| |
| norm_cfg = dict(type='SyncBN', requires_grad=True) |
| model = dict( |
| type='CascadeEncoderDecoder', |
| num_stages=2, |
| pretrained='open-mmlab://msra/hrnetv2_w18', |
| backbone=dict( |
| type='HRNet', |
| norm_cfg=norm_cfg, |
| norm_eval=False, |
| extra=dict( |
| stage1=dict( |
| num_modules=1, |
| num_branches=1, |
| block='BOTTLENECK', |
| num_blocks=(4, ), |
| num_channels=(64, )), |
| stage2=dict( |
| num_modules=1, |
| num_branches=2, |
| block='BASIC', |
| num_blocks=(4, 4), |
| num_channels=(18, 36)), |
| stage3=dict( |
| num_modules=4, |
| num_branches=3, |
| block='BASIC', |
| num_blocks=(4, 4, 4), |
| num_channels=(18, 36, 72)), |
| stage4=dict( |
| num_modules=3, |
| num_branches=4, |
| block='BASIC', |
| num_blocks=(4, 4, 4, 4), |
| num_channels=(18, 36, 72, 144)))), |
| decode_head=[ |
| dict( |
| type='FCNHead', |
| in_channels=[18, 36, 72, 144], |
| channels=sum([18, 36, 72, 144]), |
| in_index=(0, 1, 2, 3), |
| input_transform='resize_concat', |
| kernel_size=1, |
| num_convs=1, |
| concat_input=False, |
| dropout_ratio=-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=[18, 36, 72, 144], |
| in_index=(0, 1, 2, 3), |
| input_transform='resize_concat', |
| channels=512, |
| ocr_channels=256, |
| dropout_ratio=-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')) |
|
|
