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27552eb1
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mindspore
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27552eb1
编写于
7月 03, 2020
作者:
M
mindspore-ci-bot
提交者:
Gitee
7月 03, 2020
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差异文件
!2846 InvertPermutation not support Tensor
Merge pull request !2846 from jiangjinsheng/issue_fix4
上级
95c378e4
67a2c5b7
变更
1
隐藏空白更改
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Showing
1 changed file
with
3 addition
and
6 deletion
+3
-6
mindspore/ops/operations/array_ops.py
mindspore/ops/operations/array_ops.py
+3
-6
未找到文件。
mindspore/ops/operations/array_ops.py
浏览文件 @
27552eb1
...
@@ -986,11 +986,10 @@ class InvertPermutation(PrimitiveWithInfer):
...
@@ -986,11 +986,10 @@ class InvertPermutation(PrimitiveWithInfer):
values can not be negative.
values can not be negative.
Inputs:
Inputs:
- **input_x** (Union(tuple[int]
, Tensor[int])
) - The input tuple is constructed by multiple
- **input_x** (Union(tuple[int]) - The input tuple is constructed by multiple
integers, i.e., :math:`(y_1, y_2, ..., y_S)` representing the indices.
integers, i.e., :math:`(y_1, y_2, ..., y_S)` representing the indices.
The values must include 0. There can be no duplicate values or negative values.
The values must include 0. There can be no duplicate values or negative values.
If the input is Tensor, it must be 1-d and the dtype is int. Only constant value is allowed.
Only constant value is allowed.
Outputs:
Outputs:
tuple[int]. the lenth is same as input.
tuple[int]. the lenth is same as input.
...
@@ -1014,9 +1013,7 @@ class InvertPermutation(PrimitiveWithInfer):
...
@@ -1014,9 +1013,7 @@ class InvertPermutation(PrimitiveWithInfer):
raise
ValueError
(
f
'For
\'
{
self
.
name
}
\'
the input value must be const.'
)
raise
ValueError
(
f
'For
\'
{
self
.
name
}
\'
the input value must be const.'
)
validator
.
check_value_type
(
"shape"
,
x_shp
,
[
tuple
,
list
],
self
.
name
)
validator
.
check_value_type
(
"shape"
,
x_shp
,
[
tuple
,
list
],
self
.
name
)
if
mstype
.
issubclass_
(
x
[
'dtype'
],
mstype
.
tensor
):
if
mstype
.
issubclass_
(
x
[
'dtype'
],
mstype
.
tensor
):
validator
.
check
(
'x dimension'
,
len
(
x_shp
),
''
,
1
,
Rel
.
EQ
,
self
.
name
)
raise
ValueError
(
f
'For
\'
{
self
.
name
}
\'
the input value must be non-Tensor.'
)
validator
.
check_tensor_type_same
({
'x dtype'
:
x
[
'dtype'
]},
mstype
.
int_type
,
self
.
name
)
x_value
=
[
int
(
i
)
for
i
in
x_value
.
asnumpy
()]
z
=
[
x_value
[
i
]
for
i
in
range
(
len
(
x_value
))]
z
=
[
x_value
[
i
]
for
i
in
range
(
len
(
x_value
))]
z
.
sort
()
z
.
sort
()
...
...
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