mindarmour/examples/ai_fuzzer/lenet5_mnist_fuzzing.py

# 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()