Code attribution

[rvorias]

Hi,

Your implementation of PatchCore coreset selection:

anomalib/anomalib/models/patchcore/model.py

Line 212 in 3613a6e

def create_coreset(

is VERY similar to my implementation, which I have designed and coded from scratch:
https://github.com/rvorias/ind_knn_ad/blob/57de628b334f9dd62316ac7770db6e963b71ee8e/indad/utils.py#L36

Please elaborate on this.

More details:
this repo:

        select_idx = 0
        last_item = sample_set[select_idx : select_idx + 1]
        coreset_idx = [torch.tensor(select_idx).to(embedding.device)]  # pylint: disable=not-callable
        min_distances = torch.linalg.norm(sample_set - last_item, dim=1, keepdims=True)

        for _ in range(sample_count - 1):
            distances = torch.linalg.norm(sample_set - last_item, dim=1, keepdims=True)  # broadcast
            min_distances = torch.minimum(distances, min_distances)  # iterate
            select_idx = torch.argmax(min_distances)  # select

            last_item = sample_set[select_idx : select_idx + 1]
            min_distances[select_idx] = 0
            coreset_idx.append(select_idx)

        coreset_idx = torch.stack(coreset_idx)
        self.memory_bank = embedding[coreset_idx]

my repo:

    select_idx = 0
    last_item = z_lib[select_idx:select_idx+1]
    coreset_idx = [torch.tensor(select_idx)]
    min_distances = torch.linalg.norm(z_lib-last_item, dim=1, keepdims=True)
    # The line below is not faster than linalg.norm, although i'm keeping it in for
    # future reference.
    # min_distances = torch.sum(torch.pow(z_lib-last_item, 2), dim=1, keepdims=True)
    
    ...
    
    for _ in tqdm(range(n-1), **TQDM_PARAMS):
        distances = torch.linalg.norm(z_lib-last_item, dim=1, keepdims=True) # broadcasting step
        # distances = torch.sum(torch.pow(z_lib-last_item, 2), dim=1, keepdims=True) # broadcasting step
        min_distances = torch.minimum(distances, min_distances) # iterative step
        select_idx = torch.argmax(min_distances) # selection step

        # bookkeeping
        last_item = z_lib[select_idx:select_idx+1]
        min_distances[select_idx] = 0
        coreset_idx.append(select_idx.to("cpu"))

    return torch.stack(coreset_idx)

[samet-akcay]

Hi @rvorias, thank you for notifying us. This was a PR that was merged 4 hours ago. I agree that this should have been scanned before merging. I'll revert this PR right now. Apologies.

[rvorias]

I wish to let the open-source community prosper, but I demand attribution.

[samet-akcay]

Can't agree more! As I said, we perform scans before we merge, which we didn't do this time, and this happened. This has now been reverted. Apologies again!