PatchGANDiscriminator
- class deepinv.models.PatchGANDiscriminator(input_nc: int = 3, ndf: int = 64, n_layers: int = 3, use_sigmoid: bool = False, batch_norm: bool = True, bias: bool = True)[source]
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
Examples using PatchGANDiscriminator
:
Imaging inverse problems with adversarial networks