from typing import Optional
import torch
from torch import Tensor
from torch_geometric.nn.aggr import Aggregation
from torch_geometric.nn.inits import reset
from torch_geometric.utils import softmax
[docs]class AttentionalAggregation(Aggregation):
r"""The soft attention aggregation layer from the `"Graph Matching Networks
for Learning the Similarity of Graph Structured Objects"
<https://arxiv.org/abs/1904.12787>`_ paper.
.. math::
\mathbf{r}_i = \sum_{n=1}^{N_i} \mathrm{softmax} \left(
h_{\mathrm{gate}} ( \mathbf{x}_n ) \right) \cdot
h_{\mathbf{\Theta}} ( \mathbf{x}_n ),
where :math:`h_{\mathrm{gate}} \colon \mathbb{R}^F \to
\mathbb{R}` and :math:`h_{\mathbf{\Theta}}` denote neural networks, *i.e.*
MLPs.
Args:
gate_nn (torch.nn.Module): A neural network :math:`h_{\mathrm{gate}}`
that computes attention scores by mapping node features :obj:`x` of
shape :obj:`[-1, in_channels]` to shape :obj:`[-1, 1]` (for
node-level gating) or :obj:`[1, out_channels]` (for feature-level
gating), *e.g.*, defined by :class:`torch.nn.Sequential`.
nn (torch.nn.Module, optional): A neural network
:math:`h_{\mathbf{\Theta}}` that maps node features :obj:`x` of
shape :obj:`[-1, in_channels]` to shape :obj:`[-1, out_channels]`
before combining them with the attention scores, *e.g.*, defined by
:class:`torch.nn.Sequential`. (default: :obj:`None`)
"""
def __init__(
self,
gate_nn: torch.nn.Module,
nn: Optional[torch.nn.Module] = None,
):
super().__init__()
from torch_geometric.nn import MLP
self.gate_nn = self.gate_mlp = None
if isinstance(gate_nn, MLP):
self.gate_mlp = gate_nn
else:
self.gate_nn = gate_nn
self.nn = self.mlp = None
if isinstance(nn, MLP):
self.mlp = nn
else:
self.nn = nn
[docs] def reset_parameters(self):
reset(self.gate_nn)
reset(self.gate_mlp)
reset(self.nn)
reset(self.mlp)
[docs] def forward(self, x: Tensor, index: Optional[Tensor] = None,
ptr: Optional[Tensor] = None, dim_size: Optional[int] = None,
dim: int = -2) -> Tensor:
self.assert_two_dimensional_input(x, dim)
if self.gate_mlp is not None:
gate = self.gate_mlp(x, batch=index, batch_size=dim_size)
else:
gate = self.gate_nn(x)
if self.mlp is not None:
x = self.mlp(x, batch=index, batch_size=dim_size)
elif self.nn is not None:
x = self.nn(x)
gate = softmax(gate, index, ptr, dim_size, dim)
return self.reduce(gate * x, index, ptr, dim_size, dim)
def __repr__(self) -> str:
return (f'{self.__class__.__name__}('
f'gate_nn={self.gate_mlp or self.gate_nn}, '
f'nn={self.mlp or self.nn})')