Add dataset validation and config for super resolution#206
Open
iliasmahboub wants to merge 1 commit intoML4SCI:mainfrom
Open
Add dataset validation and config for super resolution#206iliasmahboub wants to merge 1 commit intoML4SCI:mainfrom
iliasmahboub wants to merge 1 commit intoML4SCI:mainfrom
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
Addresses #196.
Adds two files to
DeepLense_Physics_Informed_Super_Resolution_Anirudh_Shankar/:validate_dataset.py— Pre-training check that verifies class directoriesexist, LR/HR file counts match per class, .npy array shapes are consistent
with configured image dimensions and scale factor, and no filename index gaps.
Exits cleanly with a summary report.
dataset_config.yaml— Externalizes dataset parameters currently hardcodedacross training notebooks: data paths per telescope model, DM substructure
classes, num_samples, batch size, epochs, learning rate, train/val split,
image shape, and magnification.
How to test
cd DeepLense_Physics_Informed_Super_Resolution_Anirudh_Shankar python validate_dataset.py --config dataset_config.yamlReports missing directories gracefully when dataset is not downloaded.
With data present, validates full file integrity before training.