mirror of https://github.com/NVlabs/SegFormer.git
60 lines
1.9 KiB
Python
60 lines
1.9 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_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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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(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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# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],
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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))
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