VisualTorch aims to help visualize Torch-based neural network architectures. It currently supports generating layered-style, graph-style, and LeNet-style architectures for PyTorch Sequential and Custom models. This tool is adapted from visualkeras, pytorchviz, and pytorch-summary.

Note: VisualTorch may not yet support complex models, but contributions are welcome!

Online documentation is available at visualtorch.readthedocs.io.

The docs include usage examples, API references, and other useful information.

See the Installation page.

See the Usage Examples page.

Please feel free to send a pull request to contribute to this project by following this guideline.

This poject is available as open source under the terms of the MIT License.

Originally, this project was based on the visualkeras (under the MIT license), with additional modifications inspired by pytorchviz, and pytorch-summary, both of which are also licensed under the MIT license.

Please cite this project in your publications if it helps your research.

A ready-made citation entry is available.