Fix CelebA dataset loading, remove eval() call#3
Open
philippeitis wants to merge 1 commit intoYannDubs:masterfrom
Open
Fix CelebA dataset loading, remove eval() call#3philippeitis wants to merge 1 commit intoYannDubs:masterfrom
philippeitis wants to merge 1 commit intoYannDubs:masterfrom
Conversation
This file contains hidden or 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
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
It seems like the CelebA datasets don't filter out non-matching sizes, which causes problems when trying to run the notebooks provided in
/reproducability- the default behaviour seems to download both (3, 218, 178) and (3, 32, 32) images into the celeba32 folder, causing bugs when trying to run the models (for instance, runningCNP.ipynbfails when running everything from scratch). Adding a transform.Resize() to the__init__()method for CelebA64 fixes this - alternatively, having a set up script to filter images into separate directories could also be useful.Additionally, the eval() call in imgs.py seems like an easy source of bugs and confusion - I think it's better to split the datasets into a separate
datasets.pyfile and provide the dict with direct class references (and avoid eval()) inimgs.py