Source code for torch_geometric.nn.conv.gcn_conv

import torch
from torch.nn import Parameter
from torch_scatter import scatter_add
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import add_remaining_self_loops

from ..inits import glorot, zeros

[docs]class GCNConv(MessagePassing): r"""The graph convolutional operator from the `"Semi-supervised Classification with Graph Convolutional Networks" <>`_ paper .. math:: \mathbf{X}^{\prime} = \mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}} \mathbf{\hat{D}}^{-1/2} \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. improved (bool, optional): If set to :obj:`True`, the layer computes :math:`\mathbf{\hat{A}}` as :math:`\mathbf{A} + 2\mathbf{I}`. (default: :obj:`False`) cached (bool, optional): If set to :obj:`True`, the layer will cache the computation of :math:`\mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}} \mathbf{\hat{D}}^{-1/2}` 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, improved=False, cached=False, bias=True, **kwargs): super(GCNConv, self).__init__(aggr='add', **kwargs) self.in_channels = in_channels self.out_channels = out_channels self.improved = improved self.cached = cached self.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) zeros(self.bias) self.cached_result = None self.cached_num_edges = None
[docs] @staticmethod def norm(edge_index, num_nodes, edge_weight=None, improved=False, dtype=None): if edge_weight is None: edge_weight = torch.ones((edge_index.size(1), ), dtype=dtype, device=edge_index.device) fill_value = 1 if not improved else 2 edge_index, edge_weight = add_remaining_self_loops( edge_index, edge_weight, fill_value, num_nodes) row, col = edge_index deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes) deg_inv_sqrt = deg.pow(-0.5) deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0 return edge_index, deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
[docs] def forward(self, x, edge_index, edge_weight=None): """""" x = torch.matmul(x, self.weight) 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 or self.cached_result is None: self.cached_num_edges = edge_index.size(1) edge_index, norm = self.norm(edge_index, x.size(0), edge_weight, self.improved, x.dtype) self.cached_result = edge_index, norm edge_index, norm = self.cached_result return self.propagate(edge_index, x=x, norm=norm)
def message(self, x_j, norm): return norm.view(-1, 1) * x_j def update(self, aggr_out): if self.bias is not None: aggr_out = aggr_out + self.bias return aggr_out def __repr__(self): return '{}({}, {})'.format(self.__class__.__name__, self.in_channels, self.out_channels)