51 lines
1.5 KiB
Python
51 lines
1.5 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='EncoderDecoder',
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pretrained=None,
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backbone=dict(
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type='UNet',
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in_channels=3,
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base_channels=64,
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num_stages=5,
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strides=(1, 1, 1, 1, 1),
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enc_num_convs=(2, 2, 2, 2, 2),
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dec_num_convs=(2, 2, 2, 2),
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downsamples=(True, True, True, True),
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enc_dilations=(1, 1, 1, 1, 1),
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dec_dilations=(1, 1, 1, 1),
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with_cp=False,
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conv_cfg=None,
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norm_cfg=norm_cfg,
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act_cfg=dict(type='ReLU'),
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upsample_cfg=dict(type='InterpConv'),
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norm_eval=False),
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decode_head=dict(
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type='PSPHead',
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in_channels=64,
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in_index=4,
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channels=16,
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pool_scales=(1, 2, 3, 6),
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dropout_ratio=0.1,
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num_classes=2,
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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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auxiliary_head=dict(
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type='FCNHead',
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in_channels=128,
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in_index=3,
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channels=64,
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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=2,
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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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# model training and testing settings
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train_cfg=dict(),
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test_cfg=dict(mode='slide', crop_size=256, stride=170))
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