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_base_ = ['./segformer_mit-b0_8xb2-160k_ade20k-512x512.py'] |
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checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth' |
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crop_size = (640, 640) |
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data_preprocessor = dict(size=crop_size) |
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train_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='LoadAnnotations', reduce_zero_label=True), |
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dict( |
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type='RandomResize', |
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scale=(2048, 640), |
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ratio_range=(0.5, 2.0), |
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keep_ratio=True), |
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dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), |
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dict(type='RandomFlip', prob=0.5), |
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dict(type='PhotoMetricDistortion'), |
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dict(type='PackSegInputs') |
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] |
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test_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='Resize', scale=(2048, 640), keep_ratio=True), |
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dict(type='LoadAnnotations', reduce_zero_label=True), |
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dict(type='PackSegInputs') |
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] |
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train_dataloader = dict(dataset=dict(pipeline=train_pipeline)) |
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val_dataloader = dict(batch_size=1, dataset=dict(pipeline=test_pipeline)) |
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test_dataloader = val_dataloader |
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model = dict( |
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data_preprocessor=data_preprocessor, |
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backbone=dict( |
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init_cfg=dict(type='Pretrained', checkpoint=checkpoint), |
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embed_dims=64, |
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num_heads=[1, 2, 5, 8], |
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num_layers=[3, 6, 40, 3]), |
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decode_head=dict(in_channels=[64, 128, 320, 512])) |
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