DiscriminatorLoss

class deepinv.loss.adversarial.DiscriminatorLoss(weight_adv=1.0, D=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 formula.

Parameters:

• weight_adv (float) – weight for adversarial loss, defaults to 1.

• 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, fake, D=None)

Typical adversarial loss in GAN discriminators.

Parameters:

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

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

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

Returns:

(torch.Tensor) discriminator adversarial loss

Examples using DiscriminatorLoss:

Imaging inverse problems with adversarial networks

Imaging inverse problems with adversarial networks