def process_single_epoch():
print('**************')
parser = argparse.ArgumentParser()
parser.add_argument('-d', type=int, default=0, help='Which gpu to use')
args = parser.parse_args()
DEVICE = torch.device('cuda:{}'.format(args.d))
torch.backends.cudnn.benchmark = True
net = create_network()
net.to(DEVICE)
nat_val = load_test_dataset(10000, natural=True)
adv_val = load_test_dataset(10000, natural=False)
AttackMethod = config.create_evaluation_attack_method(DEVICE)
filename = '../ckpts/6leaf-epoch29.checkpoint'
print(filename)
if os.path.isfile(filename):
load_checkpoint(filename, net)
print('Evaluating Natural Samples')
clean_acc, adv_acc = my_eval_one_epoch(net, nat_val, DEVICE, AttackMethod)
print('clean acc -- {} adv acc -- {}'.format(clean_acc, adv_acc))
print('Evaluating Adversarial Samples')
clean_acc, adv_acc = my_eval_one_epoch(net, adv_val, DEVICE, AttackMethod)
print('clean acc -- {} adv acc -- {}'.format(clean_acc, adv_acc))
def main():
model = create_network().to(device)
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
EvalAttack = config.create_evaluation_attack_method(device)
now_train_time = 0
for epoch in range(1, args.epochs + 1):
# adjust learning rate for SGD
adjust_learning_rate(optimizer, epoch)
s_time = time()
descrip_str = 'Training epoch: {}/{}'.format(epoch, args.epochs)
# adversarial training
train(args, model, device, train_loader, optimizer, epoch, descrip_str)
now_train_time += time() - s_time
acc, advacc = eval_one_epoch(model, test_loader, device, EvalAttack)
# save checkpoint
if epoch % args.save_freq == 0:
torch.save(
model.state_dict(),
os.path.join(config.model_dir,
'model-wideres-epoch{}.pt'.format(epoch)))
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--resume',
'--resume',
default='log/models/last.checkpoint',
type=str,
metavar='PATH',
help='path to latest checkpoint (default:log/last.checkpoint)')
parser.add_argument('-d', type=int, default=0, help='Which gpu to use')
args = parser.parse_args()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
torch.backends.cudnn.benchmark = True
net = create_network()
net.to(device)
ds_val = create_test_dataset(512)
attack_method = config.create_evaluation_attack_method(device)
if os.path.isfile(args.resume):
load_checkpoint(args.resume, net)
print('Evaluating')
clean_acc, adv_acc = eval_one_epoch(net, ds_val, device, attack_method)
print('clean acc -- {} adv acc -- {}'.format(clean_acc, adv_acc))
def main():
DEVICE = torch.device('cuda:{}'.format(args.d))
torch.backends.cudnn.benchmark = True
net = create_network()
net.to(DEVICE)
criterion = config.create_loss_function().to(DEVICE)
optimizer = config.create_optimizer(net.parameters())
lr_scheduler = config.create_lr_scheduler(optimizer)
ds_train = create_train_dataset(args.batch_size)
ds_val = create_test_dataset(args.batch_size)
TrainAttack = config.create_attack_method(DEVICE)
EvalAttack = config.create_evaluation_attack_method(DEVICE)
now_epoch = 0
if args.auto_continue:
args.resume = os.path.join(config.model_dir, 'last.checkpoint')
if args.resume is not None and os.path.isfile(args.resume):
now_epoch = load_checkpoint(args.resume, net, optimizer, lr_scheduler)
while True:
if now_epoch > config.num_epochs:
break
now_epoch = now_epoch + 1
descrip_str = 'Training epoch:{}/{} -- lr:{}'.format(
now_epoch, config.num_epochs,
lr_scheduler.get_lr()[0])
train_one_epoch(net,
ds_train,
optimizer,
criterion,
DEVICE,
descrip_str,
TrainAttack,
adv_coef=args.adv_coef)
if config.eval_interval > 0 and now_epoch % config.eval_interval == 0:
eval_one_epoch(net, ds_val, DEVICE, EvalAttack)
lr_scheduler.step()
save_checkpoint(now_epoch,
net,
optimizer,
lr_scheduler,
file_name=os.path.join(
config.model_dir,
'epoch-{}.checkpoint'.format(now_epoch)))
def main():
device = 'cuda' if torch.cuda.is_available() else 'cpu'
torch.backends.cudnn.benchmark = True
net = create_network()
net.to(device)
criterion = config.create_loss_function().to(device)
optimizer = config.create_optimizer(net.parameters())
lr_scheduler = config.create_lr_scheduler(optimizer)
ds_train = create_train_dataset(args.batch_size)
ds_val = create_test_dataset(args.batch_size)
train_attack = config.create_attack_method(device)
eval_attack = config.create_evaluation_attack_method(device)
now_epoch = 0
if args.auto_continue:
args.resume = os.path.join(config.model_dir, 'last.checkpoint')
if args.resume is not None and os.path.isfile(args.resume):
now_epoch = load_checkpoint(args.resume, net, optimizer, lr_scheduler)
for i in range(now_epoch, config.num_epochs):
# if now_epoch > config.num_epochs:
# break
# now_epoch = now_epoch + 1
descrip_str = 'Training epoch:{}/{} -- lr:{}'.format(i, config.num_epochs,
lr_scheduler.get_last_lr()[0])
train_one_epoch(net, ds_train, optimizer, criterion, device,
descrip_str, train_attack, adv_coef=args.adv_coef)
if config.eval_interval > 0 and i % config.eval_interval == 0:
eval_one_epoch(net, ds_val, device, eval_attack)
lr_scheduler.step()
save_checkpoint(i, net, optimizer, lr_scheduler,
file_name=os.path.join(config.model_dir, 'epoch-{}.checkpoint'.format(i)))
Hamiltonian_func = Hamiltonian(net.layer_one, config.weight_decay)
layer_one_optimizer = optim.SGD(net.layer_one.parameters(),
lr=lr_scheduler.get_lr()[0],
momentum=0.9,
weight_decay=5e-4)
lyaer_one_optimizer_lr_scheduler = optim.lr_scheduler.MultiStepLR(
layer_one_optimizer, milestones=[15, 19], gamma=0.1)
LayerOneTrainer = FastGradientLayerOneTrainer(Hamiltonian_func,
layer_one_optimizer,
config.inner_iters, config.sigma,
config.eps)
ds_train = create_train_dataset(args.batch_size)
ds_val = create_test_dataset(args.batch_size)
EvalAttack = config.create_evaluation_attack_method(DEVICE)
now_epoch = 0
if args.auto_continue:
args.resume = os.path.join(config.model_dir, 'last.checkpoint')
if args.resume is not None and os.path.isfile(args.resume):
now_epoch = load_checkpoint(args.resume, net, optimizer, lr_scheduler)
now_train_time = 0
while True:
if now_epoch > config.num_epochs:
break
now_epoch = now_epoch + 1
descrip_str = 'Training epoch:{}/{} -- lr:{}'.format(
