Transforms with nested tensor
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- Dominant language
- Python
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Description
🚀 The feature
For batched inference on images of different sizes, we need to do the following
- Resize each image to the same size and convert to a tensor
- Stack the batch of tensors
- Do further image transformations on the batched tensors
- Run inference
It would be nice to do the following instead
- create a nested tensor of images of different sizes
- Run transformation including resizing on the nested tensor
- Run inference
Motivation, pitch
This would result in improved performance for image pre-processing
Alternatives
- Resize each image to the same size and convert to a tensor
- Stack the batch of tensors
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 reviewing the repository's image transformation entry points and any existing nested-tensor support. Determine how resizing and subsequent batched transformations are currently handled, then verify that images with different sizes can remain nested through preprocessing and inference without the existing resize-and-stack alternative.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100