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| # data pipeline | |
| test_pipeline = [ | |
| dict( | |
| type='TransformBroadcaster', | |
| transforms=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='Resize', scale=(1333, 800), keep_ratio=True), | |
| dict(type='LoadTrackAnnotations') | |
| ]), | |
| dict(type='PackTrackInputs') | |
| ] | |
| # dataloader | |
| test_dataset_tpye = 'Taov05Dataset' | |
| val_dataloader = dict( | |
| batch_size=1, | |
| num_workers=2, | |
| persistent_workers=True, | |
| # Now we support two ways to test, image_based and video_based | |
| # if you want to use video_based sampling, you can use as follows | |
| sampler=dict(type='TrackImgSampler'), # image-based sampling | |
| dataset=dict( | |
| type=test_dataset_tpye, | |
| ann_file='data/tao/annotations/tao_val_lvis_v05_classes.json', | |
| data_prefix=dict(img_path='data/tao/frames/'), | |
| test_mode=True, | |
| pipeline=test_pipeline | |
| )) | |
| test_dataloader = val_dataloader | |
| # evaluator | |
| val_evaluator = dict( | |
| type='TaoTETAMetric', | |
| dataset_type=test_dataset_tpye, | |
| format_only=False, | |
| ann_file='data/tao/annotations/tao_val_lvis_v05_classes.json', | |
| metric=['TETA']) | |
| test_evaluator = val_evaluator | |