Fix API Example and Class Description Issues

.jenkins/test/config/dependent_packages.yaml

@@ -1,2 +1,2 @@
 mindspore:
-  'mindspore/mindspore/daily/202203/20220320/master_20220320041531_3e442945369de2d9dd20e9e2e9d3c7524a128ee7_newest/'
+  'mindspore/mindspore/version/202203/20220323/master_20220323111039_5c9f2e6d5dd9c7da631461b5003bbf5920d5f792/'

mindarmour/adv_robustness/attacks/attack.py

@@ -31,6 +31,7 @@ TAG = 'Attack'
 class Attack:
     """
     The abstract base class for all attack classes creating adversarial examples.
+    The adversarial examples are generated by adding adversarial noises to the original sample.
     """
     def __init__(self):
         pass

mindarmour/adv_robustness/attacks/black/natural_evolutionary_strategy.py

@@ -142,7 +142,7 @@ class NES(Attack):
 
     def generate(self, inputs, labels):
         """
-        Main algorithm for NES.
+        Generate adversarial examples based on input data and target labels.
 
         Args:
             inputs (numpy.ndarray): Benign input samples.

mindarmour/adv_robustness/attacks/carlini_wagner.py

@@ -58,7 +58,9 @@ def _best_logits_of_other_class(logits, target_class, value=1):
 
 class CarliniWagnerL2Attack(Attack):
     """
-    The Carlini & Wagner attack using L2 norm.
+    The Carlini & Wagner attack using L2 norm generates the adversarial examples
+    by utilizing two separate losses: an adversarial loss to make the generated example
+    actually adversarial, and a distance loss to constraint the quality of the adversarial example.
 
     References: `Nicholas Carlini, David Wagner: "Towards Evaluating
     the Robustness of Neural Networks" <https://arxiv.org/abs/1608.04644>`_

mindarmour/adv_robustness/attacks/gradient_method.py

@@ -204,6 +204,8 @@ class FastGradientMethod(GradientMethod):
 class RandomFastGradientMethod(FastGradientMethod):
     """
     Fast Gradient Method use Random perturbation.
+    An one-step attack based on gradients calculation. The adversarial noises
+    are generated based on the gradients of inputs, and then randomly perturbed.
 
     References: `Florian Tramer, Alexey Kurakin, Nicolas Papernot, "Ensemble
     adversarial training: Attacks and defenses" in ICLR, 2018
@@ -260,8 +262,8 @@ class RandomFastGradientMethod(FastGradientMethod):
 
 class FastGradientSignMethod(GradientMethod):
     """
-    Use the sign instead of the value of the gradient to the input. This attack is
-    often referred to as Fast Gradient Sign Method and was introduced previously.
+    The Fast Gradient Sign Method attack calculates the gradient of the input
+    data, and then uses the sign of the gradient to create adversarial noises.
 
     References: `Ian J. Goodfellow, J. Shlens, and C. Szegedy, "Explaining
     and harnessing adversarial examples," in ICLR, 2015
@@ -335,6 +337,9 @@ class FastGradientSignMethod(GradientMethod):
 class RandomFastGradientSignMethod(FastGradientSignMethod):
     """
     Fast Gradient Sign Method using random perturbation.
+    The Random Fast Gradient Sign Method attack calculates the gradient of the input
+    data, and then uses the sign of the gradient with random perturbation
+    to create adversarial noises.
 
     References: `F. Tramer, et al., "Ensemble adversarial training: Attacks
     and defenses," in ICLR, 2018 <https://arxiv.org/abs/1705.07204>`_
@@ -387,7 +392,8 @@ class RandomFastGradientSignMethod(FastGradientSignMethod):
 
 class LeastLikelyClassMethod(FastGradientSignMethod):
     """
-    Least-Likely Class Method.
+    The Single Step Least-Likely Class Method, a variant of FGSM, targets the
+    least-likely class to generate the adversarial examples.
 
     References: `F. Tramer, et al., "Ensemble adversarial training: Attacks
     and defenses," in ICLR, 2018 <https://arxiv.org/abs/1705.07204>`_
@@ -435,6 +441,9 @@ class RandomLeastLikelyClassMethod(FastGradientSignMethod):
     """
     Least-Likely Class Method use Random perturbation.
 
+    The Single Step Least-Likely Class Method with Random Perturbation, a variant of Random FGSM,
+    targets the least-likely class to generate the adversarial examples.
+
     References: `F. Tramer, et al., "Ensemble adversarial training: Attacks
     and defenses," in ICLR, 2018 <https://arxiv.org/abs/1705.07204>`_
 

mindarmour/adv_robustness/attacks/iterative_gradient_method.py

@@ -257,7 +257,10 @@ class BasicIterativeMethod(IterativeGradientMethod):
 
 class MomentumIterativeMethod(IterativeGradientMethod):
     """
-    The Momentum Iterative Method attack.
+    The Momentum Iterative Method attack accelerates the gradient descent algorithm,
+    such as FGSM, FGM, and LLCM, by accumulating a velocity vector in the gradient
+    direction of the loss function across iterations, and thus generates the adversarial examples.
+
 
     References: `Y. Dong, et al., "Boosting adversarial attacks with
     momentum," arXiv:1710.06081, 2017 <https://arxiv.org/abs/1710.06081>`_
@@ -502,7 +505,9 @@ class ProjectedGradientDescent(BasicIterativeMethod):
 
 class DiverseInputIterativeMethod(BasicIterativeMethod):
     """
-    The Diverse Input Iterative Method attack.
+    The Diverse Input Iterative Method attack follows the basic iterative method,
+    and applies random transformation to the input data at each iteration. Such transformation
+    on the input data could improve the transferability of the adversarial examples.
 
     References: `Xie, Cihang and Zhang, et al., "Improving Transferability of
     Adversarial Examples With Input Diversity," in CVPR, 2019 <https://arxiv.org/abs/1803.06978>`_
@@ -555,7 +560,10 @@ class DiverseInputIterativeMethod(BasicIterativeMethod):
 
 class MomentumDiverseInputIterativeMethod(MomentumIterativeMethod):
     """
-    The Momentum Diverse Input Iterative Method attack.
+    The Momentum Diverse Input Iterative Method attack is a momentum iterative method,
+    and applies random transformation to the input data at each iteration. Such transformation
+    on the input data could improve the transferability of the adversarial examples.
+
 
     References: `Xie, Cihang and Zhang, et al., "Improving Transferability of
     Adversarial Examples With Input Diversity," in CVPR, 2019 <https://arxiv.org/abs/1803.06978>`_

mindarmour/adv_robustness/attacks/jsma.py

@@ -32,8 +32,10 @@ TAG = 'JSMA'
 
 class JSMAAttack(Attack):
     """
-    JSMA is an targeted & iterative attack based on saliency map of
-    input features.
+    Jacobian-based Saliency Map Attack is a targeted and iterative attack based on saliency
+    map of the input features. It uses the gradient of loss with each class labels with respect
+    to every component of the input. Then a saliency map is used to select the dimension which
+    produces the maximum error.
 
     Reference: `The limitations of deep learning in adversarial settings
     <https://arxiv.org/abs/1511.07528>`_

mindarmour/adv_robustness/attacks/lbfgs.py

@@ -34,7 +34,8 @@ TAG = 'LBFGS'
 
 class LBFGS(Attack):
     """
-    Uses L-BFGS-B to minimize the distance between the input and the adversarial example.
+    In L-BFGS-B attack, the Limited-Memory BFGS optimizaiton algorithm is used
+    to minimize the distance between the inputs and the adversarial examples.
 
     References: `Pedro Tabacof, Eduardo Valle. "Exploring the Space of
     Adversarial Images" <https://arxiv.org/abs/1510.05328>`_

mindarmour/adv_robustness/defenses/adversarial_defense.py

@@ -88,7 +88,8 @@ class AdversarialDefense(Defense):
 
 class AdversarialDefenseWithAttacks(AdversarialDefense):
     """
-    Adversarial defense with attacks.
+    Adversarial training using specific attacking method and the given
+    adversarial examples to enhance model robustness.
 
     Args:
         network (Cell): A MindSpore network to be defensed.
@@ -174,7 +175,8 @@ class AdversarialDefenseWithAttacks(AdversarialDefense):
 
 class EnsembleAdversarialDefense(AdversarialDefenseWithAttacks):
     """
-    Ensemble adversarial defense.
+    Adversarial training using a list of specific attacking methods
+    and the given adversarial examples to enhance model robustness.
 
     Args:
         network (Cell): A MindSpore network to be defensed.

mindarmour/adv_robustness/detectors/black/similarity_detector.py

@@ -98,9 +98,7 @@ 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,

mindarmour/adv_robustness/detectors/ensemble_detector.py

@@ -27,7 +27,8 @@ TAG = 'EnsembleDetector'
 
 class EnsembleDetector(Detector):
     """
-    Ensemble detector.
+    The ensemble detector uses a list of detectors to detect the adversarial
+    examples from the input samples.
 
     Args:
         detectors (Union[tuple, list]): List of detector methods.

mindarmour/adv_robustness/detectors/mag_net.py

@@ -166,7 +166,8 @@ class ErrorBasedDetector(Detector):
 
 class DivergenceBasedDetector(ErrorBasedDetector):
     """
-    This class implement a divergence-based detector.
+    The divergence-based detector learns to distinguish normal and adversarial
+    examples by their js-divergence.
 
     Reference: `MagNet: a Two-Pronged Defense against Adversarial Examples,
     by Dongyu Meng and Hao Chen, at CCS 2017.

mindarmour/adv_robustness/detectors/region_based_detector.py

@@ -34,7 +34,9 @@ TAG = 'RegionBasedDetector'
 
 class RegionBasedDetector(Detector):
     """
-    This class implement a region-based detector.
+    The region-based detector uses the fact that adversarial examples are close
+    to the classification boundary, and ensembles information around the given example
+    to predict whether it is an adversarial example or not.
 
     Reference: `Mitigating evasion attacks to deep neural networks via
     region-based classification <https://arxiv.org/abs/1709.05583>`_

mindarmour/adv_robustness/detectors/spatial_smoothing.py

@@ -38,6 +38,10 @@ def _median_filter_np(inputs, size=2):
 class SpatialSmoothing(Detector):
     """
     Detect method based on spatial smoothing.
+    Using Gaussian filtering, median filtering, and mean filtering, to blur
+    the original image. When the model has a large threshold difference
+    between the predicted values before and after the sample is blurred,
+    it is judged as an adversarial example.
 
     Args:
         model (Model): Target model.

mindarmour/fuzz_testing/fuzzing.py

@@ -169,7 +169,7 @@ class Fuzzer:
         >>> initial_seeds = []
         >>> # make initial seeds
         >>> for img, label in zip(test_images, test_labels):
-        >>>     initial_seeds.append([img, label])
+        ...     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)

mindarmour/privacy/diff_privacy/mechanisms/mechanisms.py

@@ -36,6 +36,8 @@ TAG = 'NoiseMechanism'
 class ClipMechanismsFactory:
     """
     Factory class of clip mechanisms
+    Wrapper of clip noise generating mechanisms. It supports Adaptive Clipping with
+    Gaussian Random Noise for now.
 
     For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_
 
@@ -55,7 +57,7 @@ class ClipMechanismsFactory:
             learning_rate(float): Learning rate of update norm clip. Default: 0.001.
             target_unclipped_quantile(float): Target quantile of norm clip. Default: 0.9.
             fraction_stddev(float): The stddev of Gaussian normal which used in
-                empirical_fraction, the formula is :math:`empirical fraction + N(0, fraction sstddev)`.
+                empirical_fraction, the formula is :math:`empirical_fraction + N(0, fraction_stddev)`.
                 Default: 0.01.
             seed(int): Original random seed, if seed=0 random normal will use secure
                 random number. IF seed!=0 random normal will generate values using
@@ -95,6 +97,8 @@ class ClipMechanismsFactory:
 
 class NoiseMechanismsFactory:
     """ Factory class of noise mechanisms
+    Wrapper of noise generating mechanisms. It supports Gaussian Random Noise and
+    Adaptive Gaussian Random Noise for now.
 
     For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_
 
@@ -165,7 +169,8 @@ class _Mechanisms(Cell):
 
 class NoiseGaussianRandom(_Mechanisms):
     """
-    Gaussian noise generated mechanism.
+    Generate noise in Gaussian Distribution with :math:`mean=0` and
+    :math:`standard deviation = norm_bound * initial_noise_multiplier`.
 
     Args:
         norm_bound(float): Clipping bound for the l2 norm of the gradients.
@@ -178,9 +183,6 @@ class NoiseGaussianRandom(_Mechanisms):
             given seed. Default: 0.
         decay_policy(str): Mechanisms parameters update policy. Default: None.
 
-    Returns:
-        Tensor, generated noise with shape like given gradients.
-
     Examples:
         >>> from mindspore import Tensor
         >>> from mindspore.common import dtype as mstype
@@ -230,8 +232,7 @@ class NoiseAdaGaussianRandom(NoiseGaussianRandom):
     """
     Adaptive Gaussian noise generated mechanism. Noise would be decayed with
     training. Decay mode could be 'Time' mode, 'Step' mode, 'Exp' mode.
-    `self._noise_multiplier` will be update during the model.train, using
-    _MechanismsParamsUpdater.
+    `self._noise_multiplier` will be update during model training process.
 
     Args:
         norm_bound(float): Clipping bound for the l2 norm of the gradients.
@@ -247,9 +248,6 @@ class NoiseAdaGaussianRandom(NoiseGaussianRandom):
         decay_policy(str): Noise decay strategy include 'Step', 'Time', 'Exp'.
             Default: 'Exp'.
 
-    Returns:
-        Tensor, generated noise with shape like given gradients.
-
     Examples:
         >>> from mindspore import Tensor
         >>> from mindspore.common import dtype as mstype
@@ -350,10 +348,10 @@ class _MechanismsParamsUpdater(Cell):
 
 class AdaClippingWithGaussianRandom(Cell):
     """
-    Adaptive clipping. If `decay_policy` is 'Linear', the update formula :math:`norm bound = norm bound -
-    learning rate*(beta - target unclipped quantile)`.
-    If `decay_policy` is 'Geometric', the update formula is :math:`norm bound =
-    norm bound*exp(-learning rate*(empirical fraction - target unclipped quantile))`.
+    Adaptive clipping. If `decay_policy` is 'Linear', the update formula :math:`norm_bound = norm_bound -
+    learning_rate*(beta - target_unclipped_quantile)`.
+    If `decay_policy` is 'Geometric', the update formula is :math:`norm_bound =
+    norm_bound*exp(-learning_rate*(empirical_fraction - target_unclipped_quantile))`.
     where beta is the empirical fraction of samples with the value at most
     `target_unclipped_quantile`.
 

mindarmour/privacy/diff_privacy/monitor/monitor.py

@@ -28,6 +28,8 @@ TAG = 'DP monitor'
 class PrivacyMonitorFactory:
     """
     Factory class of DP training's privacy monitor.
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_
+
     """
 
     def __init__(self):
@@ -38,8 +40,6 @@ class PrivacyMonitorFactory:
         """
         Create a privacy monitor class.
 
-        For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_
-
         Args:
             policy (str): Monitor policy, 'rdp' and 'zcdp' are supported
                 by now. If policy is 'rdp', the monitor will compute the
@@ -74,11 +74,11 @@ class RDPMonitor(Callback):
     mechanism is said to have ε'-Renyi differential privacy of order α, it
     also satisfies conventional differential privacy (ε, δ) as below:
 
-    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_
-
     .. math::
         (ε'+\frac{log(1/δ)}{α-1}, δ)
 
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_
+
     Reference: `Rényi Differential Privacy of the Sampled Gaussian Mechanism
     <https://arxiv.org/abs/1908.10530>`_
 
@@ -363,14 +363,15 @@ class ZCDPMonitor(Callback):
     if a randomized mechanism is said to have ρ-ｚCDP, it also satisfies
     conventional differential privacy (ε, δ) as below:
 
-    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_
-
     .. math::
         (ρ+２\sqrt{ρ*log(1/δ)}, δ)
 
     It should be noted that ZCDPMonitor is not suitable for subsampling
     noise mechanisms(such as NoiseAdaGaussianRandom and NoiseGaussianRandom).
     The matching noise mechanism of ZCDP will be developed in the future.
+
+    For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_
+
     Reference: `Concentrated Differentially Private Gradient Descent with
     Adaptive per-Iteration Privacy Budget <https://arxiv.org/abs/1808.09501>`_
 

mindarmour/privacy/diff_privacy/train/model.py

@@ -67,6 +67,7 @@ def tensor_grad_scale(scale, grad):
 
 class DPModel(Model):
     """
+    DPModel is used for constructing a model for differential privacy training.
     This class is overload mindspore.train.model.Model.
 
     For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_differential_privacy.html#%E5%B7%AE%E5%88%86%E9%9A%90%E7%A7%81>`_
@@ -82,7 +83,7 @@ class DPModel(Model):
             Default: None.
 
     Raises:
-        ValueError: If DPOptimizer and noise_mecn are both None or not None.
+        ValueError: If DPOptimizer and noise_mech are both None or not None.
         ValueError: If noise_mech or DPOtimizer's mech method is adaptive while clip_mech is not None.
 
     """

mindarmour/privacy/evaluation/attacker.py

@@ -139,7 +139,7 @@ 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
+        >>> 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]}}

mindarmour/privacy/evaluation/membership_inference.py

@@ -94,8 +94,11 @@ def _softmax_cross_entropy(logits, labels, epsilon=1e-12):
 
 class MembershipInference:
     """
-    Evaluation proposed by Shokri, Stronati, Song and Shmatikov is a grey-box attack.
-    The attack requires loss or logits results of training samples.
+    Proposed by Shokri, Stronati, Song and Shmatikov, membership inference is a grey-box attack
+    for inferring user's privacy data. It requires loss or logits results of the training samples.
+    (Privacy refers to some sensitive attributes of a single user).
+
+    For details, please refer to the `Tutorial <https://mindspore.cn/mindarmour/docs/en/master/test_model_security_membership_inference.html>`_
 
     References: `Reza Shokri, Marco Stronati, Congzheng Song, Vitaly Shmatikov.
     Membership Inference Attacks against Machine Learning Models. 2017.

mindarmour/privacy/sup_privacy/mask_monitor/masker.py

@@ -25,6 +25,7 @@ TAG = 'suppress masker'
 
 class SuppressMasker(Callback):
     """
+    Periodicity check suppress privacy function status and toggle suppress operation.
     For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_suppress_privacy.html#%E5%BC%95%E5%85%A5%E6%8A%91%E5%88%B6%E9%9A%90%E7%A7%81%E8%AE%AD%E7%BB%83>`_
 
     Args:
@@ -33,7 +34,6 @@ class SuppressMasker(Callback):
 
     Examples:
         >>> import mindspore.nn as nn
-        >>> import mindspore.dataset as ds
         >>> import mindspore.ops.operations as P
         >>> from mindspore import context
         >>> from mindspore.nn import Accuracy

mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py

@@ -63,7 +63,6 @@ class SuppressPrivacyFactory:
 
         Examples:
             >>> import mindspore.nn as nn
-            >>> import mindspore.dataset as ds
             >>> import mindspore.ops.operations as P
             >>> from mindspore import context
             >>> from mindspore.nn import Accuracy
@@ -113,6 +112,10 @@ class SuppressPrivacyFactory:
 
 class SuppressCtrl(Cell):
     """
+    Complete suppress privacy operation, including computing suppress ration,
+    finding the parameters that should be suppressed, and suppress these
+    parameters permanently.
+
     For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_suppress_privacy.html#%E5%BC%95%E5%85%A5%E6%8A%91%E5%88%B6%E9%9A%90%E7%A7%81%E8%AE%AD%E7%BB%83>`_
 
     Args:

mindarmour/privacy/sup_privacy/train/model.py

@@ -56,7 +56,9 @@ def tensor_grad_scale(scale, grad):
 
 class SuppressModel(Model):
     """
-    This class is overload mindspore.train.model.Model.
+    Complete model train function. The suppress privacy function is embedded into the overload
+    mindspore.train.model.Model.
+
     For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/protect_user_privacy_with_suppress_privacy.html>`_
 
     Args:

mindarmour/reliability/concept_drift/concept_drift_check_images.py

@@ -25,7 +25,7 @@ from mindarmour.utils._check_param import check_param_type, check_param_in_range
 
 class OodDetector:
     """
-    Train the OOD detector.
+    The abstract class of the out-of-distribution detector.
 
     Args:
         model (Model):The training model.
@@ -55,7 +55,9 @@ class OodDetector:
 
     def get_optimal_threshold(self, label, ds_eval):
         """
-        Get the optimal threshold.
+        Get the optimal threshold. Try to find an optimal threshold value to
+        detect OOD examples. The optimal threshold is calculated by a labeled
+        dateset `ds_eval`.
 
         Args:
             label (numpy.ndarray): The label whether an image is in-distribution and out-of-distribution.
@@ -67,7 +69,9 @@ class OodDetector:
 
     def ood_predict(self, threshold, ds_test):
         """
-        The out-of-distribution detection.
+        The out-of-distribution detection. This function aims to detect whether images,
+        regarded as `ds_test`, are OOD examples or not. If the prediction score of one
+        image is larger than `threshold`, this image is out-of-distribution.
 
         Args:
             threshold (float): the threshold to judge ood data. One can set value by experience
@@ -174,7 +178,9 @@ class OodDetectorFeatureCluster(OodDetector):
 
     def get_optimal_threshold(self, label, ds_eval):
         """
-        Get the optimal threshold.
+        Get the optimal threshold. Try to find an optimal threshold value to
+        detect OOD examples. The optimal threshold is calculated by a labeled
+        dateset `ds_eval`.
 
         Args:
             label (numpy.ndarray): The label whether an image is in-distribution and out-of-distribution.
@@ -204,7 +210,9 @@ class OodDetectorFeatureCluster(OodDetector):
 
     def ood_predict(self, threshold, ds_test):
         """
-        The out-of-distribution detection.
+        The out-of-distribution detection. This function aims to detect whether images,
+        regarded as `ds_test`, are OOD examples or not. If the prediction score of one
+        image is larger than `threshold`, this image is out-of-distribution.
 
         Args:
             threshold (float): the threshold to judge ood data. One can set value by experience

mindarmour/reliability/model_fault_injection/fault_injection.py

@@ -28,7 +28,9 @@ TAG = 'FaultInjector'
 
 class FaultInjector:
     """
-    Fault injection for deep neural networks and evaluate performance.
+    Fault injection module simulates various fault scenarios for deep neural networks and evaluates
+    performance and reliability of the model.
+
     For details, please check `Tutorial <https://mindspore.cn/mindarmour/docs/zh-CN/master/fault_injection.html>`_
 
     Args:
@@ -40,7 +42,6 @@ class FaultInjector:
 
     Examples:
         >>> from mindspore import Model
-        >>> import mindspore.dataset as ds
         >>> import mindspore.ops.operations as P
         >>> from mindarmour.reliability.model_fault_injection.fault_injection import FaultInjector
         >>> class Net(nn.Cell):
@@ -64,7 +65,7 @@ class FaultInjector:
         >>> model = Model(net)
         >>> ds_eval = ds.GeneratorDataset(dataset_generator, ['image', 'label'])
         >>> fi_type = ['bitflips_random', 'bitflips_designated', 'random', 'zeros',
-                       'nan', 'inf', 'anti_activation', 'precision_loss']
+        ...            'nan', 'inf', 'anti_activation', 'precision_loss']
         >>> fi_mode = ['single_layer', 'all_layer']
         >>> fi_size = [1]
         >>> fi = FaultInjector(model, ds_eval, fi_type, fi_mode, fi_size)

mindarmour/utils/logger.py

@@ -30,6 +30,8 @@ class LogUtil:
     """
     Logging module.
 
+    Recording the logging statistics over time in long-running scripts.
+
     Raises:
         SyntaxError: If create this class.
     """

mindarmour/utils/util.py

@@ -216,6 +216,7 @@ class GradWrap(Cell):
         ...         out = self._softmax(inputs)
         ...         out = self._Dense(out)
         ...         return self._squeeze(out)
+        >>> net = Net()
         >>> data = Tensor(np.ones([2, 1, 10]).astype(np.float32)*0.01)
         >>> labels = Tensor(np.ones([2, 10]).astype(np.float32))
         >>> num_classes = 10