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| # dataset settings | |
| dataset_type = 'DRIVEDataset' | |
| data_root = 'data/DRIVE' | |
| img_norm_cfg = dict( | |
| mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | |
| img_scale = (584, 565) | |
| crop_size = (64, 64) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations'), | |
| dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)), | |
| dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255), | |
| dict(type='DefaultFormatBundle'), | |
| dict(type='Collect', keys=['img', 'gt_semantic_seg']) | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='MultiScaleFlipAug', | |
| img_scale=img_scale, | |
| # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0], | |
| flip=False, | |
| transforms=[ | |
| dict(type='Resize', keep_ratio=True), | |
| dict(type='RandomFlip'), | |
| dict(type='Normalize', **img_norm_cfg), | |
| dict(type='ImageToTensor', keys=['img']), | |
| dict(type='Collect', keys=['img']) | |
| ]) | |
| ] | |
| data = dict( | |
| samples_per_gpu=4, | |
| workers_per_gpu=4, | |
| train=dict( | |
| type='RepeatDataset', | |
| times=40000, | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| img_dir='images/training', | |
| ann_dir='annotations/training', | |
| pipeline=train_pipeline)), | |
| val=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| img_dir='images/validation', | |
| ann_dir='annotations/validation', | |
| pipeline=test_pipeline), | |
| test=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| img_dir='images/validation', | |
| ann_dir='annotations/validation', | |
| pipeline=test_pipeline)) | |