From 8cc2a94549a20ad06786c5dda92eaacfa1572fc2 Mon Sep 17 00:00:00 2001
From: shu-kun-zhang <zhangshukun1@huawei.com>
Date: Mon, 21 Mar 2022 16:42:12 +0800
Subject: [PATCH] Fix API Issues

---
 .../adv_robustness/defenses/adversarial_defense.py |   3 -
 .../detectors/black/similarity_detector.py         |  37 +---
 mindarmour/fuzz_testing/fuzzing.py                 |   2 +-
 mindarmour/fuzz_testing/model_coverage_metrics.py  | 226 ++++++++++++++++++++-
 mindarmour/privacy/diff_privacy/monitor/monitor.py |   9 -
 mindarmour/privacy/evaluation/attacker.py          |   7 +-
 mindarmour/privacy/evaluation/inversion_attack.py  |   1 -
 .../privacy/evaluation/membership_inference.py     |   9 +-
 .../concept_drift_check_time_series.py             |   7 -
 .../model_fault_injection/fault_injection.py       |   3 +-
 mindarmour/utils/util.py                           |  59 +++++-
 .../ut/python/privacy/evaluation/test_attacker.py  |  10 +-
 12 files changed, 285 insertions(+), 88 deletions(-)

diff --git a/mindarmour/adv_robustness/defenses/adversarial_defense.py b/mindarmour/adv_robustness/defenses/adversarial_defense.py
index 1dbfc34..11a44a1 100644
--- a/mindarmour/adv_robustness/defenses/adversarial_defense.py
+++ b/mindarmour/adv_robustness/defenses/adversarial_defense.py
@@ -79,9 +79,6 @@ class AdversarialDefense(Defense):
 
         Returns:
             numpy.ndarray, loss of defense operation.
-
-        Examples:
-            >>> adv_defense.defense(inputs, labels)
         """
         inputs, labels = check_pair_numpy_param('inputs', inputs, 'labels',
                                                 labels)
diff --git a/mindarmour/adv_robustness/detectors/black/similarity_detector.py b/mindarmour/adv_robustness/detectors/black/similarity_detector.py
index a5cf583..1db6c5f 100644
--- a/mindarmour/adv_robustness/detectors/black/similarity_detector.py
+++ b/mindarmour/adv_robustness/detectors/black/similarity_detector.py
@@ -98,6 +98,9 @@ class SimilarityDetector(Detector):
         >>> detector.set_threshold(num_nearest_neighbors[-1], thresholds[-1])
         >>> detector.detect(benign_queries)
         >>> detections = detector.get_detection_interval()
+        >>> detector.detect_diff()
+        >>> detected_queries = detector.get_detected_queries()
+        >>> detector.transform(x_train)
     """
 
     def __init__(self, trans_model, max_k_neighbor=1000, chunk_size=1000,
@@ -137,13 +140,6 @@ class SimilarityDetector(Detector):
         Raises:
             ValueError: The number of training data is less than
                 max_k_neighbor!
-
-        Examples:
-            >>> x_train = np.random.rand(10, 32, 32, 3).astype(np.float32)
-            >>> perm = np.random.permutation(x_train.shape[0])
-            >>> benign_queries = x_train[perm[:10], :, :, :]
-            >>> num_nearest_neighbors, thresholds = detector.fit(inputs=x_train)
-            >>> detector.set_threshold(num_nearest_neighbors[-1], thresholds[-1])
         """
         data = check_numpy_param('inputs', inputs)
         data_len = data.shape[0]
@@ -192,14 +188,6 @@ class SimilarityDetector(Detector):
         Raises:
             ValueError: The parameters of threshold or num_of_neighbors is
                 not available.
-
-        Examples:
-            >>> x_train = np.random.rand(10, 32, 32, 3).astype(np.float32)
-            >>> perm = np.random.permutation(x_train.shape[0])
-            >>> benign_queries = x_train[perm[:10], :, :, :]
-            >>> num_nearest_neighbors, thresholds = detector.fit(inputs=x_train)
-            >>> detector.set_threshold(num_nearest_neighbors[-1], thresholds[-1])
-            >>> detector.detect(benign_queries)
         """
         if self._threshold is None or self._num_of_neighbors is None:
             msg = 'Explicit detection threshold and number of nearest ' \
@@ -247,9 +235,6 @@ class SimilarityDetector(Detector):
     def clear_buffer(self):
         """
         Clear the buffer memory.
-
-        Examples:
-            >>> detector.detect(benign_queries)
         """
         while self._buffer:
             self._buffer.pop()
@@ -261,10 +246,6 @@ class SimilarityDetector(Detector):
         Args:
             num_of_neighbors (int): Number of the nearest neighbors.
             threshold (float): Detection threshold.
-
-        Examples:
-            >>> num_nearest_neighbors, thresholds = detector.fit(inputs=x_train)
-            >>> detector.set_threshold(num_nearest_neighbors[-1], thresholds[-1])
         """
         self._num_of_neighbors = check_int_positive('num_of_neighbors',
                                                     num_of_neighbors)
@@ -276,9 +257,6 @@ class SimilarityDetector(Detector):
 
         Returns:
             list[int], number of queries between adjacent detections.
-
-        Examples:
-            >>> detector.get_detection_interval()
         """
         detected_queries = self._detected_queries
         interval = []
@@ -292,9 +270,6 @@ class SimilarityDetector(Detector):
 
         Returns:
             list[int], sequence number of detected malicious queries.
-
-        Examples:
-            >>> detector.get_detected_queries()
         """
         detected_queries = self._detected_queries
         return detected_queries
@@ -311,9 +286,6 @@ class SimilarityDetector(Detector):
         Raises:
             NotImplementedError: This function is not available
                 in class `SimilarityDetector`.
-
-        Examples:
-            >>> detector.detect_diff()
         """
         msg = 'The function detect_diff() is not available in the class ' \
               '`SimilarityDetector`.'
@@ -329,9 +301,6 @@ class SimilarityDetector(Detector):
 
         Raises:
             NotImplementedError: This function is not available in class `SimilarityDetector`.
-
-        Examples:
-            >>> detector.transform(x_train)
         """
         msg = 'The function transform() is not available in the class `SimilarityDetector`.'
         LOGGER.error(TAG, msg)
diff --git a/mindarmour/fuzz_testing/fuzzing.py b/mindarmour/fuzz_testing/fuzzing.py
index a70c57e..d6456ce 100644
--- a/mindarmour/fuzz_testing/fuzzing.py
+++ b/mindarmour/fuzz_testing/fuzzing.py
@@ -124,7 +124,7 @@ class Fuzzer:
         ...         self.reshape = P.Reshape()
         ...         self.summary = TensorSummary()
         ...
-        ...    def construct(self, x):
+        ...     def construct(self, x):
         ...         x = self.conv1(x)
         ...         x = self.relu(x)
         ...         self.summary('conv1', x)
diff --git a/mindarmour/fuzz_testing/model_coverage_metrics.py b/mindarmour/fuzz_testing/model_coverage_metrics.py
index f71ee7a..44889cb 100644
--- a/mindarmour/fuzz_testing/model_coverage_metrics.py
+++ b/mindarmour/fuzz_testing/model_coverage_metrics.py
@@ -172,8 +172,50 @@ class NeuronCoverage(CoverageMetrics):
             float, the metric of 'neuron coverage'.
 
         Examples:
+            >>> 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 NeuronCoverage
+            >>> 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)
+            >>> 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)
             >>> nc = NeuronCoverage(model, threshold=0.1)
-            >>> nc_metrics = nc.get_metrics(test_data)
+            >>> nc_metrics = nc.get_metrics(test_images)
         """
         dataset = check_numpy_param('dataset', dataset)
         batches = math.ceil(dataset.shape[0] / self.batch_size)
@@ -219,8 +261,50 @@ class TopKNeuronCoverage(CoverageMetrics):
             float, the metrics of 'top k neuron coverage'.
 
         Examples:
+            >>> 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 TopKNeuronCoverage
+            >>> 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)
+            >>> 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)
             >>> tknc = TopKNeuronCoverage(model, top_k=3)
-            >>> metrics = tknc.get_metrics(test_data)
+            >>> metrics = tknc.get_metrics(test_images)
         """
         dataset = check_numpy_param('dataset', dataset)
         batches = math.ceil(dataset.shape[0] / self.batch_size)
@@ -269,8 +353,50 @@ class SuperNeuronActivateCoverage(CoverageMetrics):
             float, the metric of 'strong neuron activation coverage'.
 
         Examples:
-            >>> snac = SuperNeuronActivateCoverage(model, train_dataset)
-            >>> metrics = snac.get_metrics(test_data)
+            >>> 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 SuperNeuronActivateCoverage
+            >>> 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)
+            >>> 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)
+            >>> snac = SuperNeuronActivateCoverage(model, train_images)
+            >>> metrics = snac.get_metrics(test_images)
         """
         dataset = check_numpy_param('dataset', dataset)
         if not self.incremental or not self._activate_table:
@@ -319,8 +445,50 @@ class NeuronBoundsCoverage(SuperNeuronActivateCoverage):
             float, the metric of 'neuron boundary coverage'.
 
         Examples:
-            >>> nbc = NeuronBoundsCoverage(model, train_dataset)
-            >>> metrics = nbc.get_metrics(test_data)
+            >>> 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 NeuronBoundsCoverage
+            >>> 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)
+            >>> 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)
+            >>> nbc = NeuronBoundsCoverage(model, train_images)
+            >>> metrics = nbc.get_metrics(test_images)
         """
         dataset = check_numpy_param('dataset', dataset)
         if not self.incremental or not self._activate_table:
@@ -383,8 +551,50 @@ class KMultisectionNeuronCoverage(SuperNeuronActivateCoverage):
             float, the metric of 'k-multisection neuron coverage'.
 
         Examples:
-            >>> kmnc = KMultisectionNeuronCoverage(model, train_dataset, segmented_num=100)
-            >>> metrics = kmnc.get_metrics(test_data)
+            >>> 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 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)
+            >>> 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)
+            >>> kmnc = KMultisectionNeuronCoverage(model, train_images, segmented_num=100)
+            >>> metrics = kmnc.get_metrics(test_images)
         """
 
         dataset = check_numpy_param('dataset', dataset)
diff --git a/mindarmour/privacy/diff_privacy/monitor/monitor.py b/mindarmour/privacy/diff_privacy/monitor/monitor.py
index 1be43a0..57cfd12 100644
--- a/mindarmour/privacy/diff_privacy/monitor/monitor.py
+++ b/mindarmour/privacy/diff_privacy/monitor/monitor.py
@@ -196,10 +196,6 @@ class RDPMonitor(Callback):
 
         Returns:
             int, the recommended maximum training epochs.
-
-        Examples:
-            >>> rdp = PrivacyMonitorFactory.create(policy='rdp', num_samples=100, batch_size=32)
-            >>> suggest_epoch = rdp.max_epoch_suggest()
         """
         if self._target_delta is not None and self._max_eps is None:
             msg = 'max_eps should be consistent with target_delta, but got None.'
@@ -450,11 +446,6 @@ class ZCDPMonitor(Callback):
 
         Returns:
             int, the recommended maximum training epochs.
-
-        Examples:
-            >>> zcdp = PrivacyMonitorFactory.create(policy='zcdp',
-            >>> num_samples=60000, batch_size=32)
-            >>> suggest_epoch = zcdp.max_epoch_suggest()
         """
         epoch = 1
         while epoch < 10000:
diff --git a/mindarmour/privacy/evaluation/attacker.py b/mindarmour/privacy/evaluation/attacker.py
index cc71c84..5aea26c 100644
--- a/mindarmour/privacy/evaluation/attacker.py
+++ b/mindarmour/privacy/evaluation/attacker.py
@@ -120,7 +120,7 @@ def _attack_rf(features, labels, random_grid, n_jobs):
     return rf_model
 
 
-def get_attack_model(features, labels, config, n_jobs=-1):
+def _get_attack_model(features, labels, config, n_jobs=-1):
     """
     Get trained attack model specify by config.
 
@@ -139,10 +139,11 @@ def get_attack_model(features, labels, config, n_jobs=-1):
         sklearn.BaseEstimator, trained model specify by config["method"].
 
     Examples:
+        >>> from mindarmour.privacy.evaluation.attacker import get_attack_model
         >>> features = np.random.randn(10, 10)
         >>> labels = np.random.randint(0, 2, 10)
-        >>> config = {"method": "knn", "params": {"n_neighbors": [3, 5, 7]}}
-        >>> attack_model = get_attack_model(features, labels, config)
+        >>> config = {"method": "knn", "params": {"n_neighbors": [3, 5]}}
+        >>> attack_model = _get_attack_model(features, labels, config)
     """
     features, labels = check_pair_numpy_param("features", features, "labels", labels)
     config = check_param_type("config", config, dict)
diff --git a/mindarmour/privacy/evaluation/inversion_attack.py b/mindarmour/privacy/evaluation/inversion_attack.py
index 9f693dc..dfc3f2b 100644
--- a/mindarmour/privacy/evaluation/inversion_attack.py
+++ b/mindarmour/privacy/evaluation/inversion_attack.py
@@ -135,7 +135,6 @@ class ImageInversionAttack:
             ...                                         loss_weights=[1, 0.2, 5])
             >>> inversion_images = inversion_attack.generate(target_features, iters=10)
             >>> evaluate_result = inversion_attack.evaluate(original_images, inversion_images)
-            >>> print(evaluate_result)
     """
     def __init__(self, network, input_shape, input_bound, loss_weights=(1, 0.2, 5)):
         self._network = check_param_type('network', network, Cell)
diff --git a/mindarmour/privacy/evaluation/membership_inference.py b/mindarmour/privacy/evaluation/membership_inference.py
index dd3c0f6..fdb8acf 100644
--- a/mindarmour/privacy/evaluation/membership_inference.py
+++ b/mindarmour/privacy/evaluation/membership_inference.py
@@ -25,7 +25,7 @@ from mindspore import Tensor
 from mindarmour.utils.logger import LogUtil
 from mindarmour.utils._check_param import check_param_type, check_param_multi_types, \
     check_model, check_numpy_param
-from .attacker import get_attack_model
+from .attacker import _get_attack_model
 from ._check_config import verify_config_params
 
 LOGGER = LogUtil.get_instance()
@@ -123,8 +123,7 @@ class MembershipInference:
         ...     data =  np.random.randn(batches * batch_size,1,10).astype(np.float32)
         ...     label =  np.random.randint(0,10, batches * batch_size).astype(np.int32)
         ...     for i in range(batches):
-        ...         yield data[i*batch_size:(i+1)*batch_size],\
-        ...               label[i*batch_size:(i+1)*batch_size]
+        ...         yield data[i*batch_size:(i+1)*batch_size], label[i*batch_size:(i+1)*batch_size]
         >>> class Net(Cell):
         ...     def __init__(self):
         ...         super(Net, self).__init__()
@@ -143,7 +142,7 @@ class MembershipInference:
         >>> config = [{
         ...     "method": "KNN",
         ...     "params": {"n_neighbors": [3, 5, 7],}
-        }]
+        ...     }]
         >>> ds_train = ds.GeneratorDataset(dataset_generator, ["image", "label"])
         >>> ds_test = ds.GeneratorDataset(dataset_generator, ["image", "label"])
         >>> inference_model.train(ds_train, ds_test, config)
@@ -195,7 +194,7 @@ class MembershipInference:
 
         features, labels = self._transform(dataset_train, dataset_test)
         for config in attack_config:
-            self._attack_list.append(get_attack_model(features, labels, config, n_jobs=self._n_jobs))
+            self._attack_list.append(_get_attack_model(features, labels, config, n_jobs=self._n_jobs))
 
 
     def eval(self, dataset_train, dataset_test, metrics):
diff --git a/mindarmour/reliability/concept_drift/concept_drift_check_time_series.py b/mindarmour/reliability/concept_drift/concept_drift_check_time_series.py
index cd2ccab..f485bcc 100644
--- a/mindarmour/reliability/concept_drift/concept_drift_check_time_series.py
+++ b/mindarmour/reliability/concept_drift/concept_drift_check_time_series.py
@@ -158,13 +158,6 @@ class ConceptDriftCheckTimeSeries:
             - numpy.ndarray, the concept drift score of the example series.
             - float, the threshold to judge concept drift.
             - list, the location of the concept drift.
-
-        Examples：
-            >>> concept = ConceptDriftCheckTimeSeries(window_size=100, rolling_window=10,
-            ...                                       step=10, threshold_index=1.5, need_label=False)
-            >>> data_example = 5*np.random.rand(1000)
-            >>> data_example[200: 800] = 20*np.random.rand(600)
-            >>> score, drift_threshold, drift_location = concept.concept_check(data_example)
         """
         # data check
         data = _check_array_not_empty('data', data)
diff --git a/mindarmour/reliability/model_fault_injection/fault_injection.py b/mindarmour/reliability/model_fault_injection/fault_injection.py
index 122726f..d917755 100644
--- a/mindarmour/reliability/model_fault_injection/fault_injection.py
+++ b/mindarmour/reliability/model_fault_injection/fault_injection.py
@@ -59,8 +59,7 @@ class FaultInjector:
         ...     data =  np.random.randn(batches * batch_size,1,10).astype(np.float32)
         ...     label =  np.random.randint(0,10, batches * batch_size).astype(np.int32)
         ...     for i in range(batches):
-        ...         yield data[i*batch_size:(i+1)*batch_size],\
-        ...               label[i*batch_size:(i+1)*batch_size]
+        ...         yield data[i*batch_size:(i+1)*batch_size], label[i*batch_size:(i+1)*batch_size]
         >>> net = Net()
         >>> model = Model(net)
         >>> ds_eval = ds.GeneratorDataset(dataset_generator, ['image', 'label'])
diff --git a/mindarmour/utils/util.py b/mindarmour/utils/util.py
index d787d39..a000ad0 100644
--- a/mindarmour/utils/util.py
+++ b/mindarmour/utils/util.py
@@ -102,12 +102,24 @@ class WithLossCell(Cell):
         loss_fn (Function): The loss function is used for computing loss.
 
     Examples:
-        >>> data = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32)*0.01)
-        >>> label = Tensor(np.ones([1, 10]).astype(np.float32))
-        >>> net = NET()
+        >>> from mindspore import Tensor
+        >>> from mindarmour.utils.util import WithLossCell
+        >>> import mindspore.ops.operations as P
+        >>> class Net(nn.Cell):
+        ...     def __init__(self):
+        ...         super(Net, self).__init__()
+        ...         self._softmax = P.Softmax()
+        ...         self._Dense = nn.Dense(10,10)
+        ...         self._squeeze = P.Squeeze(1)
+        ...     def construct(self, inputs):
+        ...         out = self._softmax(inputs)
+        ...         out = self._Dense(out)
+        ...         return self._squeeze(out)
+        >>> data = Tensor(np.ones([2, 1, 10]).astype(np.float32)*0.01)
+        >>> labels = Tensor(np.ones([2, 10]).astype(np.float32))
+        >>> net = Net()
         >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
         >>> loss_net = WithLossCell(net, loss_fn)
-        >>> loss_out = loss_net(data, label)
     """
     def __init__(self, network, loss_fn):
         super(WithLossCell, self).__init__()
@@ -138,9 +150,23 @@ class GradWrapWithLoss(Cell):
         network (Cell): The target network to wrap.
 
     Examples:
-        >>> data = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32)*0.01)
-        >>> labels = Tensor(np.ones([1, 10]).astype(np.float32))
-        >>> net = NET()
+        >>> from mindspore import Tensor
+        >>> from mindarmour.utils import GradWrapWithLoss
+        >>> from mindarmour.utils.util import WithLossCell
+        >>> import mindspore.ops.operations as P
+        >>> class Net(nn.Cell):
+        ...     def __init__(self):
+        ...         super(Net, self).__init__()
+        ...         self._softmax = P.Softmax()
+        ...         self._Dense = nn.Dense(10,10)
+        ...         self._squeeze = P.Squeeze(1)
+        ...     def construct(self, inputs):
+        ...         out = self._softmax(inputs)
+        ...         out = self._Dense(out)
+        ...         return self._squeeze(out)
+        >>> data = Tensor(np.ones([2, 1, 10]).astype(np.float32)*0.01)
+        >>> labels = Tensor(np.ones([2, 10]).astype(np.float32))
+        >>> net = Net()
         >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
         >>> loss_net = WithLossCell(net, loss_fn)
         >>> grad_all = GradWrapWithLoss(loss_net)
@@ -176,12 +202,25 @@ class GradWrap(Cell):
         network (Cell): The target network to wrap.
 
     Examples:
-        >>> data = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32)*0.01)
-        >>> label = Tensor(np.ones([1, 10]).astype(np.float32))
+        >>> from mindspore import Tensor
+        >>> from mindarmour.utils import GradWrap
+        >>> from mindarmour.utils.util import WithLossCell
+        >>> import mindspore.ops.operations as P
+        >>> class Net(nn.Cell):
+        ...     def __init__(self):
+        ...         super(Net, self).__init__()
+        ...         self._softmax = P.Softmax()
+        ...         self._Dense = nn.Dense(10,10)
+        ...         self._squeeze = P.Squeeze(1)
+        ...     def construct(self, inputs):
+        ...         out = self._softmax(inputs)
+        ...         out = self._Dense(out)
+        ...         return self._squeeze(out)
+        >>> data = Tensor(np.ones([2, 1, 10]).astype(np.float32)*0.01)
+        >>> labels = Tensor(np.ones([2, 10]).astype(np.float32))
         >>> num_classes = 10
         >>> sens = np.zeros((data.shape[0], num_classes)).astype(np.float32)
         >>> sens[:, 1] = 1.0
-        >>> net = NET()
         >>> wrap_net = GradWrap(net)
         >>> wrap_net(data, Tensor(sens))
     """
diff --git a/tests/ut/python/privacy/evaluation/test_attacker.py b/tests/ut/python/privacy/evaluation/test_attacker.py
index 9e374b0..d76af88 100644
--- a/tests/ut/python/privacy/evaluation/test_attacker.py
+++ b/tests/ut/python/privacy/evaluation/test_attacker.py
@@ -18,7 +18,7 @@ import pytest
 
 import numpy as np
 
-from mindarmour.privacy.evaluation.attacker import get_attack_model
+from mindarmour.privacy.evaluation.attacker import _get_attack_model
 
 
 @pytest.mark.level0
@@ -35,7 +35,7 @@ def test_get_knn_model():
             "n_neighbors": [3, 5, 7],
         }
     }
-    knn_attacker = get_attack_model(features, labels, config_knn, -1)
+    knn_attacker = _get_attack_model(features, labels, config_knn, -1)
     pred = knn_attacker.predict(features)
     assert pred is not None
 
@@ -54,7 +54,7 @@ def test_get_lr_model():
             "C": np.logspace(-4, 2, 10),
         }
     }
-    lr_attacker = get_attack_model(features, labels, config_lr, -1)
+    lr_attacker = _get_attack_model(features, labels, config_lr, -1)
     pred = lr_attacker.predict(features)
     assert pred is not None
 
@@ -75,7 +75,7 @@ def test_get_mlp_model():
             "alpha": [0.0001, 0.001, 0.01],
         }
     }
-    mlpc_attacker = get_attack_model(features, labels, config_mlpc, -1)
+    mlpc_attacker = _get_attack_model(features, labels, config_mlpc, -1)
     pred = mlpc_attacker.predict(features)
     assert pred is not None
 
@@ -98,6 +98,6 @@ def test_get_rf_model():
             "min_samples_leaf": [1, 2, 4],
         }
     }
-    rf_attacker = get_attack_model(features, labels, config_rf, -1)
+    rf_attacker = _get_attack_model(features, labels, config_rf, -1)
     pred = rf_attacker.predict(features)
     assert pred is not None