def main():
args = Args().get_args()
kwargs = vars(args)
checkpoint = torch.load(args.checkpoint)
base_classifier = get_architecture(checkpoint["arch"], args.dataset)
base_classifier.load_state_dict(checkpoint['state_dict'])
attacker = SmoothAttack(base_classifier)
smoothed_classifier = Smooth(base_classifier,
get_num_classes(args.dataset), args.sigma)
dataset = get_dataset(args.dataset, 'test')
average_nat = []
average_adv = []
j_header('index', 'nat_y', 'adv_y', 'nat_rad', 'adv_rad', 'success')
figure = FigureSaver()
for i in range(0, len(dataset), args.skip):
(x, label) = dataset[i]
x = x.cuda()
first_x = x.data
nat_pred, nat_rad = smoothed_classifier.certify(
x, args.N0, args.N, args.alpha, args.batch)
if nat_pred is -1:
continue
if args.dataset == DATASETS[0]: # ImageNet
targets = [j for j in range(0, 1000, 100) if j is not label]
else:
targets = [j for j in range(10) if j is not label]
best_rad = -10.0
best_image = None
best_target = -1
for target in targets:
adv_x = attacker.perturb(x=first_x, y=target, **kwargs)
# If you want to do wasserstein attack, uncomment the following and change the attacker to wasserstein
# adv_x = attacker.perturb(x=first_x, y=target, eps=args.sigma, steps=args.steps, batch=args.batch)
adv_pred, adv_rad = smoothed_classifier.certify(
adv_x, args.N0, 2 * args.N0, args.alpha, args.batch)
adv_suc = (adv_pred != label) and (adv_pred != -1) and (nat_pred !=
-1)
adv_rad = adv_rad if adv_suc else -adv_rad
if adv_rad > best_rad:
best_rad = adv_rad
best_image = adv_x.data
best_target = target
figure.save(best_image, i, 'best={}'.format(best_target))
figure.save(first_x, i, 'natural')
best_pred, best_rad = smoothed_classifier.certify(
best_image, args.N0, args.N, args.alpha, args.batch)
j_print(i, label, best_target, nat_rad, best_rad)
average_adv.append(best_rad)
average_nat.append(nat_rad)
average_nat = np.array(average_nat)
average_adv = np.array(average_adv)
print('Average nat radii {}, Average adv radii {}'.format(
average_nat.mean(), average_adv.mean()))
if not os.path.exists(os.path.dirname(args.outfile)):
os.makedirs(os.path.dirname(args.outfile))
f = open(args.outfile, 'w')
print("idx\tlabel\tpredict\tradius\tcorrect\ttime", file=f, flush=True)
# iterate through the dataset
dataset = get_dataset(args.dataset, args.split)
for i in range(len(dataset)):
# only certify every args.skip examples, and stop after args.max examples
if i % args.skip != 0:
continue
if i == args.max:
break
(x, label) = dataset[i]
before_time = time()
# certify the prediction of g around x
x = x.cuda()
prediction, radius = smoothed_classifier.certify(x, args.N0, args.N, args.alpha, args.batch)
after_time = time()
correct = int(prediction == label)
time_elapsed = str(datetime.timedelta(seconds=(after_time - before_time)))
print("{}\t{}\t{}\t{:.3}\t{}\t{}".format(
i, label, prediction, radius, correct, time_elapsed), file=f, flush=True)
print(i)
f.close()
dataset = get_dataset(args.dataset, args.split)
for i in range(len(dataset)):
# only certify every args.skip examples, and stop after args.max examples
if i % args.skip != 0:
continue
if i == args.max:
break
(x, label) = dataset[i]
before_time = time()
# certify the prediction of g around x
x = x.cuda()
prediction, exp_cdf_00, exp_cdf_25, exp_cdf_50, exp_cdf_75, exp_cdf_100, exp_cdf_125, exp_cdf_150, \
exp_00, exp_25, exp_50, exp_75, exp_100, exp_125, exp_150 = smoothed_classifier.certify(x, args.N0, args.N, args.alpha, args.batch)
after_time = time()
correct = int(prediction == label)
time_elapsed = str(
datetime.timedelta(seconds=(after_time - before_time)))
print(
"{}\t{}\t{}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{}\t{}"
.format(i, label, prediction, exp_cdf_00, exp_cdf_25, exp_cdf_50,
exp_cdf_75, exp_cdf_100, exp_cdf_125, exp_cdf_150, exp_00,
exp_25, exp_50, exp_75, exp_100, exp_125, exp_150, correct,
time_elapsed),
file=f,
flush=True)
f.close()
# prepare output file
f = open(args.outfile, 'a')
print(
"idx\tlabel\tcount\tpredict\tradiusR\tradiusG\tradiusB\tradius_L1\tradius_LInf\tradius_L2\tradius_cohen\tcorrect\ttime",
file=f,
flush=True)
# iterate through the dataset
dataset = get_dataset(args.dataset, args.split)
for i in range(args.start, args.start + (args.skip * args.max), args.skip):
(x, label) = dataset[i]
before_time = time()
# certify the prediction of g around x
x = x.cuda()
count, maxC, meanC, meanR, meanG, meanB, prediction, radiusR, radiusG, radiusB, radiusL1, radiusLInf, radiusL2, radiusOp, radiusCohen = smoothed_classifier.certify(
x, args.N0, args.N, args.alpha, args.batch, label)
after_time = time()
correct = int(prediction == label)
time_elapsed = str(
datetime.timedelta(seconds=(after_time - before_time)))
print(
"{}\t{}\t{}\t{}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{:.3}\t{}\t{}"
.format(i, label, count, prediction, radiusR, radiusG, radiusB,
radiusL1, radiusLInf, radiusL2, radiusCohen, correct,
time_elapsed),
file=f,
flush=True)
f.close()
