Add `channels_last` argument to `transforms.ToTensor()`
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- Python
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Description
🚀 The feature
Currently, for using channels_last:
- During data loading,
torchvision.transforms.ToTensor()converts the input images into CHW. - Then during training, the images are converted back to HWC:
images = images.to(memory_format=torch.channels_last).
This is highly inefficient. I suggest to add an optional boolean argument to ToTensor() that skips the permutation and outputs directly HWC:
torchvision.transforms.ToTensor(channels_last=True)
Motivation, pitch
To avoid unnecessary axis permutations and improve efficiency.
Alternatives
I had to write my custom functional.to_tensor to add the suggested argument.
Additional context
No response
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading torchvision.transforms.ToTensor() and the functional.to_tensor path, including the existing behavior that converts images to CHW. Determine how the optional channels_last setting should affect the output and verify the result against the requested efficiency goal; the issue does not name a test file.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- computer-vision
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100