Load all the networks from the disk. Parameters: epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, na

display the current losses on visdom display: dictionary of error labels and values Parameters: epoch (int) -- current

print current losses on console; also save the losses to the disk Parameters: epoch (int) -- current epoch iters (int

Print the total number of parameters in the network and (if verbose) network architecture Parameters: verbose (bool) -- if verbos

Print and save options It will print both current options and default values(if different). It will save options into a text file / [

Reset the self.saved status

Save images to the disk. Parameters: webpage (the HTML class) -- the HTML webpage class that stores these imaegs (see html.py for more de

Unpack input data from the dataloader and perform necessary pre-processing steps. Parameters: input (dict): include the data itsel

Unpack input data from the dataloader and perform necessary pre-processing steps. Parameters: input (dict): include the data itsel

Unpack input data from the dataloader and perform necessary pre-processing steps. Parameters: input (dict): include the data itsel

Converts a Tensor array into a numpy image array. Parameters: input_image (tensor) -- the input image tensor array imtype (type)

Forward function used in test time. This function wraps <forward> function in no_grad() so we don't save intermediate steps for backprop

Update learning rates for all the networks; called at the end of every epoch

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 (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:

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

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

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

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

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

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

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

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

Let's pretend the single image contains 100,000 crops for convenience.

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