TorchSig is an open-source signal processing machine learning toolkit based on the PyTorch data handling pipeline. The user-friendly toolkit simplifies common digital signal processing operations, augmentations, and transformations when dealing with both real and complex-valued signals. TorchSig streamlines the integration process of these signals processing tools building on PyTorch, enabling faster and easier development and research for machine learning techniques applied to signals data, particularly within (but not limited to) the radio frequency domain. An example dataset, Sig53, based on many unique communication signal modulations is included to accelerate the field of modulation classification. Additionally, an example wideband dataset, WidebandSig53, is also included that extends Sig53 with larger data example sizes containing multiple signals enabling accelerated research in the fields of wideband signal detection and recognition.

TorchSig is currently in beta

TorchSig provides many useful tools to facilitate and accelerate research on signals processing machine learning technologies:

• The SignalData class and its SignalDescription objects enable signals objects and meta data to be seamlessly handled and operated on throughout the TorchSig infrastructure.
• The Sig53 Dataset is a state-of-the-art static modulations-based RF dataset meant to serve as the next baseline for RFML classification development & evaluation.
• The ModulationsDataset class synthetically creates, augments, and transforms the largest communications signals modulations dataset to date in a generic, flexible fashion.
• The WidebandSig53 Dataset is a state-of-the-art static wideband RF signals dataset meant to serve as the baseline for RFML signal detection and recognition development & evaluation.
• The WidebandModulationsDataset class synthetically creates, augments, and transforms the largest wideband communications signals dataset in a generic, flexible fashion.
• Numerous signals processing transforms enable existing ML techniques to be employed on the signals data, streamline domain-specific signals augmentations in signals processing machine learning experiments, and signals-specific data transformations to speed up the field of expert feature signals processing machine learning integration.
• TorchSig also includes a model API similar to open source code in other ML domains, where several state-of-the-art convolutional and transformer-based neural architectures have been adapted to the signals domain and pretrained on the Sig53 and WidebandSig53 datasets. These models can be easily used for follow-on research in the form of additional hyperparameter tuning, out-of-the-box comparative analysis/evaluations, and/or fine-tuning to custom datasets.

Documentation can be found online or built locally by following the instructions below.

cd docs
pip install -r docs-requirements.txt
make html
firefox build/html/index.html

Clone the torchsig repository and simply install using the following commands:

cd torchsig
pip install .

If you'd like to generate the named datasets without messing with your current Python environment, you can build the development container and use it to generate data at the location of your choosing.

docker build -t torchsig -f Dockerfile .
docker run -u $(id -u ${USER}):$(id -g ${USER}) -v `pwd`:/workspace/code/torchsig torchsig python3 torchsig/scripts/generate_sig53.py --root=/workspace/code/torchsig/examples/sig53 --all=True

For the wideband dataset, you can do:

docker build -t torchsig -f Dockerfile .
docker run -u $(id -u ${USER}):$(id -g ${USER}) -v `pwd`:/workspace/code/torchsig torchsig python3 torchsig/scripts/generate_wideband_sig53.py --root=/workspace/code/torchsig/examples/wideband_sig53 --all=True

If you do not need to use Docker, you can also just generate using the regular command-line interface

python3 torchsig/scripts/generate_sig53.py --root=torchsig/examples --all=True

or for the wideband dataset:

python3 torchsig/scripts/generate_wideband_sig53.py --root=torchsig/examples --all=True

Then, be sure to point scripts looking for root to torchsig/examples.

If you have Docker installed along with compatible GPUs and drivers, you can try:

docker build -t torchsig -f Dockerfile .
docker run -d --rm --network=host --shm-size=32g --gpus all --name torchsig_workspace -v `pwd`/examples:/workspace/code/examples torchsig tail -f /dev/null
docker exec torchsig_workspace jupyter notebook --allow-root --ip=0.0.0.0 --no-browser

Then use the URL in the output in your browser to run the examples and notebooks.

TorchSig is released under the MIT License. The MIT license is a popular open-source software license enabling free use, redistribution, and modifications, even for commercial purposes, provided the license is included in all copies or substantial portions of the software. TorchSig has no connection to MIT, other than through the use of this license.

Please cite TorchSig if you use it for your research or business.

@misc{torchsig,
  title={Large Scale Radio Frequency Signal Classification},
  author={Luke Boegner and Manbir Gulati and Garrett Vanhoy and Phillip Vallance and Bradley Comar and Silvija Kokalj-Filipovic and Craig Lennon and Robert D. Miller},
  year={2022},
  archivePrefix={arXiv},
  eprint={2207.09918},
  primaryClass={cs-LG},
  note={arXiv:2207.09918}
  url={https://arxiv.org/abs/2207.09918}
}