!315 Fix API Doc Line Issues

Merge pull request !315 from 张澍坤/master

mindarmour/adv_robustness/attacks/black/genetic_attack.py

@@ -75,7 +75,6 @@ class GeneticAttack(Attack):
         >>> from mindspore.nn import Cell
         >>> from mindarmour import BlackModel
         >>> from mindarmour.adv_robustness.attacks import GeneticAttack
-        >>>
         >>> class ModelToBeAttacked(BlackModel):
         >>>     def __init__(self, network):
         >>>         super(ModelToBeAttacked, self).__init__()
@@ -83,16 +82,13 @@ class GeneticAttack(Attack):
         >>>     def predict(self, inputs):
         >>>         result = self._network(Tensor(inputs.astype(np.float32)))
         >>>         return result.asnumpy()
-        >>>
         >>> class Net(Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self._softmax = M.Softmax()
-        >>>
         >>>     def construct(self, inputs):
         >>>         out = self._softmax(inputs)
         >>>         return out
-        >>>
         >>> net = Net()
         >>> model = ModelToBeAttacked(net)
         >>> attack = GeneticAttack(model, sparse=False)

mindarmour/adv_robustness/attacks/black/hop_skip_jump_attack.py

@@ -80,7 +80,6 @@ class HopSkipJumpAttack(Attack):
         >>> from mindarmour import BlackModel
         >>> from mindarmour.adv_robustness.attacks import HopSkipJumpAttack
         >>> from tests.ut.python.utils.mock_net import Net
-        >>>
         >>> class ModelToBeAttacked(BlackModel):
         >>>     def __init__(self, network):
         >>>         super(ModelToBeAttacked, self).__init__()
@@ -90,8 +89,6 @@ class HopSkipJumpAttack(Attack):
         >>>             inputs = inputs[np.newaxis, :]
         >>>         result = self._network(Tensor(inputs.astype(np.float32)))
         >>>         return result.asnumpy()
-        >>>
-        >>>
         >>> net = Net()
         >>> model = ModelToBeAttacked(net)
         >>> attack = HopSkipJumpAttack(model)
@@ -188,12 +185,27 @@ class HopSkipJumpAttack(Attack):
             - numpy.ndarray, query times for each sample.
 
         Examples:
+            >>> import numpy as np
+            >>> from mindspore import Tensor
+            >>> from mindarmour import BlackModel
+            >>> from mindarmour.adv_robustness.attacks import HopSkipJumpAttack
+            >>> from tests.ut.python.utils.mock_net import Net
+            >>> class ModelToBeAttacked(BlackModel):
+            >>>     def __init__(self, network):
+            >>>         super(ModelToBeAttacked, self).__init__()
+            >>>         self._network = network
+            >>>     def predict(self, inputs):
+            >>>         if len(inputs.shape) == 3:
+            >>>             inputs = inputs[np.newaxis, :]
+            >>>         result = self._network(Tensor(inputs.astype(np.float32)))
+            >>>         return result.asnumpy()
+            >>> net = Net()
+            >>> model = ModelToBeAttacked(net)
             >>> attack = HopSkipJumpAttack(model)
             >>> n, c, h, w = 1, 1, 32, 32
             >>> class_num = 3
             >>> x_test = np.asarray(np.random.random((n,c,h,w)), np.float32)
             >>> y_test = np.random.randint(0, class_num, size=n)
-            >>>
             >>> _, adv_x, _= attack.generate(x_test, y_test)
         """
         if labels is not None:

mindarmour/adv_robustness/attacks/black/natural_evolutionary_strategy.py

@@ -84,7 +84,6 @@ class NES(Attack):
         >>> from mindarmour import BlackModel
         >>> from mindarmour.adv_robustness.attacks import NES
         >>> from tests.ut.python.utils.mock_net import Net
-        >>>
         >>> class ModelToBeAttacked(BlackModel):
         >>>     def __init__(self, network):
         >>>         super(ModelToBeAttacked, self).__init__()
@@ -94,7 +93,6 @@ class NES(Attack):
         >>>             inputs = inputs[np.newaxis, :]
         >>>         result = self._network(Tensor(inputs.astype(np.float32)))
         >>>         return result.asnumpy()
-        >>>
         >>> net = Net()
         >>> model = ModelToBeAttacked(net)
         >>> SCENE = 'Query_Limit'
@@ -157,12 +155,25 @@ class NES(Attack):
             ValueError: If scene is not in ['Label_Only', 'Partial_Info', 'Query_Limit']
 
         Examples:
+            >>> import numpy as np
+            >>> from mindspore import Tensor
+            >>> from mindarmour import BlackModel
+            >>> from mindarmour.adv_robustness.attacks import NES
+            >>> from tests.ut.python.utils.mock_net import Net
+            >>> class ModelToBeAttacked(BlackModel):
+            >>>     def __init__(self, network):
+            >>>         super(ModelToBeAttacked, self).__init__()
+            >>>         self._network = network
+            >>>     def predict(self, inputs):
+            >>>         if len(inputs.shape) == 3:
+            >>>             inputs = inputs[np.newaxis, :]
+            >>>         result = self._network(Tensor(inputs.astype(np.float32)))
+            >>>         return result.asnumpy()
             >>> net = Net()
             >>> model = ModelToBeAttacked(net)
             >>> SCENE = 'Query_Limit'
             >>> TOP_K = -1
             >>> attack= NES(model, SCENE, top_k=TOP_K)
-            >>>
             >>> num_class = 5
             >>> x_test = np.asarray(np.random.random((32, 32)), np.float32)
             >>> target_image  = np.asarray(np.random.random((32, 32)), np.float32)

mindarmour/adv_robustness/attacks/black/pointwise_attack.py

@@ -52,7 +52,6 @@ class PointWiseAttack(Attack):
         >>> from mindarmour import BlackModel
         >>> from mindarmour.adv_robustness.attacks import PointWiseAttack
         >>> from tests.ut.python.utils.mock_net import Net
-        >>>
         >>> class ModelToBeAttacked(BlackModel):
         >>>     def __init__(self, network):
         >>>         super(ModelToBeAttacked, self).__init__()
@@ -60,7 +59,6 @@ class PointWiseAttack(Attack):
         >>>     def predict(self, inputs):
         >>>         result = self._network(Tensor(inputs.astype(np.float32)))
         >>>         return result.asnumpy()
-        >>>
         >>> net = Net()
         >>> model = ModelToBeAttacked(net)
         >>> attack = PointWiseAttack(model)
@@ -95,6 +93,18 @@ class PointWiseAttack(Attack):
             - numpy.ndarray, query times for each sample.
 
         Examples:
+            >>> import numpy as np
+            >>> from mindspore import Tensor
+            >>> from mindarmour import BlackModel
+            >>> from mindarmour.adv_robustness.attacks import PointWiseAttack
+            >>> from tests.ut.python.utils.mock_net import Net
+            >>> class ModelToBeAttacked(BlackModel):
+            >>>     def __init__(self, network):
+            >>>         super(ModelToBeAttacked, self).__init__()
+            >>>         self._network = network
+            >>>     def predict(self, inputs):
+            >>>         result = self._network(Tensor(inputs.astype(np.float32)))
+            >>>         return result.asnumpy()
             >>> net = Net()
             >>> model = ModelToBeAttacked(net)
             >>> attack = PointWiseAttack(model)

mindarmour/adv_robustness/attacks/black/pso_attack.py

@@ -70,7 +70,6 @@ class PSOAttack(Attack):
         >>> from mindspore.nn import Cell
         >>> from mindarmour import BlackModel
         >>> from mindarmour.adv_robustness.attacks import PSOAttack
-        >>>
         >>> class ModelToBeAttacked(BlackModel):
         >>>     def __init__(self, network):
         >>>         super(ModelToBeAttacked, self).__init__()
@@ -80,7 +79,6 @@ class PSOAttack(Attack):
         >>>             inputs = np.expand_dims(inputs, axis=0)
         >>>         result = self._network(Tensor(inputs.astype(np.float32)))
         >>>         return result.asnumpy()
-        >>>
         >>> class Net(Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
@@ -89,7 +87,6 @@ class PSOAttack(Attack):
         >>>     def construct(self, inputs):
         >>>         out = self._relu(inputs)
         >>>         return out
-        >>>
         >>> net = Net()
         >>> model = ModelToBeAttacked(net)
         >>> attack = PSOAttack(model, bounds=(0.0, 1.0), pm=0.5, sparse=False)
@@ -381,17 +378,6 @@ class PSOAttack(Attack):
             - numpy.ndarray, generated adversarial examples.
 
             - numpy.ndarray, query times for each sample.
-
-        Examples:
-            >>> net = Net()
-            >>> model = ModelToBeAttacked(net)
-            >>> attack = PSOAttack(model, bounds=(0.0, 1.0), pm=0.5, sparse=False)
-            >>> batch_size = 6
-            >>> x_test = np.random.rand(batch_size, 10)
-            >>> y_test = np.random.randint(low=0, high=10, size=batch_size)
-            >>> y_test = np.eye(10)[y_test]
-            >>> y_test = y_test.astype(np.float32)
-            >>> _, adv_data, _ = attack.generate(x_test, y_test)
         """
         # inputs check
         images, auxiliary_inputs, gt_boxes, gt_labels = check_detection_inputs(inputs, labels)
@@ -523,6 +509,29 @@ class PSOAttack(Attack):
             - numpy.ndarray, query times for each sample.
 
         Examples:
+            >>> import numpy as np
+            >>> import mindspore.nn as nn
+            >>> from mindspore import Tensor
+            >>> from mindspore.nn import Cell
+            >>> from mindarmour import BlackModel
+            >>> from mindarmour.adv_robustness.attacks import PSOAttack
+            >>> class ModelToBeAttacked(BlackModel):
+            >>>     def __init__(self, network):
+            >>>         super(ModelToBeAttacked, self).__init__()
+            >>>         self._network = network
+            >>>     def predict(self, inputs):
+            >>>         if len(inputs.shape) == 1:
+            >>>             inputs = np.expand_dims(inputs, axis=0)
+            >>>         result = self._network(Tensor(inputs.astype(np.float32)))
+            >>>         return result.asnumpy()
+            >>> class Net(Cell):
+            >>>     def __init__(self):
+            >>>         super(Net, self).__init__()
+            >>>         self._relu = nn.ReLU()
+            >>>
+            >>>     def construct(self, inputs):
+            >>>         out = self._relu(inputs)
+            >>>         return out
             >>> net = Net()
             >>> model = ModelToBeAttacked(net)
             >>> attack = PSOAttack(model, bounds=(0.0, 1.0), pm=0.5, sparse=False)

mindarmour/adv_robustness/attacks/black/salt_and_pepper_attack.py

@@ -45,7 +45,6 @@ class SaltAndPepperNoiseAttack(Attack):
         >>> from mindarmour import BlackModel
         >>> from mindarmour.adv_robustness.attacks import SaltAndPepperNoiseAttack
         >>> from tests.ut.python.utils.mock_net import Net
-        >>>
         >>> class ModelToBeAttacked(BlackModel):
         >>>     def __init__(self, network):
         >>>         super(ModelToBeAttacked, self).__init__()
@@ -53,7 +52,6 @@ class SaltAndPepperNoiseAttack(Attack):
         >>>     def predict(self, inputs):
         >>>         result = self._network(Tensor(inputs.astype(np.float32)))
         >>>         return result.asnumpy()
-        >>>
         >>> net = Net()
         >>> model = ModelToBeAttacked(net)
         >>> attack = SaltAndPepperNoiseAttack(model)
@@ -85,6 +83,18 @@ class SaltAndPepperNoiseAttack(Attack):
             - numpy.ndarray, query times for each sample.
 
         Examples:
+            >>> import numpy as np
+            >>> from mindspore import Tensor
+            >>> from mindarmour import BlackModel
+            >>> from mindarmour.adv_robustness.attacks import SaltAndPepperNoiseAttack
+            >>> from tests.ut.python.utils.mock_net import Net
+            >>> class ModelToBeAttacked(BlackModel):
+            >>>     def __init__(self, network):
+            >>>         super(ModelToBeAttacked, self).__init__()
+            >>>         self._network = network
+            >>>     def predict(self, inputs):
+            >>>         result = self._network(Tensor(inputs.astype(np.float32)))
+            >>>         return result.asnumpy()
             >>> net = Net()
             >>> model = ModelToBeAttacked(net)
             >>> attack = PointWiseAttack(model)

mindarmour/adv_robustness/attacks/carlini_wagner.py

@@ -99,7 +99,6 @@ class CarliniWagnerL2Attack(Attack):
         >>> import mindspore.ops.operations as M
         >>> from mindspore.nn import Cell
         >>> from mindarmour.adv_robustness.attacks import CarliniWagnerL2Attack
-        >>>
         >>> class Net(Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
@@ -108,7 +107,6 @@ class CarliniWagnerL2Attack(Attack):
         >>>     def construct(self, inputs):
         >>>         out = self._softmax(inputs)
         >>>         return out
-        >>>
         >>> input_np = np.array([[0.1, 0.2, 0.7, 0.5, 0.4]]).astype(np.float32)
         >>> label_np = np.array([3]).astype(np.int64)
         >>> num_classes = input_np.shape[1]

mindarmour/adv_robustness/attacks/deep_fool.py

@@ -122,18 +122,16 @@ class DeepFool(Attack):
         >>> from mindspore.nn import Cell
         >>> from mindspore import Tensor
         >>> from mindarmour.adv_robustness.attacks import DeepFool
-        >>>
-        >>> def __init__(self):
-        >>>     super(Net, self).__init__()
-        >>>     self._softmax = P.Softmax()
-        >>>
-        >>> def construct(self, inputs):
-        >>>     out = self._softmax(inputs)
-        >>>     return out
-        >>>
+        >>> class Net(Cell):
+        >>>     def __init__(self):
+        >>>         super(Net, self).__init__()
+        >>>         self._softmax = P.Softmax()
+        >>>     def construct(self, inputs):
+        >>>         out = self._softmax(inputs)
+        >>>         return out
         >>> net = Net()
         >>> attack = DeepFool(net, classes, max_iters=10, norm_level=2,
-                          bounds=(0.0, 1.0))
+        ...                   bounds=(0.0, 1.0))
     """
 
     def __init__(self, network, num_classes, model_type='classification',
@@ -181,6 +179,21 @@ class DeepFool(Attack):
             NotImplementedError: If norm_level is not in [2, np.inf, '2', 'inf'].
 
         Examples:
+            >>> import numpy as np
+            >>> import mindspore.ops.operations as P
+            >>> from mindspore.nn import Cell
+            >>> from mindspore import Tensor
+            >>> from mindarmour.adv_robustness.attacks import DeepFool
+            >>> class Net(Cell):
+            >>>     def __init__(self):
+            >>>         super(Net, self).__init__()
+            >>>         self._softmax = P.Softmax()
+            >>>     def construct(self, inputs):
+            >>>         out = self._softmax(inputs)
+            >>>         return out
+            >>> net = Net()
+            >>> attack = DeepFool(net, classes, max_iters=10, norm_level=2,
+            ...                   bounds=(0.0, 1.0))
             >>> input_shape = (1, 5)
             >>> _, classes = input_shape
             >>> input_np = np.array([[0.1, 0.2, 0.7, 0.5, 0.4]]).astype(np.float32)

mindarmour/adv_robustness/attacks/gradient_method.py

@@ -51,8 +51,7 @@ class GradientMethod(Attack):
         >>> import mindspore.nn as nn
         >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits
         >>> from mindspore import Tensor
-        >>> from mindarmour.adv_robustness.attacksimport FastGradientMethod
-        >>>
+        >>> from mindarmour.adv_robustness.attacks import FastGradientMethod
         >>> class Net(Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
@@ -61,7 +60,6 @@ class GradientMethod(Attack):
         >>>     def construct(self, inputs):
         >>>         out = self._relu(inputs)
         >>>         return out
-        >>>
         >>> inputs = np.array([[0.1, 0.2, 0.6], [0.3, 0, 0.4]])
         >>> labels = np.array([[0, 1, 0, 0, 0], [0, 0, 1, 0, 0]])
         >>> net = Net()
@@ -175,16 +173,13 @@ class FastGradientMethod(GradientMethod):
         >>> import mindspore.nn as nn
         >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits
         >>> from mindarmour.adv_robustness.attacks import FastGradientMethod
-        >>>
         >>> class Net(Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self._relu = nn.ReLU()
-        >>>
         >>>     def construct(self, inputs):
         >>>         out = self._relu(inputs)
         >>>         return out
-        >>>
         >>> inputs = np.array([[0.1, 0.2, 0.6], [0.3, 0, 0.4]])
         >>> labels = np.array([[0, 1, 0, 0, 0], [0, 0, 1, 0, 0]])
         >>> net = Net()
@@ -258,16 +253,13 @@ class RandomFastGradientMethod(FastGradientMethod):
         >>> import mindspore.nn as nn
         >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits
         >>> from mindarmour.adv_robustness.attacks import RandomFastGradientMethod
-        >>>
         >>> class Net(Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self._relu = nn.ReLU()
-        >>>
         >>>     def construct(self, inputs):
         >>>         out = self._relu(inputs)
         >>>         return out
-        >>>
         >>> net = Net()
         >>> inputs = np.array([[0.1, 0.2, 0.6], [0.3, 0, 0.4]])
         >>> labels = np.array([[0, 1, 0, 0, 0], [0, 0, 1, 0, 0]])
@@ -315,16 +307,13 @@ class FastGradientSignMethod(GradientMethod):
         >>> import mindspore.nn as nn
         >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits
         >>> from mindarmour.adv_robustness.attacks import FastGradientSignMethod
-        >>>
         >>> class Net(Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self._relu = nn.ReLU()
-        >>>
         >>>     def construct(self, inputs):
         >>>         out = self._relu(inputs)
         >>>         return out
-        >>>
         >>> net = Net()
         >>> inputs = np.array([[0.1, 0.2, 0.6], [0.3, 0, 0.4]])
         >>> labels = np.array([[0, 1, 0, 0, 0], [0, 0, 1, 0, 0]])
@@ -399,11 +388,9 @@ class RandomFastGradientSignMethod(FastGradientSignMethod):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self._relu = nn.ReLU()
-        >>>
         >>>     def construct(self, inputs):
         >>>         out = self._relu(inputs)
         >>>         return out
-        >>>
         >>> net = Net()
         >>> inputs = np.array([[0.1, 0.2, 0.6], [0.3, 0, 0.4]])
         >>> labels = np.array([[0, 1, 0, 0, 0], [0, 0, 1, 0, 0]])
@@ -446,7 +433,6 @@ class LeastLikelyClassMethod(FastGradientSignMethod):
         >>> import mindspore.nn as nn
         >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits
         >>> from mindarmour.adv_robustness.attacks import LeastLikelyClassMethod
-        >>>
         >>> class Net(Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
@@ -455,7 +441,6 @@ class LeastLikelyClassMethod(FastGradientSignMethod):
         >>>     def construct(self, inputs):
         >>>         out = self._relu(inputs)
         >>>         return out
-        >>>
         >>> net = Net()
         >>> inputs = np.array([[0.1, 0.2, 0.6], [0.3, 0, 0.4]])
         >>> labels = np.array([[0, 1, 0, 0, 0], [0, 0, 1, 0, 0]])
@@ -499,16 +484,13 @@ class RandomLeastLikelyClassMethod(FastGradientSignMethod):
         >>> import mindspore.nn as nn
         >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits
         >>> from mindarmour.adv_robustness.attacks import RandomLeastLikelyClassMethod
-        >>>
         >>> class Net(Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self._relu = nn.ReLU()
-        >>>
         >>>     def construct(self, inputs):
         >>>         out = self._relu(inputs)
         >>>         return out
-        >>>
         >>> net = Net()
         >>> inputs = np.array([[0.1, 0.2, 0.6], [0.3, 0, 0.4]])
         >>> labels = np.array([[0, 1, 0, 0, 0], [0, 0, 1, 0, 0]])

mindarmour/adv_robustness/attacks/iterative_gradient_method.py

@@ -188,16 +188,13 @@ class BasicIterativeMethod(IterativeGradientMethod):
         >>> import mindspore.nn as nn
         >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits
         >>> from mindarmour.adv_robustness.attacks import BasicIterativeMethod
-        >>>
         >>> class Net(Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self._relu = nn.ReLU()
-        >>>
         >>>     def construct(self, inputs):
         >>>         out = self._relu(inputs)
         >>>         return out
-        >>>
         >>> net = Net()
         >>> attack = BasicIterativeMethod(net, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False))
     """
@@ -230,21 +227,23 @@ class BasicIterativeMethod(IterativeGradientMethod):
             numpy.ndarray, generated adversarial examples.
 
         Examples:
+            >>> import numpy as np
+            >>> import mindspore.nn as nn
+            >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits
+            >>> from mindarmour.adv_robustness.attacks import BasicIterativeMethod
             >>> class Net(Cell):
             >>>     def __init__(self):
             >>>         super(Net, self).__init__()
             >>>         self._relu = nn.ReLU()
-            >>>
             >>>     def construct(self, inputs):
             >>>         out = self._relu(inputs)
             >>>         return out
-            >>>
             >>> net = Net()
             >>> attack = BasicIterativeMethod(net, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False))
             >>> adv_x = attack.generate([[0.3, 0.2, 0.6],
-            >>>                          [0.3, 0.2, 0.4]],
-            >>>                         [[0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
-            >>>                          [0, 0, 0, 0, 0, 0, 1, 0, 0, 0]])
+            ...                          [0.3, 0.2, 0.4]],
+            ...                         [[0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
+            ...                          [0, 0, 0, 0, 0, 0, 1, 0, 0, 0]])
         """
         inputs_image, inputs, labels = check_inputs_labels(inputs, labels)
         arr_x = inputs_image
@@ -333,22 +332,19 @@ class MomentumIterativeMethod(IterativeGradientMethod):
             >>> import mindspore.nn as nn
             >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits
             >>> from mindarmour.adv_robustness.attacks import MomentumIterativeMethod
-            >>>
             >>> class Net(Cell):
             >>>     def __init__(self):
             >>>         super(Net, self).__init__()
             >>>         self._relu = nn.ReLU()
-            >>>
             >>>     def construct(self, inputs):
             >>>         out = self._relu(inputs)
             >>>         return out
-            >>>
             >>> net = Net()
             >>> attack = MomentumIterativeMethod(net, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False))
             >>> adv_x = attack.generate([[0.5, 0.2, 0.6],
-            >>>                          [0.3, 0, 0.2]],
-            >>>                         [[0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
-            >>>                          [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]])
+            ...                          [0.3, 0, 0.2]],
+            ...                         [[0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
+            ...                          [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]])
         """
         inputs_image, inputs, labels = check_inputs_labels(inputs, labels)
         arr_x = inputs_image
@@ -406,7 +402,7 @@ class MomentumIterativeMethod(IterativeGradientMethod):
 
         Examples:
             >>> grad = self._gradient([[0.5, 0.3, 0.4]],
-            >>>                       [[0, 0, 0, 1, 0, 0, 0, 0, 0, 0])
+            ...                       [[0, 0, 0, 1, 0, 0, 0, 0, 0, 0])
         """
         # get grad of loss over x
         inputs_tensor = to_tensor_tuple(inputs)
@@ -479,22 +475,19 @@ class ProjectedGradientDescent(BasicIterativeMethod):
             >>> import mindspore.nn as nn
             >>> from mindspore.nn import Cell, SoftmaxCrossEntropyWithLogits
             >>> from mindarmour.adv_robustness.attacks import ProjectedGradientDescent
-            >>>
             >>> class Net(Cell):
             >>>     def __init__(self):
             >>>         super(Net, self).__init__()
             >>>         self._relu = nn.ReLU()
-            >>>
             >>>     def construct(self, inputs):
             >>>         out = self._relu(inputs)
             >>>         return out
-            >>>
             >>> net = Net()
             >>> attack = ProjectedGradientDescent(net, loss_fn=SoftmaxCrossEntropyWithLogits(sparse=False))
             >>> adv_x = attack.generate([[0.6, 0.2, 0.6],
-            >>>                          [0.3, 0.3, 0.4]],
-            >>>                         [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
-            >>>                          [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
+            ...                          [0.3, 0.3, 0.4]],
+            ...                         [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
+            ...                          [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
         """
         inputs_image, inputs, labels = check_inputs_labels(inputs, labels)
         arr_x = inputs_image

mindarmour/adv_robustness/attacks/jsma.py

@@ -62,11 +62,9 @@ class JSMAAttack(Attack):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self._relu = nn.ReLU()
-        >>>
         >>>     def construct(self, inputs):
         >>>         out = self._relu(inputs)
         >>>         return out
-        >>>
         >>> net = Net()
         >>> input_shape = (1, 5)
         >>> batch_size, classes = input_shape
@@ -197,11 +195,22 @@ class JSMAAttack(Attack):
             numpy.ndarray, adversarial samples.
 
         Examples:
+            >>> import numpy as np
+            >>> import mindspore.nn as nn
+            >>> from mindspore.nn import Cell
+            >>> from mindarmour.adv_robustness.attacks import JSMAAttack
+            >>> class Net(Cell):
+            >>>     def __init__(self):
+            >>>         super(Net, self).__init__()
+            >>>         self._relu = nn.ReLU()
+            >>>     def construct(self, inputs):
+            >>>         out = self._relu(inputs)
+            >>>         return out
+            >>> net = Net()
             >>> input_shape = (1, 5)
+            >>> batch_size, classes = input_shape
             >>> input_np = np.random.random(input_shape).astype(np.float32)
             >>> label_np = np.random.randint(classes, size=batch_size)
-            >>> batch_size, classes = input_shape
-            >>>
             >>> attack = JSMAAttack(net, classes, max_iteration=5)
             >>> advs = attack.generate(input_np, label_np)
         """

mindarmour/adv_robustness/attacks/lbfgs.py

@@ -99,6 +99,10 @@ class LBFGS(Attack):
             numpy.ndarray, generated adversarial examples.
 
         Examples:
+            >>> import numpy as np
+            >>> from mindarmour.adv_robustness.attacks import LBFGS
+            >>> from tests.ut.python.utils.mock_net import Net
+            >>> net = Net()
             >>> attack = LBFGS(net, is_targeted=True)
             >>> adv = attack.generate([[0.1, 0.2, 0.6], [0.3, 0, 0.4]], [2, 2])
         """

mindarmour/adv_robustness/defenses/adversarial_defense.py

@@ -115,7 +115,6 @@ class AdversarialDefenseWithAttacks(AdversarialDefense):
         >>> from mindarmour.adv_robustness.defenses import AdversarialDefense
         >>> from mindspore import nn
         >>> from tests.ut.python.utils.mock_net import Net
-        >>>
         >>> net = Net()
         >>> lr = 0.001
         >>> momentum = 0.9
@@ -123,12 +122,10 @@ class AdversarialDefenseWithAttacks(AdversarialDefense):
         >>> num_class = 10
         >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
         >>> optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=momentum)
-        >>>
         >>> fgsm = FastGradientSignMethod(net, loss_fn=loss_fn)
         >>> pgd = ProjectedGradientDescent(net, loss_fn=loss_fn)
         >>> ead = AdversarialDefenseWithAttack(net, [fgsm, pgd], loss_fn=loss_fn,
-        >>>                                  optimizer=optimizer)
-        >>>
+        ...                                    optimizer=optimizer)
         >>> inputs = np.random.rand(batch_size, 1, 32, 32).astype(np.float32)
         >>> labels = np.random.randint(num_class, size=batch_size).astype(np.int32)
         >>> labels = np.eye(num_classes)[labels].astype(np.float32)

mindarmour/adv_robustness/defenses/natural_adversarial_defense.py

@@ -41,18 +41,14 @@ class NaturalAdversarialDefense(AdversarialDefenseWithAttacks):
         >>> from mindarmour.adv_robustness.defenses import NaturalAdversarialDefense
         >>> from mindspore import nn
         >>> from tests.ut.python.utils.mock_net import Net
-        >>>
         >>> net = Net()
         >>> lr = 0.001
         >>> momentum = 0.9
         >>> batch_size = 32
         >>> num_class = 10
-        >>>
         >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
         >>> optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=momentum)
-        >>>
         >>> nad = NaturalAdversarialDefense(net, loss_fn=loss_fn, optimizer=optimizer)
-        >>>
         >>> inputs = np.random.rand(batch_size, 1, 32, 32).astype(np.float32)
         >>> labels = np.random.randint(num_class, size=batch_size).astype(np.int32)
         >>> labels = np.eye(num_classes)[labels].astype(np.float32)

mindarmour/adv_robustness/defenses/projected_adversarial_defense.py

@@ -46,18 +46,14 @@ class ProjectedAdversarialDefense(AdversarialDefenseWithAttacks):
         >>> from mindarmour.adv_robustness.defenses import ProjectedAdversarialDefense
         >>> from mindspore import nn
         >>> from tests.ut.python.utils.mock_net import Net
-        >>>
         >>> net = Net()
         >>> lr = 0.001
         >>> momentum = 0.9
         >>> batch_size = 32
         >>> num_class = 10
-        >>>
         >>> loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
         >>> optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=momentum)
-        >>>
         >>> pad = ProjectedAdversarialDefense(net, loss_fn=loss_fn, optimizer=optimizer)
-        >>>
         >>> inputs = np.random.rand(batch_size, 1, 32, 32).astype(np.float32)
         >>> labels = np.random.randint(num_class, size=batch_size).astype(np.int32)
         >>> labels = np.eye(num_classes)[labels].astype(np.float32)

mindarmour/adv_robustness/detectors/black/similarity_detector.py

@@ -79,7 +79,6 @@ class SimilarityDetector(Detector):
         >>> from mindspore import Model
         >>> from mindspore import context
         >>> from mindarmour.adv_robustness.detectors import SimilarityDetector
-        >>>
         >>> class EncoderNet(Cell):
         >>>     def __init__(self, encode_dim):
         >>>         super(EncoderNet, self).__init__()
@@ -89,7 +88,6 @@ class SimilarityDetector(Detector):
         >>>         return self.add(inputs, inputs)
         >>>     def get_encode_dim(self):
         >>>         return self._encode_dim
-        >>>
         >>> np.random.seed(5)
         >>> x_train = np.random.rand(10, 32, 32, 3).astype(np.float32)
         >>> perm = np.random.permutation(x_train.shape[0])

mindarmour/adv_robustness/detectors/ensemble_detector.py

@@ -42,7 +42,6 @@ class EnsembleDetector(Detector):
         >>> from mindarmour.adv_robustness.detectors import ErrorBasedDetector
         >>> from mindarmour.adv_robustness.detectors import RegionBasedDetector
         >>> from mindarmour.adv_robustness.detectors import EnsembleDetector
-        >>>
         >>> class Net(Cell):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
@@ -56,7 +55,6 @@ class EnsembleDetector(Detector):
         >>>         self.add = Add()
         >>>     def construct(self, inputs):
         >>>         return self.add(inputs, inputs)
-        >>>
         >>> np.random.seed(6)
         >>> adv = np.random.rand(4, 4).astype(np.float32)
         >>> model = Model(Net())

mindarmour/adv_robustness/detectors/mag_net.py

@@ -58,10 +58,8 @@ class ErrorBasedDetector(Detector):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self.add = Add()
-        >>>
         >>>     def construct(self, inputs):
         >>>         return self.add(inputs, inputs)
-        >>>
         >>> np.random.seed(5)
         >>> ori = np.random.rand(4, 4, 4).astype(np.float32)
         >>> np.random.seed(6)
@@ -195,10 +193,8 @@ class DivergenceBasedDetector(ErrorBasedDetector):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self.add = Add()
-        >>>
         >>>     def construct(self, inputs):
         >>>         return self.add(inputs, inputs)
-        >>>
         >>> np.random.seed(5)
         >>> ori = np.random.rand(4, 4, 4).astype(np.float32)
         >>> np.random.seed(6)

mindarmour/adv_robustness/detectors/region_based_detector.py

@@ -62,14 +62,12 @@ class RegionBasedDetector(Detector):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self.add = Add()
-        >>>
         >>>     def construct(self, inputs):
         >>>         return self.add(inputs, inputs)
-        >>>
         >>> np.random.seed(5)
         >>> ori = np.random.rand(4, 4).astype(np.float32)
         >>> labels = np.array([[1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0],
-                              [0, 1, 0, 0]]).astype(np.int32)
+        ...                   [0, 1, 0, 0]]).astype(np.int32)
         >>> np.random.seed(6)
         >>> adv = np.random.rand(4, 4).astype(np.float32)
         >>> model = Model(Net())

mindarmour/adv_robustness/detectors/spatial_smoothing.py

@@ -59,10 +59,8 @@ class SpatialSmoothing(Detector):
         >>>     def __init__(self):
         >>>         super(Net, self).__init__()
         >>>         self._softmax = P.Softmax()
-        >>>
         >>>     def construct(self, inputs):
         >>>         return self._softmax(inputs)
-        >>>
         >>> input_shape = (50, 3)
         >>> np.random.seed(1)
         >>> input_np = np.random.randn(*input_shape).astype(np.float32)

mindarmour/adv_robustness/evaluations/attack_evaluation.py

@@ -50,11 +50,11 @@ class AttackEvaluate:
         >>> x = np.random.normal(size=(3, 512, 512, 3))
         >>> adv_x = np.random.normal(size=(3, 512, 512, 3))
         >>> y = np.array([[0.1, 0.1, 0.2, 0.6],
-        >>>               [0.1, 0.7, 0.0, 0.2],
-        >>>               [0.8, 0.1, 0.0, 0.1]])
+        ...               [0.1, 0.7, 0.0, 0.2],
+        ...               [0.8, 0.1, 0.0, 0.1]])
         >>> adv_y = np.array([[0.1, 0.1, 0.2, 0.6],
-        >>>                   [0.1, 0.0, 0.8, 0.1],
-        >>>                   [0.0, 0.9, 0.1, 0.0]])
+        ...                   [0.1, 0.0, 0.8, 0.1],
+        ...                   [0.0, 0.9, 0.1, 0.0]])
         >>> attack_eval = AttackEvaluate(x, y, adv_x, adv_y)
         >>> mr = attack_eval.mis_classification_rate()
     """

mindarmour/adv_robustness/evaluations/black/defense_evaluation.py

@@ -71,14 +71,14 @@ class BlackDefenseEvaluate:
         >>> true_labels = np.array([3, 1, 0])
         >>> max_queries = 100
         >>> def_eval = BlackDefenseEvaluate(raw_preds,
-        >>>                             def_preds,
-        >>>                             raw_query_counts,
-        >>>                             def_query_counts,
-        >>>                             raw_query_time,
-        >>>                             def_query_time,
-        >>>                             def_detection_counts,
-        >>>                             true_labels,
-        >>>                             max_queries)
+        ...                             def_preds,
+        ...                             raw_query_counts,
+        ...                             def_query_counts,
+        ...                             raw_query_time,
+        ...                             def_query_time,
+        ...                             def_detection_counts,
+        ...                             true_labels,
+        ...                             max_queries)
         >>> def_eval.qcv()
     """
 

mindarmour/adv_robustness/evaluations/defense_evaluation.py

@@ -40,15 +40,15 @@ class DefenseEvaluate:
 
     Examples:
         >>> raw_preds = np.array([[0.1, 0.1, 0.2, 0.6],
-        >>>                       [0.1, 0.7, 0.0, 0.2],
-        >>>                       [0.8, 0.1, 0.0, 0.1]])
+        ...                       [0.1, 0.7, 0.0, 0.2],
+        ...                       [0.8, 0.1, 0.0, 0.1]])
         >>> def_preds = np.array([[0.1, 0.1, 0.1, 0.7],
-        >>>                       [0.1, 0.6, 0.2, 0.1],
-        >>>                       [0.1, 0.2, 0.1, 0.6]])
+        ...                       [0.1, 0.6, 0.2, 0.1],
+        ...                       [0.1, 0.2, 0.1, 0.6]])
         >>> true_labels = np.array([3, 1, 0])
         >>> def_eval = DefenseEvaluate(raw_preds,
-        >>>                            def_preds,
-        >>>                            true_labels)
+        ...                            def_preds,
+        ...                            true_labels)
         >>> def_eval.cav()
     """
     def __init__(self, raw_preds, def_preds, true_labels):

mindarmour/adv_robustness/evaluations/visual_metrics.py

@@ -52,10 +52,10 @@ class RadarMetric:
         >>> metrics_data = [def_metrics, raw_metrics]
         >>> metrics_labels = ['before', 'after']
         >>> rm = RadarMetric(metrics_name,
-        >>>                  metrics_data,
-        >>>                  metrics_labels,
-        >>>                  title='',
-        >>>                  scale='sparse')
+        ...                  metrics_data,
+        ...                  metrics_labels,
+        ...                  title='',
+        ...                  scale='sparse')
         >>> rm.show()
     """
 

mindarmour/fuzz_testing/fuzzing.py

@@ -107,17 +107,17 @@ class Fuzzer:
         >>> net = Net()
         >>> model = Model(net)
         >>> mutate_config = [{'method': 'Blur',
-        >>>                   'params': {'auto_param': [True]}},
-        >>>                  {'method': 'Contrast',
-        >>>                   'params': {'factor': [2]}},
-        >>>                  {'method': 'Translate',
-        >>>                   'params': {'x_bias': [0.1, 0.2], 'y_bias': [0.2]}},
-        >>>                  {'method': 'FGSM',
-        >>>                   'params': {'eps': [0.1, 0.2, 0.3], 'alpha': [0.1]}}]
+        ...                   'params': {'auto_param': [True]}},
+        ...                  {'method': 'Contrast',
+        ...                   'params': {'factor': [2]}},
+        ...                  {'method': 'Translate',
+        ...                   'params': {'x_bias': [0.1, 0.2], 'y_bias': [0.2]}},
+        ...                  {'method': 'FGSM',
+        ...                   'params': {'eps': [0.1, 0.2, 0.3], 'alpha': [0.1]}}]
         >>> nc = KMultisectionNeuronCoverage(model, train_images, segmented_num=100)
         >>> model_fuzz_test = Fuzzer(model)
         >>> samples, gt_labels, preds, strategies, metrics = model_fuzz_test.fuzzing(mutate_config, initial_seeds,
-        >>>                                                                          nc, max_iters=100)
+        ...                                                                          nc, max_iters=100)
     """
 
     def __init__(self, target_model):

mindarmour/privacy/diff_privacy/mechanisms/mechanisms.py

@@ -71,10 +71,10 @@ class ClipMechanismsFactory:
             >>> target_unclipped_quantile = 0.9
             >>> clip_mechanism = ClipMechanismsFactory()
             >>> ada_clip = clip_mechanism.create('Gaussian',
-            >>>                          decay_policy=decay_policy,
-            >>>                          learning_rate=learning_rate,
-            >>>                          target_unclipped_quantile=target_unclipped_quantile,
-            >>>                          fraction_stddev=beta_stddev)
+            ...                          decay_policy=decay_policy,
+            ...                          learning_rate=learning_rate,
+            ...                          target_unclipped_quantile=target_unclipped_quantile,
+            ...                          fraction_stddev=beta_stddev)
             >>> next_norm_bound = ada_clip(beta, norm_bound)
 
         """
@@ -125,25 +125,25 @@ class NoiseMechanismsFactory:
             >>> epochs = 1
             >>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
             >>> noise_mech = NoiseMechanismsFactory().create('Gaussian',
-            >>>                                              norm_bound=norm_bound,
-            >>>                                              initial_noise_multiplier=initial_noise_multiplier)
+            ...                                              norm_bound=norm_bound,
+            ...                                              initial_noise_multiplier=initial_noise_multiplier)
             >>> clip_mech = ClipMechanismsFactory().create('Gaussian',
-            >>>                                            decay_policy='Linear',
-            >>>                                            learning_rate=0.001,
-            >>>                                            target_unclipped_quantile=0.9,
-            >>>                                            fraction_stddev=0.01)
+            ...                                            decay_policy='Linear',
+            ...                                            learning_rate=0.001,
+            ...                                            target_unclipped_quantile=0.9,
+            ...                                            fraction_stddev=0.01)
             >>> net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.1,
-            >>>                       momentum=0.9)
+            ...                       momentum=0.9)
             >>> model = DPModel(micro_batches=2,
-            >>>                 clip_mech=clip_mech,
-            >>>                 norm_bound=norm_bound,
-            >>>                 noise_mech=noise_mech,
-            >>>                 network=network,
-            >>>                 loss_fn=loss,
-            >>>                 optimizer=net_opt,
-            >>>                 metrics=None)
+            ...                 clip_mech=clip_mech,
+            ...                 norm_bound=norm_bound,
+            ...                 noise_mech=noise_mech,
+            ...                 network=network,
+            ...                 loss_fn=loss,
+            ...                 optimizer=net_opt,
+            ...                 metrics=None)
             >>> ms_ds = ds.GeneratorDataset(dataset_generator,
-            >>>                            ['data', 'label'])
+            ...                             ['data', 'label'])
             >>> model.train(epochs, ms_ds, dataset_sink_mode=False)
         """
         if mech_name == 'Gaussian':
@@ -386,9 +386,9 @@ class AdaClippingWithGaussianRandom(Cell):
         >>> learning_rate = 0.001
         >>> target_unclipped_quantile = 0.9
         >>> ada_clip = AdaClippingWithGaussianRandom(decay_policy=decay_policy,
-        >>>                                          learning_rate=learning_rate,
-        >>>                                          target_unclipped_quantile=target_unclipped_quantile,
-        >>>                                          fraction_stddev=beta_stddev)
+        ...                                          learning_rate=learning_rate,
+        ...                                          target_unclipped_quantile=target_unclipped_quantile,
+        ...                                          fraction_stddev=beta_stddev)
         >>> next_norm_bound = ada_clip(beta, norm_bound)
 
     """

mindarmour/privacy/diff_privacy/optimizer/optimizer.py

@@ -65,8 +65,8 @@ class DPOptimizerClassFactory:
         >>> GaussianSGD = DPOptimizerClassFactory(micro_batches=2)
         >>> GaussianSGD.set_mechanisms('Gaussian', norm_bound=1.0, initial_noise_multiplier=1.5)
         >>> net_opt = GaussianSGD.create('Momentum')(params=network.trainable_params(),
-        >>>                                          learning_rate=0.001,
-        >>>                                          momentum=0.9)
+        ...                                          learning_rate=0.001,
+        ...                                          momentum=0.9)
     """
 
     def __init__(self, micro_batches=2):

mindarmour/privacy/diff_privacy/train/model.py

@@ -94,25 +94,25 @@ class DPModel(Model):
         >>> loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
         >>> factory_opt = DPOptimizerClassFactory(micro_batches=micro_batches)
         >>> factory_opt.set_mechanisms('Gaussian',
-        >>>                            norm_bound=norm_bound,
-        >>>                            initial_noise_multiplier=initial_noise_multiplier)
+        ...                            norm_bound=norm_bound,
+        ...                            initial_noise_multiplier=initial_noise_multiplier)
         >>> net_opt = factory_opt.create('Momentum')(network.trainable_params(),
-        >>>                                          learning_rate=0.1, momentum=0.9)
+        ...                                          learning_rate=0.1, momentum=0.9)
         >>> clip_mech = ClipMechanismsFactory().create('Gaussian',
-        >>>                                            decay_policy='Linear',
-        >>>                                            learning_rate=0.01,
-        >>>                                            target_unclipped_quantile=0.9,
-        >>>                                            fraction_stddev=0.01)
+        ...                                            decay_policy='Linear',
+        ...                                            learning_rate=0.01,
+        ...                                            target_unclipped_quantile=0.9,
+        ...                                            fraction_stddev=0.01)
         >>> model = DPModel(micro_batches=micro_batches,
-        >>>                 norm_bound=norm_bound,
-        >>>                 clip_mech=clip_mech,
-        >>>                 noise_mech=None,
-        >>>                 network=network,
-        >>>                 loss_fn=loss,
-        >>>                 optimizer=net_opt,
-        >>>                 metrics=None)
+        ...                 norm_bound=norm_bound,
+        ...                 clip_mech=clip_mech,
+        ...                 noise_mech=None,
+        ...                 network=network,
+        ...                 loss_fn=loss,
+        ...                 optimizer=net_opt,
+        ...                 metrics=None)
         >>> ms_ds = ds.GeneratorDataset(dataset_generator,
-        >>>                             ['data', 'label'])
+        ...                             ['data', 'label'])
         >>> model.train(epochs, ms_ds, dataset_sink_mode=False)
     """
 

mindarmour/privacy/sup_privacy/mask_monitor/masker.py

@@ -34,30 +34,30 @@ class SuppressMasker(Callback):
         >>> masklayers = []
         >>> masklayers.append(MaskLayerDes("conv1.weight", 0, False, True, 10))
         >>> suppress_ctrl_instance = SuppressPrivacyFactory().create(networks=networks_l5,
-        >>>                                                     mask_layers=masklayers,
-        >>>                                                     policy="local_train",
-        >>>                                                     end_epoch=10,
-        >>>                                                     batch_num=(int)(10000/cfg.batch_size),
-        >>>                                                     start_epoch=3,
-        >>>                                                     mask_times=1000,
-        >>>                                                     lr=lr,
-        >>>                                                     sparse_end=0.90,
-        >>>                                                     sparse_start=0.0)
+        ...                                                     mask_layers=masklayers,
+        ...                                                     policy="local_train",
+        ...                                                     end_epoch=10,
+        ...                                                     batch_num=(int)(10000/cfg.batch_size),
+        ...                                                     start_epoch=3,
+        ...                                                     mask_times=1000,
+        ...                                                     lr=lr,
+        ...                                                     sparse_end=0.90,
+        ...                                                     sparse_start=0.0)
         >>> net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
         >>> net_opt = nn.Momentum(params=networks_l5.trainable_params(), learning_rate=lr, momentum=0.0)
         >>> config_ck = CheckpointConfig(save_checkpoint_steps=(int)(samples/cfg.batch_size),  keep_checkpoint_max=10)
         >>> model_instance = SuppressModel(network=networks_l5,
-        >>>                            loss_fn=net_loss,
-        >>>                            optimizer=net_opt,
-        >>>                            metrics={"Accuracy": Accuracy()})
+        ...                                loss_fn=net_loss,
+        ...                                optimizer=net_opt,
+        ...                                metrics={"Accuracy": Accuracy()})
         >>> model_instance.link_suppress_ctrl(suppress_ctrl_instance)
         >>> ds_train = generate_mnist_dataset("./MNIST_unzip/train",
-        >>>                                 batch_size=cfg.batch_size, repeat_size=1, samples=samples)
+        ...                                   batch_size=cfg.batch_size, repeat_size=1, samples=samples)
         >>> ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
-        >>>                          directory="./trained_ckpt_file/",
-        >>>                          config=config_ck)
+        ...                              directory="./trained_ckpt_file/",
+        ...                              config=config_ck)
         >>> model_instance.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor(), suppress_masker],
-        >>>                  dataset_sink_mode=False)
+        ...                      dataset_sink_mode=False)
     """
 
     def __init__(self, model, suppress_ctrl):

mindarmour/privacy/sup_privacy/sup_ctrl/conctrl.py

@@ -61,31 +61,31 @@ class SuppressPrivacyFactory:
             >>> mask_layers = []
             >>> mask_layers.append(MaskLayerDes("conv1.weight", 0, False, True, 10))
             >>> suppress_ctrl_instance = SuppressPrivacyFactory().create(networks=networks_l5,
-            >>>                                                 mask_layers=mask_layers,
-            >>>                                                 policy="local_train",
-            >>>                                                 end_epoch=10,
-            >>>                                                 batch_num=(int)(10000/cfg.batch_size),
-            >>>                                                 start_epoch=3,
-            >>>                                                 mask_times=1000,
-            >>>                                                 lr=lr,
-            >>>                                                 sparse_end=0.90,
-            >>>                                                 sparse_start=0.0)
+            ...                                                 mask_layers=mask_layers,
+            ...                                                 policy="local_train",
+            ...                                                 end_epoch=10,
+            ...                                                 batch_num=(int)(10000/cfg.batch_size),
+            ...                                                 start_epoch=3,
+            ...                                                 mask_times=1000,
+            ...                                                 lr=lr,
+            ...                                                 sparse_end=0.90,
+            ...                                                 sparse_start=0.0)
             >>> net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
             >>> net_opt = nn.Momentum(params=networks_l5.trainable_params(), learning_rate=lr, momentum=0.0)
             >>> config_ck = CheckpointConfig(save_checkpoint_steps=(int)(samples/cfg.batch_size),
-            >>>                              keep_checkpoint_max=10)
+            ...                              keep_checkpoint_max=10)
             >>> model_instance = SuppressModel(network=networks_l5,
-            >>>                             loss_fn=net_loss,
-            >>>                             optimizer=net_opt,
-            >>>                             metrics={"Accuracy": Accuracy()})
+            ...                                loss_fn=net_loss,
+            ...                                optimizer=net_opt,
+            ...                                metrics={"Accuracy": Accuracy()})
             >>> model_instance.link_suppress_ctrl(suppress_ctrl_instance)
             >>> ds_train = generate_mnist_dataset("./MNIST_unzip/train",
-            >>>                                 batch_size=cfg.batch_size, repeat_size=1, samples=samples)
+            ...                                   batch_size=cfg.batch_size, repeat_size=1, samples=samples)
             >>> ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
-            >>>                             directory="./trained_ckpt_file/",
-            >>>                             config=config_ck)
+            ...                              directory="./trained_ckpt_file/",
+            ...                              config=config_ck)
             >>> model_instance.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor(), suppress_masker],
-            >>>                 dataset_sink_mode=False)
+            ...                      dataset_sink_mode=False)
         """
         check_param_type('policy', policy, str)
         if policy == "local_train":
@@ -113,31 +113,31 @@ class SuppressCtrl(Cell):
         >>> masklayers = []
         >>> masklayers.append(MaskLayerDes("conv1.weight", 0, False, True, 10))
         >>> suppress_ctrl_instance = SuppressPrivacyFactory().create(networks=networks_l5,
-        >>>                                                 mask_layers=masklayers,
-        >>>                                                 policy="local_train",
-        >>>                                                 end_epoch=10,
-        >>>                                                 batch_num=(int)(10000/cfg.batch_size),
-        >>>                                                 start_epoch=3,
-        >>>                                                 mask_times=1000,
-        >>>                                                 lr=lr,
-        >>>                                                 sparse_end=0.90,
-        >>>                                                 sparse_start=0.0)
+        ...                                                          mask_layers=masklayers,
+        ...                                                          policy="local_train",
+        ...                                                          end_epoch=10,
+        ...                                                          batch_num=(int)(10000/cfg.batch_size),
+        ...                                                          start_epoch=3,
+        ...                                                          mask_times=1000,
+        ...                                                          lr=lr,
+        ...                                                          sparse_end=0.90,
+        ...                                                          sparse_start=0.0)
         >>> net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
         >>> net_opt = nn.Momentum(params=networks_l5.trainable_params(), learning_rate=lr, momentum=0.0)
         >>> config_ck = CheckpointConfig(save_checkpoint_steps=(int)(samples/cfg.batch_size),
-        >>>                              keep_checkpoint_max=10)
+        ...                              keep_checkpoint_max=10)
         >>> model_instance = SuppressModel(network=networks_l5,
-        >>>                             loss_fn=net_loss,
-        >>>                             optimizer=net_opt,
-        >>>                             metrics={"Accuracy": Accuracy()})
+        ...                                loss_fn=net_loss,
+        ...                                optimizer=net_opt,
+        ...                                metrics={"Accuracy": Accuracy()})
         >>> model_instance.link_suppress_ctrl(suppress_ctrl_instance)
         >>> ds_train = generate_mnist_dataset("./MNIST_unzip/train",
-        >>>                                 batch_size=cfg.batch_size, repeat_size=1, samples=samples)
+        ...                                   batch_size=cfg.batch_size, repeat_size=1, samples=samples)
         >>> ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
-        >>>                             directory="./trained_ckpt_file/",
-        >>>                             config=config_ck)
+        ...                              directory="./trained_ckpt_file/",
+        ...                              config=config_ck)
         >>> model_instance.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor(), suppress_masker],
-        >>>                 dataset_sink_mode=False)
+        ...                      dataset_sink_mode=False)
     """
     def __init__(self, networks, mask_layers, end_epoch, batch_num, start_epoch, mask_times, lr,
                  sparse_end, sparse_start):

mindarmour/privacy/sup_privacy/train/model.py

@@ -69,30 +69,30 @@ class SuppressModel(Model):
         >>> mask_layers = []
         >>> mask_layers.append(MaskLayerDes("conv1.weight", 0, False, True, 10))
         >>> suppress_ctrl_instance = SuppressPrivacyFactory().create(networks=networks_l5,
-        >>>                                                     mask_layers=mask_layers,
-        >>>                                                     policy="local_train",
-        >>>                                                     end_epoch=10,
-        >>>                                                     batch_num=(int)(10000/cfg.batch_size),
-        >>>                                                     start_epoch=3,
-        >>>                                                     mask_times=1000,
-        >>>                                                     lr=lr,
-        >>>                                                     sparse_end=0.90,
-        >>>                                                     sparse_start=0.0)
+        ...                                                          mask_layers=mask_layers,
+        ...                                                          policy="local_train",
+        ...                                                          end_epoch=10,
+        ...                                                          batch_num=(int)(10000/cfg.batch_size),
+        ...                                                          start_epoch=3,
+        ...                                                          mask_times=1000,
+        ...                                                          lr=lr,
+        ...                                                          sparse_end=0.90,
+        ...                                                          sparse_start=0.0)
         >>> net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
         >>> net_opt = nn.Momentum(params=networks_l5.trainable_params(), learning_rate=lr, momentum=0.0)
         >>> config_ck = CheckpointConfig(save_checkpoint_steps=(int)(samples/cfg.batch_size),  keep_checkpoint_max=10)
         >>> model_instance = SuppressModel(network=networks_l5,
-        >>>                            loss_fn=net_loss,
-        >>>                            optimizer=net_opt,
-        >>>                            metrics={"Accuracy": Accuracy()})
+        ...                                loss_fn=net_loss,
+        ...                                optimizer=net_opt,
+        ...                                metrics={"Accuracy": Accuracy()})
         >>> model_instance.link_suppress_ctrl(suppress_ctrl_instance)
         >>> ds_train = generate_mnist_dataset("./MNIST_unzip/train",
-        >>>                                 batch_size=cfg.batch_size, repeat_size=1, samples=samples)
+        ...                                   batch_size=cfg.batch_size, repeat_size=1, samples=samples)
         >>> ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
-        >>>                          directory="./trained_ckpt_file/",
-        >>>                          config=config_ck)
+        ...                              directory="./trained_ckpt_file/",
+        ...                              config=config_ck)
         >>> model_instance.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor(), suppress_masker],
-        >>>                  dataset_sink_mode=False)
+        ...                      dataset_sink_mode=False)
     """
 
     def __init__(self,

mindarmour/reliability/concept_drift/concept_drift_check_time_series.py

@@ -23,7 +23,7 @@ from mindarmour.utils._check_param import check_param_type, check_param_in_range
 
 
 class ConceptDriftCheckTimeSeries:
-    """
+    r"""
     ConceptDriftCheckTimeSeries is used for example series distribution change detection.
 
     Args:
@@ -39,7 +39,7 @@ class ConceptDriftCheckTimeSeries:
 
     Examples:
         >>> concept = ConceptDriftCheckTimeSeries(window_size=100, rolling_window=10,
-        >>>                   step=10, threshold_index=1.5, need_label=False)
+        ...                                       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, threshold, concept_drift_location = concept.concept_check(data_example)
@@ -161,7 +161,7 @@ class ConceptDriftCheckTimeSeries:
 
         Examples：
             >>> concept = ConceptDriftCheckTimeSeries(window_size=100, rolling_window=10,
-            >>>                   step=10, threshold_index=1.5, need_label=False)
+            ...                                       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)