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model.py
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model.py
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import math
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.nn.modules.module import Module
class GCN(Module):
"""
Graph Convolutional Network
"""
def __init__(self, in_features, out_features, bias=True):
super(GCN, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = nn.Parameter(torch.FloatTensor(in_features, out_features))
if bias:
self.bias = nn.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 GCNencoder(nn.Module):
"""
Encoder network.
"""
def __init__(self, nfeat, nhid, nout, dropout):
super(GCNencoder, self).__init__()
self.dropout = dropout
self.gc1 = GCN(nfeat, nhid)
self.gc2 = GCN(nhid, nout)
def forward(self, x, adj):
x = F.relu(self.gc1(x, adj))
x = F.dropout(x, self.dropout, training=self.training)
x = F.relu(self.gc2(x, adj))
x = F.dropout(x, self.dropout, training=self.training)
return x
class GCNdecoder(nn.Module):
"""
Decoder network.
"""
def __init__(self, nfeat, nhid, nout, dropout):
super(GCNdecoder, self).__init__()
self.dropout = dropout
self.gc1 = GCN(nfeat, nhid)
self.gc2 = GCN(nhid, nout)
def forward(self, x, adj):
x = F.relu(self.gc1(x, adj))
x = F.dropout(x, self.dropout, training=self.training)
x = F.relu(self.gc2(x, adj))
x = F.dropout(x, self.dropout, training=self.training)
return x
class Discriminator(nn.Module):
"""
Discriminator network with GCN.
"""
def __init__(self, input_size, output_size, dropout):
super(Discriminator, self).__init__()
self.dropout = dropout
self.gc1 = GCN(input_size, 32)
self.gc2 = GCN(32, 16)
self.gc3 = GCN(16, output_size)
def forward(self, x, adj):
x = F.relu(self.gc1(x, adj))
x = F.dropout(x, self.dropout, training=self.training)
x = F.relu(self.gc2(x, adj))
x = F.dropout(x, self.dropout, training=self.training)
a = self.gc3(x, adj)
x = a.view(a.shape[0])
return F.sigmoid(x), F.softmax(x, dim=0)
class Teacher(nn.Module):
"""
Teacher Network that consist of encoder and decoder
"""
def __init__(self, nfeat, nhid1, nhid2, nhid3, nhid4, dropout):
super(Teacher, self).__init__()
self.dropout = dropout
self.encoder = GCNencoder(nfeat, nhid1, nhid2, dropout)
self.decoder = GCNdecoder(nhid2, nhid3, nhid4, dropout)
def forward(self, x, adj):
embedding = self.encoder.forward(x, adj)
super_resolved_matrix = self.decoder.forward(embedding, adj)
return embedding, super_resolved_matrix
class Student(nn.Module):
"""
Student Network that consist of encoder and decoder
"""
def __init__(self, nfeat, nhid1, nhid2, nhid3, nhid4, dropout):
super(Student, self).__init__()
self.dropout = dropout
self.encoder = GCNencoder(nfeat, nhid1, nhid2, dropout)
self.decoder = GCNdecoder(nhid2, nhid3, nhid4, dropout)
def forward(self, x, adj):
embedding = self.encoder.forward(x, adj)
super_resolved_matrix = self.decoder.forward(embedding, adj)
return embedding, super_resolved_matrix