from typing import List, Optional, Tuple, Union
import torch.nn.functional as F
from torch import Tensor
from torch_geometric.nn.aggr import Aggregation, MultiAggregation
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.typing import Adj, OptPairTensor, Size, SparseTensor
from torch_geometric.utils import spmm
[docs]class SAGEConv(MessagePassing):
r"""The GraphSAGE operator from the `"Inductive Representation Learning on
Large Graphs" <https://arxiv.org/abs/1706.02216>`_ paper.
.. math::
\mathbf{x}^{\prime}_i = \mathbf{W}_1 \mathbf{x}_i + \mathbf{W}_2 \cdot
\mathrm{mean}_{j \in \mathcal{N(i)}} \mathbf{x}_j
If :obj:`project = True`, then :math:`\mathbf{x}_j` will first get
projected via
.. math::
\mathbf{x}_j \leftarrow \sigma ( \mathbf{W}_3 \mathbf{x}_j +
\mathbf{b})
as described in Eq. (3) of the paper.
Args:
in_channels (int or tuple): Size of each input sample, or :obj:`-1` to
derive the size from the first input(s) to the forward method.
A tuple corresponds to the sizes of source and target
dimensionalities.
out_channels (int): Size of each output sample.
aggr (str or Aggregation, optional): The aggregation scheme to use.
Any aggregation of :obj:`torch_geometric.nn.aggr` can be used,
*e.g.*, :obj:`"mean"`, :obj:`"max"`, or :obj:`"lstm"`.
(default: :obj:`"mean"`)
normalize (bool, optional): If set to :obj:`True`, output features
will be :math:`\ell_2`-normalized, *i.e.*,
:math:`\frac{\mathbf{x}^{\prime}_i}
{\| \mathbf{x}^{\prime}_i \|_2}`.
(default: :obj:`False`)
root_weight (bool, optional): If set to :obj:`False`, the layer will
not add transformed root node features to the output.
(default: :obj:`True`)
project (bool, optional): If set to :obj:`True`, the layer will apply a
linear transformation followed by an activation function before
aggregation (as described in Eq. (3) of the paper).
(default: :obj:`False`)
bias (bool, optional): If set to :obj:`False`, the layer will not learn
an additive bias. (default: :obj:`True`)
**kwargs (optional): Additional arguments of
:class:`torch_geometric.nn.conv.MessagePassing`.
Shapes:
- **inputs:**
node features :math:`(|\mathcal{V}|, F_{in})` or
:math:`((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))`
if bipartite,
edge indices :math:`(2, |\mathcal{E}|)`
- **outputs:** node features :math:`(|\mathcal{V}|, F_{out})` or
:math:`(|\mathcal{V_t}|, F_{out})` if bipartite
"""
def __init__(
self,
in_channels: Union[int, Tuple[int, int]],
out_channels: int,
aggr: Optional[Union[str, List[str], Aggregation]] = "mean",
normalize: bool = False,
root_weight: bool = True,
project: bool = False,
bias: bool = True,
**kwargs,
):
self.in_channels = in_channels
self.out_channels = out_channels
self.normalize = normalize
self.root_weight = root_weight
self.project = project
if isinstance(in_channels, int):
in_channels = (in_channels, in_channels)
if aggr == 'lstm':
kwargs.setdefault('aggr_kwargs', {})
kwargs['aggr_kwargs'].setdefault('in_channels', in_channels[0])
kwargs['aggr_kwargs'].setdefault('out_channels', in_channels[0])
super().__init__(aggr, **kwargs)
if self.project:
if in_channels[0] <= 0:
raise ValueError(f"'{self.__class__.__name__}' does not "
f"support lazy initialization with "
f"`project=True`")
self.lin = Linear(in_channels[0], in_channels[0], bias=True)
if isinstance(self.aggr_module, MultiAggregation):
aggr_out_channels = self.aggr_module.get_out_channels(
in_channels[0])
else:
aggr_out_channels = in_channels[0]
self.lin_l = Linear(aggr_out_channels, out_channels, bias=bias)
if self.root_weight:
self.lin_r = Linear(in_channels[1], out_channels, bias=False)
self.reset_parameters()
[docs] def reset_parameters(self):
super().reset_parameters()
if self.project:
self.lin.reset_parameters()
self.lin_l.reset_parameters()
if self.root_weight:
self.lin_r.reset_parameters()
[docs] def forward(
self,
x: Union[Tensor, OptPairTensor],
edge_index: Adj,
size: Size = None,
) -> Tensor:
if isinstance(x, Tensor):
x = (x, x)
if self.project and hasattr(self, 'lin'):
x = (self.lin(x[0]).relu(), x[1])
# propagate_type: (x: OptPairTensor)
out = self.propagate(edge_index, x=x, size=size)
out = self.lin_l(out)
x_r = x[1]
if self.root_weight and x_r is not None:
out = out + self.lin_r(x_r)
if self.normalize:
out = F.normalize(out, p=2., dim=-1)
return out
def message(self, x_j: Tensor) -> Tensor:
return x_j
def message_and_aggregate(self, adj_t: Adj, x: OptPairTensor) -> Tensor:
if isinstance(adj_t, SparseTensor):
adj_t = adj_t.set_value(None, layout=None)
return spmm(adj_t, x[0], reduce=self.aggr)
def __repr__(self) -> str:
return (f'{self.__class__.__name__}({self.in_channels}, '
f'{self.out_channels}, aggr={self.aggr})')