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models9.py
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models9.py
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#coding:utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
from layers import SpGraphAttentionLayer,GraphAttentionLayer,GraphConvolution
from layers import GraphAttentionLayer_AGCN, GraphLayer2_noAttention
import numpy as np
class AGCN(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
"""Dense version of GAT."""
super(AGCN, self).__init__()
self.dropout = dropout
self.gc1 = GraphConvolution(nfeat,nhid)
#self.gc2 = GraphConvolution(1000, 200)
#self.gc3 = GraphConvolution(200, nhid)
self.gc4 = GraphConvolution(nhid, nclass)
self.attentions = GraphAttentionLayer_AGCN(nfeat, nhid, dropout=dropout, alpha=alpha, concat=True)
#for i, attention in enumerate(self.attentions):
# self.add_module('attention_{}'.format(i), attention)
self.out_att = GraphLayer2_noAttention(nhid * nheads, nclass, dropout=dropout, alpha=alpha, concat=False)
def forward(self, x, adj):
x,attention_adj=self.attentions(x, adj)
x = F.dropout(x, self.dropout, training=self.training)
x = F.elu(self.out_att(x, attention_adj))#attention_adj
return F.log_softmax(x, dim=1)
class GAT(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
"""Dense version of GAT."""
super(GAT, self).__init__()
self.dropout = dropout
self.attentions = [GraphAttentionLayer(nfeat, nhid, dropout=dropout, alpha=alpha, concat=True) for _ in range(nheads)]
for i, attention in enumerate(self.attentions):
self.add_module('attention_{}'.format(i), attention)
self.out_att = GraphAttentionLayer(nhid * nheads, nclass, dropout=dropout, alpha=alpha, concat=False)
def forward(self, x, adj):
x = torch.cat([att(x, adj) for att in self.attentions], dim=1)
x = F.dropout(x, self.dropout, training=self.training)
x = self.out_att(x, adj)
return F.log_softmax(x, dim=1)
class SpGAT(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads):
"""Sparse version of GAT."""
super(SpGAT, self).__init__()
self.dropout = dropout
self.attentions = [SpGraphAttentionLayer(nfeat,
nhid,
dropout=dropout,
alpha=alpha,
concat=True) for _ in range(nheads)]
for i, attention in enumerate(self.attentions):
self.add_module('attention_{}'.format(i), attention)
self.out_att = SpGraphAttentionLayer(nhid * nheads,
nclass,
dropout=dropout,
alpha=alpha,
concat=False)
def forward(self, x, adj):
x = F.dropout(x, self.dropout, training=self.training)
x = torch.cat([att(x, adj) for att in self.attentions], dim=1)
x = F.dropout(x, self.dropout, training=self.training)
x = self.out_att(x, adj)
return F.log_softmax(x, dim=1)
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.netd = nn.Sequential(
nn.Conv2d(in_channels=1,out_channels=20,kernel_size=3,stride=1,bias=True),
nn.BatchNorm2d(20),
nn.LeakyReLU(0.2,inplace=False),
nn.Conv2d(20,40,3,2,bias=True),
nn.BatchNorm2d(40),
nn.LeakyReLU(0.2,inplace=False),
nn.Conv2d(40,80,3,2,bias=True),
nn.BatchNorm2d(80),
nn.LeakyReLU(0.2,inplace=False),
nn.MaxPool2d(2)#14*14-12*12-6*6
)#31*31-28*28-14*14
self.line = nn.Sequential(
nn.Linear(720,100,bias=True),
nn.BatchNorm1d(100),
nn.LeakyReLU(0.2,inplace=False)
)
self.classfier = nn.Sequential(
nn.Linear(100,2,bias=True),
#nn.BatchNorm1d(10),
#nn.LogSoftmax()
)
def forward(self, x):
x = self.netd(x)
x = x.view(x.size(0), -1)
fea = self.line(x)
x = self.classfier(fea)
return F.log_softmax(x, dim=1)#,fea
class CNN_fea(nn.Module):
def __init__(self):
super(CNN_fea, self).__init__()
self.netd = nn.Sequential(
nn.Conv2d(in_channels=1,out_channels=20,kernel_size=3,stride=1,bias=True),
nn.BatchNorm2d(20),
nn.LeakyReLU(0.2,inplace=False),
nn.Conv2d(20,40,3,2,bias=True),
nn.BatchNorm2d(40),
nn.LeakyReLU(0.2,inplace=False),
nn.Conv2d(40,80,3,2,bias=True),
nn.BatchNorm2d(80),
nn.LeakyReLU(0.2,inplace=False),
nn.MaxPool2d(2)#14*14-12*12-6*6
)#31*31-28*28-14*14
self.line = nn.Sequential(
nn.Linear(720,100,bias=True),
nn.BatchNorm1d(100),
nn.LeakyReLU(0.2,inplace=False)
)
self.classfier = nn.Sequential(
nn.Linear(100,2,bias=True),
#nn.BatchNorm1d(10),
#nn.LogSoftmax()
)
def forward(self, x):
x = self.netd(x)
x = x.view(x.size(0), -1)
fea = self.line(x)
x = self.classfier(fea)
return F.log_softmax(x, dim=1),fea
class GCN(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout):
super(GCN, self).__init__()
self.gc1 = GraphConvolution(nfeat,nhid)
#self.gc2 = GraphConvolution(1000, 200)
#self.gc3 = GraphConvolution(200, nhid)
self.gc4 = GraphConvolution(nhid, nclass)
self.dropout = dropout
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)
x = F.relu(self.gc3(x, adj))
x = F.dropout(x, self.dropout, training=self.training)'''
x = F.relu(self.gc4(x, adj))
return F.log_softmax(x, dim=1)