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Running
on
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Running
on
Zero
File size: 2,101 Bytes
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# Copyright (c) 2025 Ye Liu. Licensed under the BSD-3-Clause License.
import copy
from torch.utils.data import Dataset
from videomind.constants import GROUNDER_PROMPT, REG_TOKEN
class GroundingDataset(Dataset):
def __init__(self, processor, model_args, data_args, training_args):
super(GroundingDataset, self).__init__()
raw_annos = self.load_annos()
annos = []
for anno in raw_annos:
num_words = len(anno['query'].split(' '))
if data_args.min_num_words >= 0 and num_words < data_args.min_num_words:
continue
if data_args.max_num_words >= 0 and num_words > data_args.max_num_words:
continue
if data_args.min_video_len >= 0 and anno.get('duration', float('inf')) < data_args.min_video_len:
continue
if data_args.max_video_len >= 0 and anno.get('duration', 0) > data_args.max_video_len:
continue
annos.append(anno)
self.annos = annos
self.raw_length = len(raw_annos)
self.processor = processor
self.model_args = model_args
self.data_args = data_args
self.training_args = training_args
def __len__(self):
return len(self.annos)
def __getitem__(self, idx):
anno = copy.deepcopy(self.annos[idx])
video_path, duration, query, span = anno['video_path'], anno['duration'], anno['query'], anno['span']
messages = [{
'role':
'user',
'content': [{
'type': 'video',
'video': video_path,
'min_pixels': 36 * 28 * 28,
'max_pixels': 64 * 28 * 28,
'max_frames': 150,
'fps': 1.0
}, {
'type': 'text',
'text': GROUNDER_PROMPT.format(query)
}]
}, {
'role': 'assistant',
'content': f'The relevant moment happens in {REG_TOKEN}.'
}]
meta = dict(messages=messages, span=span, duration=duration)
return meta
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