.47 Lm: 6.538 (6.538) Lt: 5.812 (5.812) Accm: 3.21 (3.21) Acct: 5.09 (5.09) proj_loss: -0.5951 (-0.5951) time: 0.6745 data: 0.0003 [11-26 22:53:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [ 417/1669] eta: 0:14:02 tlr: 7.1e-05 tnm: 0.47 Lm: 6.476 (6.476) Lt: 5.739 (5.739) Accm: 3.35 (3.35) Acct: 5.24 (5.24) proj_loss: -0.6207 (-0.6207) time: 0.6746 data: 0.0003 [11-26 22:58:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [ 834/1669] eta: 0:09:21 tlr: 7.1e-05 tnm: 0.45 Lm: 6.471 (6.474) Lt: 5.685 (5.721) Accm: 3.53 (3.41) Acct: 5.61 (5.37) proj_loss: -0.6181 (-0.6197) time: 0.6711 data: 0.0003 [11-26 22:58:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [ 834/1669] eta: 0:09:21 tlr: 7.1e-05 tnm: 0.45 Lm: 6.289 (6.355) Lt: 5.528 (5.588) Accm: 3.91 (3.83) Acct: 6.10 (5.96) proj_loss: -0.6112 (-0.6128) time: 0.6711 data: 0.0003 [11-26 22:58:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [ 834/1669] eta: 0:09:21 tlr: 7.1e-05 tnm: 0.45 Lm: 6.562 (6.487) Lt: 5.836 (5.760) Accm: 3.36 (3.38) Acct: 4.96 (5.02) proj_loss: -0.6076 (-0.6083) time: 0.6711 data: 0.0003 [11-26 22:58:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [ 834/1669] eta: 0:09:21 tlr: 7.1e-05 tnm: 0.45 Lm: 6.534 (6.537) Lt: 5.839 (5.821) Accm: 3.35 (3.26) Acct: 5.10 (5.09) proj_loss: -0.6108 (-0.6073) time: 0.6711 data: 0.0003 [11-26 23:02:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [1251/1669] eta: 0:04:41 tlr: 7.1e-05 tnm: 0.44 Lm: 6.492 (6.507) Lt: 5.791 (5.796) Accm: 3.42 (3.32) Acct: 5.39 (5.24) proj_loss: -0.6111 (-0.6083) time: 0.6747 data: 0.0003 [11-26 23:02:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [1251/1669] eta: 0:04:41 tlr: 7.1e-05 tnm: 0.44 Lm: 6.326 (6.357) Lt: 5.582 (5.600) Accm: 3.86 (3.82) Acct: 6.03 (5.96) proj_loss: -0.6170 (-0.6154) time: 0.6747 data: 0.0003 [11-26 23:02:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [1251/1669] eta: 0:04:41 tlr: 7.1e-05 tnm: 0.44 Lm: 6.572 (6.518) Lt: 5.842 (5.782) Accm: 3.25 (3.32) Acct: 5.03 (5.04) proj_loss: -0.6114 (-0.6127) time: 0.6747 data: 0.0003 [11-26 23:02:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [1251/1669] eta: 0:04:41 tlr: 7.1e-05 tnm: 0.44 Lm: 6.509 (6.493) Lt: 5.704 (5.721) Accm: 3.38 (3.37) Acct: 5.48 (5.36) proj_loss: -0.6179 (-0.6105) time: 0.6746 data: 0.0003 [11-26 23:07:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [1668/1669] eta: 0:00:00 tlr: 7.1e-05 tnm: 0.46 Lm: 6.471 (6.459) Lt: 5.685 (5.695) Accm: 3.53 (3.48) Acct: 5.61 (5.51) proj_loss: -0.6176 (-0.6119) time: 0.7433 data: 0.0021 [11-26 23:07:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 255/350] Total time: 0:18:48 (0.676 s / it) [11-26 23:07:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [1668/1669] eta: 0:00:00 tlr: 7.1e-05 tnm: 0.46 Lm: 6.363 (6.367) Lt: 5.636 (5.611) Accm: 3.81 (3.77) Acct: 5.97 (5.84) proj_loss: -0.6228 (-0.6174) time: 0.7433 data: 0.0019 [11-26 23:07:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [1668/1669] eta: 0:00:00 tlr: 7.1e-05 tnm: 0.46 Lm: 6.505 (6.506) Lt: 5.796 (5.796) Accm: 3.50 (3.36) Acct: 5.41 (5.27) proj_loss: -0.6113 (-0.6098) time: 0.7433 data: 0.0021 [11-26 23:07:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 255/350] [1668/1669] eta: 0:00:00 tlr: 7.1e-05 tnm: 0.46 Lm: 6.562 (6.505) Lt: 5.836 (5.776) Accm: 3.36 (3.37) Acct: 5.10 (5.15) proj_loss: -0.6076 (-0.6114) time: 0.7433 data: 0.0016 [11-26 23:07:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 255/350] Total time: 0:18:48 (0.676 s / it) [11-26 23:07:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 255/350] Total time: 0:18:48 (0.676 s / it) [11-26 23:07:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 255/350] Total time: 0:18:48 (0.676 s / it) [11-26 23:07:28] (/home/user/VAR/train.py , line 279)=> [ep255] (training ) Lm: 6.445 (6.467), Lt: 5.687 (5.710), Acc m&t: 3.56 5.57, Remain: 1 day, 5:46:24, Finish: 2024-11-27 12:53 [11-26 23:07:28] (/home/user/VAR/train.py , line 279)=> [ep255] (training ) Lm: 6.445 (6.467), Lt: 5.687 (5.710), Acc m&t: 3.56 5.57, Remain: 1 day, 5:46:34, Finish: 2024-11-27 12:54 [11-26 23:07:28] (/home/user/VAR/train.py , line 279)=> [ep255] (training ) Lm: 6.445 (6.467), Lt: 5.687 (5.710), Acc m&t: 3.56 5.57, Remain: 1 day, 5:45:50, Finish: 2024-11-27 12:53 [11-26 23:07:28] (/home/user/VAR/train.py , line 279)=> [ep255] (training ) Lm: 6.445 (6.467), Lt: 5.687 (5.710), Acc m&t: 3.56 5.57, Remain: 1 day, 5:45:59, Finish: 2024-11-27 12:53 [11-26 23:07:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [ 0/1669] eta: 0:18:05 tlr: 7.1e-05 tnm: 0.45 Lm: 6.220 (6.220) Lt: 5.419 (5.419) Accm: 4.20 (4.20) Acct: 6.75 (6.75) proj_loss: -0.5980 (-0.5980) time: 0.6503 data: 0.0003 [11-26 23:07:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [ 0/1669] eta: 0:18:06 tlr: 7.1e-05 tnm: 0.45 Lm: 6.579 (6.579) Lt: 5.860 (5.860) Accm: 2.91 (2.91) Acct: 4.44 (4.44) proj_loss: -0.6514 (-0.6514) time: 0.6508 data: 0.0004 [11-26 23:07:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [ 0/1669] eta: 0:18:05 tlr: 7.1e-05 tnm: 0.45 Lm: 6.313 (6.313) Lt: 5.586 (5.586) Accm: 4.00 (4.00) Acct: 6.49 (6.49) proj_loss: -0.6254 (-0.6254) time: 0.6504 data: 0.0003 [11-26 23:07:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [ 0/1669] eta: 0:18:06 tlr: 7.1e-05 tnm: 0.45 Lm: 6.538 (6.538) Lt: 5.731 (5.731) Accm: 3.16 (3.16) Acct: 5.35 (5.35) proj_loss: -0.5880 (-0.5880) time: 0.6510 data: 0.0004 [11-26 23:12:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [ 417/1669] eta: 0:14:41 tlr: 7.1e-05 tnm: 0.45 Lm: 6.499 (6.499) Lt: 5.719 (5.719) Accm: 3.20 (3.20) Acct: 5.30 (5.30) proj_loss: -0.5880 (-0.5880) time: 0.6726 data: 0.0003 [11-26 23:12:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [ 417/1669] eta: 0:14:41 tlr: 7.1e-05 tnm: 0.45 Lm: 6.241 (6.241) Lt: 5.460 (5.460) Accm: 4.12 (4.12) Acct: 6.58 (6.58) proj_loss: -0.6068 (-0.6068) time: 0.6726 data: 0.0003 [11-26 23:12:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [ 417/1669] eta: 0:14:41 tlr: 7.1e-05 tnm: 0.45 Lm: 6.346 (6.346) Lt: 5.580 (5.580) Accm: 3.82 (3.82) Acct: 6.14 (6.14) proj_loss: -0.6157 (-0.6157) time: 0.6726 data: 0.0003 [11-26 23:12:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [ 417/1669] eta: 0:14:41 tlr: 7.1e-05 tnm: 0.45 Lm: 6.519 (6.519) Lt: 5.782 (5.782) Accm: 3.31 (3.31) Acct: 5.08 (5.08) proj_loss: -0.6203 (-0.6203) time: 0.6726 data: 0.0003 [11-26 23:17:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [ 834/1669] eta: 0:09:34 tlr: 7.1e-05 tnm: 0.47 Lm: 6.458 (6.489) Lt: 5.704 (5.738) Accm: 3.45 (3.36) Acct: 5.23 (5.13) proj_loss: -0.6214 (-0.6206) time: 0.6728 data: 0.0003 [11-26 23:17:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [ 834/1669] eta: 0:09:34 tlr: 7.1e-05 tnm: 0.47 Lm: 6.379 (6.441) Lt: 5.586 (5.679) Accm: 3.63 (3.48) Acct: 5.79 (5.62) proj_loss: -0.6179 (-0.6164) time: 0.6728 data: 0.0003 [11-26 23:17:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [ 834/1669] eta: 0:09:34 tlr: 7.1e-05 tnm: 0.47 Lm: 6.262 (6.347) Lt: 5.501 (5.586) Accm: 4.04 (3.80) Acct: 6.40 (5.95) proj_loss: -0.6156 (-0.6137) time: 0.6728 data: 0.0003 [11-26 23:17:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [ 834/1669] eta: 0:09:34 tlr: 7.1e-05 tnm: 0.47 Lm: 6.538 (6.523) Lt: 5.731 (5.768) Accm: 3.16 (3.08) Acct: 5.25 (5.00) proj_loss: -0.5881 (-0.5940) time: 0.6728 data: 0.0003 [11-26 23:21:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [1251/1669] eta: 0:04:45 tlr: 7e-05 tnm: 0.44 Lm: 6.372 (6.380) Lt: 5.590 (5.609) Accm: 3.76 (3.72) Acct: 6.07 (5.89) proj_loss: -0.6097 (-0.6112) time: 0.6740 data: 0.0003 [11-26 23:21:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [1251/1669] eta: 0:04:45 tlr: 7e-05 tnm: 0.44 Lm: 6.443 (6.446) Lt: 5.677 (5.707) Accm: 3.58 (3.49) Acct: 5.48 (5.34) proj_loss: -0.6288 (-0.6245) time: 0.6740 data: 0.0003 [11-26 23:21:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [1251/1669] eta: 0:04:45 tlr: 7e-05 tnm: 0.44 Lm: 6.505 (6.502) Lt: 5.731 (5.770) Accm: 3.22 (3.27) Acct: 5.19 (5.26) proj_loss: -0.6124 (-0.6141) time: 0.6740 data: 0.0003 [11-26 23:21:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [1251/1669] eta: 0:04:45 tlr: 7e-05 tnm: 0.44 Lm: 6.503 (6.509) Lt: 5.719 (5.748) Accm: 3.20 (3.21) Acct: 5.30 (5.23) proj_loss: -0.5902 (-0.5936) time: 0.6741 data: 0.0003 [11-26 23:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [1668/1669] eta: 0:00:00 tlr: 7e-05 tnm: 0.44 Lm: 6.475 (6.399) Lt: 5.678 (5.623) Accm: 3.74 (3.73) Acct: 5.85 (5.88) proj_loss: -0.6038 (-0.6063) time: 0.6749 data: 0.0019 [11-26 23:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [1668/1669] eta: 0:00:00 tlr: 7e-05 tnm: 0.44 Lm: 6.458 (6.484) Lt: 5.704 (5.736) Accm: 3.45 (3.40) Acct: 5.23 (5.32) proj_loss: -0.6214 (-0.6231) time: 0.6750 data: 0.0019 [11-26 23:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [1668/1669] eta: 0:00:00 tlr: 7e-05 tnm: 0.44 Lm: 6.386 (6.479) Lt: 5.601 (5.736) Accm: 3.63 (3.39) Acct: 5.79 (5.42) proj_loss: -0.6082 (-0.6129) time: 0.6750 data: 0.0015 [11-26 23:26:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 256/350] [1668/1669] eta: 0:00:00 tlr: 7e-05 tnm: 0.44 Lm: 6.514 (6.510) Lt: 5.726 (5.743) Accm: 3.16 (3.20) Acct: 5.25 (5.19) proj_loss: -0.5923 (-0.5964) time: 0.6750 data: 0.0027 [11-26 23:26:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 256/350] Total time: 0:18:56 (0.681 s / it) [11-26 23:26:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 256/350] Total time: 0:18:56 (0.681 s / it) [11-26 23:26:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 256/350] Total time: 0:18:56 (0.681 s / it) [11-26 23:26:24] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 256/350] Total time: 0:18:56 (0.681 s / it) [11-26 23:26:25] (/home/user/VAR/train.py , line 279)=> [ep256] (training ) Lm: 6.445 (6.468), Lt: 5.687 (5.717), Acc m&t: 3.56 5.57, Remain: 1 day, 5:16:29, Finish: 2024-11-27 12:42 [11-26 23:26:25] (/home/user/VAR/train.py , line 279)=> [ep256] (training ) Lm: 6.445 (6.468), Lt: 5.687 (5.717), Acc m&t: 3.56 5.57, Remain: 1 day, 5:16:13, Finish: 2024-11-27 12:42 [11-26 23:26:25] (/home/user/VAR/train.py , line 279)=> [ep256] (training ) Lm: 6.445 (6.468), Lt: 5.687 (5.717), Acc m&t: 3.56 5.57, Remain: 1 day, 5:17:08, Finish: 2024-11-27 12:43 [11-26 23:26:25] (/home/user/VAR/train.py , line 279)=> [ep256] (training ) Lm: 6.445 (6.468), Lt: 5.687 (5.717), Acc m&t: 3.56 5.57, Remain: 1 day, 5:16:07, Finish: 2024-11-27 12:42 [11-26 23:26:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [ 0/1669] eta: 0:18:10 tlr: 7e-05 tnm: 0.48 Lm: 6.558 (6.558) Lt: 5.839 (5.839) Accm: 3.04 (3.04) Acct: 4.70 (4.70) proj_loss: -0.6025 (-0.6025) time: 0.6536 data: 0.0004 [11-26 23:26:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [ 0/1669] eta: 0:18:11 tlr: 7e-05 tnm: 0.48 Lm: 6.482 (6.482) Lt: 5.734 (5.734) Accm: 3.13 (3.13) Acct: 5.01 (5.01) proj_loss: -0.6099 (-0.6099) time: 0.6540 data: 0.0004 [11-26 23:26:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [ 0/1669] eta: 0:18:11 tlr: 7e-05 tnm: 0.48 Lm: 6.469 (6.469) Lt: 5.766 (5.766) Accm: 3.22 (3.22) Acct: 4.99 (4.99) proj_loss: -0.6074 (-0.6074) time: 0.6540 data: 0.0003 [11-26 23:26:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [ 0/1669] eta: 0:18:18 tlr: 7e-05 tnm: 0.48 Lm: 6.414 (6.414) Lt: 5.663 (5.663) Accm: 3.47 (3.47) Acct: 5.41 (5.41) proj_loss: -0.5996 (-0.5996) time: 0.6585 data: 0.0004 [11-26 23:31:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [ 417/1669] eta: 0:14:02 tlr: 7e-05 tnm: 0.46 Lm: 6.429 (6.429) Lt: 5.689 (5.689) Accm: 3.51 (3.51) Acct: 5.56 (5.56) proj_loss: -0.6085 (-0.6085) time: 0.6742 data: 0.0003 [11-26 23:31:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [ 417/1669] eta: 0:14:02 tlr: 7e-05 tnm: 0.46 Lm: 6.405 (6.405) Lt: 5.623 (5.623) Accm: 3.58 (3.58) Acct: 5.67 (5.67) proj_loss: -0.6149 (-0.6149) time: 0.6742 data: 0.0003 [11-26 23:31:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [ 417/1669] eta: 0:14:02 tlr: 7e-05 tnm: 0.46 Lm: 6.562 (6.562) Lt: 5.832 (5.832) Accm: 3.00 (3.00) Acct: 4.71 (4.71) proj_loss: -0.5992 (-0.5992) time: 0.6742 data: 0.0003 [11-26 23:31:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [ 417/1669] eta: 0:14:02 tlr: 7e-05 tnm: 0.46 Lm: 6.514 (6.514) Lt: 5.801 (5.801) Accm: 3.17 (3.17) Acct: 4.95 (4.95) proj_loss: -0.6104 (-0.6104) time: 0.6742 data: 0.0003 [11-26 23:36:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [ 834/1669] eta: 0:09:37 tlr: 7e-05 tnm: 0.47 Lm: 6.558 (6.531) Lt: 5.806 (5.802) Accm: 3.18 (3.17) Acct: 4.80 (4.90) proj_loss: -0.6128 (-0.6112) time: 0.6705 data: 0.0003 [11-26 23:36:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [ 834/1669] eta: 0:09:37 tlr: 7e-05 tnm: 0.47 Lm: 6.358 (6.389) Lt: 5.564 (5.603) Accm: 4.01 (3.72) Acct: 6.34 (5.96) proj_loss: -0.6199 (-0.6167) time: 0.6705 data: 0.0003 [11-26 23:36:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [ 834/1669] eta: 0:09:37 tlr: 7e-05 tnm: 0.47 Lm: 6.444 (6.442) Lt: 5.679 (5.685) Accm: 3.55 (3.56) Acct: 5.72 (5.65) proj_loss: -0.5996 (-0.6002) time: 0.6705 data: 0.0003 [11-26 23:36:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [ 834/1669] eta: 0:09:37 tlr: 7e-05 tnm: 0.47 Lm: 6.615 (6.580) Lt: 5.834 (5.833) Accm: 3.07 (3.03) Acct: 4.92 (4.78) proj_loss: -0.5909 (-0.5950) time: 0.6705 data: 0.0003 [11-26 23:40:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [1251/1669] eta: 0:04:47 tlr: 7e-05 tnm: 0.44 Lm: 6.542 (6.548) Lt: 5.800 (5.797) Accm: 3.15 (3.12) Acct: 4.96 (4.97) proj_loss: -0.5939 (-0.5955) time: 0.6743 data: 0.0002 [11-26 23:40:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [1251/1669] eta: 0:04:47 tlr: 7e-05 tnm: 0.44 Lm: 6.420 (6.413) Lt: 5.649 (5.638) Accm: 3.67 (3.63) Acct: 5.72 (5.75) proj_loss: -0.6201 (-0.6186) time: 0.6743 data: 0.0002 [11-26 23:40:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [1251/1669] eta: 0:04:47 tlr: 7e-05 tnm: 0.44 Lm: 6.526 (6.521) Lt: 5.808 (5.805) Accm: 3.24 (3.23) Acct: 4.92 (4.94) proj_loss: -0.6155 (-0.6147) time: 0.6743 data: 0.0003 [11-26 23:40:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [1251/1669] eta: 0:04:47 tlr: 7e-05 tnm: 0.44 Lm: 6.429 (6.425) Lt: 5.671 (5.670) Accm: 3.61 (3.62) Acct: 5.65 (5.63) proj_loss: -0.6085 (-0.6067) time: 0.6743 data: 0.0003 [11-26 23:45:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 0.45 Lm: 6.444 (6.470) Lt: 5.679 (5.718) Accm: 3.55 (3.47) Acct: 5.58 (5.35) proj_loss: -0.5996 (-0.6023) time: 0.6746 data: 0.0016 [11-26 23:45:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 257/350] Total time: 0:19:02 (0.685 s / it) [11-26 23:45:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 0.45 Lm: 6.558 (6.549) Lt: 5.811 (5.847) Accm: 3.18 (3.17) Acct: 4.80 (4.76) proj_loss: -0.6128 (-0.6129) time: 0.6746 data: 0.0020 [11-26 23:45:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 0.45 Lm: 6.482 (6.429) Lt: 5.712 (5.653) Accm: 3.43 (3.59) Acct: 5.66 (5.73) proj_loss: -0.6203 (-0.6205) time: 0.6746 data: 0.0018 [11-26 23:45:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 257/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 0.45 Lm: 6.469 (6.512) Lt: 5.766 (5.761) Accm: 3.22 (3.24) Acct: 4.99 (5.18) proj_loss: -0.5969 (-0.6000) time: 0.6746 data: 0.0019 [11-26 23:45:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 257/350] Total time: 0:19:02 (0.685 s / it) [11-26 23:45:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 257/350] Total time: 0:19:02 (0.685 s / it) [11-26 23:45:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 257/350] Total time: 0:19:02 (0.685 s / it) [11-26 23:45:27] (/home/user/VAR/train.py , line 279)=> [ep257] (training ) Lm: 6.445 (6.456), Lt: 5.687 (5.700), Acc m&t: 3.56 5.57, Remain: 1 day, 5:01:53, Finish: 2024-11-27 12:47 [11-26 23:45:27] (/home/user/VAR/train.py , line 279)=> [ep257] (training ) Lm: 6.445 (6.456), Lt: 5.687 (5.700), Acc m&t: 3.56 5.57, Remain: 1 day, 5:01:58, Finish: 2024-11-27 12:47 [11-26 23:45:27] (/home/user/VAR/train.py , line 279)=> [ep257] (training ) Lm: 6.445 (6.456), Lt: 5.687 (5.700), Acc m&t: 3.56 5.57, Remain: 1 day, 5:01:56, Finish: 2024-11-27 12:47 [11-26 23:45:27] (/home/user/VAR/train.py , line 279)=> [ep257] (training ) Lm: 6.445 (6.456), Lt: 5.687 (5.700), Acc m&t: 3.56 5.57, Remain: 1 day, 5:01:36, Finish: 2024-11-27 12:47 [11-26 23:45:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [ 0/1669] eta: 0:18:11 tlr: 6.9e-05 tnm: 0.45 Lm: 6.504 (6.504) Lt: 5.715 (5.715) Accm: 3.03 (3.03) Acct: 4.46 (4.46) proj_loss: -0.5898 (-0.5898) time: 0.6537 data: 0.0004 [11-26 23:45:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [ 0/1669] eta: 0:18:19 tlr: 6.9e-05 tnm: 0.45 Lm: 6.409 (6.409) Lt: 5.638 (5.638) Accm: 3.59 (3.59) Acct: 5.77 (5.77) proj_loss: -0.6255 (-0.6255) time: 0.6587 data: 0.0004 [11-26 23:45:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [ 0/1669] eta: 0:18:21 tlr: 6.9e-05 tnm: 0.45 Lm: 6.462 (6.462) Lt: 5.762 (5.762) Accm: 3.22 (3.22) Acct: 4.99 (4.99) proj_loss: -0.6075 (-0.6075) time: 0.6601 data: 0.0004 [11-26 23:45:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [ 0/1669] eta: 0:18:19 tlr: 6.9e-05 tnm: 0.45 Lm: 6.400 (6.400) Lt: 5.653 (5.653) Accm: 3.75 (3.75) Acct: 5.89 (5.89) proj_loss: -0.6043 (-0.6043) time: 0.6586 data: 0.0004 [11-26 23:50:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [ 417/1669] eta: 0:14:02 tlr: 6.9e-05 tnm: 0.47 Lm: 6.452 (6.452) Lt: 5.711 (5.711) Accm: 3.56 (3.56) Acct: 5.61 (5.61) proj_loss: -0.6005 (-0.6005) time: 0.6760 data: 0.0003 [11-26 23:50:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [ 417/1669] eta: 0:14:02 tlr: 6.9e-05 tnm: 0.47 Lm: 6.472 (6.472) Lt: 5.713 (5.713) Accm: 3.40 (3.40) Acct: 5.19 (5.19) proj_loss: -0.6151 (-0.6151) time: 0.6759 data: 0.0003 [11-26 23:50:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [ 417/1669] eta: 0:14:02 tlr: 6.9e-05 tnm: 0.47 Lm: 6.466 (6.466) Lt: 5.754 (5.754) Accm: 3.36 (3.36) Acct: 5.34 (5.34) proj_loss: -0.6052 (-0.6052) time: 0.6759 data: 0.0003 [11-26 23:50:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [ 417/1669] eta: 0:14:02 tlr: 6.9e-05 tnm: 0.47 Lm: 6.524 (6.524) Lt: 5.736 (5.736) Accm: 3.15 (3.15) Acct: 4.84 (4.84) proj_loss: -0.5945 (-0.5945) time: 0.6760 data: 0.0003 [11-26 23:54:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [ 834/1669] eta: 0:09:22 tlr: 6.9e-05 tnm: 0.46 Lm: 6.504 (6.511) Lt: 5.747 (5.739) Accm: 3.26 (3.20) Acct: 5.22 (5.13) proj_loss: -0.5981 (-0.5957) time: 0.6754 data: 0.0003 [11-26 23:54:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [ 834/1669] eta: 0:09:22 tlr: 6.9e-05 tnm: 0.46 Lm: 6.470 (6.513) Lt: 5.762 (5.809) Accm: 3.22 (3.29) Acct: 4.99 (5.17) proj_loss: -0.6075 (-0.6102) time: 0.6753 data: 0.0003 [11-26 23:54:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [ 834/1669] eta: 0:09:22 tlr: 6.9e-05 tnm: 0.46 Lm: 6.470 (6.458) Lt: 5.672 (5.698) Accm: 3.37 (3.49) Acct: 5.73 (5.65) proj_loss: -0.6043 (-0.6064) time: 0.6754 data: 0.0003 [11-26 23:54:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [ 834/1669] eta: 0:09:22 tlr: 6.9e-05 tnm: 0.46 Lm: 6.536 (6.530) Lt: 5.788 (5.778) Accm: 3.21 (3.29) Acct: 5.08 (5.15) proj_loss: -0.6210 (-0.6171) time: 0.6753 data: 0.0003 [11-26 23:59:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [1251/1669] eta: 0:04:41 tlr: 6.9e-05 tnm: 0.45 Lm: 6.494 (6.510) Lt: 5.735 (5.754) Accm: 3.40 (3.40) Acct: 5.42 (5.32) proj_loss: -0.6129 (-0.6086) time: 0.6728 data: 0.0003 [11-26 23:59:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [1251/1669] eta: 0:04:41 tlr: 6.9e-05 tnm: 0.45 Lm: 6.466 (6.459) Lt: 5.754 (5.735) Accm: 3.36 (3.45) Acct: 5.34 (5.37) proj_loss: -0.6052 (-0.6023) time: 0.6728 data: 0.0002 [11-26 23:59:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [1251/1669] eta: 0:04:41 tlr: 6.9e-05 tnm: 0.45 Lm: 6.495 (6.463) Lt: 5.731 (5.700) Accm: 3.29 (3.45) Acct: 5.47 (5.42) proj_loss: -0.5987 (-0.5987) time: 0.6728 data: 0.0003 [11-26 23:59:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [1251/1669] eta: 0:04:41 tlr: 6.9e-05 tnm: 0.45 Lm: 6.435 (6.410) Lt: 5.663 (5.643) Accm: 3.56 (3.61) Acct: 5.81 (5.85) proj_loss: -0.6112 (-0.6110) time: 0.6728 data: 0.0003 [11-27 00:04:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 0.47 Lm: 6.454 (6.419) Lt: 5.672 (5.663) Accm: 3.75 (3.65) Acct: 5.82 (5.84) proj_loss: -0.6181 (-0.6149) time: 0.7392 data: 0.0024 [11-27 00:04:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 258/350] Total time: 0:18:47 (0.675 s / it) [11-27 00:04:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 0.47 Lm: 6.452 (6.476) Lt: 5.682 (5.725) Accm: 3.59 (3.52) Acct: 5.77 (5.50) proj_loss: -0.6210 (-0.6113) time: 0.7392 data: 0.0017 [11-27 00:04:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 0.47 Lm: 6.486 (6.451) Lt: 5.715 (5.685) Accm: 3.31 (3.46) Acct: 5.22 (5.37) proj_loss: -0.5992 (-0.6006) time: 0.7392 data: 0.0016 [11-27 00:04:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 258/350] [1668/1669] eta: 0:00:00 tlr: 6.9e-05 tnm: 0.47 Lm: 6.462 (6.452) Lt: 5.745 (5.722) Accm: 3.50 (3.49) Acct: 5.68 (5.48) proj_loss: -0.6029 (-0.6009) time: 0.7392 data: 0.0017 [11-27 00:04:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 258/350] Total time: 0:18:47 (0.675 s / it) [11-27 00:04:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 258/350] Total time: 0:18:47 (0.675 s / it) [11-27 00:04:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 258/350] Total time: 0:18:47 (0.675 s / it) [11-27 00:04:15] (/home/user/VAR/train.py , line 279)=> [ep258] (training ) Lm: 6.445 (6.445), Lt: 5.687 (5.690), Acc m&t: 3.56 5.57, Remain: 1 day, 4:38:43, Finish: 2024-11-27 12:42 [11-27 00:04:15] (/home/user/VAR/train.py , line 279)=> [ep258] (training ) Lm: 6.445 (6.445), Lt: 5.687 (5.690), Acc m&t: 3.56 5.57, Remain: 1 day, 4:38:27, Finish: 2024-11-27 12:42 [11-27 00:04:15] (/home/user/VAR/train.py , line 279)=> [ep258] (training ) Lm: 6.445 (6.445), Lt: 5.687 (5.690), Acc m&t: 3.56 5.57, Remain: 1 day, 4:38:30, Finish: 2024-11-27 12:42 [11-27 00:04:15] (/home/user/VAR/train.py , line 279)=> [ep258] (training ) Lm: 6.445 (6.445), Lt: 5.687 (5.690), Acc m&t: 3.56 5.57, Remain: 1 day, 4:38:37, Finish: 2024-11-27 12:42 [11-27 00:04:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [ 0/1669] eta: 0:18:24 tlr: 6.9e-05 tnm: 0.46 Lm: 6.542 (6.542) Lt: 5.838 (5.838) Accm: 3.31 (3.31) Acct: 5.22 (5.22) proj_loss: -0.6111 (-0.6111) time: 0.6616 data: 0.0003 [11-27 00:04:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [ 0/1669] eta: 0:18:24 tlr: 6.9e-05 tnm: 0.46 Lm: 6.443 (6.443) Lt: 5.730 (5.730) Accm: 3.34 (3.34) Acct: 5.01 (5.01) proj_loss: -0.5985 (-0.5985) time: 0.6618 data: 0.0003 [11-27 00:04:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [ 0/1669] eta: 0:18:24 tlr: 6.9e-05 tnm: 0.46 Lm: 6.385 (6.385) Lt: 5.654 (5.654) Accm: 3.78 (3.78) Acct: 5.70 (5.70) proj_loss: -0.5971 (-0.5971) time: 0.6617 data: 0.0003 [11-27 00:04:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [ 0/1669] eta: 0:18:25 tlr: 6.9e-05 tnm: 0.46 Lm: 6.468 (6.468) Lt: 5.649 (5.649) Accm: 3.54 (3.54) Acct: 5.58 (5.58) proj_loss: -0.5842 (-0.5842) time: 0.6625 data: 0.0003 [11-27 00:09:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [ 417/1669] eta: 0:14:49 tlr: 6.9e-05 tnm: 0.46 Lm: 6.510 (6.510) Lt: 5.702 (5.702) Accm: 3.47 (3.47) Acct: 5.54 (5.54) proj_loss: -0.5941 (-0.5941) time: 0.6734 data: 0.0003 [11-27 00:09:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [ 417/1669] eta: 0:14:49 tlr: 6.9e-05 tnm: 0.46 Lm: 6.414 (6.414) Lt: 5.679 (5.679) Accm: 3.65 (3.65) Acct: 5.66 (5.66) proj_loss: -0.6080 (-0.6080) time: 0.6734 data: 0.0003 [11-27 00:09:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [ 417/1669] eta: 0:14:49 tlr: 6.9e-05 tnm: 0.46 Lm: 6.509 (6.509) Lt: 5.752 (5.752) Accm: 3.50 (3.50) Acct: 5.60 (5.60) proj_loss: -0.6170 (-0.6170) time: 0.6734 data: 0.0003 [11-27 00:09:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [ 417/1669] eta: 0:14:49 tlr: 6.9e-05 tnm: 0.46 Lm: 6.411 (6.411) Lt: 5.658 (5.658) Accm: 3.57 (3.57) Acct: 5.43 (5.43) proj_loss: -0.6140 (-0.6140) time: 0.6734 data: 0.0003 [11-27 00:13:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [ 834/1669] eta: 0:09:37 tlr: 6.8e-05 tnm: 0.47 Lm: 6.443 (6.465) Lt: 5.730 (5.722) Accm: 3.34 (3.39) Acct: 5.01 (5.28) proj_loss: -0.6206 (-0.6162) time: 0.6752 data: 0.0003 [11-27 00:13:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [ 834/1669] eta: 0:09:37 tlr: 6.8e-05 tnm: 0.47 Lm: 6.385 (6.397) Lt: 5.654 (5.658) Accm: 3.51 (3.59) Acct: 5.61 (5.57) proj_loss: -0.6015 (-0.6058) time: 0.6752 data: 0.0003 [11-27 00:13:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [ 834/1669] eta: 0:09:37 tlr: 6.8e-05 tnm: 0.47 Lm: 6.484 (6.501) Lt: 5.718 (5.707) Accm: 3.42 (3.45) Acct: 5.49 (5.45) proj_loss: -0.6041 (-0.6033) time: 0.6752 data: 0.0003 [11-27 00:13:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [ 834/1669] eta: 0:09:37 tlr: 6.8e-05 tnm: 0.47 Lm: 6.475 (6.488) Lt: 5.665 (5.717) Accm: 3.58 (3.53) Acct: 5.56 (5.58) proj_loss: -0.6111 (-0.6114) time: 0.6752 data: 0.0003 [11-27 00:18:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [1251/1669] eta: 0:04:46 tlr: 6.8e-05 tnm: 0.46 Lm: 6.473 (6.484) Lt: 5.699 (5.721) Accm: 3.55 (3.53) Acct: 5.50 (5.55) proj_loss: -0.6143 (-0.6129) time: 0.6763 data: 0.0003 [11-27 00:18:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [1251/1669] eta: 0:04:46 tlr: 6.8e-05 tnm: 0.46 Lm: 6.414 (6.423) Lt: 5.679 (5.681) Accm: 3.49 (3.49) Acct: 5.51 (5.50) proj_loss: -0.5993 (-0.6031) time: 0.6763 data: 0.0002 [11-27 00:18:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [1251/1669] eta: 0:04:46 tlr: 6.8e-05 tnm: 0.46 Lm: 6.411 (6.436) Lt: 5.663 (5.691) Accm: 3.52 (3.47) Acct: 5.35 (5.38) proj_loss: -0.6249 (-0.6195) time: 0.6763 data: 0.0003 [11-27 00:18:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [1251/1669] eta: 0:04:46 tlr: 6.8e-05 tnm: 0.46 Lm: 6.518 (6.519) Lt: 5.736 (5.733) Accm: 3.41 (3.32) Acct: 5.39 (5.35) proj_loss: -0.6077 (-0.6053) time: 0.6763 data: 0.0003 [11-27 00:23:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [1668/1669] eta: 0:00:00 tlr: 6.8e-05 tnm: 0.45 Lm: 6.421 (6.423) Lt: 5.654 (5.675) Accm: 3.51 (3.54) Acct: 5.61 (5.57) proj_loss: -0.6015 (-0.6072) time: 0.6732 data: 0.0016 [11-27 00:23:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [1668/1669] eta: 0:00:00 tlr: 6.8e-05 tnm: 0.45 Lm: 6.472 (6.471) Lt: 5.665 (5.704) Accm: 3.58 (3.57) Acct: 5.56 (5.57) proj_loss: -0.6111 (-0.6077) time: 0.6732 data: 0.0020 [11-27 00:23:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [1668/1669] eta: 0:00:00 tlr: 6.8e-05 tnm: 0.45 Lm: 6.443 (6.448) Lt: 5.730 (5.704) Accm: 3.34 (3.43) Acct: 5.46 (5.40) proj_loss: -0.6206 (-0.6150) time: 0.6733 data: 0.0019 [11-27 00:23:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 259/350] [1668/1669] eta: 0:00:00 tlr: 6.8e-05 tnm: 0.45 Lm: 6.484 (6.476) Lt: 5.718 (5.681) Accm: 3.42 (3.48) Acct: 5.49 (5.60) proj_loss: -0.6113 (-0.6069) time: 0.6733 data: 0.0015 [11-27 00:23:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 259/350] Total time: 0:18:59 (0.683 s / it) [11-27 00:23:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 259/350] Total time: 0:18:59 (0.683 s / it) [11-27 00:23:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 259/350] Total time: 0:18:59 (0.683 s / it) [11-27 00:23:14] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 259/350] Total time: 0:18:59 (0.683 s / it) [11-27 00:25:33] (home/user/VAR/trainer.py, line 114)=> FID: 3.3053656771884334 [11-27 00:25:33] (/home/user/VAR/train.py , line 262)=> [*] [ep259] (val 50000) Lm: 6.4496, Lt: 5.6934, Acc m&t: 3.53 5.53, Val cost: 138.55s [11-27 00:25:33] (/home/user/VAR/train.py , line 267)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-27 00:25:56] (/home/user/VAR/train.py , line 279)=> [ep259] (training ) Lm: 6.445 (6.450), Lt: 5.687 (5.693), Acc m&t: 3.56 5.57, Remain: 1 day, 4:21:24, Finish: 2024-11-27 12:44 [11-27 00:25:56] (/home/user/VAR/train.py , line 279)=> [ep259] (training ) Lm: 6.445 (6.450), Lt: 5.687 (5.693), Acc m&t: 3.56 5.57, Remain: 1 day, 4:21:37, Finish: 2024-11-27 12:44 [11-27 00:25:56] (/home/user/VAR/train.py , line 279)=> [ep259] (training ) Lm: 6.445 (6.450), Lt: 5.687 (5.693), Acc m&t: 3.56 5.57, Remain: 1 day, 4:21:35, Finish: 2024-11-27 12:44 [11-27 00:25:56] (/home/user/VAR/train.py , line 279)=> [ep259] (training ) Lm: 6.445 (6.450), Lt: 5.687 (5.693), Acc m&t: 3.56 5.57, Remain: 1 day, 4:21:05, Finish: 2024-11-27 12:44 [11-27 00:25:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [ 0/1669] eta: 0:18:36 tlr: 6.8e-05 tnm: 0.45 Lm: 6.467 (6.467) Lt: 5.727 (5.727) Accm: 3.31 (3.31) Acct: 5.30 (5.30) proj_loss: -0.6358 (-0.6358) time: 0.6687 data: 0.0004 [11-27 00:25:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [ 0/1669] eta: 0:18:35 tlr: 6.8e-05 tnm: 0.45 Lm: 6.466 (6.466) Lt: 5.681 (5.681) Accm: 3.61 (3.61) Acct: 5.80 (5.80) proj_loss: -0.6209 (-0.6209) time: 0.6681 data: 0.0003 [11-27 00:25:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [ 0/1669] eta: 0:18:30 tlr: 6.8e-05 tnm: 0.45 Lm: 6.512 (6.512) Lt: 5.764 (5.764) Accm: 3.52 (3.52) Acct: 5.42 (5.42) proj_loss: -0.5955 (-0.5955) time: 0.6653 data: 0.0004 [11-27 00:25:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [ 0/1669] eta: 0:18:30 tlr: 6.8e-05 tnm: 0.45 Lm: 6.318 (6.318) Lt: 5.536 (5.536) Accm: 3.90 (3.90) Acct: 6.30 (6.30) proj_loss: -0.6188 (-0.6188) time: 0.6656 data: 0.0004 [11-27 00:30:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [ 417/1669] eta: 0:14:03 tlr: 6.8e-05 tnm: 0.47 Lm: 6.374 (6.374) Lt: 5.601 (5.601) Accm: 3.72 (3.72) Acct: 5.92 (5.92) proj_loss: -0.6144 (-0.6144) time: 0.6725 data: 0.0003 [11-27 00:30:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [ 417/1669] eta: 0:14:03 tlr: 6.8e-05 tnm: 0.47 Lm: 6.403 (6.403) Lt: 5.604 (5.604) Accm: 3.66 (3.66) Acct: 5.97 (5.97) proj_loss: -0.6012 (-0.6012) time: 0.6725 data: 0.0003 [11-27 00:30:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [ 417/1669] eta: 0:14:03 tlr: 6.8e-05 tnm: 0.47 Lm: 6.367 (6.367) Lt: 5.615 (5.615) Accm: 3.74 (3.74) Acct: 5.72 (5.72) proj_loss: -0.6160 (-0.6160) time: 0.6725 data: 0.0003 [11-27 00:30:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [ 417/1669] eta: 0:14:03 tlr: 6.8e-05 tnm: 0.47 Lm: 6.535 (6.535) Lt: 5.804 (5.804) Accm: 3.39 (3.39) Acct: 5.23 (5.23) proj_loss: -0.6189 (-0.6189) time: 0.6725 data: 0.0003 [11-27 00:35:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [ 834/1669] eta: 0:09:36 tlr: 6.8e-05 tnm: 0.48 Lm: 6.540 (6.537) Lt: 5.817 (5.808) Accm: 3.26 (3.30) Acct: 5.03 (5.11) proj_loss: -0.6205 (-0.6195) time: 0.6729 data: 0.0003 [11-27 00:35:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [ 834/1669] eta: 0:09:36 tlr: 6.8e-05 tnm: 0.48 Lm: 6.466 (6.468) Lt: 5.681 (5.690) Accm: 3.61 (3.34) Acct: 5.80 (5.38) proj_loss: -0.6096 (-0.6040) time: 0.6729 data: 0.0002 [11-27 00:35:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [ 834/1669] eta: 0:09:36 tlr: 6.8e-05 tnm: 0.48 Lm: 6.326 (6.358) Lt: 5.536 (5.566) Accm: 3.88 (3.77) Acct: 6.30 (6.07) proj_loss: -0.6100 (-0.6110) time: 0.6729 data: 0.0003 [11-27 00:35:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [ 834/1669] eta: 0:09:36 tlr: 6.8e-05 tnm: 0.48 Lm: 6.391 (6.375) Lt: 5.630 (5.620) Accm: 3.82 (3.76) Acct: 5.99 (5.81) proj_loss: -0.5963 (-0.6081) time: 0.6729 data: 0.0003 [11-27 00:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [1251/1669] eta: 0:04:47 tlr: 6.7e-05 tnm: 0.47 Lm: 6.423 (6.395) Lt: 5.678 (5.651) Accm: 3.58 (3.66) Acct: 5.65 (5.67) proj_loss: -0.6018 (-0.6079) time: 0.6737 data: 0.0003 [11-27 00:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [1251/1669] eta: 0:04:47 tlr: 6.7e-05 tnm: 0.47 Lm: 6.423 (6.446) Lt: 5.616 (5.655) Accm: 3.66 (3.47) Acct: 5.94 (5.56) proj_loss: -0.6046 (-0.6029) time: 0.6737 data: 0.0003 [11-27 00:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [1251/1669] eta: 0:04:47 tlr: 6.7e-05 tnm: 0.47 Lm: 6.526 (6.529) Lt: 5.790 (5.793) Accm: 3.34 (3.33) Acct: 5.11 (5.13) proj_loss: -0.6252 (-0.6221) time: 0.6737 data: 0.0003 [11-27 00:40:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [1251/1669] eta: 0:04:47 tlr: 6.7e-05 tnm: 0.47 Lm: 6.377 (6.396) Lt: 5.601 (5.609) Accm: 3.71 (3.67) Acct: 5.92 (5.85) proj_loss: -0.6070 (-0.6085) time: 0.6737 data: 0.0003 [11-27 00:44:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [1668/1669] eta: 0:00:00 tlr: 6.7e-05 tnm: 0.46 Lm: 6.368 (6.390) Lt: 5.552 (5.597) Accm: 3.72 (3.68) Acct: 6.15 (5.91) proj_loss: -0.6040 (-0.6050) time: 0.6758 data: 0.0015 [11-27 00:44:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 260/350] Total time: 0:19:01 (0.684 s / it) [11-27 00:44:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [1668/1669] eta: 0:00:00 tlr: 6.7e-05 tnm: 0.46 Lm: 6.432 (6.443) Lt: 5.681 (5.665) Accm: 3.61 (3.47) Acct: 5.80 (5.53) proj_loss: -0.5996 (-0.6010) time: 0.6758 data: 0.0015 [11-27 00:44:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [1668/1669] eta: 0:00:00 tlr: 6.7e-05 tnm: 0.46 Lm: 6.512 (6.460) Lt: 5.764 (5.711) Accm: 3.42 (3.53) Acct: 5.18 (5.45) proj_loss: -0.6287 (-0.6234) time: 0.6758 data: 0.0018 [11-27 00:44:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 260/350] [1668/1669] eta: 0:00:00 tlr: 6.7e-05 tnm: 0.46 Lm: 6.455 (6.414) Lt: 5.719 (5.664) Accm: 3.34 (3.56) Acct: 5.30 (5.53) proj_loss: -0.5963 (-0.6044) time: 0.6758 data: 0.0020 [11-27 00:44:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 260/350] Total time: 0:19:01 (0.684 s / it) [11-27 00:44:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 260/350] Total time: 0:19:01 (0.684 s / it) [11-27 00:44:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 260/350] Total time: 0:19:01 (0.684 s / it) [11-27 00:44:57] (/home/user/VAR/train.py , line 279)=> [ep260] (training ) Lm: 6.445 (6.456), Lt: 5.687 (5.700), Acc m&t: 3.56 5.57, Remain: 1 day, 4:04:03, Finish: 2024-11-27 12:49 [11-27 00:44:57] (/home/user/VAR/train.py , line 279)=> [ep260] (training ) Lm: 6.445 (6.456), Lt: 5.687 (5.700), Acc m&t: 3.56 5.57, Remain: 1 day, 4:04:17, Finish: 2024-11-27 12:49 [11-27 00:44:57] (/home/user/VAR/train.py , line 279)=> [ep260] (training ) Lm: 6.445 (6.456), Lt: 5.687 (5.700), Acc m&t: 3.56 5.57, Remain: 1 day, 4:04:21, Finish: 2024-11-27 12:49 [11-27 00:44:57] (/home/user/VAR/train.py , line 279)=> [ep260] (training ) Lm: 6.445 (6.456), Lt: 5.687 (5.700), Acc m&t: 3.56 5.57, Remain: 1 day, 4:04:18, Finish: 2024-11-27 12:49 [11-27 00:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [ 0/1669] eta: 0:18:30 tlr: 6.7e-05 tnm: 0.47 Lm: 6.309 (6.309) Lt: 5.558 (5.558) Accm: 3.92 (3.92) Acct: 6.10 (6.10) proj_loss: -0.6147 (-0.6147) time: 0.6656 data: 0.0004 [11-27 00:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [ 0/1669] eta: 0:17:53 tlr: 6.7e-05 tnm: 0.47 Lm: 6.389 (6.389) Lt: 5.637 (5.637) Accm: 3.42 (3.42) Acct: 5.46 (5.46) proj_loss: -0.6066 (-0.6066) time: 0.6430 data: 0.0005 [11-27 00:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [ 0/1669] eta: 0:17:53 tlr: 6.7e-05 tnm: 0.47 Lm: 6.393 (6.393) Lt: 5.605 (5.605) Accm: 3.33 (3.33) Acct: 5.46 (5.46) proj_loss: -0.6124 (-0.6124) time: 0.6431 data: 0.0003 [11-27 00:44:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [ 0/1669] eta: 0:17:53 tlr: 6.7e-05 tnm: 0.47 Lm: 6.711 (6.711) Lt: 6.053 (6.053) Accm: 2.85 (2.85) Acct: 4.08 (4.08) proj_loss: -0.6077 (-0.6077) time: 0.6435 data: 0.0004 [11-27 00:49:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [ 417/1669] eta: 0:14:02 tlr: 6.7e-05 tnm: 0.47 Lm: 6.431 (6.431) Lt: 5.662 (5.662) Accm: 3.50 (3.50) Acct: 5.74 (5.74) proj_loss: -0.6183 (-0.6183) time: 0.6750 data: 0.0003 [11-27 00:49:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [ 417/1669] eta: 0:14:02 tlr: 6.7e-05 tnm: 0.47 Lm: 6.516 (6.516) Lt: 5.790 (5.790) Accm: 2.96 (2.96) Acct: 4.77 (4.77) proj_loss: -0.6073 (-0.6073) time: 0.6750 data: 0.0002 [11-27 00:49:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [ 417/1669] eta: 0:14:02 tlr: 6.7e-05 tnm: 0.47 Lm: 6.527 (6.527) Lt: 5.805 (5.805) Accm: 3.52 (3.52) Acct: 5.23 (5.23) proj_loss: -0.6197 (-0.6197) time: 0.6750 data: 0.0003 [11-27 00:49:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [ 417/1669] eta: 0:14:02 tlr: 6.7e-05 tnm: 0.47 Lm: 6.339 (6.339) Lt: 5.578 (5.578) Accm: 3.79 (3.79) Acct: 6.06 (6.06) proj_loss: -0.6166 (-0.6166) time: 0.6750 data: 0.0003 [11-27 00:54:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [ 834/1669] eta: 0:09:22 tlr: 6.7e-05 tnm: 0.46 Lm: 6.369 (6.384) Lt: 5.598 (5.612) Accm: 3.66 (3.61) Acct: 6.03 (5.80) proj_loss: -0.6147 (-0.6108) time: 0.6730 data: 0.0003 [11-27 00:54:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [ 834/1669] eta: 0:09:22 tlr: 6.7e-05 tnm: 0.46 Lm: 6.439 (6.434) Lt: 5.642 (5.655) Accm: 3.67 (3.61) Acct: 6.03 (5.88) proj_loss: -0.6124 (-0.6151) time: 0.6730 data: 0.0004 [11-27 00:54:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [ 834/1669] eta: 0:09:22 tlr: 6.7e-05 tnm: 0.46 Lm: 6.411 (6.481) Lt: 5.637 (5.724) Accm: 3.42 (3.22) Acct: 5.46 (5.17) proj_loss: -0.6066 (-0.6007) time: 0.6730 data: 0.0003 [11-27 00:54:20] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [ 834/1669] eta: 0:09:22 tlr: 6.7e-05 tnm: 0.46 Lm: 6.342 (6.431) Lt: 5.557 (5.680) Accm: 4.20 (3.84) Acct: 6.39 (5.86) proj_loss: -0.6252 (-0.6215) time: 0.6730 data: 0.0003 [11-27 00:59:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [1251/1669] eta: 0:04:41 tlr: 6.7e-05 tnm: 0.47 Lm: 6.379 (6.427) Lt: 5.622 (5.682) Accm: 3.89 (3.78) Acct: 5.89 (5.74) proj_loss: -0.6165 (-0.6146) time: 0.6732 data: 0.0003 [11-27 00:59:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [1251/1669] eta: 0:04:41 tlr: 6.7e-05 tnm: 0.47 Lm: 6.450 (6.483) Lt: 5.737 (5.752) Accm: 3.35 (3.24) Acct: 5.23 (5.13) proj_loss: -0.6073 (-0.6084) time: 0.6732 data: 0.0003 [11-27 00:59:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [1251/1669] eta: 0:04:41 tlr: 6.7e-05 tnm: 0.47 Lm: 6.350 (6.370) Lt: 5.581 (5.600) Accm: 3.78 (3.68) Acct: 5.91 (5.80) proj_loss: -0.6107 (-0.6098) time: 0.6732 data: 0.0003 [11-27 00:59:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [1251/1669] eta: 0:04:41 tlr: 6.7e-05 tnm: 0.47 Lm: 6.454 (6.457) Lt: 5.680 (5.680) Accm: 3.61 (3.59) Acct: 5.99 (5.90) proj_loss: -0.6106 (-0.6122) time: 0.6732 data: 0.0003 [11-27 01:03:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [1668/1669] eta: 0:00:00 tlr: 6.7e-05 tnm: 0.46 Lm: 6.439 (6.430) Lt: 5.642 (5.652) Accm: 3.67 (3.63) Acct: 6.03 (5.96) proj_loss: -0.6088 (-0.6094) time: 0.7410 data: 0.0015 [11-27 01:03:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 261/350] Total time: 0:18:48 (0.676 s / it) [11-27 01:03:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [1668/1669] eta: 0:00:00 tlr: 6.7e-05 tnm: 0.46 Lm: 6.489 (6.486) Lt: 5.761 (5.754) Accm: 3.29 (3.22) Acct: 5.01 (5.06) proj_loss: -0.6080 (-0.6118) time: 0.7410 data: 0.0019 [11-27 01:03:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [1668/1669] eta: 0:00:00 tlr: 6.7e-05 tnm: 0.46 Lm: 6.392 (6.420) Lt: 5.658 (5.677) Accm: 3.89 (3.80) Acct: 5.97 (5.79) proj_loss: -0.6252 (-0.6188) time: 0.7410 data: 0.0015 [11-27 01:03:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 261/350] [1668/1669] eta: 0:00:00 tlr: 6.7e-05 tnm: 0.46 Lm: 6.369 (6.404) Lt: 5.598 (5.637) Accm: 3.66 (3.65) Acct: 5.80 (5.74) proj_loss: -0.6147 (-0.6121) time: 0.7410 data: 0.0016 [11-27 01:03:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 261/350] Total time: 0:18:48 (0.676 s / it) [11-27 01:03:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 261/350] Total time: 0:18:48 (0.676 s / it) [11-27 01:03:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 261/350] Total time: 0:18:48 (0.676 s / it) [11-27 01:03:46] (/home/user/VAR/train.py , line 279)=> [ep261] (training ) Lm: 6.445 (6.446), Lt: 5.687 (5.689), Acc m&t: 3.56 5.57, Remain: 1 day, 3:47:58, Finish: 2024-11-27 12:51 [11-27 01:03:46] (/home/user/VAR/train.py , line 279)=> [ep261] (training ) Lm: 6.445 (6.446), Lt: 5.687 (5.689), Acc m&t: 3.56 5.57, Remain: 1 day, 3:48:08, Finish: 2024-11-27 12:51 [11-27 01:03:46] (/home/user/VAR/train.py , line 279)=> [ep261] (training ) Lm: 6.445 (6.446), Lt: 5.687 (5.689), Acc m&t: 3.56 5.57, Remain: 1 day, 3:48:11, Finish: 2024-11-27 12:51 [11-27 01:03:46] (/home/user/VAR/train.py , line 279)=> [ep261] (training ) Lm: 6.445 (6.446), Lt: 5.687 (5.689), Acc m&t: 3.56 5.57, Remain: 1 day, 3:48:19, Finish: 2024-11-27 12:52 [11-27 01:03:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [ 0/1669] eta: 0:18:07 tlr: 6.7e-05 tnm: 0.45 Lm: 6.436 (6.436) Lt: 5.702 (5.702) Accm: 3.39 (3.39) Acct: 5.29 (5.29) proj_loss: -0.5858 (-0.5858) time: 0.6515 data: 0.0003 [11-27 01:03:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [ 0/1669] eta: 0:18:07 tlr: 6.7e-05 tnm: 0.45 Lm: 6.445 (6.445) Lt: 5.706 (5.706) Accm: 3.40 (3.40) Acct: 5.29 (5.29) proj_loss: -0.6204 (-0.6204) time: 0.6517 data: 0.0003 [11-27 01:03:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [ 0/1669] eta: 0:18:07 tlr: 6.7e-05 tnm: 0.45 Lm: 6.489 (6.489) Lt: 5.766 (5.766) Accm: 3.35 (3.35) Acct: 5.13 (5.13) proj_loss: -0.6094 (-0.6094) time: 0.6514 data: 0.0004 [11-27 01:03:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [ 0/1669] eta: 0:18:12 tlr: 6.7e-05 tnm: 0.45 Lm: 6.416 (6.416) Lt: 5.596 (5.596) Accm: 3.86 (3.86) Acct: 6.04 (6.04) proj_loss: -0.6011 (-0.6011) time: 0.6544 data: 0.0004 [11-27 01:08:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [ 417/1669] eta: 0:14:40 tlr: 6.6e-05 tnm: 0.47 Lm: 6.411 (6.411) Lt: 5.596 (5.596) Accm: 3.76 (3.76) Acct: 5.94 (5.94) proj_loss: -0.6051 (-0.6051) time: 0.6727 data: 0.0003 [11-27 01:08:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [ 417/1669] eta: 0:14:40 tlr: 6.6e-05 tnm: 0.47 Lm: 6.430 (6.430) Lt: 5.658 (5.658) Accm: 3.76 (3.76) Acct: 5.91 (5.91) proj_loss: -0.6192 (-0.6192) time: 0.6727 data: 0.0002 [11-27 01:08:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [ 417/1669] eta: 0:14:40 tlr: 6.6e-05 tnm: 0.47 Lm: 6.387 (6.387) Lt: 5.614 (5.614) Accm: 3.45 (3.45) Acct: 5.48 (5.48) proj_loss: -0.5983 (-0.5983) time: 0.6727 data: 0.0002 [11-27 01:08:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [ 417/1669] eta: 0:14:40 tlr: 6.6e-05 tnm: 0.47 Lm: 6.419 (6.419) Lt: 5.660 (5.660) Accm: 3.75 (3.75) Acct: 5.83 (5.83) proj_loss: -0.6211 (-0.6211) time: 0.6727 data: 0.0003 [11-27 01:13:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [ 834/1669] eta: 0:09:34 tlr: 6.6e-05 tnm: 0.48 Lm: 6.489 (6.494) Lt: 5.766 (5.768) Accm: 3.35 (3.40) Acct: 5.13 (5.41) proj_loss: -0.6322 (-0.6248) time: 0.6721 data: 0.0003 [11-27 01:13:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [ 834/1669] eta: 0:09:34 tlr: 6.6e-05 tnm: 0.48 Lm: 6.407 (6.394) Lt: 5.651 (5.626) Accm: 3.43 (3.44) Acct: 5.29 (5.38) proj_loss: -0.6102 (-0.6022) time: 0.6721 data: 0.0002 [11-27 01:13:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [ 834/1669] eta: 0:09:34 tlr: 6.6e-05 tnm: 0.48 Lm: 6.445 (6.479) Lt: 5.706 (5.730) Accm: 3.40 (3.54) Acct: 5.29 (5.49) proj_loss: -0.6180 (-0.6175) time: 0.6721 data: 0.0002 [11-27 01:13:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [ 834/1669] eta: 0:09:34 tlr: 6.6e-05 tnm: 0.48 Lm: 6.416 (6.484) Lt: 5.596 (5.688) Accm: 3.66 (3.53) Acct: 5.84 (5.65) proj_loss: -0.6091 (-0.6101) time: 0.6721 data: 0.0003 [11-27 01:18:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [1251/1669] eta: 0:04:45 tlr: 6.6e-05 tnm: 0.47 Lm: 6.411 (6.448) Lt: 5.596 (5.645) Accm: 3.76 (3.62) Acct: 5.85 (5.70) proj_loss: -0.6128 (-0.6117) time: 0.6731 data: 0.0003 [11-27 01:18:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [1251/1669] eta: 0:04:45 tlr: 6.6e-05 tnm: 0.47 Lm: 6.422 (6.440) Lt: 5.676 (5.683) Accm: 3.41 (3.35) Acct: 5.24 (5.23) proj_loss: -0.6104 (-0.6088) time: 0.6731 data: 0.0003 [11-27 01:18:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [1251/1669] eta: 0:04:45 tlr: 6.6e-05 tnm: 0.47 Lm: 6.549 (6.523) Lt: 5.818 (5.794) Accm: 3.14 (3.29) Acct: 4.93 (5.24) proj_loss: -0.6208 (-0.6209) time: 0.6731 data: 0.0003 [11-27 01:18:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [1251/1669] eta: 0:04:45 tlr: 6.6e-05 tnm: 0.47 Lm: 6.474 (6.485) Lt: 5.703 (5.723) Accm: 3.37 (3.49) Acct: 5.42 (5.51) proj_loss: -0.6160 (-0.6166) time: 0.6731 data: 0.0003 [11-27 01:22:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [1668/1669] eta: 0:00:00 tlr: 6.6e-05 tnm: 0.47 Lm: 6.445 (6.470) Lt: 5.701 (5.711) Accm: 3.40 (3.49) Acct: 5.56 (5.55) proj_loss: -0.6141 (-0.6156) time: 0.6733 data: 0.0019 [11-27 01:22:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 262/350] Total time: 0:18:56 (0.681 s / it) [11-27 01:22:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [1668/1669] eta: 0:00:00 tlr: 6.6e-05 tnm: 0.47 Lm: 6.407 (6.399) Lt: 5.651 (5.629) Accm: 3.43 (3.54) Acct: 5.29 (5.55) proj_loss: -0.6107 (-0.6093) time: 0.6733 data: 0.0016 [11-27 01:22:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [1668/1669] eta: 0:00:00 tlr: 6.6e-05 tnm: 0.47 Lm: 6.489 (6.481) Lt: 5.766 (5.734) Accm: 3.35 (3.42) Acct: 5.13 (5.45) proj_loss: -0.6094 (-0.6123) time: 0.6733 data: 0.0020 [11-27 01:22:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 262/350] [1668/1669] eta: 0:00:00 tlr: 6.6e-05 tnm: 0.47 Lm: 6.416 (6.445) Lt: 5.596 (5.649) Accm: 3.66 (3.54) Acct: 5.84 (5.60) proj_loss: -0.6091 (-0.6108) time: 0.6733 data: 0.0016 [11-27 01:22:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 262/350] Total time: 0:18:56 (0.681 s / it) [11-27 01:22:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 262/350] Total time: 0:18:56 (0.681 s / it) [11-27 01:22:42] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 262/350] Total time: 0:18:56 (0.681 s / it) [11-27 01:22:42] (/home/user/VAR/train.py , line 279)=> [ep262] (training ) Lm: 6.445 (6.446), Lt: 5.684 (5.684), Acc m&t: 3.56 5.57, Remain: 1 day, 3:18:24, Finish: 2024-11-27 12:41 [11-27 01:22:42] (/home/user/VAR/train.py , line 279)=> [ep262] (training ) Lm: 6.445 (6.446), Lt: 5.684 (5.684), Acc m&t: 3.56 5.57, Remain: 1 day, 3:19:54, Finish: 2024-11-27 12:42 [11-27 01:22:42] (/home/user/VAR/train.py , line 279)=> [ep262] (training ) Lm: 6.445 (6.446), Lt: 5.684 (5.684), Acc m&t: 3.56 5.57, Remain: 1 day, 3:20:01, Finish: 2024-11-27 12:42 [11-27 01:22:42] (/home/user/VAR/train.py , line 279)=> [ep262] (training ) Lm: 6.445 (6.446), Lt: 5.684 (5.684), Acc m&t: 3.56 5.57, Remain: 1 day, 3:20:10, Finish: 2024-11-27 12:42 [11-27 01:22:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [ 0/1669] eta: 0:18:45 tlr: 6.6e-05 tnm: 0.46 Lm: 6.400 (6.400) Lt: 5.692 (5.692) Accm: 3.73 (3.73) Acct: 5.46 (5.46) proj_loss: -0.6218 (-0.6218) time: 0.6746 data: 0.0003 [11-27 01:22:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [ 0/1669] eta: 0:18:27 tlr: 6.6e-05 tnm: 0.46 Lm: 6.570 (6.570) Lt: 5.816 (5.816) Accm: 3.04 (3.04) Acct: 4.91 (4.91) proj_loss: -0.6156 (-0.6156) time: 0.6636 data: 0.0004 [11-27 01:22:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [ 0/1669] eta: 0:18:27 tlr: 6.6e-05 tnm: 0.46 Lm: 6.768 (6.768) Lt: 6.110 (6.110) Accm: 2.87 (2.87) Acct: 4.15 (4.15) proj_loss: -0.6327 (-0.6327) time: 0.6636 data: 0.0003 [11-27 01:22:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [ 0/1669] eta: 0:18:27 tlr: 6.6e-05 tnm: 0.46 Lm: 6.391 (6.391) Lt: 5.631 (5.631) Accm: 3.66 (3.66) Acct: 5.65 (5.65) proj_loss: -0.6134 (-0.6134) time: 0.6636 data: 0.0004 [11-27 01:27:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [ 417/1669] eta: 0:14:03 tlr: 6.6e-05 tnm: 0.47 Lm: 6.442 (6.442) Lt: 5.700 (5.700) Accm: 3.49 (3.49) Acct: 5.31 (5.31) proj_loss: -0.6056 (-0.6056) time: 0.6748 data: 0.0003 [11-27 01:27:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [ 417/1669] eta: 0:14:03 tlr: 6.6e-05 tnm: 0.47 Lm: 6.474 (6.474) Lt: 5.781 (5.781) Accm: 3.46 (3.46) Acct: 5.13 (5.13) proj_loss: -0.6207 (-0.6207) time: 0.6748 data: 0.0002 [11-27 01:27:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [ 417/1669] eta: 0:14:03 tlr: 6.6e-05 tnm: 0.47 Lm: 6.624 (6.624) Lt: 5.928 (5.928) Accm: 3.07 (3.07) Acct: 4.59 (4.59) proj_loss: -0.6214 (-0.6214) time: 0.6748 data: 0.0003 [11-27 01:27:24] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [ 417/1669] eta: 0:14:03 tlr: 6.6e-05 tnm: 0.47 Lm: 6.450 (6.450) Lt: 5.697 (5.697) Accm: 3.62 (3.62) Acct: 5.48 (5.48) proj_loss: -0.6204 (-0.6204) time: 0.6748 data: 0.0003 [11-27 01:32:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [ 834/1669] eta: 0:09:36 tlr: 6.5e-05 tnm: 0.48 Lm: 6.428 (6.443) Lt: 5.662 (5.685) Accm: 3.45 (3.56) Acct: 5.53 (5.49) proj_loss: -0.6186 (-0.6198) time: 0.6713 data: 0.0003 [11-27 01:32:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [ 834/1669] eta: 0:09:36 tlr: 6.5e-05 tnm: 0.48 Lm: 6.410 (6.453) Lt: 5.692 (5.725) Accm: 3.55 (3.49) Acct: 5.44 (5.23) proj_loss: -0.6197 (-0.6184) time: 0.6713 data: 0.0002 [11-27 01:32:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [ 834/1669] eta: 0:09:36 tlr: 6.5e-05 tnm: 0.48 Lm: 6.480 (6.541) Lt: 5.747 (5.818) Accm: 3.28 (3.40) Acct: 5.03 (5.19) proj_loss: -0.6185 (-0.6204) time: 0.6713 data: 0.0003 [11-27 01:32:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [ 834/1669] eta: 0:09:36 tlr: 6.5e-05 tnm: 0.48 Lm: 6.493 (6.470) Lt: 5.753 (5.717) Accm: 3.34 (3.44) Acct: 5.23 (5.29) proj_loss: -0.6131 (-0.6081) time: 0.6713 data: 0.0003 [11-27 01:37:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [1251/1669] eta: 0:04:47 tlr: 6.5e-05 tnm: 0.46 Lm: 6.473 (6.466) Lt: 5.719 (5.709) Accm: 3.50 (3.50) Acct: 5.44 (5.41) proj_loss: -0.6133 (-0.6104) time: 0.6741 data: 0.0003 [11-27 01:37:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [1251/1669] eta: 0:04:47 tlr: 6.5e-05 tnm: 0.46 Lm: 6.459 (6.467) Lt: 5.732 (5.737) Accm: 3.45 (3.45) Acct: 5.33 (5.23) proj_loss: -0.6167 (-0.6135) time: 0.6741 data: 0.0003 [11-27 01:37:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [1251/1669] eta: 0:04:47 tlr: 6.5e-05 tnm: 0.46 Lm: 6.438 (6.444) Lt: 5.678 (5.687) Accm: 3.46 (3.54) Acct: 5.49 (5.48) proj_loss: -0.6171 (-0.6146) time: 0.6741 data: 0.0003 [11-27 01:37:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [1251/1669] eta: 0:04:47 tlr: 6.5e-05 tnm: 0.46 Lm: 6.516 (6.544) Lt: 5.786 (5.820) Accm: 3.13 (3.29) Acct: 4.79 (5.03) proj_loss: -0.6143 (-0.6166) time: 0.6741 data: 0.0003 [11-27 01:41:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [1668/1669] eta: 0:00:00 tlr: 6.5e-05 tnm: 0.47 Lm: 6.480 (6.522) Lt: 5.747 (5.792) Accm: 3.28 (3.38) Acct: 5.03 (5.21) proj_loss: -0.6101 (-0.6144) time: 0.6719 data: 0.0016 [11-27 01:41:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 263/350] Total time: 0:19:00 (0.684 s / it) [11-27 01:41:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [1668/1669] eta: 0:00:00 tlr: 6.5e-05 tnm: 0.47 Lm: 6.410 (6.455) Lt: 5.692 (5.721) Accm: 3.55 (3.56) Acct: 5.44 (5.41) proj_loss: -0.6139 (-0.6136) time: 0.6719 data: 0.0015 [11-27 01:41:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [1668/1669] eta: 0:00:00 tlr: 6.5e-05 tnm: 0.47 Lm: 6.493 (6.481) Lt: 5.753 (5.730) Accm: 3.34 (3.45) Acct: 5.51 (5.43) proj_loss: -0.6134 (-0.6111) time: 0.6719 data: 0.0017 [11-27 01:41:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 263/350] [1668/1669] eta: 0:00:00 tlr: 6.5e-05 tnm: 0.47 Lm: 6.428 (6.425) Lt: 5.662 (5.647) Accm: 3.47 (3.66) Acct: 5.53 (5.73) proj_loss: -0.6156 (-0.6112) time: 0.6719 data: 0.0015 [11-27 01:41:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 263/350] Total time: 0:19:00 (0.684 s / it) [11-27 01:41:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 263/350] Total time: 0:19:00 (0.684 s / it) [11-27 01:41:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 263/350] Total time: 0:19:00 (0.684 s / it) [11-27 01:41:43] (/home/user/VAR/train.py , line 279)=> [ep263] (training ) Lm: 6.445 (6.463), Lt: 5.684 (5.710), Acc m&t: 3.56 5.57, Remain: 1 day, 3:02:39, Finish: 2024-11-27 12:44 [11-27 01:41:43] (/home/user/VAR/train.py , line 279)=> [ep263] (training ) Lm: 6.445 (6.463), Lt: 5.684 (5.710), Acc m&t: 3.56 5.57, Remain: 1 day, 3:02:44, Finish: 2024-11-27 12:44 [11-27 01:41:43] (/home/user/VAR/train.py , line 279)=> [ep263] (training ) Lm: 6.445 (6.463), Lt: 5.684 (5.710), Acc m&t: 3.56 5.57, Remain: 1 day, 3:02:13, Finish: 2024-11-27 12:43 [11-27 01:41:43] (/home/user/VAR/train.py , line 279)=> [ep263] (training ) Lm: 6.445 (6.463), Lt: 5.684 (5.710), Acc m&t: 3.56 5.57, Remain: 1 day, 3:02:21, Finish: 2024-11-27 12:44 [11-27 01:41:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [ 0/1669] eta: 0:18:12 tlr: 6.5e-05 tnm: 0.46 Lm: 6.521 (6.521) Lt: 5.761 (5.761) Accm: 3.37 (3.37) Acct: 5.20 (5.20) proj_loss: -0.6255 (-0.6255) time: 0.6548 data: 0.0003 [11-27 01:41:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [ 0/1669] eta: 0:18:09 tlr: 6.5e-05 tnm: 0.46 Lm: 6.365 (6.365) Lt: 5.583 (5.583) Accm: 3.82 (3.82) Acct: 5.72 (5.72) proj_loss: -0.5912 (-0.5912) time: 0.6529 data: 0.0003 [11-27 01:41:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [ 0/1669] eta: 0:18:10 tlr: 6.5e-05 tnm: 0.46 Lm: 6.493 (6.493) Lt: 5.776 (5.776) Accm: 3.37 (3.37) Acct: 4.68 (4.68) proj_loss: -0.6262 (-0.6262) time: 0.6532 data: 0.0004 [11-27 01:41:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [ 0/1669] eta: 0:18:14 tlr: 6.5e-05 tnm: 0.46 Lm: 6.220 (6.220) Lt: 5.440 (5.440) Accm: 4.12 (4.12) Acct: 6.53 (6.53) proj_loss: -0.6241 (-0.6241) time: 0.6558 data: 0.0004 [11-27 01:46:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [ 417/1669] eta: 0:14:02 tlr: 6.5e-05 tnm: 0.46 Lm: 6.374 (6.374) Lt: 5.585 (5.585) Accm: 3.65 (3.65) Acct: 5.75 (5.75) proj_loss: -0.6154 (-0.6154) time: 0.6730 data: 0.0003 [11-27 01:46:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [ 417/1669] eta: 0:14:02 tlr: 6.5e-05 tnm: 0.46 Lm: 6.505 (6.505) Lt: 5.760 (5.760) Accm: 3.21 (3.21) Acct: 4.89 (4.89) proj_loss: -0.6035 (-0.6035) time: 0.6730 data: 0.0002 [11-27 01:46:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [ 417/1669] eta: 0:14:02 tlr: 6.5e-05 tnm: 0.46 Lm: 6.427 (6.427) Lt: 5.686 (5.686) Accm: 3.66 (3.66) Acct: 5.45 (5.45) proj_loss: -0.6267 (-0.6267) time: 0.6730 data: 0.0003 [11-27 01:46:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [ 417/1669] eta: 0:14:02 tlr: 6.5e-05 tnm: 0.46 Lm: 6.452 (6.452) Lt: 5.732 (5.732) Accm: 3.56 (3.56) Acct: 5.29 (5.29) proj_loss: -0.6176 (-0.6176) time: 0.6730 data: 0.0003 [11-27 01:51:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [ 834/1669] eta: 0:09:21 tlr: 6.5e-05 tnm: 0.46 Lm: 6.410 (6.425) Lt: 5.687 (5.687) Accm: 3.73 (3.62) Acct: 5.68 (5.42) proj_loss: -0.6096 (-0.6149) time: 0.6712 data: 0.0003 [11-27 01:51:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [ 834/1669] eta: 0:09:21 tlr: 6.5e-05 tnm: 0.46 Lm: 6.413 (6.474) Lt: 5.616 (5.712) Accm: 3.50 (3.30) Acct: 5.37 (5.05) proj_loss: -0.6070 (-0.6047) time: 0.6712 data: 0.0004 [11-27 01:51:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [ 834/1669] eta: 0:09:21 tlr: 6.5e-05 tnm: 0.46 Lm: 6.527 (6.425) Lt: 5.729 (5.665) Accm: 3.29 (3.53) Acct: 4.98 (5.47) proj_loss: -0.6066 (-0.6115) time: 0.6712 data: 0.0003 [11-27 01:51:05] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [ 834/1669] eta: 0:09:21 tlr: 6.5e-05 tnm: 0.46 Lm: 6.492 (6.449) Lt: 5.708 (5.693) Accm: 3.42 (3.58) Acct: 5.25 (5.38) proj_loss: -0.6255 (-0.6199) time: 0.6712 data: 0.0003 [11-27 01:55:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [1251/1669] eta: 0:04:41 tlr: 6.5e-05 tnm: 0.47 Lm: 6.453 (6.440) Lt: 5.693 (5.690) Accm: 3.44 (3.55) Acct: 5.23 (5.32) proj_loss: -0.6158 (-0.6147) time: 0.6734 data: 0.0003 [11-27 01:55:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [1251/1669] eta: 0:04:41 tlr: 6.5e-05 tnm: 0.47 Lm: 6.389 (6.446) Lt: 5.610 (5.685) Accm: 3.66 (3.49) Acct: 5.54 (5.27) proj_loss: -0.6052 (-0.6044) time: 0.6734 data: 0.0003 [11-27 01:55:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [1251/1669] eta: 0:04:41 tlr: 6.5e-05 tnm: 0.47 Lm: 6.528 (6.480) Lt: 5.777 (5.715) Accm: 3.23 (3.38) Acct: 4.94 (5.30) proj_loss: -0.6052 (-0.6070) time: 0.6734 data: 0.0003 [11-27 01:55:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [1251/1669] eta: 0:04:41 tlr: 6.5e-05 tnm: 0.47 Lm: 6.391 (6.405) Lt: 5.643 (5.658) Accm: 3.72 (3.64) Acct: 5.59 (5.44) proj_loss: -0.6093 (-0.6118) time: 0.6734 data: 0.0003 [11-27 02:00:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [1668/1669] eta: 0:00:00 tlr: 6.4e-05 tnm: 0.47 Lm: 6.410 (6.436) Lt: 5.687 (5.691) Accm: 3.70 (3.54) Acct: 5.49 (5.40) proj_loss: -0.6089 (-0.6108) time: 0.7409 data: 0.0017 [11-27 02:00:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 264/350] Total time: 0:18:46 (0.675 s / it) [11-27 02:00:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [1668/1669] eta: 0:00:00 tlr: 6.4e-05 tnm: 0.47 Lm: 6.413 (6.432) Lt: 5.678 (5.682) Accm: 3.46 (3.53) Acct: 5.22 (5.30) proj_loss: -0.6061 (-0.6077) time: 0.7409 data: 0.0019 [11-27 02:00:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [1668/1669] eta: 0:00:00 tlr: 6.4e-05 tnm: 0.47 Lm: 6.396 (6.436) Lt: 5.616 (5.673) Accm: 3.81 (3.55) Acct: 5.72 (5.41) proj_loss: -0.6070 (-0.6052) time: 0.7409 data: 0.0016 [11-27 02:00:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 264/350] [1668/1669] eta: 0:00:00 tlr: 6.4e-05 tnm: 0.47 Lm: 6.528 (6.499) Lt: 5.825 (5.754) Accm: 3.29 (3.37) Acct: 4.98 (5.28) proj_loss: -0.6066 (-0.6106) time: 0.7409 data: 0.0024 [11-27 02:00:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 264/350] Total time: 0:18:46 (0.675 s / it) [11-27 02:00:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 264/350] Total time: 0:18:46 (0.675 s / it) [11-27 02:00:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 264/350] Total time: 0:18:46 (0.675 s / it) [11-27 02:00:30] (/home/user/VAR/train.py , line 279)=> [ep264] (training ) Lm: 6.445 (6.454), Lt: 5.684 (5.696), Acc m&t: 3.56 5.57, Remain: 1 day, 2:49:35, Finish: 2024-11-27 12:50 [11-27 02:00:30] (/home/user/VAR/train.py , line 279)=> [ep264] (training ) Lm: 6.445 (6.454), Lt: 5.684 (5.696), Acc m&t: 3.56 5.57, Remain: 1 day, 2:50:03, Finish: 2024-11-27 12:50 [11-27 02:00:30] (/home/user/VAR/train.py , line 279)=> [ep264] (training ) Lm: 6.445 (6.454), Lt: 5.684 (5.696), Acc m&t: 3.56 5.57, Remain: 1 day, 2:49:53, Finish: 2024-11-27 12:50 [11-27 02:00:30] (/home/user/VAR/train.py , line 279)=> [ep264] (training ) Lm: 6.445 (6.454), Lt: 5.684 (5.696), Acc m&t: 3.56 5.57, Remain: 1 day, 2:49:37, Finish: 2024-11-27 12:50 [11-27 02:00:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [ 0/1669] eta: 0:18:09 tlr: 6.4e-05 tnm: 0.47 Lm: 6.493 (6.493) Lt: 5.770 (5.770) Accm: 3.37 (3.37) Acct: 5.15 (5.15) proj_loss: -0.6196 (-0.6196) time: 0.6527 data: 0.0003 [11-27 02:00:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [ 0/1669] eta: 0:18:09 tlr: 6.4e-05 tnm: 0.47 Lm: 6.462 (6.462) Lt: 5.664 (5.664) Accm: 3.45 (3.45) Acct: 5.60 (5.60) proj_loss: -0.5955 (-0.5955) time: 0.6528 data: 0.0003 [11-27 02:00:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [ 0/1669] eta: 0:18:09 tlr: 6.4e-05 tnm: 0.47 Lm: 6.428 (6.428) Lt: 5.691 (5.691) Accm: 3.25 (3.25) Acct: 5.04 (5.04) proj_loss: -0.6159 (-0.6159) time: 0.6530 data: 0.0004 [11-27 02:00:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [ 0/1669] eta: 0:18:10 tlr: 6.4e-05 tnm: 0.47 Lm: 6.370 (6.370) Lt: 5.594 (5.594) Accm: 4.15 (4.15) Acct: 6.39 (6.39) proj_loss: -0.6227 (-0.6227) time: 0.6532 data: 0.0003 [11-27 02:05:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [ 417/1669] eta: 0:14:46 tlr: 6.4e-05 tnm: 0.47 Lm: 6.442 (6.442) Lt: 5.704 (5.704) Accm: 3.77 (3.77) Acct: 5.85 (5.85) proj_loss: -0.6247 (-0.6247) time: 0.6741 data: 0.0003 [11-27 02:05:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [ 417/1669] eta: 0:14:46 tlr: 6.4e-05 tnm: 0.47 Lm: 6.500 (6.500) Lt: 5.740 (5.740) Accm: 3.42 (3.42) Acct: 5.41 (5.41) proj_loss: -0.6079 (-0.6079) time: 0.6741 data: 0.0003 [11-27 02:05:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [ 417/1669] eta: 0:14:46 tlr: 6.4e-05 tnm: 0.47 Lm: 6.527 (6.527) Lt: 5.802 (5.802) Accm: 3.26 (3.26) Acct: 5.15 (5.15) proj_loss: -0.6112 (-0.6112) time: 0.6741 data: 0.0003 [11-27 02:05:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [ 417/1669] eta: 0:14:46 tlr: 6.4e-05 tnm: 0.47 Lm: 6.446 (6.446) Lt: 5.713 (5.713) Accm: 3.36 (3.36) Acct: 5.17 (5.17) proj_loss: -0.6123 (-0.6123) time: 0.6741 data: 0.0003 [11-27 02:10:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [ 834/1669] eta: 0:09:36 tlr: 6.4e-05 tnm: 0.49 Lm: 6.428 (6.435) Lt: 5.706 (5.710) Accm: 3.47 (3.43) Acct: 5.04 (5.13) proj_loss: -0.6146 (-0.6131) time: 0.6737 data: 0.0003 [11-27 02:10:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [ 834/1669] eta: 0:09:36 tlr: 6.4e-05 tnm: 0.49 Lm: 6.462 (6.431) Lt: 5.664 (5.671) Accm: 3.45 (3.55) Acct: 5.60 (5.65) proj_loss: -0.5968 (-0.6042) time: 0.6737 data: 0.0003 [11-27 02:10:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [ 834/1669] eta: 0:09:36 tlr: 6.4e-05 tnm: 0.49 Lm: 6.493 (6.496) Lt: 5.770 (5.751) Accm: 3.37 (3.35) Acct: 5.15 (5.34) proj_loss: -0.6196 (-0.6157) time: 0.6737 data: 0.0003 [11-27 02:10:07] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [ 834/1669] eta: 0:09:36 tlr: 6.4e-05 tnm: 0.49 Lm: 6.471 (6.452) Lt: 5.707 (5.705) Accm: 3.39 (3.64) Acct: 5.35 (5.69) proj_loss: -0.6227 (-0.6220) time: 0.6737 data: 0.0003 [11-27 02:14:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [1251/1669] eta: 0:04:46 tlr: 6.4e-05 tnm: 0.47 Lm: 6.420 (6.392) Lt: 5.650 (5.628) Accm: 3.75 (3.75) Acct: 5.87 (5.93) proj_loss: -0.6197 (-0.6187) time: 0.6727 data: 0.0003 [11-27 02:14:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [1251/1669] eta: 0:04:46 tlr: 6.4e-05 tnm: 0.47 Lm: 6.500 (6.458) Lt: 5.721 (5.698) Accm: 3.42 (3.48) Acct: 5.49 (5.59) proj_loss: -0.6073 (-0.6076) time: 0.6727 data: 0.0003 [11-27 02:14:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [1251/1669] eta: 0:04:46 tlr: 6.4e-05 tnm: 0.47 Lm: 6.524 (6.511) Lt: 5.793 (5.768) Accm: 3.34 (3.34) Acct: 5.20 (5.32) proj_loss: -0.6222 (-0.6191) time: 0.6727 data: 0.0003 [11-27 02:14:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [1251/1669] eta: 0:04:46 tlr: 6.4e-05 tnm: 0.47 Lm: 6.446 (6.489) Lt: 5.720 (5.765) Accm: 3.36 (3.32) Acct: 5.04 (4.95) proj_loss: -0.6117 (-0.6116) time: 0.6727 data: 0.0003 [11-27 02:19:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [1668/1669] eta: 0:00:00 tlr: 6.4e-05 tnm: 0.47 Lm: 6.428 (6.469) Lt: 5.706 (5.733) Accm: 3.47 (3.44) Acct: 5.04 (5.27) proj_loss: -0.6087 (-0.6103) time: 0.6721 data: 0.0021 [11-27 02:19:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 265/350] Total time: 0:18:57 (0.681 s / it) [11-27 02:19:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [1668/1669] eta: 0:00:00 tlr: 6.4e-05 tnm: 0.47 Lm: 6.462 (6.458) Lt: 5.709 (5.700) Accm: 3.40 (3.44) Acct: 5.39 (5.51) proj_loss: -0.6178 (-0.6117) time: 0.6721 data: 0.0016 [11-27 02:19:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [1668/1669] eta: 0:00:00 tlr: 6.4e-05 tnm: 0.47 Lm: 6.370 (6.370) Lt: 5.594 (5.610) Accm: 3.87 (3.78) Acct: 5.87 (5.92) proj_loss: -0.6178 (-0.6185) time: 0.6721 data: 0.0015 [11-27 02:19:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 265/350] [1668/1669] eta: 0:00:00 tlr: 6.4e-05 tnm: 0.47 Lm: 6.493 (6.472) Lt: 5.770 (5.719) Accm: 3.37 (3.49) Acct: 5.25 (5.56) proj_loss: -0.6196 (-0.6158) time: 0.6721 data: 0.0017 [11-27 02:19:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 265/350] Total time: 0:18:57 (0.681 s / it) [11-27 02:19:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 265/350] Total time: 0:18:57 (0.681 s / it) [11-27 02:19:27] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 265/350] Total time: 0:18:57 (0.681 s / it) [11-27 02:19:27] (/home/user/VAR/train.py , line 279)=> [ep265] (training ) Lm: 6.445 (6.453), Lt: 5.684 (5.695), Acc m&t: 3.56 5.57, Remain: 1 day, 2:20:10, Finish: 2024-11-27 12:39 [11-27 02:19:27] (/home/user/VAR/train.py , line 279)=> [ep265] (training ) Lm: 6.445 (6.453), Lt: 5.684 (5.695), Acc m&t: 3.56 5.57, Remain: 1 day, 2:20:00, Finish: 2024-11-27 12:39 [11-27 02:19:27] (/home/user/VAR/train.py , line 279)=> [ep265] (training ) Lm: 6.445 (6.453), Lt: 5.684 (5.695), Acc m&t: 3.56 5.57, Remain: 1 day, 2:20:22, Finish: 2024-11-27 12:39 [11-27 02:19:27] (/home/user/VAR/train.py , line 279)=> [ep265] (training ) Lm: 6.445 (6.453), Lt: 5.684 (5.695), Acc m&t: 3.56 5.57, Remain: 1 day, 2:20:18, Finish: 2024-11-27 12:39 [11-27 02:19:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [ 0/1669] eta: 0:18:02 tlr: 6.4e-05 tnm: 0.47 Lm: 6.277 (6.277) Lt: 5.500 (5.500) Accm: 3.98 (3.98) Acct: 6.63 (6.63) proj_loss: -0.6339 (-0.6339) time: 0.6489 data: 0.0003 [11-27 02:19:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [ 0/1669] eta: 0:18:02 tlr: 6.4e-05 tnm: 0.47 Lm: 6.394 (6.394) Lt: 5.678 (5.678) Accm: 3.77 (3.77) Acct: 5.80 (5.80) proj_loss: -0.6246 (-0.6246) time: 0.6487 data: 0.0003 [11-27 02:19:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [ 0/1669] eta: 0:18:02 tlr: 6.4e-05 tnm: 0.47 Lm: 6.586 (6.586) Lt: 5.865 (5.865) Accm: 2.86 (2.86) Acct: 4.44 (4.44) proj_loss: -0.5915 (-0.5915) time: 0.6484 data: 0.0003 [11-27 02:19:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [ 0/1669] eta: 0:18:06 tlr: 6.4e-05 tnm: 0.47 Lm: 6.588 (6.588) Lt: 5.813 (5.813) Accm: 3.19 (3.19) Acct: 5.03 (5.03) proj_loss: -0.6090 (-0.6090) time: 0.6509 data: 0.0004 [11-27 02:24:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [ 417/1669] eta: 0:14:01 tlr: 6.3e-05 tnm: 0.46 Lm: 6.510 (6.510) Lt: 5.728 (5.728) Accm: 3.39 (3.39) Acct: 5.30 (5.30) proj_loss: -0.6079 (-0.6079) time: 0.6734 data: 0.0003 [11-27 02:24:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [ 417/1669] eta: 0:14:01 tlr: 6.3e-05 tnm: 0.46 Lm: 6.456 (6.456) Lt: 5.737 (5.737) Accm: 3.17 (3.17) Acct: 4.77 (4.77) proj_loss: -0.6041 (-0.6041) time: 0.6734 data: 0.0003 [11-27 02:24:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [ 417/1669] eta: 0:14:01 tlr: 6.3e-05 tnm: 0.46 Lm: 6.447 (6.447) Lt: 5.660 (5.660) Accm: 3.57 (3.57) Acct: 5.79 (5.79) proj_loss: -0.6209 (-0.6209) time: 0.6734 data: 0.0002 [11-27 02:24:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [ 417/1669] eta: 0:14:01 tlr: 6.3e-05 tnm: 0.46 Lm: 6.419 (6.419) Lt: 5.701 (5.701) Accm: 3.78 (3.78) Acct: 5.84 (5.84) proj_loss: -0.6205 (-0.6205) time: 0.6734 data: 0.0002 [11-27 02:29:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [ 834/1669] eta: 0:09:36 tlr: 6.3e-05 tnm: 0.47 Lm: 6.443 (6.478) Lt: 5.723 (5.752) Accm: 3.77 (3.59) Acct: 5.80 (5.54) proj_loss: -0.6165 (-0.6185) time: 0.6748 data: 0.0003 [11-27 02:29:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [ 834/1669] eta: 0:09:36 tlr: 6.3e-05 tnm: 0.47 Lm: 6.553 (6.483) Lt: 5.786 (5.702) Accm: 3.48 (3.54) Acct: 5.68 (5.76) proj_loss: -0.6079 (-0.6147) time: 0.6748 data: 0.0003 [11-27 02:29:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [ 834/1669] eta: 0:09:36 tlr: 6.3e-05 tnm: 0.47 Lm: 6.474 (6.498) Lt: 5.696 (5.717) Accm: 3.47 (3.42) Acct: 5.58 (5.45) proj_loss: -0.6069 (-0.6070) time: 0.6748 data: 0.0003 [11-27 02:29:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [ 834/1669] eta: 0:09:36 tlr: 6.3e-05 tnm: 0.47 Lm: 6.489 (6.467) Lt: 5.733 (5.736) Accm: 3.29 (3.21) Acct: 5.10 (4.92) proj_loss: -0.6153 (-0.6078) time: 0.6748 data: 0.0003 [11-27 02:33:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [1251/1669] eta: 0:04:47 tlr: 6.3e-05 tnm: 0.48 Lm: 6.492 (6.470) Lt: 5.734 (5.697) Accm: 3.42 (3.49) Acct: 5.48 (5.64) proj_loss: -0.6141 (-0.6161) time: 0.7395 data: 0.0003 [11-27 02:33:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [1251/1669] eta: 0:04:47 tlr: 6.3e-05 tnm: 0.48 Lm: 6.462 (6.486) Lt: 5.716 (5.722) Accm: 3.44 (3.42) Acct: 5.37 (5.38) proj_loss: -0.6079 (-0.6078) time: 0.7395 data: 0.0003 [11-27 02:33:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [1251/1669] eta: 0:04:47 tlr: 6.3e-05 tnm: 0.48 Lm: 6.470 (6.482) Lt: 5.765 (5.766) Accm: 3.53 (3.52) Acct: 5.37 (5.35) proj_loss: -0.6175 (-0.6185) time: 0.7395 data: 0.0002 [11-27 02:33:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [1251/1669] eta: 0:04:47 tlr: 6.3e-05 tnm: 0.48 Lm: 6.460 (6.458) Lt: 5.716 (5.727) Accm: 3.39 (3.38) Acct: 5.16 (5.21) proj_loss: -0.6104 (-0.6072) time: 0.7395 data: 0.0003 [11-27 02:38:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [1668/1669] eta: 0:00:00 tlr: 6.3e-05 tnm: 0.49 Lm: 6.489 (6.466) Lt: 5.733 (5.729) Accm: 3.29 (3.36) Acct: 5.13 (5.20) proj_loss: -0.6153 (-0.6091) time: 0.6734 data: 0.0018 [11-27 02:38:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 266/350] Total time: 0:19:01 (0.684 s / it) [11-27 02:38:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [1668/1669] eta: 0:00:00 tlr: 6.3e-05 tnm: 0.49 Lm: 6.443 (6.451) Lt: 5.723 (5.724) Accm: 3.77 (3.58) Acct: 5.66 (5.41) proj_loss: -0.6165 (-0.6118) time: 0.6734 data: 0.0015 [11-27 02:38:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [1668/1669] eta: 0:00:00 tlr: 6.3e-05 tnm: 0.49 Lm: 6.553 (6.488) Lt: 5.754 (5.708) Accm: 3.37 (3.41) Acct: 5.29 (5.53) proj_loss: -0.6079 (-0.6130) time: 0.6734 data: 0.0018 [11-27 02:38:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 266/350] [1668/1669] eta: 0:00:00 tlr: 6.3e-05 tnm: 0.49 Lm: 6.474 (6.488) Lt: 5.735 (5.731) Accm: 3.41 (3.39) Acct: 5.17 (5.33) proj_loss: -0.6069 (-0.6065) time: 0.6734 data: 0.0021 [11-27 02:38:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 266/350] Total time: 0:19:01 (0.684 s / it) [11-27 02:38:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 266/350] Total time: 0:19:01 (0.684 s / it) [11-27 02:38:29] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 266/350] Total time: 0:19:01 (0.684 s / it) [11-27 02:38:29] (/home/user/VAR/train.py , line 279)=> [ep266] (training ) Lm: 6.445 (6.452), Lt: 5.684 (5.701), Acc m&t: 3.56 5.57, Remain: 1 day, 2:07:38, Finish: 2024-11-27 12:46 [11-27 02:38:29] (/home/user/VAR/train.py , line 279)=> [ep266] (training ) Lm: 6.445 (6.452), Lt: 5.684 (5.701), Acc m&t: 3.56 5.57, Remain: 1 day, 2:08:12, Finish: 2024-11-27 12:46 [11-27 02:38:29] (/home/user/VAR/train.py , line 279)=> [ep266] (training ) Lm: 6.445 (6.452), Lt: 5.684 (5.701), Acc m&t: 3.56 5.57, Remain: 1 day, 2:08:08, Finish: 2024-11-27 12:46 [11-27 02:38:29] (/home/user/VAR/train.py , line 279)=> [ep266] (training ) Lm: 6.445 (6.452), Lt: 5.684 (5.701), Acc m&t: 3.56 5.57, Remain: 1 day, 2:08:03, Finish: 2024-11-27 12:46 [11-27 02:38:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [ 0/1669] eta: 0:18:25 tlr: 6.3e-05 tnm: 0.47 Lm: 6.483 (6.483) Lt: 5.734 (5.734) Accm: 3.59 (3.59) Acct: 5.60 (5.60) proj_loss: -0.6037 (-0.6037) time: 0.6625 data: 0.0003 [11-27 02:38:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [ 0/1669] eta: 0:18:26 tlr: 6.3e-05 tnm: 0.47 Lm: 6.534 (6.534) Lt: 5.773 (5.773) Accm: 3.19 (3.19) Acct: 5.06 (5.06) proj_loss: -0.6044 (-0.6044) time: 0.6629 data: 0.0004 [11-27 02:38:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [ 0/1669] eta: 0:18:26 tlr: 6.3e-05 tnm: 0.47 Lm: 6.592 (6.592) Lt: 5.792 (5.792) Accm: 3.21 (3.21) Acct: 5.23 (5.23) proj_loss: -0.5893 (-0.5893) time: 0.6629 data: 0.0003 [11-27 02:38:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [ 0/1669] eta: 0:18:25 tlr: 6.3e-05 tnm: 0.47 Lm: 6.417 (6.417) Lt: 5.711 (5.711) Accm: 3.63 (3.63) Acct: 5.48 (5.48) proj_loss: -0.6178 (-0.6178) time: 0.6626 data: 0.0004 [11-27 02:43:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [ 417/1669] eta: 0:14:01 tlr: 6.3e-05 tnm: 0.48 Lm: 6.407 (6.407) Lt: 5.664 (5.664) Accm: 3.57 (3.57) Acct: 5.48 (5.48) proj_loss: -0.6143 (-0.6143) time: 0.6725 data: 0.0003 [11-27 02:43:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [ 417/1669] eta: 0:14:01 tlr: 6.3e-05 tnm: 0.48 Lm: 6.507 (6.507) Lt: 5.732 (5.732) Accm: 3.19 (3.19) Acct: 5.12 (5.12) proj_loss: -0.6090 (-0.6090) time: 0.6725 data: 0.0003 [11-27 02:43:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [ 417/1669] eta: 0:14:01 tlr: 6.3e-05 tnm: 0.48 Lm: 6.516 (6.516) Lt: 5.704 (5.704) Accm: 3.55 (3.55) Acct: 5.66 (5.66) proj_loss: -0.5930 (-0.5930) time: 0.6725 data: 0.0003 [11-27 02:43:10] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [ 417/1669] eta: 0:14:01 tlr: 6.3e-05 tnm: 0.48 Lm: 6.406 (6.406) Lt: 5.649 (5.649) Accm: 3.82 (3.82) Acct: 5.99 (5.99) proj_loss: -0.6098 (-0.6098) time: 0.6725 data: 0.0003 [11-27 02:47:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [ 834/1669] eta: 0:09:21 tlr: 6.3e-05 tnm: 0.48 Lm: 6.328 (6.340) Lt: 5.564 (5.571) Accm: 4.04 (3.99) Acct: 6.39 (6.27) proj_loss: -0.6160 (-0.6149) time: 0.6730 data: 0.0003 [11-27 02:47:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [ 834/1669] eta: 0:09:21 tlr: 6.3e-05 tnm: 0.48 Lm: 6.479 (6.480) Lt: 5.741 (5.735) Accm: 3.19 (3.24) Acct: 5.17 (5.14) proj_loss: -0.6135 (-0.6114) time: 0.6730 data: 0.0003 [11-27 02:47:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [ 834/1669] eta: 0:09:21 tlr: 6.3e-05 tnm: 0.48 Lm: 6.440 (6.477) Lt: 5.639 (5.683) Accm: 3.77 (3.63) Acct: 5.85 (5.73) proj_loss: -0.5968 (-0.6003) time: 0.6730 data: 0.0003 [11-27 02:47:51] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [ 834/1669] eta: 0:09:21 tlr: 6.3e-05 tnm: 0.48 Lm: 6.417 (6.451) Lt: 5.711 (5.702) Accm: 3.51 (3.47) Acct: 5.48 (5.38) proj_loss: -0.6129 (-0.6138) time: 0.6730 data: 0.0003 [11-27 02:52:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [1251/1669] eta: 0:04:41 tlr: 6.2e-05 tnm: 0.48 Lm: 6.407 (6.436) Lt: 5.678 (5.687) Accm: 3.56 (3.51) Acct: 5.48 (5.41) proj_loss: -0.6119 (-0.6100) time: 0.6723 data: 0.0003 [11-27 02:52:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [1251/1669] eta: 0:04:41 tlr: 6.2e-05 tnm: 0.48 Lm: 6.406 (6.378) Lt: 5.649 (5.624) Accm: 3.82 (3.84) Acct: 5.99 (5.98) proj_loss: -0.6170 (-0.6157) time: 0.6723 data: 0.0003 [11-27 02:52:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [1251/1669] eta: 0:04:41 tlr: 6.2e-05 tnm: 0.48 Lm: 6.483 (6.482) Lt: 5.729 (5.730) Accm: 3.27 (3.30) Acct: 5.17 (5.26) proj_loss: -0.6149 (-0.6150) time: 0.6723 data: 0.0002 [11-27 02:52:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [1251/1669] eta: 0:04:41 tlr: 6.2e-05 tnm: 0.48 Lm: 6.419 (6.424) Lt: 5.628 (5.643) Accm: 3.83 (3.71) Acct: 5.97 (5.88) proj_loss: -0.6059 (-0.6056) time: 0.6723 data: 0.0003 [11-27 02:57:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [1668/1669] eta: 0:00:00 tlr: 6.2e-05 tnm: 0.47 Lm: 6.413 (6.422) Lt: 5.617 (5.635) Accm: 3.77 (3.70) Acct: 5.85 (5.84) proj_loss: -0.6083 (-0.6062) time: 0.7420 data: 0.0019 [11-27 02:57:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 267/350] Total time: 0:18:46 (0.675 s / it) [11-27 02:57:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [1668/1669] eta: 0:00:00 tlr: 6.2e-05 tnm: 0.47 Lm: 6.413 (6.385) Lt: 5.685 (5.637) Accm: 3.63 (3.80) Acct: 5.60 (5.88) proj_loss: -0.6180 (-0.6176) time: 0.7420 data: 0.0016 [11-27 02:57:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [1668/1669] eta: 0:00:00 tlr: 6.2e-05 tnm: 0.47 Lm: 6.417 (6.442) Lt: 5.711 (5.694) Accm: 3.51 (3.50) Acct: 5.48 (5.44) proj_loss: -0.6109 (-0.6069) time: 0.7420 data: 0.0019 [11-27 02:57:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 267/350] [1668/1669] eta: 0:00:00 tlr: 6.2e-05 tnm: 0.47 Lm: 6.486 (6.496) Lt: 5.741 (5.749) Accm: 3.19 (3.25) Acct: 5.17 (5.14) proj_loss: -0.6164 (-0.6158) time: 0.7420 data: 0.0018 [11-27 02:57:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 267/350] Total time: 0:18:46 (0.675 s / it) [11-27 02:57:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 267/350] Total time: 0:18:46 (0.675 s / it) [11-27 02:57:16] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 267/350] Total time: 0:18:46 (0.675 s / it) [11-27 02:57:16] (/home/user/VAR/train.py , line 279)=> [ep267] (training ) Lm: 6.445 (6.448), Lt: 5.684 (5.696), Acc m&t: 3.56 5.57, Remain: 1 day, 1:54:09, Finish: 2024-11-27 12:51 [11-27 02:57:16] (/home/user/VAR/train.py , line 279)=> [ep267] (training ) Lm: 6.445 (6.448), Lt: 5.684 (5.696), Acc m&t: 3.56 5.57, Remain: 1 day, 1:54:34, Finish: 2024-11-27 12:51 [11-27 02:57:16] (/home/user/VAR/train.py , line 279)=> [ep267] (training ) Lm: 6.445 (6.448), Lt: 5.684 (5.696), Acc m&t: 3.56 5.57, Remain: 1 day, 1:54:40, Finish: 2024-11-27 12:51 [11-27 02:57:16] (/home/user/VAR/train.py , line 279)=> [ep267] (training ) Lm: 6.445 (6.448), Lt: 5.684 (5.696), Acc m&t: 3.56 5.57, Remain: 1 day, 1:54:42, Finish: 2024-11-27 12:51 [11-27 02:57:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [ 0/1669] eta: 0:18:18 tlr: 6.2e-05 tnm: 0.48 Lm: 6.426 (6.426) Lt: 5.676 (5.676) Accm: 3.63 (3.63) Acct: 5.61 (5.61) proj_loss: -0.6213 (-0.6213) time: 0.6585 data: 0.0003 [11-27 02:57:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [ 0/1669] eta: 0:18:19 tlr: 6.2e-05 tnm: 0.48 Lm: 6.342 (6.342) Lt: 5.570 (5.570) Accm: 4.00 (4.00) Acct: 6.30 (6.30) proj_loss: -0.6168 (-0.6168) time: 0.6591 data: 0.0003 [11-27 02:57:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [ 0/1669] eta: 0:18:20 tlr: 6.2e-05 tnm: 0.48 Lm: 6.453 (6.453) Lt: 5.683 (5.683) Accm: 3.52 (3.52) Acct: 5.54 (5.54) proj_loss: -0.6138 (-0.6138) time: 0.6592 data: 0.0004 [11-27 02:57:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [ 0/1669] eta: 0:18:28 tlr: 6.2e-05 tnm: 0.48 Lm: 6.493 (6.493) Lt: 5.725 (5.725) Accm: 3.24 (3.24) Acct: 5.20 (5.20) proj_loss: -0.5930 (-0.5930) time: 0.6640 data: 0.0004 [11-27 03:02:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [ 417/1669] eta: 0:14:44 tlr: 6.2e-05 tnm: 0.46 Lm: 6.487 (6.487) Lt: 5.722 (5.722) Accm: 3.22 (3.22) Acct: 5.23 (5.23) proj_loss: -0.5972 (-0.5972) time: 0.6730 data: 0.0003 [11-27 03:02:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [ 417/1669] eta: 0:14:44 tlr: 6.2e-05 tnm: 0.46 Lm: 6.430 (6.430) Lt: 5.643 (5.643) Accm: 3.64 (3.64) Acct: 5.85 (5.85) proj_loss: -0.6117 (-0.6117) time: 0.6730 data: 0.0003 [11-27 03:02:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [ 417/1669] eta: 0:14:44 tlr: 6.2e-05 tnm: 0.46 Lm: 6.443 (6.443) Lt: 5.689 (5.689) Accm: 3.57 (3.57) Acct: 5.60 (5.60) proj_loss: -0.6060 (-0.6060) time: 0.6730 data: 0.0003 [11-27 03:02:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [ 417/1669] eta: 0:14:44 tlr: 6.2e-05 tnm: 0.46 Lm: 6.455 (6.455) Lt: 5.701 (5.701) Accm: 3.53 (3.53) Acct: 5.62 (5.62) proj_loss: -0.6119 (-0.6119) time: 0.6730 data: 0.0003 [11-27 03:06:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [ 834/1669] eta: 0:09:35 tlr: 6.2e-05 tnm: 0.49 Lm: 6.453 (6.423) Lt: 5.683 (5.664) Accm: 3.54 (3.62) Acct: 5.70 (5.77) proj_loss: -0.6138 (-0.6125) time: 0.6745 data: 0.0003 [11-27 03:06:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [ 834/1669] eta: 0:09:35 tlr: 6.2e-05 tnm: 0.49 Lm: 6.493 (6.499) Lt: 5.725 (5.741) Accm: 3.24 (3.26) Acct: 5.20 (5.21) proj_loss: -0.6014 (-0.6008) time: 0.6745 data: 0.0003 [11-27 03:06:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [ 834/1669] eta: 0:09:35 tlr: 6.2e-05 tnm: 0.49 Lm: 6.435 (6.441) Lt: 5.676 (5.683) Accm: 3.63 (3.64) Acct: 5.63 (5.77) proj_loss: -0.6213 (-0.6165) time: 0.6745 data: 0.0003 [11-27 03:06:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [ 834/1669] eta: 0:09:35 tlr: 6.2e-05 tnm: 0.49 Lm: 6.465 (6.450) Lt: 5.691 (5.690) Accm: 3.56 (3.57) Acct: 5.72 (5.64) proj_loss: -0.5952 (-0.6013) time: 0.6745 data: 0.0003 [11-27 03:11:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [1251/1669] eta: 0:04:46 tlr: 6.2e-05 tnm: 0.48 Lm: 6.449 (6.446) Lt: 5.687 (5.688) Accm: 3.58 (3.58) Acct: 5.60 (5.60) proj_loss: -0.6060 (-0.6080) time: 0.6738 data: 0.0003 [11-27 03:11:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [1251/1669] eta: 0:04:46 tlr: 6.2e-05 tnm: 0.48 Lm: 6.430 (6.434) Lt: 5.655 (5.670) Accm: 3.64 (3.64) Acct: 5.67 (5.76) proj_loss: -0.6138 (-0.6140) time: 0.6738 data: 0.0002 [11-27 03:11:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [1251/1669] eta: 0:04:46 tlr: 6.2e-05 tnm: 0.48 Lm: 6.500 (6.501) Lt: 5.729 (5.739) Accm: 3.29 (3.35) Acct: 5.23 (5.33) proj_loss: -0.6022 (-0.6013) time: 0.6738 data: 0.0003 [11-27 03:11:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [1251/1669] eta: 0:04:46 tlr: 6.2e-05 tnm: 0.48 Lm: 6.422 (6.415) Lt: 5.680 (5.667) Accm: 3.67 (3.69) Acct: 5.88 (5.91) proj_loss: -0.6138 (-0.6181) time: 0.6738 data: 0.0003 [11-27 03:16:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [1668/1669] eta: 0:00:00 tlr: 6.1e-05 tnm: 0.49 Lm: 6.453 (6.431) Lt: 5.683 (5.680) Accm: 3.54 (3.62) Acct: 5.70 (5.82) proj_loss: -0.6139 (-0.6173) time: 0.6747 data: 0.0016 [11-27 03:16:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 268/350] Total time: 0:18:57 (0.682 s / it) [11-27 03:16:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [1668/1669] eta: 0:00:00 tlr: 6.1e-05 tnm: 0.49 Lm: 6.435 (6.446) Lt: 5.676 (5.681) Accm: 3.63 (3.62) Acct: 5.72 (5.78) proj_loss: -0.6121 (-0.6136) time: 0.6747 data: 0.0016 [11-27 03:16:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [1668/1669] eta: 0:00:00 tlr: 6.1e-05 tnm: 0.49 Lm: 6.460 (6.449) Lt: 5.689 (5.688) Accm: 3.58 (3.58) Acct: 5.68 (5.62) proj_loss: -0.6054 (-0.6075) time: 0.6747 data: 0.0016 [11-27 03:16:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 268/350] [1668/1669] eta: 0:00:00 tlr: 6.1e-05 tnm: 0.49 Lm: 6.493 (6.486) Lt: 5.725 (5.728) Accm: 3.33 (3.41) Acct: 5.25 (5.46) proj_loss: -0.6030 (-0.6034) time: 0.6747 data: 0.0017 [11-27 03:16:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 268/350] Total time: 0:18:57 (0.682 s / it) [11-27 03:16:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 268/350] Total time: 0:18:57 (0.682 s / it) [11-27 03:16:13] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 268/350] Total time: 0:18:57 (0.682 s / it) [11-27 03:16:13] (/home/user/VAR/train.py , line 279)=> [ep268] (training ) Lm: 6.440 (6.440), Lt: 5.682 (5.682), Acc m&t: 3.58 5.61, Remain: 1 day, 1:33:34, Finish: 2024-11-27 12:49 [11-27 03:16:13] (/home/user/VAR/train.py , line 279)=> [ep268] (training ) Lm: 6.440 (6.440), Lt: 5.682 (5.682), Acc m&t: 3.58 5.61, Remain: 1 day, 1:33:24, Finish: 2024-11-27 12:49 [11-27 03:16:13] (/home/user/VAR/train.py , line 279)=> [ep268] (training ) Lm: 6.440 (6.440), Lt: 5.682 (5.682), Acc m&t: 3.58 5.61, Remain: 1 day, 1:33:21, Finish: 2024-11-27 12:49 [11-27 03:16:13] (/home/user/VAR/train.py , line 279)=> [ep268] (training ) Lm: 6.440 (6.440), Lt: 5.682 (5.682), Acc m&t: 3.58 5.61, Remain: 1 day, 1:33:27, Finish: 2024-11-27 12:49 [11-27 03:16:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [ 0/1669] eta: 0:18:35 tlr: 6.1e-05 tnm: 0.48 Lm: 6.521 (6.521) Lt: 5.764 (5.764) Accm: 3.31 (3.31) Acct: 5.03 (5.03) proj_loss: -0.6210 (-0.6210) time: 0.6681 data: 0.0003 [11-27 03:16:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [ 0/1669] eta: 0:18:34 tlr: 6.1e-05 tnm: 0.48 Lm: 6.500 (6.500) Lt: 5.739 (5.739) Accm: 3.53 (3.53) Acct: 5.39 (5.39) proj_loss: -0.6134 (-0.6134) time: 0.6680 data: 0.0003 [11-27 03:16:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [ 0/1669] eta: 0:18:36 tlr: 6.1e-05 tnm: 0.48 Lm: 6.279 (6.279) Lt: 5.532 (5.532) Accm: 4.01 (4.01) Acct: 6.16 (6.16) proj_loss: -0.6286 (-0.6286) time: 0.6690 data: 0.0004 [11-27 03:16:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [ 0/1669] eta: 0:18:36 tlr: 6.1e-05 tnm: 0.48 Lm: 6.532 (6.532) Lt: 5.811 (5.811) Accm: 3.35 (3.35) Acct: 5.25 (5.25) proj_loss: -0.6069 (-0.6069) time: 0.6688 data: 0.0004 [11-27 03:20:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [ 417/1669] eta: 0:14:03 tlr: 6.1e-05 tnm: 0.47 Lm: 6.466 (6.466) Lt: 5.728 (5.728) Accm: 3.43 (3.43) Acct: 5.35 (5.35) proj_loss: -0.6178 (-0.6178) time: 0.6747 data: 0.0003 [11-27 03:20:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [ 417/1669] eta: 0:14:03 tlr: 6.1e-05 tnm: 0.47 Lm: 6.539 (6.539) Lt: 5.767 (5.767) Accm: 3.47 (3.47) Acct: 5.46 (5.46) proj_loss: -0.6087 (-0.6087) time: 0.6747 data: 0.0003 [11-27 03:20:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [ 417/1669] eta: 0:14:03 tlr: 6.1e-05 tnm: 0.47 Lm: 6.428 (6.428) Lt: 5.656 (5.656) Accm: 3.66 (3.66) Acct: 5.70 (5.70) proj_loss: -0.6171 (-0.6171) time: 0.6747 data: 0.0003 [11-27 03:20:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [ 417/1669] eta: 0:14:03 tlr: 6.1e-05 tnm: 0.47 Lm: 6.330 (6.330) Lt: 5.556 (5.556) Accm: 3.78 (3.78) Acct: 5.97 (5.97) proj_loss: -0.6167 (-0.6167) time: 0.6747 data: 0.0003 [11-27 03:25:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [ 834/1669] eta: 0:09:37 tlr: 6.1e-05 tnm: 0.45 Lm: 6.323 (6.327) Lt: 5.568 (5.560) Accm: 4.01 (3.96) Acct: 6.16 (6.20) proj_loss: -0.6277 (-0.6203) time: 0.6726 data: 0.0003 [11-27 03:25:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [ 834/1669] eta: 0:09:37 tlr: 6.1e-05 tnm: 0.45 Lm: 6.500 (6.444) Lt: 5.739 (5.690) Accm: 3.53 (3.73) Acct: 5.53 (5.77) proj_loss: -0.6067 (-0.6080) time: 0.6726 data: 0.0003 [11-27 03:25:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [ 834/1669] eta: 0:09:37 tlr: 6.1e-05 tnm: 0.45 Lm: 6.402 (6.445) Lt: 5.668 (5.708) Accm: 3.52 (3.54) Acct: 5.44 (5.46) proj_loss: -0.6239 (-0.6198) time: 0.6726 data: 0.0003 [11-27 03:25:50] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [ 834/1669] eta: 0:09:37 tlr: 6.1e-05 tnm: 0.45 Lm: 6.335 (6.343) Lt: 5.548 (5.563) Accm: 4.01 (3.95) Acct: 6.37 (6.18) proj_loss: -0.6131 (-0.6155) time: 0.6726 data: 0.0003 [11-27 03:30:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [1251/1669] eta: 0:04:46 tlr: 6.1e-05 tnm: 0.49 Lm: 6.427 (6.387) Lt: 5.656 (5.629) Accm: 3.68 (3.80) Acct: 5.76 (5.92) proj_loss: -0.6171 (-0.6200) time: 0.6705 data: 0.0002 [11-27 03:30:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [1251/1669] eta: 0:04:46 tlr: 6.1e-05 tnm: 0.49 Lm: 6.402 (6.409) Lt: 5.640 (5.653) Accm: 3.69 (3.76) Acct: 5.90 (5.89) proj_loss: -0.6062 (-0.6074) time: 0.6705 data: 0.0003 [11-27 03:30:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [1251/1669] eta: 0:04:46 tlr: 6.1e-05 tnm: 0.49 Lm: 6.401 (6.416) Lt: 5.656 (5.650) Accm: 3.63 (3.67) Acct: 5.56 (5.79) proj_loss: -0.6154 (-0.6157) time: 0.6705 data: 0.0003 [11-27 03:30:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [1251/1669] eta: 0:04:46 tlr: 6.1e-05 tnm: 0.49 Lm: 6.352 (6.348) Lt: 5.574 (5.584) Accm: 3.78 (3.83) Acct: 5.97 (6.03) proj_loss: -0.6220 (-0.6193) time: 0.6705 data: 0.0003 [11-27 03:35:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [1668/1669] eta: 0:00:00 tlr: 6.1e-05 tnm: 0.47 Lm: 6.381 (6.397) Lt: 5.580 (5.650) Accm: 3.55 (3.71) Acct: 5.79 (5.80) proj_loss: -0.6277 (-0.6210) time: 0.6750 data: 0.0020 [11-27 03:35:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 269/350] Total time: 0:19:01 (0.684 s / it) [11-27 03:35:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [1668/1669] eta: 0:00:00 tlr: 6.1e-05 tnm: 0.47 Lm: 6.389 (6.387) Lt: 5.581 (5.620) Accm: 3.77 (3.79) Acct: 5.97 (5.93) proj_loss: -0.6131 (-0.6182) time: 0.6750 data: 0.0015 [11-27 03:35:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [1668/1669] eta: 0:00:00 tlr: 6.1e-05 tnm: 0.47 Lm: 6.401 (6.400) Lt: 5.645 (5.641) Accm: 3.74 (3.74) Acct: 5.68 (5.94) proj_loss: -0.6086 (-0.6143) time: 0.6750 data: 0.0023 [11-27 03:35:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 269/350] [1668/1669] eta: 0:00:00 tlr: 6.1e-05 tnm: 0.47 Lm: 6.497 (6.427) Lt: 5.739 (5.672) Accm: 3.53 (3.68) Acct: 5.53 (5.78) proj_loss: -0.6067 (-0.6090) time: 0.6750 data: 0.0018 [11-27 03:35:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 269/350] Total time: 0:19:01 (0.684 s / it) [11-27 03:35:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 269/350] Total time: 0:19:01 (0.684 s / it) [11-27 03:35:15] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 269/350] Total time: 0:19:01 (0.684 s / it) [11-27 03:37:35] (home/user/VAR/trainer.py, line 114)=> FID: 3.0656037811475016 [11-27 03:37:36] (/home/user/VAR/train.py , line 262)=> [*] [ep269] (val 50000) Lm: 6.4578, Lt: 5.7031, Acc m&t: 3.53 5.53, Val cost: 140.04s [11-27 03:37:36] (/home/user/VAR/train.py , line 267)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-27 03:38:12] (/home/user/VAR/train.py , line 279)=> [ep269] (training ) Lm: 6.440 (6.458), Lt: 5.682 (5.703), Acc m&t: 3.58 5.61, Remain: 1 day, 1:11:55, Finish: 2024-11-27 12:47 [11-27 03:38:12] (/home/user/VAR/train.py , line 279)=> [ep269] (training ) Lm: 6.440 (6.458), Lt: 5.682 (5.703), Acc m&t: 3.58 5.61, Remain: 1 day, 1:12:38, Finish: 2024-11-27 12:47 [11-27 03:38:12] (/home/user/VAR/train.py , line 279)=> [ep269] (training ) Lm: 6.440 (6.458), Lt: 5.682 (5.703), Acc m&t: 3.58 5.61, Remain: 1 day, 1:12:44, Finish: 2024-11-27 12:47 [11-27 03:38:12] (/home/user/VAR/train.py , line 279)=> [ep269] (training ) Lm: 6.440 (6.458), Lt: 5.682 (5.703), Acc m&t: 3.58 5.61, Remain: 1 day, 1:12:59, Finish: 2024-11-27 12:48 [11-27 03:38:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [ 0/1669] eta: 0:18:22 tlr: 6.1e-05 tnm: 0.47 Lm: 6.584 (6.584) Lt: 5.868 (5.868) Accm: 3.21 (3.21) Acct: 5.17 (5.17) proj_loss: -0.6079 (-0.6079) time: 0.6608 data: 0.0004 [11-27 03:38:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [ 0/1669] eta: 0:18:20 tlr: 6.1e-05 tnm: 0.47 Lm: 6.605 (6.605) Lt: 5.878 (5.878) Accm: 2.93 (2.93) Acct: 4.55 (4.55) proj_loss: -0.6000 (-0.6000) time: 0.6596 data: 0.0003 [11-27 03:38:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [ 0/1669] eta: 0:18:23 tlr: 6.1e-05 tnm: 0.47 Lm: 6.422 (6.422) Lt: 5.681 (5.681) Accm: 3.32 (3.32) Acct: 4.94 (4.94) proj_loss: -0.5996 (-0.5996) time: 0.6614 data: 0.0003 [11-27 03:38:13] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [ 0/1669] eta: 0:18:49 tlr: 6.1e-05 tnm: 0.47 Lm: 6.292 (6.292) Lt: 5.512 (5.512) Accm: 4.55 (4.55) Acct: 7.08 (7.08) proj_loss: -0.6114 (-0.6114) time: 0.6768 data: 0.0003 [11-27 03:42:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [ 417/1669] eta: 0:14:01 tlr: 6.1e-05 tnm: 0.49 Lm: 6.522 (6.522) Lt: 5.774 (5.774) Accm: 3.74 (3.74) Acct: 5.73 (5.73) proj_loss: -0.6125 (-0.6125) time: 0.6715 data: 0.0003 [11-27 03:42:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [ 417/1669] eta: 0:14:01 tlr: 6.1e-05 tnm: 0.49 Lm: 6.459 (6.459) Lt: 5.684 (5.684) Accm: 3.61 (3.61) Acct: 5.85 (5.85) proj_loss: -0.5985 (-0.5985) time: 0.6715 data: 0.0003 [11-27 03:42:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [ 417/1669] eta: 0:14:01 tlr: 6.1e-05 tnm: 0.49 Lm: 6.454 (6.454) Lt: 5.690 (5.690) Accm: 3.42 (3.42) Acct: 5.25 (5.25) proj_loss: -0.5981 (-0.5981) time: 0.6715 data: 0.0003 [11-27 03:42:53] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [ 417/1669] eta: 0:14:01 tlr: 6.1e-05 tnm: 0.49 Lm: 6.401 (6.401) Lt: 5.631 (5.631) Accm: 3.67 (3.67) Acct: 5.75 (5.75) proj_loss: -0.5966 (-0.5966) time: 0.6715 data: 0.0002 [11-27 03:47:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [ 834/1669] eta: 0:09:21 tlr: 6e-05 tnm: 0.47 Lm: 6.447 (6.416) Lt: 5.672 (5.644) Accm: 3.48 (3.61) Acct: 5.80 (5.77) proj_loss: -0.6079 (-0.6020) time: 0.6765 data: 0.0003 [11-27 03:47:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [ 834/1669] eta: 0:09:21 tlr: 6e-05 tnm: 0.47 Lm: 6.312 (6.409) Lt: 5.489 (5.613) Accm: 4.19 (3.80) Acct: 6.77 (6.16) proj_loss: -0.6000 (-0.6043) time: 0.6765 data: 0.0003 [11-27 03:47:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [ 834/1669] eta: 0:09:21 tlr: 6e-05 tnm: 0.47 Lm: 6.525 (6.523) Lt: 5.743 (5.764) Accm: 3.22 (3.57) Acct: 5.20 (5.56) proj_loss: -0.6114 (-0.6015) time: 0.6765 data: 0.0003 [11-27 03:47:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [ 834/1669] eta: 0:09:21 tlr: 6e-05 tnm: 0.47 Lm: 6.486 (6.486) Lt: 5.699 (5.723) Accm: 3.32 (3.25) Acct: 4.94 (4.99) proj_loss: -0.5996 (-0.6067) time: 0.6765 data: 0.0003 [11-27 03:52:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [1251/1669] eta: 0:04:41 tlr: 6e-05 tnm: 0.48 Lm: 6.454 (6.455) Lt: 5.690 (5.702) Accm: 3.42 (3.40) Acct: 5.25 (5.16) proj_loss: -0.6048 (-0.6075) time: 0.6763 data: 0.0003 [11-27 03:52:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [1251/1669] eta: 0:04:41 tlr: 6e-05 tnm: 0.48 Lm: 6.426 (6.441) Lt: 5.637 (5.656) Accm: 3.67 (3.64) Acct: 5.99 (5.92) proj_loss: -0.5985 (-0.5996) time: 0.6763 data: 0.0003 [11-27 03:52:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [1251/1669] eta: 0:04:41 tlr: 6e-05 tnm: 0.48 Lm: 6.369 (6.385) Lt: 5.595 (5.613) Accm: 3.65 (3.66) Acct: 5.89 (5.82) proj_loss: -0.6080 (-0.6035) time: 0.6763 data: 0.0003 [11-27 03:52:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [1251/1669] eta: 0:04:41 tlr: 6e-05 tnm: 0.48 Lm: 6.408 (6.455) Lt: 5.627 (5.681) Accm: 3.62 (3.68) Acct: 5.62 (5.68) proj_loss: -0.6068 (-0.6017) time: 0.6763 data: 0.0003 [11-27 03:57:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [1668/1669] eta: 0:00:00 tlr: 6e-05 tnm: 0.49 Lm: 6.510 (6.466) Lt: 5.743 (5.696) Accm: 3.22 (3.57) Acct: 5.20 (5.55) proj_loss: -0.6114 (-0.6058) time: 0.7454 data: 0.0018 [11-27 03:57:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 270/350] Total time: 0:18:47 (0.675 s / it) [11-27 03:57:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [1668/1669] eta: 0:00:00 tlr: 6e-05 tnm: 0.49 Lm: 6.447 (6.431) Lt: 5.672 (5.658) Accm: 3.48 (3.57) Acct: 5.80 (5.67) proj_loss: -0.6079 (-0.6042) time: 0.7454 data: 0.0022 [11-27 03:57:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [1668/1669] eta: 0:00:00 tlr: 6e-05 tnm: 0.49 Lm: 6.486 (6.495) Lt: 5.699 (5.748) Accm: 3.32 (3.29) Acct: 4.94 (5.05) proj_loss: -0.6100 (-0.6084) time: 0.7454 data: 0.0016 [11-27 03:57:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 270/350] [1668/1669] eta: 0:00:00 tlr: 6e-05 tnm: 0.49 Lm: 6.517 (6.456) Lt: 5.739 (5.673) Accm: 3.45 (3.60) Acct: 5.37 (5.81) proj_loss: -0.5969 (-0.5969) time: 0.7454 data: 0.0018 [11-27 03:57:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 270/350] Total time: 0:18:47 (0.675 s / it) [11-27 03:57:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 270/350] Total time: 0:18:47 (0.675 s / it) [11-27 03:57:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 270/350] Total time: 0:18:47 (0.675 s / it) [11-27 03:57:00] (/home/user/VAR/train.py , line 279)=> [ep270] (training ) Lm: 6.440 (6.446), Lt: 5.682 (5.694), Acc m&t: 3.58 5.61, Remain: 1 day, 1:10:10, Finish: 2024-11-27 13:07 [11-27 03:57:00] (/home/user/VAR/train.py , line 279)=> [ep270] (training ) Lm: 6.440 (6.446), Lt: 5.682 (5.694), Acc m&t: 3.58 5.61, Remain: 1 day, 1:09:01, Finish: 2024-11-27 13:06 [11-27 03:57:00] (/home/user/VAR/train.py , line 279)=> [ep270] (training ) Lm: 6.440 (6.446), Lt: 5.682 (5.694), Acc m&t: 3.58 5.61, Remain: 1 day, 1:09:36, Finish: 2024-11-27 13:06 [11-27 03:57:00] (/home/user/VAR/train.py , line 279)=> [ep270] (training ) Lm: 6.440 (6.446), Lt: 5.682 (5.694), Acc m&t: 3.58 5.61, Remain: 1 day, 1:09:03, Finish: 2024-11-27 13:06 [11-27 03:57:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [ 0/1669] eta: 0:18:05 tlr: 6e-05 tnm: 0.50 Lm: 6.570 (6.570) Lt: 5.799 (5.799) Accm: 2.95 (2.95) Acct: 4.75 (4.75) proj_loss: -0.6110 (-0.6110) time: 0.6506 data: 0.0004 [11-27 03:57:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [ 0/1669] eta: 0:18:05 tlr: 6e-05 tnm: 0.50 Lm: 6.405 (6.405) Lt: 5.609 (5.609) Accm: 3.39 (3.39) Acct: 5.18 (5.18) proj_loss: -0.6042 (-0.6042) time: 0.6506 data: 0.0003 [11-27 03:57:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [ 0/1669] eta: 0:18:07 tlr: 6e-05 tnm: 0.50 Lm: 6.383 (6.383) Lt: 5.688 (5.688) Accm: 3.65 (3.65) Acct: 5.15 (5.15) proj_loss: -0.6150 (-0.6150) time: 0.6513 data: 0.0004 [11-27 03:57:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [ 0/1669] eta: 0:18:20 tlr: 6e-05 tnm: 0.50 Lm: 6.556 (6.556) Lt: 5.835 (5.835) Accm: 3.05 (3.05) Acct: 4.82 (4.82) proj_loss: -0.5771 (-0.5771) time: 0.6596 data: 0.0004 [11-27 04:01:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [ 417/1669] eta: 0:14:46 tlr: 6e-05 tnm: 0.48 Lm: 6.480 (6.480) Lt: 5.716 (5.716) Accm: 3.34 (3.34) Acct: 5.26 (5.26) proj_loss: -0.5820 (-0.5820) time: 0.6726 data: 0.0003 [11-27 04:01:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [ 417/1669] eta: 0:14:46 tlr: 6e-05 tnm: 0.48 Lm: 6.488 (6.488) Lt: 5.774 (5.774) Accm: 3.26 (3.26) Acct: 4.84 (4.84) proj_loss: -0.6114 (-0.6114) time: 0.6726 data: 0.0003 [11-27 04:01:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [ 417/1669] eta: 0:14:46 tlr: 6e-05 tnm: 0.48 Lm: 6.496 (6.496) Lt: 5.725 (5.725) Accm: 3.29 (3.29) Acct: 5.03 (5.03) proj_loss: -0.6074 (-0.6074) time: 0.6726 data: 0.0003 [11-27 04:01:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [ 417/1669] eta: 0:14:46 tlr: 6e-05 tnm: 0.48 Lm: 6.369 (6.369) Lt: 5.552 (5.552) Accm: 3.74 (3.74) Acct: 5.98 (5.98) proj_loss: -0.6116 (-0.6116) time: 0.6726 data: 0.0003 [11-27 04:06:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [ 834/1669] eta: 0:09:36 tlr: 6e-05 tnm: 0.46 Lm: 6.278 (6.338) Lt: 5.489 (5.531) Accm: 4.25 (3.91) Acct: 6.78 (6.25) proj_loss: -0.6122 (-0.6149) time: 0.6707 data: 0.0003 [11-27 04:06:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [ 834/1669] eta: 0:09:36 tlr: 6e-05 tnm: 0.46 Lm: 6.430 (6.474) Lt: 5.664 (5.705) Accm: 3.37 (3.32) Acct: 5.18 (5.17) proj_loss: -0.6105 (-0.6178) time: 0.6707 data: 0.0003 [11-27 04:06:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [ 834/1669] eta: 0:09:36 tlr: 6e-05 tnm: 0.46 Lm: 6.442 (6.467) Lt: 5.639 (5.690) Accm: 3.63 (3.46) Acct: 5.70 (5.45) proj_loss: -0.5870 (-0.5930) time: 0.6707 data: 0.0003 [11-27 04:06:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [ 834/1669] eta: 0:09:36 tlr: 6e-05 tnm: 0.46 Lm: 6.592 (6.546) Lt: 5.861 (5.822) Accm: 2.98 (3.17) Acct: 4.68 (4.79) proj_loss: -0.6078 (-0.6078) time: 0.6707 data: 0.0003 [11-27 04:11:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [1251/1669] eta: 0:04:46 tlr: 5.9e-05 tnm: 0.49 Lm: 6.488 (6.500) Lt: 5.774 (5.761) Accm: 3.31 (3.35) Acct: 4.92 (5.26) proj_loss: -0.6108 (-0.6093) time: 0.6729 data: 0.0004 [11-27 04:11:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [1251/1669] eta: 0:04:46 tlr: 5.9e-05 tnm: 0.49 Lm: 6.444 (6.470) Lt: 5.673 (5.699) Accm: 3.38 (3.44) Acct: 5.31 (5.39) proj_loss: -0.6179 (-0.6197) time: 0.6729 data: 0.0003 [11-27 04:11:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [1251/1669] eta: 0:04:46 tlr: 5.9e-05 tnm: 0.49 Lm: 6.423 (6.443) Lt: 5.618 (5.658) Accm: 3.67 (3.54) Acct: 5.77 (5.60) proj_loss: -0.5919 (-0.5939) time: 0.6729 data: 0.0003 [11-27 04:11:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [1251/1669] eta: 0:04:46 tlr: 5.9e-05 tnm: 0.49 Lm: 6.260 (6.314) Lt: 5.459 (5.505) Accm: 4.22 (3.98) Acct: 6.65 (6.32) proj_loss: -0.6126 (-0.6144) time: 0.6730 data: 0.0003 [11-27 04:15:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [1668/1669] eta: 0:00:00 tlr: 5.9e-05 tnm: 0.47 Lm: 6.278 (6.360) Lt: 5.489 (5.560) Accm: 4.18 (3.84) Acct: 6.53 (6.10) proj_loss: -0.6122 (-0.6087) time: 0.6770 data: 0.0019 [11-27 04:15:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 271/350] Total time: 0:18:58 (0.682 s / it) [11-27 04:15:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [1668/1669] eta: 0:00:00 tlr: 5.9e-05 tnm: 0.47 Lm: 6.404 (6.401) Lt: 5.597 (5.606) Accm: 3.70 (3.70) Acct: 5.84 (5.88) proj_loss: -0.5913 (-0.5934) time: 0.6770 data: 0.0016 [11-27 04:15:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [1668/1669] eta: 0:00:00 tlr: 5.9e-05 tnm: 0.47 Lm: 6.476 (6.495) Lt: 5.743 (5.758) Accm: 3.18 (3.31) Acct: 4.84 (5.17) proj_loss: -0.6137 (-0.6112) time: 0.6770 data: 0.0016 [11-27 04:15:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 271/350] [1668/1669] eta: 0:00:00 tlr: 5.9e-05 tnm: 0.47 Lm: 6.458 (6.482) Lt: 5.681 (5.706) Accm: 3.37 (3.41) Acct: 5.30 (5.37) proj_loss: -0.6105 (-0.6126) time: 0.6770 data: 0.0018 [11-27 04:15:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 271/350] Total time: 0:18:58 (0.682 s / it) [11-27 04:15:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 271/350] Total time: 0:18:58 (0.682 s / it) [11-27 04:15:58] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 271/350] Total time: 0:18:58 (0.682 s / it) [11-27 04:15:58] (/home/user/VAR/train.py , line 279)=> [ep271] (training ) Lm: 6.440 (6.442), Lt: 5.682 (5.687), Acc m&t: 3.58 5.61, Remain: 1 day, 0:37:58, Finish: 2024-11-27 12:53 [11-27 04:15:58] (/home/user/VAR/train.py , line 279)=> [ep271] (training ) Lm: 6.440 (6.442), Lt: 5.682 (5.687), Acc m&t: 3.58 5.61, Remain: 1 day, 0:38:09, Finish: 2024-11-27 12:54 [11-27 04:15:58] (/home/user/VAR/train.py , line 279)=> [ep271] (training ) Lm: 6.440 (6.442), Lt: 5.682 (5.687), Acc m&t: 3.58 5.61, Remain: 1 day, 0:37:58, Finish: 2024-11-27 12:53 [11-27 04:15:58] (/home/user/VAR/train.py , line 279)=> [ep271] (training ) Lm: 6.440 (6.442), Lt: 5.682 (5.687), Acc m&t: 3.58 5.61, Remain: 1 day, 0:37:54, Finish: 2024-11-27 12:53 [11-27 04:15:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [ 0/1669] eta: 0:18:09 tlr: 5.9e-05 tnm: 0.48 Lm: 6.519 (6.519) Lt: 5.794 (5.794) Accm: 3.42 (3.42) Acct: 5.34 (5.34) proj_loss: -0.6265 (-0.6265) time: 0.6530 data: 0.0003 [11-27 04:15:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [ 0/1669] eta: 0:19:27 tlr: 5.9e-05 tnm: 0.48 Lm: 6.298 (6.298) Lt: 5.446 (5.446) Accm: 4.03 (4.03) Acct: 6.66 (6.66) proj_loss: -0.6112 (-0.6112) time: 0.6993 data: 0.0004 [11-27 04:15:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [ 0/1669] eta: 0:19:27 tlr: 5.9e-05 tnm: 0.48 Lm: 6.543 (6.543) Lt: 5.807 (5.807) Accm: 3.21 (3.21) Acct: 5.06 (5.06) proj_loss: -0.6252 (-0.6252) time: 0.6993 data: 0.0004 [11-27 04:15:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [ 0/1669] eta: 0:19:27 tlr: 5.9e-05 tnm: 0.48 Lm: 6.222 (6.222) Lt: 5.405 (5.405) Accm: 4.52 (4.52) Acct: 7.21 (7.21) proj_loss: -0.6100 (-0.6100) time: 0.6995 data: 0.0004 [11-27 04:20:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [ 417/1669] eta: 0:14:02 tlr: 5.9e-05 tnm: 0.50 Lm: 6.341 (6.341) Lt: 5.535 (5.535) Accm: 4.10 (4.10) Acct: 6.49 (6.49) proj_loss: -0.6031 (-0.6031) time: 0.6734 data: 0.0003 [11-27 04:20:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [ 417/1669] eta: 0:14:02 tlr: 5.9e-05 tnm: 0.50 Lm: 6.442 (6.442) Lt: 5.696 (5.696) Accm: 3.57 (3.57) Acct: 5.72 (5.72) proj_loss: -0.6070 (-0.6070) time: 0.6734 data: 0.0003 [11-27 04:20:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [ 417/1669] eta: 0:14:02 tlr: 5.9e-05 tnm: 0.50 Lm: 6.496 (6.496) Lt: 5.767 (5.767) Accm: 3.53 (3.53) Acct: 5.51 (5.51) proj_loss: -0.6205 (-0.6205) time: 0.6734 data: 0.0003 [11-27 04:20:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [ 417/1669] eta: 0:14:02 tlr: 5.9e-05 tnm: 0.50 Lm: 6.493 (6.493) Lt: 5.715 (5.715) Accm: 3.47 (3.47) Acct: 5.51 (5.51) proj_loss: -0.6154 (-0.6154) time: 0.6734 data: 0.0003 [11-27 04:25:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [ 834/1669] eta: 0:09:36 tlr: 5.9e-05 tnm: 0.48 Lm: 6.467 (6.483) Lt: 5.686 (5.706) Accm: 3.47 (3.47) Acct: 5.37 (5.46) proj_loss: -0.6102 (-0.6136) time: 0.6716 data: 0.0003 [11-27 04:25:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [ 834/1669] eta: 0:09:36 tlr: 5.9e-05 tnm: 0.48 Lm: 6.410 (6.432) Lt: 5.622 (5.671) Accm: 3.60 (3.58) Acct: 5.94 (5.80) proj_loss: -0.6112 (-0.6136) time: 0.6716 data: 0.0003 [11-27 04:25:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [ 834/1669] eta: 0:09:36 tlr: 5.9e-05 tnm: 0.48 Lm: 6.543 (6.517) Lt: 5.791 (5.775) Accm: 3.21 (3.41) Acct: 5.37 (5.46) proj_loss: -0.6157 (-0.6097) time: 0.6716 data: 0.0003 [11-27 04:25:35] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [ 834/1669] eta: 0:09:36 tlr: 5.9e-05 tnm: 0.48 Lm: 6.351 (6.345) Lt: 5.629 (5.566) Accm: 3.69 (3.96) Acct: 5.77 (6.17) proj_loss: -0.6100 (-0.6126) time: 0.6716 data: 0.0003 [11-27 04:30:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [1251/1669] eta: 0:04:46 tlr: 5.9e-05 tnm: 0.49 Lm: 6.387 (6.364) Lt: 5.638 (5.587) Accm: 3.68 (3.88) Acct: 5.75 (6.06) proj_loss: -0.6148 (-0.6144) time: 0.6745 data: 0.0003 [11-27 04:30:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [1251/1669] eta: 0:04:46 tlr: 5.9e-05 tnm: 0.49 Lm: 6.425 (6.434) Lt: 5.663 (5.680) Accm: 3.37 (3.47) Acct: 5.57 (5.65) proj_loss: -0.6156 (-0.6152) time: 0.6745 data: 0.0003 [11-27 04:30:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [1251/1669] eta: 0:04:46 tlr: 5.9e-05 tnm: 0.49 Lm: 6.465 (6.477) Lt: 5.694 (5.705) Accm: 3.50 (3.52) Acct: 5.53 (5.61) proj_loss: -0.6107 (-0.6130) time: 0.6745 data: 0.0003 [11-27 04:30:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [1251/1669] eta: 0:04:46 tlr: 5.9e-05 tnm: 0.49 Lm: 6.496 (6.482) Lt: 5.759 (5.746) Accm: 3.44 (3.47) Acct: 5.66 (5.62) proj_loss: -0.6092 (-0.6080) time: 0.6745 data: 0.0003 [11-27 04:35:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [1668/1669] eta: 0:00:00 tlr: 5.9e-05 tnm: 0.48 Lm: 6.543 (6.503) Lt: 5.791 (5.772) Accm: 3.21 (3.42) Acct: 5.37 (5.49) proj_loss: -0.6143 (-0.6093) time: 0.6738 data: 0.0017 [11-27 04:35:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 272/350] Total time: 0:19:01 (0.684 s / it) [11-27 04:35:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [1668/1669] eta: 0:00:00 tlr: 5.9e-05 tnm: 0.48 Lm: 6.410 (6.414) Lt: 5.622 (5.659) Accm: 3.60 (3.55) Acct: 5.94 (5.76) proj_loss: -0.6112 (-0.6116) time: 0.6738 data: 0.0018 [11-27 04:35:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [1668/1669] eta: 0:00:00 tlr: 5.9e-05 tnm: 0.48 Lm: 6.423 (6.378) Lt: 5.629 (5.583) Accm: 3.69 (3.89) Acct: 5.77 (6.18) proj_loss: -0.6100 (-0.6101) time: 0.6738 data: 0.0016 [11-27 04:35:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 272/350] [1668/1669] eta: 0:00:00 tlr: 5.9e-05 tnm: 0.48 Lm: 6.463 (6.461) Lt: 5.686 (5.686) Accm: 3.53 (3.62) Acct: 5.68 (5.69) proj_loss: -0.6102 (-0.6097) time: 0.6738 data: 0.0018 [11-27 04:35:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 272/350] Total time: 0:19:01 (0.684 s / it) [11-27 04:35:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 272/350] Total time: 0:19:01 (0.684 s / it) [11-27 04:35:00] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 272/350] Total time: 0:19:01 (0.684 s / it) [11-27 04:35:00] (/home/user/VAR/train.py , line 279)=> [ep272] (training ) Lm: 6.439 (6.439), Lt: 5.679 (5.679), Acc m&t: 3.58 5.63, Remain: 1 day, 0:13:06, Finish: 2024-11-27 12:48 [11-27 04:35:00] (/home/user/VAR/train.py , line 279)=> [ep272] (training ) Lm: 6.439 (6.439), Lt: 5.679 (5.679), Acc m&t: 3.58 5.63, Remain: 1 day, 0:12:56, Finish: 2024-11-27 12:47 [11-27 04:35:00] (/home/user/VAR/train.py , line 279)=> [ep272] (training ) Lm: 6.439 (6.439), Lt: 5.679 (5.679), Acc m&t: 3.58 5.63, Remain: 1 day, 0:13:11, Finish: 2024-11-27 12:48 [11-27 04:35:00] (/home/user/VAR/train.py , line 279)=> [ep272] (training ) Lm: 6.439 (6.439), Lt: 5.679 (5.679), Acc m&t: 3.58 5.63, Remain: 1 day, 0:13:13, Finish: 2024-11-27 12:48 [11-27 04:35:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [ 0/1669] eta: 0:18:23 tlr: 5.9e-05 tnm: 0.49 Lm: 6.410 (6.410) Lt: 5.656 (5.656) Accm: 3.41 (3.41) Acct: 5.15 (5.15) proj_loss: -0.6353 (-0.6353) time: 0.6611 data: 0.0004 [11-27 04:35:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [ 0/1669] eta: 0:18:24 tlr: 5.9e-05 tnm: 0.49 Lm: 6.416 (6.416) Lt: 5.630 (5.630) Accm: 3.84 (3.84) Acct: 6.15 (6.15) proj_loss: -0.5949 (-0.5949) time: 0.6615 data: 0.0004 [11-27 04:35:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [ 0/1669] eta: 0:18:19 tlr: 5.9e-05 tnm: 0.49 Lm: 6.364 (6.364) Lt: 5.642 (5.642) Accm: 4.00 (4.00) Acct: 5.97 (5.97) proj_loss: -0.6336 (-0.6336) time: 0.6588 data: 0.0004 [11-27 04:35:01] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [ 0/1669] eta: 0:18:24 tlr: 5.9e-05 tnm: 0.49 Lm: 6.496 (6.496) Lt: 5.769 (5.769) Accm: 3.31 (3.31) Acct: 5.11 (5.11) proj_loss: -0.5972 (-0.5972) time: 0.6618 data: 0.0004 [11-27 04:39:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [ 417/1669] eta: 0:14:02 tlr: 5.8e-05 tnm: 0.49 Lm: 6.503 (6.503) Lt: 5.792 (5.792) Accm: 3.30 (3.30) Acct: 5.11 (5.11) proj_loss: -0.6068 (-0.6068) time: 0.6754 data: 0.0003 [11-27 04:39:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [ 417/1669] eta: 0:14:02 tlr: 5.8e-05 tnm: 0.49 Lm: 6.459 (6.459) Lt: 5.698 (5.698) Accm: 3.62 (3.62) Acct: 5.68 (5.68) proj_loss: -0.6075 (-0.6075) time: 0.6754 data: 0.0003 [11-27 04:39:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [ 417/1669] eta: 0:14:02 tlr: 5.8e-05 tnm: 0.49 Lm: 6.351 (6.351) Lt: 5.609 (5.609) Accm: 4.05 (4.05) Acct: 6.03 (6.03) proj_loss: -0.6353 (-0.6353) time: 0.6754 data: 0.0003 [11-27 04:39:41] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [ 417/1669] eta: 0:14:02 tlr: 5.8e-05 tnm: 0.49 Lm: 6.413 (6.413) Lt: 5.683 (5.683) Accm: 3.46 (3.46) Acct: 5.19 (5.19) proj_loss: -0.6220 (-0.6220) time: 0.6754 data: 0.0003 [11-27 04:44:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [ 834/1669] eta: 0:09:21 tlr: 5.8e-05 tnm: 0.49 Lm: 6.410 (6.374) Lt: 5.656 (5.620) Accm: 3.51 (3.78) Acct: 5.23 (5.65) proj_loss: -0.6164 (-0.6201) time: 0.6730 data: 0.0003 [11-27 04:44:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [ 834/1669] eta: 0:09:21 tlr: 5.8e-05 tnm: 0.49 Lm: 6.416 (6.405) Lt: 5.630 (5.633) Accm: 3.84 (3.72) Acct: 6.15 (5.84) proj_loss: -0.6054 (-0.6068) time: 0.6730 data: 0.0003 [11-27 04:44:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [ 834/1669] eta: 0:09:21 tlr: 5.8e-05 tnm: 0.49 Lm: 6.337 (6.340) Lt: 5.576 (5.586) Accm: 4.00 (3.99) Acct: 6.10 (6.07) proj_loss: -0.6336 (-0.6295) time: 0.6730 data: 0.0003 [11-27 04:44:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [ 834/1669] eta: 0:09:21 tlr: 5.8e-05 tnm: 0.49 Lm: 6.496 (6.423) Lt: 5.769 (5.703) Accm: 3.31 (3.69) Acct: 5.11 (5.64) proj_loss: -0.6165 (-0.6124) time: 0.6730 data: 0.0003 [11-27 04:49:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [1251/1669] eta: 0:04:41 tlr: 5.8e-05 tnm: 0.48 Lm: 6.484 (6.435) Lt: 5.722 (5.696) Accm: 3.30 (3.57) Acct: 5.11 (5.50) proj_loss: -0.6068 (-0.6077) time: 0.6722 data: 0.0003 [11-27 04:49:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [1251/1669] eta: 0:04:41 tlr: 5.8e-05 tnm: 0.48 Lm: 6.445 (6.422) Lt: 5.673 (5.654) Accm: 3.62 (3.59) Acct: 5.68 (5.66) proj_loss: -0.6127 (-0.6112) time: 0.6722 data: 0.0003 [11-27 04:49:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [1251/1669] eta: 0:04:41 tlr: 5.8e-05 tnm: 0.48 Lm: 6.351 (6.350) Lt: 5.597 (5.594) Accm: 3.94 (3.95) Acct: 6.03 (6.04) proj_loss: -0.6258 (-0.6259) time: 0.6722 data: 0.0003 [11-27 04:49:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [1251/1669] eta: 0:04:41 tlr: 5.8e-05 tnm: 0.48 Lm: 6.413 (6.427) Lt: 5.683 (5.673) Accm: 3.46 (3.63) Acct: 5.19 (5.52) proj_loss: -0.6125 (-0.6163) time: 0.6722 data: 0.0003 [11-27 04:53:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [1668/1669] eta: 0:00:00 tlr: 5.8e-05 tnm: 0.48 Lm: 6.416 (6.437) Lt: 5.656 (5.665) Accm: 3.50 (3.60) Acct: 5.23 (5.56) proj_loss: -0.6086 (-0.6125) time: 0.7419 data: 0.0018 [11-27 04:53:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 273/350] Total time: 0:18:46 (0.675 s / it) [11-27 04:53:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [1668/1669] eta: 0:00:00 tlr: 5.8e-05 tnm: 0.48 Lm: 6.474 (6.436) Lt: 5.716 (5.671) Accm: 3.41 (3.54) Acct: 5.30 (5.59) proj_loss: -0.6054 (-0.6091) time: 0.7419 data: 0.0016 [11-27 04:53:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [1668/1669] eta: 0:00:00 tlr: 5.8e-05 tnm: 0.48 Lm: 6.364 (6.387) Lt: 5.618 (5.634) Accm: 3.88 (3.74) Acct: 5.97 (5.74) proj_loss: -0.6181 (-0.6238) time: 0.7419 data: 0.0017 [11-27 04:53:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 273/350] [1668/1669] eta: 0:00:00 tlr: 5.8e-05 tnm: 0.48 Lm: 6.496 (6.458) Lt: 5.769 (5.723) Accm: 3.30 (3.48) Acct: 5.11 (5.36) proj_loss: -0.6165 (-0.6113) time: 0.7419 data: 0.0018 [11-27 04:53:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 273/350] Total time: 0:18:46 (0.675 s / it) [11-27 04:53:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 273/350] Total time: 0:18:46 (0.675 s / it) [11-27 04:53:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 273/350] Total time: 0:18:46 (0.675 s / it) [11-27 04:53:46] (/home/user/VAR/train.py , line 279)=> [ep273] (training ) Lm: 6.438 (6.438), Lt: 5.679 (5.683), Acc m&t: 3.58 5.63, Remain: 23:55:58, Finish: 2024-11-27 12:49 [11-27 04:53:46] (/home/user/VAR/train.py , line 279)=> [ep273] (training ) Lm: 6.438 (6.438), Lt: 5.679 (5.683), Acc m&t: 3.58 5.63, Remain: 23:55:28, Finish: 2024-11-27 12:49 [11-27 04:53:46] (/home/user/VAR/train.py , line 279)=> [ep273] (training ) Lm: 6.438 (6.438), Lt: 5.679 (5.683), Acc m&t: 3.58 5.63, Remain: 23:55:52, Finish: 2024-11-27 12:49 [11-27 04:53:46] (/home/user/VAR/train.py , line 279)=> [ep273] (training ) Lm: 6.438 (6.438), Lt: 5.679 (5.683), Acc m&t: 3.58 5.63, Remain: 23:56:01, Finish: 2024-11-27 12:49 [11-27 04:53:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [ 0/1669] eta: 0:18:06 tlr: 5.8e-05 tnm: 0.48 Lm: 6.543 (6.543) Lt: 5.871 (5.871) Accm: 3.31 (3.31) Acct: 4.98 (4.98) proj_loss: -0.6149 (-0.6149) time: 0.6511 data: 0.0004 [11-27 04:53:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [ 0/1669] eta: 0:18:07 tlr: 5.8e-05 tnm: 0.48 Lm: 6.551 (6.551) Lt: 5.782 (5.782) Accm: 3.15 (3.15) Acct: 4.98 (4.98) proj_loss: -0.6020 (-0.6020) time: 0.6515 data: 0.0004 [11-27 04:53:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [ 0/1669] eta: 0:18:08 tlr: 5.8e-05 tnm: 0.48 Lm: 6.493 (6.493) Lt: 5.716 (5.716) Accm: 3.31 (3.31) Acct: 5.20 (5.20) proj_loss: -0.6190 (-0.6190) time: 0.6521 data: 0.0004 [11-27 04:53:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [ 0/1669] eta: 0:18:08 tlr: 5.8e-05 tnm: 0.48 Lm: 6.293 (6.293) Lt: 5.505 (5.505) Accm: 4.18 (4.18) Acct: 6.75 (6.75) proj_loss: -0.6156 (-0.6156) time: 0.6520 data: 0.0004 [11-27 04:58:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [ 417/1669] eta: 0:14:46 tlr: 5.8e-05 tnm: 0.48 Lm: 6.390 (6.390) Lt: 5.660 (5.660) Accm: 3.89 (3.89) Acct: 6.05 (6.05) proj_loss: -0.6179 (-0.6179) time: 0.6756 data: 0.0003 [11-27 04:58:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [ 417/1669] eta: 0:14:46 tlr: 5.8e-05 tnm: 0.48 Lm: 6.499 (6.499) Lt: 5.752 (5.752) Accm: 3.30 (3.30) Acct: 5.33 (5.33) proj_loss: -0.6047 (-0.6047) time: 0.6756 data: 0.0003 [11-27 04:58:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [ 417/1669] eta: 0:14:46 tlr: 5.8e-05 tnm: 0.48 Lm: 6.463 (6.463) Lt: 5.693 (5.693) Accm: 3.44 (3.44) Acct: 5.43 (5.43) proj_loss: -0.6225 (-0.6225) time: 0.6756 data: 0.0003 [11-27 04:58:42] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [ 417/1669] eta: 0:14:46 tlr: 5.8e-05 tnm: 0.48 Lm: 6.476 (6.476) Lt: 5.777 (5.777) Accm: 3.53 (3.53) Acct: 5.42 (5.42) proj_loss: -0.6136 (-0.6136) time: 0.6756 data: 0.0003 [11-27 05:03:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [ 834/1669] eta: 0:09:36 tlr: 5.7e-05 tnm: 0.48 Lm: 6.543 (6.499) Lt: 5.832 (5.796) Accm: 3.31 (3.44) Acct: 5.10 (5.31) proj_loss: -0.6123 (-0.6123) time: 0.6735 data: 0.0003 [11-27 05:03:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [ 834/1669] eta: 0:09:36 tlr: 5.7e-05 tnm: 0.48 Lm: 6.527 (6.508) Lt: 5.782 (5.764) Accm: 3.20 (3.27) Acct: 5.03 (5.23) proj_loss: -0.6074 (-0.6063) time: 0.6735 data: 0.0002 [11-27 05:03:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [ 834/1669] eta: 0:09:36 tlr: 5.7e-05 tnm: 0.48 Lm: 6.338 (6.372) Lt: 5.609 (5.643) Accm: 3.88 (3.89) Acct: 6.16 (6.09) proj_loss: -0.6156 (-0.6082) time: 0.6735 data: 0.0006 [11-27 05:03:23] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [ 834/1669] eta: 0:09:36 tlr: 5.7e-05 tnm: 0.48 Lm: 6.491 (6.472) Lt: 5.716 (5.713) Accm: 3.31 (3.39) Acct: 5.20 (5.28) proj_loss: -0.6190 (-0.6168) time: 0.6736 data: 0.0003 [11-27 05:08:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [1251/1669] eta: 0:04:46 tlr: 5.7e-05 tnm: 0.49 Lm: 6.462 (6.459) Lt: 5.693 (5.700) Accm: 3.44 (3.44) Acct: 5.39 (5.35) proj_loss: -0.6123 (-0.6138) time: 0.6722 data: 0.0003 [11-27 05:08:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [1251/1669] eta: 0:04:46 tlr: 5.7e-05 tnm: 0.49 Lm: 6.500 (6.488) Lt: 5.783 (5.780) Accm: 3.38 (3.44) Acct: 5.25 (5.34) proj_loss: -0.6110 (-0.6112) time: 0.6722 data: 0.0003 [11-27 05:08:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [1251/1669] eta: 0:04:46 tlr: 5.7e-05 tnm: 0.49 Lm: 6.412 (6.405) Lt: 5.663 (5.661) Accm: 3.76 (3.82) Acct: 5.91 (5.98) proj_loss: -0.6119 (-0.6082) time: 0.6722 data: 0.0003 [11-27 05:08:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [1251/1669] eta: 0:04:46 tlr: 5.7e-05 tnm: 0.49 Lm: 6.528 (6.513) Lt: 5.784 (5.770) Accm: 3.32 (3.31) Acct: 5.19 (5.26) proj_loss: -0.6060 (-0.6059) time: 0.6722 data: 0.0002 [11-27 05:12:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [1668/1669] eta: 0:00:00 tlr: 5.7e-05 tnm: 0.49 Lm: 6.528 (6.549) Lt: 5.787 (5.803) Accm: 3.20 (3.18) Acct: 5.03 (5.10) proj_loss: -0.6074 (-0.6068) time: 0.6731 data: 0.0017 [11-27 05:12:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 274/350] Total time: 0:18:58 (0.682 s / it) [11-27 05:12:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [1668/1669] eta: 0:00:00 tlr: 5.7e-05 tnm: 0.49 Lm: 6.389 (6.401) Lt: 5.654 (5.660) Accm: 3.64 (3.78) Acct: 5.65 (5.91) proj_loss: -0.6106 (-0.6087) time: 0.6731 data: 0.0020 [11-27 05:12:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [1668/1669] eta: 0:00:00 tlr: 5.7e-05 tnm: 0.49 Lm: 6.543 (6.524) Lt: 5.832 (5.800) Accm: 3.31 (3.31) Acct: 5.10 (5.18) proj_loss: -0.6098 (-0.6102) time: 0.6731 data: 0.0025 [11-27 05:12:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 274/350] [1668/1669] eta: 0:00:00 tlr: 5.7e-05 tnm: 0.49 Lm: 6.491 (6.472) Lt: 5.716 (5.712) Accm: 3.31 (3.40) Acct: 5.20 (5.28) proj_loss: -0.6056 (-0.6106) time: 0.6731 data: 0.0021 [11-27 05:12:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 274/350] Total time: 0:18:58 (0.682 s / it) [11-27 05:12:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 274/350] Total time: 0:18:58 (0.682 s / it) [11-27 05:12:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 274/350] Total time: 0:18:58 (0.682 s / it) [11-27 05:12:44] (/home/user/VAR/train.py , line 279)=> [ep274] (training ) Lm: 6.436 (6.436), Lt: 5.679 (5.681), Acc m&t: 3.58 5.63, Remain: 23:36:35, Finish: 2024-11-27 12:49 [11-27 05:12:44] (/home/user/VAR/train.py , line 279)=> [ep274] (training ) Lm: 6.436 (6.436), Lt: 5.679 (5.681), Acc m&t: 3.58 5.63, Remain: 23:36:14, Finish: 2024-11-27 12:48 [11-27 05:12:44] (/home/user/VAR/train.py , line 279)=> [ep274] (training ) Lm: 6.436 (6.436), Lt: 5.679 (5.681), Acc m&t: 3.58 5.63, Remain: 23:36:16, Finish: 2024-11-27 12:49 [11-27 05:12:44] (/home/user/VAR/train.py , line 279)=> [ep274] (training ) Lm: 6.436 (6.436), Lt: 5.679 (5.681), Acc m&t: 3.58 5.63, Remain: 23:36:59, Finish: 2024-11-27 12:49 [11-27 05:12:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [ 0/1669] eta: 0:18:27 tlr: 5.7e-05 tnm: 0.51 Lm: 6.573 (6.573) Lt: 5.822 (5.822) Accm: 3.23 (3.23) Acct: 5.06 (5.06) proj_loss: -0.6071 (-0.6071) time: 0.6633 data: 0.0003 [11-27 05:12:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [ 0/1669] eta: 0:18:27 tlr: 5.7e-05 tnm: 0.51 Lm: 6.509 (6.509) Lt: 5.806 (5.806) Accm: 2.99 (2.99) Acct: 4.51 (4.51) proj_loss: -0.6285 (-0.6285) time: 0.6635 data: 0.0004 [11-27 05:12:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [ 0/1669] eta: 0:18:28 tlr: 5.7e-05 tnm: 0.51 Lm: 6.472 (6.472) Lt: 5.718 (5.718) Accm: 3.39 (3.39) Acct: 5.08 (5.08) proj_loss: -0.6000 (-0.6000) time: 0.6639 data: 0.0003 [11-27 05:12:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [ 0/1669] eta: 0:18:28 tlr: 5.7e-05 tnm: 0.51 Lm: 6.493 (6.493) Lt: 5.811 (5.811) Accm: 3.31 (3.31) Acct: 4.79 (4.79) proj_loss: -0.6124 (-0.6124) time: 0.6642 data: 0.0003 [11-27 05:17:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [ 417/1669] eta: 0:14:02 tlr: 5.7e-05 tnm: 0.50 Lm: 6.548 (6.548) Lt: 5.845 (5.845) Accm: 3.07 (3.07) Acct: 4.65 (4.65) proj_loss: -0.6147 (-0.6147) time: 0.6714 data: 0.0003 [11-27 05:17:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [ 417/1669] eta: 0:14:02 tlr: 5.7e-05 tnm: 0.50 Lm: 6.513 (6.513) Lt: 5.762 (5.762) Accm: 3.39 (3.39) Acct: 5.41 (5.41) proj_loss: -0.6088 (-0.6088) time: 0.6714 data: 0.0003 [11-27 05:17:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [ 417/1669] eta: 0:14:02 tlr: 5.7e-05 tnm: 0.50 Lm: 6.424 (6.424) Lt: 5.690 (5.690) Accm: 3.41 (3.41) Acct: 5.29 (5.29) proj_loss: -0.6166 (-0.6166) time: 0.6714 data: 0.0003 [11-27 05:17:26] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [ 417/1669] eta: 0:14:02 tlr: 5.7e-05 tnm: 0.50 Lm: 6.451 (6.451) Lt: 5.681 (5.681) Accm: 3.66 (3.66) Acct: 5.57 (5.57) proj_loss: -0.6118 (-0.6118) time: 0.6714 data: 0.0003 [11-27 05:22:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [ 834/1669] eta: 0:09:36 tlr: 5.7e-05 tnm: 0.48 Lm: 6.430 (6.353) Lt: 5.644 (5.573) Accm: 3.93 (3.94) Acct: 6.06 (6.07) proj_loss: -0.6236 (-0.6202) time: 0.6760 data: 0.0002 [11-27 05:22:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [ 834/1669] eta: 0:09:36 tlr: 5.7e-05 tnm: 0.48 Lm: 6.454 (6.464) Lt: 5.702 (5.697) Accm: 3.56 (3.50) Acct: 5.75 (5.58) proj_loss: -0.6106 (-0.6095) time: 0.6760 data: 0.0003 [11-27 05:22:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [ 834/1669] eta: 0:09:36 tlr: 5.7e-05 tnm: 0.48 Lm: 6.509 (6.456) Lt: 5.772 (5.718) Accm: 3.34 (3.38) Acct: 5.25 (5.28) proj_loss: -0.6220 (-0.6184) time: 0.6760 data: 0.0003 [11-27 05:22:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [ 834/1669] eta: 0:09:36 tlr: 5.7e-05 tnm: 0.48 Lm: 6.493 (6.522) Lt: 5.811 (5.782) Accm: 3.31 (3.18) Acct: 4.79 (4.91) proj_loss: -0.6124 (-0.6032) time: 0.6760 data: 0.0003 [11-27 05:27:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [1251/1669] eta: 0:04:46 tlr: 5.7e-05 tnm: 0.50 Lm: 6.482 (6.504) Lt: 5.745 (5.756) Accm: 3.35 (3.33) Acct: 5.11 (5.18) proj_loss: -0.6084 (-0.6035) time: 0.6737 data: 0.0003 [11-27 05:27:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [1251/1669] eta: 0:04:46 tlr: 5.7e-05 tnm: 0.50 Lm: 6.451 (6.392) Lt: 5.681 (5.631) Accm: 3.66 (3.78) Acct: 5.65 (5.86) proj_loss: -0.6186 (-0.6185) time: 0.6738 data: 0.0002 [11-27 05:27:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [1251/1669] eta: 0:04:46 tlr: 5.7e-05 tnm: 0.50 Lm: 6.423 (6.446) Lt: 5.684 (5.690) Accm: 3.63 (3.55) Acct: 5.74 (5.62) proj_loss: -0.6107 (-0.6099) time: 0.6737 data: 0.0003 [11-27 05:27:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [1251/1669] eta: 0:04:46 tlr: 5.7e-05 tnm: 0.50 Lm: 6.432 (6.431) Lt: 5.717 (5.704) Accm: 3.52 (3.46) Acct: 5.57 (5.43) proj_loss: -0.6252 (-0.6222) time: 0.6738 data: 0.0003 [11-27 05:31:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [1668/1669] eta: 0:00:00 tlr: 5.6e-05 tnm: 0.49 Lm: 6.355 (6.400) Lt: 5.662 (5.664) Accm: 3.70 (3.63) Acct: 5.89 (5.71) proj_loss: -0.6285 (-0.6237) time: 0.6751 data: 0.0018 [11-27 05:31:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 275/350] Total time: 0:19:01 (0.684 s / it) [11-27 05:31:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [1668/1669] eta: 0:00:00 tlr: 5.6e-05 tnm: 0.49 Lm: 6.430 (6.395) Lt: 5.684 (5.641) Accm: 3.58 (3.74) Acct: 5.39 (5.76) proj_loss: -0.6135 (-0.6172) time: 0.6752 data: 0.0016 [11-27 05:31:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [1668/1669] eta: 0:00:00 tlr: 5.6e-05 tnm: 0.49 Lm: 6.471 (6.478) Lt: 5.679 (5.715) Accm: 3.39 (3.43) Acct: 5.44 (5.34) proj_loss: -0.6124 (-0.6068) time: 0.6751 data: 0.0020 [11-27 05:31:46] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 275/350] [1668/1669] eta: 0:00:00 tlr: 5.6e-05 tnm: 0.49 Lm: 6.454 (6.457) Lt: 5.702 (5.701) Accm: 3.63 (3.56) Acct: 5.75 (5.64) proj_loss: -0.6109 (-0.6109) time: 0.6751 data: 0.0018 [11-27 05:31:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 275/350] Total time: 0:19:01 (0.684 s / it) [11-27 05:31:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 275/350] Total time: 0:19:01 (0.684 s / it) [11-27 05:31:46] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 275/350] Total time: 0:19:01 (0.684 s / it) [11-27 05:31:46] (/home/user/VAR/train.py , line 279)=> [ep275] (training ) Lm: 6.436 (6.444), Lt: 5.679 (5.691), Acc m&t: 3.58 5.63, Remain: 23:18:27, Finish: 2024-11-27 12:50 [11-27 05:31:46] (/home/user/VAR/train.py , line 279)=> [ep275] (training ) Lm: 6.436 (6.444), Lt: 5.679 (5.691), Acc m&t: 3.58 5.63, Remain: 23:18:40, Finish: 2024-11-27 12:50 [11-27 05:31:46] (/home/user/VAR/train.py , line 279)=> [ep275] (training ) Lm: 6.436 (6.444), Lt: 5.679 (5.691), Acc m&t: 3.58 5.63, Remain: 23:18:24, Finish: 2024-11-27 12:50 [11-27 05:31:46] (/home/user/VAR/train.py , line 279)=> [ep275] (training ) Lm: 6.436 (6.444), Lt: 5.679 (5.691), Acc m&t: 3.58 5.63, Remain: 23:18:27, Finish: 2024-11-27 12:50 [11-27 05:31:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [ 0/1669] eta: 0:18:08 tlr: 5.6e-05 tnm: 0.49 Lm: 6.399 (6.399) Lt: 5.697 (5.697) Accm: 3.47 (3.47) Acct: 5.22 (5.22) proj_loss: -0.6043 (-0.6043) time: 0.6523 data: 0.0005 [11-27 05:31:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [ 0/1669] eta: 0:18:09 tlr: 5.6e-05 tnm: 0.49 Lm: 6.539 (6.539) Lt: 5.776 (5.776) Accm: 3.24 (3.24) Acct: 5.20 (5.20) proj_loss: -0.6106 (-0.6106) time: 0.6526 data: 0.0004 [11-27 05:31:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [ 0/1669] eta: 0:18:08 tlr: 5.6e-05 tnm: 0.49 Lm: 6.539 (6.539) Lt: 5.850 (5.850) Accm: 3.10 (3.10) Acct: 4.89 (4.89) proj_loss: -0.6097 (-0.6097) time: 0.6523 data: 0.0004 [11-27 05:31:47] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [ 0/1669] eta: 0:18:09 tlr: 5.6e-05 tnm: 0.49 Lm: 6.416 (6.416) Lt: 5.643 (5.643) Accm: 3.84 (3.84) Acct: 5.89 (5.89) proj_loss: -0.5961 (-0.5961) time: 0.6526 data: 0.0004 [11-27 05:36:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [ 417/1669] eta: 0:14:01 tlr: 5.6e-05 tnm: 0.50 Lm: 6.367 (6.367) Lt: 5.569 (5.569) Accm: 4.16 (4.16) Acct: 6.42 (6.42) proj_loss: -0.6059 (-0.6059) time: 0.6733 data: 0.0003 [11-27 05:36:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [ 417/1669] eta: 0:14:01 tlr: 5.6e-05 tnm: 0.50 Lm: 6.528 (6.528) Lt: 5.745 (5.745) Accm: 3.36 (3.36) Acct: 5.39 (5.39) proj_loss: -0.6064 (-0.6064) time: 0.6733 data: 0.0002 [11-27 05:36:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [ 417/1669] eta: 0:14:01 tlr: 5.6e-05 tnm: 0.50 Lm: 6.552 (6.552) Lt: 5.837 (5.837) Accm: 3.02 (3.02) Acct: 4.73 (4.73) proj_loss: -0.6075 (-0.6075) time: 0.6733 data: 0.0003 [11-27 05:36:27] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [ 417/1669] eta: 0:14:01 tlr: 5.6e-05 tnm: 0.50 Lm: 6.469 (6.469) Lt: 5.741 (5.741) Accm: 3.26 (3.26) Acct: 4.92 (4.92) proj_loss: -0.5961 (-0.5961) time: 0.6733 data: 0.0003 [11-27 05:41:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [ 834/1669] eta: 0:09:21 tlr: 5.6e-05 tnm: 0.48 Lm: 6.535 (6.491) Lt: 5.785 (5.756) Accm: 3.09 (3.20) Acct: 4.98 (4.94) proj_loss: -0.6043 (-0.6018) time: 0.6735 data: 0.0003 [11-27 05:41:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [ 834/1669] eta: 0:09:21 tlr: 5.6e-05 tnm: 0.48 Lm: 6.516 (6.467) Lt: 5.715 (5.660) Accm: 3.47 (3.56) Acct: 5.58 (5.66) proj_loss: -0.6106 (-0.6104) time: 0.6735 data: 0.0003 [11-27 05:41:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [ 834/1669] eta: 0:09:21 tlr: 5.6e-05 tnm: 0.48 Lm: 6.539 (6.512) Lt: 5.823 (5.780) Accm: 3.10 (3.09) Acct: 4.89 (4.86) proj_loss: -0.6097 (-0.6110) time: 0.6735 data: 0.0003 [11-27 05:41:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [ 834/1669] eta: 0:09:21 tlr: 5.6e-05 tnm: 0.48 Lm: 6.416 (6.416) Lt: 5.643 (5.651) Accm: 3.84 (3.84) Acct: 5.89 (5.85) proj_loss: -0.5961 (-0.6018) time: 0.6735 data: 0.0003 [11-27 05:45:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [1251/1669] eta: 0:04:41 tlr: 5.6e-05 tnm: 0.47 Lm: 6.406 (6.410) Lt: 5.637 (5.646) Accm: 3.74 (3.79) Acct: 5.81 (5.82) proj_loss: -0.6023 (-0.6034) time: 0.6712 data: 0.0003 [11-27 05:45:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [1251/1669] eta: 0:04:41 tlr: 5.6e-05 tnm: 0.47 Lm: 6.520 (6.481) Lt: 5.745 (5.691) Accm: 3.46 (3.53) Acct: 5.50 (5.60) proj_loss: -0.6092 (-0.6097) time: 0.6712 data: 0.0003 [11-27 05:45:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [1251/1669] eta: 0:04:41 tlr: 5.6e-05 tnm: 0.47 Lm: 6.507 (6.488) Lt: 5.770 (5.756) Accm: 3.23 (3.24) Acct: 5.10 (5.02) proj_loss: -0.6057 (-0.6031) time: 0.6712 data: 0.0003 [11-27 05:45:48] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [1251/1669] eta: 0:04:41 tlr: 5.6e-05 tnm: 0.47 Lm: 6.508 (6.503) Lt: 5.751 (5.755) Accm: 3.17 (3.18) Acct: 5.00 (5.11) proj_loss: -0.6131 (-0.6124) time: 0.6712 data: 0.0003 [11-27 05:50:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [1668/1669] eta: 0:00:00 tlr: 5.6e-05 tnm: 0.50 Lm: 6.477 (6.482) Lt: 5.680 (5.723) Accm: 3.24 (3.33) Acct: 5.11 (5.42) proj_loss: -0.6164 (-0.6159) time: 0.7388 data: 0.0018 [11-27 05:50:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 276/350] Total time: 0:18:46 (0.675 s / it) [11-27 05:50:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [1668/1669] eta: 0:00:00 tlr: 5.6e-05 tnm: 0.50 Lm: 6.516 (6.477) Lt: 5.741 (5.701) Accm: 3.44 (3.51) Acct: 5.42 (5.54) proj_loss: -0.6105 (-0.6099) time: 0.7388 data: 0.0018 [11-27 05:50:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [1668/1669] eta: 0:00:00 tlr: 5.6e-05 tnm: 0.50 Lm: 6.479 (6.463) Lt: 5.755 (5.732) Accm: 3.37 (3.35) Acct: 5.22 (5.19) proj_loss: -0.6071 (-0.6048) time: 0.7388 data: 0.0016 [11-27 05:50:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 276/350] [1668/1669] eta: 0:00:00 tlr: 5.6e-05 tnm: 0.50 Lm: 6.395 (6.368) Lt: 5.632 (5.602) Accm: 3.84 (3.88) Acct: 5.89 (6.02) proj_loss: -0.6084 (-0.6059) time: 0.7388 data: 0.0019 [11-27 05:50:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 276/350] Total time: 0:18:46 (0.675 s / it) [11-27 05:50:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 276/350] Total time: 0:18:46 (0.675 s / it) [11-27 05:50:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 276/350] Total time: 0:18:46 (0.675 s / it) [11-27 05:50:33] (/home/user/VAR/train.py , line 279)=> [ep276] (training ) Lm: 6.436 (6.448), Lt: 5.679 (5.695), Acc m&t: 3.58 5.63, Remain: 22:59:48, Finish: 2024-11-27 12:50 [11-27 05:50:33] (/home/user/VAR/train.py , line 279)=> [ep276] (training ) Lm: 6.436 (6.448), Lt: 5.679 (5.695), Acc m&t: 3.58 5.63, Remain: 22:59:34, Finish: 2024-11-27 12:50 [11-27 05:50:33] (/home/user/VAR/train.py , line 279)=> [ep276] (training ) Lm: 6.436 (6.448), Lt: 5.679 (5.695), Acc m&t: 3.58 5.63, Remain: 23:00:09, Finish: 2024-11-27 12:50 [11-27 05:50:33] (/home/user/VAR/train.py , line 279)=> [ep276] (training ) Lm: 6.436 (6.448), Lt: 5.679 (5.695), Acc m&t: 3.58 5.63, Remain: 22:58:44, Finish: 2024-11-27 12:49 [11-27 05:50:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [ 0/1669] eta: 0:18:47 tlr: 5.6e-05 tnm: 0.50 Lm: 6.451 (6.451) Lt: 5.716 (5.716) Accm: 3.64 (3.64) Acct: 5.39 (5.39) proj_loss: -0.6114 (-0.6114) time: 0.6755 data: 0.0004 [11-27 05:50:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [ 0/1669] eta: 0:18:45 tlr: 5.6e-05 tnm: 0.50 Lm: 6.404 (6.404) Lt: 5.650 (5.650) Accm: 3.37 (3.37) Acct: 5.37 (5.37) proj_loss: -0.6023 (-0.6023) time: 0.6743 data: 0.0004 [11-27 05:50:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [ 0/1669] eta: 0:18:45 tlr: 5.6e-05 tnm: 0.50 Lm: 6.479 (6.479) Lt: 5.691 (5.691) Accm: 3.60 (3.60) Acct: 6.06 (6.06) proj_loss: -0.5835 (-0.5835) time: 0.6745 data: 0.0004 [11-27 05:50:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [ 0/1669] eta: 0:18:48 tlr: 5.6e-05 tnm: 0.50 Lm: 6.402 (6.402) Lt: 5.644 (5.644) Accm: 3.53 (3.53) Acct: 5.66 (5.66) proj_loss: -0.6206 (-0.6206) time: 0.6759 data: 0.0003 [11-27 05:55:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [ 417/1669] eta: 0:14:46 tlr: 5.5e-05 tnm: 0.48 Lm: 6.385 (6.385) Lt: 5.605 (5.605) Accm: 3.74 (3.74) Acct: 5.73 (5.73) proj_loss: -0.6177 (-0.6177) time: 0.6747 data: 0.0002 [11-27 05:55:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [ 417/1669] eta: 0:14:46 tlr: 5.5e-05 tnm: 0.48 Lm: 6.408 (6.408) Lt: 5.649 (5.649) Accm: 3.40 (3.40) Acct: 5.33 (5.33) proj_loss: -0.6077 (-0.6077) time: 0.6747 data: 0.0003 [11-27 05:55:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [ 417/1669] eta: 0:14:46 tlr: 5.5e-05 tnm: 0.48 Lm: 6.474 (6.474) Lt: 5.721 (5.721) Accm: 3.55 (3.55) Acct: 5.72 (5.72) proj_loss: -0.6088 (-0.6088) time: 0.6747 data: 0.0003 [11-27 05:55:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [ 417/1669] eta: 0:14:46 tlr: 5.5e-05 tnm: 0.48 Lm: 6.425 (6.425) Lt: 5.694 (5.694) Accm: 3.42 (3.42) Acct: 5.17 (5.17) proj_loss: -0.6121 (-0.6121) time: 0.6747 data: 0.0003 [11-27 06:00:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [ 834/1669] eta: 0:09:36 tlr: 5.5e-05 tnm: 0.51 Lm: 6.451 (6.491) Lt: 5.716 (5.739) Accm: 3.21 (3.33) Acct: 5.04 (5.13) proj_loss: -0.6114 (-0.6069) time: 0.6761 data: 0.0003 [11-27 06:00:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [ 834/1669] eta: 0:09:36 tlr: 5.5e-05 tnm: 0.51 Lm: 6.402 (6.438) Lt: 5.644 (5.665) Accm: 3.53 (3.54) Acct: 5.66 (5.54) proj_loss: -0.6147 (-0.6101) time: 0.6761 data: 0.0003 [11-27 06:00:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [ 834/1669] eta: 0:09:36 tlr: 5.5e-05 tnm: 0.51 Lm: 6.413 (6.432) Lt: 5.650 (5.674) Accm: 3.37 (3.37) Acct: 5.37 (5.34) proj_loss: -0.6049 (-0.6068) time: 0.6761 data: 0.0003 [11-27 06:00:09] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [ 834/1669] eta: 0:09:36 tlr: 5.5e-05 tnm: 0.51 Lm: 6.470 (6.398) Lt: 5.691 (5.641) Accm: 3.60 (3.80) Acct: 6.06 (6.12) proj_loss: -0.6264 (-0.6147) time: 0.6761 data: 0.0003 [11-27 06:04:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [1251/1669] eta: 0:04:45 tlr: 5.5e-05 tnm: 0.48 Lm: 6.421 (6.391) Lt: 5.646 (5.631) Accm: 3.63 (3.77) Acct: 5.83 (5.99) proj_loss: -0.6287 (-0.6187) time: 0.6719 data: 0.0003 [11-27 06:04:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [1251/1669] eta: 0:04:45 tlr: 5.5e-05 tnm: 0.48 Lm: 6.394 (6.425) Lt: 5.636 (5.656) Accm: 3.38 (3.46) Acct: 5.41 (5.42) proj_loss: -0.6077 (-0.6078) time: 0.6719 data: 0.0002 [11-27 06:04:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [1251/1669] eta: 0:04:45 tlr: 5.5e-05 tnm: 0.48 Lm: 6.446 (6.445) Lt: 5.686 (5.690) Accm: 3.39 (3.38) Acct: 5.33 (5.29) proj_loss: -0.6072 (-0.6074) time: 0.6719 data: 0.0003 [11-27 06:04:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [1251/1669] eta: 0:04:45 tlr: 5.5e-05 tnm: 0.48 Lm: 6.504 (6.507) Lt: 5.743 (5.747) Accm: 3.19 (3.29) Acct: 5.07 (5.12) proj_loss: -0.6121 (-0.6087) time: 0.6719 data: 0.0003 [11-27 06:09:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [1668/1669] eta: 0:00:00 tlr: 5.5e-05 tnm: 0.51 Lm: 6.451 (6.469) Lt: 5.716 (5.709) Accm: 3.21 (3.39) Acct: 5.10 (5.27) proj_loss: -0.6127 (-0.6110) time: 0.6755 data: 0.0015 [11-27 06:09:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 277/350] Total time: 0:18:57 (0.681 s / it) [11-27 06:09:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [1668/1669] eta: 0:00:00 tlr: 5.5e-05 tnm: 0.51 Lm: 6.419 (6.440) Lt: 5.650 (5.679) Accm: 3.40 (3.40) Acct: 5.37 (5.38) proj_loss: -0.6094 (-0.6083) time: 0.6755 data: 0.0018 [11-27 06:09:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [1668/1669] eta: 0:00:00 tlr: 5.5e-05 tnm: 0.51 Lm: 6.389 (6.391) Lt: 5.646 (5.634) Accm: 3.60 (3.72) Acct: 5.68 (5.93) proj_loss: -0.6264 (-0.6201) time: 0.6755 data: 0.0016 [11-27 06:09:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 277/350] [1668/1669] eta: 0:00:00 tlr: 5.5e-05 tnm: 0.51 Lm: 6.386 (6.405) Lt: 5.629 (5.625) Accm: 3.53 (3.57) Acct: 5.66 (5.64) proj_loss: -0.6067 (-0.6076) time: 0.6755 data: 0.0016 [11-27 06:09:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 277/350] Total time: 0:18:57 (0.681 s / it) [11-27 06:09:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 277/350] Total time: 0:18:57 (0.681 s / it) [11-27 06:09:30] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 277/350] Total time: 0:18:57 (0.681 s / it) [11-27 06:09:30] (/home/user/VAR/train.py , line 279)=> [ep277] (training ) Lm: 6.434 (6.434), Lt: 5.676 (5.676), Acc m&t: 3.60 5.65, Remain: 22:43:00, Finish: 2024-11-27 12:52 [11-27 06:09:30] (/home/user/VAR/train.py , line 279)=> [ep277] (training ) Lm: 6.434 (6.434), Lt: 5.676 (5.676), Acc m&t: 3.60 5.65, Remain: 22:42:53, Finish: 2024-11-27 12:52 [11-27 06:09:30] (/home/user/VAR/train.py , line 279)=> [ep277] (training ) Lm: 6.434 (6.434), Lt: 5.676 (5.676), Acc m&t: 3.60 5.65, Remain: 22:43:02, Finish: 2024-11-27 12:52 [11-27 06:09:30] (/home/user/VAR/train.py , line 279)=> [ep277] (training ) Lm: 6.434 (6.434), Lt: 5.676 (5.676), Acc m&t: 3.60 5.65, Remain: 22:42:26, Finish: 2024-11-27 12:51 [11-27 06:09:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [ 0/1669] eta: 0:18:40 tlr: 5.5e-05 tnm: 0.50 Lm: 6.493 (6.493) Lt: 5.774 (5.774) Accm: 3.70 (3.70) Acct: 5.68 (5.68) proj_loss: -0.6169 (-0.6169) time: 0.6715 data: 0.0003 [11-27 06:09:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [ 0/1669] eta: 0:18:40 tlr: 5.5e-05 tnm: 0.50 Lm: 6.373 (6.373) Lt: 5.614 (5.614) Accm: 3.84 (3.84) Acct: 6.30 (6.30) proj_loss: -0.6194 (-0.6194) time: 0.6713 data: 0.0004 [11-27 06:09:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [ 0/1669] eta: 0:18:41 tlr: 5.5e-05 tnm: 0.50 Lm: 6.340 (6.340) Lt: 5.610 (5.610) Accm: 3.86 (3.86) Acct: 6.20 (6.20) proj_loss: -0.5994 (-0.5994) time: 0.6719 data: 0.0004 [11-27 06:09:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [ 0/1669] eta: 0:18:41 tlr: 5.5e-05 tnm: 0.50 Lm: 6.349 (6.349) Lt: 5.604 (5.604) Accm: 4.07 (4.07) Acct: 6.44 (6.44) proj_loss: -0.6361 (-0.6361) time: 0.6722 data: 0.0004 [11-27 06:14:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [ 417/1669] eta: 0:14:01 tlr: 5.5e-05 tnm: 0.49 Lm: 6.424 (6.424) Lt: 5.636 (5.636) Accm: 3.82 (3.82) Acct: 6.12 (6.12) proj_loss: -0.6186 (-0.6186) time: 0.6755 data: 0.0003 [11-27 06:14:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [ 417/1669] eta: 0:14:01 tlr: 5.5e-05 tnm: 0.49 Lm: 6.520 (6.520) Lt: 5.789 (5.789) Accm: 3.45 (3.45) Acct: 5.29 (5.29) proj_loss: -0.6229 (-0.6229) time: 0.6755 data: 0.0002 [11-27 06:14:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [ 417/1669] eta: 0:14:01 tlr: 5.5e-05 tnm: 0.49 Lm: 6.414 (6.414) Lt: 5.666 (5.666) Accm: 3.62 (3.62) Acct: 5.65 (5.65) proj_loss: -0.5990 (-0.5990) time: 0.6755 data: 0.0003 [11-27 06:14:11] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [ 417/1669] eta: 0:14:01 tlr: 5.5e-05 tnm: 0.49 Lm: 6.447 (6.447) Lt: 5.738 (5.738) Accm: 3.48 (3.48) Acct: 5.63 (5.63) proj_loss: -0.6174 (-0.6174) time: 0.6755 data: 0.0003 [11-27 06:19:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [ 834/1669] eta: 0:09:36 tlr: 5.5e-05 tnm: 0.49 Lm: 6.437 (6.444) Lt: 5.632 (5.703) Accm: 3.72 (3.56) Acct: 5.56 (5.61) proj_loss: -0.6153 (-0.6068) time: 0.6747 data: 0.0003 [11-27 06:19:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [ 834/1669] eta: 0:09:36 tlr: 5.5e-05 tnm: 0.49 Lm: 6.488 (6.442) Lt: 5.721 (5.688) Accm: 3.39 (3.51) Acct: 5.39 (5.56) proj_loss: -0.5994 (-0.6036) time: 0.6748 data: 0.0003 [11-27 06:19:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [ 834/1669] eta: 0:09:36 tlr: 5.5e-05 tnm: 0.49 Lm: 6.447 (6.432) Lt: 5.667 (5.648) Accm: 3.59 (3.75) Acct: 5.80 (5.99) proj_loss: -0.6361 (-0.6246) time: 0.6748 data: 0.0003 [11-27 06:19:06] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [ 834/1669] eta: 0:09:36 tlr: 5.5e-05 tnm: 0.49 Lm: 6.493 (6.449) Lt: 5.774 (5.711) Accm: 3.70 (3.62) Acct: 5.68 (5.49) proj_loss: -0.6257 (-0.6238) time: 0.6748 data: 0.0003 [11-27 06:23:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [1251/1669] eta: 0:04:46 tlr: 5.4e-05 tnm: 0.51 Lm: 6.486 (6.456) Lt: 5.753 (5.716) Accm: 3.55 (3.56) Acct: 5.48 (5.43) proj_loss: -0.6213 (-0.6205) time: 0.6711 data: 0.0003 [11-27 06:23:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [1251/1669] eta: 0:04:46 tlr: 5.4e-05 tnm: 0.51 Lm: 6.490 (6.454) Lt: 5.727 (5.709) Accm: 3.51 (3.54) Acct: 5.54 (5.60) proj_loss: -0.6060 (-0.6100) time: 0.6711 data: 0.0003 [11-27 06:23:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [1251/1669] eta: 0:04:46 tlr: 5.4e-05 tnm: 0.51 Lm: 6.412 (6.418) Lt: 5.648 (5.643) Accm: 3.67 (3.75) Acct: 5.76 (5.91) proj_loss: -0.6253 (-0.6221) time: 0.6711 data: 0.0003 [11-27 06:23:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [1251/1669] eta: 0:04:46 tlr: 5.4e-05 tnm: 0.51 Lm: 6.405 (6.407) Lt: 5.623 (5.666) Accm: 3.78 (3.67) Acct: 5.92 (5.78) proj_loss: -0.6147 (-0.6086) time: 0.6711 data: 0.0003 [11-27 06:28:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [1668/1669] eta: 0:00:00 tlr: 5.4e-05 tnm: 0.49 Lm: 6.389 (6.404) Lt: 5.632 (5.665) Accm: 3.72 (3.66) Acct: 5.63 (5.75) proj_loss: -0.6153 (-0.6121) time: 0.6695 data: 0.0021 [11-27 06:28:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 278/350] Total time: 0:19:00 (0.684 s / it) [11-27 06:28:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [1668/1669] eta: 0:00:00 tlr: 5.4e-05 tnm: 0.49 Lm: 6.488 (6.439) Lt: 5.721 (5.692) Accm: 3.63 (3.57) Acct: 5.68 (5.61) proj_loss: -0.6056 (-0.6091) time: 0.6695 data: 0.0019 [11-27 06:28:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [1668/1669] eta: 0:00:00 tlr: 5.4e-05 tnm: 0.49 Lm: 6.480 (6.448) Lt: 5.732 (5.712) Accm: 3.70 (3.59) Acct: 5.68 (5.53) proj_loss: -0.6257 (-0.6216) time: 0.6695 data: 0.0016 [11-27 06:28:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 278/350] [1668/1669] eta: 0:00:00 tlr: 5.4e-05 tnm: 0.49 Lm: 6.447 (6.431) Lt: 5.667 (5.663) Accm: 3.59 (3.65) Acct: 5.72 (5.72) proj_loss: -0.6146 (-0.6203) time: 0.6695 data: 0.0018 [11-27 06:28:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 278/350] Total time: 0:19:00 (0.684 s / it) [11-27 06:28:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 278/350] Total time: 0:19:00 (0.684 s / it) [11-27 06:28:31] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 278/350] Total time: 0:19:00 (0.684 s / it) [11-27 06:28:31] (/home/user/VAR/train.py , line 279)=> [ep278] (training ) Lm: 6.434 (6.452), Lt: 5.676 (5.702), Acc m&t: 3.60 5.65, Remain: 22:09:29, Finish: 2024-11-27 12:38 [11-27 06:28:31] (/home/user/VAR/train.py , line 279)=> [ep278] (training ) Lm: 6.434 (6.452), Lt: 5.676 (5.702), Acc m&t: 3.60 5.65, Remain: 22:09:26, Finish: 2024-11-27 12:37 [11-27 06:28:31] (/home/user/VAR/train.py , line 279)=> [ep278] (training ) Lm: 6.434 (6.452), Lt: 5.676 (5.702), Acc m&t: 3.60 5.65, Remain: 22:09:33, Finish: 2024-11-27 12:38 [11-27 06:28:31] (/home/user/VAR/train.py , line 279)=> [ep278] (training ) Lm: 6.434 (6.452), Lt: 5.676 (5.702), Acc m&t: 3.60 5.65, Remain: 22:09:28, Finish: 2024-11-27 12:37 [11-27 06:28:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [ 0/1669] eta: 0:18:18 tlr: 5.4e-05 tnm: 0.50 Lm: 6.459 (6.459) Lt: 5.652 (5.652) Accm: 3.31 (3.31) Acct: 5.32 (5.32) proj_loss: -0.5963 (-0.5963) time: 0.6579 data: 0.0004 [11-27 06:28:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [ 0/1669] eta: 0:18:16 tlr: 5.4e-05 tnm: 0.50 Lm: 6.396 (6.396) Lt: 5.701 (5.701) Accm: 3.36 (3.36) Acct: 4.96 (4.96) proj_loss: -0.6149 (-0.6149) time: 0.6567 data: 0.0004 [11-27 06:28:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [ 0/1669] eta: 0:18:18 tlr: 5.4e-05 tnm: 0.50 Lm: 6.500 (6.500) Lt: 5.736 (5.736) Accm: 3.37 (3.37) Acct: 5.20 (5.20) proj_loss: -0.6110 (-0.6110) time: 0.6583 data: 0.0004 [11-27 06:28:31] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [ 0/1669] eta: 0:18:19 tlr: 5.4e-05 tnm: 0.50 Lm: 6.538 (6.538) Lt: 5.842 (5.842) Accm: 2.99 (2.99) Acct: 4.77 (4.77) proj_loss: -0.6078 (-0.6078) time: 0.6588 data: 0.0003 [11-27 06:33:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [ 417/1669] eta: 0:14:02 tlr: 5.4e-05 tnm: 0.49 Lm: 6.474 (6.474) Lt: 5.771 (5.771) Accm: 3.26 (3.26) Acct: 5.14 (5.14) proj_loss: -0.6147 (-0.6147) time: 0.6725 data: 0.0003 [11-27 06:33:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [ 417/1669] eta: 0:14:02 tlr: 5.4e-05 tnm: 0.49 Lm: 6.528 (6.528) Lt: 5.772 (5.772) Accm: 3.21 (3.21) Acct: 5.01 (5.01) proj_loss: -0.6144 (-0.6144) time: 0.6725 data: 0.0003 [11-27 06:33:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [ 417/1669] eta: 0:14:02 tlr: 5.4e-05 tnm: 0.49 Lm: 6.412 (6.412) Lt: 5.683 (5.683) Accm: 3.41 (3.41) Acct: 5.11 (5.11) proj_loss: -0.6219 (-0.6219) time: 0.6725 data: 0.0003 [11-27 06:33:12] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [ 417/1669] eta: 0:14:02 tlr: 5.4e-05 tnm: 0.49 Lm: 6.427 (6.427) Lt: 5.644 (5.644) Accm: 3.50 (3.50) Acct: 5.53 (5.53) proj_loss: -0.5988 (-0.5988) time: 0.6725 data: 0.0003 [11-27 06:37:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [ 834/1669] eta: 0:09:21 tlr: 5.4e-05 tnm: 0.51 Lm: 6.395 (6.410) Lt: 5.636 (5.628) Accm: 3.69 (3.60) Acct: 5.73 (5.70) proj_loss: -0.6013 (-0.6038) time: 0.6727 data: 0.0003 [11-27 06:37:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [ 834/1669] eta: 0:09:21 tlr: 5.4e-05 tnm: 0.51 Lm: 6.549 (6.535) Lt: 5.794 (5.780) Accm: 3.21 (3.21) Acct: 5.17 (5.06) proj_loss: -0.6110 (-0.6086) time: 0.6727 data: 0.0003 [11-27 06:37:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [ 834/1669] eta: 0:09:21 tlr: 5.4e-05 tnm: 0.51 Lm: 6.428 (6.459) Lt: 5.700 (5.725) Accm: 3.53 (3.45) Acct: 5.51 (5.58) proj_loss: -0.6158 (-0.6150) time: 0.6727 data: 0.0003 [11-27 06:37:52] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [ 834/1669] eta: 0:09:21 tlr: 5.4e-05 tnm: 0.51 Lm: 6.396 (6.407) Lt: 5.679 (5.682) Accm: 3.46 (3.43) Acct: 5.25 (5.21) proj_loss: -0.6149 (-0.6174) time: 0.6727 data: 0.0003 [11-27 06:42:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [1251/1669] eta: 0:04:41 tlr: 5.4e-05 tnm: 0.49 Lm: 6.412 (6.421) Lt: 5.690 (5.702) Accm: 3.42 (3.41) Acct: 5.15 (5.17) proj_loss: -0.6177 (-0.6182) time: 0.6705 data: 0.0003 [11-27 06:42:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [1251/1669] eta: 0:04:41 tlr: 5.4e-05 tnm: 0.49 Lm: 6.525 (6.498) Lt: 5.765 (5.737) Accm: 3.29 (3.35) Acct: 5.18 (5.29) proj_loss: -0.6116 (-0.6095) time: 0.6705 data: 0.0003 [11-27 06:42:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [1251/1669] eta: 0:04:41 tlr: 5.4e-05 tnm: 0.49 Lm: 6.424 (6.449) Lt: 5.685 (5.712) Accm: 3.63 (3.53) Acct: 5.76 (5.69) proj_loss: -0.6135 (-0.6141) time: 0.6705 data: 0.0003 [11-27 06:42:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [1251/1669] eta: 0:04:41 tlr: 5.4e-05 tnm: 0.49 Lm: 6.385 (6.398) Lt: 5.616 (5.601) Accm: 3.71 (3.63) Acct: 5.80 (5.74) proj_loss: -0.5988 (-0.6017) time: 0.6705 data: 0.0003 [11-27 06:47:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [1668/1669] eta: 0:00:00 tlr: 5.3e-05 tnm: 0.51 Lm: 6.395 (6.409) Lt: 5.636 (5.628) Accm: 3.69 (3.62) Acct: 5.73 (5.67) proj_loss: -0.6013 (-0.6052) time: 0.7397 data: 0.0018 [11-27 06:47:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 279/350] Total time: 0:18:46 (0.675 s / it) [11-27 06:47:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [1668/1669] eta: 0:00:00 tlr: 5.3e-05 tnm: 0.51 Lm: 6.396 (6.395) Lt: 5.679 (5.670) Accm: 3.46 (3.53) Acct: 5.25 (5.37) proj_loss: -0.6204 (-0.6192) time: 0.7397 data: 0.0019 [11-27 06:47:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [1668/1669] eta: 0:00:00 tlr: 5.3e-05 tnm: 0.51 Lm: 6.500 (6.477) Lt: 5.736 (5.720) Accm: 3.37 (3.42) Acct: 5.20 (5.37) proj_loss: -0.6110 (-0.6058) time: 0.7397 data: 0.0017 [11-27 06:47:17] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 279/350] [1668/1669] eta: 0:00:00 tlr: 5.3e-05 tnm: 0.51 Lm: 6.428 (6.446) Lt: 5.688 (5.707) Accm: 3.53 (3.48) Acct: 5.51 (5.52) proj_loss: -0.6112 (-0.6110) time: 0.7397 data: 0.0015 [11-27 06:47:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 279/350] Total time: 0:18:46 (0.675 s / it) [11-27 06:47:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 279/350] Total time: 0:18:46 (0.675 s / it) [11-27 06:47:17] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 279/350] Total time: 0:18:46 (0.675 s / it) [11-27 06:49:37] (home/user/VAR/trainer.py, line 114)=> FID: 3.140709001171217 [11-27 06:49:37] (/home/user/VAR/train.py , line 262)=> [*] [ep279] (val 50000) Lm: 6.4307, Lt: 5.6774, Acc m&t: 3.58 5.62, Val cost: 139.96s [11-27 06:49:37] (/home/user/VAR/train.py , line 267)=> [saving ckpt] ... [saving ckpt](*) finished! @ /sensei-fs/users/xiangl/exp141-var-d24/ar-ckpt-last.pth [11-27 06:49:59] (/home/user/VAR/train.py , line 279)=> [ep279] (training ) Lm: 6.431 (6.431), Lt: 5.676 (5.677), Acc m&t: 3.60 5.65, Remain: 22:04:16, Finish: 2024-11-27 12:51 [11-27 06:49:59] (/home/user/VAR/train.py , line 279)=> [ep279] (training ) Lm: 6.431 (6.431), Lt: 5.676 (5.677), Acc m&t: 3.60 5.65, Remain: 22:03:55, Finish: 2024-11-27 12:51 [11-27 06:49:59] (/home/user/VAR/train.py , line 279)=> [ep279] (training ) Lm: 6.431 (6.431), Lt: 5.676 (5.677), Acc m&t: 3.60 5.65, Remain: 22:04:10, Finish: 2024-11-27 12:51 [11-27 06:49:59] (/home/user/VAR/train.py , line 279)=> [ep279] (training ) Lm: 6.431 (6.431), Lt: 5.676 (5.677), Acc m&t: 3.60 5.65, Remain: 22:04:38, Finish: 2024-11-27 12:51 [11-27 06:49:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [ 0/1669] eta: 0:21:25 tlr: 5.3e-05 tnm: 0.51 Lm: 6.637 (6.637) Lt: 5.997 (5.997) Accm: 2.94 (2.94) Acct: 4.20 (4.20) proj_loss: -0.6350 (-0.6350) time: 0.7701 data: 0.0004 [11-27 06:49:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [ 0/1669] eta: 0:18:12 tlr: 5.3e-05 tnm: 0.51 Lm: 6.351 (6.351) Lt: 5.599 (5.599) Accm: 3.65 (3.65) Acct: 5.89 (5.89) proj_loss: -0.6286 (-0.6286) time: 0.6544 data: 0.0004 [11-27 06:49:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [ 0/1669] eta: 0:21:24 tlr: 5.3e-05 tnm: 0.51 Lm: 6.329 (6.329) Lt: 5.553 (5.553) Accm: 4.00 (4.00) Acct: 6.30 (6.30) proj_loss: -0.6082 (-0.6082) time: 0.7694 data: 0.0004 [11-27 06:49:59] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [ 0/1669] eta: 0:21:25 tlr: 5.3e-05 tnm: 0.51 Lm: 6.452 (6.452) Lt: 5.715 (5.715) Accm: 3.57 (3.57) Acct: 5.75 (5.75) proj_loss: -0.6026 (-0.6026) time: 0.7701 data: 0.0003 [11-27 06:54:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [ 417/1669] eta: 0:14:46 tlr: 5.3e-05 tnm: 0.51 Lm: 6.476 (6.476) Lt: 5.734 (5.734) Accm: 3.47 (3.47) Acct: 5.47 (5.47) proj_loss: -0.5991 (-0.5991) time: 0.6735 data: 0.0003 [11-27 06:54:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [ 417/1669] eta: 0:14:47 tlr: 5.3e-05 tnm: 0.51 Lm: 6.585 (6.585) Lt: 5.931 (5.931) Accm: 3.13 (3.13) Acct: 4.66 (4.66) proj_loss: -0.6312 (-0.6312) time: 0.6735 data: 0.0003 [11-27 06:54:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [ 417/1669] eta: 0:14:46 tlr: 5.3e-05 tnm: 0.51 Lm: 6.335 (6.335) Lt: 5.589 (5.589) Accm: 4.04 (4.04) Acct: 6.26 (6.26) proj_loss: -0.6095 (-0.6095) time: 0.6735 data: 0.0003 [11-27 06:54:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [ 417/1669] eta: 0:14:46 tlr: 5.3e-05 tnm: 0.51 Lm: 6.405 (6.405) Lt: 5.677 (5.677) Accm: 3.53 (3.53) Acct: 5.59 (5.59) proj_loss: -0.6224 (-0.6224) time: 0.6735 data: 0.0003 [11-27 06:59:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [ 834/1669] eta: 0:09:37 tlr: 5.3e-05 tnm: 0.49 Lm: 6.340 (6.368) Lt: 5.625 (5.635) Accm: 4.00 (3.83) Acct: 6.22 (5.92) proj_loss: -0.6109 (-0.6116) time: 0.6753 data: 0.0003 [11-27 06:59:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [ 834/1669] eta: 0:09:37 tlr: 5.3e-05 tnm: 0.49 Lm: 6.452 (6.420) Lt: 5.715 (5.660) Accm: 3.57 (3.66) Acct: 5.75 (5.80) proj_loss: -0.6026 (-0.6022) time: 0.6753 data: 0.0003 [11-27 06:59:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [ 834/1669] eta: 0:09:37 tlr: 5.3e-05 tnm: 0.49 Lm: 6.534 (6.510) Lt: 5.865 (5.817) Accm: 3.31 (3.32) Acct: 5.11 (5.06) proj_loss: -0.6274 (-0.6285) time: 0.6753 data: 0.0003 [11-27 06:59:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [ 834/1669] eta: 0:09:36 tlr: 5.3e-05 tnm: 0.49 Lm: 6.390 (6.400) Lt: 5.667 (5.673) Accm: 3.65 (3.58) Acct: 5.84 (5.67) proj_loss: -0.6162 (-0.6197) time: 0.6754 data: 0.0003 [11-27 07:04:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [1251/1669] eta: 0:04:46 tlr: 5.3e-05 tnm: 0.49 Lm: 6.393 (6.399) Lt: 5.642 (5.659) Accm: 3.66 (3.66) Acct: 5.86 (5.82) proj_loss: -0.6152 (-0.6131) time: 0.6736 data: 0.0003 [11-27 07:04:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [1251/1669] eta: 0:04:46 tlr: 5.3e-05 tnm: 0.49 Lm: 6.388 (6.402) Lt: 5.676 (5.659) Accm: 3.74 (3.74) Acct: 5.95 (5.86) proj_loss: -0.6134 (-0.6148) time: 0.6736 data: 0.0003 [11-27 07:04:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [1251/1669] eta: 0:04:46 tlr: 5.3e-05 tnm: 0.49 Lm: 6.504 (6.501) Lt: 5.807 (5.800) Accm: 3.36 (3.34) Acct: 5.11 (5.07) proj_loss: -0.6312 (-0.6327) time: 0.6734 data: 0.0002 [11-27 07:04:16] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [1251/1669] eta: 0:04:46 tlr: 5.3e-05 tnm: 0.49 Lm: 6.424 (6.414) Lt: 5.713 (5.673) Accm: 3.62 (3.66) Acct: 5.60 (5.71) proj_loss: -0.6055 (-0.6050) time: 0.6736 data: 0.0003 [11-27 07:08:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [1668/1669] eta: 0:00:00 tlr: 5.3e-05 tnm: 0.50 Lm: 6.428 (6.417) Lt: 5.711 (5.670) Accm: 3.67 (3.76) Acct: 5.75 (5.86) proj_loss: -0.6026 (-0.6025) time: 0.6729 data: 0.0016 [11-27 07:08:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 280/350] Total time: 0:18:57 (0.682 s / it) [11-27 07:08:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [1668/1669] eta: 0:00:00 tlr: 5.3e-05 tnm: 0.50 Lm: 6.390 (6.386) Lt: 5.617 (5.645) Accm: 3.65 (3.65) Acct: 5.84 (5.77) proj_loss: -0.6142 (-0.6095) time: 0.6729 data: 0.0015 [11-27 07:08:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [1668/1669] eta: 0:00:00 tlr: 5.3e-05 tnm: 0.50 Lm: 6.436 (6.435) Lt: 5.727 (5.702) Accm: 3.47 (3.63) Acct: 5.68 (5.66) proj_loss: -0.6158 (-0.6193) time: 0.6729 data: 0.0018 [11-27 07:08:56] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 280/350] [1668/1669] eta: 0:00:00 tlr: 5.3e-05 tnm: 0.50 Lm: 6.475 (6.494) Lt: 5.749 (5.788) Accm: 3.34 (3.34) Acct: 5.11 (5.10) proj_loss: -0.6274 (-0.6262) time: 0.6729 data: 0.0020 [11-27 07:08:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 280/350] Total time: 0:18:57 (0.681 s / it) [11-27 07:08:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 280/350] Total time: 0:18:57 (0.682 s / it) [11-27 07:08:56] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 280/350] Total time: 0:18:57 (0.682 s / it) [11-27 07:08:56] (/home/user/VAR/train.py , line 279)=> [ep280] (training ) Lm: 6.431 (6.441), Lt: 5.676 (5.690), Acc m&t: 3.60 5.65, Remain: 21:38:28, Finish: 2024-11-27 12:47 [11-27 07:08:56] (/home/user/VAR/train.py , line 279)=> [ep280] (training ) Lm: 6.431 (6.441), Lt: 5.676 (5.690), Acc m&t: 3.60 5.65, Remain: 21:38:22, Finish: 2024-11-27 12:47 [11-27 07:08:56] (/home/user/VAR/train.py , line 279)=> [ep280] (training ) Lm: 6.431 (6.441), Lt: 5.676 (5.690), Acc m&t: 3.60 5.65, Remain: 21:38:33, Finish: 2024-11-27 12:47 [11-27 07:08:56] (/home/user/VAR/train.py , line 279)=> [ep280] (training ) Lm: 6.431 (6.441), Lt: 5.676 (5.690), Acc m&t: 3.60 5.65, Remain: 21:38:10, Finish: 2024-11-27 12:47 [11-27 07:08:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [ 0/1669] eta: 0:18:06 tlr: 5.3e-05 tnm: 0.49 Lm: 6.595 (6.595) Lt: 5.854 (5.854) Accm: 2.80 (2.80) Acct: 4.41 (4.41) proj_loss: -0.6094 (-0.6094) time: 0.6508 data: 0.0003 [11-27 07:08:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [ 0/1669] eta: 0:18:05 tlr: 5.3e-05 tnm: 0.49 Lm: 6.458 (6.458) Lt: 5.732 (5.732) Accm: 3.36 (3.36) Acct: 5.04 (5.04) proj_loss: -0.6092 (-0.6092) time: 0.6501 data: 0.0003 [11-27 07:08:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [ 0/1669] eta: 0:18:33 tlr: 5.3e-05 tnm: 0.49 Lm: 6.420 (6.420) Lt: 5.623 (5.623) Accm: 3.75 (3.75) Acct: 5.94 (5.94) proj_loss: -0.6181 (-0.6181) time: 0.6671 data: 0.0004 [11-27 07:08:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [ 0/1669] eta: 0:18:07 tlr: 5.3e-05 tnm: 0.49 Lm: 6.408 (6.408) Lt: 5.664 (5.664) Accm: 3.74 (3.74) Acct: 5.77 (5.77) proj_loss: -0.6086 (-0.6086) time: 0.6513 data: 0.0004 [11-27 07:13:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [ 417/1669] eta: 0:14:01 tlr: 5.3e-05 tnm: 0.48 Lm: 6.471 (6.471) Lt: 5.730 (5.730) Accm: 3.38 (3.38) Acct: 5.32 (5.32) proj_loss: -0.6173 (-0.6173) time: 0.6729 data: 0.0003 [11-27 07:13:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [ 417/1669] eta: 0:14:01 tlr: 5.3e-05 tnm: 0.48 Lm: 6.417 (6.417) Lt: 5.624 (5.624) Accm: 3.63 (3.63) Acct: 5.72 (5.72) proj_loss: -0.6188 (-0.6188) time: 0.6729 data: 0.0003 [11-27 07:13:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [ 417/1669] eta: 0:14:01 tlr: 5.3e-05 tnm: 0.48 Lm: 6.376 (6.376) Lt: 5.550 (5.550) Accm: 3.73 (3.73) Acct: 6.03 (6.03) proj_loss: -0.5988 (-0.5988) time: 0.6729 data: 0.0003 [11-27 07:13:37] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [ 417/1669] eta: 0:14:01 tlr: 5.3e-05 tnm: 0.48 Lm: 6.405 (6.405) Lt: 5.665 (5.665) Accm: 3.64 (3.64) Acct: 5.65 (5.65) proj_loss: -0.6039 (-0.6039) time: 0.6729 data: 0.0003 [11-27 07:18:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [ 834/1669] eta: 0:09:36 tlr: 5.2e-05 tnm: 0.49 Lm: 6.446 (6.419) Lt: 5.732 (5.695) Accm: 3.52 (3.60) Acct: 5.34 (5.54) proj_loss: -0.6092 (-0.6132) time: 0.6731 data: 0.0003 [11-27 07:18:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [ 834/1669] eta: 0:09:36 tlr: 5.2e-05 tnm: 0.49 Lm: 6.420 (6.447) Lt: 5.624 (5.683) Accm: 3.50 (3.55) Acct: 5.51 (5.48) proj_loss: -0.6181 (-0.6177) time: 0.6731 data: 0.0003 [11-27 07:18:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [ 834/1669] eta: 0:09:36 tlr: 5.2e-05 tnm: 0.49 Lm: 6.447 (6.399) Lt: 5.697 (5.599) Accm: 3.37 (3.61) Acct: 5.49 (5.85) proj_loss: -0.5977 (-0.5984) time: 0.6731 data: 0.0003 [11-27 07:18:32] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [ 834/1669] eta: 0:09:36 tlr: 5.2e-05 tnm: 0.49 Lm: 6.408 (6.408) Lt: 5.664 (5.647) Accm: 3.74 (3.66) Acct: 5.77 (5.76) proj_loss: -0.6134 (-0.6160) time: 0.6731 data: 0.0003 [11-27 07:23:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [1251/1669] eta: 0:04:46 tlr: 5.2e-05 tnm: 0.51 Lm: 6.449 (6.428) Lt: 5.702 (5.670) Accm: 3.42 (3.52) Acct: 5.32 (5.53) proj_loss: -0.6110 (-0.6114) time: 0.6714 data: 0.0003 [11-27 07:23:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [1251/1669] eta: 0:04:46 tlr: 5.2e-05 tnm: 0.51 Lm: 6.452 (6.436) Lt: 5.713 (5.695) Accm: 3.47 (3.56) Acct: 5.36 (5.51) proj_loss: -0.6075 (-0.6113) time: 0.6714 data: 0.0002 [11-27 07:23:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [1251/1669] eta: 0:04:46 tlr: 5.2e-05 tnm: 0.51 Lm: 6.489 (6.432) Lt: 5.743 (5.646) Accm: 3.41 (3.57) Acct: 5.52 (5.78) proj_loss: -0.6030 (-0.6009) time: 0.6714 data: 0.0003 [11-27 07:23:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [1251/1669] eta: 0:04:46 tlr: 5.2e-05 tnm: 0.51 Lm: 6.417 (6.421) Lt: 5.625 (5.668) Accm: 3.63 (3.67) Acct: 5.72 (5.63) proj_loss: -0.6169 (-0.6156) time: 0.6714 data: 0.0003 [11-27 07:27:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [1668/1669] eta: 0:00:00 tlr: 5.2e-05 tnm: 0.49 Lm: 6.420 (6.453) Lt: 5.625 (5.701) Accm: 3.50 (3.51) Acct: 5.51 (5.44) proj_loss: -0.6157 (-0.6111) time: 0.6749 data: 0.0018 [11-27 07:27:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 281/350] Total time: 0:19:00 (0.684 s / it) [11-27 07:27:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [1668/1669] eta: 0:00:00 tlr: 5.2e-05 tnm: 0.49 Lm: 6.464 (6.439) Lt: 5.698 (5.657) Accm: 3.45 (3.55) Acct: 5.49 (5.69) proj_loss: -0.5977 (-0.5992) time: 0.6749 data: 0.0019 [11-27 07:27:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [1668/1669] eta: 0:00:00 tlr: 5.2e-05 tnm: 0.49 Lm: 6.446 (6.404) Lt: 5.694 (5.674) Accm: 3.52 (3.72) Acct: 5.39 (5.67) proj_loss: -0.6092 (-0.6133) time: 0.6749 data: 0.0020 [11-27 07:27:57] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 281/350] [1668/1669] eta: 0:00:00 tlr: 5.2e-05 tnm: 0.49 Lm: 6.408 (6.415) Lt: 5.664 (5.653) Accm: 3.74 (3.61) Acct: 5.77 (5.73) proj_loss: -0.6086 (-0.6085) time: 0.6749 data: 0.0018 [11-27 07:27:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 281/350] Total time: 0:19:00 (0.684 s / it) [11-27 07:27:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 281/350] Total time: 0:19:00 (0.684 s / it) [11-27 07:27:57] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 281/350] Total time: 0:19:00 (0.684 s / it) [11-27 07:27:57] (/home/user/VAR/train.py , line 279)=> [ep281] (training ) Lm: 6.431 (6.446), Lt: 5.676 (5.695), Acc m&t: 3.60 5.65, Remain: 21:25:05, Finish: 2024-11-27 12:53 [11-27 07:27:57] (/home/user/VAR/train.py , line 279)=> [ep281] (training ) Lm: 6.431 (6.446), Lt: 5.676 (5.695), Acc m&t: 3.60 5.65, Remain: 21:25:29, Finish: 2024-11-27 12:53 [11-27 07:27:57] (/home/user/VAR/train.py , line 279)=> [ep281] (training ) Lm: 6.431 (6.446), Lt: 5.676 (5.695), Acc m&t: 3.60 5.65, Remain: 21:25:36, Finish: 2024-11-27 12:53 [11-27 07:27:57] (/home/user/VAR/train.py , line 279)=> [ep281] (training ) Lm: 6.431 (6.446), Lt: 5.676 (5.695), Acc m&t: 3.60 5.65, Remain: 21:25:00, Finish: 2024-11-27 12:52 [11-27 07:27:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [ 0/1669] eta: 0:18:40 tlr: 5.2e-05 tnm: 0.49 Lm: 6.285 (6.285) Lt: 5.566 (5.566) Accm: 3.93 (3.93) Acct: 6.01 (6.01) proj_loss: -0.6378 (-0.6378) time: 0.6713 data: 0.0004 [11-27 07:27:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [ 0/1669] eta: 0:18:41 tlr: 5.2e-05 tnm: 0.49 Lm: 6.411 (6.411) Lt: 5.661 (5.661) Accm: 3.39 (3.39) Acct: 5.41 (5.41) proj_loss: -0.6114 (-0.6114) time: 0.6717 data: 0.0003 [11-27 07:27:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [ 0/1669] eta: 0:18:41 tlr: 5.2e-05 tnm: 0.49 Lm: 6.383 (6.383) Lt: 5.652 (5.652) Accm: 3.69 (3.69) Acct: 5.68 (5.68) proj_loss: -0.6264 (-0.6264) time: 0.6719 data: 0.0004 [11-27 07:27:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [ 0/1669] eta: 0:18:40 tlr: 5.2e-05 tnm: 0.49 Lm: 6.382 (6.382) Lt: 5.644 (5.644) Accm: 4.04 (4.04) Acct: 6.44 (6.44) proj_loss: -0.5940 (-0.5940) time: 0.6713 data: 0.0004 [11-27 07:32:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [ 417/1669] eta: 0:14:01 tlr: 5.2e-05 tnm: 0.51 Lm: 6.404 (6.404) Lt: 5.646 (5.646) Accm: 3.98 (3.98) Acct: 6.33 (6.33) proj_loss: -0.6115 (-0.6115) time: 0.6744 data: 0.0003 [11-27 07:32:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [ 417/1669] eta: 0:14:01 tlr: 5.2e-05 tnm: 0.51 Lm: 6.404 (6.404) Lt: 5.676 (5.676) Accm: 3.56 (3.56) Acct: 5.52 (5.52) proj_loss: -0.6236 (-0.6236) time: 0.6744 data: 0.0003 [11-27 07:32:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [ 417/1669] eta: 0:14:01 tlr: 5.2e-05 tnm: 0.51 Lm: 6.415 (6.415) Lt: 5.698 (5.698) Accm: 3.42 (3.42) Acct: 5.40 (5.40) proj_loss: -0.6156 (-0.6156) time: 0.6744 data: 0.0002 [11-27 07:32:38] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [ 417/1669] eta: 0:14:01 tlr: 5.2e-05 tnm: 0.51 Lm: 6.397 (6.397) Lt: 5.663 (5.663) Accm: 3.70 (3.70) Acct: 5.66 (5.66) proj_loss: -0.6337 (-0.6337) time: 0.6744 data: 0.0003 [11-27 07:37:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [ 834/1669] eta: 0:09:21 tlr: 5.2e-05 tnm: 0.51 Lm: 6.412 (6.425) Lt: 5.673 (5.675) Accm: 3.69 (3.61) Acct: 5.65 (5.66) proj_loss: -0.6264 (-0.6209) time: 0.6733 data: 0.0003 [11-27 07:37:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [ 834/1669] eta: 0:09:21 tlr: 5.2e-05 tnm: 0.51 Lm: 6.364 (6.391) Lt: 5.635 (5.662) Accm: 3.78 (3.63) Acct: 5.96 (5.66) proj_loss: -0.6093 (-0.6156) time: 0.6733 data: 0.0003 [11-27 07:37:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [ 834/1669] eta: 0:09:21 tlr: 5.2e-05 tnm: 0.51 Lm: 6.427 (6.416) Lt: 5.644 (5.642) Accm: 3.93 (3.76) Acct: 6.22 (5.97) proj_loss: -0.6005 (-0.6078) time: 0.6733 data: 0.0003 [11-27 07:37:19] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [ 834/1669] eta: 0:09:21 tlr: 5.2e-05 tnm: 0.51 Lm: 6.411 (6.391) Lt: 5.661 (5.654) Accm: 3.45 (3.64) Acct: 5.41 (5.68) proj_loss: -0.6114 (-0.6134) time: 0.6733 data: 0.0002 [11-27 07:42:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [1251/1669] eta: 0:04:41 tlr: 5.1e-05 tnm: 0.50 Lm: 6.415 (6.424) Lt: 5.698 (5.680) Accm: 3.42 (3.55) Acct: 5.42 (5.62) proj_loss: -0.6103 (-0.6120) time: 0.6741 data: 0.0003 [11-27 07:42:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [1251/1669] eta: 0:04:41 tlr: 5.1e-05 tnm: 0.50 Lm: 6.411 (6.408) Lt: 5.668 (5.672) Accm: 3.70 (3.63) Acct: 5.74 (5.63) proj_loss: -0.6046 (-0.6113) time: 0.6741 data: 0.0002 [11-27 07:42:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [1251/1669] eta: 0:04:41 tlr: 5.1e-05 tnm: 0.50 Lm: 6.404 (6.383) Lt: 5.639 (5.611) Accm: 3.98 (3.87) Acct: 6.33 (6.18) proj_loss: -0.6078 (-0.6096) time: 0.6741 data: 0.0003 [11-27 07:42:00] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [1251/1669] eta: 0:04:41 tlr: 5.1e-05 tnm: 0.50 Lm: 6.446 (6.456) Lt: 5.686 (5.723) Accm: 3.57 (3.48) Acct: 5.65 (5.43) proj_loss: -0.6251 (-0.6216) time: 0.6741 data: 0.0003 [11-27 07:46:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [1668/1669] eta: 0:00:00 tlr: 5.1e-05 tnm: 0.52 Lm: 6.427 (6.450) Lt: 5.673 (5.709) Accm: 3.57 (3.50) Acct: 5.65 (5.47) proj_loss: -0.6237 (-0.6197) time: 0.7416 data: 0.0015 [11-27 07:46:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 282/350] Total time: 0:18:47 (0.675 s / it) [11-27 07:46:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [1668/1669] eta: 0:00:00 tlr: 5.1e-05 tnm: 0.52 Lm: 6.411 (6.417) Lt: 5.661 (5.676) Accm: 3.45 (3.57) Acct: 5.44 (5.64) proj_loss: -0.6114 (-0.6134) time: 0.7416 data: 0.0028 [11-27 07:46:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [1668/1669] eta: 0:00:00 tlr: 5.1e-05 tnm: 0.52 Lm: 6.458 (6.430) Lt: 5.701 (5.682) Accm: 3.63 (3.59) Acct: 5.53 (5.57) proj_loss: -0.6093 (-0.6147) time: 0.7416 data: 0.0015 [11-27 07:46:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 282/350] [1668/1669] eta: 0:00:00 tlr: 5.1e-05 tnm: 0.52 Lm: 6.382 (6.370) Lt: 5.634 (5.603) Accm: 4.04 (3.93) Acct: 6.40 (6.22) proj_loss: -0.6116 (-0.6100) time: 0.7416 data: 0.0014 [11-27 07:46:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 282/350] Total time: 0:18:47 (0.675 s / it) [11-27 07:46:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 282/350] Total time: 0:18:47 (0.675 s / it) [11-27 07:46:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 282/350] Total time: 0:18:47 (0.675 s / it) [11-27 07:46:44] (/home/user/VAR/train.py , line 279)=> [ep282] (training ) Lm: 6.420 (6.420), Lt: 5.662 (5.662), Acc m&t: 3.65 5.72, Remain: 21:06:00, Finish: 2024-11-27 12:52 [11-27 07:46:44] (/home/user/VAR/train.py , line 279)=> [ep282] (training ) Lm: 6.420 (6.420), Lt: 5.662 (5.662), Acc m&t: 3.65 5.72, Remain: 21:05:45, Finish: 2024-11-27 12:52 [11-27 07:46:44] (/home/user/VAR/train.py , line 279)=> [ep282] (training ) Lm: 6.420 (6.420), Lt: 5.662 (5.662), Acc m&t: 3.65 5.72, Remain: 21:05:37, Finish: 2024-11-27 12:52 [11-27 07:46:44] (/home/user/VAR/train.py , line 279)=> [ep282] (training ) Lm: 6.420 (6.420), Lt: 5.662 (5.662), Acc m&t: 3.65 5.72, Remain: 21:05:47, Finish: 2024-11-27 12:52 [11-27 07:46:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [ 0/1669] eta: 0:18:07 tlr: 5.1e-05 tnm: 0.50 Lm: 6.431 (6.431) Lt: 5.752 (5.752) Accm: 3.26 (3.26) Acct: 4.98 (4.98) proj_loss: -0.6449 (-0.6449) time: 0.6518 data: 0.0004 [11-27 07:46:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [ 0/1669] eta: 0:18:07 tlr: 5.1e-05 tnm: 0.50 Lm: 6.410 (6.410) Lt: 5.662 (5.662) Accm: 3.69 (3.69) Acct: 5.84 (5.84) proj_loss: -0.6050 (-0.6050) time: 0.6516 data: 0.0003 [11-27 07:46:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [ 0/1669] eta: 0:18:08 tlr: 5.1e-05 tnm: 0.50 Lm: 6.522 (6.522) Lt: 5.840 (5.840) Accm: 3.02 (3.02) Acct: 4.58 (4.58) proj_loss: -0.6275 (-0.6275) time: 0.6522 data: 0.0003 [11-27 07:46:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [ 0/1669] eta: 0:18:08 tlr: 5.1e-05 tnm: 0.50 Lm: 6.507 (6.507) Lt: 5.745 (5.745) Accm: 3.21 (3.21) Acct: 5.20 (5.20) proj_loss: -0.6115 (-0.6115) time: 0.6522 data: 0.0003 [11-27 07:51:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [ 417/1669] eta: 0:14:41 tlr: 5.1e-05 tnm: 0.50 Lm: 6.556 (6.556) Lt: 5.834 (5.834) Accm: 3.24 (3.24) Acct: 5.11 (5.11) proj_loss: -0.6004 (-0.6004) time: 0.6749 data: 0.0003 [11-27 07:51:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [ 417/1669] eta: 0:14:41 tlr: 5.1e-05 tnm: 0.50 Lm: 6.471 (6.471) Lt: 5.767 (5.767) Accm: 3.25 (3.25) Acct: 4.90 (4.90) proj_loss: -0.6118 (-0.6118) time: 0.6749 data: 0.0003 [11-27 07:51:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [ 417/1669] eta: 0:14:41 tlr: 5.1e-05 tnm: 0.50 Lm: 6.449 (6.449) Lt: 5.770 (5.770) Accm: 3.31 (3.31) Acct: 4.98 (4.98) proj_loss: -0.6390 (-0.6390) time: 0.6749 data: 0.0003 [11-27 07:51:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [ 417/1669] eta: 0:14:41 tlr: 5.1e-05 tnm: 0.50 Lm: 6.428 (6.428) Lt: 5.646 (5.646) Accm: 3.71 (3.71) Acct: 5.97 (5.97) proj_loss: -0.5959 (-0.5959) time: 0.6749 data: 0.0003 [11-27 07:56:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [ 834/1669] eta: 0:09:37 tlr: 5.1e-05 tnm: 0.50 Lm: 6.431 (6.429) Lt: 5.630 (5.640) Accm: 3.72 (3.71) Acct: 5.99 (5.97) proj_loss: -0.6050 (-0.6008) time: 0.6737 data: 0.0003 [11-27 07:56:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [ 834/1669] eta: 0:09:37 tlr: 5.1e-05 tnm: 0.50 Lm: 6.466 (6.463) Lt: 5.752 (5.746) Accm: 3.36 (3.36) Acct: 4.99 (5.11) proj_loss: -0.6330 (-0.6262) time: 0.6737 data: 0.0003 [11-27 07:56:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [ 834/1669] eta: 0:09:37 tlr: 5.1e-05 tnm: 0.50 Lm: 6.420 (6.435) Lt: 5.694 (5.725) Accm: 3.48 (3.34) Acct: 5.22 (5.02) proj_loss: -0.6089 (-0.6108) time: 0.6737 data: 0.0002 [11-27 07:56:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [ 834/1669] eta: 0:09:37 tlr: 5.1e-05 tnm: 0.50 Lm: 6.507 (6.480) Lt: 5.745 (5.748) Accm: 3.28 (3.46) Acct: 5.20 (5.36) proj_loss: -0.6107 (-0.6038) time: 0.6737 data: 0.0003 [11-27 08:01:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [1251/1669] eta: 0:04:46 tlr: 5.1e-05 tnm: 0.51 Lm: 6.497 (6.482) Lt: 5.736 (5.743) Accm: 3.34 (3.45) Acct: 5.39 (5.41) proj_loss: -0.6111 (-0.6072) time: 0.6753 data: 0.0003 [11-27 08:01:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [1251/1669] eta: 0:04:46 tlr: 5.1e-05 tnm: 0.51 Lm: 6.459 (6.451) Lt: 5.722 (5.732) Accm: 3.34 (3.31) Acct: 5.17 (5.04) proj_loss: -0.6068 (-0.6093) time: 0.6753 data: 0.0003 [11-27 08:01:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [1251/1669] eta: 0:04:46 tlr: 5.1e-05 tnm: 0.51 Lm: 6.452 (6.456) Lt: 5.725 (5.726) Accm: 3.41 (3.42) Acct: 5.17 (5.23) proj_loss: -0.6266 (-0.6247) time: 0.6753 data: 0.0003 [11-27 08:01:03] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [1251/1669] eta: 0:04:46 tlr: 5.1e-05 tnm: 0.51 Lm: 6.439 (6.443) Lt: 5.646 (5.659) Accm: 3.70 (3.64) Acct: 5.91 (5.90) proj_loss: -0.6078 (-0.6086) time: 0.6753 data: 0.0003 [11-27 08:05:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [1668/1669] eta: 0:00:00 tlr: 5.1e-05 tnm: 0.51 Lm: 6.440 (6.442) Lt: 5.662 (5.681) Accm: 3.69 (3.61) Acct: 5.84 (5.87) proj_loss: -0.6105 (-0.6138) time: 0.6741 data: 0.0018 [11-27 08:05:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 283/350] Total time: 0:18:58 (0.682 s / it) [11-27 08:05:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [1668/1669] eta: 0:00:00 tlr: 5.1e-05 tnm: 0.51 Lm: 6.499 (6.465) Lt: 5.750 (5.753) Accm: 3.28 (3.30) Acct: 5.13 (5.04) proj_loss: -0.6089 (-0.6138) time: 0.6741 data: 0.0021 [11-27 08:05:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [1668/1669] eta: 0:00:00 tlr: 5.1e-05 tnm: 0.51 Lm: 6.437 (6.439) Lt: 5.698 (5.680) Accm: 3.46 (3.52) Acct: 5.35 (5.51) proj_loss: -0.6201 (-0.6190) time: 0.6741 data: 0.0014 [11-27 08:05:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 283/350] [1668/1669] eta: 0:00:00 tlr: 5.1e-05 tnm: 0.51 Lm: 6.487 (6.454) Lt: 5.726 (5.716) Accm: 3.39 (3.55) Acct: 5.58 (5.56) proj_loss: -0.6107 (-0.6075) time: 0.6741 data: 0.0019 [11-27 08:05:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 283/350] Total time: 0:18:58 (0.682 s / it) [11-27 08:05:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 283/350] Total time: 0:18:58 (0.682 s / it) [11-27 08:05:43] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 283/350] Total time: 0:18:58 (0.682 s / it) [11-27 08:05:43] (/home/user/VAR/train.py , line 279)=> [ep283] (training ) Lm: 6.420 (6.440), Lt: 5.662 (5.689), Acc m&t: 3.65 5.72, Remain: 20:47:52, Finish: 2024-11-27 12:53 [11-27 08:05:43] (/home/user/VAR/train.py , line 279)=> [ep283] (training ) Lm: 6.420 (6.440), Lt: 5.662 (5.689), Acc m&t: 3.65 5.72, Remain: 20:47:20, Finish: 2024-11-27 12:53 [11-27 08:05:43] (/home/user/VAR/train.py , line 279)=> [ep283] (training ) Lm: 6.420 (6.440), Lt: 5.662 (5.689), Acc m&t: 3.65 5.72, Remain: 20:47:33, Finish: 2024-11-27 12:53 [11-27 08:05:43] (/home/user/VAR/train.py , line 279)=> [ep283] (training ) Lm: 6.420 (6.440), Lt: 5.662 (5.689), Acc m&t: 3.65 5.72, Remain: 20:47:52, Finish: 2024-11-27 12:53 [11-27 08:05:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [ 0/1669] eta: 0:18:14 tlr: 5.1e-05 tnm: 0.52 Lm: 6.461 (6.461) Lt: 5.758 (5.758) Accm: 3.33 (3.33) Acct: 5.25 (5.25) proj_loss: -0.6173 (-0.6173) time: 0.6557 data: 0.0003 [11-27 08:05:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [ 0/1669] eta: 0:18:15 tlr: 5.1e-05 tnm: 0.52 Lm: 6.459 (6.459) Lt: 5.701 (5.701) Accm: 3.55 (3.55) Acct: 5.46 (5.46) proj_loss: -0.6019 (-0.6019) time: 0.6562 data: 0.0004 [11-27 08:05:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [ 0/1669] eta: 0:18:14 tlr: 5.1e-05 tnm: 0.52 Lm: 6.310 (6.310) Lt: 5.505 (5.505) Accm: 4.19 (4.19) Acct: 6.96 (6.96) proj_loss: -0.5988 (-0.5988) time: 0.6561 data: 0.0004 [11-27 08:05:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [ 0/1669] eta: 0:18:15 tlr: 5.1e-05 tnm: 0.52 Lm: 6.581 (6.581) Lt: 5.890 (5.890) Accm: 3.07 (3.07) Acct: 4.99 (4.99) proj_loss: -0.6027 (-0.6027) time: 0.6562 data: 0.0004 [11-27 08:10:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [ 417/1669] eta: 0:14:03 tlr: 5e-05 tnm: 0.50 Lm: 6.466 (6.466) Lt: 5.764 (5.764) Accm: 3.37 (3.37) Acct: 5.24 (5.24) proj_loss: -0.6171 (-0.6171) time: 0.6723 data: 0.0003 [11-27 08:10:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [ 417/1669] eta: 0:14:03 tlr: 5e-05 tnm: 0.50 Lm: 6.410 (6.410) Lt: 5.662 (5.662) Accm: 3.61 (3.61) Acct: 5.52 (5.52) proj_loss: -0.6169 (-0.6169) time: 0.6723 data: 0.0003 [11-27 08:10:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [ 417/1669] eta: 0:14:03 tlr: 5e-05 tnm: 0.50 Lm: 6.316 (6.316) Lt: 5.504 (5.504) Accm: 4.09 (4.09) Acct: 6.74 (6.74) proj_loss: -0.6033 (-0.6033) time: 0.6723 data: 0.0003 [11-27 08:10:25] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [ 417/1669] eta: 0:14:03 tlr: 5e-05 tnm: 0.50 Lm: 6.510 (6.510) Lt: 5.771 (5.771) Accm: 3.33 (3.33) Acct: 5.32 (5.32) proj_loss: -0.6210 (-0.6210) time: 0.6723 data: 0.0003 [11-27 08:15:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [ 834/1669] eta: 0:09:37 tlr: 5e-05 tnm: 0.50 Lm: 6.461 (6.462) Lt: 5.758 (5.736) Accm: 3.33 (3.41) Acct: 5.39 (5.37) proj_loss: -0.6173 (-0.6180) time: 0.6758 data: 0.0003 [11-27 08:15:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [ 834/1669] eta: 0:09:37 tlr: 5e-05 tnm: 0.50 Lm: 6.321 (6.322) Lt: 5.505 (5.510) Accm: 3.99 (4.02) Acct: 6.53 (6.60) proj_loss: -0.6078 (-0.6068) time: 0.6758 data: 0.0003 [11-27 08:15:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [ 834/1669] eta: 0:09:37 tlr: 5e-05 tnm: 0.50 Lm: 6.360 (6.385) Lt: 5.622 (5.629) Accm: 3.67 (3.84) Acct: 5.58 (5.96) proj_loss: -0.6113 (-0.6150) time: 0.6758 data: 0.0003 [11-27 08:15:21] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [ 834/1669] eta: 0:09:37 tlr: 5e-05 tnm: 0.50 Lm: 6.445 (6.459) Lt: 5.664 (5.730) Accm: 3.58 (3.44) Acct: 5.49 (5.38) proj_loss: -0.6079 (-0.6140) time: 0.6758 data: 0.0003 [11-27 08:20:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [1251/1669] eta: 0:04:47 tlr: 5e-05 tnm: 0.51 Lm: 6.397 (6.389) Lt: 5.651 (5.644) Accm: 3.63 (3.64) Acct: 5.58 (5.61) proj_loss: -0.6075 (-0.6123) time: 0.6735 data: 0.0003 [11-27 08:20:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [1251/1669] eta: 0:04:47 tlr: 5e-05 tnm: 0.51 Lm: 6.327 (6.381) Lt: 5.514 (5.589) Accm: 3.94 (3.84) Acct: 6.42 (6.24) proj_loss: -0.6108 (-0.6088) time: 0.6735 data: 0.0003 [11-27 08:20:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [1251/1669] eta: 0:04:47 tlr: 5e-05 tnm: 0.51 Lm: 6.410 (6.434) Lt: 5.662 (5.677) Accm: 3.61 (3.67) Acct: 5.52 (5.80) proj_loss: -0.6069 (-0.6119) time: 0.6735 data: 0.0003 [11-27 08:20:04] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [1251/1669] eta: 0:04:47 tlr: 5e-05 tnm: 0.51 Lm: 6.439 (6.451) Lt: 5.712 (5.715) Accm: 3.45 (3.48) Acct: 5.43 (5.46) proj_loss: -0.6146 (-0.6112) time: 0.6735 data: 0.0003 [11-27 08:24:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [1668/1669] eta: 0:00:00 tlr: 5e-05 tnm: 0.51 Lm: 6.416 (6.439) Lt: 5.665 (5.696) Accm: 3.58 (3.54) Acct: 5.48 (5.55) proj_loss: -0.6173 (-0.6167) time: 0.6756 data: 0.0013 [11-27 08:24:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 284/350] Total time: 0:19:01 (0.684 s / it) [11-27 08:24:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [1668/1669] eta: 0:00:00 tlr: 5e-05 tnm: 0.51 Lm: 6.407 (6.393) Lt: 5.664 (5.653) Accm: 3.67 (3.69) Acct: 5.66 (5.71) proj_loss: -0.6079 (-0.6142) time: 0.6755 data: 0.0025 [11-27 08:24:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [1668/1669] eta: 0:00:00 tlr: 5e-05 tnm: 0.51 Lm: 6.334 (6.372) Lt: 5.522 (5.601) Accm: 3.99 (3.90) Acct: 6.32 (6.23) proj_loss: -0.6110 (-0.6092) time: 0.6755 data: 0.0022 [11-27 08:24:44] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 284/350] [1668/1669] eta: 0:00:00 tlr: 5e-05 tnm: 0.51 Lm: 6.370 (6.421) Lt: 5.622 (5.655) Accm: 3.67 (3.73) Acct: 5.58 (5.87) proj_loss: -0.6025 (-0.6095) time: 0.6755 data: 0.0022 [11-27 08:24:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 284/350] Total time: 0:19:01 (0.684 s / it) [11-27 08:24:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 284/350] Total time: 0:19:01 (0.684 s / it) [11-27 08:24:44] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 284/350] Total time: 0:19:01 (0.684 s / it) [11-27 08:24:44] (/home/user/VAR/train.py , line 279)=> [ep284] (training ) Lm: 6.420 (6.435), Lt: 5.662 (5.685), Acc m&t: 3.65 5.72, Remain: 20:30:04, Finish: 2024-11-27 12:54 [11-27 08:24:44] (/home/user/VAR/train.py , line 279)=> [ep284] (training ) Lm: 6.420 (6.435), Lt: 5.662 (5.685), Acc m&t: 3.65 5.72, Remain: 20:29:21, Finish: 2024-11-27 12:54 [11-27 08:24:44] (/home/user/VAR/train.py , line 279)=> [ep284] (training ) Lm: 6.420 (6.435), Lt: 5.662 (5.685), Acc m&t: 3.65 5.72, Remain: 20:29:44, Finish: 2024-11-27 12:54 [11-27 08:24:44] (/home/user/VAR/train.py , line 279)=> [ep284] (training ) Lm: 6.420 (6.435), Lt: 5.662 (5.685), Acc m&t: 3.65 5.72, Remain: 20:29:43, Finish: 2024-11-27 12:54 [11-27 08:24:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [ 0/1669] eta: 0:18:27 tlr: 5e-05 tnm: 0.53 Lm: 6.532 (6.532) Lt: 5.836 (5.836) Accm: 3.17 (3.17) Acct: 4.99 (4.99) proj_loss: -0.6276 (-0.6276) time: 0.6637 data: 0.0004 [11-27 08:24:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [ 0/1669] eta: 0:18:27 tlr: 5e-05 tnm: 0.53 Lm: 6.593 (6.593) Lt: 5.836 (5.836) Accm: 3.28 (3.28) Acct: 5.11 (5.11) proj_loss: -0.6069 (-0.6069) time: 0.6636 data: 0.0003 [11-27 08:24:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [ 0/1669] eta: 0:18:27 tlr: 5e-05 tnm: 0.53 Lm: 6.398 (6.398) Lt: 5.670 (5.670) Accm: 3.56 (3.56) Acct: 5.32 (5.32) proj_loss: -0.6213 (-0.6213) time: 0.6638 data: 0.0004 [11-27 08:24:45] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [ 0/1669] eta: 0:18:26 tlr: 5e-05 tnm: 0.53 Lm: 6.335 (6.335) Lt: 5.574 (5.574) Accm: 3.93 (3.93) Acct: 5.91 (5.91) proj_loss: -0.6291 (-0.6291) time: 0.6627 data: 0.0004 [11-27 08:29:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [ 417/1669] eta: 0:14:08 tlr: 5e-05 tnm: 0.51 Lm: 6.424 (6.424) Lt: 5.702 (5.702) Accm: 3.54 (3.54) Acct: 5.25 (5.25) proj_loss: -0.6201 (-0.6201) time: 0.6740 data: 0.0003 [11-27 08:29:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [ 417/1669] eta: 0:14:08 tlr: 5e-05 tnm: 0.51 Lm: 6.385 (6.385) Lt: 5.588 (5.588) Accm: 3.93 (3.93) Acct: 6.22 (6.22) proj_loss: -0.6145 (-0.6145) time: 0.6740 data: 0.0002 [11-27 08:29:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [ 417/1669] eta: 0:14:08 tlr: 5e-05 tnm: 0.51 Lm: 6.314 (6.314) Lt: 5.564 (5.564) Accm: 3.76 (3.76) Acct: 5.89 (5.89) proj_loss: -0.6193 (-0.6193) time: 0.6740 data: 0.0003 [11-27 08:29:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [ 417/1669] eta: 0:14:08 tlr: 5e-05 tnm: 0.51 Lm: 6.543 (6.543) Lt: 5.836 (5.836) Accm: 3.21 (3.21) Acct: 4.93 (4.93) proj_loss: -0.6210 (-0.6210) time: 0.6740 data: 0.0003 [11-27 08:34:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [ 834/1669] eta: 0:09:23 tlr: 4.9e-05 tnm: 0.50 Lm: 6.553 (6.547) Lt: 5.836 (5.800) Accm: 3.24 (3.30) Acct: 4.99 (5.18) proj_loss: -0.6152 (-0.6191) time: 0.6725 data: 0.0003 [11-27 08:34:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [ 834/1669] eta: 0:09:23 tlr: 4.9e-05 tnm: 0.50 Lm: 6.398 (6.382) Lt: 5.670 (5.635) Accm: 3.56 (3.65) Acct: 5.51 (5.76) proj_loss: -0.6207 (-0.6198) time: 0.6725 data: 0.0003 [11-27 08:34:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [ 834/1669] eta: 0:09:23 tlr: 4.9e-05 tnm: 0.50 Lm: 6.481 (6.417) Lt: 5.738 (5.638) Accm: 3.59 (3.81) Acct: 5.34 (5.93) proj_loss: -0.6069 (-0.6081) time: 0.6725 data: 0.0005 [11-27 08:34:08] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [ 834/1669] eta: 0:09:23 tlr: 4.9e-05 tnm: 0.50 Lm: 6.432 (6.427) Lt: 5.679 (5.695) Accm: 3.80 (3.63) Acct: 5.91 (5.61) proj_loss: -0.6217 (-0.6206) time: 0.6725 data: 0.0003 [11-27 08:38:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [1251/1669] eta: 0:04:41 tlr: 4.9e-05 tnm: 0.53 Lm: 6.543 (6.477) Lt: 5.782 (5.741) Accm: 3.37 (3.58) Acct: 5.34 (5.56) proj_loss: -0.6176 (-0.6193) time: 0.6748 data: 0.0003 [11-27 08:38:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [1251/1669] eta: 0:04:41 tlr: 4.9e-05 tnm: 0.53 Lm: 6.402 (6.413) Lt: 5.658 (5.680) Accm: 3.80 (3.67) Acct: 5.97 (5.72) proj_loss: -0.6164 (-0.6146) time: 0.6748 data: 0.0003 [11-27 08:38:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [1251/1669] eta: 0:04:41 tlr: 4.9e-05 tnm: 0.53 Lm: 6.405 (6.390) Lt: 5.645 (5.631) Accm: 3.76 (3.73) Acct: 5.97 (5.93) proj_loss: -0.6194 (-0.6193) time: 0.6748 data: 0.0003 [11-27 08:38:49] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [1251/1669] eta: 0:04:41 tlr: 4.9e-05 tnm: 0.53 Lm: 6.390 (6.387) Lt: 5.611 (5.599) Accm: 3.71 (3.82) Acct: 5.59 (5.91) proj_loss: -0.6090 (-0.6089) time: 0.6748 data: 0.0003 [11-27 08:43:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [1668/1669] eta: 0:00:00 tlr: 4.9e-05 tnm: 0.51 Lm: 6.452 (6.400) Lt: 5.699 (5.619) Accm: 3.59 (3.72) Acct: 5.34 (5.77) proj_loss: -0.6069 (-0.6067) time: 0.7386 data: 0.0018 [11-27 08:43:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 285/350] Total time: 0:18:48 (0.676 s / it) [11-27 08:43:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [1668/1669] eta: 0:00:00 tlr: 4.9e-05 tnm: 0.51 Lm: 6.413 (6.401) Lt: 5.670 (5.645) Accm: 3.63 (3.71) Acct: 5.75 (5.89) proj_loss: -0.6180 (-0.6165) time: 0.7386 data: 0.0020 [11-27 08:43:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [1668/1669] eta: 0:00:00 tlr: 4.9e-05 tnm: 0.51 Lm: 6.532 (6.444) Lt: 5.728 (5.708) Accm: 3.50 (3.62) Acct: 5.51 (5.55) proj_loss: -0.6152 (-0.6158) time: 0.7386 data: 0.0018 [11-27 08:43:33] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 285/350] [1668/1669] eta: 0:00:00 tlr: 4.9e-05 tnm: 0.51 Lm: 6.432 (6.453) Lt: 5.679 (5.708) Accm: 3.80 (3.54) Acct: 5.91 (5.49) proj_loss: -0.6111 (-0.6137) time: 0.7386 data: 0.0015 [11-27 08:43:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 285/350] Total time: 0:18:48 (0.676 s / it) [11-27 08:43:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 285/350] Total time: 0:18:48 (0.676 s / it) [11-27 08:43:33] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 285/350] Total time: 0:18:48 (0.676 s / it) [11-27 08:43:33] (/home/user/VAR/train.py , line 279)=> [ep285] (training ) Lm: 6.420 (6.431), Lt: 5.662 (5.675), Acc m&t: 3.65 5.72, Remain: 20:09:33, Finish: 2024-11-27 12:53 [11-27 08:43:33] (/home/user/VAR/train.py , line 279)=> [ep285] (training ) Lm: 6.420 (6.431), Lt: 5.662 (5.675), Acc m&t: 3.65 5.72, Remain: 20:10:12, Finish: 2024-11-27 12:53 [11-27 08:43:33] (/home/user/VAR/train.py , line 279)=> [ep285] (training ) Lm: 6.420 (6.431), Lt: 5.662 (5.675), Acc m&t: 3.65 5.72, Remain: 20:09:19, Finish: 2024-11-27 12:52 [11-27 08:43:33] (/home/user/VAR/train.py , line 279)=> [ep285] (training ) Lm: 6.420 (6.431), Lt: 5.662 (5.675), Acc m&t: 3.65 5.72, Remain: 20:09:30, Finish: 2024-11-27 12:53 [11-27 08:43:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [ 0/1669] eta: 0:18:22 tlr: 4.9e-05 tnm: 0.52 Lm: 6.460 (6.460) Lt: 5.697 (5.697) Accm: 3.30 (3.30) Acct: 5.30 (5.30) proj_loss: -0.6146 (-0.6146) time: 0.6605 data: 0.0004 [11-27 08:43:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [ 0/1669] eta: 0:18:22 tlr: 4.9e-05 tnm: 0.52 Lm: 6.359 (6.359) Lt: 5.564 (5.564) Accm: 4.38 (4.38) Acct: 6.71 (6.71) proj_loss: -0.6205 (-0.6205) time: 0.6607 data: 0.0003 [11-27 08:43:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [ 0/1669] eta: 0:18:22 tlr: 4.9e-05 tnm: 0.52 Lm: 6.484 (6.484) Lt: 5.672 (5.672) Accm: 3.64 (3.64) Acct: 5.73 (5.73) proj_loss: -0.5898 (-0.5898) time: 0.6607 data: 0.0004 [11-27 08:43:34] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [ 0/1669] eta: 0:18:25 tlr: 4.9e-05 tnm: 0.52 Lm: 6.407 (6.407) Lt: 5.690 (5.690) Accm: 3.61 (3.61) Acct: 5.30 (5.30) proj_loss: -0.6150 (-0.6150) time: 0.6623 data: 0.0004 [11-27 08:48:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [ 417/1669] eta: 0:14:49 tlr: 4.9e-05 tnm: 0.53 Lm: 6.418 (6.418) Lt: 5.659 (5.659) Accm: 3.61 (3.61) Acct: 5.49 (5.49) proj_loss: -0.6161 (-0.6161) time: 0.6745 data: 0.0003 [11-27 08:48:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [ 417/1669] eta: 0:14:49 tlr: 4.9e-05 tnm: 0.53 Lm: 6.491 (6.491) Lt: 5.706 (5.706) Accm: 3.48 (3.48) Acct: 5.42 (5.42) proj_loss: -0.6009 (-0.6009) time: 0.6745 data: 0.0003 [11-27 08:48:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [ 417/1669] eta: 0:14:49 tlr: 4.9e-05 tnm: 0.53 Lm: 6.365 (6.365) Lt: 5.579 (5.579) Accm: 3.59 (3.59) Acct: 5.89 (5.89) proj_loss: -0.6161 (-0.6161) time: 0.6745 data: 0.0003 [11-27 08:48:30] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [ 417/1669] eta: 0:14:49 tlr: 4.9e-05 tnm: 0.53 Lm: 6.408 (6.408) Lt: 5.610 (5.610) Accm: 3.95 (3.95) Acct: 6.12 (6.12) proj_loss: -0.6124 (-0.6124) time: 0.6745 data: 0.0003 [11-27 08:53:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [ 834/1669] eta: 0:09:40 tlr: 4.9e-05 tnm: 0.53 Lm: 6.458 (6.443) Lt: 5.657 (5.661) Accm: 3.52 (3.67) Acct: 5.53 (5.64) proj_loss: -0.6105 (-0.6118) time: 0.6746 data: 0.0002 [11-27 08:53:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [ 834/1669] eta: 0:09:40 tlr: 4.9e-05 tnm: 0.53 Lm: 6.484 (6.457) Lt: 5.672 (5.678) Accm: 3.64 (3.66) Acct: 5.73 (5.70) proj_loss: -0.6121 (-0.6048) time: 0.6746 data: 0.0003 [11-27 08:53:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [ 834/1669] eta: 0:09:40 tlr: 4.9e-05 tnm: 0.53 Lm: 6.428 (6.438) Lt: 5.690 (5.687) Accm: 3.61 (3.52) Acct: 5.30 (5.43) proj_loss: -0.6150 (-0.6090) time: 0.6746 data: 0.0003 [11-27 08:53:14] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [ 834/1669] eta: 0:09:40 tlr: 4.9e-05 tnm: 0.53 Lm: 6.460 (6.407) Lt: 5.697 (5.641) Accm: 3.40 (3.53) Acct: 5.30 (5.65) proj_loss: -0.6175 (-0.6175) time: 0.6746 data: 0.0003 [11-27 08:57:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [1251/1669] eta: 0:04:47 tlr: 4.9e-05 tnm: 0.50 Lm: 6.392 (6.386) Lt: 5.621 (5.617) Accm: 3.64 (3.67) Acct: 5.75 (5.79) proj_loss: -0.6161 (-0.6162) time: 0.6763 data: 0.0003 [11-27 08:57:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [1251/1669] eta: 0:04:47 tlr: 4.9e-05 tnm: 0.50 Lm: 6.485 (6.470) Lt: 5.709 (5.691) Accm: 3.44 (3.59) Acct: 5.45 (5.57) proj_loss: -0.6078 (-0.6101) time: 0.6763 data: 0.0003 [11-27 08:57:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [1251/1669] eta: 0:04:47 tlr: 4.9e-05 tnm: 0.50 Lm: 6.483 (6.463) Lt: 5.705 (5.693) Accm: 3.57 (3.62) Acct: 5.58 (5.63) proj_loss: -0.6123 (-0.6086) time: 0.6763 data: 0.0003 [11-27 08:57:55] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [1251/1669] eta: 0:04:47 tlr: 4.9e-05 tnm: 0.50 Lm: 6.442 (6.442) Lt: 5.701 (5.693) Accm: 3.47 (3.44) Acct: 5.30 (5.29) proj_loss: -0.6161 (-0.6136) time: 0.6763 data: 0.0003 [11-27 09:02:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [1668/1669] eta: 0:00:00 tlr: 4.8e-05 tnm: 0.52 Lm: 6.455 (6.449) Lt: 5.711 (5.698) Accm: 3.47 (3.45) Acct: 5.30 (5.33) proj_loss: -0.6172 (-0.6147) time: 0.6762 data: 0.0021 [11-27 09:02:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 286/350] Total time: 0:19:02 (0.684 s / it) [11-27 09:02:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [1668/1669] eta: 0:00:00 tlr: 4.8e-05 tnm: 0.52 Lm: 6.484 (6.474) Lt: 5.739 (5.704) Accm: 3.63 (3.62) Acct: 5.73 (5.68) proj_loss: -0.6121 (-0.6066) time: 0.6762 data: 0.0018 [11-27 09:02:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [1668/1669] eta: 0:00:00 tlr: 4.8e-05 tnm: 0.52 Lm: 6.460 (6.414) Lt: 5.697 (5.649) Accm: 3.40 (3.58) Acct: 5.30 (5.64) proj_loss: -0.6175 (-0.6167) time: 0.6762 data: 0.0013 [11-27 09:02:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 286/350] [1668/1669] eta: 0:00:00 tlr: 4.8e-05 tnm: 0.52 Lm: 6.476 (6.471) Lt: 5.725 (5.697) Accm: 3.52 (3.58) Acct: 5.51 (5.56) proj_loss: -0.6092 (-0.6099) time: 0.6762 data: 0.0016 [11-27 09:02:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 286/350] Total time: 0:19:02 (0.684 s / it) [11-27 09:02:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 286/350] Total time: 0:19:02 (0.684 s / it) [11-27 09:02:36] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 286/350] Total time: 0:19:02 (0.684 s / it) [11-27 09:02:36] (/home/user/VAR/train.py , line 279)=> [ep286] (training ) Lm: 6.420 (6.428), Lt: 5.662 (5.672), Acc m&t: 3.65 5.72, Remain: 19:55:30, Finish: 2024-11-27 12:58 [11-27 09:02:36] (/home/user/VAR/train.py , line 279)=> [ep286] (training ) Lm: 6.420 (6.428), Lt: 5.662 (5.672), Acc m&t: 3.65 5.72, Remain: 19:54:45, Finish: 2024-11-27 12:57 [11-27 09:02:36] (/home/user/VAR/train.py , line 279)=> [ep286] (training ) Lm: 6.420 (6.428), Lt: 5.662 (5.672), Acc m&t: 3.65 5.72, Remain: 19:54:44, Finish: 2024-11-27 12:57 [11-27 09:02:36] (/home/user/VAR/train.py , line 279)=> [ep286] (training ) Lm: 6.420 (6.428), Lt: 5.662 (5.672), Acc m&t: 3.65 5.72, Remain: 19:54:15, Finish: 2024-11-27 12:56 [11-27 09:02:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [ 0/1669] eta: 0:18:11 tlr: 4.8e-05 tnm: 0.50 Lm: 6.605 (6.605) Lt: 5.898 (5.898) Accm: 3.13 (3.13) Acct: 4.91 (4.91) proj_loss: -0.6128 (-0.6128) time: 0.6542 data: 0.0004 [11-27 09:02:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [ 0/1669] eta: 0:18:12 tlr: 4.8e-05 tnm: 0.50 Lm: 6.439 (6.439) Lt: 5.694 (5.694) Accm: 3.75 (3.75) Acct: 5.96 (5.96) proj_loss: -0.6267 (-0.6267) time: 0.6547 data: 0.0003 [11-27 09:02:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [ 0/1669] eta: 0:18:13 tlr: 4.8e-05 tnm: 0.50 Lm: 6.320 (6.320) Lt: 5.479 (5.479) Accm: 4.31 (4.31) Acct: 6.66 (6.66) proj_loss: -0.5941 (-0.5941) time: 0.6549 data: 0.0004 [11-27 09:02:36] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [ 0/1669] eta: 0:18:12 tlr: 4.8e-05 tnm: 0.50 Lm: 6.428 (6.428) Lt: 5.701 (5.701) Accm: 3.62 (3.62) Acct: 5.58 (5.58) proj_loss: -0.6488 (-0.6488) time: 0.6549 data: 0.0004 [11-27 09:07:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [ 417/1669] eta: 0:14:03 tlr: 4.8e-05 tnm: 0.52 Lm: 6.457 (6.457) Lt: 5.717 (5.717) Accm: 3.53 (3.53) Acct: 5.37 (5.37) proj_loss: -0.6458 (-0.6458) time: 0.6734 data: 0.0003 [11-27 09:07:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [ 417/1669] eta: 0:14:03 tlr: 4.8e-05 tnm: 0.52 Lm: 6.416 (6.416) Lt: 5.702 (5.702) Accm: 3.59 (3.59) Acct: 5.54 (5.54) proj_loss: -0.6272 (-0.6272) time: 0.6734 data: 0.0003 [11-27 09:07:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [ 417/1669] eta: 0:14:03 tlr: 4.8e-05 tnm: 0.52 Lm: 6.514 (6.514) Lt: 5.788 (5.788) Accm: 3.40 (3.40) Acct: 5.41 (5.41) proj_loss: -0.6207 (-0.6207) time: 0.6734 data: 0.0003 [11-27 09:07:18] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [ 417/1669] eta: 0:14:03 tlr: 4.8e-05 tnm: 0.52 Lm: 6.329 (6.329) Lt: 5.514 (5.514) Accm: 4.05 (4.05) Acct: 6.26 (6.26) proj_loss: -0.5915 (-0.5915) time: 0.6734 data: 0.0003 [11-27 09:12:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [ 834/1669] eta: 0:09:39 tlr: 4.8e-05 tnm: 0.51 Lm: 6.338 (6.336) Lt: 5.549 (5.526) Accm: 4.01 (4.03) Acct: 6.03 (6.18) proj_loss: -0.5941 (-0.5963) time: 0.6720 data: 0.0003 [11-27 09:12:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [ 834/1669] eta: 0:09:39 tlr: 4.8e-05 tnm: 0.51 Lm: 6.422 (6.481) Lt: 5.677 (5.748) Accm: 3.64 (3.48) Acct: 5.65 (5.49) proj_loss: -0.6128 (-0.6153) time: 0.6720 data: 0.0003 [11-27 09:12:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [ 834/1669] eta: 0:09:39 tlr: 4.8e-05 tnm: 0.51 Lm: 6.400 (6.411) Lt: 5.694 (5.674) Accm: 3.72 (3.63) Acct: 5.80 (5.63) proj_loss: -0.6267 (-0.6123) time: 0.6720 data: 0.0003 [11-27 09:12:15] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [ 834/1669] eta: 0:09:39 tlr: 4.8e-05 tnm: 0.51 Lm: 6.485 (6.472) Lt: 5.733 (5.728) Accm: 3.45 (3.44) Acct: 5.20 (5.31) proj_loss: -0.6428 (-0.6305) time: 0.6720 data: 0.0003 [11-27 09:16:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [1251/1669] eta: 0:04:47 tlr: 4.8e-05 tnm: 0.52 Lm: 6.457 (6.438) Lt: 5.717 (5.679) Accm: 3.53 (3.62) Acct: 5.39 (5.67) proj_loss: -0.6380 (-0.6312) time: 0.6730 data: 0.0003 [11-27 09:16:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [1251/1669] eta: 0:04:47 tlr: 4.8e-05 tnm: 0.52 Lm: 6.419 (6.435) Lt: 5.702 (5.703) Accm: 3.57 (3.55) Acct: 5.47 (5.49) proj_loss: -0.6184 (-0.6118) time: 0.6730 data: 0.0003 [11-27 09:16:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [1251/1669] eta: 0:04:47 tlr: 4.8e-05 tnm: 0.52 Lm: 6.419 (6.438) Lt: 5.673 (5.706) Accm: 3.66 (3.56) Acct: 5.66 (5.54) proj_loss: -0.6159 (-0.6162) time: 0.6730 data: 0.0003 [11-27 09:16:58] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [1251/1669] eta: 0:04:47 tlr: 4.8e-05 tnm: 0.52 Lm: 6.343 (6.418) Lt: 5.550 (5.627) Accm: 3.90 (3.86) Acct: 5.94 (5.98) proj_loss: -0.6000 (-0.5998) time: 0.6730 data: 0.0003 [11-27 09:21:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [1668/1669] eta: 0:00:00 tlr: 4.8e-05 tnm: 0.51 Lm: 6.400 (6.398) Lt: 5.694 (5.657) Accm: 3.72 (3.72) Acct: 5.80 (5.77) proj_loss: -0.6132 (-0.6121) time: 0.6758 data: 0.0020 [11-27 09:21:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [1668/1669] eta: 0:00:00 tlr: 4.8e-05 tnm: 0.51 Lm: 6.428 (6.431) Lt: 5.701 (5.666) Accm: 3.62 (3.68) Acct: 5.58 (5.81) proj_loss: -0.6331 (-0.6256) time: 0.6758 data: 0.0022 [11-27 09:21:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [1668/1669] eta: 0:00:00 tlr: 4.8e-05 tnm: 0.51 Lm: 6.349 (6.431) Lt: 5.550 (5.646) Accm: 3.79 (3.81) Acct: 5.85 (5.90) proj_loss: -0.6060 (-0.6027) time: 0.6758 data: 0.0017 [11-27 09:21:39] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 287/350] [1668/1669] eta: 0:00:00 tlr: 4.8e-05 tnm: 0.51 Lm: 6.415 (6.397) Lt: 5.668 (5.655) Accm: 3.67 (3.67) Acct: 5.68 (5.75) proj_loss: -0.6128 (-0.6117) time: 0.6758 data: 0.0020 [11-27 09:21:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 287/350] Total time: 0:19:03 (0.685 s / it) [11-27 09:21:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 287/350] Total time: 0:19:03 (0.685 s / it) [11-27 09:21:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 287/350] Total time: 0:19:03 (0.685 s / it) [11-27 09:21:39] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 287/350] Total time: 0:19:03 (0.685 s / it) [11-27 09:21:39] (/home/user/VAR/train.py , line 279)=> [ep287] (training ) Lm: 6.416 (6.416), Lt: 5.662 (5.662), Acc m&t: 3.66 5.72, Remain: 19:32:40, Finish: 2024-11-27 12:54 [11-27 09:21:39] (/home/user/VAR/train.py , line 279)=> [ep287] (training ) Lm: 6.416 (6.416), Lt: 5.662 (5.662), Acc m&t: 3.66 5.72, Remain: 19:32:40, Finish: 2024-11-27 12:54 [11-27 09:21:39] (/home/user/VAR/train.py , line 279)=> [ep287] (training ) Lm: 6.416 (6.416), Lt: 5.662 (5.662), Acc m&t: 3.66 5.72, Remain: 19:32:35, Finish: 2024-11-27 12:54 [11-27 09:21:39] (/home/user/VAR/train.py , line 279)=> [ep287] (training ) Lm: 6.416 (6.416), Lt: 5.662 (5.662), Acc m&t: 3.66 5.72, Remain: 19:32:41, Finish: 2024-11-27 12:54 [11-27 09:21:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [ 0/1669] eta: 0:18:13 tlr: 4.8e-05 tnm: 0.52 Lm: 6.418 (6.418) Lt: 5.688 (5.688) Accm: 3.57 (3.57) Acct: 5.63 (5.63) proj_loss: -0.5932 (-0.5932) time: 0.6549 data: 0.0004 [11-27 09:21:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [ 0/1669] eta: 0:18:11 tlr: 4.8e-05 tnm: 0.52 Lm: 6.353 (6.353) Lt: 5.572 (5.572) Accm: 3.79 (3.79) Acct: 6.04 (6.04) proj_loss: -0.5968 (-0.5968) time: 0.6541 data: 0.0003 [11-27 09:21:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [ 0/1669] eta: 0:18:12 tlr: 4.8e-05 tnm: 0.52 Lm: 6.442 (6.442) Lt: 5.675 (5.675) Accm: 3.39 (3.39) Acct: 5.30 (5.30) proj_loss: -0.6016 (-0.6016) time: 0.6544 data: 0.0004 [11-27 09:21:40] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [ 0/1669] eta: 0:18:13 tlr: 4.8e-05 tnm: 0.52 Lm: 6.437 (6.437) Lt: 5.745 (5.745) Accm: 3.68 (3.68) Acct: 5.75 (5.75) proj_loss: -0.6144 (-0.6144) time: 0.6550 data: 0.0004 [11-27 09:26:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [ 417/1669] eta: 0:14:08 tlr: 4.7e-05 tnm: 0.51 Lm: 6.392 (6.392) Lt: 5.663 (5.663) Accm: 3.80 (3.80) Acct: 5.95 (5.95) proj_loss: -0.6078 (-0.6078) time: 0.6754 data: 0.0002 [11-27 09:26:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [ 417/1669] eta: 0:14:08 tlr: 4.7e-05 tnm: 0.51 Lm: 6.434 (6.434) Lt: 5.646 (5.646) Accm: 3.49 (3.49) Acct: 5.41 (5.41) proj_loss: -0.6015 (-0.6015) time: 0.6754 data: 0.0003 [11-27 09:26:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [ 417/1669] eta: 0:14:08 tlr: 4.7e-05 tnm: 0.51 Lm: 6.377 (6.377) Lt: 5.591 (5.591) Accm: 3.89 (3.89) Acct: 6.32 (6.32) proj_loss: -0.5951 (-0.5951) time: 0.6754 data: 0.0003 [11-27 09:26:22] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [ 417/1669] eta: 0:14:08 tlr: 4.7e-05 tnm: 0.51 Lm: 6.420 (6.420) Lt: 5.663 (5.663) Accm: 3.49 (3.49) Acct: 5.77 (5.77) proj_loss: -0.6001 (-0.6001) time: 0.6754 data: 0.0003 [11-27 09:31:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [ 834/1669] eta: 0:09:23 tlr: 4.7e-05 tnm: 0.52 Lm: 6.422 (6.442) Lt: 5.688 (5.675) Accm: 3.57 (3.53) Acct: 5.63 (5.68) proj_loss: -0.6070 (-0.6060) time: 0.6706 data: 0.0003 [11-27 09:31:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [ 834/1669] eta: 0:09:23 tlr: 4.7e-05 tnm: 0.52 Lm: 6.353 (6.364) Lt: 5.572 (5.581) Accm: 3.99 (3.95) Acct: 6.16 (6.27) proj_loss: -0.5968 (-0.5987) time: 0.6706 data: 0.0003 [11-27 09:31:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [ 834/1669] eta: 0:09:23 tlr: 4.7e-05 tnm: 0.52 Lm: 6.437 (6.407) Lt: 5.668 (5.664) Accm: 3.83 (3.81) Acct: 5.87 (5.92) proj_loss: -0.6144 (-0.6109) time: 0.6706 data: 0.0002 [11-27 09:31:02] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [ 834/1669] eta: 0:09:23 tlr: 4.7e-05 tnm: 0.52 Lm: 6.442 (6.482) Lt: 5.675 (5.700) Accm: 3.39 (3.31) Acct: 5.30 (5.13) proj_loss: -0.6016 (-0.6057) time: 0.6706 data: 0.0003 [11-27 09:35:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [1251/1669] eta: 0:04:41 tlr: 4.7e-05 tnm: 0.53 Lm: 6.434 (6.440) Lt: 5.646 (5.674) Accm: 3.48 (3.37) Acct: 5.41 (5.28) proj_loss: -0.6079 (-0.6085) time: 0.6739 data: 0.0003 [11-27 09:35:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [1251/1669] eta: 0:04:41 tlr: 4.7e-05 tnm: 0.53 Lm: 6.438 (6.428) Lt: 5.696 (5.679) Accm: 3.76 (3.71) Acct: 5.81 (5.74) proj_loss: -0.6104 (-0.6098) time: 0.6739 data: 0.0002 [11-27 09:35:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [1251/1669] eta: 0:04:41 tlr: 4.7e-05 tnm: 0.53 Lm: 6.377 (6.385) Lt: 5.591 (5.600) Accm: 3.89 (3.88) Acct: 6.10 (6.21) proj_loss: -0.6014 (-0.6019) time: 0.6739 data: 0.0003 [11-27 09:35:43] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [1251/1669] eta: 0:04:41 tlr: 4.7e-05 tnm: 0.53 Lm: 6.420 (6.436) Lt: 5.672 (5.670) Accm: 3.58 (3.55) Acct: 5.68 (5.69) proj_loss: -0.6124 (-0.6090) time: 0.6739 data: 0.0003 [11-27 09:40:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [1668/1669] eta: 0:00:00 tlr: 4.7e-05 tnm: 0.50 Lm: 6.353 (6.372) Lt: 5.572 (5.575) Accm: 3.99 (3.91) Acct: 6.16 (6.27) proj_loss: -0.6060 (-0.6034) time: 0.7397 data: 0.0016 [11-27 09:40:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [1668/1669] eta: 0:00:00 tlr: 4.7e-05 tnm: 0.50 Lm: 6.442 (6.489) Lt: 5.675 (5.731) Accm: 3.39 (3.22) Acct: 5.30 (5.10) proj_loss: -0.6016 (-0.6061) time: 0.7398 data: 0.0019 [11-27 09:40:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [1668/1669] eta: 0:00:00 tlr: 4.7e-05 tnm: 0.50 Lm: 6.418 (6.423) Lt: 5.655 (5.663) Accm: 3.60 (3.56) Acct: 5.63 (5.63) proj_loss: -0.6070 (-0.6069) time: 0.7398 data: 0.0022 [11-27 09:40:28] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 288/350] [1668/1669] eta: 0:00:00 tlr: 4.7e-05 tnm: 0.50 Lm: 6.438 (6.439) Lt: 5.722 (5.688) Accm: 3.68 (3.70) Acct: 5.87 (5.80) proj_loss: -0.6079 (-0.6094) time: 0.7397 data: 0.0016 [11-27 09:40:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 288/350] Total time: 0:18:49 (0.676 s / it) [11-27 09:40:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 288/350] Total time: 0:18:49 (0.676 s / it) [11-27 09:40:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 288/350] Total time: 0:18:49 (0.676 s / it) [11-27 09:40:28] (e/user/VAR/utils/misc.py, line 336)=> [Ep]: [ 288/350] Total time: 0:18:49 (0.676 s / it) [11-27 09:40:28] (/home/user/VAR/train.py , line 279)=> [ep288] (training ) Lm: 6.416 (6.428), Lt: 5.662 (5.667), Acc m&t: 3.66 5.72, Remain: 19:11:59, Finish: 2024-11-27 12:52 [11-27 09:40:28] (/home/user/VAR/train.py , line 279)=> [ep288] (training ) Lm: 6.416 (6.428), Lt: 5.662 (5.667), Acc m&t: 3.66 5.72, Remain: 19:11:47, Finish: 2024-11-27 12:52 [11-27 09:40:28] (/home/user/VAR/train.py , line 279)=> [ep288] (training ) Lm: 6.416 (6.428), Lt: 5.662 (5.667), Acc m&t: 3.66 5.72, Remain: 19:11:42, Finish: 2024-11-27 12:52 [11-27 09:40:28] (/home/user/VAR/train.py , line 279)=> [ep288] (training ) Lm: 6.416 (6.428), Lt: 5.662 (5.667), Acc m&t: 3.66 5.72, Remain: 19:11:54, Finish: 2024-11-27 12:52 [11-27 09:40:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 289/350] [ 0/1669] eta: 0:20:18 tlr: 4.7e-05 tnm: 0.52 Lm: 6.493 (6.493) Lt: 5.727 (5.727) Accm: 3.27 (3.27) Acct: 5.48 (5.48) proj_loss: -0.5972 (-0.5972) time: 0.7301 data: 0.0003 [11-27 09:40:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 289/350] [ 0/1669] eta: 0:17:40 tlr: 4.7e-05 tnm: 0.52 Lm: 6.494 (6.494) Lt: 5.755 (5.755) Accm: 3.61 (3.61) Acct: 5.79 (5.79) proj_loss: -0.6145 (-0.6145) time: 0.6352 data: 0.0002 [11-27 09:40:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 289/350] [ 0/1669] eta: 0:17:53 tlr: 4.7e-05 tnm: 0.52 Lm: 6.334 (6.334) Lt: 5.590 (5.590) Accm: 3.92 (3.92) Acct: 6.16 (6.16) proj_loss: -0.6285 (-0.6285) time: 0.6434 data: 0.0004 [11-27 09:40:29] (e/user/VAR/utils/misc.py, line 314)=> [Ep]: [ 289/350] [ 0/1669] eta: 0:17:38 tlr: 4.7e-05 tnm: 0.52 Lm: 6.352 (6.352) Lt: 5.618 (5.618) Accm: 3.47 (3.47) Acct: 5.34 (5.34) proj_loss: -0.6154 (-0.6154) time: 0.6341 data: 0.0004