import torch
from torch import Tensor
from torch.nn import Parameter
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.nn.inits import zeros
from torch_geometric.typing import OptTensor
[docs]class DenseGCNConv(torch.nn.Module):
r"""See :class:`torch_geometric.nn.conv.GCNConv`."""
def __init__(
self,
in_channels: int,
out_channels: int,
improved: bool = False,
bias: bool = True,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.improved = improved
self.lin = Linear(in_channels, out_channels, bias=False,
weight_initializer='glorot')
if bias:
self.bias = Parameter(torch.empty(out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
[docs] def reset_parameters(self):
r"""Resets all learnable parameters of the module."""
self.lin.reset_parameters()
zeros(self.bias)
[docs] def forward(self, x: Tensor, adj: Tensor, mask: OptTensor = None,
add_loop: bool = True) -> Tensor:
r"""Forward pass.
Args:
x (torch.Tensor): Node feature tensor
:math:`\mathbf{X} \in \mathbb{R}^{B \times N \times F}`, with
batch-size :math:`B`, (maximum) number of nodes :math:`N` for
each graph, and feature dimension :math:`F`.
adj (torch.Tensor): Adjacency tensor
:math:`\mathbf{A} \in \mathbb{R}^{B \times N \times N}`.
The adjacency tensor is broadcastable in the batch dimension,
resulting in a shared adjacency matrix for the complete batch.
mask (torch.Tensor, optional): Mask matrix
:math:`\mathbf{M} \in {\{ 0, 1 \}}^{B \times N}` indicating
the valid nodes for each graph. (default: :obj:`None`)
add_loop (bool, optional): If set to :obj:`False`, the layer will
not automatically add self-loops to the adjacency matrices.
(default: :obj:`True`)
"""
x = x.unsqueeze(0) if x.dim() == 2 else x
adj = adj.unsqueeze(0) if adj.dim() == 2 else adj
B, N, _ = adj.size()
if add_loop:
adj = adj.clone()
idx = torch.arange(N, dtype=torch.long, device=adj.device)
adj[:, idx, idx] = 1 if not self.improved else 2
out = self.lin(x)
deg_inv_sqrt = adj.sum(dim=-1).clamp(min=1).pow(-0.5)
adj = deg_inv_sqrt.unsqueeze(-1) * adj * deg_inv_sqrt.unsqueeze(-2)
out = torch.matmul(adj, out)
if self.bias is not None:
out = out + self.bias
if mask is not None:
out = out * mask.view(B, N, 1).to(x.dtype)
return out
def __repr__(self) -> str:
return (f'{self.__class__.__name__}({self.in_channels}, '
f'{self.out_channels})')