Migrate to register_full_backward_hook #837
Closed
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
NarineK
reviewed
Jan 28, 2022
NarineK
reviewed
Jan 28, 2022
NarineK
reviewed
Jan 28, 2022
NarineK
reviewed
Jan 28, 2022
NarineK
approved these changes
Feb 18, 2022
|
@vivekmig has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
|
Does this warning mean the output for GuidedBackPropagation will not be correct? (I am currently using the following pretrained torchvision models: Alexnet, VGG16, Resnet50, DenseNet121) |
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Outdated suggestions cannot be applied.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.
Modifying all module backward hooks to utilize the new register_full_backward hook API documented here. This new API resolves many issues we previously encountered with backward module hooks.
Since this API is available only in torch 1.8, allowing a fall-back option to the original backward hook approach.
Due to issues described here, we are also deprecating attribution with respect to neuron outputs for NeuronDeepLift, NeuronGuidedBackprop, and NeuronDeconvolution; these methods require attributing with respect to neuron input (which is typically equivalent to attributing with respect to the previous layer output).
Additionally, in-place modules are no longer supported for full backward hooks, so these are no longer supported for DeepLift, LRP, and GuidedBackprop / Deconvolution and corresponding variants. Documentation has been updated accordingly.