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| _base_ = [ | |
| '../_base_/models/setr_naive.py', '../_base_/datasets/ade20k.py', | |
| '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' | |
| ] | |
| crop_size = (512, 512) | |
| data_preprocessor = dict(size=crop_size) | |
| norm_cfg = dict(type='SyncBN', requires_grad=True) | |
| model = dict( | |
| data_preprocessor=data_preprocessor, | |
| pretrained=None, | |
| backbone=dict( | |
| img_size=(512, 512), | |
| drop_rate=0., | |
| init_cfg=dict( | |
| type='Pretrained', checkpoint='pretrain/vit_large_p16.pth')), | |
| decode_head=dict(num_classes=150), | |
| auxiliary_head=[ | |
| dict( | |
| type='SETRUPHead', | |
| in_channels=1024, | |
| channels=256, | |
| in_index=0, | |
| num_classes=150, | |
| dropout_ratio=0, | |
| norm_cfg=norm_cfg, | |
| act_cfg=dict(type='ReLU'), | |
| num_convs=2, | |
| kernel_size=1, | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | |
| dict( | |
| type='SETRUPHead', | |
| in_channels=1024, | |
| channels=256, | |
| in_index=1, | |
| num_classes=150, | |
| dropout_ratio=0, | |
| norm_cfg=norm_cfg, | |
| act_cfg=dict(type='ReLU'), | |
| num_convs=2, | |
| kernel_size=1, | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | |
| dict( | |
| type='SETRUPHead', | |
| in_channels=1024, | |
| channels=256, | |
| in_index=2, | |
| num_classes=150, | |
| dropout_ratio=0, | |
| norm_cfg=norm_cfg, | |
| act_cfg=dict(type='ReLU'), | |
| num_convs=2, | |
| kernel_size=1, | |
| align_corners=False, | |
| loss_decode=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)) | |
| ], | |
| test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(341, 341)), | |
| ) | |
| optimizer = dict(lr=0.01, weight_decay=0.0) | |
| optim_wrapper = dict( | |
| type='OptimWrapper', | |
| optimizer=optimizer, | |
| paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10.)})) | |
| # num_gpus: 8 -> batch_size: 16 | |
| train_dataloader = dict(batch_size=2) | |
| val_dataloader = dict(batch_size=1) | |
| test_dataloader = val_dataloader | |