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体验新版 GitCode,发现更多精彩内容 >>
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f6cea357
编写于
1月 22, 2018
作者:
Y
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电子邮件补丁
差异文件
fix rendering error of transpose operator.
上级
eaa8d680
变更
1
隐藏空白更改
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1 changed file
with
23 addition
and
28 deletion
+23
-28
paddle/operators/transpose_op.cc
paddle/operators/transpose_op.cc
+23
-28
未找到文件。
paddle/operators/transpose_op.cc
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f6cea357
...
...
@@ -59,44 +59,39 @@ class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(Tensor)
The input tensor, tensors with rank at most 6 are supported
"
);
AddOutput
(
"Out"
,
"(Tensor)The output tensor"
);
"(Tensor)
The input tensor, tensors with rank up to 6 are supported.
"
);
AddOutput
(
"Out"
,
"(Tensor)The output tensor
.
"
);
AddAttr
<
std
::
vector
<
int
>>
(
"axis"
,
"(vector<int>)A list of values, and the size of the list should be "
"the same with the input tensor rank
, the tensor will
"
"
permute the axes according the the values given
"
);
"(vector<int>)
A list of values, and the size of the list should be "
"the same with the input tensor rank
. This operator permutes the input
"
"
tensor's axes according to the values given.
"
);
AddComment
(
R"DOC(
Transpose Operator.
The input tensor will be permuted according to the ax
is valu
es given.
The
op functions is similar to how numpy.transpose works in python
.
The input tensor will be permuted according to the axes given.
The
behavior of this operator is similar to how `numpy.transpose` works
.
For example:
- suppose the input `X` is a 2-D tensor:
$$
X = \begin{pmatrix}
0 &1 &2 \\
3 &4 &5
\end{pmatrix}$$
.. code-block:: text
the given `axes` is: $[1, 0]$, and $Y$ = transpose($X$, axis)
input = numpy.arange(6).reshape((2,3))
then the output $Y$ is:
the input is:
$$
Y = \begin{pmatrix}
0 &3 \\
1 &4 \\
2 &5
\end{pmatrix}$$
array([[0, 1, 2],
[3, 4, 5]])
given axis is:
[1, 0]
output = input.transpose(axis)
then the output is:
array([[0, 3],
[1, 4],
[2, 5]])
So, given a input tensor of shape(N, C, H, W) and the axis is {0, 2, 3, 1},
the output tensor shape will be (N, H, W, C)
- Given a input tensor with shape $(N, C, H, W)$ and the `axes` is
$[0, 2, 3, 1]$, then shape of the output tensor will be: $(N, H, W, C)$.
)DOC"
);
}
...
...
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