def run():
torch.multiprocessing.freeze_support()
use_cuda=True
image_nc=1
epochs = 60
batch_size = 128
BOX_MIN = 0
BOX_MAX = 1
# Define what device we are using
print("CUDA Available: ",torch.cuda.is_available())
device = torch.device("cuda" if (use_cuda and torch.cuda.is_available()) else "cpu")
pretrained_model = "./MNIST_target_model.pth"
targeted_model = MNIST_target_net().to(device)
targeted_model.load_state_dict(torch.load(pretrained_model))
targeted_model.eval()
model_num_labels = 10
# MNIST train dataset and dataloader declaration
mnist_dataset = torchvision.datasets.MNIST('./dataset', train=True, transform=transforms.ToTensor(), download=True)
dataloader = DataLoader(mnist_dataset, batch_size=batch_size, shuffle=True, num_workers=1)
advGAN = AdvGAN_Attack(device,
targeted_model,
model_num_labels,
image_nc,
BOX_MIN,
BOX_MAX)
advGAN.train(dataloader, epochs)
if not os.path.exists(models_path):
os.makedirs(models_path)
targeted_model = targeted_model.cuda()
targeted_model = nn.DataParallel(targeted_model)
targeted_model.eval()
model_num_labels = 101
cfg = get_opts(data_name)
# MNIST train dataset and dataloader declaration
if data_name == 'cifar10':
trainset = torchvision.datasets.CIFAR10(root=os.path.join(args.dir, 'data/cifar10', train=True, transform=cfg['transform_train'], download=True))
elif data_name == 'imagenette':
trainset = ImageFolder(os.path.join(args.dir, 'imagenette2/train'), transform=cfg['transform_train'])
elif data_name == 'caltech101':
trainset = ImageFolder(os.path.join(args.dir, 'Caltech101/train'), transform=cfg['transform_train'])
train_loader = DataLoader(
trainset,
batch_size=4,
shuffle=True,
num_workers=4,
pin_memory=True
)
advGAN = AdvGAN_Attack(device,
targeted_model,
model_num_labels,
image_nc,
BOX_MIN,
BOX_MAX, models_path)
advGAN.train(train_loader, epochs)
if not os.path.exists(models_path):
os.makedirs(models_path)
if not os.path.exists(models_path + model_name):
os.makedirs(models_path + model_name)
# Define what device we are using print("CUDA Available: ",torch.cuda.is_available())
device = torch.device("cuda" if (
use_cuda and torch.cuda.is_available()) else "cpu")
pretrained_model = "./MNIST_target_model.pth"
targeted_model = MNIST_target_net().to(device)
targeted_model.load_state_dict(
torch.load(pretrained_model, map_location=device))
targeted_model.eval()
model_num_labels = 10
# MNIST train dataset and dataloader declaration
mnist_dataset = torchvision.datasets.MNIST('./dataset',
train=True,
transform=transforms.ToTensor(),
download=True)
dataloader = DataLoader(mnist_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
advGAN = AdvGAN_Attack(device, targeted_model, model_num_labels, image_nc,
BOX_MIN, BOX_MAX, eps, pgd_iter, models_path, out_path,
model_name, writer, args.E_lr, args.defG_lr)
advGAN.train(dataloader, args.epochs)
target_model.eval()
model_num_labels = 10
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, 4),
transforms.ToTensor(),
normalize,
])
# MNIST train dataset and dataloader declaration
# mnist_dataset = torchvision.datasets.MNIST('./dataset', train=True, transform=transforms.ToTensor(), download=True)
# transform = transforms.Compose([transforms.ToTensor()])
# dataloader = DataLoader(
# datasets.MNIST('./dataset/MNIST',
# train=True, download=True, transform=transform),
# batch_size=batch_size, shuffle=True)
# cifar10
dataloader = DataLoader(datasets.CIFAR10('../cifar-10-batches-py',
train=True,
download=True,
transform=transform),
batch_size=batch_size,
shuffle=True)
advGAN = AdvGAN_Attack(device, target_model, model_num_labels, image_nc,
BOX_MIN, BOX_MAX, args)
advGAN.train(dataloader, args.epoch)
epochs=EPOCHS_TARGET_MODEL,
train_dataloader=train_dataloader,
test_dataloader=test_dataloader,
dataset_size=test_set_size)
print('TARGET LOADED!')
# train AdvGAN
print('\nTRAINING ADVGAN...')
advGAN = AdvGAN_Attack(device,
target_model,
n_labels,
n_channels,
target=TARGET,
lr=LR,
l_inf_bound=l_inf_bound,
alpha=ALPHA,
beta=BETA,
gamma=GAMMA,
kappa=KAPPA,
c=C,
n_steps_D=N_STEPS_D,
n_steps_G=N_STEPS_G,
is_relativistic=IS_RELATIVISTIC)
advGAN.train(train_dataloader, EPOCHS)
# load the trained AdvGAN
print('\nLOADING TRAINED ADVGAN!')
adv_GAN_path = './checkpoints/AdvGAN/G_epoch_{}.pth'.format(EPOCHS)
adv_GAN = models.Generator(n_channels, n_channels, TARGET).to(device)
adv_GAN.load_state_dict(torch.load(adv_GAN_path))
adv_GAN.eval()
def main():
device = torch.device("cuda:0" if FLAGS.cuda else "cpu")
print('Loading data...\n')
train_transform, _ = _transforms_catsdogs(FLAGS)
train_data = dset.ImageFolder(root=FLAGS.data_dir,
transform=train_transform)
assert train_data
num_train = len(train_data)
indices = list(range(num_train))
random.shuffle(indices)
split = int(np.floor(FLAGS.data_split * num_train))
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=FLAGS.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(indices[:split]),
num_workers=2)
valid_loader = torch.utils.data.DataLoader(
train_data,
batch_size=FLAGS.batch_size,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[split:num_train]),
num_workers=2)
if FLAGS.train_single:
print('Transfer training model {}...\n'.format(FLAGS.model))
model = torch.hub.load('pytorch/vision', FLAGS.model, pretrained=True)
for param in model.parameters():
param.requires_grad = False
model, param_to_train = transfer_init(model, FLAGS.model,
FLAGS.classes)
model.to(device)
optimizer = torch.optim.SGD(param_to_train,
FLAGS.lr,
momentum=0.9,
weight_decay=5e-4)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=5,
gamma=0.1)
criterion = nn.CrossEntropyLoss()
# Train
best_acc = 0.0
for epoch in range(25):
model.train()
scheduler.step()
print('Epoch {}, lr: {}'.format(epoch, scheduler.get_lr()[0]))
prefetcher = data_prefetcher(train_loader)
data, target = prefetcher.next()
batch_idx = 0
while data is not None:
optimizer.zero_grad()
output = model(data)
pred = output.max(1, keepdim=True)[1]
loss = criterion(output, target)
loss.backward()
optimizer.step()
correct = pred.eq(target.view_as(pred)).sum().item()
if batch_idx % FLAGS.log_interval == 0:
print('[{}/{}]\tloss: {:.4f}\tbatch accuracy: {:.4f}%'.
format(batch_idx * FLAGS.batch_size, num_train,
loss.item(), 100 * correct / data.size(0)))
data, target = prefetcher.next()
batch_idx += 1
# Eval
model.eval()
test_loss = 0
test_correct = 0
with torch.no_grad():
valid_prefetcher = data_prefetcher(valid_loader)
data, target = valid_prefetcher.next()
while data is not None:
output = model(data)
test_loss += criterion(output, target).item()
pred = output.max(1, keepdim=True)[1]
test_correct += pred.eq(target.view_as(pred)).sum().item()
data, target = valid_prefetcher.next()
test_loss /= len(valid_loader)
test_correct = 100 * test_correct / (1 -
FLAGS.data_split) / num_train
print('Eval loss: {:.4f}, accuracy: {:.4f}'.format(
test_loss, test_correct))
if (test_correct > best_acc):
best_acc = test_correct
torch.save(
model,
os.path.join(FLAGS.model_dir,
'{}.pth'.format(FLAGS.model)))
print('Best test accuracy for model {}: {:.4f}'.format(
FLAGS.model, best_acc))
elif FLAGS.train_ensemble:
print('Loading model...\n')
model_names = [
'mobilenet_v2', 'resnet18', 'densenet121', 'googlenet',
'resnext50_32x4d'
]
model = ModelEnsemble(model_names, FLAGS.classes, FLAGS.model_dir)
model.to(device)
optimizer = torch.optim.SGD(model.vote_layer.parameters(),
FLAGS.lr,
momentum=0.9,
weight_decay=5e-4)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=1,
gamma=0.1)
criterion = nn.CrossEntropyLoss()
# Train
print('Training ensemble model...\n')
# model = torch.load(os.path.join(FLAGS.model_dir, 'ensemble.pth'))
for epoch in range(2):
model.train()
scheduler.step()
print('Epoch {}, lr: {}'.format(epoch, scheduler.get_lr()[0]))
prefetcher = data_prefetcher(train_loader)
data, target = prefetcher.next()
batch_idx = 0
while data is not None:
optimizer.zero_grad()
output = model(data)
pred = output.max(1, keepdim=True)[1]
loss = criterion(output, target)
loss.backward()
optimizer.step()
correct = pred.eq(target.view_as(pred)).sum().item()
if batch_idx % FLAGS.log_interval == 0:
print('[{}/{}]\tloss: {:.4f}\tbatch accuracy: {:.4f}%'.
format(batch_idx * FLAGS.batch_size, num_train,
loss.item(), 100 * correct / data.size(0)))
data, target = prefetcher.next()
batch_idx += 1
# Eval
model.eval()
test_loss = 0
test_correct = 0
valid_prefetcher = data_prefetcher(valid_loader)
data, target = valid_prefetcher.next()
while data is not None:
output = model(data)
test_loss += criterion(output, target).item()
pred = output.max(1, keepdim=True)[1]
test_correct += pred.eq(target.view_as(pred)).sum().item()
data, target = valid_prefetcher.next()
test_loss /= len(valid_loader)
print('Eval loss: {:.4f}, accuracy: {:.4f}'.format(
test_loss,
100 * test_correct / (1 - FLAGS.data_split) / num_train))
torch.save(model, os.path.join(FLAGS.model_dir, 'ensemble.pth'))
# Adversarial attack
print('Training GAN for adversarial attack...\n')
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=16,
sampler=torch.utils.data.sampler.SubsetRandomSampler(
indices[:split]),
num_workers=2)
model.eval()
advGAN = AdvGAN_Attack(device, model, FLAGS.classes, FLAGS.channels, 0,
1, FLAGS.model_dir)
# advGAN.netG = torch.load(os.path.join(FLAGS.model_dir, 'netG_epoch_{}.pth'.format(FLAGS.pretrained_epoch)))
advGAN.train(train_loader, FLAGS.epochs)
print('Attacking ensemble model...\n')
test_loss = 0
test_correct = 0
adv_examples = []
# enough = False
with torch.no_grad():
valid_prefetcher = data_prefetcher(valid_loader)
data, target = valid_prefetcher.next()
while data is not None:
# for i in range(64):
# adv_ex = data[i].squeeze().detach().cpu().numpy()
# adv_examples.append((0, 0, adv_ex))
# break
output = model(data)
init_pred = output.max(1, keepdim=True)[1]
init_pred = init_pred.view_as(target)
perturbed_data = advGAN.adv_example(data)
output = model(perturbed_data)
test_loss += criterion(output, target).item()
final_pred = output.max(1, keepdim=True)[1]
final_pred = final_pred.view_as(target)
test_correct += final_pred.eq(target).sum().item()
if len(adv_examples) < 64 and not (final_pred == target).all():
indices = torch.ne(final_pred.ne(target),
init_pred.ne(target)).nonzero()
for i in range(indices.shape[0]):
adv_ex = perturbed_data[
indices[i]].squeeze().detach().cpu().numpy()
adv_examples.append(
(init_pred[indices[i]].item(),
final_pred[indices[i]].item(), adv_ex))
# adv_ex = perturbed_data[indices[i]].squeeze()
# adv_examples.append(adv_ex)
if (len(adv_examples) >= 64):
# enough = True
break
# if enough:
# break
data, target = valid_prefetcher.next()
test_loss /= len(valid_loader)
print('Eval loss: {:.4f}, accuracy: {:.4f}'.format(
test_loss,
100 * test_correct / (1 - FLAGS.data_split) / num_train))
# show 64 results
if True:
cnt = 0
plt.figure(figsize=(8, 10))
for i in range(8):
for j in range(8):
cnt += 1
plt.subplot(8, 8, cnt)
plt.xticks([], [])
plt.yticks([], [])
orig, adv, ex = adv_examples[i * 8 + j]
ex = np.transpose(ex, (1, 2, 0))
plt.title("{} -> {}".format(orig, adv))
plt.imshow(ex)
plt.tight_layout()
plt.show()
else:
viz_sample = torch.stack(adv_examples, dim=0)
print(viz_sample.shape)
vutils.save_image(viz_sample,
'adv_examples.png',
nrow=8,
normalize=True)
pretrained_model1 = "./MNIST_target_model.pth"
pretrained_model2 = "./MNIST_target_modelA.pth"
targeted_model1 = MNIST_target_net().to(device)
targeted_model1.load_state_dict(torch.load(pretrained_model1))
targeted_model1.eval()
targeted_model2 = MNIST_target_netA().to(device)
targeted_model2.load_state_dict(torch.load(pretrained_model2))
targeted_model2.eval()
model_num_labels = 10
# MNIST train dataset and dataloader declaration
mnist_dataset = torchvision.datasets.MNIST('./dataset', train=True, transform=transforms.ToTensor(), download=True)
dataloader = DataLoader(mnist_dataset, batch_size=batch_size, shuffle=True, num_workers=1)
start_time = time.time()
advGAN = AdvGAN_Attack(device,
targeted_model1,
targeted_model2,
model_num_labels,
image_nc,
BOX_MIN,
BOX_MAX)
advGAN.train(dataloader, epochs)
end_time = time.time()
print("advGAN training time is {}".format(end_time - start_time))
device = torch.device("cuda" if (
use_cuda and torch.cuda.is_available()) else "cpu")
pretrained_model = "./target_model.pth"
target_modeled, input_size = target_net(model_name, num_classes,
feature_extract,
use_pretrained).initialize_model()
targeted_model = target_modeled.to(
device) # 除非forward否则精确写出到函数才能to(device)
targeted_model.load_state_dict(torch.load(pretrained_model))
targeted_model.eval()
# train dataset and dataloader declaration
train_data_transform = transforms.Compose([
transforms.RandomResizedCrop(input_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
dataset = torchvision.datasets.CIFAR10('./dataset',
train=True,
transform=train_data_transform,
download=False)
dataloader = DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
advGAN = AdvGAN_Attack(device, targeted_model, num_classes, BOX_MIN,
BOX_MAX)
advGAN.train(dataloader, epochs)
