DiscriminatorLoss

class deepinv.loss.adversarial.DiscriminatorLoss(weight_adv: float = 1.0, D: Module | None = None, device='cpu', **kwargs)

Bases: Loss

Base discriminator adversarial loss.

Override the forward function to call adversarial_loss with quantities depending on your specific GAN model.

For examples, see deepinv.loss.adversarial.SupAdversarialDiscriminatorLoss, deepinv.loss.adversarial.UnsupAdversarialDiscriminatorLoss.

See Imaging inverse problems with adversarial networks for formulae.

Parameters:

• weight_adv (float) – weight for adversarial loss, defaults to 1.0

• D (torch.nn.Module) – discriminator network. If not specified, D must be provided in forward, defaults to None.

• device (str) – torch device, defaults to “cpu”

adversarial_loss(real: Tensor, fake: Tensor, D: Module | None = None)

Typical adversarial loss in GAN discriminators.

Parameters:

• real (Tensor) – image labelled as real, typically one originating from training set

• fake (Tensor) – image labelled as fake, typically a reconstructed image

• D (nn.Module) – discriminator/critic/classifier model. If None, then D passed from __init__ used. Defaults to None.

Return Tensor:

discriminator adversarial loss

forward(*args, D: Module | None = None, **kwargs) → Tensor

Computes the loss.

Parameters:

• x_net (torch.Tensor) – Reconstructed image \(\inverse{y}\).

• x (torch.Tensor) – Reference image.

• y (torch.Tensor) – Measurement.

• physics (deepinv.physics.Physics) – Forward operator associated with the measurements.

• model (torch.nn.Module) – Reconstruction function.

Returns:

(torch.Tensor) loss, the tensor size might be (1,) or (batch size,).

Examples using DiscriminatorLoss:

Imaging inverse problems with adversarial networks

Imaging inverse problems with adversarial networks