Source code for torch_geometric.nn.conv.simple_conv

from typing import List, Optional, Union

import torch
from torch import Tensor

from torch_geometric.nn.aggr import Aggregation
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.typing import (
from torch_geometric.utils import add_self_loops, spmm

[docs]class SimpleConv(MessagePassing): r"""A simple message passing operator that performs (non-trainable) propagation. .. math:: \mathbf{x}^{\prime}_i = \bigoplus_{j \in \mathcal{N(i)}} e_{ji} \cdot \mathbf{x}_j where :math:`\bigoplus` defines a custom aggregation scheme. Args: aggr (str or [str] or Aggregation, optional): The aggregation scheme to use, *e.g.*, :obj:`"add"`, :obj:`"sum"` :obj:`"mean"`, :obj:`"min"`, :obj:`"max"` or :obj:`"mul"`. In addition, can be any :class:`~torch_geometric.nn.aggr.Aggregation` module (or any string that automatically resolves to it). (default: :obj:`"sum"`) combine_root (str, optional): Specifies whether or how to combine the central node representation (one of :obj:`"sum"`, :obj:`"cat"`, :obj:`"self_loop"`, :obj:`None`). (default: :obj:`None`) **kwargs (optional): Additional arguments of :class:`torch_geometric.nn.conv.MessagePassing`. Shapes: - **inputs:** node features :math:`(|\mathcal{V}|, F)` or :math:`((|\mathcal{V_s}|, F), (|\mathcal{V_t}|, *))` if bipartite, edge indices :math:`(2, |\mathcal{E}|)` - **outputs:** node features :math:`(|\mathcal{V}|, F)` or :math:`(|\mathcal{V_t}|, F)` if bipartite """ def __init__( self, aggr: Optional[Union[str, List[str], Aggregation]] = "sum", combine_root: Optional[str] = None, **kwargs, ): if combine_root not in ['sum', 'cat', 'self_loop', None]: raise ValueError(f"Received invalid value for 'combine_root' " f"(got '{combine_root}')") super().__init__(aggr, **kwargs) self.combine_root = combine_root
[docs] def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj, edge_weight: OptTensor = None, size: Size = None) -> Tensor: if self.combine_root is not None: if self.combine_root == 'self_loop': if not isinstance(x, Tensor) or (size is not None and size[0] != size[1]): raise ValueError("Cannot use `combine_root='self_loop'` " "for bipartite message passing") if isinstance(edge_index, Tensor): edge_index, edge_weight = add_self_loops( edge_index, edge_weight, num_nodes=x.size(0)) elif isinstance(edge_index, SparseTensor): edge_index = torch_sparse.set_diag(edge_index) if isinstance(x, Tensor): x = (x, x) # propagate_type: (x: OptPairTensor, edge_weight: OptTensor) out = self.propagate(edge_index, x=x, edge_weight=edge_weight, size=size) x_dst = x[1] if x_dst is not None and self.combine_root is not None: if self.combine_root == 'sum': out = out + x_dst elif self.combine_root == 'cat': out =[x_dst, out], dim=-1) return out
def message(self, x_j: Tensor, edge_weight: OptTensor) -> Tensor: 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: OptPairTensor) -> Tensor: assert isinstance(self.aggr, str) return spmm(adj_t, x[0], reduce=self.aggr)