Source code for torch_geometric.nn.conv.cg_conv

import torch
from torch.nn import Linear
import torch.nn.functional as F
from torch_geometric.nn.conv import MessagePassing

[docs]class CGConv(MessagePassing): r"""The crystal graph convolutional operator from the `"Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties" <>`_ paper .. math:: \mathbf{x}^{\prime}_i = \mathbf{x}_i + \sum_{j \in \mathcal{N}(i)} \sigma \left( \mathbf{z}_{i,j} \mathbf{W}_f + \mathbf{b}_f \right) \odot g \left( \mathbf{z}_{i,j} \mathbf{W}_s + \mathbf{b}_s \right) where :math:`\mathbf{z}_{i,j} = [ \mathbf{x}_i, \mathbf{x}_j, \mathbf{e}_{i,j} ]` denotes the concatenation of central node features, neighboring node features and edge features. In addition, :math:`\sigma` and :math:`g` denote the sigmoid and softplus functions, respectively. Args: channels (int): Size of each input sample. dim (int): Edge feature dimensionality. aggr (string, optional): The aggregation operator to use (:obj:`"add"`, :obj:`"mean"`, :obj:`"max"`). (default: :obj:`"add"`) bias (bool, optional): If set to :obj:`False`, the layer will not learn an additive bias. (default: :obj:`True`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.MessagePassing`. """ def __init__(self, channels, dim, aggr='add', bias=True, **kwargs): super(CGConv, self).__init__(aggr=aggr, **kwargs) self.in_channels = channels self.out_channels = channels self.dim = dim self.lin_f = Linear(2 * channels + dim, channels, bias=bias) self.lin_s = Linear(2 * channels + dim, channels, bias=bias) self.reset_parameters()
[docs] def reset_parameters(self): self.lin_f.reset_parameters() self.lin_s.reset_parameters()
[docs] def forward(self, x, edge_index, edge_attr): """""" return self.propagate(edge_index, x=x, edge_attr=edge_attr)
def message(self, x_i, x_j, edge_attr): z =[x_i, x_j, edge_attr], dim=-1) return self.lin_f(z).sigmoid() * F.softplus(self.lin_s(z)) def update(self, aggr_out, x): return aggr_out + x def __repr__(self): return '{}({}, {}, dim={})'.format(self.__class__.__name__, self.in_channels, self.out_channels, self.dim)