Source code for torch_geometric.nn.conv.gated_graph_conv

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" <>`_ 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 =[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})')