add validation

mindspore/nn/wrap/loss_scale.py

@@ -179,7 +179,7 @@ class TrainOneStepWithLossScaleCell(Cell):
         network (Cell): The training network. The network only supports single output.
         optimizer (Cell): Optimizer for updating the weights.
         scale_sense (Union[Tensor, Cell]): If this value is Cell type, the loss scaling update logic cell.If this value
-                                          is Tensor type, Tensor with shape :math:`()`. Default: None.
+                                          is Tensor type, Tensor with shape :math:`()`.
 
     Inputs:
         - **(*inputs)** (Tuple(Tensor)) - Tuple of input tensors with shape :math:`(N, \ldots)`.
@@ -189,6 +189,7 @@ class TrainOneStepWithLossScaleCell(Cell):
 
         - **loss** (Tensor) -  Tensor with shape :math:`()`.
         - **overflow** (Tensor) -  Tensor with shape :math:`()`, type is bool.
+        - **loss scaling value** (Tensor) -  Tensor with shape :math:`()`
 
     Examples:
         >>> net_with_loss = Net()
@@ -203,7 +204,7 @@ class TrainOneStepWithLossScaleCell(Cell):
         >>> output = train_network(inputs, label, scaling_sens)
     """
 
-    def __init__(self, network, optimizer, scale_sense=None):
+    def __init__(self, network, optimizer, scale_sense):
         super(TrainOneStepWithLossScaleCell, self).__init__(auto_prefix=False)
         self.network = network
         self.network.set_grad()
@@ -236,14 +237,15 @@ class TrainOneStepWithLossScaleCell(Cell):
             self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree)
         self.is_distributed = self.parallel_mode != ParallelMode.STAND_ALONE
 
-        self.scale_sense = None
         self.loss_scaling_manager = None
         if isinstance(scale_sense, Cell):
             self.loss_scaling_manager = scale_sense
             self.scale_sense = Parameter(Tensor(scale_sense.get_loss_scale(), dtype=mstype.float32),
                                          name="scale_sense")
-        if isinstance(scale_sense, Tensor):
+        elif isinstance(scale_sense, Tensor):
             self.scale_sense = Parameter(scale_sense, name='scale_sense')
+        else:
+            raise TypeError("The scale_sense must be Cell or Tensor, but got {}".format(type(scale_sense)))
 
     @C.add_flags(has_effect=True)
     def construct(self, *inputs):
@@ -293,4 +295,6 @@ class TrainOneStepWithLossScaleCell(Cell):
         """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.self.scale_sense.set_data(sens)
+            self.scale_sense.set_data(sens)
+        else:
+            raise TypeError("The input type must be Tensor,but got {}".format(type(sens)))