norm_cfg = dict(type='BN', requires_grad=True) data_preprocessor = dict( type='SegDataPreProcessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True, pad_val=0, seg_pad_val=255) model = dict( type='EncoderDecoder', data_preprocessor=data_preprocessor, pretrained=None, backbone=dict( type='STDCContextPathNet', backbone_cfg=dict( type='STDCNet', stdc_type='STDCNet1', in_channels=3, channels=(32, 64, 256, 512, 1024), bottleneck_type='cat', num_convs=4, norm_cfg=norm_cfg, act_cfg=dict(type='ReLU'), with_final_conv=False), last_in_channels=(1024, 512), out_channels=128, ffm_cfg=dict(in_channels=384, out_channels=256, scale_factor=4)), decode_head=dict( type='FCNHead', in_channels=256, channels=256, num_convs=1, num_classes=19, in_index=3, concat_input=False, dropout_ratio=0.1, norm_cfg=norm_cfg, align_corners=True, sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000), loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), auxiliary_head=[ dict( type='FCNHead', in_channels=128, channels=64, num_convs=1, num_classes=19, in_index=2, norm_cfg=norm_cfg, concat_input=False, align_corners=False, sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000), loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), dict( type='FCNHead', in_channels=128, channels=64, num_convs=1, num_classes=19, in_index=1, norm_cfg=norm_cfg, concat_input=False, align_corners=False, sampler=dict(type='OHEMPixelSampler', thresh=0.7, min_kept=10000), loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), dict( type='STDCHead', in_channels=256, channels=64, num_convs=1, num_classes=2, boundary_threshold=0.1, in_index=0, norm_cfg=norm_cfg, concat_input=False, align_corners=True, loss_decode=[ dict( type='CrossEntropyLoss', loss_name='loss_ce', use_sigmoid=True, loss_weight=1.0), dict(type='DiceLoss', loss_name='loss_dice', loss_weight=1.0) ]), ], # model training and testing settings train_cfg=dict(), test_cfg=dict(mode='whole'))