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TorchGAN is based on PyTorch Of GAN Design and development framework. The framework is designed to provide building blocks for popular GANs and allows for customization of cutting-edge research.

Using TorchGAN's modular structure you can:

  • Try the popular GAN model on the dataset;
  • Insert new loss functions, new architectures, etc. for traditional loss functions, architectures, etc.
  • Seamlessly visualize the GAN training process with multiple logging backends.

project address:https://github.com/torchgan/torchgan

The TorchGAN package consists of various generation-resistance networks and utilities that are useful during training. This package provides an easy-to-use API for training popular GANs and developing GAN variants. The author has written a tutorial document to help you use the package.

Document address:https://torchgan.readthedocs.io/en/latest/

Document directory

TorchGAN's documentation contains three parts: Getting Started, API Documentation, and Tutorials.

The Getting Started section describes TorchGAN's various installation methods, the necessary dependency package installation, and a guide to contributing to the project.

The API documentation describes the various common modules or layers that build a GAN network, loss functions, evaluation metrics, classic models (such as InfoGAN, DCGAN, etc.) and trainers that help you quickly customize your model from the architectural level.

The tutorial section will introduce the specific GAN project construction process, involving the complete process of data set construction, architecture design, hyperparameter and optimizer settings, loss function definition, visualization, training and other machine learning project implementation. The author uses SAGAN and CycleGAN As a typical case show, I also wrote a chapter to introduce how to customize the loss function, which is actually one of the most important links in the whole project. Whether the training of the model converges, the convergence speed and the final convergence effect are affected by the loss function. The big impact of the definition.

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SAGAN tutorial example

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