import torch
from torch.nn import Parameter as Param
from torch_geometric.nn.conv import MessagePassing
from ..inits import uniform
[docs]class RGCNConv(MessagePassing):
r"""The relational graph convolutional operator from the `"Modeling
Relational Data with Graph Convolutional Networks"
<https://arxiv.org/abs/1703.06103>`_ paper
.. math::
\mathbf{x}^{\prime}_i = \mathbf{\Theta}_{\textrm{root}} \cdot
\mathbf{x}_i + \sum_{r \in \mathcal{R}} \sum_{j \in \mathcal{N}_r(i)}
\frac{1}{|\mathcal{N}_r(i)|} \mathbf{\Theta}_r \cdot \mathbf{x}_j,
where :math:`\mathcal{R}` denotes the set of relations, *i.e.* edge types.
Edge type needs to be a one-dimensional :obj:`torch.long` tensor which
stores a relation identifier
:math:`\in \{ 0, \ldots, |\mathcal{R}| - 1\}` for each edge.
Args:
in_channels (int): Size of each input sample.
out_channels (int): Size of each output sample.
num_relations (int): Number of relations.
num_bases (int): Number of bases used for basis-decomposition.
root_weight (bool, optional): If set to :obj:`False`, the layer will
not add transformed root node features to the output.
(default: :obj:`True`)
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, in_channels, out_channels, num_relations, num_bases,
root_weight=True, bias=True, **kwargs):
super(RGCNConv, self).__init__(aggr='add', **kwargs)
self.in_channels = in_channels
self.out_channels = out_channels
self.num_relations = num_relations
self.num_bases = num_bases
self.basis = Param(torch.Tensor(num_bases, in_channels, out_channels))
self.att = Param(torch.Tensor(num_relations, num_bases))
if root_weight:
self.root = Param(torch.Tensor(in_channels, out_channels))
else:
self.register_parameter('root', None)
if bias:
self.bias = Param(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
[docs] def reset_parameters(self):
size = self.num_bases * self.in_channels
uniform(size, self.basis)
uniform(size, self.att)
uniform(size, self.root)
uniform(size, self.bias)
[docs] def forward(self, x, edge_index, edge_type, edge_norm=None, size=None):
""""""
return self.propagate(edge_index, size=size, x=x, edge_type=edge_type,
edge_norm=edge_norm)
def message(self, x_j, edge_index_j, edge_type, edge_norm):
w = torch.matmul(self.att, self.basis.view(self.num_bases, -1))
# If no node features are given, we implement a simple embedding
# loopkup based on the target node index and its edge type.
if x_j is None:
w = w.view(-1, self.out_channels)
index = edge_type * self.in_channels + edge_index_j
out = torch.index_select(w, 0, index)
else:
w = w.view(self.num_relations, self.in_channels, self.out_channels)
w = torch.index_select(w, 0, edge_type)
out = torch.bmm(x_j.unsqueeze(1), w).squeeze(-2)
return out if edge_norm is None else out * edge_norm.view(-1, 1)
def update(self, aggr_out, x):
if self.root is not None:
if x is None:
aggr_out = aggr_out + self.root
else:
aggr_out = aggr_out + torch.matmul(x, self.root)
if self.bias is not None:
aggr_out = aggr_out + self.bias
return aggr_out
def __repr__(self):
return '{}({}, {}, num_relations={})'.format(self.__class__.__name__,
self.in_channels,
self.out_channels,
self.num_relations)