40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
_base_ = './ocr_hr18_4x3_484e_cityscapes.py'
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norm_cfg = dict(type='SyncBN', requires_grad=True, momentum=0.01)
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model = dict(
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pretrained='pretrain_model/hrnetv2_w48-d2186c55.pth',
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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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drop_out_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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drop_out_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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