| |
| norm_cfg = dict(type='SyncBN', requires_grad=True) |
| model = dict( |
| type='CascadeEncoderDecoder', |
| num_stages=2, |
| pretrained='open-mmlab://resnet50_v1c', |
| backbone=dict( |
| type='ResNetV1c', |
| depth=50, |
| num_stages=4, |
| out_indices=(0, 1, 2, 3), |
| dilations=(1, 1, 2, 4), |
| strides=(1, 2, 1, 1), |
| norm_cfg=norm_cfg, |
| norm_eval=False, |
| style='pytorch', |
| contract_dilation=True), |
| decode_head=[ |
| dict( |
| type='FCNHead', |
| in_channels=1024, |
| 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)), |
| dict( |
| type='OCRHead', |
| in_channels=2048, |
| in_index=3, |
| channels=512, |
| ocr_channels=256, |
| 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)) |
| ], |
| |
| train_cfg=dict(), |
| test_cfg=dict(mode='whole')) |
|
|
