def test(epoch):
net.eval()
test_loss = 0
correct = 0
nominal_correct = 0
total = 0
total_epsilon = 0
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
inputs_pgd, _, epsilons = attack(torch.clamp(unnormalize(inputs),
min=0),
targets,
net,
p=args.p,
normalize=normalize,
epsilon_factor=args.epsilon_factor,
epsilon=args.init_epsilon,
maxiters=args.maxiters,
epsilon_iters=args.epsilon_iters,
regularization=args.reg,
alpha=args.alpha,
norm=args.norm,
ball=args.ball,
multiply=True)
with torch.no_grad():
outputs = net(normalize(inputs_pgd))
loss = criterion(outputs, targets)
outputs_nominal = net(inputs)
_, predicted_nominal = outputs_nominal.max(1)
nominal_correct += predicted_nominal.eq(targets).sum().item()
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
total_epsilon += epsilons.sum().item()
progress_bar(
batch_idx, len(testloader),
'Loss: %.3f | Adv Acc: %.3f%% (%d/%d) | Acc: %.3f%% (%d/%d) | Eps: %.3f%%'
% (test_loss / (batch_idx + 1), 100. * correct / total,
correct, total, 100. * nominal_correct / total,
nominal_correct, total, total_epsilon / total))
if epoch % 10 == 0:
# Save checkpoint.
acc = 100. * correct / total
eps = total_epsilon / total
print('Saving..')
state = {
'net': net.state_dict(),
'acc': acc,
'eps': eps,
'epoch': epoch,
}
if not os.path.isdir(args.outdir):
os.mkdir(args.outdir)
torch.save(state, checkpoint_file.format(epoch))
def test_attack():
net.eval()
test_loss = 0
correct = 0
total = 0
all_epsilons = []
succeed_epsilons = []
L1_delta = []
W_delta = []
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
if args.binarize:
inputs = (inputs >= 0.5).float()
inputs_pgd, _, epsilons = attack(
torch.clamp(inputs, min=0),
targets,
net,
regularization=regularization,
p=args.p,
alpha=args.alpha,
norm=args.norm,
ball=args.ball,
epsilon_iters=args.epsilon_iters,
epsilon_factor=args.epsilon_factor,
epsilon=args.init_epsilon,
maxiters=args.maxiters,
kernel_size=args.kernel,
use_tqdm=True,
clamping=not args.no_clamping,
constrained_sinkhorn=not args.unconstrained)
outputs_pgd = net(inputs_pgd)
loss = criterion(outputs_pgd, targets)
test_loss += loss.item()
_, predicted = outputs_pgd.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
epsilons[predicted == targets] = float('inf')
which_correct = epsilons == float('inf')
succeed_epsilons.append(epsilons[~which_correct])
all_epsilons.append(epsilons)
progress_bar(
batch_idx, len(testloader),
'Loss: %.3f | Acc: %.3f%% (%d/%d) | Avg epsilon: %.3f' %
(test_loss / (batch_idx + 1), 100. * correct / total, correct,
total, torch.cat(succeed_epsilons).float().mean().item()))
acc = 100. * correct / total
all_epsilons = torch.cat(all_epsilons)
with open(save_name, 'w') as f:
f.write('index\tradius\n')
for i in range(len(all_epsilons)):
f.write(f'{i+1}\t{all_epsilons[i].item()}\n')
def train(epoch):
print('\nEpoch: %d' % epoch)
train_loss = 0
correct = 0
nominal_correct = 0
total_epsilon = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(device), targets.to(device)
net.eval()
inputs_pgd, _, epsilons = attack(torch.clamp(unnormalize(inputs),
min=0),
targets,
net,
p=args.p,
normalize=normalize,
epsilon_factor=args.epsilon_factor,
epsilon=args.init_epsilon,
maxiters=args.maxiters,
epsilon_iters=args.epsilon_iters,
regularization=args.reg,
alpha=args.alpha,
norm=args.norm,
ball=args.ball,
sinkhorn_maxiters=10,
training=True,
kernel_size=5,
l1_delta=args.L1D,
multiply=True)
net.train()
optimizer.zero_grad()
outputs = net(normalize(inputs_pgd.detach()))
_, predicted = outputs.max(1)
correct += predicted.eq(targets).sum().item()
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
with torch.no_grad():
net.eval()
outputs_nominal = net(inputs)
_, predicted_nominal = outputs_nominal.max(1)
nominal_correct += predicted_nominal.eq(targets).sum().item()
train_loss += loss.item()
total += targets.size(0)
total_epsilon += epsilons.sum().item()
progress_bar(
batch_idx, len(trainloader),
'Loss: %.3f | Adv Acc: %.3f%% (%d/%d) | Acc: %.3f%% (%d/%d) | Eps: %.3f%%'
% (train_loss / (batch_idx + 1), 100. * correct / total, correct,
total, 100. * nominal_correct / total, nominal_correct, total,
total_epsilon / total))
def test_attack():
net.eval()
test_loss = 0
correct = 0
total = 0
all_epsilons = []
for batch_idx, (inputs, targets) in enumerate(testloader):
inputs, targets = inputs.to(device), targets.to(device)
if binarize:
inputs = (inputs >= 0.5).float()
inputs_pgd, _, epsilons = attack(torch.clamp(inputs, min=0),
targets,
net,
regularization=regularization,
p=args.p,
alpha=args.alpha,
norm=args.norm,
ball=args.ball,
epsilon=0.7,
maxiters=200,
kernel_size=7)
outputs_pgd = net(inputs_pgd)
loss = criterion(outputs_pgd, targets)
test_loss += loss.item()
_, predicted = outputs_pgd.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
epsilons[predicted == targets] = -1
all_epsilons.append(epsilons)
progress_bar(
batch_idx, len(testloader),
'Loss: %.3f | Acc: %.3f%% (%d/%d) | Avg epsilon: %.3f' %
(test_loss / (batch_idx + 1), 100. * correct / total, correct,
total, torch.cat(all_epsilons).float().mean().item()))
acc = 100. * correct / total
torch.save((acc, torch.cat(all_epsilons)), save_name)
def train(epoch):
print('\nEpoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
nominal_correct = 0
total_epsilon = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(device), targets.to(device)
inputs_pgd, _, epsilons = attack(inputs, targets, net,
epsilon_factor=1.4, epsilon=0.1,
maxiters=50, epsilon_iters=5,
p=args.p,
regularization=args.reg,
alpha=args.alpha,
norm=args.norm,
ball=args.ball)
optimizer.zero_grad()
outputs = net(inputs_pgd.detach())
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
with torch.no_grad():
outputs_nominal = net(inputs)
_, predicted_nominal = outputs_nominal.max(1)
nominal_correct += predicted_nominal.eq(targets).sum().item()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
total_epsilon += (epsilons < float("inf")).sum().item()
progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Adv Acc: %.3f%% (%d/%d) | Acc: %.3f%% (%d/%d) | Eps: %.3f%%'
% (train_loss/(batch_idx+1), 100.*correct/total, correct, total,
100.*nominal_correct/total, nominal_correct, total, total_epsilon/total))
