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

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

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

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

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

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

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

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

Return the total number of images.

Return the total number of images in the dataset.

Fix InstanceNorm checkpoints incompatibility (prior to 0.4)

Print warning information about image size(only print once)

The feature network netF is defined in terms of the shape of the intermediate, extracted features of the encoder portion of netG. Bec

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)