2022-12-12

Added

• L=12 spherical harmonics

Fixed

• TensorProduct.visualize now works even if the TP is on the GPU.
• Github actions only trigger a push to coveralls if the corresponding token is set in github secrets.
• Batchnorm

2022-04-13

[0.5.0] - 2022-04-13

Added

• Sparse Voxel Convolution
• Clebsch-Gordan coefficients are computed via a change of basis from the complex to real basis. (see #341)
• o3, nn and io are accessible through e3nn. For instance e3nn.o3.rand_axis_angle.

Changed

• Since now the code is no more tested against torch==1.8.0, only tested against torch>=1.10.0

Fixed

• wigner_3j now always returns a contiguous copy regardless of dtype or device

2021-12-15

[0.4.4] - 2021-12-15

Fixed

• Remove CartesianTensor._rtp. Instead recompute the ReducedTensorProduct everytime. The user can save the ReducedTensorProduct to avoid creating it each time.
• *equivariance_error no longer keeps around unneeded autograd graphs
• CartesianTensor builds ReducedTensorProduct with correct device/dtype when called without one

Added

• Created module for reflected imports allowing for nice syntax for creating irreps, e.g. from e3nn.o3.irreps import l3o # same as Irreps("o3")
• Add uvu<v mode for TensorProduct. Compute only the upper triangular part of the uv terms.
• (beta) TensorSquare. computes x \otimes x and decompose it.
• *equivariance_error now tell you which arguments had which error

Changed

• Give up the support of python 3.6, set python_requires='>=3.7' in setup
• Optimize a little bit ReducedTensorProduct: solve linear system only once per irrep instead of 2L+1 times.
• Do not scale line width by path_weight in TensorProduct.visualize
• *equivariance_error now transforms its inputs in float64 by default, regardless of the dtype used for the calculation itself

2021-11-18

[0.4.3] - 2021-11-18

Fixed

• ReducedTensorProduct: replace QR decomposition by orthonormalize the projector X.T @ X.
  This keeps ReducedTensorProduct deterministic because the projectors and orthonormalize are both deterministic.
  The output of orthonormalize apears also to be highly sparse (luckily).

2021-11-08

[0.4.2] - 2021-11-08

This release coupled with the release of opt-einsum-fx=0.1.4 aim to fix slowness in the instantiation of TensorProduct.
The two main change that improved the instantiation time are

• Turning off the compilation of TensorProduct.right by default
• Replacing actual computation of torch.einsum and torch.tensordot by prediction of their output shape in the tracer used by opt-einsum-fx to collect tensor shapes

Added

• irrep_normalization and path_normalization for TensorProduct
• compile_right flag to TensorProduct
• Add new global flag jit_script_fx to optionally turn off torch.jit.script of fx code

2021-10-29

[0.4.1] - 2021-10-29

Added

• Add to_cartesian() to CartesianTensor

Fixed

• make it work with pytorch 1.10.0

2021-10-05

[0.4.0] - 2021-10-05

Changed

• Breaking change. normalization constants for TensorProduct and Linear. Now Linear(2x0e + 7x0e, 0e) is equivalent to Linear(9x0e, 0e). Models with inhomogeneous multiplicities will be affected by this change!

Fixed

• remove profiler.record_function calls that caused troubles with torchscript
• the home made implementation of radius_graph was ignoring the argument r_max

2021-08-27

[0.3.5] - 2021-08-27

Fixed

• Extract uses CodeGenMixin to avoid strange recursion errors during training
• Add missing call to normalize in axis_angle_to_quaternion

2021-08-20

[0.3.4] - 2021-08-20

Fixed

• ReducedTensorProducts: normalization and filter_ir_mid where not properly propagated through the recusive calls, this bug has no effects if the default values where used
• Use torch.linalg.eigh instead of the deprecated torch.symeig

Added

• (dev only) Pre-commit hooks that run pylint and flake8. These catch some common mistakes/style issues.
• classes to do SO(3) Grid transform (not fast) and Activation function using it
• Add f_in and f_out to o3.Linear
• PBC guide in the doc

2021-06-21

[0.3.3] - 2021-06-21

Changed

• FullyConnectedNet is now a torch.nn.Sequential

Fixed

• BatchNorm was not equivariant for pseudo-scalars

Added

• biases argument to o3.Linear
• nn.models.v2106: MessagePassing takes a sequence of irreps
• nn.models.v2106: Convolution inpired from Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks