layers.py

import math

import torch

from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
from torch.nn import init
import torch.nn.functional as F


class GraphConvolution(Module):
    """
    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
    """

    def __init__(self, in_features, out_features, nfeat, bias=True):
        super(GraphConvolution, self).__init__()
        self.original_feat_size = nfeat
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.FloatTensor(in_features, out_features))
        if bias:
            self.bias = Parameter(torch.FloatTensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        if self.weight is not None:
            init.xavier_uniform_(self.weight)
        if self.bias is not None:
            init.zeros_(self.bias)

    def forward(self, input, adj, h):
        support = torch.mm(input, self.weight)
        output = torch.spmm(adj, support)

        if self.bias is not None:
            return output + self.bias
        else:
            return output

    def __repr__(self):
        return self.__class__.__name__ + ' (' \
               + str(self.in_features) + ' -> ' \
               + str(self.out_features) + ')'