Initialize this dataset class. Parameters: opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseO

Initialize this class Step 1: create a dataset instance given the name [dataset_mode] Step 2: create a multi-threaded data loader.

Initialize this dataset class. Parameters: opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseO

Initialize this dataset class. Parameters: opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseO

Initialize this dataset class. Parameters: opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseO

Initialize this dataset class. Parameters: opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseO

Initialize this dataset class. Parameters: opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseO

Initialize this dataset class. Parameters: opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseO

Reset the class; indicates the class hasn't been initailized

Initialize the pix2pix class. Parameters: opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOpt

Initialize the pix2pix class. Parameters: opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOpt

Initialize the pix2pix class. Parameters: opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOpt

Initialize the pix2pix class. Parameters: opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOpt

Initialize the CycleGAN class. Parameters: opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOp

Initialize the pix2pix class. Parameters: opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOpt

Initialize the BaseModel class. Parameters: opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseO

Initialize the pix2pix class. Parameters: opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOpt

Construct a Resnet-based generator Parameters: input_nc (int) -- the number of channels in input images output_n

Initialize the Resnet block A resnet block is a conv block with skip connections We construct a conv block with build_conv_block func

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

Construct a Unet submodule with skip connections. Parameters: outer_nc (int) -- the number of filters in the outer conv layer

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

Construct a 1x1 PatchGAN discriminator Parameters: input_nc (int) -- the number of channels in input images ndf (int

Initialize this model class. Parameters: opt -- training/test options A few things can be done here. - (required

Initialize the pix2pix class. Parameters: opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOpt

Construct a Resnet-based generator Parameters: input_nc (int) -- the number of channels in input images output_n

Construct a Resnet-based generator Parameters: input_nc (int) -- the number of channels in input images output_n

Initialize the Resnet block A resnet block is a conv block with skip connections We construct a conv block with build_conv_block func

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

Construct a Unet submodule with skip connections. Parameters: outer_nc (int) -- the number of filters in the outer conv layer

Construct a Unet submodule with skip connections. Parameters: outer_nc (int) -- the number of filters in the outer conv layer

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

Construct a Unet submodule with skip connections. Parameters: outer_nc (int) -- the number of filters in the outer conv layer

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

Construct a Unet submodule with skip connections. Parameters: outer_nc (int) -- the number of filters in the outer conv layer

Construct a Unet submodule with skip connections. Parameters: outer_nc (int) -- the number of filters in the outer conv layer

Construct a Unet submodule with skip connections. Parameters: outer_nc (int) -- the number of filters in the outer conv layer

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

Construct a 1x1 PatchGAN discriminator Parameters: input_nc (int) -- the number of channels in input images ndf (int

Initialize the HTML classes Parameters: web_dir (str) -- a directory that stores the webpage. HTML file will be created at <web_d

Initialize the Visualizer class Parameters: opt -- stores all the experiment flags; needs to be a subclass of BaseOptions

Initialize the ImagePool class Parameters: pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created

Return a batch of data

Return the total number of images in the dataset.

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.

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 total number of images in the dataset.

Return the total number of images in the dataset.

Return the total number of images.

Return the total number of images in the dataset.

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

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

Run forward pass.

Standard forward

Forward function (with skip connections)

Standard forward

Standard forward.

Standard forward.

Standard forward

Standard forward

Forward function (with skip connections)

Standard forward

Standard forward

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

create empty directories if they don't exist Parameters: paths (str list) -- a list of directory paths

Add new dataset-specific options, and rewrite default values for existing options. Parameters: parser -- original option

Add new dataset-specific options, and rewrite default values for existing options. Parameters: parser -- original option

Add new dataset-specific options, and rewrite default values for existing options. Parameters: parser -- original option

Add new dataset-specific options, and rewrite default values for existing options. Parameters: parser -- original option

Add new dataset-specific options, and rewrite default values for existing options. Parameters: parser -- original option