Source code for torch_geometric.nn.aggr.set2set

from typing import Optional

import torch
from torch import Tensor

from torch_geometric.nn.aggr import Aggregation
from torch_geometric.utils import softmax

[docs]class Set2Set(Aggregation): r"""The Set2Set aggregation operator based on iterative content-based attention, as described in the `"Order Matters: Sequence to sequence for Sets" <>`_ paper. .. math:: \mathbf{q}_t &= \mathrm{LSTM}(\mathbf{q}^{*}_{t-1}) \alpha_{i,t} &= \mathrm{softmax}(\mathbf{x}_i \cdot \mathbf{q}_t) \mathbf{r}_t &= \sum_{i=1}^N \alpha_{i,t} \mathbf{x}_i \mathbf{q}^{*}_t &= \mathbf{q}_t \, \Vert \, \mathbf{r}_t, where :math:`\mathbf{q}^{*}_T` defines the output of the layer with twice the dimensionality as the input. Args: in_channels (int): Size of each input sample. processing_steps (int): Number of iterations :math:`T`. **kwargs (optional): Additional arguments of :class:`torch.nn.LSTM`. """ def __init__(self, in_channels: int, processing_steps: int, **kwargs): super().__init__() self.in_channels = in_channels self.out_channels = 2 * in_channels self.processing_steps = processing_steps self.lstm = torch.nn.LSTM(self.out_channels, in_channels, **kwargs) self.reset_parameters()
[docs] def reset_parameters(self): self.lstm.reset_parameters()
[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_index_present(index) self.assert_two_dimensional_input(x, dim) h = (x.new_zeros((self.lstm.num_layers, dim_size, x.size(-1))), x.new_zeros((self.lstm.num_layers, dim_size, x.size(-1)))) q_star = x.new_zeros(dim_size, self.out_channels) for _ in range(self.processing_steps): q, h = self.lstm(q_star.unsqueeze(0), h) q = q.view(dim_size, self.in_channels) e = (x * q[index]).sum(dim=-1, keepdim=True) a = softmax(e, index, ptr, dim_size, dim) r = self.reduce(a * x, index, ptr, dim_size, dim, reduce='sum') q_star =[q, r], dim=-1) return q_star
def __repr__(self) -> str: return (f'{self.__class__.__name__}({self.in_channels}, ' f'{self.out_channels})')