from typing import Optional
import torch
from torch import Tensor
from torch_geometric.nn.aggr import Aggregation
from torch_geometric.utils import softmax
from ..inits import reset
[docs]class AttentionalAggregation(Aggregation):
r"""The soft attention aggregation layer from the `"Gated Graph Sequence
Neural Networks" <https://arxiv.org/abs/1511.05493>`_ paper
.. math::
\mathbf{r}_i = \sum_{n=1}^{N_i} \mathrm{softmax} \left(
h_{\mathrm{gate}} ( \mathbf{x}_n ) \right) \odot
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]`, *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__()
self.gate_nn = gate_nn
self.nn = nn
self.reset_parameters()
def reset_parameters(self):
reset(self.gate_nn)
reset(self.nn)
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)
gate = self.gate_nn(x).view(-1, 1)
x = self.nn(x) if self.nn is not None else 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__}(gate_nn={self.gate_nn}, '
f'nn={self.nn})')