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DFM

DFM#

DFM: Deep Feature Modeling.

https://arxiv.org/abs/1909.11786

class anomalib.models.image.dfm.lightning_model.Dfm(backbone='resnet50', layer='layer3', pre_trained=True, pooling_kernel_size=4, pca_level=0.97, score_type='fre')#

Bases: MemoryBankMixin, AnomalyModule

DFM: Deep Featured Kernel Density Estimation.

Parameters:

  • backbone (str) – Backbone CNN network Defaults to "resnet50".

  • layer (str) – Layer to extract features from the backbone CNN Defaults to "layer3".

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

  • pooling_kernel_size (int, optional) – Kernel size to pool features extracted from the CNN. Defaults to 4.

  • pca_level (float, optional) – Ratio from which number of components for PCA are calculated. Defaults to 0.97.

  • score_type (str, optional) – Scoring type. Options are fre and nll. Defaults to fre.

static configure_optimizers()#

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

Return type:

None

fit()#

Fit a PCA transformation and a Gaussian model to dataset.

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 DFM-specific trainer arguments.

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

Perform the training step of DFM.

For each batch, features are extracted from the CNN.

Parameters:

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

  • args – Arguments.

  • kwargs – Keyword arguments.

Return type:

None

Returns:

Deep CNN features.

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

Perform the validation step of DFM.

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 FRE anomaly scores and anomaly maps.

PyTorch model for DFM model implementation.

class anomalib.models.image.dfm.torch_model.DFMModel(backbone, layer, pre_trained=True, pooling_kernel_size=4, n_comps=0.97, score_type='fre')#

Bases: Module

Model for the DFM algorithm.

Parameters:

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

  • layer (str) – Layer from which to extract features.

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

  • pooling_kernel_size (int, optional) – Kernel size to pool features extracted from the CNN. Defaults to 4.

  • n_comps (float, optional) – Ratio from which number of components for PCA are calculated. Defaults to 0.97.

  • score_type (str, optional) – Scoring type. Options are fre and nll. Anomaly Defaults to fre. Segmentation is supported with fre only. If using nll, set task in config.yaml to classification Defaults to classification.

fit(dataset)#

Fit a pca transformation and a Gaussian model to dataset.

Parameters:

dataset (torch.Tensor) – Input dataset to fit the model.

Return type:

None

forward(batch)#

Compute score from input images.

Parameters:

batch (torch.Tensor) – Input images

Returns:

Scores

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

score(features, feature_shapes)#

Compute scores.

Scores are either PCA-based feature reconstruction error (FRE) scores or the Gaussian density-based NLL scores

Parameters:

  • features (torch.Tensor) – semantic features on which PCA and density modeling is performed.

  • feature_shapes (tuple) – shape of features tensor. Used to generate anomaly map of correct shape.

Returns:

numpy array of scores

Return type:

score (torch.Tensor)

class anomalib.models.image.dfm.torch_model.SingleClassGaussian#

Bases: DynamicBufferMixin

Model Gaussian distribution over a set of points.

fit(dataset)#

Fit a Gaussian model to dataset X.

Covariance matrix is not calculated directly using: C = X.X^T Instead, it is represented in terms of the Singular Value Decomposition of X: X = U.S.V^T Hence, C = U.S^2.U^T This simplifies the calculation of the log-likelihood without requiring full matrix inversion.

Parameters:

dataset (torch.Tensor) – Input dataset to fit the model.

Return type:

None

forward(dataset)#

Provide the same functionality as fit.

Transforms the input dataset based on singular values calculated earlier.

Parameters:

dataset (torch.Tensor) – Input dataset

Return type:

None

score_samples(features)#

Compute the NLL (negative log likelihood) scores.

Parameters:

features (torch.Tensor) – semantic features on which density modeling is performed.

Returns:

Torch tensor of scores

Return type:

nll (torch.Tensor)

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