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DFKDE

DFKDE#

DFKDE: Deep Feature Kernel Density Estimation.

class anomalib.models.image.dfkde.lightning_model.Dfkde(backbone='resnet18', layers=('layer4',), pre_trained=True, n_pca_components=16, feature_scaling_method=FeatureScalingMethod.SCALE, max_training_points=40000)#

Bases: MemoryBankMixin, AnomalyModule

DFKDE: Deep Feature Kernel Density Estimation.

Parameters:

  • backbone (str) – Pre-trained model backbone. Defaults to "resnet18".

  • layers (Sequence[str], optional) – Layers to extract features from. Defaults to ("layer4",).

  • pre_trained (bool, optional) – Boolean to check whether to use a pre_trained backbone. Defaults to True.

  • n_pca_components (int, optional) – Number of PCA components. Defaults to 16.

  • feature_scaling_method (FeatureScalingMethod, optional) – Feature scaling method. Defaults to FeatureScalingMethod.SCALE.

  • max_training_points (int, optional) – Number of training points to fit the KDE model. Defaults to 40000.

static configure_optimizers()#

DFKDE doesn’t require optimization, therefore returns no optimizers.

Return type:

None

fit()#

Fit a KDE Model to the embedding collected from the training set.

Return type:

None

property learning_type: LearningType#

Return the learning type of the model.

Returns:

Learning type of the model.

Return type:

LearningType

property trainer_arguments: dict[str, Any]#

Return DFKDE-specific trainer arguments.

training_step(batch, *args, **kwargs)#

Perform the training step of DFKDE. For each batch, features are extracted from the CNN.

Parameters:

  • (batch (batch) – dict[str, str | torch.Tensor]): Batch containing image filename, image, label and mask

  • args – Arguments.

  • kwargs – Keyword arguments.

Return type:

None

Returns:

Deep CNN features.

validation_step(batch, *args, **kwargs)#

Perform the validation step of DFKDE.

Similar to the training step, features are extracted from the CNN for each batch.

Parameters:

  • batch (dict[str, str | torch.Tensor]) – Input batch

  • args – Arguments.

  • kwargs – Keyword arguments.

Return type:

Union[Tensor, Mapping[str, Any], None]

Returns:

Dictionary containing probability, prediction and ground truth values.

Normality model of DFKDE.

class anomalib.models.image.dfkde.torch_model.DfkdeModel(backbone, layers, pre_trained=True, n_pca_components=16, feature_scaling_method=FeatureScalingMethod.SCALE, max_training_points=40000)#

Bases: Module

Normality Model for the DFKDE algorithm.

Parameters:

  • backbone (str) – Pre-trained model backbone.

  • layers (Sequence[str]) – Layers to extract features from.

  • pre_trained (bool, optional) – Boolean to check whether to use a pre_trained backbone. Defaults to True.

  • n_pca_components (int, optional) – Number of PCA components. Defaults to 16.

  • feature_scaling_method (FeatureScalingMethod, optional) – Feature scaling method. Defaults to FeatureScalingMethod.SCALE.

  • max_training_points (int, optional) – Number of training points to fit the KDE model. Defaults to 40000.

forward(batch)#

Prediction by normality model.

Parameters:

batch (torch.Tensor) – Input images.

Returns:

Predictions

Return type:

Tensor

get_features(batch)#

Extract features from the pretrained network.

Parameters:

batch (torch.Tensor) – Image batch.

Returns:

torch.Tensor containing extracted features.

Return type:

Tensor

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