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Zero
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# Copyright (c) 2025 Ye Liu. Licensed under the BSD-3-Clause License.
import re
from collections import OrderedDict
import nncore
from videomind.dataset.hybrid import DATASETS
from videomind.dataset.wrappers import GroundingDataset
from videomind.utils.parser import parse_query
@DATASETS.register(name='activitynet_rtl')
class ActivitynetRTLDataset(GroundingDataset):
ANNO_PATH_TRAIN = 'data/activitynet_rtl/activitynet_train_gpt-4-0613_temp_6_f10009.json'
ANNO_PATH_TEST = 'data/activitynet_rtl/annot_val_1_q229.json'
VIDEO_ROOT = 'data/activitynet/videos_3fps_480_noaudio'
UNIT = 0.01
@classmethod
def load_annos(self, split='train'):
if split == 'train':
raw_annos = nncore.load(self.ANNO_PATH_TRAIN, object_pairs_hook=OrderedDict)
annos = []
for vid, raw_anno in raw_annos.items():
for meta in raw_anno['QA']:
match = re.findall(r'<(\d+(\.\d+)?)>', meta['a'])
span = [float(m[0]) for m in match[:2]]
# some samples do not have timestamps
if len(span) != 2:
continue
anno = dict(
source='activitynet_rtl',
data_type='grounding',
video_path=nncore.join(self.VIDEO_ROOT, vid + '.mp4'),
duration=raw_anno['duration'],
query=parse_query(meta['q']),
span=[span])
annos.append(anno)
else:
raw_annos = nncore.load(self.ANNO_PATH_TEST, object_pairs_hook=OrderedDict)
annos = []
for raw_anno in raw_annos:
vid = f"v_{raw_anno['vid']}"
match = re.findall(r'<(\d+(\.\d+)?)>', raw_anno['answer'])
span = [float(m[0]) for m in match[:2]]
assert len(span) == 2
anno = dict(
source='activitynet_rtl',
data_type='grounding',
video_path=nncore.join(self.VIDEO_ROOT, vid + '.mp4'),
duration=raw_anno['duration'],
query=parse_query(raw_anno['question']),
span=[span])
annos.append(anno)
return annos
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