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TorchGAN is a GAN design and development framework based on PyTorch. This framework aims to provide construction modules for popular GANs and allows customization for cutting-edge research.
Using the modular structure of TorchGAN, you can:
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Experiment with popular GAN models on datasets;
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Insert new loss functions, architectures, etc., into traditional loss functions and architectures;
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Seamlessly visualize the GAN training process with various logging backends.
Project Address: https://github.com/torchgan/torchgan
The TorchGAN package consists of various generative adversarial networks and utilities that are very useful during training. The 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 this package.
Documentation Address: https://torchgan.readthedocs.io/en/latest/
Documentation Directory
The documentation for TorchGAN includes three main sections: Getting Started, API Documentation, and Tutorials.
The Getting Started section introduces various installation methods for TorchGAN, installation of necessary dependencies, and guidelines for contributing to the project.
The API documentation introduces various commonly used modules or layers for building GAN networks, loss functions, evaluation metrics, classic models (such as InfoGAN, DCGAN, etc.), and trainers. These APIs can help you quickly customize models from an architectural perspective.
The tutorial section will introduce the specific process of building GAN projects, covering dataset construction, architecture design, hyperparameter and optimizer settings, loss function definitions, visualization, training, and the complete process of implementing machine learning projects. The author uses SAGAN and CycleGAN as typical case studies and has specifically written a chapter on how to customize loss functions, which is actually one of the most important aspects of the entire project. The convergence of the model, the speed of convergence, and the final convergence results are greatly influenced by the definition of the loss function.
SAGAN Tutorial Example
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