Extend avg pool count include pad false to1d3d #4035
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
Get latest changes
…ere count_include_pad = false
|
Hi @AmosLewis , @rsuderman , Could you please review this change when you get a chance? The change is just to generalize the average pooling divisor computation when count_include_pad = false. This was implemented for 2 dimensions. This change makes it work for N dimensions, and does some refactoring to make the methods smaller. Thanks, |
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Outdated suggestions cannot be applied.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.
Currently the avg_pool2d PyTorch operation supports the cases where count_include_pad is true and false, but the avg_pool1d and avg_pool3d only the true case is supported (which is simpler).
The count_include_pad = false support for avg_pool2d was added by @AmosLewis in this change (reviewed by @rsuderman and @nirvedhmeshram) : #3235
In this change I generalized the logic added above. I also did some refactoring to the original code to reduce the size of the functions and to avoid redundancy when possible.
@sahas3 @dixinzhou @rafaelubal