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https://github.com/open-mmlab/mmsegmentation.git
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3d18775113
* add RandomRotate for transforms * change rotation function to mmcv.imrotate * refactor * add unittest * fixed test * fixed docstring * fixed test * add more test * fixed repr * rename to prob * fixed unittest Co-authored-by: hkzhang95 <GodBlessZhk@outlook.com>
36 lines
1.3 KiB
Python
36 lines
1.3 KiB
Python
_base_ = './cityscapes.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 = (769, 769)
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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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# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
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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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