File size: 5,083 Bytes
64c992d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | import torch
import torch.nn as nn
import copy
from .so3 import SO3_Embedding
from .radial_function import RadialFunction
class EdgeDegreeEmbedding(torch.nn.Module):
"""
Args:
sphere_channels (int): Number of spherical channels
lmax_list (list:int): List of degrees (l) for each resolution
mmax_list (list:int): List of orders (m) for each resolution
SO3_rotation (list:SO3_Rotation): Class to calculate Wigner-D matrices and rotate embeddings
mappingReduced (CoefficientMappingModule): Class to convert l and m indices once node embedding is rotated
max_num_elements (int): Maximum number of atomic numbers
edge_channels_list (list:int): List of sizes of invariant edge embedding. For example, [input_channels, hidden_channels, hidden_channels].
The last one will be used as hidden size when `use_atom_edge_embedding` is `True`.
use_atom_edge_embedding (bool): Whether to use atomic embedding along with relative distance for edge scalar features
rescale_factor (float): Rescale the sum aggregation
"""
def __init__(
self,
sphere_channels,
lmax_list,
mmax_list,
SO3_rotation,
mappingReduced,
max_num_elements,
edge_channels_list,
use_atom_edge_embedding,
rescale_factor
):
super(EdgeDegreeEmbedding, self).__init__()
self.sphere_channels = sphere_channels
self.lmax_list = lmax_list
self.mmax_list = mmax_list
self.num_resolutions = len(self.lmax_list)
self.SO3_rotation = SO3_rotation
self.mappingReduced = mappingReduced
self.m_0_num_coefficients = self.mappingReduced.m_size[0]
self.m_all_num_coefficents = len(self.mappingReduced.l_harmonic)
# Create edge scalar (invariant to rotations) features
# Embedding function of the atomic numbers
self.max_num_elements = max_num_elements
self.edge_channels_list = copy.deepcopy(edge_channels_list)
self.use_atom_edge_embedding = use_atom_edge_embedding
if self.use_atom_edge_embedding:
self.source_embedding = nn.Embedding(self.max_num_elements, self.edge_channels_list[-1])
self.target_embedding = nn.Embedding(self.max_num_elements, self.edge_channels_list[-1])
nn.init.uniform_(self.source_embedding.weight.data, -0.001, 0.001)
nn.init.uniform_(self.target_embedding.weight.data, -0.001, 0.001)
self.edge_channels_list[0] = self.edge_channels_list[0] + 2 * self.edge_channels_list[-1]
else:
self.source_embedding, self.target_embedding = None, None
# Embedding function of distance
self.edge_channels_list.append(self.m_0_num_coefficients * self.sphere_channels)
self.rad_func = RadialFunction(self.edge_channels_list)
self.rescale_factor = rescale_factor
def forward(
self,
atomic_numbers,
edge_distance,
edge_index
):
if self.use_atom_edge_embedding:
source_element = atomic_numbers[edge_index[0]] # Source atom atomic number
target_element = atomic_numbers[edge_index[1]] # Target atom atomic number
source_embedding = self.source_embedding(source_element)
target_embedding = self.target_embedding(target_element)
x_edge = torch.cat((edge_distance, source_embedding, target_embedding), dim=1)
else:
x_edge = edge_distance
x_edge_m_0 = self.rad_func(x_edge)
x_edge_m_0 = x_edge_m_0.reshape(-1, self.m_0_num_coefficients, self.sphere_channels)
x_edge_m_pad = torch.zeros((
x_edge_m_0.shape[0],
(self.m_all_num_coefficents - self.m_0_num_coefficients),
self.sphere_channels),
device=x_edge_m_0.device)
x_edge_m_all = torch.cat((x_edge_m_0, x_edge_m_pad), dim=1)
x_edge_embedding = SO3_Embedding(
0,
self.lmax_list.copy(),
self.sphere_channels,
device=x_edge_m_all.device,
dtype=x_edge_m_all.dtype
)
x_edge_embedding.set_embedding(x_edge_m_all)
x_edge_embedding.set_lmax_mmax(self.lmax_list.copy(), self.mmax_list.copy())
# Reshape the spherical harmonics based on l (degree)
x_edge_embedding._l_primary(self.mappingReduced)
# Rotate back the irreps
x_edge_embedding._rotate_inv(self.SO3_rotation, self.mappingReduced)
# Compute the sum of the incoming neighboring messages for each target node
x_edge_embedding._reduce_edge(edge_index[1], atomic_numbers.shape[0])
x_edge_embedding.embedding = x_edge_embedding.embedding / self.rescale_factor
return x_edge_embedding
|