anomalib.models.components.sampling.k_center_greedy

This module comprises PatchCore Sampling Methods for the embedding.

Module Contents

Classes

KCenterGreedy

Implements k-center-greedy method.
class anomalib.models.components.sampling.k_center_greedy.KCenterGreedy(embedding: torch.Tensor, sampling_ratio: float)[source]

Implements k-center-greedy method.

Parameters

  • embedding (Tensor) – Embedding vector extracted from a CNN

  • sampling_ratio (float) – Ratio to choose coreset size from the embedding size.

Example

>>> embedding.shape
torch.Size([219520, 1536])
>>> sampler = KCenterGreedy(embedding=embedding)
>>> sampled_idxs = sampler.select_coreset_idxs()
>>> coreset = embedding[sampled_idxs]
>>> coreset.shape
torch.Size([219, 1536])
reset_distances() → None[source]

Reset minimum distances.

update_distances(cluster_centers: List[int]) → None[source]

Update min distances given cluster centers.

Parameters

cluster_centers (List[int]) – indices of cluster centers

get_new_idx() → int[source]

Get index value of a sample.

Based on minimum distance of the cluster

Returns

Sample index

Return type

int

select_coreset_idxs(selected_idxs: Optional[List[int]] = None) → List[int][source]

Greedily form a coreset to minimize the maximum distance of a cluster.

Parameters

selected_idxs – index of samples already selected. Defaults to an empty set.

Returns

indices of samples selected to minimize distance to cluster centers

sample_coreset(selected_idxs: Optional[List[int]] = None) → torch.Tensor[source]

Select coreset from the embedding.

Parameters

selected_idxs – index of samples already selected. Defaults to an empty set.

Returns

Output coreset

Return type

Tensor

Example

>>> embedding.shape
torch.Size([219520, 1536])
>>> sampler = KCenterGreedy(...)
>>> coreset = sampler.sample_coreset()
>>> coreset.shape
torch.Size([219, 1536])