|
|
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dataset_type = 'STAREDataset' |
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data_root = 'data/STARE' |
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img_norm_cfg = dict( |
|
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) |
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img_scale = (605, 700) |
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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(type='Resize', img_scale=img_scale, 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=img_scale, |
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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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samples_per_gpu=4, |
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workers_per_gpu=4, |
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train=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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img_dir='images/training', |
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ann_dir='annotations/training', |
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pipeline=train_pipeline)), |
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val=dict( |
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type=dataset_type, |
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data_root=data_root, |
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img_dir='images/validation', |
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ann_dir='annotations/validation', |
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pipeline=test_pipeline), |
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test=dict( |
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type=dataset_type, |
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data_root=data_root, |
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img_dir='images/validation', |
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ann_dir='annotations/validation', |
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pipeline=test_pipeline)) |
|
|
