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783f9ead
编写于
1月 02, 2018
作者:
S
sweetsky0901
浏览文件
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差异文件
del using in .h
上级
e12d1a1c
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1
隐藏空白更改
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并排
Showing
1 changed file
with
47 addition
and
34 deletion
+47
-34
paddle/operators/norm_op.h
paddle/operators/norm_op.h
+47
-34
未找到文件。
paddle/operators/norm_op.h
浏览文件 @
783f9ead
...
...
@@ -19,13 +19,6 @@ limitations under the License. */
namespace
paddle
{
namespace
operators
{
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenVector
=
framework
::
EigenVector
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
DeviceContext
,
typename
T
,
typename
AttrType
=
T
>
class
NormKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
...
...
@@ -42,29 +35,37 @@ class NormKernel : public framework::OpKernel<T> {
int
fea_len
=
height
*
width
;
auto
*
place
=
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
auto
x
=
EigenMatrix
<
T
>::
From
(
*
in_x
,
framework
::
make_ddim
({
batch_size
,
fea_len
*
channels
}));
auto
x
=
framework
::
EigenMatrix
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
From
(
*
in_x
,
framework
::
make_ddim
({
batch_size
,
fea_len
*
channels
}));
// get square
framework
::
Tensor
x_square
;
x_square
.
mutable_data
<
T
>
(
in_x
->
dims
(),
context
.
GetPlace
());
auto
x_square_eigen
=
EigenMatrix
<
T
>::
From
(
x_square
,
framework
::
make_ddim
({
batch_size
,
fea_len
*
channels
}));
auto
x_square_eigen
=
framework
::
EigenMatrix
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
From
(
x_square
,
framework
::
make_ddim
({
batch_size
,
fea_len
*
channels
}));
x_square_eigen
.
device
(
*
place
)
=
x
.
square
();
auto
scale_eigen
=
EigenVector
<
T
>::
Flatten
(
*
scale
);
auto
scale_eigen
=
framework
::
EigenVector
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
Flatten
(
*
scale
);
for
(
int
n
=
0
;
n
<
batch_size
;
++
n
)
{
framework
::
Tensor
in_x_batch
=
in_x
->
Slice
(
n
,
n
+
1
);
auto
in_x_batch_eigen
=
EigenMatrix
<
T
>::
From
(
in_x_batch
,
framework
::
make_ddim
({
channels
,
fea_len
}));
auto
in_x_batch_eigen
=
framework
::
EigenMatrix
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
From
(
in_x_batch
,
framework
::
make_ddim
({
channels
,
fea_len
}));
framework
::
Tensor
x_square_batch
=
x_square
.
Slice
(
n
,
n
+
1
);
auto
x_square_batch_eigen
=
EigenMatrix
<
T
>::
From
(
x_square_batch
,
framework
::
make_ddim
({
channels
,
fea_len
}));
auto
x_square_batch_eigen
=
framework
::
EigenMatrix
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
From
(
x_square_batch
,
framework
::
make_ddim
({
channels
,
fea_len
}));
framework
::
Tensor
out_batch
=
out
->
Slice
(
n
,
n
+
1
);
auto
out_batch_eigen
=
EigenMatrix
<
T
>::
From
(
out_batch
,
framework
::
make_ddim
({
channels
,
fea_len
}));
auto
out_batch_eigen
=
framework
::
EigenMatrix
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
From
(
out_batch
,
framework
::
make_ddim
({
channels
,
fea_len
}));
framework
::
Tensor
tmp_tensor
;
tmp_tensor
.
mutable_data
<
T
>
(
framework
::
make_ddim
({
1
,
fea_len
}),
context
.
GetPlace
());
auto
tmp
=
EigenVector
<
T
>::
Flatten
(
tmp_tensor
);
auto
tmp
=
framework
::
EigenVector
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
Flatten
(
tmp_tensor
);
// get colsum and sqrt , inverse
auto
dim
=
Eigen
::
array
<
int
,
1
>
({{
0
}});
tmp
.
device
(
*
place
)
=
x_square_batch_eigen
.
sum
(
dim
);
...
...
@@ -102,40 +103,52 @@ class NormGradKernel : public framework::OpKernel<T> {
auto
*
place
=
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
auto
scale_eigen
=
EigenVector
<
T
>::
Flatten
(
*
scale
);
auto
x
=
EigenMatrix
<
T
>::
From
(
*
in_x
,
framework
::
make_ddim
({
batch_size
,
fea_len
*
channels
}));
auto
scale_eigen
=
framework
::
EigenVector
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
Flatten
(
*
scale
);
auto
x
=
framework
::
EigenMatrix
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
From
(
*
in_x
,
framework
::
make_ddim
({
batch_size
,
fea_len
*
channels
}));
// get square
framework
::
Tensor
x_square
;
x_square
.
mutable_data
<
T
>
(
in_x
->
dims
(),
context
.
GetPlace
());
auto
x_square_eigen
=
EigenMatrix
<
T
>::
From
(
x_square
,
framework
::
make_ddim
({
batch_size
,
fea_len
*
channels
}));
auto
x_square_eigen
=
framework
::
EigenMatrix
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
From
(
x_square
,
framework
::
make_ddim
({
batch_size
,
fea_len
*
channels
}));
x_square_eigen
.
device
(
*
place
)
=
x
.
square
();
for
(
int
n
=
0
;
n
<
batch_size
;
++
n
)
{
framework
::
Tensor
in_x_batch
=
in_x
->
Slice
(
n
,
n
+
1
);
auto
in_x_batch_eigen
=
EigenMatrix
<
T
>::
From
(
in_x_batch
,
framework
::
make_ddim
({
channels
,
fea_len
}));
auto
in_x_batch_eigen
=
framework
::
EigenMatrix
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
From
(
in_x_batch
,
framework
::
make_ddim
({
channels
,
fea_len
}));
framework
::
Tensor
in_g_batch
=
in_x_grad
->
Slice
(
n
,
n
+
1
);
auto
in_g_batch_eigen
=
EigenMatrix
<
T
>::
From
(
in_g_batch
,
framework
::
make_ddim
({
channels
,
fea_len
}));
auto
in_g_batch_eigen
=
framework
::
EigenMatrix
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
From
(
in_g_batch
,
framework
::
make_ddim
({
channels
,
fea_len
}));
framework
::
Tensor
x_square_batch
=
x_square
.
Slice
(
n
,
n
+
1
);
auto
x_square_batch_eigen
=
EigenMatrix
<
T
>::
From
(
x_square_batch
,
framework
::
make_ddim
({
channels
,
fea_len
}));
auto
x_square_batch_eigen
=
framework
::
EigenMatrix
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
From
(
x_square_batch
,
framework
::
make_ddim
({
channels
,
fea_len
}));
framework
::
Tensor
outg_batch
=
out_grad
->
Slice
(
n
,
n
+
1
);
auto
outg_batch_eigen
=
EigenMatrix
<
T
>::
From
(
outg_batch
,
framework
::
make_ddim
({
channels
,
fea_len
}));
auto
outg_batch_eigen
=
framework
::
EigenMatrix
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
From
(
outg_batch
,
framework
::
make_ddim
({
channels
,
fea_len
}));
framework
::
Tensor
tmp_tensor
;
tmp_tensor
.
mutable_data
<
T
>
(
framework
::
make_ddim
({
1
,
fea_len
}),
context
.
GetPlace
());
auto
tmp_eigen
=
EigenVector
<
T
>::
Flatten
(
tmp_tensor
);
auto
tmp_eigen
=
framework
::
EigenVector
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
Flatten
(
tmp_tensor
);
auto
dim
=
Eigen
::
array
<
int
,
1
>
({{
0
}});
tmp_eigen
.
device
(
*
place
)
=
(
in_x_batch_eigen
*
outg_batch_eigen
).
sum
(
dim
);
framework
::
Tensor
norm_tmp_tensor
;
norm_tmp_tensor
.
mutable_data
<
T
>
(
framework
::
make_ddim
({
1
,
fea_len
}),
context
.
GetPlace
());
auto
norm_tmp_eigen
=
EigenVector
<
T
>::
Flatten
(
norm_tmp_tensor
);
auto
norm_tmp_eigen
=
framework
::
EigenVector
<
T
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>::
Flatten
(
norm_tmp_tensor
);
norm_tmp_eigen
.
device
(
*
place
)
=
(
x_square_batch_eigen
.
sum
(
dim
)
+
epsilon
).
sqrt
();
Eigen
::
array
<
int
,
2
>
broadcast_dim_col
;
...
...
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