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

.. py:module:: anomalib.utils.callbacks.min_max_normalization

.. autoapi-nested-parse::

   Anomaly Score Normalization Callback that uses min-max normalization.



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

Classes
~~~~~~~

.. autoapisummary::

   anomalib.utils.callbacks.min_max_normalization.MinMaxNormalizationCallback




.. py:class:: MinMaxNormalizationCallback

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

   Callback that normalizes the image-level and pixel-level anomaly scores using min-max normalization.

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

      Adds min_max 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_batch_end(_trainer: pytorch_lightning.Trainer, pl_module: anomalib.models.components.AnomalyModule, outputs: pytorch_lightning.utilities.types.STEP_OUTPUT, _batch: Any, _batch_idx: int, _dataloader_idx: int) -> None

      Called when the validation batch ends, update the min and max observed values.


   .. py:method:: on_test_batch_end(_trainer: pytorch_lightning.Trainer, pl_module: anomalib.models.components.AnomalyModule, outputs: 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:: _normalize_batch(outputs, pl_module)
      :staticmethod:

      Normalize a batch of predictions.