Source code for torch_geometric.nn.conv.pdn_conv

import torch
from torch import Tensor
from torch.nn import Linear, Parameter, ReLU, Sequential, Sigmoid

from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.conv.gcn_conv import gcn_norm
from torch_geometric.nn.inits import glorot, zeros
from torch_geometric.typing import Adj, OptTensor, SparseTensor
from torch_geometric.utils import spmm

[docs]class PDNConv(MessagePassing): r"""The pathfinder discovery network convolutional operator from the `"Pathfinder Discovery Networks for Neural Message Passing" <>`_ paper. .. math:: \mathbf{x}^{\prime}_i = \sum_{j \in \mathcal{N}(i) \cup \{i\}}f_{\Theta}(\textbf{e}_{(j,i)}) \cdot f_{\Omega}(\mathbf{x}_{j}) where :math:`z_{i,j}` denotes the edge feature vector from source node :math:`j` to target node :math:`i`, and :math:`\mathbf{x}_{j}` denotes the node feature vector of node :math:`j`. Args: in_channels (int): Size of each input sample. out_channels (int): Size of each output sample. edge_dim (int): Edge feature dimensionality. hidden_channels (int): Hidden edge feature dimensionality. add_self_loops (bool, optional): If set to :obj:`False`, will not add self-loops to the input graph. (default: :obj:`True`) normalize (bool, optional): Whether to add self-loops and compute symmetric normalization coefficients on the fly. (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`. Shapes: - **input:** node features :math:`(|\mathcal{V}|, F_{in})`, edge indices :math:`(2, |\mathcal{E}|)`, edge features :math:`(|\mathcal{E}|, D)` *(optional)* - **output:** node features :math:`(|\mathcal{V}|, F_{out})` """ def __init__(self, in_channels: int, out_channels: int, edge_dim: int, hidden_channels: int, add_self_loops: bool = True, normalize: bool = True, bias: bool = True, **kwargs): kwargs.setdefault("aggr", "add") super().__init__(**kwargs) self.in_channels = in_channels self.out_channels = out_channels self.edge_dim = edge_dim self.hidden_channels = hidden_channels self.add_self_loops = add_self_loops self.normalize = normalize self.lin = Linear(in_channels, out_channels, bias=False) self.mlp = Sequential( Linear(edge_dim, hidden_channels), ReLU(inplace=True), Linear(hidden_channels, 1), Sigmoid(), ) if bias: self.bias = Parameter(torch.empty(out_channels)) else: self.register_parameter("bias", None) self.reset_parameters()
[docs] def reset_parameters(self): super().reset_parameters() glorot(self.lin.weight) glorot(self.mlp[0].weight) glorot(self.mlp[2].weight) zeros(self.mlp[0].bias) zeros(self.mlp[2].bias) zeros(self.bias)
[docs] def forward(self, x: Tensor, edge_index: Adj, edge_attr: OptTensor = None) -> Tensor: if isinstance(edge_index, SparseTensor): edge_attr = if edge_attr is not None: edge_attr = self.mlp(edge_attr).squeeze(-1) if isinstance(edge_index, SparseTensor): edge_index = edge_index.set_value(edge_attr, layout='coo') if self.normalize: if isinstance(edge_index, Tensor): edge_index, edge_attr = gcn_norm(edge_index, edge_attr, x.size(self.node_dim), False, self.add_self_loops, self.flow, x.dtype) elif isinstance(edge_index, SparseTensor): edge_index = gcn_norm(edge_index, None, x.size(self.node_dim), False, self.add_self_loops, self.flow, x.dtype) x = self.lin(x) # propagate_type: (x: Tensor, edge_weight: OptTensor) out = self.propagate(edge_index, x=x, edge_weight=edge_attr) if self.bias is not None: out = out + self.bias return out
def message(self, x_j: Tensor, edge_weight: Tensor) -> Tensor: return 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): return (f'{self.__class__.__name__}({self.in_channels}, ' f'{self.out_channels})')