anomalib.models.ganomaly.loss

Loss function for the GANomaly Model Implementation.

Module Contents

Classes

GeneratorLoss

Generator loss for the GANomaly model.

DiscriminatorLoss

Discriminator loss for the GANomaly model.
class anomalib.models.ganomaly.loss.GeneratorLoss(wadv=1, wcon=50, wenc=1)[source]

Bases: torch.nn.Module

Generator loss for the GANomaly model.

Parameters

  • 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.

forward(self, latent_i: torch.Tensor, latent_o: torch.Tensor, images: torch.Tensor, fake: torch.Tensor, pred_real: torch.Tensor, pred_fake: torch.Tensor) → torch.Tensor[source]

Compute the loss for a batch.

Parameters

  • latent_i (Tensor) – Latent features of the first encoder.

  • latent_o (Tensor) – Latent features of the second encoder.

  • images (Tensor) – Real image that served as input of the generator.

  • fake (Tensor) – Generated image.

  • pred_real (Tensor) – Discriminator predictions for the real image.

  • pred_fake (Tensor) – Discriminator predictions for the fake image.

Returns

The computed generator loss.

Return type

Tensor

class anomalib.models.ganomaly.loss.DiscriminatorLoss[source]

Bases: torch.nn.Module

Discriminator loss for the GANomaly model.

forward(self, pred_real, pred_fake)[source]

Compye the loss for a predicted batch.

Parameters

  • pred_real (Tensor) – Discriminator predictions for the real image.

  • pred_fake (Tensor) – Discriminator predictions for the fake image.

Returns

The computed discriminator loss.

Return type

Tensor