photontorch 0.3.3

PhotonTorch: a photonic simulation framework based on the deep learning framework PyTorch.

Photontorch

Photontorch is a photonic simulator for highly parallel simulation and optimization of photonic circuits in time and frequency domain. Photontorch features CUDA enabled simulation and optimization of photonic circuits. It leverages the deep learning framework PyTorch to view the photonic circuit as essentially a recurrent neural network. This enables the use of native PyTorch optimizers to optimize the (physical) parameters of the circuit.

• Floris Laporte [[e-mail]]
• Peter Bienstman [[e-mail]]
• Website: photontorch.com

Installation

Stable version

Photontorch can be installed with pip:

pip install photontorch

Development version

During development or to use the most recent Photontorch version, clone the repository and link with pip:

git clone https://git.photontorch.com/photontorch.git
./install-git-hooks.sh # Unix [Linux/Mac/BSD/...]
install-git-hooks.bat  # Windows
pip install -e photontorch

During development, use pytest to run the tests from within the root of the git-repository:

pytest tests

Documentation

Read the full documentation here: https://docs.photontorch.com

Dependencies

Required dependencies

• Python 2.7 (Linux only) or 3.6+. It's recommended to use the Anaconda distribution.
• pytorch>=1.5.0: conda install pytorch (see pytorch.org for more installation options for your CUDA version)
• numpy: conda install numpy
• scipy: conda install scipy

Optional (but recommended) dependencies

• tqdm: conda install tqdm [progress bars]
• networkx: conda install networkx [network visualization]
• matplotlib: conda install matplotlib [visualization]
• pytest: conda install pytest [to run tests]
• pandoc: conda install pandoc [to generate docs]
• sphinx: pip install sphinx nbsphinx [to generate docs]
• torch-lfilter: pip install torch-lfilter [faster lfilter for detectors]

Reference

If you're using Photontorch in your work or feel in any way inspired by it, we ask you to cite us in your work:

Floris Laporte, Joni Dambre, and Peter Bienstman. "Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch." Scientific reports 9.1 (2019): 5918.

Known issues

• Complex tensor support. Complex tensors are not supported in PyTorch/Photontorch. Wherever complex tensors would be applicable, Photontorch expects a real-valued tensor with the real and imag part stacked in the first dimension. The Photontorch issue can be followed here and the PyTorch issue here.
• Sparse tensor support. A lot of memory usage can probably be avoided when transitioning to sparse tensor representations under the hood. The Photontorch issue can be followed here

License

Photontorch is available under an Academic License. This means that there are no restrictions on the usage in a purely non-commercial or academic context. For commercial applications you can always contact the authors.

Copyright © 2020, Floris Laporte - Universiteit Gent - Ghent University - Academic License