anomalib.utils.callbacks.visualizer_callback

Visualizer Callback.

Module Contents

Classes

VisualizerCallback

Callback that visualizes the inference results of a model.
class anomalib.utils.callbacks.visualizer_callback.VisualizerCallback(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 inference results of a model.

The callback generates a figure showing the original image, the ground truth segmentation mask, the predicted error heat map, and the predicted segmentation mask.

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(self, image: numpy.ndarray, module: anomalib.models.components.AnomalyModule, trainer: pytorch_lightning.Trainer, filename: pathlib.Path)[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_predict_batch_end(self, _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[source]

Show images at the end of every batch.

Parameters

  • _trainer (Trainer) – Pytorch lightning trainer object (unused).

  • _pl_module (LightningModule) – Lightning modules derived from BaseAnomalyLightning object as

  • images. (currently only they support logging) –

  • outputs (Dict[str, Any]) – Outputs of the current test step.

  • _batch (Any) – Input batch of the current test step (unused).

  • _batch_idx (int) – Index of the current test batch (unused).

  • _dataloader_idx (int) – Index of the dataloader that yielded the current batch (unused).

on_test_batch_end(self, 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[source]

Log images at the end of every batch.

Parameters

  • trainer (Trainer) – Pytorch lightning trainer object (unused).

  • pl_module (LightningModule) – Lightning modules derived from BaseAnomalyLightning object as

  • images. (currently only they support logging) –

  • outputs (Dict[str, Any]) – Outputs of the current test step.

  • _batch (Any) – Input batch of the current test step (unused).

  • _batch_idx (int) – Index of the current test batch (unused).

  • _dataloader_idx (int) – Index of the dataloader that yielded the current batch (unused).

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

Sync logs.

Currently only AnomalibWandbLogger 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 (unused)

  • pl_module (AnomalyModule) – Anomaly module