def code_test(model_path):
from base_model.cifar_resnet18 import cifar_resnet18
from dataset import create_test_dataset
ds_val = create_test_dataset()
#model_path = '../exps/exp0/checkpoint.pth.tar'
net = cifar_resnet18()
if model_path is not None:
checkpoint = torch.load(model_path)
net.load_state_dict(checkpoint['state_dict'])
net.cuda()
epoch = next(ds_val.epoch_generator())
roboustness = evalRoboustness(net, epoch)
print(roboustness)
if model_path is not None:
s = model_path.split('/')[-1]
val_res_path = os.path.join(model_path[:-18], 'r_results.txt')
with open(val_res_path, 'a') as f:
f.write('\n')
json.dump({'Roboustness': roboustness}, f)
def code_test():
from base_model.cifar_resnet18 import cifar_resnet18
from dataset import Dataset
from my_snip.base import EpochDataset
ds_val = Dataset(dataset_name = 'val')
ds_val.load()
ds_val = EpochDataset(ds_val)
model_path = '../exps/exp0/checkpoint.pth.tar'
net = cifar_resnet18()
checkpoint = torch.load(model_path)
net.load_state_dict(checkpoint['state_dict'])
net.cuda()
epoch = next(ds_val.epoch_generator())
evalRoboustness(net, epoch)
def test_IPGD():
model_path = './exps/exp0/model_best.pth.tar'
from base_model.cifar_resnet18 import cifar_resnet18
from dataset import Dataset
from my_snip.base import EpochDataset
pgd = IPGD()
net = cifar_resnet18()
net.load_state_dict(torch.load(model_path)['state_dict'])
net.cuda()
ds_train = Dataset(dataset_name='train')
ds_train.load()
ds_train = EpochDataset(ds_train)
epoch = next(ds_train.epoch_generator())
count = 0
for mn_batch in epoch:
count += 1
if count > 100:
break
data = mn_batch['data']
label = mn_batch['label']
#print(data.shape)
data = torch.tensor(data, dtype=torch.float32).cuda()
label = torch.tensor(label, dtype=torch.int64).cuda()
acc = pgd.get_batch_accuracy(net, data, label)
print('acc: {:.2f}%'.format(acc))
def create_network():
net = cifar_resnet18(num_class=10, expansion=1)
return net
def create_network():
net = cifar_resnet18()
return net
#global get_lea
lr = get_learing_rate(epoch)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
torch.backends.cudnn.benchmark = True
ds_train = Dataset(dataset_name = 'train')
ds_train.load()
ds_train = EpochDataset(ds_train)
ds_val = Dataset(dataset_name = 'val')
ds_val.load()
ds_val = EpochDataset(ds_val)
net = cifar_resnet18()
net.cuda()
criterion = nn.CrossEntropyLoss().cuda()
optimizer = optim.SGD(net.parameters(), lr = get_learing_rate(0), momentum = 0.9, weight_decay=args.weight_decay)
PgdAttack = IPGD(eps = args.eps, sigma = args.eps // 2, nb_iter = args.iter, norm = np.inf)
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']
def adjust_learning_rate(optimzier, epoch):
#global get_lea
lr = get_learing_rate(epoch)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
torch.backends.cudnn.benchmark = True
ds_train = create_train_dataset()
ds_val = create_test_dataset()
net = cifar_resnet18(expansion=args.wide)
net.cuda()
criterion = nn.CrossEntropyLoss().cuda()
optimizer = optim.SGD(net.parameters(),
lr=get_learing_rate(0),
momentum=0.9,
weight_decay=args.weight_decay)
PgdAttack = IPGD(eps=args.eps,
sigma=args.eps // 2,
nb_iter=args.iter,
norm=np.inf)
best_prec = 0.0
if args.resume:
if os.path.isfile(args.resume):
