# dataset settings dataset_type = 'DRIVEDataset' data_root = 'data/DRIVE' img_scale = (584, 565) crop_size = (64, 64) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='RandomResize', scale=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') ] test_pipeline = [ dict(type='LoadImageFromFile'), dict(type='Resize', scale=img_scale, keep_ratio=True), # add loading annotation after ``Resize`` because ground truth # does not need to do resize data transform dict(type='LoadAnnotations'), dict(type='PackSegInputs') ] img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] tta_pipeline = [ dict(type='LoadImageFromFile', backend_args=None), 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='RepeatDataset', times=40000, 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=test_pipeline)) test_dataloader = val_dataloader val_evaluator = dict(type='IoUMetric', iou_metrics=['mDice']) test_evaluator = val_evaluator