Calculate losses, gradients, and update network weights; called in every training iteration

save the current content to the HMTL file

Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights Parameters: net (network)

Parse our options, create checkpoints directory suffix, and set up gpu device.

Set requies_grad=Fasle for all the networks to avoid unnecessary computations Parameters: nets (network list) -- a list of netwo

If the program could not connect to Visdom server, this function will start a new server at port < self.port >

Insert a header to the HTML file Parameters: text (str) -- the header text

add images to the HTML file Parameters: ims (str list) -- a list of image paths txts (str list) -- a list of image

Create a dataset given the option. This function wraps the class CustomDatasetDataLoader. This is the main interface between this package

Download a dataset. Parameters: save_path (str) -- A directory to save the data to. dataset (str) -- (opt

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

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

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

Initialize the GANLoss class. Parameters: gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, an

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

Calculate GAN loss for the discriminator

Calculate GAN loss for the discriminator

Calculate GAN and NCE loss for the generator

Calculate GAN and NCE loss for the generator

Calculate additional output images for visdom and HTML visualization

Create a model given the option. This function warps the class CustomDatasetDataLoader. This is the main interface between this package and '

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

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

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

Import the module "data/[dataset_name]_dataset.py". In the file, the class called DatasetNameDataset() will be instantiated. It has to be a s

Import the module "models/[model_name]_model.py". In the file, the class called DatasetNameModel() will be instantiated. It has to be a subcl

Run forward pass; called by both functions <optimize_parameters> and <test>.

Run forward pass; called by both functions <optimize_parameters> and <test>.

Return visualization images. train.py will display these images with visdom, and save the images to a HTML

Return a normalization layer Parameters: norm_type (str) -- the name of the normalization layer: batch | instance | none For BatchNo

create a single empty directory if it didn't exist Parameters: path (str) -- a single directory path

Load and print networks; create schedulers Parameters: opt (Option class) -- stores all the experiment flags; needs to be a subcl

Construct a convolutional block. Parameters: dim (int) -- the number of channels in the conv layer. padding

Calculate Adversarial loss for the negative samples

Display current results on visdom; save current results to an HTML file. Parameters: visuals (OrderedDict) - - dictionary of imag

Make models eval mode during test time

Standard forward.

Run forward pass; called by both functions <optimize_parameters> and <test>.

Run forward pass. This will be called by both functions <optimize_parameters> and <test>.

Initialize our parser with basic options(only once). Add additional model-specific and dataset-specific options. These options are def

Return traning losses / errors. train.py will print out these errors on console, and save them to a file

Return the directory that stores images

Return image paths that are used to load current data

Create label tensors with the same size as the input. Parameters: prediction (tensor) - - tpyically the prediction from a discrim

Initialize network weights. Parameters: net (network) -- network to be initialized init_type (str) -- the name of an initializa

Define the common options that are used in both training and test.

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