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Running
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Zero
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# Copyright (c) 2025 Ye Liu. Licensed under the BSD-3-Clause License.
import nncore
from videomind.dataset.hybrid import DATASETS
from videomind.dataset.wrappers import GroundingDataset
from videomind.utils.parser import parse_query
@DATASETS.register(name='tacos')
class TACoSDataset(GroundingDataset):
ANNO_PATH_TRAIN = 'data/tacos/train.jsonl'
ANNO_PATH_VALID = 'data/tacos/val.jsonl'
ANNO_PATH_TEST = 'data/tacos/test.jsonl'
VIDEO_ROOT = 'data/tacos/videos_3fps_480_noaudio'
UNIT = 0.001
@classmethod
def load_annos(self, split='train'):
if split == 'train':
raw_annos = nncore.load(self.ANNO_PATH_TRAIN)
elif split == 'val':
raw_annos = nncore.load(self.ANNO_PATH_VALID)
else:
raw_annos = nncore.load(self.ANNO_PATH_TEST)
annos = []
for raw_anno in raw_annos:
assert len(raw_anno['relevant_windows']) == 1
vid = raw_anno['vid']
anno = dict(
source='tacos',
data_type='grounding',
video_path=nncore.join(self.VIDEO_ROOT, vid + '-cam-002.mp4'),
duration=raw_anno['duration'],
query=parse_query(raw_anno['query']),
span=raw_anno['relevant_windows'])
annos.append(anno)
return annos
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