Spaces:
Runtime error
Runtime error
| # dataset settings | |
| dataset_type = 'REFUGEDataset' | |
| data_root = 'data/REFUGE' | |
| train_img_scale = (2056, 2124) | |
| val_img_scale = (1634, 1634) | |
| test_img_scale = (1634, 1634) | |
| crop_size = (512, 512) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', reduce_zero_label=False), | |
| dict( | |
| type='RandomResize', | |
| scale=train_img_scale, | |
| ratio_range=(0.5, 2.0), | |
| keep_ratio=True), | |
| dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='PhotoMetricDistortion'), | |
| dict(type='PackSegInputs') | |
| ] | |
| val_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='Resize', scale=val_img_scale, keep_ratio=True), | |
| # add loading annotation after ``Resize`` because ground truth | |
| # does not need to do resize data transform | |
| dict(type='LoadAnnotations', reduce_zero_label=False), | |
| dict(type='PackSegInputs') | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='Resize', scale=test_img_scale, keep_ratio=True), | |
| # add loading annotation after ``Resize`` because ground truth | |
| # does not need to do resize data transform | |
| dict(type='LoadAnnotations', reduce_zero_label=False), | |
| dict(type='PackSegInputs') | |
| ] | |
| img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] | |
| tta_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=dict(backend='local')), | |
| dict( | |
| type='TestTimeAug', | |
| transforms=[ | |
| [ | |
| dict(type='Resize', scale_factor=r, keep_ratio=True) | |
| for r in img_ratios | |
| ], | |
| [ | |
| dict(type='RandomFlip', prob=0., direction='horizontal'), | |
| dict(type='RandomFlip', prob=1., direction='horizontal') | |
| ], [dict(type='LoadAnnotations')], [dict(type='PackSegInputs')] | |
| ]) | |
| ] | |
| train_dataloader = dict( | |
| batch_size=4, | |
| num_workers=4, | |
| persistent_workers=True, | |
| sampler=dict(type='InfiniteSampler', shuffle=True), | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| data_prefix=dict( | |
| img_path='images/training', seg_map_path='annotations/training'), | |
| pipeline=train_pipeline)) | |
| val_dataloader = dict( | |
| batch_size=1, | |
| num_workers=4, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| data_prefix=dict( | |
| img_path='images/validation', | |
| seg_map_path='annotations/validation'), | |
| pipeline=val_pipeline)) | |
| test_dataloader = dict( | |
| batch_size=1, | |
| num_workers=4, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| data_prefix=dict( | |
| img_path='images/test', seg_map_path='annotations/test'), | |
| pipeline=val_pipeline)) | |
| val_evaluator = dict(type='IoUMetric', iou_metrics=['mDice']) | |
| test_evaluator = val_evaluator | |