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https://github.com/open-mmlab/mmsegmentation.git
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124b87ce90
## Motivation Use the new fileio from mmengine https://github.com/open-mmlab/mmengine/pull/533 ## Modification 1. Use `mmengine.fileio` to repalce FileClient in mmseg/datasets 2. Use `mmengine.fileio` to repalce FileClient in mmseg/datasets/transforms 3. Use `mmengine.fileio` to repalce FileClient in mmseg/visualization ## BC-breaking (Optional) we modify all the dataset configurations, so please use the latest config file.
76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
# dataset settings
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dataset_type = 'ChaseDB1Dataset'
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data_root = 'data/CHASE_DB1'
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img_scale = (960, 999)
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crop_size = (128, 128)
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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(
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type='RandomResize',
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scale=img_scale,
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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=img_scale, keep_ratio=True),
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# add loading annotation after ``Resize`` because ground truth
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# does not need to do resize data transform
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dict(type='LoadAnnotations'),
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dict(type='PackSegInputs')
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]
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img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
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tta_pipeline = [
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dict(type='LoadImageFromFile', backend_args=dict(backend='local')),
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dict(
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type='TestTimeAug',
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transforms=[
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[
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dict(type='Resize', scale_factor=r, keep_ratio=True)
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for r in img_ratios
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],
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[
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dict(type='RandomFlip', prob=0., direction='horizontal'),
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dict(type='RandomFlip', prob=1., direction='horizontal')
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], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')]
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])
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]
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train_dataloader = dict(
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batch_size=4,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type='InfiniteSampler', shuffle=True),
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dataset=dict(
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type='RepeatDataset',
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times=40000,
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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data_prefix=dict(
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img_path='images/training',
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seg_map_path='annotations/training'),
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pipeline=train_pipeline)))
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val_dataloader = dict(
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batch_size=1,
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num_workers=4,
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persistent_workers=True,
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sampler=dict(type='DefaultSampler', shuffle=False),
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dataset=dict(
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type=dataset_type,
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data_root=data_root,
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data_prefix=dict(
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img_path='images/validation',
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seg_map_path='annotations/validation'),
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pipeline=test_pipeline))
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test_dataloader = val_dataloader
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val_evaluator = dict(type='IoUMetric', iou_metrics=['mDice'])
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test_evaluator = val_evaluator
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