!2412 fix Inv

Merge pull request !2412 from jiangjinsheng/issue_fix4

mindspore/ops/_op_impl/tbe/inv.py

@@ -28,8 +28,6 @@ inv_op_info = TBERegOp("Inv") \
     .dtype_format(DataType.I32_Default, DataType.I32_Default) \
     .dtype_format(DataType.F32_Default, DataType.F32_Default) \
     .dtype_format(DataType.F16_Default, DataType.F16_Default) \
-    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
-    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
     .get_op_info()
 
 

mindspore/ops/operations/array_ops.py

@@ -2506,7 +2506,7 @@ class BatchToSpace(PrimitiveWithInfer):
     dimension and block_size with given amount to crop from dimension, respectively.
 
     Args:
-        block_size (int): The block size of dividing block with value >= 1.
+        block_size (int): The block size of dividing block with value >= 2.
         crops (list): The crop value for H and W dimension, containing 2 sub list, each containing 2 int value.
             All values must be >= 0. crops[i] specifies the crop values for spatial dimension i, which corresponds to
             input dimension i+2. It is required that input_shape[i+2]*block_size >= crops[i][0]+crops[i][1].
@@ -2540,7 +2540,7 @@ class BatchToSpace(PrimitiveWithInfer):
     def __init__(self, block_size, crops):
         """Init BatchToSpace"""
         validator.check_value_type('block_size', block_size, [int], self.name)
-        validator.check('block_size', block_size, '', 1, Rel.GE, self.name)
+        validator.check('block_size', block_size, '', 2, Rel.GE, self.name)
         self.block_size = block_size
         validator.check('crops shape', np.array(crops).shape, '', (2, 2))
         for elem in itertools.chain(*crops):

mindspore/ops/operations/math_ops.py

@@ -3016,9 +3016,10 @@ class Inv(PrimitiveWithInfer):
 
     Inputs:
         - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
+          Must be one of the following types: float16, float32, int32.
 
     Outputs:
-        Tensor, has the same shape as `input_x`.
+        Tensor, has the same shape and data type as `input_x`.
 
     Examples:
         >>> inv = P.Inv()
@@ -3036,8 +3037,7 @@ class Inv(PrimitiveWithInfer):
 
     def infer_dtype(self, x_dtype):
         validator.check_tensor_type_same({'x_dtype': x_dtype}, [mstype.float16, mstype.float32,
-                                                                mstype.int32, mstype.int8,
-                                                                mstype.uint8], self.name)
+                                                                mstype.int32], self.name)
         return x_dtype