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)))
import os
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,