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Runtime error
| # dataset settings | |
| dataset_type = 'CityscapesDataset' | |
| data_root = 'data/cityscapes/' | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='RandomResize', | |
| scale=[(2048, 800), (2048, 1024)], | |
| keep_ratio=True), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='PackDetInputs') | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='Resize', scale=(2048, 1024), keep_ratio=True), | |
| # If you don't have a gt annotation, delete the pipeline | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='PackDetInputs', | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor')) | |
| ] | |
| train_dataloader = dict( | |
| batch_size=1, | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| batch_sampler=dict(type='AspectRatioBatchSampler'), | |
| dataset=dict( | |
| type='RepeatDataset', | |
| times=8, | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='annotations/instancesonly_filtered_gtFine_train.json', | |
| data_prefix=dict(img='leftImg8bit/train/'), | |
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | |
| pipeline=train_pipeline))) | |
| val_dataloader = dict( | |
| batch_size=1, | |
| num_workers=2, | |
| persistent_workers=True, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='annotations/instancesonly_filtered_gtFine_val.json', | |
| data_prefix=dict(img='leftImg8bit/val/'), | |
| test_mode=True, | |
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | |
| pipeline=test_pipeline)) | |
| test_dataloader = val_dataloader | |
| val_evaluator = dict( | |
| type='CocoMetric', | |
| ann_file=data_root + 'annotations/instancesonly_filtered_gtFine_val.json', | |
| metric='bbox') | |
| test_evaluator = val_evaluator | |