| norm_cfg = dict(type='SyncBN', requires_grad=True) |
| custom_imports = dict(imports='mmcls.models', allow_failed_imports=False) |
| checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-base_3rdparty_32xb128-noema_in1k_20220301-2a0ee547.pth' |
| model = dict( |
| type='EncoderDecoder', |
| pretrained=None, |
| backbone=dict( |
| type='mmcls.ConvNeXt', |
| arch='base', |
| out_indices=[0, 1, 2, 3], |
| drop_path_rate=0.4, |
| layer_scale_init_value=1.0, |
| gap_before_final_norm=False, |
| init_cfg=dict( |
| type='Pretrained', checkpoint=checkpoint_file, |
| prefix='backbone.')), |
| decode_head=dict( |
| type='UPerHead', |
| in_channels=[128, 256, 512, 1024], |
| in_index=[0, 1, 2, 3], |
| pool_scales=(1, 2, 3, 6), |
| channels=512, |
| dropout_ratio=0.1, |
| num_classes=19, |
| norm_cfg=norm_cfg, |
| align_corners=False, |
| loss_decode=dict( |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), |
| auxiliary_head=dict( |
| type='FCNHead', |
| in_channels=384, |
| in_index=2, |
| channels=256, |
| num_convs=1, |
| concat_input=False, |
| dropout_ratio=0.1, |
| num_classes=19, |
| norm_cfg=norm_cfg, |
| align_corners=False, |
| loss_decode=dict( |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), |
| |
| train_cfg=dict(), |
| test_cfg=dict(mode='whole')) |
|
|
