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- # 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
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