Fix issues of code review.

mindarmour/fuzz_testing/image_transform.py

@@ -350,9 +350,9 @@ class Translate(ImageTransform):
     Translate an image.
 
     Args:
-        x_bias ([int, float): X-direction translation, x=x+x_bias*image_length.
+        x_bias (Union[int, float]): X-direction translation, x = x + x_bias*image_length.
             Default: 0.
-        y_bias ([int, float): Y-direction translation,  y=y+y_bias*image_wide.
+        y_bias (Union[int, float]): Y-direction translation,  y = y + y_bias*image_wide.
             Default: 0.
     """
 
@@ -365,10 +365,8 @@ class Translate(ImageTransform):
         Set translate parameters.
 
         Args:
-            x_bias ([float, int]): X-direction translation, x=x+x_bias*image_length.
-                Default: 0.
-            y_bias ([float, int]): Y-direction translation, y=y+y_bias*image_wide.
-                Default: 0.
+            x_bias (Union[float, int]): X-direction translation. Default: 0.
+            y_bias (Union[float, int]): Y-direction translation. Default: 0.
             auto_param (bool): True if auto generate parameters. Default: False.
         """
         self.auto_param = auto_param

mindarmour/privacy/diff_privacy/mechanisms/mechanisms.py

@@ -426,7 +426,6 @@ class AdaClippingWithGaussianRandom(Cell):
             next_norm_bound = self._sub(norm_bound,
                                         self._mul(self._learning_rate, grad_clip))
 
-        # decay_policy == 'Geometric'
         else:
             grad_clip = self._sub(empirical_fraction,
                                   self._target_unclipped_quantile)

mindarmour/privacy/diff_privacy/monitor/monitor.py

@@ -62,7 +62,7 @@ class PrivacyMonitorFactory:
             return RDPMonitor(*args, **kwargs)
         if policy == 'zcdp':
             return ZCDPMonitor(*args, **kwargs)
-        raise ValueError("Only RDP-policy or ZCDP-policy is supported by now")
+        raise ValueError("The policy must be 'rdp' or 'zcdp', but got {}".format(policy))
 
 
 class RDPMonitor(Callback):
@@ -86,7 +86,8 @@ class RDPMonitor(Callback):
             be used to calculate privacy spent. Default: 1.5.
         max_eps (Union[float, int, None]): The maximum acceptable epsilon
             budget for DP training, which is used for estimating the max
-            training epochs. Default: 10.0.
+            training epochs. 'None' means there is no limit to epsilon budget.
+            Default: 10.0.
         target_delta (Union[float, int, None]): Target delta budget for DP
             training. If target_delta is set to be δ, then the privacy budget
             δ would be fixed during the whole training process. Default: 1e-3.
@@ -94,7 +95,7 @@ class RDPMonitor(Callback):
             budget for DP training, which is used for estimating the max
             training epochs. Max_delta must be less than 1 and suggested
             to be less than 1e-3, otherwise overflow would be encountered.
-            Default: None.
+            'None' means there is no limit to delta budget. Default: None.
         target_eps (Union[float, int, None]): Target epsilon budget for DP
             training. If target_eps is set to be ε, then the privacy budget
             ε would be fixed during the whole training process. Default: None.
@@ -192,6 +193,7 @@ class RDPMonitor(Callback):
             msg = 'One of target eps and target delta must be None'
             LOGGER.error(TAG, msg)
             raise ValueError(msg)
+
         if dataset_sink_mode:
             self._per_print_times = int(self._num_samples / self._batch_size)
 
@@ -208,6 +210,14 @@ class RDPMonitor(Callback):
             >>> num_samples=60000, batch_size=32)
             >>> suggest_epoch = rdp.max_epoch_suggest()
         """
+        if self._target_delta is not None and self._max_eps is None:
+            msg = 'max_eps should be consistent with target_delta, but got None.'
+            LOGGER.error(TAG, msg)
+            raise ValueError(msg)
+        if self._target_eps is not None and self._max_delta is None:
+            msg = 'max_delta should be consistent with target_eps, but got None.'
+            LOGGER.error(TAG, msg)
+            raise ValueError(msg)
         epoch = 1
         while epoch < 10000:
             steps = self._num_samples // self._batch_size
@@ -427,7 +437,8 @@ class ZCDPMonitor(Callback):
                              initial_noise_multiplier)
         if noise_decay_mode is not None:
             if noise_decay_mode not in ('Step', 'Time', 'Exp'):
-                msg = "Noise decay mode must be in ('Step', 'Time', 'Exp')"
+                msg = "Noise decay mode must be in ('Step', 'Time', 'Exp'), but got {}.".\
+                    format(noise_decay_mode)
                 LOGGER.error(TAG, msg)
                 raise ValueError(msg)
             noise_decay_rate = check_param_type('noise_decay_rate', noise_decay_rate, float)

mindarmour/privacy/diff_privacy/optimizer/optimizer.py

@@ -36,7 +36,7 @@ _reciprocal = P.Reciprocal()
 @_grad_scale.register("Tensor", "Tensor")
 def tensor_grad_scale(scale, grad):
     """ grad scaling """
-    return grad * _reciprocal(scale)
+    return grad*_reciprocal(scale)
 
 
 class _TupleAdd(nn.Cell):
@@ -59,14 +59,14 @@ class DPOptimizerClassFactory:
         micro_batches (int): The number of small batches split from an original batch. Default: 2.
 
     Returns:
-        Optimizer, Optimizer class
+        Optimizer, Optimizer class.
 
     Examples:
         >>> 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=cfg.lr,
-        >>>                                          momentum=cfg.momentum)
+        >>>                                          learning_rate=0.001,
+        >>>                                          momentum=0.9)
     """
 
     def __init__(self, micro_batches=2):
@@ -76,7 +76,9 @@ class DPOptimizerClassFactory:
 
     def set_mechanisms(self, policy, *args, **kwargs):
         """
-        Get noise mechanism object.
+        Get noise mechanism object. Policies can be 'sgd', 'momentum'
+        or 'adam'. Candidate args and kwargs can be seen in class
+        NoiseMechanismsFactory of mechanisms.py.
 
         Args:
             policy (str): Choose mechanism type.
@@ -85,15 +87,15 @@ class DPOptimizerClassFactory:
 
     def create(self, policy):
         """
-        Create DP optimizer.
+        Create DP optimizer. Policies can be 'sgd', 'momentum'
+        or 'adam'.
 
         Args:
             policy (str): Choose original optimizer type.
 
         Returns:
-            Optimizer, A optimizer with DP.
+            Optimizer, an optimizer with DP.
         """
-        dp_opt_class = None
         policy_ = policy.lower()
         if policy_ == 'sgd':
             dp_opt_class = self._get_dp_optimizer_class(nn.SGD)
@@ -102,7 +104,7 @@ class DPOptimizerClassFactory:
         elif policy_ == 'adam':
             dp_opt_class = self._get_dp_optimizer_class(nn.Adam)
         else:
-            msg = "The {} optimizer is not implement, please choose ['SGD', 'Momentum', 'Adam']" \
+            msg = "The policy must be in ('SGD', 'Momentum', 'Adam'), but got {}." \
                 .format(policy)
             LOGGER.error(TAG, msg)
             raise NameError(msg)
@@ -112,6 +114,10 @@ class DPOptimizerClassFactory:
         """
         Wrap original mindspore optimizer with `self._mech`.
         """
+        if self._mech is None:
+            msg = 'Noise mechanism should be given through set_mechanisms(), but got None.'
+            LOGGER.error(TAG, msg)
+            raise ValueError(msg)
         mech = self._mech
         micro_batches = self._micro_batches
 

mindarmour/privacy/diff_privacy/train/model.py

@@ -306,8 +306,7 @@ class _ClipGradients(nn.Cell):
         """
         construct a compute flow.
         """
-        # pylint: disable=consider-using-in
-        if clip_type != 0 and clip_type != 1:
+        if clip_type not in (0, 1):
             return grads
 
         new_grads = ()