layers.py

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


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

    def __init__(self, in_features, out_features):
        super(GraphConvolution, self).__init__()

        self.linear = nn.Linear(in_features, out_features)

    def forward(self, x, adj):
        y = x
        y = torch.mm(adj, self.linear(y))

        return y


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

#     def __init__(self, in_features, out_features, bias=True):
#         super(GraphConvolution, self).__init__()
#         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):
#         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, in_put, adj):
#         # # input('enter for input')
#         # print(in_put, in_put.shape)
#         # input('enter for weight')
#         # print(self.weight, self.weight.shape)
#         support = torch.mm(in_put, self.weight)
#         # input('enter for support')
#         # print(support, support.shape) 
#         output = torch.mm(adj, support)
#         # input('enter for output')
#         # print(output, output.shape)
#         # print(support.sum())
#         # input('continue')
#         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) + ')'