from torch_geometric.typing import Adj, Size, OptTensor
import torch
from torch import Tensor
from torch.nn import Parameter
from torch_sparse import SparseTensor, matmul, set_diag, sum
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import remove_self_loops, add_self_loops, degree
from ..inits import glorot, zeros
[docs]class ClusterGCNConv(MessagePassing):
r"""The ClusterGCN graph convolutional operator from the
`"Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph
Convolutional Networks" <https://arxiv.org/abs/1905.07953>`_ paper
.. math::
\mathbf{X}^{\prime} = \left( \mathbf{\hat{A}} + \lambda \cdot
\textrm{diag}(\mathbf{\hat{A}}) \right) \mathbf{X} \mathbf{W}_1 +
\mathbf{X} \mathbf{W}_2
where :math:`\mathbf{\hat{A}} = {(\mathbf{D} + \mathbf{I})}^{-1}(\mathbf{A}
+ \mathbf{I})`.
Args:
in_channels (int or tuple): Size of each input sample.
out_channels (int): Size of each output sample.
diag_lambda: (float, optional): Diagonal enhancement value
:math:`\lambda`. (default: :obj:`0.`)
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`.
"""
def __init__(self, in_channels: int, out_channels: int,
diag_lambda: float = 0., add_self_loops: bool = True,
bias: bool = True, **kwargs):
super(ClusterGCNConv, self).__init__(aggr='add', **kwargs)
self.in_channels = in_channels
self.out_channels = out_channels
self.diag_lambda = diag_lambda
self.add_self_loops = add_self_loops
self.weight = Parameter(torch.Tensor(in_channels, out_channels))
self.root_weight = Parameter(torch.Tensor(in_channels, out_channels))
if bias:
self.bias = Parameter(torch.Tensor(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
[docs] def reset_parameters(self):
glorot(self.weight)
glorot(self.root_weight)
zeros(self.bias)
[docs] def forward(self, x: Tensor, edge_index: Adj, size: Size = None) -> Tensor:
""""""
edge_weight: OptTensor = None
if isinstance(edge_index, Tensor):
num_nodes = size[1] if size is not None else x.size(self.node_dim)
if self.add_self_loops:
edge_index, _ = remove_self_loops(edge_index)
edge_index, _ = add_self_loops(edge_index, num_nodes=num_nodes)
row, col = edge_index[0], edge_index[1]
deg_inv = 1. / degree(col, num_nodes=num_nodes).clamp_(1.)
edge_weight = deg_inv[col]
edge_weight[row == col] += self.diag_lambda * deg_inv
elif isinstance(edge_index, SparseTensor):
if self.add_self_loops:
edge_index = set_diag(edge_index)
col, row, _ = edge_index.coo() # Transposed.
deg_inv = 1. / sum(edge_index, dim=1).clamp_(1.)
edge_weight = deg_inv[col]
edge_weight[row == col] += self.diag_lambda * deg_inv
edge_index = edge_index.set_value(edge_weight, layout='coo')
# propagate_type: (x: Tensor, edge_weight: OptTensor)
out = self.propagate(edge_index, x=x, edge_weight=edge_weight,
size=None)
out = out @ self.weight + x @ self.root_weight
if self.bias is not None:
out += self.bias
return out
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 matmul(adj_t, x, reduce=self.aggr)
def __repr__(self):
return '{}({}, {}, diag_lambda={})'.format(self.__class__.__name__,
self.in_channels,
self.out_channels,
self.diag_lambda)