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| _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 | |