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import functools
from io import BytesIO
import torch
import torchvision
import torchvision.transforms.v2 as transforms
import wids
from torch.utils.data import DataLoader
def _video_shortener(video_tensor, length, generator=None):
start = torch.randint(0, video_tensor.shape[0] - length, (1,), generator=generator)
return video_tensor[start:start + length]
def select_video_extract(length=16, generator=None):
return functools.partial(_video_shortener, length=length, generator=generator)
def my_collate_fn(batch):
videos = torch.stack([sample[0] for sample in batch])
txts = [sample[1] for sample in batch]
return videos, txts
class WebVidDataset(wids.ShardListDataset):
def __init__(self, shards, cache_dir, video_length=16, video_size=256, video_length_offset=1, val=False, seed=42,
**kwargs):
self.val = val
self.generator = torch.Generator()
self.generator.manual_seed(seed)
self.generator_init_state = self.generator.get_state()
super().__init__(shards, cache_dir=cache_dir, keep=True, **kwargs)
if isinstance(video_size, int):
video_size = (video_size, video_size)
self.video_size = video_size
for size in video_size:
if size % 8 != 0:
raise ValueError("video_size must be divisible by 8")
self.transform = transforms.Compose(
[
select_video_extract(length=video_length + video_length_offset, generator=self.generator),
transforms.Resize(size=video_size),
transforms.RandomCrop(size=video_size) if not self.val else transforms.CenterCrop(size=video_size),
transforms.RandomHorizontalFlip() if not self.val else transforms.Identity(),
]
)
self.add_transform(self._make_sample)
def _make_sample(self, sample):
if self.val:
self.generator.set_state(self.generator_init_state)
video = torchvision.io.read_video(BytesIO(sample[".mp4"].read()), output_format="TCHW", pts_unit='sec')[0]
label = sample[".txt"]
return self.transform(video), label
if __name__ == "__main__":
dataset = WebVidDataset(
tar_index=0,
root_path='/users/Etu9/3711799/onlyflow/data/webvid/desc.json',
video_length=16,
video_size=256,
video_length_offset=0,
)
sampler = wids.DistributedChunkedSampler(dataset, chunksize=1000, shuffle=True)
dataloader = DataLoader(
dataset,
collate_fn=my_collate_fn,
batch_size=4,
sampler=sampler,
num_workers=4
)
for i, (images, labels) in enumerate(dataloader):
print(i, images.shape, labels)
if i > 10:
break
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