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lenet5_mnist_fuzzing.py 5.81 KB
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# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from mindspore import Model
from mindspore import context
from mindspore import load_checkpoint, load_param_into_net
from mindarmour.fuzz_testing import Fuzzer
from mindarmour.fuzz_testing import KMultisectionNeuronCoverage
from mindarmour.utils import LogUtil
from examples.common.dataset.data_processing import generate_mnist_dataset
from examples.common.networks.lenet5.lenet5_net_for_fuzzing import LeNet5
LOGGER = LogUtil.get_instance()
TAG = 'Fuzz_test'
LOGGER.set_level('INFO')
def test_lenet_mnist_fuzzing():
# upload trained network
ckpt_path = '../common/networks/lenet5/trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
net = LeNet5()
load_dict = load_checkpoint(ckpt_path)
load_param_into_net(net, load_dict)
model = Model(net)
mutate_config = [
{'method': 'GaussianBlur',
'params': {'ksize': [1, 2, 3, 5],
'auto_param': [True, False]}},
{'method': 'MotionBlur',
'params': {'degree': [1, 2, 5], 'angle': [45, 10, 100, 140, 210, 270, 300], 'auto_param': [True]}},
{'method': 'GradientBlur',
'params': {'point': [[10, 10]], 'auto_param': [True]}},
{'method': 'UniformNoise',
'params': {'factor': [0.1, 0.2, 0.3], 'auto_param': [False, True]}},
{'method': 'GaussianNoise',
'params': {'factor': [0.1, 0.2, 0.3], 'auto_param': [False, True]}},
{'method': 'SaltAndPepperNoise',
'params': {'factor': [0.1, 0.2, 0.3], 'auto_param': [False, True]}},
{'method': 'NaturalNoise',
'params': {'ratio': [0.1, 0.2, 0.3], 'k_x_range': [(1, 3), (1, 5)], 'k_y_range': [(1, 5)],
'auto_param': [False, True]}},
{'method': 'Contrast',
'params': {'alpha': [0.5, 1, 1.5], 'beta': [-10, 0, 10], 'auto_param': [False, True]}},
{'method': 'GradientLuminance',
'params': {'color_start': [(0, 0, 0)], 'color_end': [(255, 255, 255)], 'start_point': [(10, 10)],
'scope': [0.5], 'pattern': ['light'], 'bright_rate': [0.3], 'mode': ['circle'],
'auto_param': [False, True]}},
{'method': 'Translate',
'params': {'x_bias': [0, 0.05, -0.05], 'y_bias': [0, -0.05, 0.05], 'auto_param': [False, True]}},
{'method': 'Scale',
'params': {'factor_x': [1, 0.9], 'factor_y': [1, 0.9], 'auto_param': [False, True]}},
{'method': 'Shear',
'params': {'factor': [0.2, 0.1], 'direction': ['horizontal', 'vertical'], 'auto_param': [False, True]}},
{'method': 'Rotate',
'params': {'angle': [20, 90], 'auto_param': [False, True]}},
{'method': 'Perspective',
'params': {'ori_pos': [[[0, 0], [0, 800], [800, 0], [800, 800]]],
'dst_pos': [[[50, 0], [0, 800], [780, 0], [800, 800]]], 'auto_param': [False, True]}},
{'method': 'Curve',
'params': {'curves': [5], 'depth': [2], 'mode': ['vertical'], 'auto_param': [False, True]}},
{'method': 'FGSM',
'params': {'eps': [0.3, 0.2, 0.4], 'alpha': [0.1], 'bounds': [(0, 1)]}},
{'method': 'PGD',
'params': {'eps': [0.1, 0.2, 0.4], 'eps_iter': [0.05, 0.1], 'nb_iter': [1, 3]}},
{'method': 'MDIIM',
'params': {'eps': [0.1, 0.2, 0.4], 'prob': [0.5, 0.1],
'norm_level': [1, 2, '1', '2', 'l1', 'l2', 'inf', 'np.inf', 'linf']}}
]
# get training data
data_list = "../common/dataset/MNIST/train"
batch_size = 32
ds = generate_mnist_dataset(data_list, batch_size, sparse=False)
train_images = []
for data in ds.create_tuple_iterator(output_numpy=True):
images = data[0].astype(np.float32)
train_images.append(images)
train_images = np.concatenate(train_images, axis=0)
# fuzz test with original test data
# get test data
data_list = "../common/dataset/MNIST/test"
batch_size = 32
ds = generate_mnist_dataset(data_list, batch_size, sparse=False)
test_images = []
test_labels = []
for data in ds.create_tuple_iterator(output_numpy=True):
images = data[0].astype(np.float32)
labels = data[1]
test_images.append(images)
test_labels.append(labels)
test_images = np.concatenate(test_images, axis=0)
test_labels = np.concatenate(test_labels, axis=0)
initial_seeds = []
# make initial seeds
for img, label in zip(test_images, test_labels):
initial_seeds.append([img, label])
coverage = KMultisectionNeuronCoverage(model, train_images, segmented_num=100, incremental=True)
kmnc = coverage.get_metrics(test_images[:100])
print('KMNC of initial seeds is: ', kmnc)
initial_seeds = initial_seeds[:100]
model_fuzz_test = Fuzzer(model)
_, _, _, _, metrics = model_fuzz_test.fuzzing(mutate_config,
initial_seeds, coverage,
evaluate=True,
max_iters=10,
mutate_num_per_seed=20)
if metrics:
for key in metrics:
print(key + ': ', metrics[key])
if __name__ == '__main__':
# device_target can be "CPU"GPU, "" or "Ascend"
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
test_lenet_mnist_fuzzing()
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