:py:mod:`anomalib.models.padim`
===============================

.. py:module:: anomalib.models.padim

.. autoapi-nested-parse::

   PADIM model.



Submodules
----------
.. toctree::
   :titlesonly:
   :maxdepth: 1

   anomaly_map/index.rst
   lightning_model/index.rst
   torch_model/index.rst


Package Contents
----------------

Classes
~~~~~~~

.. autoapisummary::

   anomalib.models.padim.Padim
   anomalib.models.padim.PadimLightning




.. py:class:: Padim(layers: List[str], input_size: Tuple[int, int], backbone: str, pre_trained: bool = True)

   Bases: :py:obj:`anomalib.models.components.AnomalyModule`

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

   :param layers: Layers to extract features from the backbone CNN
   :type layers: List[str]
   :param input_size: Size of the model input.
   :type input_size: Tuple[int, int]
   :param backbone: Backbone CNN network
   :type backbone: str
   :param pre_trained: Boolean to check whether to use a pre_trained backbone.
   :type pre_trained: bool, optional

   .. py:method:: configure_optimizers()
      :staticmethod:

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


   .. py:method:: training_step(batch, _batch_idx)

      Training Step of PADIM. For each batch, hierarchical features are extracted from the CNN.

      :param batch: Batch containing image filename, image, label and mask
      :type batch: Dict[str, Any]
      :param _batch_idx: Index of the batch.

      :returns: Hierarchical feature map


   .. py:method:: on_validation_start() -> None

      Fit a Gaussian to the embedding collected from the training set.


   .. py:method:: validation_step(batch, _)

      Validation Step of PADIM.

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

      :param batch: Input batch
      :param _: Index of the batch.

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



.. py:class:: PadimLightning(hparams: Union[omegaconf.DictConfig, omegaconf.ListConfig])

   Bases: :py:obj:`Padim`

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

   :param hparams: Model params
   :type hparams: Union[DictConfig, ListConfig]