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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 random
import nncore
from videomind.dataset.hybrid import DATASETS
from videomind.dataset.wrappers import AnsweringCropDataset, AnsweringDataset, GroundingDataset
from videomind.utils.parser import parse_query, parse_question
@DATASETS.register(name='ego_timeqa')
class EgoTimeQADataset(AnsweringDataset):
ANNO_PATH_TRAIN = 'data/ego_timeqa/annotations.EgoTimeQA.json'
VIDEO_ROOT = 'data/ego4d/v2/videos_3fps_480_noaudio'
DURATIONS = 'data/ego4d/v2/durations.json'
SOURCE = 'ego_timeqa'
DATA_TYPE = 'multimodal'
UNIT = 0.001
@classmethod
def load_annos(self, split='train'):
assert split == 'train'
raw_annos = nncore.load(self.ANNO_PATH_TRAIN)
durations = nncore.load(self.DURATIONS)
annos = []
for raw_anno in raw_annos:
vid = raw_anno['video_id']
duration = durations[vid]
# 303k -> 284k (to be verified)
if duration < 10 or duration > 600:
continue
span = [raw_anno['moment_start_frame'] / 30, raw_anno['moment_end_frame'] / 30]
span = [round(span[0], 3), round(span[1], 3)]
# this would remove many samples (284k -> 37k)
# if span[1] - span[0] < 2:
# continue
question = raw_anno['question'].replace(' l ', ' I ').capitalize()
question = parse_question(question)
query = parse_query(question)
# too short or too long samples
num_words = len(query.split(' '))
if split == 'train' and (num_words < 3 or num_words > 30):
continue
answer = raw_anno['answer'].capitalize()
assert len(raw_anno['wrong_answers']) == 3
idx = random.randint(0, 3)
ans = chr(ord('A') + idx)
options = [o.capitalize() for o in raw_anno['wrong_answers']]
options.insert(idx, answer)
anno = dict(
source=self.SOURCE,
data_type=self.DATA_TYPE,
video_path=nncore.join(self.VIDEO_ROOT, vid + '.mp4'),
duration=duration,
query=query,
question=question,
options=options,
answer=answer,
ans=ans,
span=[span])
annos.append(anno)
return annos
@DATASETS.register(name='ego_timeqa_crop')
class EgoTimeQACropDataset(AnsweringCropDataset, EgoTimeQADataset):
SOURCE = 'ego_timeqa_crop'
@DATASETS.register(name='ego_timeqa_grounding')
class EgoTimeQAGroundingDataset(GroundingDataset, EgoTimeQADataset):
SOURCE = 'ego_timeqa_grounding'
DATA_TYPE = 'grounding'
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