33 lines
1008 B
Python
33 lines
1008 B
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='EncoderDecoder',
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pretrained=None,
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backbone=dict(
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type='ERFNet',
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in_channels=3,
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enc_downsample_channels=(16, 64, 128),
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enc_stage_non_bottlenecks=(5, 8),
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enc_non_bottleneck_dilations=(2, 4, 8, 16),
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enc_non_bottleneck_channels=(64, 128),
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dec_upsample_channels=(64, 16),
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dec_stages_non_bottleneck=(2, 2),
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dec_non_bottleneck_channels=(64, 16),
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dropout_ratio=0.1,
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init_cfg=None),
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decode_head=dict(
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type='FCNHead',
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in_channels=16,
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channels=128,
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num_convs=1,
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concat_input=False,
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dropout_ratio=0.1,
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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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# 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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