[PIMO] speed up

[jpcbertoldo]

📝 Description

without numba, aupimo became annoyingly slow for full resolution test, so this idea show speed it up by removing unnecessayr computation

this parameter is what mostly makes it so inefficient (num_thresholds = 300_000)
https://github.com/openvinotoolkit/anomalib/blob/1465b05fd9ff5c20bfe6df661187e6866e04cec7/src/anomalib/metrics/pimo/functional.py#L118

it has to be so big to make sure that there will be enough points in the AUC integration within the integration range

the current implementation thresholds the anomaly score maps from their min to max value, which is the inefficient because we only need it in a much smaller range

strategy to improve it:
- use binary search to find the thresholds corresponding to the fpr integration bounds
- compute the binary classification curves within those bounds
- decrease the number of thresholds from 300_000 to 300

• review tutorial notebooks

✨ Changes

Select what type of change your PR is:

• 🐞 Bug fix (non-breaking change which fixes an issue)
• 🔨 Refactor (non-breaking change which refactors the code base)
• [] 🚀 New feature (non-breaking change which adds functionality)
• 💥 Breaking change (fix or feature that would cause existing functionality to not work as expected)
• 📚 Documentation update
• 🔒 Security update

✅ Checklist

Before you submit your pull request, please make sure you have completed the following steps:

• 📋 I have summarized my changes in the CHANGELOG and followed the guidelines for my type of change (skip for minor changes, documentation updates, and test enhancements).
• 📚 I have made the necessary updates to the documentation (if applicable).
• 🧪 I have written tests that support my changes and prove that my fix is effective or my feature works (if applicable).

For more information about code review checklists, see the Code Review Checklist.

[review-notebook-app]

Check out this pull request on 

See visual diffs & provide feedback on Jupyter Notebooks.

---

Powered by ReviewNB

[samet-akcay]

Suggested change

	anomaly_maps_normal_images = anomaly_maps[image_classes == 0]
	normal_anomaly_maps = anomaly_maps[image_classes == 0]

[samet-akcay]

Suggested change

	anomaly_maps_normals: torch.Tensor,
	normal_anomaly_maps: torch.Tensor,

[djdameln]

Thanks, this is a nice optimization. Just out of curiosity, do you have some numbers on the amount of speed up is achieved by this change?

[djdameln]

It would be good to add a more detailed description to this docstring, explaining why we need to apply the binary search and how it is performed. This would be useful for future reference.

[samet-akcay]

Looks like one of the pimo notebook tests are failing