parser = argparse.ArgumentParser()
parser.add_argument('--val', action='store_true', help='weither to run validation')
parser.add_argument('--bench', action='store_true',
help = 'weither to generate results on benchmark dataset')
parser.add_argument('--smooth', action='store_true',
help = 'weither to generator smootGrad results on benchmark dataset')
parser.add_argument('--gen', action = 'store_true',
help = 'weither to generate Large eps adversarial examples on benchmark data')
parser.add_argument('--resume', type=str, default=None,
help='checkpoint path')
args = parser.parse_args()
clock = TrainClock()
clock.epoch = 21
net = ant_model()
net.cuda()
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
check_point = torch.load(args.resume)
net.load_state_dict(check_point['state_dict'])
print('Modeled loaded from {} with metrics:'.format(args.resume))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
base_path = os.path.split(args.resume)[0]
else:
base_path = './'
best_prec = 0.0
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
check_point = torch.load(args.resume)
args.start_epoch = check_point['epoch']
net.load_state_dict(check_point['state_dict'])
best_prec = check_point['best_prec']
print('Modeled loaded from {} with metrics:'.format(args.resume))
print(results)
else:
print("=> no checkpoint found at '{}'".format(args.resume))
clock.epoch = args.start_epoch
#for epoch in ds_train.epoch_generator():
while True:
if clock.epoch > args.epochs:
break
adjust_learning_rate(optimizer, clock.epoch)
Trainresults = adversairal_train_one_epoch(net,
optimizer,
ds_train,
criterion,
PgdAttack,
clock,
attack_freq=args.adv_freq,
use_adv=not args.no_adv,
DEVICE=DEVICE)
