import torch
from torch import Tensor
from torch.nn import Parameter as Param
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.inits import uniform
from torch_geometric.typing import Adj, OptTensor
from torch_geometric.utils import spmm
[docs]class GatedGraphConv(MessagePassing):
r"""The gated graph convolution operator from the `"Gated Graph Sequence
Neural Networks" <https://arxiv.org/abs/1511.05493>`_ paper.
.. math::
\mathbf{h}_i^{(0)} &= \mathbf{x}_i \, \Vert \, \mathbf{0}
\mathbf{m}_i^{(l+1)} &= \sum_{j \in \mathcal{N}(i)} e_{j,i} \cdot
\mathbf{\Theta} \cdot \mathbf{h}_j^{(l)}
\mathbf{h}_i^{(l+1)} &= \textrm{GRU} (\mathbf{m}_i^{(l+1)},
\mathbf{h}_i^{(l)})
up to representation :math:`\mathbf{h}_i^{(L)}`.
The number of input channels of :math:`\mathbf{x}_i` needs to be less or
equal than :obj:`out_channels`.
:math:`e_{j,i}` denotes the edge weight from source node :obj:`j` to target
node :obj:`i` (default: :obj:`1`)
Args:
out_channels (int): Size of each output sample.
num_layers (int): The sequence length :math:`L`.
aggr (str, optional): The aggregation scheme 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`.
Shapes:
- **input:**
node features :math:`(|\mathcal{V}|, F_{in})`,
edge indices :math:`(2, |\mathcal{E}|)`
- **output:** node features :math:`(|\mathcal{V}|, F_{out})`
"""
def __init__(self, out_channels: int, num_layers: int, aggr: str = 'add',
bias: bool = True, **kwargs):
super().__init__(aggr=aggr, **kwargs)
self.out_channels = out_channels
self.num_layers = num_layers
self.weight = Param(Tensor(num_layers, out_channels, out_channels))
self.rnn = torch.nn.GRUCell(out_channels, out_channels, bias=bias)
self.reset_parameters()
[docs] def reset_parameters(self):
super().reset_parameters()
uniform(self.out_channels, self.weight)
self.rnn.reset_parameters()
[docs] def forward(self, x: Tensor, edge_index: Adj,
edge_weight: OptTensor = None) -> Tensor:
if x.size(-1) > self.out_channels:
raise ValueError('The number of input channels is not allowed to '
'be larger than the number of output channels')
if x.size(-1) < self.out_channels:
zero = x.new_zeros(x.size(0), self.out_channels - x.size(-1))
x = torch.cat([x, zero], dim=1)
for i in range(self.num_layers):
m = torch.matmul(x, self.weight[i])
# propagate_type: (x: Tensor, edge_weight: OptTensor)
m = self.propagate(edge_index, x=m, edge_weight=edge_weight)
x = self.rnn(m, x)
return x
def message(self, x_j: Tensor, edge_weight: OptTensor):
return x_j if edge_weight is None else edge_weight.view(-1, 1) * x_j
def message_and_aggregate(self, adj_t: Adj, x: Tensor) -> Tensor:
return spmm(adj_t, x, reduce=self.aggr)
def __repr__(self) -> str:
return (f'{self.__class__.__name__}({self.out_channels}, '
f'num_layers={self.num_layers})')