e3nn-jax 0.9.2

Equivariant convolutional neural networks for the group E(3) of 3 dimensional rotations, translations, and mirrors.

e3nn-jax

Documentation

:boom: Warning :boom:

Please always check the ChangeLog for breaking changes.

Installation

To install the latest released version:

pip install --upgrade e3nn-jax

To install the latest GitHub version:

pip install git+https://github.com/e3nn/e3nn-jax.git

To install from a local copy for development, we recommend creating a virtual enviroment:

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

To check that the tests are running:

pip install pytest
pytest tests/tensor_products_test.py

What is different from the PyTorch version?

• No more shared_weights and internal_weights in TensorProduct. Extensive use of jax.vmap instead (see example below)
• Support of python structure IrrepsArray that contains a contiguous version of the data and a list of jnp.ndarray for the data. This allows to avoid unnecessary jnp.concatenante followed by indexing to reverse the concatenation (even that jax.jit is probably able to unroll the concatenations)
• Support of None in the list of jnp.ndarray to avoid unnecessary computation with zeros (basically imposing 0 * x = 0, which is not simplified by default by jax because 0 * nan = nan)

Examples

The examples are moved in the documentation.