[MA][diff_privacy][Func] micro_batches and dp_mech not checked

https://gitee.com/mindspore/dashboard/issues?id=I1IS9G

mindarmour/diff_privacy/optimizer/optimizer.py

@@ -27,7 +27,7 @@ class DPOptimizerClassFactory:
     Factory class of Optimizer.
 
     Args:
-        micro_batches (int): The number of small batches split from an origianl batch. Default: None.
+        micro_batches (int): The number of small batches split from an origianl batch. Default: 2.
 
     Returns:
         Optimizer, Optimizer class
@@ -39,7 +39,7 @@ class DPOptimizerClassFactory:
         >>>                                     learning_rate=cfg.lr,
         >>>                                     momentum=cfg.momentum)
     """
-    def __init__(self, micro_batches=None):
+    def __init__(self, micro_batches=2):
         self._mech_factory = MechanismsFactory()
         self.mech = None
         self._micro_batches = check_int_positive('micro_batches', micro_batches)
@@ -72,17 +72,7 @@ class DPOptimizerClassFactory:
         if policy == 'Adam':
             cls = self._get_dp_optimizer_class(nn.Adam, self.mech, self._micro_batches, *args, **kwargs)
             return cls
-        if policy == 'AdamWeightDecay':
-            cls = self._get_dp_optimizer_class(nn.AdamWeightDecay, self.mech, self._micro_batches, *args, **kwargs)
-            return cls
-        if policy == 'AdamWeightDecayDynamicLR':
-            cls = self._get_dp_optimizer_class(nn.AdamWeightDecayDynamicLR,
-                                               self.mech,
-                                               self._micro_batches,
-                                               *args, **kwargs)
-            return cls
-        raise NameError("The {} is not implement, please choose ['SGD', 'Momentum', 'AdamWeightDecay', "
-                        "'Adam', 'AdamWeightDecayDynamicLR']".format(policy))
+        raise NameError("The {} is not implement, please choose ['SGD', 'Momentum', 'Adam']".format(policy))
 
     def _get_dp_optimizer_class(self, cls, mech, micro_batches):
         """

mindarmour/diff_privacy/train/model.py

@@ -48,8 +48,11 @@ from mindspore.nn.wrap.loss_scale import _grad_overflow
 from mindspore.nn import Cell
 from mindspore import ParameterTuple
 
+from mindarmour.diff_privacy.mechanisms import mechanisms
 from mindarmour.utils._check_param import check_param_type
 from mindarmour.utils._check_param import check_value_positive
+from mindarmour.utils._check_param import check_int_positive
+
 
 GRADIENT_CLIP_TYPE = 1
 grad_scale = C.MultitypeFuncGraph("grad_scale")
@@ -67,7 +70,7 @@ class DPModel(Model):
     This class is overload mindspore.train.model.Model.
 
     Args:
-        micro_batches (int): The number of small batches split from an origianl batch. Default: None.
+        micro_batches (int): The number of small batches split from an origianl batch. Default: 2.
         norm_clip (float): Use to clip the bound, if set 1, will retun the original data. Default: 1.0.
         dp_mech (Mechanisms): The object can generate the different type of noise. Default: None.
 
@@ -106,14 +109,17 @@ class DPModel(Model):
         >>> dataset = get_dataset()
         >>> model.train(2, dataset)
     """
-    def __init__(self, micro_batches=None, norm_clip=1.0, dp_mech=None, **kwargs):
+    def __init__(self, micro_batches=2, norm_clip=1.0, dp_mech=None, **kwargs):
         if micro_batches:
-            self._micro_batches = int(micro_batches)
+            self._micro_batches = check_int_positive('micro_batches', micro_batches)
         else:
             self._micro_batches = None
         float_norm_clip = check_param_type('l2_norm_clip', norm_clip, float)
         self._norm_clip = check_value_positive('l2_norm_clip', float_norm_clip)
-        self._dp_mech = dp_mech
+        if isinstance(dp_mech, mechanisms.Mechanisms):
+            self._dp_mech = dp_mech
+        else:
+            raise TypeError('dp mechanisms should be instance of class Mechansms, but got {}'.format(type(dp_mech)))
         super(DPModel, self).__init__(**kwargs)
 
     def _amp_build_train_network(self, network, optimizer, loss_fn=None, level='O0', **kwargs):