core.models.gemnet.layers.efficient

core.models.gemnet.layers.efficient#

Copyright (c) Meta, Inc. and its affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.

Classes#

EfficientInteractionDownProjection

Down projection in the efficient reformulation.

EfficientInteractionBilinear

Efficient reformulation of the bilinear layer and subsequent summation.

Module Contents#

class core.models.gemnet.layers.efficient.EfficientInteractionDownProjection(num_spherical: int, num_radial: int, emb_size_interm: int)#

Bases: torch.nn.Module

Down projection in the efficient reformulation.

Parameters:

  • emb_size_interm (int) – Intermediate embedding size (down-projection size).

  • kernel_initializer (callable) – Initializer of the weight matrix.

num_spherical#
num_radial#
emb_size_interm#
reset_parameters() → None#
forward(rbf, sph, id_ca, id_ragged_idx)#
Parameters:

  • rbf (torch.Tensor, shape=(1, nEdges, num_radial))

  • sph (torch.Tensor, shape=(nEdges, Kmax, num_spherical))

  • id_ca

  • id_ragged_idx

Returns:

  • rbf_W1 (torch.Tensor, shape=(nEdges, emb_size_interm, num_spherical))

  • sph (torch.Tensor, shape=(nEdges, Kmax, num_spherical)) – Kmax = maximum number of neighbors of the edges

class core.models.gemnet.layers.efficient.EfficientInteractionBilinear(emb_size: int, emb_size_interm: int, units_out: int)#

Bases: torch.nn.Module

Efficient reformulation of the bilinear layer and subsequent summation.

Parameters:

  • units_out (int) – Embedding output size of the bilinear layer.

  • kernel_initializer (callable) – Initializer of the weight matrix.

emb_size#
emb_size_interm#
units_out#
reset_parameters() → None#
forward(basis, m, id_reduce, id_ragged_idx) → torch.Tensor#
Parameters:

  • basis

  • m (quadruplets: m = m_db , triplets: m = m_ba)

  • id_reduce

  • id_ragged_idx

Returns:

m_ca – Edge embeddings.

Return type:

torch.Tensor, shape=(nEdges, units_out)

Contents