Comments (3)
The accepted batch size depends on the dataset and your config.
Weizmann Action Dataset has 72 videos and since the drop_last=True in image loader and in video_loader, the max batch size is the dataset length.
To solve the issue, you can duplicate the data to cover your needed batch size (e.g. batch_size = 128, 72*2 > 128). Note that simply setting drop_last=False will not solve your issue.
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I solve the problem by edit the file of 'data.py' at line 22.
from:' if cache is not None and os.path.exitsis(cache):
to :'if (cache is not None) and (os.path.getsize(cache) != 0):'
because: the cache file maybe is a 0 byte files and meanwhile, it can not open to write
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Facing a similar issue for Weizmann Action Dataset on batch sizes larger than 64
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Related Issues (20)
- Image-to-video Translation HOT 3
- Inception Score on UCF101 HOT 9
- Output size is too small. HOT 1
- Unwanted repeatition of the action in the Generated videos HOT 1
- Future frame prediction
- Has the Taichi dataset been published?
- Has the Taichi dataset been published? HOT 1
- Code for Image-to-video Translation HOT 3
- Pre trained Model
- Video generation error HOT 1
- about Taichi dataset
- Video generation ffmpeg error HOT 1
- RuntimeError: arguments are located on different GPUs HOT 4
- ValueError: Expected target size (16, 16, 256, 256), got torch.Size([16])
- Dataset Formatter HOT 2
- Invariable Image Size HOT 1
- RuntimeError: arguments are located on different GPUs HOT 1
- About evaluation code
- Zero length video generation
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