gcn_conv.py

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 remove_self_loops, add_self_loops, softmax
from torch_geometric.nn.inits import glorot, zeros
import pdb

class GCNConv(MessagePassing):
    
    def __init__(self,
                 in_channels,
                 out_channels,
                 improved=False,
                 cached=False,
                 bias=True,
                 edge_norm=True,
                 gfn=False):
        super(GCNConv, self).__init__('add')

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.improved = improved
        self.cached = cached
        self.cached_result = None
        self.edge_norm = edge_norm
        self.gfn = gfn
        self.message_mask = None
        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()

    def reset_parameters(self):
        glorot(self.weight)
        zeros(self.bias)
        self.cached_result = None

    @staticmethod
    def norm(edge_index, num_nodes, edge_weight, improved=False, dtype=None):
        if edge_weight is None:
            edge_weight = torch.ones((edge_index.size(1), ),
                                     dtype=dtype,
                                     device=edge_index.device)
        
        edge_weight = edge_weight.view(-1)
        
        
        assert edge_weight.size(0) == edge_index.size(1)
        
        edge_index, edge_weight = remove_self_loops(edge_index, edge_weight)
        edge_index, _ = add_self_loops(edge_index, num_nodes=num_nodes)
        # Add edge_weight for loop edges.
        loop_weight = torch.full((num_nodes, ),
                                 1 if not improved else 2,
                                 dtype=edge_weight.dtype,
                                 device=edge_weight.device)
        edge_weight = torch.cat([edge_weight, loop_weight], dim=0)

        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]

    def forward(self, x, edge_index, edge_weight=None):
        """"""
        
        x = torch.matmul(x, self.weight)
        if self.gfn:
            return x
    
        if not self.cached or self.cached_result is None:
            if self.edge_norm:
                edge_index, norm = GCNConv.norm(
                    edge_index, 
                    x.size(0), 
                    edge_weight, 
                    self.improved, 
                    x.dtype)
            else:
                norm = None
            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):

        if self.edge_norm:
            return norm.view(-1, 1) * x_j
        else:
            return 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)