anomalib.models.ganomaly.lightning_model

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training.

https://arxiv.org/abs/1805.06725

Module Contents

Classes

Ganomaly

PL Lightning Module for the GANomaly Algorithm.

GanomalyLightning

PL Lightning Module for the GANomaly Algorithm.

Attributes

logger

anomalib.models.ganomaly.lightning_model.logger[source]
class anomalib.models.ganomaly.lightning_model.Ganomaly(batch_size: int, input_size: Tuple[int, int], n_features: int, latent_vec_size: int, extra_layers: int = 0, add_final_conv_layer: bool = True, wadv: int = 1, wcon: int = 50, wenc: int = 1, lr: float = 0.0002, beta1: float = 0.5, beta2: float = 0.999)[source]

Bases: anomalib.models.components.AnomalyModule

PL Lightning Module for the GANomaly Algorithm.

Parameters

  • batch_size (int) – Batch size.

  • input_size (Tuple[int,int]) – Input dimension.

  • n_features (int) – Number of features layers in the CNNs.

  • latent_vec_size (int) – Size of autoencoder latent vector.

  • extra_layers (int, optional) – Number of extra layers for encoder/decoder. Defaults to 0.

  • add_final_conv_layer (bool, optional) – Add convolution layer at the end. Defaults to True.

  • wadv (int, optional) – Weight for adversarial loss. Defaults to 1.

  • wcon (int, optional) – Image regeneration weight. Defaults to 50.

  • wenc (int, optional) – Latent vector encoder weight. Defaults to 1.

_reset_min_max()[source]

Resets min_max scores.

configure_optimizers() → List[torch.optim.Optimizer][source]

Configures optimizers for each decoder.

Note

This method is used for the existing CLI. When PL CLI is introduced, configure optimizers method will be

deprecated, and optimizers will be configured from either config.yaml file or from CLI.

Returns

Adam optimizer for each decoder

Return type

Optimizer

training_step(batch, _, optimizer_idx)[source]

Training step.

Parameters

  • batch (Dict) – Input batch containing images.

  • optimizer_idx (int) – Optimizer which is being called for current training step.

Returns

Loss

Return type

Dict[str, Tensor]

on_validation_start() → None[source]

Reset min and max values for current validation epoch.

validation_step(batch, _) → Dict[str, torch.Tensor][source]

Update min and max scores from the current step.

Parameters

batch (Dict[str, Tensor]) – Predicted difference between z and z_hat.

Returns

batch

Return type

Dict[str, Tensor]

validation_epoch_end(outputs)[source]

Normalize outputs based on min/max values.

on_test_start() → None[source]

Reset min max values before test batch starts.

test_step(batch, _)[source]

Update min and max scores from the current step.

test_epoch_end(outputs)[source]

Normalize outputs based on min/max values.

_normalize(scores: torch.Tensor) → torch.Tensor[source]

Normalize the scores based on min/max of entire dataset.

Parameters

scores (Tensor) – Un-normalized scores.

Returns

Normalized scores.

Return type

Tensor

class anomalib.models.ganomaly.lightning_model.GanomalyLightning(hparams: Union[omegaconf.DictConfig, omegaconf.ListConfig])[source]

Bases: Ganomaly

PL Lightning Module for the GANomaly Algorithm.

Parameters

hparams (Union[DictConfig, ListConfig]) – Model params

configure_callbacks()[source]

Configure model-specific callbacks.

Note

This method is used for the existing CLI. When PL CLI is introduced, configure callback method will be

deprecated, and callbacks will be configured from either config.yaml file or from CLI.