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basic_training.py
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basic_training.py
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import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
from models import *
learning_rate = 0.1
epsilon = 0.0314
k = 7
alpha = 0.00784
file_name = 'basic_training'
device = 'cuda' if torch.cuda.is_available() else 'cpu'
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
])
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=4)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=100, shuffle=False, num_workers=4)
class LinfPGDAttack(object):
def __init__(self, model):
self.model = model
def perturb(self, x_natural, y):
x = x_natural.detach()
x = x + torch.zeros_like(x).uniform_(-epsilon, epsilon)
for i in range(k):
x.requires_grad_()
with torch.enable_grad():
logits = self.model(x)
loss = F.cross_entropy(logits, y)
grad = torch.autograd.grad(loss, [x])[0]
x = x.detach() + alpha * torch.sign(grad.detach())
x = torch.min(torch.max(x, x_natural - epsilon), x_natural + epsilon)
x = torch.clamp(x, 0, 1)
return x
def attack(x, y, model, adversary):
model_copied = copy.deepcopy(model)
model_copied.eval()
adversary.model = model_copied
adv = adversary.perturb(x, y)
return adv
net = ResNet18()
net = net.to(device)
net = torch.nn.DataParallel(net)
cudnn.benchmark = True
adversary = LinfPGDAttack(net)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9, weight_decay=0.0002)
def train(epoch):
print('\n[ Train epoch: %d ]' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(train_loader):
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
benign_outputs = net(inputs)
loss = criterion(benign_outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = benign_outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
if batch_idx % 10 == 0:
print('\nCurrent batch:', str(batch_idx))
print('Current benign train accuracy:', str(predicted.eq(targets).sum().item() / targets.size(0)))
print('Current benign train loss:', loss.item())
print('\nTotal benign train accuarcy:', 100. * correct / total)
print('Total benign train loss:', train_loss)
def test(epoch):
print('\n[ Test epoch: %d ]' % epoch)
net.eval()
benign_loss = 0
adv_loss = 0
benign_correct = 0
adv_correct = 0
total = 0
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(test_loader):
inputs, targets = inputs.to(device), targets.to(device)
total += targets.size(0)
outputs = net(inputs)
loss = criterion(outputs, targets)
benign_loss += loss.item()
_, predicted = outputs.max(1)
benign_correct += predicted.eq(targets).sum().item()
if batch_idx % 10 == 0:
print('\nCurrent batch:', str(batch_idx))
print('Current benign test accuracy:', str(predicted.eq(targets).sum().item() / targets.size(0)))
print('Current benign test loss:', loss.item())
adv = adversary.perturb(inputs, targets)
adv_outputs = net(adv)
loss = criterion(adv_outputs, targets)
adv_loss += loss.item()
_, predicted = adv_outputs.max(1)
adv_correct += predicted.eq(targets).sum().item()
if batch_idx % 10 == 0:
print('Current adversarial test accuracy:', str(predicted.eq(targets).sum().item() / targets.size(0)))
print('Current adversarial test loss:', loss.item())
print('\nTotal benign test accuarcy:', 100. * benign_correct / total)
print('Total adversarial test Accuarcy:', 100. * adv_correct / total)
print('Total benign test loss:', benign_loss)
print('Total adversarial test loss:', adv_loss)
state = {
'net': net.state_dict()
}
if not os.path.isdir('checkpoint'):
os.mkdir('checkpoint')
torch.save(state, './checkpoint/' + file_name)
print('Model Saved!')
def adjust_learning_rate(optimizer, epoch):
lr = learning_rate
if epoch >= 100:
lr /= 10
if epoch >= 150:
lr /= 10
for param_group in optimizer.param_groups:
param_group['lr'] = lr
for epoch in range(0, 200):
adjust_learning_rate(optimizer, epoch)
train(epoch)
test(epoch)