Source code for torch_geometric.nn.dense.dense_gcn_conv

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})')