52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
# dataset settings
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dataset_type = 'HRFDataset'
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data_root = 'data/HRF'
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img_scale = (2336, 3504)
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crop_size = (256, 256)
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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='RandomResize', 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='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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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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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', 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', metrics=['mIoU'])
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test_evaluator = val_evaluator
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