!327 modify fuzz to call new image transform method

Merge pull request !327 from ZhidanLiu/master

examples/ai_fuzzer/fuzz_testing_and_model_enhense.py

@@ -27,7 +27,7 @@ from mindspore.nn.optim.momentum import Momentum
 
 from mindarmour.adv_robustness.defenses import AdversarialDefense
 from mindarmour.fuzz_testing import Fuzzer
-from mindarmour.fuzz_testing import ModelCoverageMetrics
+from mindarmour.fuzz_testing import KMultisectionNeuronCoverage
 from mindarmour.utils.logger import LogUtil
 
 from examples.common.dataset.data_processing import generate_mnist_dataset
@@ -38,33 +38,66 @@ TAG = 'Fuzz_testing and enhance model'
 LOGGER.set_level('INFO')
 
 
+def split_dataset(image, label, proportion):
+    """
+    Split the generated fuzz data into train and test set.
+    """
+    indices = np.arange(len(image))
+    random.shuffle(indices)
+    train_length = int(len(image) * proportion)
+    train_image = [image[i] for i in indices[:train_length]]
+    train_label = [label[i] for i in indices[:train_length]]
+    test_image = [image[i] for i in indices[:train_length]]
+    test_label = [label[i] for i in indices[:train_length]]
+    return train_image, train_label, test_image, test_label
+
+
 def example_lenet_mnist_fuzzing():
     """
     An example of fuzz testing and then enhance the non-robustness model.
     """
     # upload trained network
-    ckpt_path = '../common/networks/lenet5/trained_ckpt_file/lenet_m1-10_1250.ckpt'
+    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': 'Blur',
-                      'params': {'auto_param': [True]}},
-                     {'method': 'Contrast',
-                      'params': {'auto_param': [True]}},
-                     {'method': 'Translate',
-                      'params': {'auto_param': [True]}},
-                     {'method': 'Brightness',
-                      'params': {'auto_param': [True]}},
-                     {'method': 'Noise',
-                      'params': {'auto_param': [True]}},
-                     {'method': 'Scale',
-                      'params': {'auto_param': [True]}},
-                     {'method': 'Shear',
-                      'params': {'auto_param': [True]}},
-                     {'method': 'FGSM',
-                      'params': {'eps': [0.3, 0.2, 0.4], 'alpha': [0.1]}}
-                     ]
+    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], '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)]}}]
 
     # get training data
     data_list = "../common/dataset/MNIST/train"
@@ -75,49 +108,36 @@ def example_lenet_mnist_fuzzing():
         images = data[0].astype(np.float32)
         train_images.append(images)
     train_images = np.concatenate(train_images, axis=0)
-    neuron_num = 10
-    segmented_num = 1000
-
-    # initialize fuzz test with training dataset
-    model_coverage_test = ModelCoverageMetrics(model, neuron_num, segmented_num, train_images)
+    segmented_num = 100
 
     # fuzz test with original test data
-    # get test data
     data_list = "../common/dataset/MNIST/test"
-    batch_size = 32
-    init_samples = 5000
-    max_iters = 50000
+    batch_size = batch_size
+    init_samples = 50
+    max_iters = 500
     mutate_num_per_seed = 10
-    ds = generate_mnist_dataset(data_list, batch_size, num_samples=init_samples,
-                                sparse=False)
+    ds = generate_mnist_dataset(data_list, batch_size=batch_size, num_samples=init_samples, 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.append(data[0].astype(np.float32))
+        test_labels.append(data[1])
     test_images = np.concatenate(test_images, axis=0)
     test_labels = np.concatenate(test_labels, axis=0)
-    initial_seeds = []
+
+    coverage = KMultisectionNeuronCoverage(model, train_images, segmented_num=segmented_num, incremental=True)
+    kmnc = coverage.get_metrics(test_images[:100])
+    print('kmnc: ', kmnc)
 
     # make initial seeds
+    initial_seeds = []
     for img, label in zip(test_images, test_labels):
         initial_seeds.append([img, label])
 
-    model_coverage_test.calculate_coverage(
-        np.array(test_images[:100]).astype(np.float32))
-    LOGGER.info(TAG, 'KMNC of test dataset before fuzzing is : %s',
-                model_coverage_test.get_kmnc())
-    LOGGER.info(TAG, 'NBC of test dataset before fuzzing is : %s',
-                model_coverage_test.get_nbc())
-    LOGGER.info(TAG, 'SNAC of test dataset before fuzzing is : %s',
-                model_coverage_test.get_snac())
-
-    model_fuzz_test = Fuzzer(model, train_images, 10, 1000)
+    model_fuzz_test = Fuzzer(model)
     gen_samples, gt, _, _, metrics = model_fuzz_test.fuzzing(mutate_config,
-                                                             initial_seeds,
-                                                             eval_metrics='auto',
+                                                             initial_seeds, coverage,
+                                                             evaluate=True,
                                                              max_iters=max_iters,
                                                              mutate_num_per_seed=mutate_num_per_seed)
 
@@ -125,24 +145,10 @@ def example_lenet_mnist_fuzzing():
         for key in metrics:
             LOGGER.info(TAG, key + ': %s', metrics[key])
 
-    def split_dataset(image, label, proportion):
-        """
-        Split the generated fuzz data into train and test set.
-        """
-        indices = np.arange(len(image))
-        random.shuffle(indices)
-        train_length = int(len(image) * proportion)
-        train_image = [image[i] for i in indices[:train_length]]
-        train_label = [label[i] for i in indices[:train_length]]
-        test_image = [image[i] for i in indices[:train_length]]
-        test_label = [label[i] for i in indices[:train_length]]
-        return train_image, train_label, test_image, test_label
-
-    train_image, train_label, test_image, test_label = split_dataset(
-        gen_samples, gt, 0.7)
+    train_image, train_label, test_image, test_label = split_dataset(gen_samples, gt, 0.7)
 
     # load model B and test it on the test set
-    ckpt_path = '../common/networks/lenet5/trained_ckpt_file/lenet_m2-10_1250.ckpt'
+    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)
@@ -154,12 +160,11 @@ def example_lenet_mnist_fuzzing():
     # enhense model robustness
     lr = 0.001
     momentum = 0.9
-    loss_fn = SoftmaxCrossEntropyWithLogits(Sparse=True)
+    loss_fn = SoftmaxCrossEntropyWithLogits(sparse=True)
     optimizer = Momentum(net.trainable_params(), lr, momentum)
 
     adv_defense = AdversarialDefense(net, loss_fn, optimizer)
-    adv_defense.batch_defense(np.array(train_image).astype(np.float32),
-                              np.argmax(train_label, axis=1).astype(np.int32))
+    adv_defense.batch_defense(np.array(train_image).astype(np.float32), np.argmax(train_label, axis=1).astype(np.int32))
     preds_en = net(Tensor(test_image, dtype=mindspore.float32)).asnumpy()
     acc_en = np.sum(np.argmax(preds_en, axis=1) == np.argmax(test_label, axis=1)) / len(test_label)
     print('Accuracy of enhensed model on test set is ', acc_en)
@@ -167,5 +172,5 @@ def example_lenet_mnist_fuzzing():
 
 if __name__ == '__main__':
     # device_target can be "CPU", "GPU" or "Ascend"
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
     example_lenet_mnist_fuzzing()

examples/ai_fuzzer/lenet5_mnist_fuzzing.py

@@ -35,24 +35,50 @@ def test_lenet_mnist_fuzzing():
     load_dict = load_checkpoint(ckpt_path)
     load_param_into_net(net, load_dict)
     model = Model(net)
-    mutate_config = [{'method': 'Blur',
-                      'params': {'radius': [0.1, 0.2, 0.3],
-                                 'auto_param': [True, False]}},
-                     {'method': 'Contrast',
-                      'params': {'auto_param': [True]}},
-                     {'method': 'Translate',
-                      'params': {'auto_param': [True]}},
-                     {'method': 'Brightness',
-                      'params': {'auto_param': [True]}},
-                     {'method': 'Noise',
-                      'params': {'auto_param': [True]}},
-                     {'method': 'Scale',
-                      'params': {'auto_param': [True]}},
-                     {'method': 'Shear',
-                      'params': {'auto_param': [True]}},
-                     {'method': 'FGSM',
-                      'params': {'eps': [0.3, 0.2, 0.4], 'alpha': [0.1], 'bounds': [(0, 1)]}}
-                     ]
+    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"
@@ -88,7 +114,10 @@ def test_lenet_mnist_fuzzing():
     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,
+    _, _, _, _, metrics = model_fuzz_test.fuzzing(mutate_config,
+                                                  initial_seeds, coverage,
+                                                  evaluate=True,
+                                                  max_iters=10,
                                                   mutate_num_per_seed=20)
 
     if metrics:

mindarmour/fuzz_testing/fuzzing.py

@@ -24,10 +24,11 @@ from mindspore import nn
 from mindarmour.utils._check_param import check_model, check_numpy_param, check_param_multi_types, check_norm_level, \
     check_param_in_range, check_param_type, check_int_positive, check_param_bounds
 from mindarmour.utils.logger import LogUtil
-from ..adv_robustness.attacks import FastGradientSignMethod, \
+from mindarmour.adv_robustness.attacks import FastGradientSignMethod, \
     MomentumDiverseInputIterativeMethod, ProjectedGradientDescent
-from .image_transform import Contrast, Brightness, Blur, \
-    Noise, Translate, Scale, Shear, Rotate
+from mindarmour.natural_robustness.transform.image import GaussianBlur, MotionBlur, GradientBlur, UniformNoise,\
+    GaussianNoise, SaltAndPepperNoise, NaturalNoise, Contrast, GradientLuminance, Translate, Scale, Shear, Rotate, \
+    Perspective, Curve
 from .model_coverage_metrics import CoverageMetrics, KMultisectionNeuronCoverage
 
 LOGGER = LogUtil.get_instance()
@@ -104,17 +105,79 @@ class Fuzzer:
         target_model (Model): Target fuzz model.
 
     Examples:
+        >>> import numpy as np
+        >>> from mindspore import context
+        >>> from mindspore import nn
+        >>> from mindspore.common.initializer import TruncatedNormal
+        >>> from mindspore.ops import operations as P
+        >>> from mindspore.train import Model
+        >>> from mindspore.ops import TensorSummary
+        >>> from mindarmour.fuzz_testing import Fuzzer
+        >>> from mindarmour.fuzz_testing import KMultisectionNeuronCoverage
+        >>>
+        >>> class Net(nn.Cell):
+        >>>     def __init__(self):
+        >>>         super(Net, self).__init__()
+        >>>         self.conv1 = nn.Conv2d(1, 6, 5, padding=0, weight_init=TruncatedNormal(0.02), pad_mode="valid")
+        >>>         self.conv2 = nn.Conv2d(6, 16, 5, padding=0, weight_init=TruncatedNormal(0.02), pad_mode="valid")
+        >>>         self.fc1 = nn.Dense(16 * 5 * 5, 120, TruncatedNormal(0.02), TruncatedNormal(0.02))
+        >>>         self.fc2 = nn.Dense(120, 84, TruncatedNormal(0.02), TruncatedNormal(0.02))
+        >>>         self.fc3 = nn.Dense(84, 10, TruncatedNormal(0.02), TruncatedNormal(0.02))
+        >>>         self.relu = nn.ReLU()
+        >>>         self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        >>>         self.reshape = P.Reshape()
+        >>>         self.summary = TensorSummary()
+        >>>
+        >>>    def construct(self, x):
+        >>>         x = self.conv1(x)
+        >>>         x = self.relu(x)
+        >>>         self.summary('conv1', x)
+        >>>         x = self.max_pool2d(x)
+        >>>         x = self.conv2(x)
+        >>>         x = self.relu(x)
+        >>>         self.summary('conv2', x)
+        >>>         x = self.max_pool2d(x)
+        >>>         x = self.reshape(x, (-1, 16 * 5 * 5))
+        >>>         x = self.fc1(x)
+        >>>         x = self.relu(x)
+        >>>         self.summary('fc1', x)
+        >>>         x = self.fc2(x)
+        >>>         x = self.relu(x)
+        >>>         self.summary('fc2', x)
+        >>>         x = self.fc3(x)
+        >>>         self.summary('fc3', x)
+        >>>         return x
+        >>>
         >>> net = Net()
         >>> model = Model(net)
-        >>> mutate_config = [{'method': 'Blur',
-        ...                   'params': {'auto_param': [True]}},
+        >>> 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': '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': 'Contrast',
-        ...                   'params': {'factor': [2]}},
-        ...                  {'method': 'Translate',
-        ...                   'params': {'x_bias': [0.1, 0.2], 'y_bias': [0.2]}},
+        ...                   'params': {'alpha': [0.5, 1, 1.5], 'beta': [-10, 0, 10], 'auto_param': [False, True]}},
+        ...                  {'method': 'Rotate',
+        ...                   'params': {'angle': [20, 90], 'auto_param': [False, True]}},
         ...                  {'method': 'FGSM',
-        ...                   'params': {'eps': [0.1, 0.2, 0.3], 'alpha': [0.1]}}]
-        >>> nc = KMultisectionNeuronCoverage(model, train_images, segmented_num=100)
+        ...                   'params': {'eps': [0.3, 0.2, 0.4], 'alpha': [0.1], 'bounds': [(0, 1)]}}]
+        >>> batch_size = 8
+        >>> num_classe = 10
+        >>> train_images = np.random.rand(32, 1, 32, 32).astype(np.float32)
+        >>> test_images = np.random.rand(batch_size, 1, 32, 32).astype(np.float32)
+        >>> test_labels = np.random.randint(num_classe, size=batch_size).astype(np.int32)
+        >>> test_labels = (np.eye(num_classe)[test_labels]).astype(np.float32)
+        >>> initial_seeds = []
+        >>> # make initial seeds
+        >>> for img, label in zip(test_images, test_labels):
+        >>>     initial_seeds.append([img, label])
+
+        >>> initial_seeds = initial_seeds[:10]
+        >>> nc = KMultisectionNeuronCoverage(model, train_images, segmented_num=100, incremental=True)
         >>> model_fuzz_test = Fuzzer(model)
         >>> samples, gt_labels, preds, strategies, metrics = model_fuzz_test.fuzzing(mutate_config, initial_seeds,
         ...                                                                          nc, max_iters=100)
@@ -125,18 +188,26 @@ class Fuzzer:
 
         # Allowed mutate strategies so far.
         self._strategies = {'Contrast': Contrast,
-                            'Brightness': Brightness,
-                            'Blur': Blur,
-                            'Noise': Noise,
+                            'GradientLuminance': GradientLuminance,
+                            'GaussianBlur': GaussianBlur,
+                            'MotionBlur': MotionBlur,
+                            'GradientBlur': GradientBlur,
+                            'UniformNoise': UniformNoise,
+                            'GaussianNoise': GaussianNoise,
+                            'SaltAndPepperNoise': SaltAndPepperNoise,
+                            'NaturalNoise': NaturalNoise,
                             'Translate': Translate,
                             'Scale': Scale,
                             'Shear': Shear,
                             'Rotate': Rotate,
+                            'Perspective': Perspective,
+                            'Curve': Curve,
                             'FGSM': FastGradientSignMethod,
                             'PGD': ProjectedGradientDescent,
                             'MDIIM': MomentumDiverseInputIterativeMethod}
-        self._affine_trans_list = ['Translate', 'Scale', 'Shear', 'Rotate']
-        self._pixel_value_trans_list = ['Contrast', 'Brightness', 'Blur', 'Noise']
+        self._affine_trans_list = ['Translate', 'Scale', 'Shear', 'Rotate', 'Perspective', 'Curve']
+        self._pixel_value_trans_list = ['Contrast', 'GradientLuminance', 'GaussianBlur', 'MotionBlur', 'GradientBlur',
+                                        'UniformNoise', 'GaussianNoise', 'SaltAndPepperNoise', 'NaturalNoise']
         self._attacks_list = ['FGSM', 'PGD', 'MDIIM']
         self._attack_param_checklists = {
             'FGSM': {'eps': {'dtype': [float], 'range': [0, 1]},
@@ -144,10 +215,11 @@ class Fuzzer:
                      'bounds': {'dtype': [tuple, list]}},
             'PGD': {'eps': {'dtype': [float], 'range': [0, 1]},
                     'eps_iter': {'dtype': [float], 'range': [0, 1]},
-                    'nb_iter': {'dtype': [int], 'range': [0, 100000]},
+                    'nb_iter': {'dtype': [int]},
                     'bounds': {'dtype': [tuple, list]}},
             'MDIIM': {'eps': {'dtype': [float], 'range': [0, 1]},
-                      'norm_level': {'dtype': [str, int], 'range': [1, 2, '1', '2', 'l1', 'l2', 'inf', 'np.inf']},
+                      'norm_level': {'dtype': [str, int],
+                                     'range': [1, 2, '1', '2', 'l1', 'l2', 'inf', 'linf', 'np.inf']},
                       'prob': {'dtype': [float], 'range': [0, 1]},
                       'bounds': {'dtype': [tuple, list]}}}
 
@@ -157,18 +229,26 @@ class Fuzzer:
 
         Args:
             mutate_config (list): Mutate configs. The format is
-                [{'method': 'Blur',
-                'params': {'radius': [0.1, 0.2], 'auto_param': [True, False]}},
-                {'method': 'Contrast',
-                'params': {'factor': [1, 1.5, 2]}},
-                {'method': 'FGSM',
-                'params': {'eps': [0.3, 0.2, 0.4], 'alpha': [0.1]}},
-                ...].
+                 [{'method': 'GaussianBlur',
+                   'params': {'ksize': [1, 2, 3, 5], 'auto_param': [True, False]}},
+                  {'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': 'Contrast',
+                   'params': {'alpha': [0.5, 1, 1.5], 'beta': [-10, 0, 10], 'auto_param': [False, True]}},
+                  {'method': 'Rotate',
+                   'params': {'angle': [20, 90], 'auto_param': [False, True]}},
+                  {'method': 'FGSM',
+                   'params': {'eps': [0.3, 0.2, 0.4], 'alpha': [0.1], 'bounds': [(0, 1)]}}]
+                   ...].
                 The supported methods list is in `self._strategies`, and the params of each method must within the
                 range of optional parameters. Supported methods are grouped in three types: Firstly, pixel value based
                 transform methods include: 'Contrast', 'Brightness', 'Blur' and 'Noise'. Secondly, affine transform
                 methods include: 'Translate', 'Scale', 'Shear' and 'Rotate'. Thirdly, attack methods include: 'FGSM',
-                'PGD' and 'MDIIM'. `mutate_config` must have method in the type of pixel value based transform methods.
+                'PGD' and 'MDIIM'. 'FGSM', 'PGD' and 'MDIIM'. are abbreviations of FastGradientSignMethod,
+                ProjectedGradientDescent and MomentumDiverseInputIterativeMethod.
+                `mutate_config` must have method in the type of pixel value based transform methods.
                 The way of setting parameters for first and second type methods can be seen in
                 'mindarmour/fuzz_testing/image_transform.py'. For third type methods, the optional parameters refer to
                 `self._attack_param_checklists`.
@@ -278,7 +358,6 @@ class Fuzzer:
             if only_pixel_trans:
                 while strategy['method'] not in self._pixel_value_trans_list:
                     strategy = choice(mutate_config)
-            transform = mutates[strategy['method']]
             params = strategy['params']
             method = strategy['method']
             selected_param = {}
@@ -290,9 +369,10 @@ class Fuzzer:
                     shear_keys = selected_param.keys()
                     if 'factor_x' in shear_keys and 'factor_y' in shear_keys:
                         selected_param[choice(['factor_x', 'factor_y'])] = 0
-                transform.set_params(**selected_param)
-                mutate_sample = transform.transform(seed[0])
+                transform = mutates[strategy['method']](**selected_param)
+                mutate_sample = transform(seed[0])
             else:
+                transform = mutates[strategy['method']]
                 for param_name in selected_param:
                     transform.__setattr__('_' + str(param_name), selected_param[param_name])
                 mutate_sample = transform.generate(np.array([seed[0].astype(np.float32)]), np.array([seed[1]]))[0]
@@ -360,6 +440,8 @@ class Fuzzer:
                     _ = check_param_bounds('bounds', param_value)
                 elif param_name == 'norm_level':
                     _ = check_norm_level(param_value)
+                elif param_name == 'nb_iter':
+                    _ = check_int_positive(param_name, param_value)
                 else:
                     allow_type = self._attack_param_checklists[method][param_name]['dtype']
                     allow_range = self._attack_param_checklists[method][param_name]['range']
@@ -372,7 +454,8 @@ class Fuzzer:
         for mutate in mutate_config:
             method = mutate['method']
             if method not in self._attacks_list:
-                mutates[method] = self._strategies[method]()
+                # mutates[method] = self._strategies[method]()
+                mutates[method] = self._strategies[method]
             else:
                 network = self._target_model._network
                 loss_fn = self._target_model._loss_fn
@@ -414,7 +497,6 @@ class Fuzzer:
         else:
             attack_success_rate = None
         metrics_report['Attack_success_rate'] = attack_success_rate
-
         metrics_report['Coverage_metrics'] = coverage.get_metrics(fuzz_samples)
 
         return metrics_report

mindarmour/fuzz_testing/image_transform.py

@@ -1,609 +0,0 @@
-# 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.
-"""
-Image transform
-"""
-import numpy as np
-from PIL import Image, ImageEnhance, ImageFilter
-
-from mindspore.dataset.vision.py_transforms_util import is_numpy, \
-    to_pil, hwc_to_chw
-from mindarmour.utils._check_param import check_param_multi_types, check_param_in_range, check_numpy_param
-from mindarmour.utils.logger import LogUtil
-
-LOGGER = LogUtil.get_instance()
-TAG = 'Image Transformation'
-
-
-def chw_to_hwc(img):
-    """
-    Transpose the input image; shape (C, H, W) to shape (H, W, C).
-
-    Args:
-        img (numpy.ndarray): Image to be converted.
-
-    Returns:
-        img (numpy.ndarray), Converted image.
-    """
-    if is_numpy(img):
-        return img.transpose(1, 2, 0).copy()
-    raise TypeError('img should be numpy.ndarray. Got {}'.format(type(img)))
-
-
-def is_hwc(img):
-    """
-    Check if the input image is shape (H, W, C).
-
-    Args:
-        img (numpy.ndarray): Image to be checked.
-
-    Returns:
-        Bool, True if input is shape (H, W, C).
-    """
-    if is_numpy(img):
-        img_shape = np.shape(img)
-        if img_shape[2] == 3 and img_shape[1] > 3 and img_shape[0] > 3:
-            return True
-        return False
-    raise TypeError('img should be numpy.ndarray. Got {}'.format(type(img)))
-
-
-def is_chw(img):
-    """
-    Check if the input image is shape (H, W, C).
-
-    Args:
-        img (numpy.ndarray): Image to be checked.
-
-    Returns:
-        Bool, True if input is shape (H, W, C).
-    """
-    if is_numpy(img):
-        img_shape = np.shape(img)
-        if img_shape[0] == 3 and img_shape[1] > 3 and img_shape[2] > 3:
-            return True
-        return False
-    raise TypeError('img should be numpy.ndarray. Got {}'.format(type(img)))
-
-
-def is_rgb(img):
-    """
-    Check if the input image is RGB.
-
-    Args:
-        img (numpy.ndarray): Image to be checked.
-
-    Returns:
-        Bool, True if input is RGB.
-    """
-    if is_numpy(img):
-        img_shape = np.shape(img)
-        if len(np.shape(img)) == 3 and (img_shape[0] == 3 or img_shape[2] == 3):
-            return True
-        return False
-    raise TypeError('img should be numpy.ndarray. Got {}'.format(type(img)))
-
-
-def is_normalized(img):
-    """
-    Check if the input image is normalized between 0 to 1.
-
-    Args:
-        img (numpy.ndarray): Image to be checked.
-
-    Returns:
-        Bool, True if input is normalized between 0 to 1.
-    """
-    if is_numpy(img):
-        minimal = np.min(img)
-        maximun = np.max(img)
-        if minimal >= 0 and maximun <= 1:
-            return True
-        return False
-    raise TypeError('img should be Numpy array. Got {}'.format(type(img)))
-
-
-class ImageTransform:
-    """
-    The abstract base class for all image transform classes.
-    """
-
-    def __init__(self):
-        pass
-
-    def _check(self, image):
-        """ Check image format. If input image is RGB and its shape
-        is (C, H, W), it will be transposed to (H, W, C). If the value
-        of the image is not normalized , it will be normalized between 0 to 1."""
-        rgb = is_rgb(image)
-        chw = False
-        gray3dim = False
-        normalized = is_normalized(image)
-        if rgb:
-            chw = is_chw(image)
-            if chw:
-                image = chw_to_hwc(image)
-            else:
-                image = image
-        else:
-            if len(np.shape(image)) == 3:
-                gray3dim = True
-                image = image[0]
-            else:
-                image = image
-        if normalized:
-            image = image*255
-        return rgb, chw, normalized, gray3dim, np.uint8(image)
-
-    def _original_format(self, image, chw, normalized, gray3dim):
-        """ Return transformed image with original format. """
-        if not is_numpy(image):
-            image = np.array(image)
-        if chw:
-            image = hwc_to_chw(image)
-        if normalized:
-            image = image / 255
-        if gray3dim:
-            image = np.expand_dims(image, 0)
-        return image
-
-    def transform(self, image):
-        pass
-
-
-class Contrast(ImageTransform):
-    """
-    Contrast of an image.
-
-    Args:
-        factor (Union[float, int]): Control the contrast of an image. If 1.0,
-            gives the original image. If 0, gives a gray image. Default: 1.
-    """
-
-    def __init__(self, factor=1):
-        super(Contrast, self).__init__()
-        self.set_params(factor)
-
-    def set_params(self, factor=1, auto_param=False):
-        """
-        Set contrast parameters.
-
-        Args:
-            factor (Union[float, int]): Control the contrast of an image. If 1.0
-                gives the original image. If 0 gives a gray image. Default: 1.
-            auto_param (bool): True if auto generate parameters. Default: False.
-        """
-        if auto_param:
-            self.factor = np.random.uniform(-5, 5)
-        else:
-            self.factor = check_param_multi_types('factor', factor, [int, float])
-
-    def transform(self, image):
-        """
-        Transform the image.
-
-        Args:
-            image (numpy.ndarray): Original image to be transformed.
-
-        Returns:
-            numpy.ndarray, transformed image.
-        """
-        image = check_numpy_param('image', image)
-        ori_dtype = image.dtype
-        _, chw, normalized, gray3dim, image = self._check(image)
-        image = to_pil(image)
-        img_contrast = ImageEnhance.Contrast(image)
-        trans_image = img_contrast.enhance(self.factor)
-        trans_image = self._original_format(trans_image, chw, normalized,
-                                            gray3dim)
-
-        return trans_image.astype(ori_dtype)
-
-
-class Brightness(ImageTransform):
-    """
-    Brightness of an image.
-
-    Args:
-        factor (Union[float, int]): Control the brightness of an image. If 1.0
-            gives the original image. If 0 gives a black image. Default: 1.
-    """
-
-    def __init__(self, factor=1):
-        super(Brightness, self).__init__()
-        self.set_params(factor)
-
-    def set_params(self, factor=1, auto_param=False):
-        """
-        Set brightness parameters.
-
-        Args:
-            factor (Union[float, int]): Control the brightness of an image. If 1
-                gives the original image. If 0 gives a black image. Default: 1.
-            auto_param (bool): True if auto generate parameters. Default: False.
-        """
-        if auto_param:
-            self.factor = np.random.uniform(0, 5)
-        else:
-            self.factor = check_param_multi_types('factor', factor, [int, float])
-
-    def transform(self, image):
-        """
-        Transform the image.
-
-        Args:
-            image (numpy.ndarray): Original image to be transformed.
-
-        Returns:
-            numpy.ndarray, transformed image.
-        """
-        image = check_numpy_param('image', image)
-        ori_dtype = image.dtype
-        _, chw, normalized, gray3dim, image = self._check(image)
-        image = to_pil(image)
-        img_contrast = ImageEnhance.Brightness(image)
-        trans_image = img_contrast.enhance(self.factor)
-        trans_image = self._original_format(trans_image, chw, normalized,
-                                            gray3dim)
-        return trans_image.astype(ori_dtype)
-
-
-class Blur(ImageTransform):
-    """
-    Blurs the image using Gaussian blur filter.
-
-    Args:
-        radius(Union[float, int]): Blur radius, 0 means no blur. Default: 0.
-    """
-
-    def __init__(self, radius=0):
-        super(Blur, self).__init__()
-        self.set_params(radius)
-
-    def set_params(self, radius=0, auto_param=False):
-        """
-        Set blur parameters.
-
-        Args:
-            radius (Union[float, int]): Blur radius, 0 means no blur. Default: 0.
-            auto_param (bool): True if auto generate parameters. Default: False.
-        """
-        if auto_param:
-            self.radius = np.random.uniform(-1.5, 1.5)
-        else:
-            self.radius = check_param_multi_types('radius', radius, [int, float])
-
-    def transform(self, image):
-        """
-        Transform the image.
-
-        Args:
-            image (numpy.ndarray): Original image to be transformed.
-
-        Returns:
-            numpy.ndarray, transformed image.
-        """
-        image = check_numpy_param('image', image)
-        ori_dtype = image.dtype
-        _, chw, normalized, gray3dim, image = self._check(image)
-        image = to_pil(image)
-        trans_image = image.filter(ImageFilter.GaussianBlur(radius=self.radius))
-        trans_image = self._original_format(trans_image, chw, normalized,
-                                            gray3dim)
-        return trans_image.astype(ori_dtype)
-
-
-class Noise(ImageTransform):
-    """
-    Add noise of an image.
-
-    Args:
-        factor (float): factor is the ratio of pixels to add noise.
-            If 0 gives the original image. Default 0.
-    """
-
-    def __init__(self, factor=0):
-        super(Noise, self).__init__()
-        self.set_params(factor)
-
-    def set_params(self, factor=0, auto_param=False):
-        """
-        Set noise parameters.
-
-        Args:
-            factor (Union[float, int]): factor is the ratio of pixels to
-                add noise. If 0 gives the original image. Default 0.
-            auto_param (bool): True if auto generate parameters. Default: False.
-        """
-        if auto_param:
-            self.factor = np.random.uniform(0, 1)
-        else:
-            self.factor = check_param_multi_types('factor', factor, [int, float])
-
-    def transform(self, image):
-        """
-        Transform the image.
-
-        Args:
-            image (numpy.ndarray): Original image to be transformed.
-
-        Returns:
-            numpy.ndarray, transformed image.
-        """
-        image = check_numpy_param('image', image)
-        ori_dtype = image.dtype
-        _, chw, normalized, gray3dim, image = self._check(image)
-        noise = np.random.uniform(low=-1, high=1, size=np.shape(image))
-        trans_image = np.copy(image)
-        threshold = 1 - self.factor
-        trans_image[noise < -threshold] = 0
-        trans_image[noise > threshold] = 1
-        trans_image = self._original_format(trans_image, chw, normalized,
-                                            gray3dim)
-        return trans_image.astype(ori_dtype)
-
-
-class Translate(ImageTransform):
-    """
-    Translate an image.
-
-    Args:
-        x_bias (Union[int, float]): X-direction translation, x = x + x_bias*image_length.
-            Default: 0.
-        y_bias (Union[int, float]): Y-direction translation,  y = y + y_bias*image_wide.
-            Default: 0.
-    """
-
-    def __init__(self, x_bias=0, y_bias=0):
-        super(Translate, self).__init__()
-        self.set_params(x_bias, y_bias)
-
-    def set_params(self, x_bias=0, y_bias=0, auto_param=False):
-        """
-        Set translate parameters.
-
-        Args:
-            x_bias (Union[float, int]): X-direction translation, and x_bias should be in range of (-1, 1). Default: 0.
-            y_bias (Union[float, int]): Y-direction translation, and y_bias should be in range of (-1, 1). Default: 0.
-            auto_param (bool): True if auto generate parameters. Default: False.
-        """
-        x_bias = check_param_in_range('x_bias', x_bias, -1, 1)
-        y_bias = check_param_in_range('y_bias', y_bias, -1, 1)
-        self.auto_param = auto_param
-        if auto_param:
-            self.x_bias = np.random.uniform(-0.3, 0.3)
-            self.y_bias = np.random.uniform(-0.3, 0.3)
-        else:
-            self.x_bias = check_param_multi_types('x_bias', x_bias,
-                                                  [int, float])
-            self.y_bias = check_param_multi_types('y_bias', y_bias,
-                                                  [int, float])
-
-    def transform(self, image):
-        """
-        Transform the image.
-
-        Args:
-            image(numpy.ndarray): Original image to be transformed.
-
-        Returns:
-            numpy.ndarray, transformed image.
-        """
-        image = check_numpy_param('image', image)
-        ori_dtype = image.dtype
-        _, chw, normalized, gray3dim, image = self._check(image)
-        img = to_pil(image)
-        image_shape = np.shape(image)
-        self.x_bias = image_shape[1]*self.x_bias
-        self.y_bias = image_shape[0]*self.y_bias
-        trans_image = img.transform(img.size, Image.AFFINE,
-                                    (1, 0, self.x_bias, 0, 1, self.y_bias))
-        trans_image = self._original_format(trans_image, chw, normalized,
-                                            gray3dim)
-        return trans_image.astype(ori_dtype)
-
-
-class Scale(ImageTransform):
-    """
-    Scale an image in the middle.
-
-    Args:
-        factor_x (Union[float, int]): Rescale in X-direction, x=factor_x*x.
-            Default: 1.
-        factor_y (Union[float, int]): Rescale in Y-direction, y=factor_y*y.
-            Default: 1.
-    """
-
-    def __init__(self, factor_x=1, factor_y=1):
-        super(Scale, self).__init__()
-        self.set_params(factor_x, factor_y)
-
-    def set_params(self, factor_x=1, factor_y=1, auto_param=False):
-
-        """
-        Set scale parameters.
-
-        Args:
-            factor_x (Union[float, int]): Rescale in X-direction, x=factor_x*x.
-                Default: 1.
-            factor_y (Union[float, int]): Rescale in Y-direction, y=factor_y*y.
-                Default: 1.
-            auto_param (bool): True if auto generate parameters. Default: False.
-        """
-        if auto_param:
-            self.factor_x = np.random.uniform(0.7, 3)
-            self.factor_y = np.random.uniform(0.7, 3)
-        else:
-            self.factor_x = check_param_multi_types('factor_x', factor_x,
-                                                    [int, float])
-            self.factor_y = check_param_multi_types('factor_y', factor_y,
-                                                    [int, float])
-
-    def transform(self, image):
-        """
-        Transform the image.
-
-        Args:
-            image(numpy.ndarray): Original image to be transformed.
-
-        Returns:
-            numpy.ndarray, transformed image.
-        """
-        image = check_numpy_param('image', image)
-        ori_dtype = image.dtype
-        rgb, chw, normalized, gray3dim, image = self._check(image)
-        if rgb:
-            h, w, _ = np.shape(image)
-        else:
-            h, w = np.shape(image)
-        move_x_centor = w / 2*(1 - self.factor_x)
-        move_y_centor = h / 2*(1 - self.factor_y)
-        img = to_pil(image)
-        trans_image = img.transform(img.size, Image.AFFINE,
-                                    (self.factor_x, 0, move_x_centor,
-                                     0, self.factor_y, move_y_centor))
-        trans_image = self._original_format(trans_image, chw, normalized,
-                                            gray3dim)
-        return trans_image.astype(ori_dtype)
-
-
-class Shear(ImageTransform):
-    """
-    Shear an image, for each pixel (x, y) in the sheared image, the new value is
-    taken from a position (x+factor_x*y, factor_y*x+y) in the origin image. Then
-    the sheared image will be rescaled to fit original size.
-
-    Args:
-        factor_x (Union[float, int]): Shear factor of horizontal direction.
-            Default: 0.
-        factor_y (Union[float, int]): Shear factor of vertical direction.
-            Default: 0.
-
-    """
-
-    def __init__(self, factor_x=0, factor_y=0):
-        super(Shear, self).__init__()
-        self.set_params(factor_x, factor_y)
-
-    def set_params(self, factor_x=0, factor_y=0, auto_param=False):
-        """
-        Set shear parameters.
-
-        Args:
-            factor_x (Union[float, int]): Shear factor of horizontal direction.
-                Default: 0.
-            factor_y (Union[float, int]): Shear factor of vertical direction.
-                Default: 0.
-            auto_param (bool): True if auto generate parameters. Default: False.
-        """
-        if factor_x != 0 and factor_y != 0:
-            msg = 'At least one of factor_x and factor_y is zero.'
-            LOGGER.error(TAG, msg)
-            raise ValueError(msg)
-        if auto_param:
-            if np.random.uniform(-1, 1) > 0:
-                self.factor_x = np.random.uniform(-2, 2)
-                self.factor_y = 0
-            else:
-                self.factor_x = 0
-                self.factor_y = np.random.uniform(-2, 2)
-        else:
-            self.factor_x = check_param_multi_types('factor', factor_x,
-                                                    [int, float])
-            self.factor_y = check_param_multi_types('factor', factor_y,
-                                                    [int, float])
-
-    def transform(self, image):
-        """
-        Transform the image.
-
-        Args:
-            image(numpy.ndarray): Original image to be transformed.
-
-        Returns:
-            numpy.ndarray, transformed image.
-        """
-        image = check_numpy_param('image', image)
-        ori_dtype = image.dtype
-        rgb, chw, normalized, gray3dim, image = self._check(image)
-        img = to_pil(image)
-        if rgb:
-            h, w, _ = np.shape(image)
-        else:
-            h, w = np.shape(image)
-        if self.factor_x != 0:
-            boarder_x = [0, -w, -self.factor_x*h, -w - self.factor_x*h]
-            min_x = min(boarder_x)
-            max_x = max(boarder_x)
-            scale = (max_x - min_x) / w
-            move_x_cen = (w - scale*w - scale*h*self.factor_x) / 2
-            move_y_cen = h*(1 - scale) / 2
-        else:
-            boarder_y = [0, -h, -self.factor_y*w, -h - self.factor_y*w]
-            min_y = min(boarder_y)
-            max_y = max(boarder_y)
-            scale = (max_y - min_y) / h
-            move_y_cen = (h - scale*h - scale*w*self.factor_y) / 2
-            move_x_cen = w*(1 - scale) / 2
-        trans_image = img.transform(img.size, Image.AFFINE,
-                                    (scale, scale*self.factor_x, move_x_cen,
-                                     scale*self.factor_y, scale, move_y_cen))
-        trans_image = self._original_format(trans_image, chw, normalized,
-                                            gray3dim)
-        return trans_image.astype(ori_dtype)
-
-
-class Rotate(ImageTransform):
-    """
-    Rotate an image of degrees counter clockwise around its center.
-
-    Args:
-        angle(Union[float, int]): Degrees counter clockwise. Default: 0.
-    """
-
-    def __init__(self, angle=0):
-        super(Rotate, self).__init__()
-        self.set_params(angle)
-
-    def set_params(self, angle=0, auto_param=False):
-        """
-        Set rotate parameters.
-
-        Args:
-            angle(Union[float, int]): Degrees counter clockwise. Default: 0.
-            auto_param (bool): True if auto generate parameters. Default: False.
-        """
-        if auto_param:
-            self.angle = np.random.uniform(0, 360)
-        else:
-            self.angle = check_param_multi_types('angle', angle, [int, float])
-
-    def transform(self, image):
-        """
-        Transform the image.
-
-        Args:
-            image(numpy.ndarray): Original image to be transformed.
-
-        Returns:
-            numpy.ndarray, transformed image.
-        """
-        image = check_numpy_param('image', image)
-        ori_dtype = image.dtype
-        _, chw, normalized, gray3dim, image = self._check(image)
-        img = to_pil(image)
-        trans_image = img.rotate(self.angle, expand=False)
-        trans_image = self._original_format(trans_image, chw, normalized,
-                                            gray3dim)
-        return trans_image.astype(ori_dtype)

tests/ut/python/fuzzing/test_fuzzer.py

@@ -99,15 +99,17 @@ def test_fuzzing_ascend():
     model = Model(net)
     batch_size = 8
     num_classe = 10
-    mutate_config = [{'method': 'Blur',
-                      'params': {'auto_param': [True]}},
+    mutate_config = [{'method': 'GaussianBlur',
+                      'params': {'ksize': [1, 2, 3, 5],
+                                 'auto_param': [True, False]}},
+                     {'method': 'UniformNoise',
+                      'params': {'factor': [0.1, 0.2, 0.3], 'auto_param': [False, True]}},
                      {'method': 'Contrast',
-                      'params': {'factor': [2, 1]}},
-                     {'method': 'Translate',
-                      'params': {'x_bias': [0.1, 0.3], 'y_bias': [0.2]}},
+                      'params': {'alpha': [0.5, 1, 1.5], 'beta': [-10, 0, 10], 'auto_param': [False, True]}},
+                     {'method': 'Rotate',
+                      'params': {'angle': [20, 90], 'auto_param': [False, True]}},
                      {'method': 'FGSM',
-                      'params': {'eps': [0.1, 0.2, 0.3], 'alpha': [0.1]}}
-                     ]
+                      'params': {'eps': [0.3, 0.2, 0.4], 'alpha': [0.1], 'bounds': [(0, 1)]}}]
 
     train_images = np.random.rand(32, 1, 32, 32).astype(np.float32)
     # fuzz test with original test data
@@ -142,15 +144,17 @@ def test_fuzzing_cpu():
     model = Model(net)
     batch_size = 8
     num_classe = 10
-    mutate_config = [{'method': 'Blur',
-                      'params': {'auto_param': [True]}},
+    mutate_config = [{'method': 'GaussianBlur',
+                      'params': {'ksize': [1, 2, 3, 5],
+                                 'auto_param': [True, False]}},
+                     {'method': 'UniformNoise',
+                      'params': {'factor': [0.1, 0.2, 0.3], 'auto_param': [False, True]}},
                      {'method': 'Contrast',
-                      'params': {'factor': [2, 1]}},
-                     {'method': 'Translate',
-                      'params': {'x_bias': [0.1, 0.3], 'y_bias': [0.2]}},
+                      'params': {'alpha': [0.5, 1, 1.5], 'beta': [-10, 0, 10], 'auto_param': [False, True]}},
+                     {'method': 'Rotate',
+                      'params': {'angle': [20, 90], 'auto_param': [False, True]}},
                      {'method': 'FGSM',
-                      'params': {'eps': [0.1, 0.2, 0.3], 'alpha': [0.1]}}
-                     ]
+                      'params': {'eps': [0.3, 0.2, 0.4], 'alpha': [0.1], 'bounds': [(0, 1)]}}]
     # initialize fuzz test with training dataset
     train_images = np.random.rand(32, 1, 32, 32).astype(np.float32)
 

tests/ut/python/fuzzing/test_image_transform.py

@@ -1,126 +0,0 @@
-# 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.
-"""
-Image transform test.
-"""
-import numpy as np
-import pytest
-
-from mindarmour.utils.logger import LogUtil
-from mindarmour.fuzz_testing.image_transform import Contrast, Brightness, \
-    Blur, Noise, Translate, Scale, Shear, Rotate
-
-LOGGER = LogUtil.get_instance()
-TAG = 'Image transform test'
-LOGGER.set_level('INFO')
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.env_onecard
-@pytest.mark.component_mindarmour
-def test_contrast():
-    image = (np.random.rand(32, 32)).astype(np.float32)
-    trans = Contrast()
-    trans.set_params(auto_param=True)
-    _ = trans.transform(image)
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.env_onecard
-@pytest.mark.component_mindarmour
-def test_brightness():
-    image = (np.random.rand(32, 32)).astype(np.float32)
-    trans = Brightness()
-    trans.set_params(auto_param=True)
-    _ = trans.transform(image)
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.env_onecard
-@pytest.mark.component_mindarmour
-def test_blur():
-    image = (np.random.rand(32, 32)).astype(np.float32)
-    trans = Blur()
-    trans.set_params(auto_param=True)
-    _ = trans.transform(image)
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.env_onecard
-@pytest.mark.component_mindarmour
-def test_noise():
-    image = (np.random.rand(32, 32)).astype(np.float32)
-    trans = Noise()
-    trans.set_params(auto_param=True)
-    _ = trans.transform(image)
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.env_onecard
-@pytest.mark.component_mindarmour
-def test_translate():
-    image = (np.random.rand(32, 32)).astype(np.float32)
-    trans = Translate()
-    trans.set_params(auto_param=True)
-    _ = trans.transform(image)
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.env_onecard
-@pytest.mark.component_mindarmour
-def test_shear():
-    image = (np.random.rand(32, 32)).astype(np.float32)
-    trans = Shear()
-    trans.set_params(auto_param=True)
-    _ = trans.transform(image)
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.env_onecard
-@pytest.mark.component_mindarmour
-def test_scale():
-    image = (np.random.rand(32, 32)).astype(np.float32)
-    trans = Scale()
-    trans.set_params(auto_param=True)
-    _ = trans.transform(image)
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.env_onecard
-@pytest.mark.component_mindarmour
-def test_rotate():
-    image = (np.random.rand(32, 32)).astype(np.float32)
-    trans = Rotate()
-    trans.set_params(auto_param=True)
-    _ = trans.transform(image)