Padim

Padim#

PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization.

Paper https://arxiv.org/abs/2011.08785

class anomalib.models.image.padim.lightning_model.Padim(backbone='resnet18', layers=['layer1', 'layer2', 'layer3'], pre_trained=True, n_features=None)#

Bases: MemoryBankMixin, AnomalyModule

PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization.

Parameters:

  • backbone (str) – Backbone CNN network Defaults to resnet18.

  • layers (list[str]) – Layers to extract features from the backbone CNN Defaults to ["layer1", "layer2", "layer3"].

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

  • n_features (int, optional) – Number of features to retain in the dimension reduction step. Default values from the paper are available for: resnet18 (100), wide_resnet50_2 (550). Defaults to None.

static configure_optimizers()#

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

Return type:

None

static configure_transforms(image_size=None)#

Default transform for Padim.

Return type:

Transform

fit()#

Fit a Gaussian 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, int | float]#

Return PADIM trainer arguments.

Since the model does not require training, we limit the max_epochs to 1. Since we need to run training epoch before validation, we also set the sanity steps to 0

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

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

Parameters:

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

  • args – Additional arguments.

  • kwargs – Additional keyword arguments.

Return type:

None

Returns:

Hierarchical feature map

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

Perform a validation step of PADIM.

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

Parameters:

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

  • args – Additional arguments.

  • kwargs – Additional keyword arguments.

Return type:

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

Returns:

Dictionary containing images, features, true labels and masks. These are required in validation_epoch_end for feature concatenation.

PyTorch model for the PaDiM model implementation.

class anomalib.models.image.padim.torch_model.PadimModel(backbone='resnet18', layers=['layer1', 'layer2', 'layer3'], pre_trained=True, n_features=None)#

Bases: Module

Padim Module.

Parameters:

  • layers (list[str]) – Layers used for feature extraction

  • backbone (str, optional) – Pre-trained model backbone. Defaults to “resnet18”. Defaults to resnet18.

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

  • n_features (int, optional) – Number of features to retain in the dimension reduction step. Default values from the paper are available for: resnet18 (100), wide_resnet50_2 (550). Defaults to None.

forward(input_tensor)#

Forward-pass image-batch (N, C, H, W) into model to extract features.

Parameters:

  • input_tensor (Tensor) – Image-batch (N, C, H, W)

  • input_tensor – torch.Tensor:

Return type:

Tensor

Returns:

Features from single/multiple layers.

Example

>>> x = torch.randn(32, 3, 224, 224)
>>> features = self.extract_features(input_tensor)
>>> features.keys()
dict_keys(['layer1', 'layer2', 'layer3'])

>>> [v.shape for v in features.values()]
[torch.Size([32, 64, 56, 56]),
torch.Size([32, 128, 28, 28]),
torch.Size([32, 256, 14, 14])]
generate_embedding(features)#

Generate embedding from hierarchical feature map.

Parameters:

features (dict[str, torch.Tensor]) – Hierarchical feature map from a CNN (ResNet18 or WideResnet)

Return type:

Tensor

Returns:

Embedding vector

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