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_base_ = './ocrnet_hr18_512x1024_160k_cityscapes.py' |
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norm_cfg = dict(type='SyncBN', requires_grad=True) |
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model = dict( |
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pretrained='open-mmlab://msra/hrnetv2_w48', |
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backbone=dict( |
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extra=dict( |
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stage2=dict(num_channels=(48, 96)), |
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stage3=dict(num_channels=(48, 96, 192)), |
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stage4=dict(num_channels=(48, 96, 192, 384)))), |
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decode_head=[ |
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dict( |
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type='FCNHead', |
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in_channels=[48, 96, 192, 384], |
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channels=sum([48, 96, 192, 384]), |
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input_transform='resize_concat', |
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in_index=(0, 1, 2, 3), |
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kernel_size=1, |
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num_convs=1, |
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norm_cfg=norm_cfg, |
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concat_input=False, |
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dropout_ratio=-1, |
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num_classes=19, |
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align_corners=False, |
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loss_decode=dict( |
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), |
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dict( |
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type='OCRHead', |
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in_channels=[48, 96, 192, 384], |
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channels=512, |
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ocr_channels=256, |
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input_transform='resize_concat', |
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in_index=(0, 1, 2, 3), |
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norm_cfg=norm_cfg, |
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dropout_ratio=-1, |
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num_classes=19, |
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align_corners=False, |
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loss_decode=dict( |
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)) |
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]) |
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