overview.md

Overview of Code Structure

To help users better understand and use our codebase, we briefly overview the functionality and implementation of each package and each module. Please see the documentation in each file for more details. If you have questions, you may find useful information in training/test tips and frequently asked questions.

train.py is a general-purpose training script. It works for various models (with option --model: e.g., pix2pix, cyclegan, colorization) and different datasets (with option --dataset_mode: e.g., aligned, unaligned, single, colorization). See the main README and training/test tips for more details.

test.py is a general-purpose test script. Once you have trained your model with train.py, you can use this script to test the model. It will load a saved model from --checkpoints_dir and save the results to --results_dir. See the main README and training/test tips for more details.

data directory contains all the modules related to data loading and preprocessing. To add a custom dataset class called dummy, you need to add a file called dummy_dataset.py and define a subclass DummyDataset inherited from BaseDataset. You need to implement four functions: __init__ (initialize the class, you need to first call BaseDataset.__init__(self, opt)), __len__ (return the size of dataset), __getitem__　(get a data point), and optionally modify_commandline_options (add dataset-specific options and set default options). Now you can use the dataset class by specifying flag --dataset_mode dummy. See our template dataset class for an example. Below we explain each file in details.

• __init__.py implements the interface between this package and training and test scripts. train.py and test.py call from data import create_dataset and dataset = create_dataset(opt) to create a dataset given the option opt.
• base_dataset.py implements an abstract base class (ABC) for datasets. It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses.
• image_folder.py implements an image folder class. We modify the official PyTorch image folder code so that this class can load images from both the current directory and its subdirectories.
• template_dataset.py provides a dataset template with detailed documentation. Check out this file if you plan to implement your own dataset.
• aligned_dataset.py includes a dataset class that can load image pairs. It assumes a single image directory /path/to/data/train, which contains image pairs in the form of {A,B}. See here on how to prepare aligned datasets. During test time, you need to prepare a directory /path/to/data/test as test data.
• unaligned_dataset.py includes a dataset class that can load unaligned/unpaired datasets. It assumes that two directories to host training images from domain A /path/to/data/trainA and from domain B /path/to/data/trainB respectively. Then you can train the model with the dataset flag --dataroot /path/to/data. Similarly, you need to prepare two directories /path/to/data/testA and /path/to/data/testB during test time.
• single_dataset.py includes a dataset class that can load a set of single images specified by the path --dataroot /path/to/data. It can be used for generating CycleGAN results only for one side with the model option -model test.
• colorization_dataset.py implements a dataset class that can load a set of nature images in RGB, and convert RGB format into (L, ab) pairs in Lab color space. It is required by pix2pix-based colorization model (--model colorization).

models directory contains modules related to objective functions, optimizations, and network architectures. To add a custom model class called dummy, you need to add a file called dummy_model.py and define a subclass DummyModel inherited from BaseModel. You need to implement four functions: __init__ (initialize the class; you need to first call BaseModel.__init__(self, opt)), set_input (unpack data from dataset and apply preprocessing), forward (generate intermediate results), optimize_parameters (calculate loss, gradients, and update network weights), and optionally modify_commandline_options (add model-specific options and set default options). Now you can use the model class by specifying flag --model dummy. See our template model class for an example. Below we explain each file in details.

• __init__.py implements the interface between this package and training and test scripts. train.py and test.py call from models import create_model and model = create_model(opt) to create a model given the option opt. You also need to call model.setup(opt) to properly initialize the model.
• base_model.py implements an abstract base class (ABC) for models. It also includes commonly used helper functions (e.g., setup, test, update_learning_rate, save_networks, load_networks), which can be later used in subclasses.
• template_model.py provides a model template with detailed documentation. Check out this file if you plan to implement your own model.
• pix2pix_model.py implements the pix2pix model, for learning a mapping from input images to output images given paired data. The model training requires --dataset_mode aligned dataset. By default, it uses a --netG unet256 U-Net generator, a --netD basic discriminator (PatchGAN), and a --gan_mode vanilla GAN loss (standard cross-entropy objective).
• colorization_model.py implements a subclass of Pix2PixModel for image colorization (black & white image to colorful image). The model training requires -dataset_model colorization dataset. It trains a pix2pix model, mapping from L channel to ab channels in Lab color space. By default, the colorization dataset will automatically set --input_nc 1 and --output_nc 2.
• cycle_gan_model.py implements the CycleGAN model, for learning image-to-image translation without paired data. The model training requires --dataset_mode unaligned dataset. By default, it uses a --netG resnet_9blocks ResNet generator, a --netD basic discriminator (PatchGAN introduced by pix2pix), and a least-square GANs objective (--gan_mode lsgan).
• networks.py module implements network architectures (both generators and discriminators), as well as normalization layers, initialization methods, optimization scheduler (i.e., learning rate policy), and GAN objective function (vanilla, lsgan, wgangp).
• test_model.py implements a model that can be used to generate CycleGAN results for only one direction. This model will automatically set --dataset_mode single, which only loads the images from one set. See the test instruction for more details.

options directory includes our option modules: training options, test options, and basic options (used in both training and test). TrainOptions and TestOptions are both subclasses of BaseOptions. They will reuse the options defined in BaseOptions.

• __init__.py is required to make Python treat the directory options as containing packages,
• base_options.py includes options that are used in both training and test. It also implements a few helper functions such as parsing, printing, and saving the options. It also gathers additional options defined in modify_commandline_options functions in both dataset class and model class.
• train_options.py includes options that are only used during training time.
• test_options.py includes options that are only used during test time.

util directory includes a miscellaneous collection of useful helper functions.

• __init__.py is required to make Python treat the directory util as containing packages,
• get_data.py provides a Python script for downloading CycleGAN and pix2pix datasets. Alternatively, You can also use bash scripts such as download_pix2pix_model.sh and download_cyclegan_model.sh.
• html.py implements a module that saves images into a single HTML file. It consists of functions such as add_header (add a text header to the HTML file), add_images (add a row of images to the HTML file), save (save the HTML to the disk). It is based on Python library dominate, a Python library for creating and manipulating HTML documents using a DOM API.
• image_pool.py implements an image buffer that stores previously generated images. This buffer enables us to update discriminators using a history of generated images rather than the ones produced by the latest generators. The original idea was discussed in this paper. The size of the buffer is controlled by the flag --pool_size.
• visualizer.py includes several functions that can display/save images and print/save logging information. It uses a Python library visdom for display and a Python library dominate (wrapped in HTML) for creating HTML files with images.
• util.py consists of simple helper functions such as tensor2im (convert a tensor array to a numpy image array), diagnose_network (calculate and print the mean of average absolute value of gradients), and mkdirs (create multiple directories).