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

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

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

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)

Print warning information about image size(only print once)

Calculate GAN and L1 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 '

Make models eval mode during test time

Run forward pass; 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

Get a yacs CfgNode object with default values for my_project.

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

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

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

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

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

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

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

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

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

group conv2d to calculate cross correlation

for loop to calculate cross correlation

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

foreground_paths: [folder, imagepath_list, image path] mask_paths: [folter, imagepath_list, image path] background_paths:

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

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

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

Compute the instance normalization within only the background region, in which the mean and standard variance are measured from the featur

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

Initialize the ImagePool class Parameters: pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created

Return the total number of images.

Return the total number of images.

Return the total number of images in the dataset.

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

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

group conv2d to calculate cross correlation