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anomalib.utils.callbacks.cdf_normalization

Anomaly Score Normalization Callback.

Module Contents

Classes

CdfNormalizationCallback

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

Attributes

logger

anomalib.utils.callbacks.cdf_normalization.logger

class anomalib.utils.callbacks.cdf_normalization.CdfNormalizationCallback

Bases: pytorch_lightning.Callback

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

on_test_start(self, _trainer: pytorch_lightning.Trainer, pl_module: anomalib.models.components.AnomalyModule) → None

Called when the test begins.

on_validation_epoch_start(self, 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.

on_validation_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

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

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

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

on_predict_batch_end(self, _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.

_collect_stats(self, 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.

static _create_inference_model(pl_module)

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

static _standardize_batch(outputs: pytorch_lightning.utilities.types.STEP_OUTPUT, pl_module) → None

static _normalize_batch(outputs: pytorch_lightning.utilities.types.STEP_OUTPUT, pl_module: anomalib.models.components.AnomalyModule) → None