/
models.py
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/
models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
class GraphConvolution(nn.Module):
def __init__(self, input_size, output_size, bias=True):
super(GraphConvolution, self).__init__()
self.fc = nn.Linear(input_size, output_size, bias=bias)
def forward(self, input, propagation_adj):
support = self.fc(input)
# CAUTION: Pytorch only supports sparse * dense matrix multiplication
# CAUTION: Pytorch does not support sparse * sparse matrix multiplication !!!
output = torch.sparse.mm(propagation_adj, support)
return output
class GCN(nn.Module):
def __init__(self, input_size, hidden_size, num_classes, dropout=0, num_middle_layers=0):
super(GCN, self).__init__()
self.gc1 = GraphConvolution(input_size, hidden_size)
self.gc2 = GraphConvolution(hidden_size, num_classes)
self.dropout = dropout
self.conv_middle= nn.ModuleList([GraphConvolution(hidden_size, hidden_size) for i in range(num_middle_layers)])
def forward(self, x, propagation_adj):
x = F.dropout(x, self.dropout, training=self.training) # drop out for input
x = self.gc1.forward(x, propagation_adj)
x = F.relu(x)
for conv_middle in self.conv_middle:
x = F.dropout(x, self.dropout, training=self.training) # drop out for input
x = conv_middle.forward(x, propagation_adj)
x = F.relu(x)
x = F.dropout(x, self.dropout, training=self.training) # drop out for hidden layers
x = self.gc2.forward(x, propagation_adj)
return x
class DeepGCN(nn.Module):
def __init__(self, input_size, hidden_size, num_classes, dropout=0, num_middle_layers=0):
super(DeepGCN, self).__init__()
self.conv1 = GraphConvolution(input_size, hidden_size)
self.conv_middle = nn.ModuleList([GraphConvolution(hidden_size, hidden_size) for i in range(num_middle_layers)])
self.conv2 = GraphConvolution(hidden_size, num_classes)
self.dropout = dropout
self.dropout_layer = nn.Dropout(self.dropout)
self.gc1Linear = nn.Linear(input_size, hidden_size, bias=False)
self.gc2Linear = nn.Linear(hidden_size, num_classes, bias=False)
self.time_step = nn.Parameter(torch.FloatTensor([0.1])) # [0.01])) # np.random.rand(1) *
# self.bn1 = nn.BatchNorm1d(num_features=hidden_size)
# self.bn2 = nn.BatchNorm1d(num_features=num_classes)
def forward(self, x, propagation_adj):
"""
:param x:
:param propagation_adj:
:return: x(t+\delta) = x(t) + f(x(t)) * \delta
"""
# x = F.dropout(x, self.dropout, training=self.training) # drop out for input
# x = self.gc1.forward(x, propagation_adj)
# residual = self.gc1Linear.forward(x)
# x = F.dropout(x, self.dropout, training=self.training) # drop out for input
# x = F.dropout(x, 0.2, training=self.training) # drop out for input
x = self.dropout_layer(x)
x = self.conv1.forward(x, propagation_adj)
# x = self.bn1(x)
x = F.relu(x)
# x += residual
# x = x * self.time_step + residual
for conv_middle in self.conv_middle:
f = F.dropout(x, self.dropout, training=self.training) # drop out for input
f = conv_middle.forward(f, propagation_adj)
f = F.relu(f)
x = x + f * self.time_step
# x = self.gc2Linear.forward(x)
# f = self.gc2Linear.forward(x)
x = self.dropout_layer(x)
# x = F.dropout(x, self.dropout, training=self.training) # drop out for hidden layers
x = self.conv2.forward(x, propagation_adj)
# x = F.relu(x)
# x = self.bn2(x)
# x = self.gc2Linear.forward(x)
# x += f
# x = x + f * self.time_step
return x
class DeepGCN2(nn.Module):
def __init__(self, filter_matrix, input_size, hidden_size, num_classes, dropout=0, num_middle_layers=0):
super(DeepGCN2, self).__init__()
self.filter_matrix = filter_matrix
self.linear1 = nn.Linear(input_size, hidden_size, bias=True)
# self.dropout = dropout
self.dropout_layer = nn.Dropout(dropout)
self.linear2 = nn.Linear(hidden_size, num_classes, bias=True)
self.time_step = nn.Parameter(torch.FloatTensor([0.1])) # [0.01])) # np.random.rand(1) *
def forward(self, x, propagation_adj):
"""
:param x:
:param propagation_adj:
:return: x(t+\delta) = x(t) + f(x(t)) * \delta
"""
x = torch.sparse.mm(self.filter_matrix, x)
x = self.dropout_layer(x)
x = self.linear1(x)
x = F.relu(x)
# for conv_middle in self.conv_middle:
# f = F.dropout(x, self.dropout, training=self.training) # drop out for input
# f = conv_middle.forward(f, propagation_adj)
# f = F.relu(f)
# x = x + f * self.time_step
x = torch.sparse.mm(self.filter_matrix, x)
x = self.dropout_layer(x)
x = self.linear2(x)
# x = F.relu(x)
return x
class DeepGCN3(nn.Module):
def __init__(self, input_size, hidden_size, num_classes, num_nodes, dropout=0, num_middle_layers=0):
super(DeepGCN3, self).__init__()
# self.filter_matrix = filter_matrix
self.linear1 = nn.Linear(input_size, hidden_size, bias=True)
# self.dropout = dropout
self.conv_middle = nn.ModuleList([nn.Linear(hidden_size, hidden_size, bias=False) for i in range(num_middle_layers)])
self.dropout_layer = nn.Dropout(dropout)
self.linear2 = nn.Linear(hidden_size, num_classes, bias=True)
self.linear_oldDim = nn.Linear(input_size, num_classes, bias=True)
self.time_step = nn.Parameter(torch.FloatTensor([0.1])) # [0.01])) # np.random.rand(1) *
#self.A = adj.to_dense()
self.AW = nn.Parameter(torch.FloatTensor(torch.rand(num_nodes, num_nodes)))
# indices = torch.from_numpy(
# np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64))
# values = torch.from_numpy(sparse_mx.data)
# shape = torch.Size(sparse_mx.shape)
# torch.sparse.FloatTensor(indices, values, shape)
def forward(self, x, propagation_adj):
"""
:param x:
:param propagation_adj:
:return: x(t+\delta) = x(t) + f(x(t)) * \delta
"""
# x = torch.sparse.mm(self.filter_matrix, x)
# x = self.dropout_layer(x)
x = self.linear1(x)
# x = F.relu(x)
A = self.AW * propagation_adj
D = torch.diag(A.sum(1))
L = A - D
for conv_middle in self.conv_middle:
# f = torch.sparse.mm(propagation_adj, x)
f = torch.mm(L, x)
# f = self.dropout_layer(f) # drop out for input
# f = conv_middle.forward(f)
f = F.relu(f)
# f = conv_middle.forward(f)
# f = torch.sparse.mm(propagation_adj, f)
# f = self.dropout_layer(f) # drop out for input
# f = F.relu(f)
x = x + f * self.time_step
# x = conv_middle.forward(x)
# x = F.relu(x)
# x = conv_middle.forward(x)
# x = F.relu(x)
# x = torch.sparse.mm(self.filter_matrix, x)
# x = self.dropout_layer(x)
x = self.linear2(x)
# x = self.linear_oldDim(x)
# x = F.relu(x)
return x
class DiagLinear(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, n_features, bias=True):
super(DiagLinear, self).__init__()
self.n_features = n_features
self.weight = nn.Parameter(torch.FloatTensor(n_features))
if bias:
self.bias = nn.Parameter(torch.FloatTensor(n_features))
else:
self.register_parameter('bias', None)
self.reset_parameters()
# self.weight = nn.Parameter(torch.diag(self.weight))
# print(self.weight.shape)
def reset_parameters(self):
stdv = 1. / math.sqrt(self.n_features)
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward(self, input):
output = torch.spmm(input, torch.diag(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 DeepGCN4(nn.Module):
def __init__(self, input_size, hidden_size, num_classes, dropout=0, num_middle_layers=0):
super(DeepGCN4, self).__init__()
# self.filter_matrix = filter_matrix
self.linear1 = nn.Linear(input_size, hidden_size, bias=True)
# self.dropout = dropout
self.conv_middle = nn.ModuleList(
[DiagLinear(hidden_size, bias=False) for i in range(num_middle_layers)])
# self.conv_middle = nn.ModuleList(
# [nn.Linear(hidden_size, hidden_size, bias=True) for i in range(num_middle_layers)])
self.dropout_layer = nn.Dropout(dropout)
self.linear2 = nn.Linear(hidden_size, num_classes, bias=True)
self.linear_oldDim = nn.Linear(input_size, num_classes, bias=True)
self.time_step = nn.Parameter(torch.FloatTensor([0.1])) # [0.01])) # np.random.rand(1) *
self.time_step_list = nn.Parameter(torch.FloatTensor([0.1 for i in range(num_middle_layers)])) # [0.01])) # np.random.rand(1) *
def forward(self, x, propagation_adj):
"""
:param x:
:param propagation_adj:
:return: x(t+\delta) = x(t) + f(x(t)) * \delta
"""
# x = torch.sparse.mm(propagation_adj, x)
# x = self.dropout_layer(x)
x = self.linear1(x)
x = F.relu(x)
# x = torch.sigmoid(x)
nl = 0
for conv_middle in self.conv_middle:
f = torch.sparse.mm(propagation_adj, x)
f = self.dropout_layer(f) # drop out for input
# f = conv_middle.forward(f)
f = F.relu(f)
# f = F.tanh(f)
# f = torch.sigmoid(f)
# f = conv_middle.forward(f)
# f = torch.sparse.mm(propagation_adj, f)
# f = self.dropout_layer(f) # drop out for input
# f = F.relu(f)
# x = x + f * self.time_step
x = x + f * self.time_step_list[nl]
# x = f
nl += 1
# x = torch.sparse.mm(self.filter_matrix, x)
# x = self.dropout_layer(x)
x = self.linear2(x)
# x = self.linear_oldDim(x)
# x = F.relu(x)
return x