sparsegnc_layer.py

import math
import torch
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module

class sparsegcn(Module):
    """
    Sparse GCN layer
    """

    def __init__(self, in_features, out_features, bias=False):
        super(sparsegcn, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.FloatTensor(in_features, in_features))
        if bias:
            self.bias = Parameter(torch.FloatTensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        ##kaiming_uniform
        stdv = 1. / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward(self, input, gene_adj):
        output = torch.mm(input, (self.weight * gene_adj))
        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) + ')'