mirror of https://github.com/NVlabs/SegFormer.git
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
# model settings
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norm_cfg = dict(type='SyncBN', requires_grad=True)
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model = dict(
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type='CascadeEncoderDecoder',
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num_stages=2,
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pretrained='open-mmlab://msra/hrnetv2_w18',
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backbone=dict(
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type='HRNet',
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norm_cfg=norm_cfg,
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norm_eval=False,
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extra=dict(
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stage1=dict(
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num_modules=1,
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num_branches=1,
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block='BOTTLENECK',
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num_blocks=(4, ),
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num_channels=(64, )),
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stage2=dict(
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num_modules=1,
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num_branches=2,
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block='BASIC',
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num_blocks=(4, 4),
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num_channels=(18, 36)),
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stage3=dict(
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num_modules=4,
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num_branches=3,
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block='BASIC',
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num_blocks=(4, 4, 4),
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num_channels=(18, 36, 72)),
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stage4=dict(
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num_modules=3,
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num_branches=4,
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block='BASIC',
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num_blocks=(4, 4, 4, 4),
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num_channels=(18, 36, 72, 144)))),
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decode_head=[
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dict(
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type='FCNHead',
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in_channels=[18, 36, 72, 144],
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channels=sum([18, 36, 72, 144]),
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in_index=(0, 1, 2, 3),
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input_transform='resize_concat',
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kernel_size=1,
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num_convs=1,
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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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norm_cfg=norm_cfg,
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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=[18, 36, 72, 144],
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in_index=(0, 1, 2, 3),
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input_transform='resize_concat',
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channels=512,
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ocr_channels=256,
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dropout_ratio=-1,
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num_classes=19,
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norm_cfg=norm_cfg,
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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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# model training and testing settings
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train_cfg=dict(),
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test_cfg=dict(mode='whole'))
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