!6873 fix bugs of op LeakyReLU, Argmax, Select and ReduceAll

Merge pull request !6873 from lihongkang/v2_master

mindspore/nn/layer/activation.py

@@ -22,6 +22,7 @@ from mindspore.common.tensor import Tensor
 from mindspore._extends import cell_attr_register
 from mindspore.ops import _selected_ops
 from ..cell import Cell
+from ..._checkparam import Validator as validator
 
 
 __all__ = ['Softmax',
@@ -228,7 +229,7 @@ class LeakyReLU(Cell):
     See https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf
 
     Args:
-        alpha (float): Slope of the activation function at x < 0. Default: 0.2.
+        alpha (Union[int, float]): Slope of the activation function at x < 0. Default: 0.2.
 
     Inputs:
         - **input_x** (Tensor) - The input of LeakyReLU.
@@ -246,6 +247,7 @@ class LeakyReLU(Cell):
 
     def __init__(self, alpha=0.2):
         super(LeakyReLU, self).__init__()
+        validator.check_value_type('alpha', alpha, [float, int], self.cls_name)
         self.greater_equal = P.GreaterEqual()
         self.mul = P.Mul()
         self.alpha = alpha

mindspore/ops/operations/array_ops.py

@@ -1185,6 +1185,7 @@ class Argmax(PrimitiveWithInfer):
     Examples:
         >>> input_x = Tensor(np.array([2.0, 3.1, 1.2]), mindspore.float32)
         >>> index = P.Argmax(output_type=mindspore.int32)(input_x)
+        1
     """
 
     @prim_attr_register
@@ -1192,7 +1193,7 @@ class Argmax(PrimitiveWithInfer):
         """Initialize Argmax"""
         self.init_prim_io_names(inputs=['x'], outputs=['output'])
         validator.check_value_type("axis", axis, [int], self.name)
-        validator.check_type_same({'output': output_type}, [mstype.int32, mstype.int64], self.name)
+        validator.check_type_same({'output': output_type}, [mstype.int32], self.name)
         self.axis = axis
         self.add_prim_attr('output_type', output_type)
 
@@ -1996,7 +1997,7 @@ class Select(PrimitiveWithInfer):
     and :math:`y`.
 
     Inputs:
-        - **input_x** (Tensor[bool]) - The shape is :math:`(x_1, x_2, ..., x_N)`.
+        - **input_x** (Tensor[bool]) - The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`.
           The condition tensor, decides which element is chosen.
         - **input_y** (Tensor) - The shape is :math:`(x_1, x_2, ..., x_N, ..., x_R)`.
           The first input tensor.

mindspore/ops/operations/math_ops.py

@@ -404,7 +404,7 @@ class ReduceAll(_Reduce):
 
         - If axis is (), and keep_dims is False,
           the output is a 0-D tensor representing the "logical and" of all elements in the input tensor.
-        - If axis is int, set as 2, and keep_dims is alse,
+        - If axis is int, set as 2, and keep_dims is False,
           the shape of output is :math:`(x_1, x_3, ..., x_R)`.
         - If axis is tuple(int), set as (2, 3), and keep_dims is False,
           the shape of output is :math:`(x_1, x_4, ..., x_R)`.