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Quaternion PyTorch

⚠️ this is still heavily experimental. Use at your own discretion!

This repository contains code to extend PyTorch for applications defined in the quaternion domain (H). We provide quaternion-valued tensors, layers, and examples. Code is designed to be as inter-operable as possible with basic real-valued PyTorch tensors and operations.

This code draws in large part from Titouan Parcollet's code, which inspired this library.

Installation

After cloning the repository, install the requirements as:

pip install -r requirements.txt

If you want to run the unit tests, you will also need the pyquaternion library. Then, install the library by running:

pip install -e .

Using the library

The basic unit of the library is the QuaternionTensor, an extension of the PyTorch's tensor class to handle quaternion-valued elements. You can initialize a quaternion tensor by specifying the four components, or by providing a (..., 4)-dimensional tensor:

# A vector with two quaternions
x = quaternion.QuaternionTensor(torch.rand(2, 4, requires_grad=True))

We provide a number of operations from quaternion algebra and inter-operability with PyTorch:

x = x * torch.rand(2) # Multiply with real-valued scalars
x.norm().sum().backward() # Take the absolute value, sum, and take the gradient

We also provide layers and utilities to work with PyTorch modules, e.g.:

model = torch.nn.Sequential(
    layers.QLinear(10, 20),
    torch.nn.ReLU(),
    layers.QLinear(20, 10),
    layers.QuaternionToReal(10), # Take the absolute value in output
)

See the Basic notebook for an introduction to the basic concepts in the library, and the Training notebook for an example of training a quaternion-valued CNN.

Code organization

  1. The QuaternionTensor class is defined in htorch/quaternion.py.
  2. Layers for building quaternion-valued NN are found in htorch/layers.py.
  3. A few utilities to load real-valued datasets or convert existing real-valued models can be found in htorch/utils.py.

Most operations are documented in the example notebooks.

Testing

To manually run the unit tests:

python -m unittest discover -s ./tests -p *_test.py

If you have coverage installed:

coverage run -m unittest discover -s ./tests -p *_test.py

To generate again the coverage badge (not automated yet), install coverage-badge, then run:

coverage-badge -o coverage.svg -f

References

[1] Chase Gaudet, Anthony Maida (2018). Deep Quaternion Networks. 2018 International Joint Conference on Neural Networks (IJCNN).

[2] Chiheb Trabelsi et al. (2017). Deep Complex Networks.

[3] Titouan Parcollet, Mohamed Morchid, G. Linarès (2019). A survey of quaternion neural networks. Artificial Intelligence Review.