add comment and param check

example/mnist_demo/lenet5_mnist_coverage.py

@@ -67,7 +67,7 @@ def test_lenet_mnist_coverage():
         test_labels.append(labels)
     test_images = np.concatenate(test_images, axis=0)
     test_labels = np.concatenate(test_labels, axis=0)
-    model_fuzz_test.test_adequacy_coverage_calculate(test_images)
+    model_fuzz_test.calculate_coverage(test_images)
     LOGGER.info(TAG, 'KMNC of this test is : %s', model_fuzz_test.get_kmnc())
     LOGGER.info(TAG, 'NBC of this test is : %s', model_fuzz_test.get_nbc())
     LOGGER.info(TAG, 'SNAC of this test is : %s', model_fuzz_test.get_snac())
@@ -76,14 +76,13 @@ def test_lenet_mnist_coverage():
     loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
     attack = FastGradientSignMethod(net, eps=0.3, loss_fn=loss)
     adv_data = attack.batch_generate(test_images, test_labels, batch_size=32)
-    model_fuzz_test.test_adequacy_coverage_calculate(adv_data,
-                                                     bias_coefficient=0.5)
-    LOGGER.info(TAG, 'KMNC of this test is : %s', model_fuzz_test.get_kmnc())
-    LOGGER.info(TAG, 'NBC of this test is : %s', model_fuzz_test.get_nbc())
-    LOGGER.info(TAG, 'SNAC of this test is : %s', model_fuzz_test.get_snac())
+    model_fuzz_test.calculate_coverage(adv_data, bias_coefficient=0.5)
+    LOGGER.info(TAG, 'KMNC of this adv data is : %s', model_fuzz_test.get_kmnc())
+    LOGGER.info(TAG, 'NBC of this adv data is : %s', model_fuzz_test.get_nbc())
+    LOGGER.info(TAG, 'SNAC of this adv data is : %s', model_fuzz_test.get_snac())
 
 
 if __name__ == '__main__':
     # device_target can be "CPU", "GPU" or "Ascend"
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
     test_lenet_mnist_coverage()

example/mnist_demo/lenet5_mnist_fuzzing.py

@@ -87,9 +87,9 @@ def test_lenet_mnist_fuzzing():
     LOGGER.info(TAG, 'KMNC of this test is : %s',
                 model_coverage_test.get_kmnc())
 
-    model_fuzz_test = Fuzzer(model, train_images, 1000, 10)
+    model_fuzz_test = Fuzzer(model, train_images, 10, 1000)
     _, _, _, _, metrics = model_fuzz_test.fuzzing(mutate_config, initial_seeds,
-                                                  eval_metric=True)
+                                                  eval_metrics='auto')
     if metrics:
         for key in metrics:
             LOGGER.info(TAG, key + ': %s', metrics[key])
@@ -97,5 +97,5 @@ def test_lenet_mnist_fuzzing():
 
 if __name__ == '__main__':
     # device_target can be "CPU", "GPU" or "Ascend"
-    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
     test_lenet_mnist_fuzzing()

mindarmour/fuzzing/fuzzing.py

@@ -27,6 +27,48 @@ from mindarmour.fuzzing.image_transform import Contrast, Brightness, Blur, \
     Noise, Translate, Scale, Shear, Rotate
 from mindarmour.attacks import FastGradientSignMethod, \
     MomentumDiverseInputIterativeMethod, ProjectedGradientDescent
+from mindarmour.utils.logger import LogUtil
+
+LOGGER = LogUtil.get_instance()
+TAG = 'Fuzzer'
+
+
+def _select_next(initial_seeds):
+    """ Randomly select a seed from `initial_seeds`."""
+    seed_num = choice(range(len(initial_seeds)))
+    seed = initial_seeds[seed_num]
+    del initial_seeds[seed_num]
+    return seed, initial_seeds
+
+
+def _coverage_gains(coverages):
+    """ Calculate the coverage gains of mutated samples. """
+    gains = [0] + coverages[:-1]
+    gains = np.array(coverages) - np.array(gains)
+    return gains
+
+
+def _is_trans_valid(seed, mutate_sample):
+    """ Check a mutated sample is valid. If the number of changed pixels in
+    a seed is less than pixels_change_rate*size(seed), this mutate is valid.
+    Else check the infinite norm of seed changes, if the value of the
+    infinite norm less than pixel_value_change_rate*255, this mutate is
+    valid too. Otherwise the opposite.
+    """
+    is_valid = False
+    pixels_change_rate = 0.02
+    pixel_value_change_rate = 0.2
+    diff = np.array(seed - mutate_sample).flatten()
+    size = np.shape(diff)[0]
+    l0_norm = np.linalg.norm(diff, ord=0)
+    linf = np.linalg.norm(diff, ord=np.inf)
+    if l0_norm > pixels_change_rate*size:
+        if linf < 256:
+            is_valid = True
+    else:
+        if linf < pixel_value_change_rate*255:
+            is_valid = True
+    return is_valid
 
 
 class Fuzzer:
@@ -40,71 +82,203 @@ class Fuzzer:
         target_model (Model): Target fuzz model.
         train_dataset (numpy.ndarray): Training dataset used for determining
             the neurons' output boundaries.
-        segmented_num (int): The number of segmented sections of neurons'
-            output intervals.
         neuron_num (int): The number of testing neurons.
+        segmented_num (int): The number of segmented sections of neurons'
+            output intervals. Default: 1000.
+
+    Examples:
+        >>> net = Net()
+        >>> mutate_config = [{'method': 'Blur', 'params': {'auto_param': True}},
+        >>>                  {'method': 'Contrast','params': {'factor': 2}},
+        >>>                  {'method': 'Translate', 'params': {'x_bias': 0.1, 'y_bias': 0.2}},
+        >>>                  {'method': 'FGSM', 'params': {'eps': 0.1, 'alpha': 0.1}}]
+        >>> train_images = np.random.rand(32, 1, 32, 32).astype(np.float32)
+        >>> model_fuzz_test = Fuzzer(model, train_images, 1000, 10)
+        >>> samples, labels, preds, strategies, report = model_fuzz_test.fuzzing(mutate_config, initial_seeds)
     """
 
-    def __init__(self, target_model, train_dataset, segmented_num, neuron_num):
-        self.target_model = check_model('model', target_model, Model)
-        self.train_dataset = check_numpy_param('train_dataset', train_dataset)
-        self.coverage_metrics = ModelCoverageMetrics(target_model,
-                                                     segmented_num,
-                                                     neuron_num, train_dataset)
+    def __init__(self, target_model, train_dataset, neuron_num, segmented_num=1000):
+        self._target_model = check_model('model', target_model, Model)
+        train_dataset = check_numpy_param('train_dataset', train_dataset)
+        self._coverage_metrics = ModelCoverageMetrics(target_model,
+                                                      segmented_num,
+                                                      neuron_num, train_dataset)
         # Allowed mutate strategies so far.
-        self.strategies = {'Contrast': Contrast, 'Brightness': Brightness,
-                           'Blur': Blur, 'Noise': Noise, 'Translate': Translate,
-                           'Scale': Scale, 'Shear': Shear, 'Rotate': Rotate,
-                           'FGSM': FastGradientSignMethod,
-                           'PGD': ProjectedGradientDescent,
-                           'MDIIM': MomentumDiverseInputIterativeMethod}
-        self.affine_trans_list = ['Translate', 'Scale', 'Shear', 'Rotate']
-        self.pixel_value_trans_list = ['Contrast', 'Brightness', 'Blur',
-                                       'Noise']
-        self.attacks_list = ['FGSM', 'PGD', 'MDIIM']
-        self.attack_param_checklists = {
+        self._strategies = {'Contrast': Contrast, 'Brightness': Brightness,
+                            'Blur': Blur, 'Noise': Noise, 'Translate': Translate,
+                            'Scale': Scale, 'Shear': Shear, 'Rotate': Rotate,
+                            'FGSM': FastGradientSignMethod,
+                            'PGD': ProjectedGradientDescent,
+                            'MDIIM': MomentumDiverseInputIterativeMethod}
+        self._affine_trans_list = ['Translate', 'Scale', 'Shear', 'Rotate']
+        self._pixel_value_trans_list = ['Contrast', 'Brightness', 'Blur',
+                                        'Noise']
+        self._attacks_list = ['FGSM', 'PGD', 'MDIIM']
+        self._attack_param_checklists = {
             'FGSM': {'params': {'eps': {'dtype': [float, int], 'range': [0, 1]},
                                 'alpha': {'dtype': [float, int],
                                           'range': [0, 1]},
                                 'bounds': {'dtype': [list, tuple],
-                                           'range': None},
-                                }},
+                                           'range': None}}},
             'PGD': {'params': {'eps': {'dtype': [float, int], 'range': [0, 1]},
                                'eps_iter': {'dtype': [float, int],
                                             'range': [0, 1e5]},
                                'nb_iter': {'dtype': [float, int],
                                            'range': [0, 1e5]},
                                'bounds': {'dtype': [list, tuple],
-                                          'range': None},
-                               }},
+                                          'range': None}}},
             'MDIIM': {
                 'params': {'eps': {'dtype': [float, int], 'range': [0, 1]},
                            'norm_level': {'dtype': [str], 'range': None},
                            'prob': {'dtype': [float, int], 'range': [0, 1]},
-                           'bounds': {'dtype': [list, tuple], 'range': None},
-                           }}}
+                           'bounds': {'dtype': [list, tuple], 'range': None}}}}
+
+    def fuzzing(self, mutate_config, initial_seeds, coverage_metric='KMNC',
+                eval_metrics='auto', max_iters=10000, mutate_num_per_seed=20):
+        """
+        Fuzzing tests for deep neural networks.
+
+        Args:
+            mutate_config (list): Mutate configs. The format is
+                [{'method': 'Blur', 'params': {'auto_param': True}}, {'method': 'Contrast', 'params': {'factor': 2}}].
+                 The support methods list is in `self._strategies`, and the params of each
+                 method must within the range of changeable parameters.
+            initial_seeds (numpy.ndarray): Initial seeds used to generate
+                mutated samples.
+            coverage_metric (str): Model coverage metric of neural networks.
+                Default: 'KMNC'.
+            eval_metrics (Union[list, tuple, str]): Evaluation metrics. If the type is 'auto',
+                it will calculate all the metrics, else if the type is list or tuple, it will
+                calculate the metrics specified by user. Default: 'auto'.
+            max_iters (int): Max number of select a seed to mutate.
+                Default: 10000.
+            mutate_num_per_seed (int): The number of mutate times for a seed.
+                Default: 20.
+
+        Returns:
+            - list, mutated samples in fuzzing.
+
+            - list, ground truth labels of mutated samples.
+
+            - list, preds of mutated samples.
+
+            - list, strategies of mutated samples.
+
+            - dict, metrics report of fuzzer.
+
+        Raises:
+            TypeError: If the type of `eval_metrics` is not str, list or tuple.
+            TypeError: If the type of metric in list `eval_metrics` is not str.
+            ValueError: If `eval_metrics` is not equal to 'auto' when it's type is str.
+            ValueError: If metric in list `eval_metrics` is not in ['accuracy', 'attack_success_rate',
+                'kmnc', 'nbc', 'snac'].
+        """
+        eval_metrics_ = None
+        if isinstance(eval_metrics, (list, tuple)):
+            eval_metrics_ = []
+            avaliable_metrics = ['accuracy', 'attack_success_rate', 'kmnc', 'nbc', 'snac']
+            for elem in eval_metrics:
+                if not isinstance(elem, str):
+                    msg = 'the type of metric in list `eval_metrics` must be str, but got {}.' \
+                        .format(type(elem))
+                    LOGGER.error(TAG, msg)
+                    raise TypeError(msg)
+                if elem not in avaliable_metrics:
+                    msg = 'metric in list `eval_metrics` must be in {}, but got {}.' \
+                        .format(avaliable_metrics, elem)
+                    LOGGER.error(TAG, msg)
+                    raise ValueError(msg)
+                eval_metrics_.append(elem.lower())
+        elif isinstance(eval_metrics, str):
+            if eval_metrics != 'auto':
+                msg = "the value of `eval_metrics` must be 'auto' if it's type is str, " \
+                      "but got {}.".format(eval_metrics)
+                LOGGER.error(TAG, msg)
+                raise ValueError(msg)
+            eval_metrics_ = 'auto'
+        else:
+            msg = "the type of `eval_metrics` must be str, list or tuple, but got {}." \
+                .format(type(eval_metrics))
+            LOGGER.error(TAG, msg)
+            raise TypeError(msg)
+
+        # Check whether the mutate_config meet the specification.
+        mutates = self._init_mutates(mutate_config)
+        seed, initial_seeds = _select_next(initial_seeds)
+        fuzz_samples = []
+        gt_labels = []
+        fuzz_preds = []
+        fuzz_strategies = []
+        iter_num = 0
+        while initial_seeds and iter_num < max_iters:
+            # Mutate a seed.
+            mutate_samples, mutate_strategies = self._metamorphic_mutate(seed,
+                                                                         mutates,
+                                                                         mutate_config,
+                                                                         mutate_num_per_seed)
+            # Calculate the coverages and predictions of generated samples.
+            coverages, predicts = self._run(mutate_samples, coverage_metric)
+            coverage_gains = _coverage_gains(coverages)
+            for mutate, cov, pred, strategy in zip(mutate_samples,
+                                                   coverage_gains,
+                                                   predicts, mutate_strategies):
+                fuzz_samples.append(mutate[0])
+                gt_labels.append(mutate[1])
+                fuzz_preds.append(pred)
+                fuzz_strategies.append(strategy)
+                # if the mutate samples has coverage gains add this samples in
+                # the initial seeds to guide new mutates.
+                if cov > 0:
+                    initial_seeds.append(mutate)
+            seed, initial_seeds = _select_next(initial_seeds)
+            iter_num += 1
+        metrics_report = None
+        if eval_metrics_ is not None:
+            metrics_report = self._evaluate(fuzz_samples, gt_labels, fuzz_preds,
+                                            fuzz_strategies, eval_metrics_)
+        return fuzz_samples, gt_labels, fuzz_preds, fuzz_strategies, metrics_report
+
+    def _run(self, mutate_samples, coverage_metric="KNMC"):
+        """ Calculate the coverages and predictions of generated samples."""
+        samples = [s[0] for s in mutate_samples]
+        samples = np.array(samples)
+        coverages = []
+        predictions = self._target_model.predict(Tensor(samples.astype(np.float32)))
+        predictions = predictions.asnumpy()
+        for index in range(len(samples)):
+            mutate = samples[:index + 1]
+            self._coverage_metrics.calculate_coverage(mutate.astype(np.float32))
+            if coverage_metric == "KMNC":
+                coverages.append(self._coverage_metrics.get_kmnc())
+            if coverage_metric == 'NBC':
+                coverages.append(self._coverage_metrics.get_nbc())
+            if coverage_metric == 'SNAC':
+                coverages.append(self._coverage_metrics.get_snac())
+        return coverages, predictions
 
     def _check_attack_params(self, method, params):
         """Check input parameters of attack methods."""
-        allow_params = self.attack_param_checklists[method]['params'].keys()
-        for p in params:
-            if p not in allow_params:
+        allow_params = self._attack_param_checklists[method]['params'].keys()
+        for param_name in params:
+            if param_name not in allow_params:
                 msg = "parameters of {} must in {}".format(method, allow_params)
                 raise ValueError(msg)
-            if p == 'bounds':
-                bounds = check_param_multi_types('bounds', params[p],
+
+            param_value = params[param_name]
+            if param_name == 'bounds':
+                bounds = check_param_multi_types('bounds', param_value,
                                                  [list, tuple])
-                for b in bounds:
-                    _ = check_param_multi_types('bound', b, [int, float])
-            elif p == 'norm_level':
-                _ = check_norm_level(params[p])
+                for bound_value in bounds:
+                    _ = check_param_multi_types('bound', bound_value, [int, float])
+            elif param_name == 'norm_level':
+                _ = check_norm_level(param_value)
             else:
-                allow_type = self.attack_param_checklists[method]['params'][p][
+                allow_type = self._attack_param_checklists[method]['params'][param_name][
                     'dtype']
-                allow_range = self.attack_param_checklists[method]['params'][p][
+                allow_range = self._attack_param_checklists[method]['params'][param_name][
                     'range']
-                _ = check_param_multi_types(str(p), params[p], allow_type)
-                _ = check_param_in_range(str(p), params[p], allow_range[0],
+                _ = check_param_multi_types(str(param_name), param_value, allow_type)
+                _ = check_param_in_range(str(param_name), param_value, allow_range[0],
                                          allow_range[1])
 
     def _metamorphic_mutate(self, seed, mutates, mutate_config,
@@ -117,23 +291,23 @@ class Fuzzer:
             strage = choice(mutate_config)
             # Choose a pixel value based transform method
             if only_pixel_trans:
-                while strage['method'] not in self.pixel_value_trans_list:
+                while strage['method'] not in self._pixel_value_trans_list:
                     strage = choice(mutate_config)
             transform = mutates[strage['method']]
             params = strage['params']
             method = strage['method']
-            if method in list(self.pixel_value_trans_list + self.affine_trans_list):
+            if method in list(self._pixel_value_trans_list + self._affine_trans_list):
                 transform.set_params(**params)
                 mutate_sample = transform.transform(seed[0])
             else:
-                for p in params:
-                    transform.__setattr__('_'+str(p), params[p])
+                for param_name in params:
+                    transform.__setattr__('_' + str(param_name), params[param_name])
                 mutate_sample = transform.generate([seed[0].astype(np.float32)],
                                                    [seed[1]])[0]
-            if method not in self.pixel_value_trans_list:
+            if method not in self._pixel_value_trans_list:
                 only_pixel_trans = 1
             mutate_sample = [mutate_sample, seed[1], only_pixel_trans]
-            if self._is_trans_valid(seed[0], mutate_sample[0]):
+            if _is_trans_valid(seed[0], mutate_sample[0]):
                 mutate_samples.append(mutate_sample)
                 mutate_strategies.append(method)
         if not mutate_samples:
@@ -145,29 +319,29 @@ class Fuzzer:
         """ Check whether the mutate_config meet the specification."""
         has_pixel_trans = False
         for mutate in mutate_config:
-            if mutate['method'] in self.pixel_value_trans_list:
+            if mutate['method'] in self._pixel_value_trans_list:
                 has_pixel_trans = True
                 break
         if not has_pixel_trans:
             msg = "mutate methods in mutate_config at lease have one in {}".format(
-                self.pixel_value_trans_list)
+                self._pixel_value_trans_list)
             raise ValueError(msg)
         mutates = {}
         for mutate in mutate_config:
             method = mutate['method']
             params = mutate['params']
-            if method not in self.attacks_list:
-                mutates[method] = self.strategies[method]()
+            if method not in self._attacks_list:
+                mutates[method] = self._strategies[method]()
             else:
                 self._check_attack_params(method, params)
-                network = self.target_model._network
-                loss_fn = self.target_model._loss_fn
-                mutates[method] = self.strategies[method](network,
-                                                          loss_fn=loss_fn)
+                network = self._target_model._network
+                loss_fn = self._target_model._loss_fn
+                mutates[method] = self._strategies[method](network,
+                                                           loss_fn=loss_fn)
         return mutates
 
-    def evaluate(self, fuzz_samples, gt_labels, fuzz_preds,
-                 fuzz_strategies):
+    def _evaluate(self, fuzz_samples, gt_labels, fuzz_preds,
+                  fuzz_strategies, metrics):
         """
         Evaluate generated fuzzing samples in three dimention: accuracy,
         attack success rate and neural coverage.
@@ -177,147 +351,40 @@ class Fuzzer:
             gt_labels (numpy.ndarray): Ground Truth of seeds.
             fuzz_preds (numpy.ndarray): Predictions of generated fuzz samples.
             fuzz_strategies (numpy.ndarray): Mutate strategies of fuzz samples.
+            metrics (Union[list, tuple, str]): evaluation metrics.
 
         Returns:
             dict, evaluate metrics include accuarcy, attack success rate
                 and neural coverage.
         """
-
-        gt_labels = np.asarray(gt_labels)
-        fuzz_preds = np.asarray(fuzz_preds)
         temp = np.argmax(gt_labels, axis=1) == np.argmax(fuzz_preds, axis=1)
-        acc = np.sum(temp) / np.size(temp)
-
-        cond = [elem in self.attacks_list for elem in fuzz_strategies]
-        temp = temp[cond]
-        attack_success_rate = 1 - np.sum(temp) / np.size(temp)
-
-        self.coverage_metrics.calculate_coverage(
-            np.array(fuzz_samples).astype(np.float32))
-        kmnc = self.coverage_metrics.get_kmnc()
-        nbc = self.coverage_metrics.get_nbc()
-        snac = self.coverage_metrics.get_snac()
-
-        metrics = {}
-        metrics['Accuracy'] = acc
-        metrics['Attack_succrss_rate'] = attack_success_rate
-        metrics['Neural_coverage_KMNC'] = kmnc
-        metrics['Neural_coverage_NBC'] = nbc
-        metrics['Neural_coverage_SNAC'] = snac
-        return metrics
+        metrics_report = {}
+        if metrics == 'auto' or 'accuracy' in metrics:
+            gt_labels = np.asarray(gt_labels)
+            fuzz_preds = np.asarray(fuzz_preds)
+            acc = np.sum(temp) / np.size(temp)
+            metrics_report['Accuracy'] = acc
 
-    def fuzzing(self, mutate_config, initial_seeds, coverage_metric='KMNC',
-                eval_metric=True, max_iters=10000, mutate_num_per_seed=20):
-        """
-        Fuzzing tests for deep neural networks.
+        if metrics == 'auto' or 'attack_success_rate' in metrics:
+            cond = [elem in self._attacks_list for elem in fuzz_strategies]
+            temp = temp[cond]
+            attack_success_rate = 1 - np.sum(temp) / np.size(temp)
+            metrics_report['Attack_success_rate'] = attack_success_rate
 
-        Args:
-            mutate_config (list): Mutate configs. The format is
-                [{'method': 'Blur',
-                  'params': {'auto_param': True}},
-                 {'method': 'Contrast',
-                  'params': {'factor': 2}},
-                 ...]. The support methods list is in `self.strategies`,
-                 The params of each method must within the range of changeable
-                 parameters.
-            initial_seeds (numpy.ndarray): Initial seeds used to generate
-                mutated samples.
-            coverage_metric (str): Model coverage metric of neural networks.
-                Default: 'KMNC'.
-            eval_metric (bool): Whether to evaluate the generated fuzz samples.
-                Default: True.
-            max_iters (int): Max number of select a seed to mutate.
-                Default: 10000.
-            mutate_num_per_seed (int): The number of mutate times for a seed.
-                Default: 20.
+        if metrics == 'auto' or 'kmnc' in metrics or 'nbc' in metrics or 'snac' in metrics:
+            self._coverage_metrics.calculate_coverage(
+                np.array(fuzz_samples).astype(np.float32))
 
-        Returns:
-            list, mutated samples.
-        """
-        # Check whether the mutate_config meet the specification.
-        mutates = self._init_mutates(mutate_config)
-        seed, initial_seeds = self._select_next(initial_seeds)
-        fuzz_samples = []
-        gt_labels = []
-        fuzz_preds = []
-        fuzz_strategies = []
-        iter_num = 0
-        while initial_seeds and iter_num < max_iters:
-            # Mutate a seed.
-            mutate_samples, mutate_strategies = self._metamorphic_mutate(seed,
-                                                                         mutates,
-                                                                         mutate_config,
-                                                                         mutate_num_per_seed)
-            # Calculate the coverages and predictions of generated samples.
-            coverages, predicts = self._run(mutate_samples, coverage_metric)
-            coverage_gains = self._coverage_gains(coverages)
-            for mutate, cov, pred, strategy in zip(mutate_samples,
-                                                   coverage_gains,
-                                                   predicts, mutate_strategies):
-                fuzz_samples.append(mutate[0])
-                gt_labels.append(mutate[1])
-                fuzz_preds.append(pred)
-                fuzz_strategies.append(strategy)
-                # if the mutate samples has coverage gains add this samples in
-                # the initial seeds to guide new mutates.
-                if cov > 0:
-                    initial_seeds.append(mutate)
-            seed, initial_seeds = self._select_next(initial_seeds)
-            iter_num += 1
-        metrics = None
-        if eval_metric:
-            metrics = self.evaluate(fuzz_samples, gt_labels, fuzz_preds,
-                                    fuzz_strategies)
-        return fuzz_samples, gt_labels, fuzz_preds, fuzz_strategies, metrics
-
-    def _coverage_gains(self, coverages):
-        """ Calculate the coverage gains of mutated samples. """
-        gains = [0] + coverages[:-1]
-        gains = np.array(coverages) - np.array(gains)
-        return gains
+        if metrics == 'auto' or 'kmnc' in metrics:
+            kmnc = self._coverage_metrics.get_kmnc()
+            metrics_report['Neural_coverage_KMNC'] = kmnc
 
-    def _run(self, mutate_samples, coverage_metric="KNMC"):
-        """ Calculate the coverages and predictions of generated samples."""
-        samples = [s[0] for s in mutate_samples]
-        samples = np.array(samples)
-        coverages = []
-        predictions = self.target_model.predict(Tensor(samples.astype(np.float32)))
-        predictions = predictions.asnumpy()
-        for index in range(len(samples)):
-            mutate = samples[:index + 1]
-            self.coverage_metrics.calculate_coverage(mutate.astype(np.float32))
-            if coverage_metric == "KMNC":
-                coverages.append(self.coverage_metrics.get_kmnc())
-            if coverage_metric == 'NBC':
-                coverages.append(self.coverage_metrics.get_nbc())
-            if coverage_metric == 'SNAC':
-                coverages.append(self.coverage_metrics.get_snac())
-        return coverages, predictions
+        if metrics == 'auto' or 'nbc' in metrics:
+            nbc = self._coverage_metrics.get_nbc()
+            metrics_report['Neural_coverage_NBC'] = nbc
 
-    def _select_next(self, initial_seeds):
-        """Randomly select a seed from `initial_seeds`."""
-        seed_num = choice(range(len(initial_seeds)))
-        seed = initial_seeds[seed_num]
-        del initial_seeds[seed_num]
-        return seed, initial_seeds
-
-    def _is_trans_valid(self, seed, mutate_sample):
-        """ Check a mutated sample is valid. If the number of changed pixels in
-        a seed is less than pixels_change_rate*size(seed), this mutate is valid.
-        Else check the infinite norm of seed changes, if the value of the
-        infinite norm less than pixel_value_change_rate*255, this mutate is
-        valid too. Otherwise the opposite."""
-        is_valid = False
-        pixels_change_rate = 0.02
-        pixel_value_change_rate = 0.2
-        diff = np.array(seed - mutate_sample).flatten()
-        size = np.shape(diff)[0]
-        l0 = np.linalg.norm(diff, ord=0)
-        linf = np.linalg.norm(diff, ord=np.inf)
-        if l0 > pixels_change_rate*size:
-            if linf < 256:
-                is_valid = True
-        else:
-            if linf < pixel_value_change_rate*255:
-                is_valid = True
-        return is_valid
+        if metrics == 'auto' or 'snac' in metrics:
+            snac = self._coverage_metrics.get_snac()
+            metrics_report['Neural_coverage_SNAC'] = snac
+
+        return metrics_report

tests/ut/python/fuzzing/test_fuzzer.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-Model-fuzz coverage test.
+Model-fuzzer test.
 """
 import numpy as np
 import pytest
@@ -121,7 +121,7 @@ def test_fuzzing_ascend():
     LOGGER.info(TAG, 'KMNC of this test is : %s',
                 model_coverage_test.get_kmnc())
 
-    model_fuzz_test = Fuzzer(model, train_images, 1000, 10)
+    model_fuzz_test = Fuzzer(model, train_images, 10, 1000)
     _, _, _, _, metrics = model_fuzz_test.fuzzing(mutate_config, initial_seeds)
     print(metrics)
 
@@ -167,6 +167,6 @@ def test_fuzzing_cpu():
     LOGGER.info(TAG, 'KMNC of this test is : %s',
                 model_coverage_test.get_kmnc())
 
-    model_fuzz_test = Fuzzer(model, train_images, 1000, 10)
+    model_fuzz_test = Fuzzer(model, train_images, 10, 1000)
     _, _, _, _, metrics = model_fuzz_test.fuzzing(mutate_config, initial_seeds)
     print(metrics)