Return the number of images.

Return the total number of images in the dataset.

Return the number of data in the dataset

Return the total number of images in the dataset. As we have two datasets with potentially different number of images, we take a maxi

Return the number of images.

Create a generator and a discriminator.

Create a generator and a discriminator.

Calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028 Arguments: netD (network) -- dis

Calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028 Arguments: netD (network) -- dis

Compute the ith block of the KID estimate.

Create a discriminator Parameters: input_nc (int) -- the number of channels in input images ndf (int) -- the number

Create a discriminator Parameters: input_nc (int) -- the number of channels in input images ndf (int) -- the number

Create a generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels

Create a generator Parameters: input_nc (int) -- the number of channels in input images output_nc (int) -- the number of channels

Calculate and print the mean of average absolute(gradients) Parameters: net (torch network) -- Torch network name (str) -- the na

Standard forward

Forward function (with skip connections)

Standard forward

Standard forward.

Standard forward.

Standard forward

Forward function (with skip connections)

Standard forward

Standard forward.

Standard forward.

Return the static method <modify_commandline_options> of the dataset class.

Return the static method <modify_commandline_options> of the model class.

Return a learning rate scheduler Parameters: optimizer -- the optimizer of the network opt (option class) -- stores all

Return a learning rate scheduler Parameters: optimizer -- the optimizer of the network opt (option class) -- stores all