def main():
model = StandardModel(args.dataset,
args.arch,
no_grad=False,
load_pretrained=False)
model.cuda()
model.train()
device = torch.device("cuda")
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
model_path = '{}/train_pytorch_model/adversarial_train/TRADES/{}@{}@epoch_{}@batch_{}.pth.tar'.format(
PY_ROOT, args.dataset, args.arch, args.epochs, args.batch_size)
os.makedirs(os.path.dirname(model_path), exist_ok=True)
print("After trained, the model will save to {}".format(model_path))
for epoch in range(1, args.epochs + 1):
# adjust learning rate for SGD
adjust_learning_rate(optimizer, epoch)
# adversarial training
train(args, model, device, train_loader, optimizer, epoch)
# evaluation on natural examples
print(
'================================================================')
eval_train(model, device, train_loader)
eval_test(model, device, test_loader)
print(
'================================================================')
# save checkpoint
if epoch % args.save_freq == 0:
state = {
'state_dict': model.state_dict(),
'epoch': epoch,
'optimizer': optimizer.state_dict()
}
torch.save(state, os.path.join(model_dir, model_path))
ma_img = ma[:, :, None, None]
inputs1 = inputs1 * torch.from_numpy(ma_img).cuda().float()
inputs2 = inputs2 * torch.from_numpy(1 - ma_img).cuda().float()
targets1 = targets1.float() * torch.from_numpy(ma).cuda().float()
targets2 = targets2.float() * torch.from_numpy(1 - ma).cuda().float()
inputs_shuffle = (inputs1 + inputs2).cuda()
targets_shuffle = (targets1 + targets2).cuda()
return inputs_shuffle, targets_shuffle
criterion = soft_cross_entropy
optimizer = optim.SGD(model.parameters(), lr=args.learning_rate, momentum=0.9)
lr_scheduler = optim.lr_scheduler.LambdaLR(optimizer, [anneal_lr])
for epoch in range(EPOCH_TOTAL): # loop over the dataset multiple times
logger.info("Start Epoch {}".format(epoch))
running_loss_1, running_loss_2 = 0.0, 0.0
lr_scheduler.step(epoch)
for i, data_batch in enumerate(train_loader):
# get the inputs; data is a list of [inputs, labels]
# if i == 19:
# break
img_batch, label_batch = data_batch
img_batch, label_batch = img_batch.cuda(), label_batch.cuda()
train_img_batch, train_label_batch = [], []
def main():
args = get_args()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
logger.info(args)
model_path = '{}/train_pytorch_model/adversarial_train/fast_adv_train/{}@{}@epoch_{}.pth.tar'.format(
PY_ROOT, args.dataset, args.arch, args.epochs)
out_dir = os.path.dirname(model_path)
os.makedirs(out_dir, exist_ok=True)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
start_start_time = time.time()
train_loader = DataLoaderMaker.get_img_label_data_loader(args.dataset, args.batch_size, True)
test_loader = DataLoaderMaker.get_img_label_data_loader(args.dataset, args.batch_size, False)
epsilon = (args.epsilon / 255.) / std
pgd_alpha = (args.pgd_alpha / 255.) / std
model = StandardModel(args.dataset, args.arch, no_grad=False)
model.apply(initialize_weights)
model.cuda()
model.train()
opt = torch.optim.SGD(model.parameters(), lr=args.lr_max, momentum=0.9, weight_decay=5e-4)
model, opt = amp.initialize(model, opt, opt_level="O2", loss_scale=1.0, master_weights=False)
criterion = nn.CrossEntropyLoss()
if args.attack == 'free':
delta = torch.zeros(args.batch_size, 3, 32, 32).cuda()
delta.requires_grad = True
elif args.attack == 'fgsm' and args.fgsm_init == 'previous':
delta = torch.zeros(args.batch_size, 3, 32, 32).cuda()
delta.requires_grad = True
if args.attack == 'free':
assert args.epochs % args.attack_iters == 0
epochs = int(math.ceil(args.epochs / args.attack_iters))
else:
epochs = args.epochs
if args.lr_schedule == 'cyclic':
lr_schedule = lambda t: np.interp([t], [0, args.epochs * 2 // 5, args.epochs], [0, args.lr_max, 0])[0]
elif args.lr_schedule == 'piecewise':
def lr_schedule(t):
if t / args.epochs < 0.5:
return args.lr_max
elif t / args.epochs < 0.75:
return args.lr_max / 10.
else:
return args.lr_max / 100.
prev_robust_acc = 0.
logger.info('Epoch \t Time \t LR \t \t Train Loss \t Train Acc')
for epoch in range(epochs):
start_time = time.time()
train_loss = 0
train_acc = 0
train_n = 0
for i, (X, y) in enumerate(train_loader):
X = X.cuda().float()
y = y.cuda().long()
if i == 0:
first_batch = X, y
lr = lr_schedule(epoch + (i + 1) / len(train_loader))
opt.param_groups[0].update(lr=lr)
if args.attack == 'pgd':
delta = attack_pgd(model, X, y, epsilon, pgd_alpha, args.attack_iters, args.restarts, opt)
elif args.attack == 'fgsm':
if args.fgsm_init == 'zero':
delta = torch.zeros_like(X, requires_grad=True)
delta.requires_grad = True
elif args.fgsm_init == 'random':
delta = torch.zeros_like(X).cuda()
delta[:, 0, :, :].uniform_(-epsilon[0][0][0].item(), epsilon[0][0][0].item())
delta[:, 1, :, :].uniform_(-epsilon[1][0][0].item(), epsilon[1][0][0].item())
delta[:, 2, :, :].uniform_(-epsilon[2][0][0].item(), epsilon[2][0][0].item())
delta.requires_grad = True
elif args.fgsm_init == 'previous':
delta.requires_grad = True
output = model(X + delta[:X.size(0)])
loss = F.cross_entropy(output, y)
with amp.scale_loss(loss, opt) as scaled_loss:
scaled_loss.backward()
grad = delta.grad.detach()
delta.data = clamp(delta + args.fgsm_alpha * epsilon * torch.sign(grad), -epsilon, epsilon)
delta = delta.detach()
elif args.attack == 'free':
delta.requires_grad = True
for j in range(args.attack_iters):
epoch_iters = epoch * args.attack_iters + (i * args.attack_iters + j + 1) / len(train_loader)
lr = lr_schedule(epoch_iters)
opt.param_groups[0].update(lr=lr)
output = model(clamp(X + delta[:X.size(0)], lower_limit, upper_limit))
loss = F.cross_entropy(output, y)
opt.zero_grad()
with amp.scale_loss(loss, opt) as scaled_loss:
scaled_loss.backward()
grad = delta.grad.detach()
delta.data = clamp(delta + epsilon * torch.sign(grad), -epsilon, epsilon)
nn.utils.clip_grad_norm_(model.parameters(), 0.5)
opt.step()
delta.grad.zero_()
elif args.attack == 'none':
delta = torch.zeros_like(X)
output = model(clamp(X + delta[:X.size(0)], lower_limit, upper_limit))
loss = criterion(output, y)
if args.attack != 'free':
opt.zero_grad()
with amp.scale_loss(loss, opt) as scaled_loss:
scaled_loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 0.5)
opt.step()
train_loss += loss.item() * y.size(0)
train_acc += (output.max(1)[1] == y).sum().item()
train_n += y.size(0)
if args.overfit_check:
# Check current PGD robustness of model using random minibatch
X, y = first_batch['input'], first_batch['target']
pgd_delta = attack_pgd(model, X, y, epsilon, pgd_alpha, args.attack_iters, args.restarts, opt)
with torch.no_grad():
output = model(clamp(X + pgd_delta[:X.size(0)], lower_limit, upper_limit))
robust_acc = (output.max(1)[1] == y).sum().item() / y.size(0)
if robust_acc - prev_robust_acc < -0.5:
break
prev_robust_acc = robust_acc
best_state_dict = copy.deepcopy(model.state_dict())
train_time = time.time()
logger.info('%d \t %.1f \t %.4f \t %.4f \t %.4f',
epoch, train_time - start_time, lr, train_loss/train_n, train_acc/train_n)
torch.save(best_state_dict, model_path)
logger.info('Total time: %.4f', train_time - start_start_time)
