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"
<https://arxiv.org/abs/2010.12878>`_ 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 = edge_index.storage.value()
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})')