Upload 5 files
Browse files- README.md +9 -3
- best_lsm_hawp.safetensors +3 -0
- best_lsm_hawp.yaml +36 -0
- last.safetensors +3 -0
- last.yaml +104 -0
README.md
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# ZITS-PlusPlus models for Gyre
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Models from https://github.com/ewrfcas/ZITS-PlusPlus
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Distributed under the Apache-2.0 license
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Changes:
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- Converted to safetensors
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- lsm_hawp config converted to yaml
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best_lsm_hawp.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:68d8f0020eb979a4951678a4336f351d9ec6f73d6560e8245e886fa69fcef169
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size 41525248
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best_lsm_hawp.yaml
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MODEL:
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DEVICE: cuda
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HEAD_SIZE:
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HGNETS:
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DEPTH: 4
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INPLANES: 64
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NUM_BLOCKS: 1
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NUM_FEATS: 128
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NUM_STACKS: 2
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LOSS_WEIGHTS: {}
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NAME: Hourglass
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OUT_FEATURE_CHANNELS: 256
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PARSING_HEAD:
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DIM_FC: 1024
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DIM_LOI: 128
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MATCHING_STRATEGY: junction
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MAX_DISTANCE: 5.0
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N_DYN_JUNC: 300
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N_DYN_NEGL: 300
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N_DYN_OTHR: 0
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N_DYN_OTHR2: 300
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N_DYN_POSL: 300
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N_OUT_JUNC: 250
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N_OUT_LINE: 2500
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N_PTS0: 32
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N_PTS1: 8
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N_STC_NEGL: 40
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N_STC_POSL: 300
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USE_RESIDUAL: true
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SCALE: 1.0
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WEIGHTS: ''
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last.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:bd42c1d27c29114d8bb231b8af275e1eaae4d2eacdd755fecfdf39d42dd16b4b
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size 785269879
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last.yaml
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train_flist: '/home/wmlce/places365_standard/places2_all/train_list.txt'
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val_flist: '/home/wmlce/places365_standard/places2_all/test_sub_list.txt'
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test_path: '/home/wmlce/places365_standard/val_512img_for_eval'
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train_mask_flist: [ '/home/wmlce/irregular_mask/irregular_lama_mask_list.txt',
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'/home/wmlce/coco_mask/coco_mask_list.txt' ]
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test_mask_flist: '/home/wmlce/Image-Transformer-Inpainting/data/indoor/test_mask'
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batch_size: 12 # input batch size for training
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num_workers: 12
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sample_size: 12
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fp16: false
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# Dataset settings
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data_class: 'base.dataset.DynamicDataset_gradient_line'
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dataset:
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rect_mask_rate: 0.0
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train_line_path: "places2_train_wireframes"
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eval_line_path: "places2_val_wireframes"
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round: 64
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str_size: 256
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input_size: 512 # size for eval
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# model settings
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structure_upsample_class: 'networks.upsample.StructureUpsampling4'
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edgeline_tsr_class: 'networks.tsr.EdgeLineGPT256RelBCE_edge_pred_infer'
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grad_tsr_class: 'networks.tsr.GradientGPT256RelBCE'
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PLTrainer: 'trainers.pl_trainers.FinetunePLTrainer_nms_threshold'
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g_class: 'networks.generators.FTRModel'
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g_args:
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use_gradient: False
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use_GFBlock: False
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activation: 'swish'
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use_VAN_between_FFC: False
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van_kernel_size: 21
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van_dilation: 3
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prior_ch: 3
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rezero_for_mpe: True
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rel_pos_num: 128
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d_class: 'networks.discriminators.NLayerDiscriminator'
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d_args:
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input_nc: 3
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# pretrained ckpt settings
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# resume_structure_upsample: '/mnt/storage/dongqiaole/dql_inpainting/CNN_final/ckpt/StructureUpsampling_V5_last.pth'
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# resume_edgeline_tsr: '/mnt/storage/dongqiaole/dql_inpainting/Transformer_final_places2/ckpt/places2_line_cats_edge_pred_infer/best.pth'
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# resume_grad_tsr: '/mnt/storage/dongqiaole/dql_inpainting/Transformer_final_places2/ckpt/places2_gradient/best.pth'
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# resume_ftr: '/mnt/storage/dongqiaole/dql_inpainting/TPAMI2022-final/ckpts/Places2_lightning_converted_weights/converted_from_pl_800k_3sfe.pth'
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resume_structure_upsample: '/home/wmlce/dql_inpainting/CNN_final/ckpt/StructureUpsampling_V5_last.pth'
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resume_edgeline_tsr: '/home/wmlce/dql_inpainting/Transformer_final_places2/ckpt/places2_line_cats_edge_pred_infer/best.pth'
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resume_grad_tsr: '/home/wmlce/dql_inpainting/Transformer_final_places2/ckpt/places2_gradient/best.pth'
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resume_ftr: '/home/wmlce/dongqiaole/dql_inpainting/TPAMI2022-final/ckpts/Places2_lightning_converted_weights/converted_from_pl_800k_3sfe.pth'
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# Trainer settings
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trainer:
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fix_256: False
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Turning_Point: 10000
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total_step: 150000
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sample_period: 1000
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eval_period: 2000
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save_period: 1000
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logging_every: 50
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ema_beta: 0.995
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sample_with_center_mask: false
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# loss
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l1:
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use_l1: true
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weight_missing: 0
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weight_known: 10.0
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adversarial:
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weight: 10.0
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gp_coef: 0.001
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mask_as_fake_target: true
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allow_scale_mask: true
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extra_mask_weight_for_gen: 0.0
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use_unmasked_for_gen: true
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use_unmasked_for_discr: true
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mask_scale_mode: 'maxpool'
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perceptual:
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weight: 0
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resnet_pl:
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weight: 30.0
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weights_path: '/home/wmlce/dql_inpainting'
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feature_matching:
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weight: 100.0
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# opt settings
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optimizer:
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warmup_steps: 0
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decay_steps: [50000, 100000]
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decay_rate: 0.5
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g_opt:
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lr: 3.0e-4
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beta1: 0
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beta2: 0.99
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d_opt:
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lr: 1.0e-4
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beta1: 0
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beta2: 0.99
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