|
|
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_base_ = './FoodSeg103.py' |
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img_norm_cfg = dict( |
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) |
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crop_size = (768, 768) |
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train_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='LoadAnnotations'), |
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dict(type='Resize', img_scale=(2049, 1025), ratio_range=(0.5, 2.0)), |
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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='Normalize', **img_norm_cfg), |
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dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), |
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dict(type='DefaultFormatBundle'), |
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dict(type='Collect', keys=['img', 'gt_semantic_seg']), |
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] |
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test_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict( |
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type='MultiScaleFlipAug', |
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img_scale=(2049, 1025), |
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|
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flip=False, |
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transforms=[ |
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dict(type='Resize', keep_ratio=True), |
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dict(type='RandomFlip'), |
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dict(type='Normalize', **img_norm_cfg), |
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dict(type='ImageToTensor', keys=['img']), |
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dict(type='Collect', keys=['img']), |
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]) |
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] |
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data = dict( |
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train=dict(pipeline=train_pipeline), |
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val=dict(pipeline=test_pipeline), |
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test=dict(pipeline=test_pipeline)) |
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|
