File size: 2,955 Bytes
d6b2940 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | import sys
import os
# Importing the parent directory
# This line must be preceded by
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) # nopep8
from robustbench.utils import load_model # nopep8
from robustbench.utils import clean_accuracy # nopep8
from robustbench.data import load_cifar10 # nopep8
import torchattacks # nopep8
import torch # nopep8
import pytest # nopep8
import time # nopep8
CACHE = {}
def get_model(model_name='Standard', device='cpu', model_dir='./models'):
model = load_model(model_name, model_dir=model_dir, norm='Linf')
# fsize = os.path.getsize(filePath)
return model.to(device)
def get_data(data_name='CIFAR10', device='cpu', n_examples=5, data_dir='./data'):
images, labels = load_cifar10(n_examples=n_examples, data_dir=data_dir)
return images.to(device), labels.to(device)
@torch.no_grad()
@pytest.mark.parametrize('atk_class', [atk_class for atk_class in torchattacks.__all__ if atk_class not in torchattacks.__wrapper__])
def test_atks_on_cifar10(atk_class, device='cpu', n_examples=5, model_dir='./models', data_dir='./data'):
global CACHE
if CACHE.get('model') is None:
model = get_model(device=device, model_dir=model_dir)
CACHE['model'] = model
else:
model = CACHE['model']
if CACHE.get('images') is None:
images, labels = get_data(
device=device, n_examples=n_examples, data_dir=data_dir)
CACHE['images'] = images
CACHE['labels'] = labels
else:
images = CACHE['images']
labels = CACHE['labels']
if CACHE.get('clean_acc') is None:
clean_acc = clean_accuracy(model, images, labels)
CACHE['clean_acc'] = clean_acc
else:
clean_acc = CACHE['clean_acc']
try:
kargs = {}
if atk_class in ['SPSA']:
kargs['max_batch_size'] = 5
atk = eval("torchattacks."+atk_class)(model, **kargs)
start = time.time()
with torch.enable_grad():
adv_images = atk(images, labels)
end = time.time()
robust_acc = clean_accuracy(model, adv_images, labels)
sec = float(end - start)
print('{0:<12}: clean_acc={1:2.2f} robust_acc={2:2.2f} sec={3:2.2f}'.format(
atk_class, clean_acc, robust_acc, sec))
if 'targeted' in atk.supported_mode:
atk.set_mode_targeted_random(quiet=True)
with torch.enable_grad():
adv_images = atk(images, labels)
robust_acc = clean_accuracy(model, adv_images, labels)
sec = float(end - start)
print('{0:<12}: clean_acc={1:2.2f} robust_acc={2:2.2f} sec={3:2.2f}'.format(
"- targeted", clean_acc, robust_acc, sec))
except Exception as e:
robust_acc = clean_acc + 1 # It will cuase assertion.
print('{0:<12} test acc Error'.format(atk_class))
print(e)
assert clean_acc >= robust_acc
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