59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
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# model settings
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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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type='EncoderDecoder',
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backbone=dict(
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type='FastSCNN',
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downsample_dw_channels=(32, 48),
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global_in_channels=64,
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global_block_channels=(64, 96, 128),
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global_block_strides=(2, 2, 1),
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global_out_channels=128,
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higher_in_channels=64,
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lower_in_channels=128,
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fusion_out_channels=128,
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out_indices=(0, 1, 2),
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norm_cfg=norm_cfg,
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align_corners=False),
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decode_head=dict(
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type='DepthwiseSeparableFCNHead',
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in_channels=128,
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channels=128,
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concat_input=False,
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num_classes=19,
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in_index=-1,
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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.)),
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auxiliary_head=[
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dict(
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type='FCNHead',
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in_channels=128,
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channels=32,
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num_convs=1,
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num_classes=19,
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in_index=-2,
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norm_cfg=norm_cfg,
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concat_input=False,
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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='FCNHead',
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in_channels=64,
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channels=32,
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num_convs=1,
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num_classes=19,
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in_index=-3,
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norm_cfg=norm_cfg,
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concat_input=False,
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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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])
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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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