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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 = 'Taov1Dataset'
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_v1_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_v1_classes.json',
metric=['TETA'])
test_evaluator = val_evaluator
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