from typing import Optional
from torch import Tensor
from torch_geometric.utils 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.
Functional method of the
:class:`~torch_geometric.nn.aggr.SumAggregation` module.
Args:
x (torch.Tensor): Node feature matrix
:math:`\mathbf{X} \in \mathbb{R}^{(N_1 + \ldots + N_B) \times F}`.
batch (torch.Tensor, optional): The batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns
each node to a specific example.
size (int, optional): The number of examples :math:`B`.
Automatically calculated if not given. (default: :obj:`None`)
"""
dim = -1 if x.dim() == 1 else -2
if batch is None:
return x.sum(dim=dim, keepdim=x.dim() <= 2)
size = int(batch.max().item() + 1) if size is None else size
return scatter(x, batch, dim=dim, dim_size=size, reduce='sum')
[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.
Functional method of the
:class:`~torch_geometric.nn.aggr.MeanAggregation` module.
Args:
x (torch.Tensor): Node feature matrix
:math:`\mathbf{X} \in \mathbb{R}^{(N_1 + \ldots + N_B) \times F}`.
batch (torch.Tensor, optional): The batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns
each node to a specific example.
size (int, optional): The number of examples :math:`B`.
Automatically calculated if not given. (default: :obj:`None`)
"""
dim = -1 if x.dim() == 1 else -2
if batch is None:
return x.mean(dim=dim, keepdim=x.dim() <= 2)
size = int(batch.max().item() + 1) if size is None else size
return scatter(x, batch, dim=dim, 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.
Functional method of the
:class:`~torch_geometric.nn.aggr.MaxAggregation` module.
Args:
x (torch.Tensor): Node feature matrix
:math:`\mathbf{X} \in \mathbb{R}^{(N_1 + \ldots + N_B) \times F}`.
batch (torch.Tensor, optional): The batch vector
:math:`\mathbf{b} \in {\{ 0, \ldots, B-1\}}^N`, which assigns
each element to a specific example.
size (int, optional): The number of examples :math:`B`.
Automatically calculated if not given. (default: :obj:`None`)
"""
dim = -1 if x.dim() == 1 else -2
if batch is None:
return x.max(dim=dim, keepdim=x.dim() <= 2)[0]
size = int(batch.max().item() + 1) if size is None else size
return scatter(x, batch, dim=dim, dim_size=size, reduce='max')