| # model settings | |
| 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)), | |
| # model training and testing settings | |
| train_cfg=dict(), | |
| test_cfg=dict(mode='whole')) | |