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Sampling Components

Sampling Components#

Sampling methods.

class anomalib.models.components.sampling.KCenterGreedy(embedding, sampling_ratio)#

Bases: object

Implements k-center-greedy method.

Parameters:

  • embedding (torch.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])
get_new_idx()#

Get index value of a sample.

Based on minimum distance of the cluster

Returns:

Sample index

Return type:

int

reset_distances()#

Reset minimum distances.

Return type:

None

sample_coreset(selected_idxs=None)#

Select coreset from the embedding.

Parameters:

selected_idxs (list[int] | None) – 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])
select_coreset_idxs(selected_idxs=None)#

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

Parameters:

selected_idxs (list[int] | None) – index of samples already selected. Defaults to an empty set.

Return type:

list[int]

Returns:

indices of samples selected to minimize distance to cluster centers

update_distances(cluster_centers)#

Update min distances given cluster centers.

Parameters:

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

Return type:

None

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