40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
norm_cfg = dict(type='SyncBN', requires_grad=True)
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data_preprocessor = dict(
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type='SegDataPreProcessor',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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bgr_to_rgb=True,
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pad_val=0,
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seg_pad_val=255)
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model = dict(
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type='EncoderDecoder',
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data_preprocessor=data_preprocessor,
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pretrained='pretrain/vit-b16_p16_224-80ecf9dd.pth', # noqa
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backbone=dict(
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type='VisionTransformer',
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img_size=224,
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embed_dims=768,
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num_layers=12,
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num_heads=12,
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out_indices=(2, 5, 8, 11),
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final_norm=False,
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with_cls_token=True,
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output_cls_token=True),
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decode_head=dict(
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type='DPTHead',
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in_channels=(768, 768, 768, 768),
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channels=256,
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embed_dims=768,
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post_process_channels=[96, 192, 384, 768],
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num_classes=150,
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readout_type='project',
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input_transform='multiple_select',
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in_index=(0, 1, 2, 3),
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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=None,
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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')) # yapf: disable
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