convert crop_slices to tuple #26
Conversation
![gemini-code-assist[bot]](https://avatars.githubusercontent.com/in/956858?s=60&v=4){kind=small}
There was a problem hiding this comment.
Summary of Changes
Hello @vcasellesb, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request introduces a small but crucial change to prevent a UserWarning that arises from upcoming changes in PyTorch's multidimensional indexing behavior. The modification ensures that the crop_slices variable is a tuple, thereby maintaining compatibility and avoiding potential errors or unexpected results in future PyTorch versions.
Highlights
- Deprecation Warning Fix: I've addressed a
UserWarningfrom PyTorch related to multidimensional indexing. The fix involves explicitly converting thecrop_sliceslist to a tuple before it's used for indexing, aligning with future PyTorch behavior.
Using Gemini Code Assist
The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.
Invoking Gemini
You can request assistance from Gemini at any point in your pull request via creating an issue comment (i.e. comment on the pull request page) using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands.
| Feature | Command | Description |
|---|---|---|
| Code Review | /gemini review |
Performs a code review for the current pull request in its current state. |
| Pull Request Summary | /gemini summary |
Provides a summary of the current pull request in its current state. |
| Comment | @gemini-code-assist | Responds in comments when explicitly tagged, both in issue comments and review comments. |
| Help | /gemini help |
Displays a list of available commands. |
Customization
To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.
Limitations & Feedback
Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments or fill out our survey to provide feedback.
You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.
Footnotes
-
Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩
| crop_slices = tuple( | ||
| [slice(None), slice(None)] + [ | ||
| slice(0, (signal_size[i] - kernel.size(i) + 1), stride_[i - 2]) | ||
| for i in range(2, signal.ndim) | ||
| ] | ||
| ) |
There was a problem hiding this comment.
Converting crop_slices to a tuple resolves the deprecation warning. For improved efficiency and conciseness, consider constructing the tuple directly using tuple concatenation with a generator expression. This avoids creating intermediate lists.
crop_slices = (slice(None), slice(None)) + tuple(
slice(0, (signal_size[i] - kernel.size(i) + 1), stride_[i - 2])
for i in range(2, signal.ndim)
)
Hi! Thanks for your package, I have been using it as part of the batchgeneratorsv2 data augmentations pipeline, and I kept getting the following UserWarning:
/opt/homebrew/Caskroom/miniconda/base/envs/tm-torch/lib/python3.12/site-packages/fft_conv_pytorch/fft_conv.py:139: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/torch/csrc/autograd/python_variable_indexing.cpp:312.)I think that with the little change I've commited it should be fixed, but I haven't tested it! Although it's such a small change I don't think it can break anything.
UPDATE: I've tested it and it works same as before.