_base_ = [ '../_base_/models/upernet_mae.py', '../_base_/datasets/ade20k.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' ] crop_size = (512, 512) data_preprocessor = dict(size=crop_size) model = dict( data_preprocessor=data_preprocessor, pretrained='./pretrain/mae_pretrain_vit_base_mmcls.pth', backbone=dict( type='MAE', img_size=(512, 512), patch_size=16, embed_dims=768, num_layers=12, num_heads=12, mlp_ratio=4, init_values=1.0, drop_path_rate=0.1, out_indices=[3, 5, 7, 11]), neck=dict(embed_dim=768, rescales=[4, 2, 1, 0.5]), decode_head=dict( in_channels=[768, 768, 768, 768], num_classes=150, channels=768), auxiliary_head=dict(in_channels=768, num_classes=150), test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(341, 341))) optim_wrapper = dict( _delete_=True, type='OptimWrapper', optimizer=dict( type='AdamW', lr=1e-4, betas=(0.9, 0.999), weight_decay=0.05), paramwise_cfg=dict(num_layers=12, layer_decay_rate=0.65), constructor='LayerDecayOptimizerConstructor') param_scheduler = [ dict( type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500), dict( type='PolyLR', eta_min=0.0, power=1.0, begin=1500, end=160000, by_epoch=False, ) ] # mixed precision fp16 = dict(loss_scale='dynamic') # By default, models are trained on 8 GPUs with 2 images per GPU train_dataloader = dict(batch_size=2) val_dataloader = dict(batch_size=1) test_dataloader = val_dataloader