save the current content to the HMTL file

Make models eval mode during test time

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

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

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

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): includes the data its

Fix InstanceNorm checkpoints incompatibility (prior to 0.4)

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>.

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

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

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

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

Print warning information about image size(only print once)

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

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

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

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

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

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 / [

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

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

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

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

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

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

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