!18015 [clean code] nn-opt/wrap

Merge pull request !18015 from kingxian/master

mindspore/nn/optim/adam.py

@@ -365,15 +365,16 @@ class Adam(Optimizer):
 
     @Optimizer.target.setter
     def target(self, value):
-        """If the input value is set to "CPU", the parameters will be updated on the host using the Fused
-           optimizer operation."""
+        """
+        If the input value is set to "CPU", the parameters will be updated on the host using the Fused
+        optimizer operation."""
         if not isinstance(value, str):
             raise TypeError("The value must be str type, but got value type is {}".format(type(value)))
 
         if value not in ('CPU', 'Ascend', 'GPU'):
             raise ValueError("The value must be 'CPU', 'Ascend' or 'GPU', but got value {}".format(value))
 
-        if self._target == "CPU" and value in('Ascend', 'GPU'):
+        if self._target == "CPU" and value in ('Ascend', 'GPU'):
             raise ValueError("In the CPU environment, target cannot be set to 'GPU' and 'Ascend'.")
 
         if self._target == "Ascend" and value == 'GPU':

mindspore/nn/optim/ftrl.py

@@ -234,8 +234,9 @@ class FTRL(Optimizer):
 
     @Optimizer.target.setter
     def target(self, value):
-        """If the input value is set to "CPU", the parameters will be updated on the host using the Fused
-           optimizer operation."""
+        """
+        If the input value is set to "CPU", the parameters will be updated on the host using the Fused
+        optimizer operation."""
         if not isinstance(value, str):
             raise TypeError("The value must be str type, but got value type is {}".format(type(value)))
 

mindspore/nn/optim/lazyadam.py

@@ -283,8 +283,9 @@ class LazyAdam(Optimizer):
 
     @Optimizer.target.setter
     def target(self, value):
-        """If the input value is set to "CPU", the parameters will be updated on the host using the Fused
-           optimizer operation."""
+        """
+        If the input value is set to "CPU", the parameters will be updated on the host using the Fused
+        optimizer operation."""
         if not isinstance(value, str):
             raise TypeError("The value must be str type, but got value type is {}".format(type(value)))
 

mindspore/nn/optim/optimizer.py

@@ -236,14 +236,16 @@ class Optimizer(Cell):
 
     @property
     def target(self):
-        """The method is used to determine whether the parameter is updated on host or device. The input type is str
-           and can only be 'CPU', 'Ascend' or 'GPU'."""
+        """
+        The method is used to determine whether the parameter is updated on host or device. The input type is str
+        and can only be 'CPU', 'Ascend' or 'GPU'."""
         return self._target
 
     @target.setter
     def target(self, value):
-        """If the input value is set to "CPU", the parameters will be updated on the host using the Fused
-           optimizer operation."""
+        """
+        If the input value is set to "CPU", the parameters will be updated on the host using the Fused
+        optimizer operation."""
         raise NotImplementedError
 
     def decay_weight(self, gradients):

mindspore/nn/optim/proximal_ada_grad.py

@@ -183,8 +183,9 @@ class ProximalAdagrad(Optimizer):
 
     @Optimizer.target.setter
     def target(self, value):
-        """If the input value is set to "CPU", the parameters will be updated on the host using the Fused
-           optimizer operation."""
+        """
+        If the input value is set to "CPU", the parameters will be updated on the host using the Fused
+        optimizer operation."""
         if not isinstance(value, str):
             raise TypeError("The value must be str type, but got value type is {}".format(type(value)))
 

mindspore/nn/wrap/loss_scale.py

@@ -326,7 +326,8 @@ class TrainOneStepWithLossScaleCell(TrainOneStepCell):
         return loss, cond, scaling_sens
 
     def set_sense_scale(self, sens):
-        """If the user has set the sens in the training process and wants to reassign the value, he can call
+        """
+        If the user has set the sens in the training process and wants to reassign the value, he can call
         this function again to make modification, and sens needs to be of type Tensor."""
         if self.scale_sense and isinstance(sens, Tensor):
             self.scale_sense.set_data(sens)