Renamed transform functionals to avoid overridden imports #11
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
TorchDSP
deleted the
2-torchsigtransforms-subpackage-does-not-export-its-contents-correctly
branch
ereoh
pushed a commit
that referenced
this pull request
Sep 25, 2024
Co-authored-by: Luke Boegner <luke.boegner@peratonlabs.com>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This update changes the naming scheme of the transform/functional code. Previously, each transform category contained a similarly named
functional.pyfile. Issue #2 identified that this identical naming scheme resulted in the functional code being overridden when imported. While this is not necessarily an issue for the transform pipeline, it limits the functional codes' use-cases and is misleading when attempting to import these functions directly. By renaming the functional code to be more specific to each category, we overcome the import overriding issue (closes issue #2 ) and we make the toolkit more flexible for individuals who may want to use the functional code directly, outside of the expected ML training pipeline.