!204 Update verion number from 1.2.0-rc1 to 1.2.0

From: @pkuliuliu
Reviewed-by: @zhidanliu,@jxlang910
Signed-off-by: @jxlang910

RELEASE.md

@@ -1,3 +1,44 @@
+# MindArmour 1.2.0
+
+## MindArmour 1.2.0 Release Notes
+
+### Major Features and Improvements
+
+#### Privacy
+
+* [STABLE]Tailored-based privacy protection technology (Pynative)
+* [STABLE]Model Inversion. Reverse analysis technology of privacy information
+
+### API Change
+
+#### Backwards Incompatible Change
+
+##### C++ API
+
+[Modify] ...
+[Add] ...
+[Delete] ...
+
+##### Java API
+
+[Add] ...
+
+#### Deprecations
+
+##### C++ API
+
+##### Java API
+
+### Bug fixes
+
+[BUGFIX] ...
+
+### Contributors
+
+Thanks goes to these wonderful people:
+
+han.yin
+
 # MindArmour 1.1.0 Release Notes
 
 ## MindArmour

mindarmour/adv_robustness/evaluations/attack_evaluation.py

@@ -136,10 +136,10 @@ class AttackEvaluate:
             - float, return average l0, l2, or linf distance of all success
               adversarial examples, return value includes following cases.
 
-                - If return value :math:`>=` 0, average lp distance. The lower,
-                  the more successful the attack is.
+              - If return value :math:`>=` 0, average lp distance. The lower,
+                the more successful the attack is.
 
-                - If return value is -1, there is no success adversarial examples.
+              - If return value is -1, there is no success adversarial examples.
         """
         idxes = self._success_idxes
         success_num = idxes.shape[0]
@@ -164,10 +164,10 @@ class AttackEvaluate:
         Returns:
             - float, average structural similarity.
 
-                - If return value ranges between (0, 1), the higher, the more
-                  successful the attack is.
+              - If return value ranges between (0, 1), the higher, the more
+                successful the attack is.
 
-                - If return value is -1: there is no success adversarial examples.
+              - If return value is -1: there is no success adversarial examples.
         """
         success_num = self._success_idxes.shape[0]
         if success_num == 0:

mindarmour/adv_robustness/evaluations/defense_evaluation.py

@@ -106,7 +106,7 @@ class DefenseEvaluate:
         Returns:
             - float, the lower, the more successful the defense is.
 
-                - If return value == -1, len(idxes) == 0.
+              - If return value == -1, len(idxes) == 0.
         """
         idxes = np.arange(self._num_samples)
         cond1 = np.argmax(self._def_preds, axis=1) == self._true_labels

mindarmour/privacy/diff_privacy/mechanisms/mechanisms.py

@@ -183,8 +183,8 @@ class NoiseGaussianRandom(_Mechanisms):
         initial_noise_multiplier(float): Ratio of the standard deviation of
             Gaussian noise divided by the norm_bound, which will be used to
             calculate privacy spent. Default: 1.0.
-        seed(int): Original random seed, if seed=0 random normal will use secure
-            random number. IF seed!=0 random normal will generate values using
+        seed(int): Original random seed, if seed=0, random normal will use secure
+            random number. If seed!=0, random normal will generate values using
             given seed. Default: 0.
         decay_policy(str): Mechanisms parameters update policy. Default: None.
 

mindarmour/privacy/diff_privacy/train/model.py

@@ -38,7 +38,6 @@ from mindspore.ops.operations import NPUAllocFloatStatus
 from mindspore.ops.operations import NPUClearFloatStatus
 from mindspore.ops.operations import ReduceSum
 from mindspore.ops.operations import LessEqual
-from mindspore.ops.operations import ControlDepend
 from mindspore.parallel._utils import _get_gradients_mean
 from mindspore.parallel._utils import _get_device_num
 from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
@@ -395,7 +394,6 @@ class _TrainOneStepWithLossScaleCell(Cell):
         self.reduce_sum = ReduceSum(keep_dims=False)
         self.base = Tensor(1, mstype.float32)
         self.less_equal = LessEqual()
-        self.depend_parameter_use = ControlDepend(depend_mode=1)
         self.allreduce = P.AllReduce()
         self.parallel_mode = _get_parallel_mode()
         self.grad_reducer = F.identity

mindarmour/privacy/evaluation/membership_inference.py

@@ -95,7 +95,7 @@ def _softmax_cross_entropy(logits, labels):
 class MembershipInference:
     """
     Evaluation proposed by Shokri, Stronati, Song and Shmatikov is a grey-box attack.
-    The attack requires obtain loss or logits results of training samples.
+    The attack requires loss or logits results of training samples.
 
     References: `Reza Shokri, Marco Stronati, Congzheng Song, Vitaly Shmatikov.
     Membership Inference Attacks against Machine Learning Models. 2017.