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mnist_code.py
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from torch_geometric.data import DataLoader
import torch
import scipy.io as sio
from torch_geometric.data.data import Data
import numpy as np
import os.path as osp
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import (NNConv, graclus, max_pool, max_pool_x,GINConv,global_add_pool,
global_mean_pool,GATConv,ChebConv,GCNConv)
from torch_geometric.datasets import MNISTSuperpixels
from utils import DegreeMaxEigTransform
#select if node degree and location of superpixel region would be used by model or not.
#after any chnageing please remove MNIST/processed folder in order to preprocess changes again.
transform=DegreeMaxEigTransform(adddegree=True,addposition=False)
train_dataset = MNISTSuperpixels(root='dataset/MNIST/', train=True, pre_transform=transform)
test_dataset = MNISTSuperpixels(root='dataset/MNIST/', train=False, pre_transform=transform)
train_loader = DataLoader(train_dataset[0:55000], batch_size=64, shuffle=True)
val_loader = DataLoader(train_dataset[55000:60000], batch_size=64, shuffle=False)
test_loader = DataLoader(test_dataset[0:10000], batch_size=64, shuffle=False)
trsize=55000
tsize=10000
vsize=5000
class GcnNet(nn.Module):
def __init__(self):
super(GcnNet, self).__init__()
ninp=train_dataset.num_features
nout=train_dataset.num_classes
nn=64
self.conv1 = GCNConv(ninp, nn, cached=False)
self.conv2 = GCNConv(nn, nn, cached=False)
self.conv3 = GCNConv(nn, nn, cached=False)
self.bn1 = torch.nn.BatchNorm1d(nn)
self.fc1 = torch.nn.Linear(nn, 32)
self.fc2 = torch.nn.Linear(32, nout)
def forward(self, data):
x=data.x
edge_index=data.edge_index
x = F.relu(self.conv1(x, edge_index))
x = F.relu(self.conv2(x, edge_index))
x = F.relu(self.conv3(x, edge_index))
x = global_mean_pool(x, data.batch)
x=self.bn1(x)
x = F.relu(self.fc1(x))
return F.log_softmax(self.fc2(x), dim=1)
class GatNet(nn.Module):
def __init__(self):
super(GatNet, self).__init__()
ninp=train_dataset.num_features
nout=train_dataset.num_classes
self.conv1 = GATConv(ninp, 8, heads=8, dropout=0.0)
self.conv2 = GATConv(8 * 8, 16, heads=8, concat=True, dropout=0.0)
self.conv3 = GATConv(8 * 16, 16, heads=8, concat=True, dropout=0.0)
self.bn1 = torch.nn.BatchNorm1d(128)
self.fc1 = torch.nn.Linear(128, 32)
self.fc2 = torch.nn.Linear(32, nout)
def forward(self, data):
x=data.x
x = F.dropout(x, p=0.1, training=self.training)
x = F.elu(self.conv1(x, data.edge_index))
x = F.dropout(x, p=0.1, training=self.training)
x = F.elu(self.conv2(x, data.edge_index))
x = F.dropout(x, p=0.1, training=self.training)
x = F.elu(self.conv3(x, data.edge_index))
x = global_mean_pool(x, data.batch)
x=self.bn1(x)
x = F.relu(self.fc1(x))
#x = F.dropout(x, training=self.training)
return F.log_softmax(self.fc2(x), dim=1)
class ChebNet(nn.Module):
def __init__(self):
super(ChebNet, self).__init__()
S=5
ninp=train_dataset.num_features
nout=train_dataset.num_classes
self.conv1 = ChebConv(ninp, 64,S)
self.conv2 = ChebConv(64, 128, S)
self.conv3 = ChebConv(128, 128, S)
self.bn1 = torch.nn.BatchNorm1d(128)
self.fc1 = torch.nn.Linear(128, 32)
self.fc2 = torch.nn.Linear(32, nout) #int(d.num_classes))
def forward(self, data):
x=data.x
edge_index=data.edge_index
x = F.dropout(x, p=0.1, training=self.training)
#x = F.relu(self.conv1(x, edge_index))
x = F.relu(self.conv1(x, edge_index,lambda_max=data.lmax,batch=data.batch))
x = F.dropout(x, p=0.1, training=self.training)
#x = F.relu(self.conv2(x, edge_index))
x = F.relu(self.conv2(x, edge_index,lambda_max=data.lmax,batch=data.batch))
x = F.dropout(x, p=0.1, training=self.training)
#x = F.relu(self.conv3(x, edge_index))
x = F.relu(self.conv3(x, edge_index,lambda_max=data.lmax,batch=data.batch))
x = global_mean_pool(x, data.batch)
x=self.bn1(x)
x = F.relu(self.fc1(x))
return F.log_softmax(self.fc2(x), dim=1)
class GinNet(nn.Module):
def __init__(self):
super(GinNet, self).__init__()
ninp=train_dataset.num_features
nout=train_dataset.num_classes
nn1 = Sequential(Linear(ninp, 64), ReLU(), Linear(64, 64))
self.conv1 = GINConv(nn1,train_eps=True)
self.bn1 = torch.nn.BatchNorm1d(64)
nn2 = Sequential(Linear(64, 64), ReLU(), Linear(64, 64))
self.conv2 = GINConv(nn2,train_eps=True)
self.bn2 = torch.nn.BatchNorm1d(64)
nn3 = Sequential(Linear(64, 64), ReLU(), Linear(64, 64))
self.conv3 = GINConv(nn3,train_eps=True)
self.bn3 = torch.nn.BatchNorm1d(64)
self.fc1 = torch.nn.Linear(64, 32)
self.fc2 = torch.nn.Linear(32, nout)
def forward(self, data):
x=data.x
edge_index=data.edge_index
x = F.relu(self.conv1(x, edge_index))
x = self.bn1(x)
x = F.relu(self.conv2(x, edge_index))
x = self.bn2(x)
x = F.relu(self.conv3(x, edge_index))
x = global_mean_pool(x, data.batch)
x = self.bn3(x)
x = F.elu(self.fc1(x))
#x = F.dropout(x, training=self.training)
return F.log_softmax(self.fc2(x), dim=1)
class MlpNet(nn.Module):
def __init__(self):
super(MlpNet, self).__init__()
ninp=train_dataset.num_features
nout=train_dataset.num_classes
self.conv1 = torch.nn.Linear(ninp, 64)
self.conv2 = torch.nn.Linear(64, 64)
self.conv3 = torch.nn.Linear(64, 64)
self.bn1 = torch.nn.BatchNorm1d(64)
self.fc1 = torch.nn.Linear(64, 32)
self.fc2 = torch.nn.Linear(32, nout)
def forward(self, data):
x=data.x
edge_index=data.edge_index
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = F.relu(self.conv3(x))
x = global_mean_pool(x, data.batch)
x=self.bn1(x)
x = F.relu(self.fc1(x))
return F.log_softmax(self.fc2(x), dim=1)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = ChebNet().to(device) # GcnNet GatNet ChebNet GinNet MlpNet
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
def train(epoch):
model.train()
L=0
correct = 0
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
pred = model(data)
lss=F.nll_loss(pred, data.y,reduction='sum')
lss.backward()
L+=lss.cpu().detach().numpy()
optimizer.step()
pred = pred.max(1)[1]
correct += pred.eq(data.y).sum().item()
s1= correct / trsize
return L/trsize,s1
def test():
model.eval()
correct = 0
Lt=0
for data in test_loader:
data = data.to(device)
pred = model(data)
lss=F.nll_loss(pred, data.y,reduction='sum')
Lt+=lss.cpu().detach().numpy()
pred = pred.max(1)[1]
correct += pred.eq(data.y).sum().item()
s1= correct / tsize
Lt=Lt/tsize
correct = 0
Lv=0
for data in val_loader:
data = data.to(device)
pred = model(data)
lss=F.nll_loss(pred, data.y,reduction='sum')
Lv+=lss.cpu().detach().numpy()
pred = pred.max(1)[1]
correct += pred.eq(data.y).sum().item()
s2= correct / vsize
Lv=Lv/vsize
return s1,Lt,s2,Lv
bval=0
btest=0
for epoch in range(1, 3001):
trloss,tr_acc=train(epoch)
test_acc,tloss,val_acc,vloss = test()
if bval<val_acc:
bval=val_acc
btest=test_acc
print('Epoch: {:02d}, train: {:.4f},{:.4f}, Val: {:.4f},{:.4f}, Test: {:.4f}, {:.4f} besttest:{:.4f} '.format(epoch,trloss,tr_acc,vloss,val_acc,tloss, test_acc,btest))