:py:mod:`anomalib.models.draem.lightning_model`
===============================================

.. py:module:: anomalib.models.draem.lightning_model

.. autoapi-nested-parse::

   DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection.

   Paper https://arxiv.org/abs/2108.07610



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   anomalib.models.draem.lightning_model.Draem
   anomalib.models.draem.lightning_model.DraemLightning




.. py:class:: Draem(enable_sspcab: bool = False, sspcab_lambda: float = 0.1, anomaly_source_path: Optional[str] = None)

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

   DRÆM: A discriminatively trained reconstruction embedding for surface anomaly detection.

   :param anomaly_source_path: Path to folder that contains the anomaly source images. Random noise will
                               be used if left empty.
   :type anomaly_source_path: Optional[str]

   .. py:method:: setup_sspcab()

      Prepare the model for the SSPCAB training step by adding forward hooks for the SSPCAB layer activations.


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

      Training Step of DRAEM.

      Feeds the original image and the simulated anomaly
      image through the network and computes the training loss.

      :param batch: Batch containing image filename, image, label and mask
      :type batch: Dict[str, Any]

      :returns: Loss dictionary


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

      Validation step of DRAEM. The Softmax predictions of the anomalous class are used as anomaly map.

      :param batch: Batch of input images

      :returns: Dictionary to which predicted anomaly maps have been added.



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

   Bases: :py:obj:`Draem`

   DRÆM: A discriminatively trained reconstruction embedding for surface anomaly detection.

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

   .. py:method:: configure_callbacks()

      Configure model-specific callbacks.

      .. note::

         This method is used for the existing CLI.
         When PL CLI is introduced, configure callback method will be
             deprecated, and callbacks will be configured from either
             config.yaml file or from CLI.


   .. py:method:: configure_optimizers()

      Configure the Adam optimizer.