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| # Copyright (c) OpenMMLab. All rights reserved. | |
| from mmcv.transforms import (LoadImageFromFile, RandomResize, | |
| TransformBroadcaster) | |
| from mmdet.datasets import MOTChallengeDataset | |
| from mmdet.datasets.samplers import TrackImgSampler | |
| from mmdet.datasets.transforms import (LoadTrackAnnotations, PackTrackInputs, | |
| PhotoMetricDistortion, RandomCrop, | |
| RandomFlip, Resize, | |
| UniformRefFrameSample) | |
| from mmdet.evaluation import MOTChallengeMetric | |
| # dataset settings | |
| dataset_type = MOTChallengeDataset | |
| data_root = 'data/MOT17/' | |
| img_scale = (1088, 1088) | |
| backend_args = None | |
| # data pipeline | |
| train_pipeline = [ | |
| dict( | |
| type=UniformRefFrameSample, | |
| num_ref_imgs=1, | |
| frame_range=10, | |
| filter_key_img=True), | |
| dict( | |
| type=TransformBroadcaster, | |
| share_random_params=True, | |
| transforms=[ | |
| dict(type=LoadImageFromFile, backend_args=backend_args), | |
| dict(type=LoadTrackAnnotations), | |
| dict( | |
| type=RandomResize, | |
| scale=img_scale, | |
| ratio_range=(0.8, 1.2), | |
| keep_ratio=True, | |
| clip_object_border=False), | |
| dict(type=PhotoMetricDistortion) | |
| ]), | |
| dict( | |
| type=TransformBroadcaster, | |
| # different cropped positions for different frames | |
| share_random_params=False, | |
| transforms=[ | |
| dict(type=RandomCrop, crop_size=img_scale, bbox_clip_border=False) | |
| ]), | |
| dict( | |
| type=TransformBroadcaster, | |
| share_random_params=True, | |
| transforms=[ | |
| dict(type=RandomFlip, prob=0.5), | |
| ]), | |
| dict(type=PackTrackInputs) | |
| ] | |
| test_pipeline = [ | |
| dict( | |
| type=TransformBroadcaster, | |
| transforms=[ | |
| dict(type=LoadImageFromFile, backend_args=backend_args), | |
| dict(type=Resize, scale=img_scale, keep_ratio=True), | |
| dict(type=LoadTrackAnnotations) | |
| ]), | |
| dict(type=PackTrackInputs) | |
| ] | |
| # dataloader | |
| train_dataloader = dict( | |
| batch_size=2, | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(type=TrackImgSampler), # image-based sampling | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| visibility_thr=-1, | |
| ann_file='annotations/half-train_cocoformat.json', | |
| data_prefix=dict(img_path='train'), | |
| metainfo=dict(classes=('pedestrian', )), | |
| pipeline=train_pipeline)) | |
| 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='DefaultSampler', shuffle=False, round_up=False), | |
| sampler=dict(type=TrackImgSampler), # image-based sampling | |
| dataset=dict( | |
| type=dataset_type, | |
| data_root=data_root, | |
| ann_file='annotations/half-val_cocoformat.json', | |
| data_prefix=dict(img_path='train'), | |
| test_mode=True, | |
| pipeline=test_pipeline)) | |
| test_dataloader = val_dataloader | |
| # evaluator | |
| val_evaluator = dict( | |
| type=MOTChallengeMetric, metric=['HOTA', 'CLEAR', 'Identity']) | |
| test_evaluator = val_evaluator | |