DiscriminatorLoss

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

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.

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)[source]

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[source]

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`: