PatchGANDiscriminator

class deepinv.models.PatchGANDiscriminator(input_nc=3, ndf=64, n_layers=3, use_sigmoid=False, batch_norm=True, bias=True)

Bases: Module

PatchGAN Discriminator model.

This discriminator model was originally proposed in Image-to-Image Translation with Conditional Adversarial Networks (Isola et al.) and classifies whether each patch of an image is real or fake.

Implementation adapted from DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks (Kupyn et al.).

See Imaging inverse problems with adversarial networks for how to use this for adversarial training.

Parameters:

• input_nc (int) – number of input channels, defaults to 3

• ndf (int) – hidden layer size, defaults to 64

• n_layers (int) – number of hidden conv layers, defaults to 3

• use_sigmoid (bool) – use sigmoid activation at end, defaults to False

• batch_norm (bool) – whether to use batch norm layers, defaults to True

• bias (bool) – whether to use bias in conv layers, defaults to True

forward(x)

Forward pass of discriminator model.

Parameters:

x (torch.Tensor) – input image

Examples using PatchGANDiscriminator:

Imaging inverse problems with adversarial networks

Imaging inverse problems with adversarial networks