torch.cdist works faster than distance_matrix.

[paining]

and it needs more small gpu-memory.

There is a memory overload in torch.pow(x-y,p).sum()
When I try with this code, I already reserve 8GB and try to allocate another 8GB.

I have a RTX 3080 GPU with 10GB memory.
And when I input big image (3,896,896), distance matrix needs 14.36GB but torch.cdist works well.

It means we can use more large sampling rates by using torch.cdist function.
I can run sampling rate = 0.01 with torch.cdist but I got CUDA out of memory with distance_matrix.

---

Actual GPU memory usages with MVTec AD.
#19 (comment)

[royarahimzadeh]

Great, I can run with a higher sampling rate too.
I have a problem, not sure if it is related to the current higher sampling rate. I see that the false negative rate is higher than before. Any suggestions for improving?

[paining]

Great, I can run with a higher sampling rate too. I have a problem, not sure if it is related to the current higher sampling rate. I see that the false negative rate is higher than before. Any suggestions for improving?

Humm... That is interesting...
I don't know why the false negative rate become higher.
But as you said, I guess the higher sampling rate makes the high false negative rate too.
I think too many samples decrease the distance between new test features (it may be normal or defect) and sampled features.
Variance of distance become smaller, it makes bigger trade-off problem.

We can see the table 4 in PatchCore paper, 10% sampling rate has higher pixel AUROC and PRO score than 25%.

That is my best guess.....
Another person can give us more clear solution!

[hcw-00]

Thank you!