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!2412 fix Inv 

Merge pull request !2412 from jiangjinsheng/issue_fix4
This commit is contained in:
mindspore-ci-bot 2020-06-22 12:09:46 +08:00 committed by Gitee
parent 9b1b93eede 304dbfaa0b
commit 83b53559f5
3 changed files with 5 additions and 7 deletions
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2
mindspore/ops/_op_impl/tbe/inv.py
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@ -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()

4
mindspore/ops/operations/array_ops.py
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@ -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):

6
mindspore/ops/operations/math_ops.py
View File

@ -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