:py:mod:`anomalib.utils.callbacks.visualizer.visualizer_metric`
===============================================================

.. py:module:: anomalib.utils.callbacks.visualizer.visualizer_metric

.. autoapi-nested-parse::

   Metric Visualizer Callback.



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

Classes
~~~~~~~

.. autoapisummary::

   anomalib.utils.callbacks.visualizer.visualizer_metric.MetricVisualizerCallback




.. py:class:: MetricVisualizerCallback(task: str, mode: str, image_save_path: str, inputs_are_normalized: bool = True, show_images: bool = False, log_images: bool = True, save_images: bool = True)

   Bases: :py:obj:`anomalib.utils.callbacks.visualizer.visualizer_base.BaseVisualizerCallback`

   Callback that visualizes the metric results of a model by plotting the corresponding curves.

   To save the images to the filesystem, add the 'local' keyword to the `project.log_images_to` parameter in the
   config.yaml file.

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

      Log images of the metrics contained in pl_module.

      In order to also plot custom metrics, they need to have implemented a `generate_figure` function that returns
      Tuple[matplotlib.figure.Figure, str].

      :param trainer: pytorch lightning trainer.
      :type trainer: pl.Trainer
      :param pl_module: pytorch lightning module.
      :type pl_module: AnomalyModule