| | |
| | norm_cfg = dict(type='SyncBN', requires_grad=True) |
| | model = dict( |
| | type='EncoderDecoder', |
| | 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], |
| | in_index=(0, 1, 2, 3), |
| | channels=sum([18, 36, 72, 144]), |
| | 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=1.0)), |
| | |
| | train_cfg=dict(), |
| | test_cfg=dict(mode='whole')) |
| |
|
