models_gcn.py

# -*- coding: utf-8 -*-
"""
Created on Sun Oct 18 21:17:31 2020
@author: Mohammed Amine
"""

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

import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import pickle


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, input, adj):
        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) + ')'


class GCN(nn.Module):
    def __init__(self, nfeat, nhid, nclass, dropout):
        super(GCN, self).__init__()
        self.gc1 = GraphConvolution(nfeat, nhid)
        #print('gc1: ', self.gc1.weight.shape, self.gc1.bias.shape) # gc1:  torch.Size([35, 64]) torch.Size([64])
        self.gc2 = GraphConvolution(nhid, nclass)
        #print('gc2: ', self.gc2.weight.shape, self.gc2.bias.shape) # gc2:  torch.Size([64, 2]) torch.Size([2])
        self.dropout = dropout
        self.LinearLayer = nn.Linear(nfeat,1)
        self.is_trained = False

    def forward(self, x, adj):
        x = F.relu(self.gc1(x, adj))
        x = F.dropout(x, self.dropout, training=self.training)
        x = self.gc2(x, adj)
        x = F.log_softmax(x, dim=1)
        x = self.LinearLayer(torch.transpose(x,0,1))
        if self.is_trained:
          #w_dict = {"w": self.LinearLayer.weight}
          #with open("GCN_W.pickle", 'wb') as f:
          #  pickle.dump(w_dict, f)
          self.is_trained = False
          #print("GCN Weights are saved:")
          #print(self.LinearLayer.weight)
        x = torch.transpose(x,0,1)
        return x
    
    def loss(self, pred, label, type='softmax'):
        # softmax + CE
        
        return F.cross_entropy(pred, label, reduction='mean')