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exp_iso.py
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exp_iso.py
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import os.path as osp
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear
import torch_geometric.transforms as T
from torch_geometric.data import DataLoader
from torch_geometric.nn import (GINConv,global_add_pool,GATConv,ChebConv,GCNConv)
import numpy as np
from libs.spect_conv import SpectConv,ML3Layer
from libs.utils import PlanarSATPairsDataset,SpectralDesign
transform = SpectralDesign(nmax=64,recfield=1,dv=2,nfreq=5,adddegree=True)
dataset = PlanarSATPairsDataset(root="dataset/EXP/",pre_transform=transform)
train_loader = DataLoader(dataset, batch_size=100, shuffle=False)
class PPGN(torch.nn.Module):
def __init__(self,nmax=64,nneuron=20):
super(PPGN, self).__init__()
self.nmax=nmax
self.nneuron=nneuron
ninp=dataset.data.X2.shape[1]
bias=False
self.mlp1_1 = torch.nn.Conv2d(ninp,nneuron,1,bias=bias)
self.mlp1_2 = torch.nn.Conv2d(ninp,nneuron,1,bias=bias)
self.mlp1_3 = torch.nn.Conv2d(nneuron+ninp, nneuron,1,bias=bias)
self.mlp2_1 = torch.nn.Conv2d(nneuron,nneuron,1,bias=bias)
self.mlp2_2 = torch.nn.Conv2d(nneuron,nneuron,1,bias=bias)
self.mlp2_3 = torch.nn.Conv2d(2*nneuron,nneuron,1,bias=bias)
self.mlp3_1 = torch.nn.Conv2d(nneuron,nneuron,1,bias=bias)
self.mlp3_2 = torch.nn.Conv2d(nneuron,nneuron,1,bias=bias)
self.mlp3_3 = torch.nn.Conv2d(2*nneuron,nneuron,1,bias=bias)
self.h1 = torch.nn.Linear(2*3*nneuron, 10)
def forward(self,data):
x=data.X2
M=torch.sum(data.M,(1),True)
x1=F.relu(self.mlp1_1(x)*M)
x2=F.relu(self.mlp1_2(x)*M)
x1x2 = torch.matmul(x1, x2)*M
x=F.relu(self.mlp1_3(torch.cat([x1x2,x],1))*M)
xo1=torch.cat([torch.sum(x*data.M[:,0:1,:,:],(2)),torch.sum(x*data.M[:,1:2,:,:],(2))],1)
x1=F.relu(self.mlp2_1(x)*M)
x2=F.relu(self.mlp2_2(x)*M)
x1x2 = torch.matmul(x1, x2)*M
x=F.relu(self.mlp2_3(torch.cat([x1x2,x],1))*M)
xo2=torch.cat([torch.sum(x*data.M[:,0:1,:,:],(2)),torch.sum(x*data.M[:,1:2,:,:],(2))],1)
x1=F.relu(self.mlp3_1(x)*M)
x2=F.relu(self.mlp3_2(x)*M)
x1x2 = torch.matmul(x1, x2)*M
x=F.relu(self.mlp3_3(torch.cat([x1x2,x],1))*M)
xo3=torch.cat([torch.sum(x*data.M[:,0:1,:,:],(2)),torch.sum(x*data.M[:,1:2,:,:],(2))],1)
x=torch.cat([xo1,xo2,xo3],1)
x=torch.sum(x,2)
x=torch.tanh(self.h1(x))
return x
class GinNet(nn.Module):
def __init__(self):
super(GinNet, self).__init__()
neuron=64
r1=np.random.uniform()
r2=np.random.uniform()
r3=np.random.uniform()
nn1 = Sequential(Linear(dataset.num_features, neuron))
self.conv1 = GINConv(nn1,eps=r1,train_eps=True)
nn2 = Sequential(Linear(neuron, neuron))
self.conv2 = GINConv(nn2,eps=r2,train_eps=True)
nn3 = Sequential(Linear(neuron, neuron))
self.conv3 = GINConv(nn3,eps=r3,train_eps=True)
self.fc1 = torch.nn.Linear(neuron, 10)
def forward(self, data):
x=data.x
edge_index=data.edge_index
x = torch.tanh(self.conv1(x, edge_index))
x = torch.tanh(self.conv2(x, edge_index))
x = torch.tanh(self.conv3(x, edge_index))
x = global_add_pool(x, data.batch)
x = torch.tanh(self.fc1(x))
return x
class GcnNet(nn.Module):
def __init__(self):
super(GcnNet, self).__init__()
neuron=64
self.conv1 = GCNConv(dataset.num_features, neuron, cached=False)
self.conv2 = GCNConv(neuron, neuron, cached=False)
self.conv3 = GCNConv(neuron, neuron, cached=False)
self.fc1 = torch.nn.Linear(neuron, 10)
def forward(self, data):
x=data.x
edge_index=data.edge_index
x = torch.tanh(self.conv1(x, edge_index))
x = torch.tanh(self.conv2(x, edge_index))
x = torch.tanh(self.conv3(x, edge_index))
x = global_add_pool(x, data.batch)
x = torch.tanh(self.fc1(x))
return x
class MlpNet(nn.Module):
def __init__(self):
super(MlpNet, self).__init__()
neuron=64
self.conv1 = torch.nn.Linear(dataset.num_features, neuron)
self.conv2 = torch.nn.Linear(neuron, neuron)
self.conv3 = torch.nn.Linear(neuron, neuron)
self.fc1 = torch.nn.Linear(neuron, 10)
def forward(self, data):
x=data.x
edge_index=data.edge_index
x = torch.tanh(self.conv1(x))
x = torch.tanh(self.conv2(x))
x = torch.tanh(self.conv3(x))
x = global_add_pool(x, data.batch)
x = torch.tanh(self.fc1(x))
return x
class ChebNet(nn.Module):
def __init__(self,S=4):
super(ChebNet, self).__init__()
self.conv1 = ChebConv(dataset.num_features, 32,S)
self.conv2 = ChebConv(32, 64, S)
self.conv3 = ChebConv(64, 64, S)
self.fc1 = torch.nn.Linear(64, 10)
def forward(self, data):
x=data.x
edge_index=data.edge_index
x = torch.tanh(self.conv1(x, edge_index,lambda_max=data.lmax,batch=data.batch))
x = torch.tanh(self.conv2(x, edge_index,lambda_max=data.lmax,batch=data.batch))
x = torch.tanh(self.conv3(x, edge_index,lambda_max=data.lmax,batch=data.batch))
x = global_add_pool(x, data.batch)
x = torch.tanh(self.fc1(x))
return x
class GatNet(nn.Module):
def __init__(self):
super(GatNet, self).__init__()
'''number of param (in+3)*head*out
'''
self.conv1 = GATConv(dataset.num_features, 16, heads=8,concat=True, dropout=0.0)
self.conv2 = GATConv(128, 16, heads=8, concat=True, dropout=0.0)
self.conv3 = GATConv(128, 16, heads=8, concat=True, dropout=0.0)
self.fc1 = torch.nn.Linear(128, 10)
def forward(self, data):
x=data.x
edge_index=data.edge_index
x = torch.tanh(self.conv1(x, edge_index))
x = torch.tanh(self.conv2(x, edge_index))
x = torch.tanh(self.conv3(x, edge_index))
x = global_add_pool(x, data.batch)
x = torch.tanh(self.fc1(x))
return x
class GNNML1(nn.Module):
def __init__(self):
super(GNNML1, self).__init__()
# number of neuron
nout=64
# three part concatenate or sum?
self.concat=False
if self.concat:
nin=3*nout
else:
nin=nout
self.conv11 = SpectConv(dataset.num_features, nout,selfconn=False)
self.conv21 = SpectConv(nin, nout, selfconn=False)
self.conv31 = SpectConv(nin, nout, selfconn=False)
self.fc11 = torch.nn.Linear(dataset.num_features, nout)
self.fc21 = torch.nn.Linear(nin, nout)
self.fc31 = torch.nn.Linear(nin, nout)
self.fc12 = torch.nn.Linear(dataset.num_features, nout)
self.fc22 = torch.nn.Linear(nin, nout)
self.fc32 = torch.nn.Linear(nin, nout)
self.fc13 = torch.nn.Linear(dataset.num_features, nout)
self.fc23 = torch.nn.Linear(nin, nout)
self.fc33 = torch.nn.Linear(nin, nout)
self.fc1 = torch.nn.Linear(nin, 10)
def forward(self, data):
x=data.x
edge_index=data.edge_index
edge_attr=torch.ones(edge_index.shape[1],1).to('cuda')
if self.concat:
x = torch.cat([torch.tanh(self.fc11(x)), torch.tanh(self.conv11(x, edge_index,edge_attr)),torch.tanh(self.fc12(x)*self.fc13(x))],1)
x = torch.cat([torch.tanh(self.fc21(x)), torch.tanh(self.conv21(x, edge_index,edge_attr)),torch.tanh(self.fc22(x)*self.fc23(x))],1)
x = torch.cat([torch.tanh(self.fc31(x)), torch.tanh(self.conv31(x, edge_index,edge_attr)),torch.tanh(self.fc32(x)*self.fc33(x))],1)
else:
x = torch.tanh(self.fc11(x)+self.conv11(x, edge_index,edge_attr)+self.fc12(x)*self.fc13(x))
x = torch.tanh(self.fc21(x)+self.conv21(x, edge_index,edge_attr)+self.fc22(x)*self.fc23(x))
x = torch.tanh(self.fc31(x)+self.conv31(x, edge_index,edge_attr)+self.fc32(x)*self.fc33(x))
x = global_add_pool(x, data.batch)
x = self.fc1(x)
return x
class GNNML3(nn.Module):
def __init__(self):
super(GNNML3, self).__init__()
# number of neuron for for part1 and part2
nout1=32
nout2=16
nin=nout1+nout2
ne=dataset.data.edge_attr2.shape[1]
ninp=dataset.num_features
self.conv1=ML3Layer(learnedge=True,nedgeinput=ne,nedgeoutput=ne,ninp=ninp,nout1=nout1,nout2=nout2)
self.conv2=ML3Layer(learnedge=True,nedgeinput=ne,nedgeoutput=ne,ninp=nin ,nout1=nout1,nout2=nout2)
self.conv3=ML3Layer(learnedge=True,nedgeinput=ne,nedgeoutput=ne,ninp=nin ,nout1=nout1,nout2=nout2)
self.fc1 = torch.nn.Linear(nin, 10)
def forward(self, data):
x=data.x
edge_index=data.edge_index2
edge_attr=data.edge_attr2
x=(self.conv1(x, edge_index,edge_attr))
x=(self.conv2(x, edge_index,edge_attr))
x=(self.conv3(x, edge_index,edge_attr))
x = global_add_pool(x, data.batch)
x = torch.tanh(self.fc1(x))
return x
M=0
for iter in range(0,100):
torch.manual_seed(iter)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# select your model
model = GNNML3().to(device) # GatNet ChebNet GcnNet GinNet MlpNet PPGN GNNML1 GNNML3
embeddings=[]
model.eval()
for data in train_loader:
data = data.to(device)
pre=model(data)
embeddings.append(pre)
E=torch.cat(embeddings).cpu().detach().numpy()
M=M+1*(np.abs(E[0::2]-E[1::2]).sum(1)>0.001)
sm=(M==0).sum()
print('similar:',sm)