43 lines
1.3 KiB
Python
43 lines
1.3 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='pretrain_model/resnet50_v1c-66047269.pth',
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backbone=dict(
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type='ResNetV1c',
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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dilations=(1, 1, 1, 1),
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strides=(1, 2, 2, 2),
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norm_cfg=norm_cfg,
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norm_eval=False,
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style='pytorch',
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contract_dilation=True),
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decode_head=dict(
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type='UPerHead',
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in_channels=[256, 512, 1024, 2048],
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in_index=[0, 1, 2, 3],
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pool_scales=(1, 2, 3, 6),
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channels=512,
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drop_out_ratio=0.1,
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num_classes=19,
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norm_cfg=norm_cfg,
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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=1024,
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in_index=2,
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channels=256,
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num_convs=1,
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concat_input=False,
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drop_out_ratio=0.1,
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num_classes=19,
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norm_cfg=norm_cfg,
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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=(769, 769), stride=(513, 513))
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