:py:mod:`anomalib.utils.callbacks.cdf_normalization`
====================================================

.. py:module:: anomalib.utils.callbacks.cdf_normalization

.. autoapi-nested-parse::

   Anomaly Score Normalization Callback.



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

Classes
~~~~~~~

.. autoapisummary::

   anomalib.utils.callbacks.cdf_normalization.CdfNormalizationCallback




Attributes
~~~~~~~~~~

.. autoapisummary::

   anomalib.utils.callbacks.cdf_normalization.logger


.. py:data:: logger
   

   

.. py:class:: CdfNormalizationCallback

   Bases: :py:obj:`pytorch_lightning.Callback`

   Callback that standardizes the image-level and pixel-level anomaly scores.

   .. py:method:: setup(trainer: pytorch_lightning.Trainer, pl_module: anomalib.models.components.AnomalyModule, stage: Optional[str] = None) -> None

      Adds training_distribution metrics to normalization metrics.


   .. py:method:: on_test_start(trainer: pytorch_lightning.Trainer, pl_module: anomalib.models.components.AnomalyModule) -> None

      Called when the test begins.


   .. py:method:: on_validation_epoch_start(trainer: pytorch_lightning.Trainer, pl_module: anomalib.models.components.AnomalyModule) -> None

      Called when the validation starts after training.

      Use the current model to compute the anomaly score distributions
      of the normal training data. This is needed after every epoch, because the statistics must be
      stored in the state dict of the checkpoint file.


   .. py:method:: on_validation_batch_end(_trainer: pytorch_lightning.Trainer, pl_module: anomalib.models.components.AnomalyModule, outputs: Optional[pytorch_lightning.utilities.types.STEP_OUTPUT], _batch: Any, _batch_idx: int, _dataloader_idx: int) -> None

      Called when the validation batch ends, standardizes the predicted scores and anomaly maps.


   .. py:method:: on_test_batch_end(_trainer: pytorch_lightning.Trainer, pl_module: anomalib.models.components.AnomalyModule, outputs: Optional[pytorch_lightning.utilities.types.STEP_OUTPUT], _batch: Any, _batch_idx: int, _dataloader_idx: int) -> None

      Called when the test batch ends, normalizes the predicted scores and anomaly maps.


   .. py:method:: on_predict_batch_end(_trainer: pytorch_lightning.Trainer, pl_module: anomalib.models.components.AnomalyModule, outputs: Dict, _batch: Any, _batch_idx: int, _dataloader_idx: int) -> None

      Called when the predict batch ends, normalizes the predicted scores and anomaly maps.


   .. py:method:: _collect_stats(trainer, pl_module)

      Collect the statistics of the normal training data.

      Create a trainer and use it to predict the anomaly maps and scores of the normal training data. Then
       estimate the distribution of anomaly scores for normal data at the image and pixel level by computing
       the mean and standard deviations. A dictionary containing the computed statistics is stored in self.stats.


   .. py:method:: _create_inference_model(pl_module)
      :staticmethod:

      Create a duplicate of the PL module that can be used to perform inference on the training set.


   .. py:method:: _standardize_batch(outputs: pytorch_lightning.utilities.types.STEP_OUTPUT, pl_module) -> None
      :staticmethod:


   .. py:method:: _normalize_batch(outputs: pytorch_lightning.utilities.types.STEP_OUTPUT, pl_module: anomalib.models.components.AnomalyModule) -> None
      :staticmethod: