Calculate loss given Discriminator's output and grount truth labels. Parameters: prediction (tensor) - - tpyically the prediction

Return a data point and its metadata information. Parameters: index - - a random integer for data indexing Returns:

Return a data point and its metadata information. Parameters: index (int) -- a random integer for data indexing Ret

Return a data point and its metadata information. Parameters: index -- a random integer for data indexing Returns:

Initialize the class; save the options in the class Parameters: opt (Option class)-- stores all the experiment flags; needs to be

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

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

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

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

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 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 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 total number of images.

Sets the learning rate to the initial LR decayed by 10 every 30 epochs

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

Calculate additional output images for visdom and HTML visualization

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

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

if only_encode: return self.encoder.forward(input, predict=True) else:

Run forward pass.

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.