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Zero
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# Copyright (c) 2025 Ye Liu. Licensed under the BSD-3-Clause License.
import csv
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='nextgqa')
class NExTGQADataset(AnsweringDataset):
ANNO_PATH_VALID = 'data/nextgqa/val.csv'
ANNO_PATH_TEST = 'data/nextgqa/test.csv'
SPAN_PATH_VALID = 'data/nextgqa/gsub_val.json'
SPAN_PATH_TEST = 'data/nextgqa/gsub_test.json'
VIDEO_ID_MAP = 'data/nextgqa/map_vid_vidorID.json'
VIDEO_ROOT = 'data/nextqa/videos'
SOURCE = 'nextgqa'
DATA_TYPE = 'multimodal'
UNIT = 0.1
@classmethod
def load_annos(self, split='valid'):
assert split in ('valid', 'test')
if split == 'valid':
anno_path = self.ANNO_PATH_VALID
raw_spans = nncore.load(self.SPAN_PATH_VALID)
else:
anno_path = self.ANNO_PATH_TEST
raw_spans = nncore.load(self.SPAN_PATH_TEST)
with open(anno_path, mode='r') as f:
reader = csv.DictReader(f)
raw_annos = [d for d in reader]
video_id_map = nncore.load(self.VIDEO_ID_MAP)
annos = []
for raw_anno in raw_annos:
vid = raw_anno['video_id']
qid = raw_anno['qid']
video_id = video_id_map[vid]
query = parse_query(raw_anno['question'].capitalize() + '?')
question = parse_question(raw_anno['question'].capitalize() + '?')
options = [raw_anno[k].capitalize() for k in ('a0', 'a1', 'a2', 'a3', 'a4')]
answer = raw_anno['answer'].capitalize()
ans = chr(ord('A') + options.index(answer))
anno = dict(
source=self.SOURCE,
data_type=self.DATA_TYPE,
video_path=nncore.join(self.VIDEO_ROOT, video_id + '.mp4'),
duration=raw_spans[vid]['duration'],
query=query,
question=question,
options=options,
answer=answer,
ans=ans,
span=raw_spans[vid]['location'][qid],
task=raw_anno['type'])
annos.append(anno)
return annos
@DATASETS.register(name='nextgqa_crop')
class NExTGQACropDataset(AnsweringCropDataset, NExTGQADataset):
SOURCE = 'nextgqa_crop'
@DATASETS.register(name='nextgqa_grounding')
class NExTGQAGroundingDataset(GroundingDataset, NExTGQADataset):
SOURCE = 'nextgqa_grounding'
DATA_TYPE = 'grounding'
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