[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)

anomalib/src/anomalib/metrics/pimo/functional.py

Line 118 in 1465b05

num_thresholds: int = 300_000,

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

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[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