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test_CIFAR100.py
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test_CIFAR100.py
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from autoattack import AutoAttack
from CIFAR100_models import *
from utils import *
import argparse
import sys
import os
sys.path.insert(0, '..')
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str, default='/apdcephfs/share_1290939/jiaxiaojun/cifar-data100')
parser.add_argument('--norm', type=str, default='Linf')
parser.add_argument('--epsilon', type=int, default=8)
parser.add_argument('--model_path', type=str, default='/apdcephfs/private_xiaojunjia/LAS-AT/weights/LAS-AT/CIFAR100/LAS_Trades/model.pth')
parser.add_argument('--model', default='WideResNet', type=str, help='model name')
parser.add_argument('--n_ex', type=int, default=10000)
parser.add_argument('--batch_size', type=int, default=500)
parser.add_argument('--out_dir', type=str, default='./data')
arguments = parser.parse_args()
return arguments
args = get_args()
if not os.path.exists(args.out_dir):
os.makedirs(args.out_dir)
logfile1 = os.path.join(args.out_dir, 'log_file1.txt')
logfile2 = os.path.join(args.out_dir, 'log_file2.txt')
if os.path.exists(logfile1):
os.remove(logfile1)
if os.path.exists(logfile2):
os.remove(logfile2)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
if args.model == "VGG":
target_model = VGG('VGG19')
elif args.model == "ResNet18":
target_model = ResNet18()
elif args.model == "PreActResNest18":
target_model = PreActResNet18()
elif args.model == "WideResNet":
target_model = WideResNet()
target_model = target_model.to(device)
checkpoint = torch.load(args.model_path)
from collections import OrderedDict
try:
target_model.load_state_dict(checkpoint)
except:
new_state_dict = OrderedDict()
for k, v in checkpoint.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
target_model.load_state_dict(new_state_dict, False)
#torch.save(target_model.state_dict(), '/apdcephfs/private_xiaojunjia/LAS-AT/weights/LAS-AT/CIFAR100/LAS_Trades/model.pth')
target_model.eval()
train_loader, test_loader = get_loaders_cifar100(args.data_dir, args.batch_size)
epsilon=args.epsilon
epsilon=float(epsilon)/255.
print(epsilon)
AT_fgsm_loss,AT_fgsm_acc=evaluate_fgsm(test_loader, target_model, 1)
AT_pgd_loss_10, AT_pgd_acc_10 = evaluate_pgd(test_loader, target_model, 10, 1, epsilon / std)
AT_pgd_loss_20, AT_pgd_acc_20 = evaluate_pgd(test_loader, target_model, 20, 1, epsilon / std)
AT_pgd_loss_50, AT_pgd_acc_50 = evaluate_pgd(test_loader, target_model, 50, 1, epsilon / std)
AT_CW_loss_20, AT_pgd_cw_acc_20 = evaluate_pgd_cw(test_loader, target_model, 20, 1)
AT_models_test_loss, AT_models_test_acc = evaluate_standard(test_loader, target_model)
print('AT_models_test_acc:', AT_models_test_acc)
print('AT_fgsm_acc:', AT_fgsm_acc)
print('AT_pgd_acc_10:', AT_pgd_acc_10)
print('AT_pgd_acc_20:', AT_pgd_acc_20)
print('AT_pgd_acc_50:', AT_pgd_acc_50)
print('AT_pgd_cw_acc_20:', AT_pgd_cw_acc_20)
adversary1 = AutoAttack(target_model, norm=args.norm, eps=epsilon, version='standard',log_path=logfile1)
#adversary2 = AutoAttack(target_model, norm=args.norm, eps=epsilon, version='standard',log_path=logfile2)
l = [x for (x, y) in test_loader]
x_test = torch.cat(l, 0)
l = [y for (x, y) in test_loader]
y_test = torch.cat(l, 0)
adv_complete = adversary1.run_standard_evaluation(x_test[:args.n_ex], y_test[:args.n_ex],
bs=args.batch_size)
# adv_complete1 = adversary2.run_standard_evaluation_individual(x_test[:args.n_ex], y_test[:args.n_ex],
# bs=args.batch_size)