from torch.nn import Linear
from torch_geometric.nn.conv import MessagePassing, GCNConv
[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.
Args:
in_channels (int): Size of each input sample.
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` 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`)
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`.
"""
def __init__(self, in_channels, out_channels, K=1, cached=False, bias=True,
**kwargs):
super(SGConv, self).__init__(aggr='add', **kwargs)
self.in_channels = in_channels
self.out_channels = out_channels
self.K = K
self.cached = cached
self.lin = Linear(in_channels, out_channels, bias=bias)
self.reset_parameters()
[docs] def reset_parameters(self):
self.lin.reset_parameters()
self.cached_result = None
self.cached_num_edges = None
[docs] def forward(self, x, edge_index, edge_weight=None):
""""""
if self.cached and self.cached_result is not None:
if edge_index.size(1) != self.cached_num_edges:
raise RuntimeError(
'Cached {} number of edges, but found {}. Please '
'disable the caching behavior of this layer by removing '
'the `cached=True` argument in its constructor.'.format(
self.cached_num_edges, edge_index.size(1)))
if not self.cached:
x = self.lin(x)
if not self.cached or self.cached_result is None:
self.cached_num_edges = edge_index.size(1)
edge_index, norm = GCNConv.norm(edge_index, x.size(0), edge_weight,
dtype=x.dtype)
for k in range(self.K):
x = self.propagate(edge_index, x=x, norm=norm)
self.cached_result = x
if self.cached:
x = self.lin(self.cached_result)
return x
def message(self, x_j, norm):
return norm.view(-1, 1) * x_j
def __repr__(self):
return '{}({}, {}, K={})'.format(self.__class__.__name__,
self.in_channels, self.out_channels,
self.K)