Source code for torch_geometric.nn.glob.glob

from typing import List, Optional, Union

import torch
from torch import Tensor
from torch_scatter import scatter


[docs]def global_add_pool(x: Tensor, batch: Optional[Tensor], size: Optional[int] = None) -> Tensor: r"""Returns batch-wise graph-level-outputs by adding node features across the node dimension, so that for a single graph :math:`\mathcal{G}_i` its output is computed by .. math:: \mathbf{r}_i = \sum_{n=1}^{N_i} \mathbf{x}_n Args: x (Tensor): Node feature matrix :math:`\mathbf{X} \in \mathbb{R}^{(N_1 + \ldots + N_B) \times F}`. batch (LongTensor, optional): Batch vector :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each node to a specific example. size (int, optional): Batch-size :math:`B`. Automatically calculated if not given. (default: :obj:`None`) """ if batch is None: return x.sum(dim=0, keepdim=True) size = int(batch.max().item() + 1) if size is None else size return scatter(x, batch, dim=0, dim_size=size, reduce='add')
[docs]def global_mean_pool(x: Tensor, batch: Optional[Tensor], size: Optional[int] = None) -> Tensor: r"""Returns batch-wise graph-level-outputs by averaging node features across the node dimension, so that for a single graph :math:`\mathcal{G}_i` its output is computed by .. math:: \mathbf{r}_i = \frac{1}{N_i} \sum_{n=1}^{N_i} \mathbf{x}_n Args: x (Tensor): Node feature matrix :math:`\mathbf{X} \in \mathbb{R}^{(N_1 + \ldots + N_B) \times F}`. batch (LongTensor, optional): Batch vector :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each node to a specific example. size (int, optional): Batch-size :math:`B`. Automatically calculated if not given. (default: :obj:`None`) """ if batch is None: return x.mean(dim=0, keepdim=True) size = int(batch.max().item() + 1) if size is None else size return scatter(x, batch, dim=0, dim_size=size, reduce='mean')
[docs]def global_max_pool(x: Tensor, batch: Optional[Tensor], size: Optional[int] = None) -> Tensor: r"""Returns batch-wise graph-level-outputs by taking the channel-wise maximum across the node dimension, so that for a single graph :math:`\mathcal{G}_i` its output is computed by .. math:: \mathbf{r}_i = \mathrm{max}_{n=1}^{N_i} \, \mathbf{x}_n Args: x (Tensor): Node feature matrix :math:`\mathbf{X} \in \mathbb{R}^{(N_1 + \ldots + N_B) \times F}`. batch (LongTensor, optional): Batch vector :math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns each node to a specific example. size (int, optional): Batch-size :math:`B`. Automatically calculated if not given. (default: :obj:`None`) """ if batch is None: return x.max(dim=0, keepdim=True)[0] size = int(batch.max().item() + 1) if size is None else size return scatter(x, batch, dim=0, dim_size=size, reduce='max')
[docs]class GlobalPooling(torch.nn.Module): r"""A global pooling module that wraps the usage of :meth:`~torch_geometric.nn.glob.global_add_pool`, :meth:`~torch_geometric.nn.glob.global_mean_pool` and :meth:`~torch_geometric.nn.glob.global_max_pool` into a single module. Args: aggr (string or List[str]): The aggregation scheme to use (:obj:`"add"`, :obj:`"mean"`, :obj:`"max"`). If given as a list, will make use of multiple aggregations in which different outputs will get concatenated in the last dimension. """ def __init__(self, aggr: Union[str, List[str]]): super().__init__() self.aggrs = [aggr] if isinstance(aggr, str) else aggr assert len(self.aggrs) > 0 assert len(set(self.aggrs) | {'sum', 'add', 'mean', 'max'}) == 4
[docs] def forward(self, x: Tensor, batch: Optional[Tensor], size: Optional[int] = None) -> Tensor: """""" xs: List[Tensor] = [] for aggr in self.aggrs: if aggr == 'sum' or aggr == 'add': xs.append(global_add_pool(x, batch, size)) elif aggr == 'mean': xs.append(global_mean_pool(x, batch, size)) elif aggr == 'max': xs.append(global_max_pool(x, batch, size)) return xs[0] if len(xs) == 1 else torch.cat(xs, dim=-1)
def __repr__(self) -> str: aggr = self.aggrs[0] if len(self.aggrs) == 1 else self.aggrs return f'{self.__class__.__name__}(aggr={aggr})'