from typing import Optional
from torch import Tensor
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.conv.gcn_conv import gcn_norm
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.typing import Adj, OptTensor, SparseTensor
from torch_geometric.utils import spmm
[docs]class SGConv(MessagePassing):
r"""The simple graph convolutional operator from the `"Simplifying Graph
Convolutional Networks" <https://arxiv.org/abs/1902.07153>`_ paper
.. math::
\mathbf{X}^{\prime} = {\left(\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}}
\mathbf{\hat{D}}^{-1/2} \right)}^K \mathbf{X} \mathbf{\Theta},
where :math:`\mathbf{\hat{A}} = \mathbf{A} + \mathbf{I}` denotes the
adjacency matrix with inserted self-loops and
:math:`\hat{D}_{ii} = \sum_{j=0} \hat{A}_{ij}` its diagonal degree matrix.
The adjacency matrix can include other values than :obj:`1` representing
edge weights via the optional :obj:`edge_weight` tensor.
Args:
in_channels (int): Size of each input sample, or :obj:`-1` to derive
the size from the first input(s) to the forward method.
out_channels (int): Size of each output sample.
K (int, optional): Number of hops :math:`K`. (default: :obj:`1`)
cached (bool, optional): If set to :obj:`True`, the layer will cache
the computation of :math:`{\left(\mathbf{\hat{D}}^{-1/2}
\mathbf{\hat{A}} \mathbf{\hat{D}}^{-1/2} \right)}^K \mathbf{X}` on
first execution, and will use the cached version for further
executions.
This parameter should only be set to :obj:`True` in transductive
learning scenarios. (default: :obj:`False`)
add_self_loops (bool, optional): If set to :obj:`False`, will not add
self-loops to the input graph. (default: :obj:`True`)
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:
- **input:**
node features :math:`(|\mathcal{V}|, F_{in})`,
edge indices :math:`(2, |\mathcal{E}|)`,
edge weights :math:`(|\mathcal{E}|)` *(optional)*
- **output:**
node features :math:`(|\mathcal{V}|, F_{out})`
"""
_cached_x: Optional[Tensor]
def __init__(self, in_channels: int, out_channels: int, K: int = 1,
cached: bool = False, add_self_loops: bool = True,
bias: bool = True, **kwargs):
kwargs.setdefault('aggr', 'add')
super().__init__(**kwargs)
self.in_channels = in_channels
self.out_channels = out_channels
self.K = K
self.cached = cached
self.add_self_loops = add_self_loops
self._cached_x = None
self.lin = Linear(in_channels, out_channels, bias=bias)
self.reset_parameters()
[docs] def reset_parameters(self):
super().reset_parameters()
self.lin.reset_parameters()
self._cached_x = None
[docs] def forward(self, x: Tensor, edge_index: Adj,
edge_weight: OptTensor = None) -> Tensor:
cache = self._cached_x
if cache is None:
if isinstance(edge_index, Tensor):
edge_index, edge_weight = gcn_norm( # yapf: disable
edge_index, edge_weight, x.size(self.node_dim), False,
self.add_self_loops, self.flow, dtype=x.dtype)
elif isinstance(edge_index, SparseTensor):
edge_index = gcn_norm( # yapf: disable
edge_index, edge_weight, x.size(self.node_dim), False,
self.add_self_loops, self.flow, dtype=x.dtype)
for k in range(self.K):
# propagate_type: (x: Tensor, edge_weight: OptTensor)
x = self.propagate(edge_index, x=x, edge_weight=edge_weight,
size=None)
if self.cached:
self._cached_x = x
else:
x = cache.detach()
return self.lin(x)
def message(self, x_j: Tensor, edge_weight: Tensor) -> Tensor:
return edge_weight.view(-1, 1) * x_j
def message_and_aggregate(self, adj_t: SparseTensor, x: Tensor) -> Tensor:
return spmm(adj_t, x, reduce=self.aggr)
def __repr__(self) -> str:
return (f'{self.__class__.__name__}({self.in_channels}, '
f'{self.out_channels}, K={self.K})')