anomalib.utils.callbacks.visualizer.visualizer_base

Base Visualizer Callback.

Module Contents

Classes

BaseVisualizerCallback

Callback that visualizes the results of a model.
class anomalib.utils.callbacks.visualizer.visualizer_base.BaseVisualizerCallback(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)[source]

Bases: pytorch_lightning.Callback

Callback that visualizes the results of a model.

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

_add_to_logger(image: numpy.ndarray, module: anomalib.models.components.AnomalyModule, trainer: pytorch_lightning.Trainer, filename: Union[pathlib.Path, str])[source]

Log image from a visualizer to each of the available loggers in the project.

Parameters

  • image (np.ndarray) – Image that should be added to the loggers.

  • module (AnomalyModule) – Anomaly module.

  • trainer (Trainer) – Pytorch Lightning trainer which holds reference to logger

  • filename (Path) – Path of the input image. This name is used as name for the generated image.

on_test_end(trainer: pytorch_lightning.Trainer, pl_module: anomalib.models.components.AnomalyModule) → None[source]

Sync logs.

Currently only AnomalibWandbLogger.save is called from this method. This is because logging as a single batch ensures that all images appear as part of the same step.

Parameters

  • trainer (pl.Trainer) – Pytorch Lightning trainer

  • pl_module (AnomalyModule) – Anomaly module (unused)