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:
SignalData
class and its SignalMetadata
objects enable signals objects and meta data to be seamlessly handled and operated on throughout the TorchSig infrastructure.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.ModulationsDataset
class synthetically creates, augments, and transforms the largest communications signals modulations dataset to date in a generic, flexible fashion.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.WidebandModulationsDataset
class synthetically creates, augments, and transforms the largest wideband communications signals dataset in a generic, flexible fashion.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/data --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/data --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/data --all=True
or for the wideband dataset:
python3 torchsig/scripts/generate_wideband_sig53.py --root=torchsig/data --all=True
Then, be sure to point scripts looking for root
to torchsig/data
.
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 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}
}