fixed erfc, geswitch, merge etc

mindspore/ops/operations/__init__.py

@@ -142,6 +142,7 @@ __all__ = [
     'ReLUV2',
     'Elu',
     'Erf',
+    'Erfc',
     'Sigmoid',
     'HSwish',
     'HSigmoid',

mindspore/ops/operations/control_ops.py

@@ -84,7 +84,7 @@ class GeSwitch(PrimitiveWithInfer):
     the true branch will be activated, or vise verse.
 
     Inputs:
-        - **data** (Tensor) - The data to be used for switch control.
+        - **data** (Union[Tensor, Number]) - The data to be used for switch control.
         - **pred** (Tensor) - It should be a scalar whose type is bool and shape is `()`, It is used as condition for
           switch control.
     Outputs:
@@ -144,7 +144,7 @@ class Merge(PrimitiveWithInfer):
     One and only one of the inputs should be selected as the output
 
     Inputs:
-        - **inputs** (Tuple) - The data to be merged.
+        - **inputs** (Tuple) - The data to be merged. All tuple elements should have same data type.
 
     Outputs:
         tuple. Output is tuple(`data`, `output_index`). The `data` has the same shape of `inputs` element.
@@ -171,6 +171,5 @@ class Merge(PrimitiveWithInfer):
         for i, item in enumerate(inputs):
             args['inputs[%d]' % i] = item
 
-        validator.check_tensor_type_same(
-            args, (mstype.bool_,) + mstype.number_type, self.name)
+        validator.check_scalar_or_tensor_type_same(args, (mstype.bool_,) + mstype.number_type, self.name)
         return (inputs[0], mstype.int32)

mindspore/ops/operations/math_ops.py

@@ -1397,14 +1397,14 @@ class EqualCount(PrimitiveWithInfer):
     """
     Computes the number of the same elements of two tensors.
 
-    The two input tensors should have same shape.
+    The two input tensors should have same shape and same data type.
 
     Inputs:
         - **input_x** (Tensor) - The first input tensor.
         - **input_y** (Tensor) - The second input tensor.
 
     Outputs:
-        Tensor, with the type as `mindspore.int32` and size as (1,).
+        Tensor, with the type same as input tensor and size as (1,).
 
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)