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

.. py:module:: anomalib.models.dfkde

.. autoapi-nested-parse::

   Deep Feature Kernel Density Estimation model.



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

   lightning_model/index.rst
   torch_model/index.rst


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

Classes
~~~~~~~

.. autoapisummary::

   anomalib.models.dfkde.Dfkde
   anomalib.models.dfkde.DfkdeLightning




.. py:class:: Dfkde(backbone: str, max_training_points: int = 40000, pre_processing: str = 'scale', n_components: int = 16, threshold_steepness: float = 0.05, threshold_offset: int = 12)

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

   DFKDE: Deep Feature Kernel Density Estimation.

   :param backbone: Pre-trained model backbone.
   :type backbone: str
   :param max_training_points: Number of training points to fit the KDE model.
                               Defaults to 40000.
   :type max_training_points: int, optional
   :param pre_processing: Preprocess features before passing to KDE.
                          Options are between `norm` and `scale`. Defaults to "scale".
   :type pre_processing: str, optional
   :param n_components: Number of PCA components. Defaults to 16.
   :type n_components: int, optional
   :param threshold_steepness: Controls how quickly the value saturates around zero.
                               Defaults to 0.05.
   :type threshold_steepness: float, optional
   :param threshold_offset: Offset of the density function from 0. Defaults to 12.0.
   :type threshold_offset: float, optional

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

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


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

      Training Step of DFKDE. For each batch, 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: Deep CNN features.


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

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


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

      Validation Step of DFKDE.

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

      :param batch: Input batch

      :returns: Dictionary containing probability, prediction and ground truth values.



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

   Bases: :py:obj:`Dfkde`

   DFKDE: Deep Feature Kernel Density Estimation.

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