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体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
c7e739f5
编写于
12月 06, 2017
作者:
G
gongweibao
提交者:
GitHub
12月 06, 2017
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add LRN efficient GPU implement. (#5894)
Add LRN efficient GPU implement
上级
1d1555e2
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
289 addition
and
93 deletion
+289
-93
paddle/operators/lrn_op.cc
paddle/operators/lrn_op.cc
+100
-4
paddle/operators/lrn_op.cu
paddle/operators/lrn_op.cu
+158
-2
paddle/operators/lrn_op.h
paddle/operators/lrn_op.h
+30
-85
python/paddle/v2/fluid/tests/test_lrn_op.py
python/paddle/v2/fluid/tests/test_lrn_op.py
+1
-2
未找到文件。
paddle/operators/lrn_op.cc
浏览文件 @
c7e739f5
...
...
@@ -19,6 +19,103 @@ namespace operators {
using
framework
::
Tensor
;
template
<
typename
T
>
struct
LRNFunctor
<
platform
::
CPUPlace
,
T
>
{
void
operator
()(
const
framework
::
ExecutionContext
&
ctx
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
*
out
,
framework
::
Tensor
*
mid
,
int
N
,
int
C
,
int
H
,
int
W
,
int
n
,
T
k
,
T
alpha
,
T
beta
)
{
auto
x_v
=
framework
::
EigenVector
<
T
>::
Flatten
(
input
);
const
int
start
=
-
(
n
-
1
)
/
2
;
const
int
end
=
start
+
n
;
auto
e_mid
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
*
mid
);
e_mid
=
e_mid
.
constant
(
k
);
auto
e_x
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
input
);
for
(
int
m
=
0
;
m
<
N
;
m
++
)
{
for
(
int
i
=
0
;
i
<
C
;
i
++
)
{
for
(
int
c
=
start
;
c
<=
end
;
c
++
)
{
int
ch
=
i
+
c
;
if
(
ch
>=
0
&&
ch
<
C
)
{
auto
s
=
e_mid
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
i
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
auto
r
=
e_x
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
ch
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
s
+=
alpha
*
r
.
square
();
}
}
}
}
auto
out_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
out
);
out_e
=
x_v
*
e_mid
.
reshape
(
Eigen
::
DSizes
<
int
,
1
>
(
e_mid
.
size
())).
pow
(
-
beta
);
}
};
template
struct
LRNFunctor
<
platform
::
CPUPlace
,
float
>;
template
struct
LRNFunctor
<
platform
::
CPUPlace
,
double
>;
template
<
typename
T
>
struct
LRNGradFunctor
<
platform
::
CPUPlace
,
T
>
{
void
operator
()(
const
framework
::
ExecutionContext
&
ctx
,
const
framework
::
Tensor
&
x
,
const
framework
::
Tensor
&
out
,
const
framework
::
Tensor
&
mid
,
framework
::
Tensor
*
x_g
,
const
framework
::
Tensor
&
out_g
,
int
N
,
int
C
,
int
H
,
int
W
,
int
n
,
T
alpha
,
T
beta
)
{
T
ratio
=
-
2
*
alpha
*
beta
;
auto
x_g_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
x_g
);
x_g_e
=
x_g_e
.
constant
(
0.0
);
auto
e_x
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
x
);
auto
e_x_g
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
*
x_g
);
auto
e_out
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
out
);
auto
e_out_g
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
out_g
);
auto
e_mid
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
mid
);
const
int
start
=
-
(
n
-
1
)
/
2
;
const
int
end
=
start
+
n
;
for
(
int
m
=
0
;
m
<
N
;
m
++
)
{
for
(
int
i
=
0
;
i
<
C
;
i
++
)
{
auto
i_x
=
e_x
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
i
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
auto
i_x_g
=
e_x_g
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
i
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
auto
i_out_g
=
e_out_g
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
i
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
auto
i_mid
=
e_mid
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
i
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
i_x_g
=
i_mid
.
pow
(
-
beta
)
*
i_out_g
;
for
(
int
c
=
start
;
c
<=
end
;
c
++
)
{
int
ch
=
i
+
c
;
if
(
ch
<
0
||
ch
>=
C
)
{
continue
;
}
auto
c_out
=
e_out
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
ch
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
auto
c_mid
=
e_mid
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
ch
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
auto
c_out_g
=
e_out_g
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
ch
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
i_x_g
+=
ratio
*
c_out_g
*
c_out
*
i_x
/
c_mid
;
}
}
}
}
};
template
struct
LRNGradFunctor
<
platform
::
CPUPlace
,
float
>;
template
struct
LRNGradFunctor
<
platform
::
CPUPlace
,
double
>;
class
LRNOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
...
...
@@ -83,8 +180,8 @@ class LRNOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment
(
R"DOC(
Local Response Normalization Operator.
This operator comes from the paper
"ImageNet Classification with Deep Convolutional Neural Networks"
.
This operator comes from the paper
:
<<ImageNet Classification with Deep Convolutional Neural Networks>>
.
The original formula is:
...
...
@@ -119,8 +216,7 @@ class LRNOpGrad : public framework::OperatorWithKernel {
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"MidOut"
)),
"Input(MidOut@GRAD) should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"MidOut"
),
"Input(MidOut) should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null"
);
...
...
paddle/operators/lrn_op.cu
浏览文件 @
c7e739f5
...
...
@@ -12,11 +12,167 @@
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/lrn_op.h"
namespace
ops
=
paddle
::
operators
;
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
__global__
void
KeCMRNormFillScale
(
int
img_size
,
const
T
*
in
,
T
*
mid
,
int
C
,
int
H
,
int
W
,
int
size
,
T
k
,
T
alpha
)
{
const
int
idx
=
threadIdx
.
x
+
blockIdx
.
x
*
blockDim
.
x
;
if
(
idx
<
img_size
)
{
const
int
w
=
idx
%
W
;
const
int
h
=
(
idx
/
W
)
%
H
;
const
int
n
=
idx
/
W
/
H
;
const
int
offset
=
(
n
*
C
*
H
+
h
)
*
W
+
w
;
in
+=
offset
;
mid
+=
offset
;
const
int
step
=
H
*
W
;
const
int
pre_pad
=
(
size
-
1
)
/
2
;
const
int
post_pad
=
size
-
pre_pad
-
1
;
T
accum
=
0
;
int
index
=
0
;
while
(
index
<
C
+
post_pad
)
{
if
(
index
<
C
)
{
T
val
=
in
[
index
*
step
];
accum
+=
val
*
val
;
}
if
(
index
>=
size
)
{
T
val
=
in
[(
index
-
size
)
*
step
];
accum
-=
val
*
val
;
}
if
(
index
>=
post_pad
)
{
mid
[(
index
-
post_pad
)
*
step
]
=
k
+
accum
*
alpha
;
}
++
index
;
}
}
}
template
<
typename
T
>
__global__
void
KeCMRNormOutput
(
int
input_size
,
const
T
*
in
,
const
T
*
mid
,
T
negative_beta
,
T
*
out
)
{
const
int
index
=
threadIdx
.
x
+
blockIdx
.
x
*
blockDim
.
x
;
if
(
index
<
input_size
)
{
out
[
index
]
=
in
[
index
]
*
pow
(
mid
[
index
],
negative_beta
);
}
}
template
<
typename
T
>
void
CrossMapNormal
(
const
framework
::
ExecutionContext
&
ctx
,
const
T
*
inputs
,
T
*
outputs
,
T
*
mid
,
int
N
,
int
C
,
int
H
,
int
W
,
int
n
,
T
k
,
T
alpha
,
T
beta
)
{
int
img_size
=
N
*
H
*
W
;
const
int
block_size
=
1024
;
int
grid_size
=
(
img_size
+
block_size
-
1
)
/
block_size
;
KeCMRNormFillScale
<
T
><<<
grid_size
,
block_size
,
0
,
ctx
.
cuda_device_context
().
stream
()
>>>
(
img_size
,
inputs
,
mid
,
C
,
H
,
W
,
n
,
k
,
alpha
);
int
input_size
=
N
*
H
*
W
*
C
;
grid_size
=
(
input_size
+
block_size
-
1
)
/
block_size
;
KeCMRNormOutput
<
T
><<<
grid_size
,
block_size
,
0
,
ctx
.
cuda_device_context
().
stream
()
>>>
(
input_size
,
inputs
,
mid
,
-
beta
,
outputs
);
}
template
<
typename
T
>
struct
LRNFunctor
<
platform
::
GPUPlace
,
T
>
{
void
operator
()(
const
framework
::
ExecutionContext
&
ctx
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
*
out
,
framework
::
Tensor
*
mid
,
int
N
,
int
C
,
int
H
,
int
W
,
int
n
,
T
k
,
T
alpha
,
T
beta
)
{
CrossMapNormal
<
T
>
(
ctx
,
input
.
data
<
T
>
(),
out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
()),
mid
->
mutable_data
<
T
>
(
ctx
.
GetPlace
()),
N
,
C
,
H
,
W
,
n
,
k
,
alpha
,
beta
);
}
};
template
struct
LRNFunctor
<
platform
::
GPUPlace
,
float
>;
template
struct
LRNFunctor
<
platform
::
GPUPlace
,
double
>;
template
<
typename
T
>
__global__
void
KeCMRNormDiff
(
int
img_size
,
const
T
*
x
,
const
T
*
out
,
const
T
*
mid
,
T
*
x_g
,
const
T
*
out_g
,
int
C
,
int
H
,
int
W
,
int
size
,
T
negative_beta
,
T
ratio
)
{
const
int
idx
=
threadIdx
.
x
+
blockIdx
.
x
*
blockDim
.
x
;
if
(
idx
<
img_size
)
{
const
int
w
=
idx
%
W
;
const
int
h
=
(
idx
/
W
)
%
H
;
const
int
n
=
idx
/
W
/
H
;
const
int
offset
=
(
n
*
C
*
H
+
h
)
*
W
+
w
;
x
+=
offset
;
out
+=
offset
;
mid
+=
offset
;
out_g
+=
offset
;
x_g
+=
offset
;
const
int
step
=
H
*
W
;
const
int
pre_pad
=
size
-
(
size
+
1
)
/
2
;
const
int
post_pad
=
size
-
pre_pad
-
1
;
int
index
=
0
;
T
accum
=
0
;
// TODO(gongwb): optimize this with thread shared array.
while
(
index
<
C
+
post_pad
)
{
if
(
index
<
C
)
{
x_g
[
index
*
step
]
=
0.0
;
accum
+=
out_g
[
index
*
step
]
*
out
[
index
*
step
]
/
mid
[
index
*
step
];
}
if
(
index
>=
size
)
{
accum
-=
out_g
[(
index
-
size
)
*
step
]
*
out
[(
index
-
size
)
*
step
]
/
mid
[(
index
-
size
)
*
step
];
}
if
(
index
>=
post_pad
)
{
x_g
[(
index
-
post_pad
)
*
step
]
+=
out_g
[(
index
-
post_pad
)
*
step
]
*
pow
(
mid
[(
index
-
post_pad
)
*
step
],
negative_beta
)
-
ratio
*
x
[(
index
-
post_pad
)
*
step
]
*
accum
;
}
++
index
;
}
}
}
template
<
typename
T
>
void
CrossMapNormalGrad
(
const
framework
::
ExecutionContext
&
ctx
,
const
T
*
x
,
const
T
*
out
,
const
T
*
mid
,
T
*
x_g
,
const
T
*
out_g
,
int
N
,
int
C
,
int
H
,
int
W
,
int
n
,
T
alpha
,
T
beta
)
{
int
img_size
=
N
*
H
*
W
;
const
int
block_size
=
1024
;
int
grid_size
=
(
img_size
+
block_size
-
1
)
/
block_size
;
KeCMRNormDiff
<
T
><<<
grid_size
,
block_size
,
0
,
ctx
.
cuda_device_context
().
stream
()
>>>
(
img_size
,
x
,
out
,
mid
,
x_g
,
out_g
,
C
,
H
,
W
,
n
,
-
beta
,
2.0
f
*
alpha
*
beta
);
}
template
<
typename
T
>
struct
LRNGradFunctor
<
platform
::
GPUPlace
,
T
>
{
void
operator
()(
const
framework
::
ExecutionContext
&
ctx
,
const
framework
::
Tensor
&
x
,
const
framework
::
Tensor
&
out
,
const
framework
::
Tensor
&
mid
,
framework
::
Tensor
*
x_g
,
const
framework
::
Tensor
&
out_g
,
int
N
,
int
C
,
int
H
,
int
W
,
int
n
,
T
alpha
,
T
beta
)
{
CrossMapNormalGrad
<
T
>
(
ctx
,
x
.
data
<
T
>
(),
out
.
data
<
T
>
(),
mid
.
data
<
T
>
(),
x_g
->
mutable_data
<
T
>
(
ctx
.
GetPlace
()),
out_g
.
data
<
T
>
(),
N
,
C
,
H
,
W
,
n
,
alpha
,
beta
);
}
};
template
struct
LRNGradFunctor
<
platform
::
GPUPlace
,
float
>;
template
struct
LRNGradFunctor
<
platform
::
GPUPlace
,
double
>;
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
lrn
,
ops
::
LRNKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
lrn_grad
,
ops
::
LRNGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/lrn_op.h
浏览文件 @
c7e739f5
...
...
@@ -21,6 +21,14 @@
namespace
paddle
{
namespace
operators
{
template
<
typename
place
,
typename
T
>
struct
LRNFunctor
{
void
operator
()(
const
framework
::
ExecutionContext
&
ctx
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
*
out
,
framework
::
Tensor
*
mid
,
int
N
,
int
C
,
int
H
,
int
W
,
int
n
,
T
k
,
T
alpha
,
T
beta
);
};
template
<
typename
Place
,
typename
T
>
class
LRNKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
...
...
@@ -31,8 +39,8 @@ class LRNKernel : public framework::OpKernel<T> {
// f(x) represents outputs
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
// input
const
Tensor
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
x_dims
=
x
->
dims
();
const
Tensor
&
x
=
*
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
x_dims
=
x
.
dims
();
// NCHW
int
N
=
x_dims
[
0
];
...
...
@@ -57,38 +65,20 @@ class LRNKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE
(
beta
>=
0.0
,
"beta should >= 0.0"
);
PADDLE_ENFORCE
(
k
>=
0.0
,
"k should >= 0.0"
);
auto
x_v
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
x
);
const
int
start
=
-
(
n
-
1
)
/
2
;
const
int
end
=
start
+
n
;
auto
e_mid
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
*
mid
);
e_mid
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
=
e_mid
.
constant
(
k
);
auto
e_x
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
*
x
);
for
(
int
m
=
0
;
m
<
N
;
m
++
)
{
for
(
int
i
=
0
;
i
<
C
;
i
++
)
{
for
(
int
c
=
start
;
c
<=
end
;
c
++
)
{
int
ch
=
i
+
c
;
if
(
ch
>=
0
&&
ch
<
C
)
{
auto
s
=
e_mid
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
i
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
auto
r
=
e_x
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
ch
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
s
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
+=
alpha
*
r
.
square
();
}
}
}
}
auto
out_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
out
);
out_e
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
=
x_v
*
e_mid
.
reshape
(
Eigen
::
DSizes
<
int
,
1
>
(
e_mid
.
size
())).
pow
(
-
beta
);
LRNFunctor
<
Place
,
T
>
f
;
f
(
ctx
,
x
,
out
,
mid
,
N
,
C
,
H
,
W
,
n
,
k
,
alpha
,
beta
);
}
};
template
<
typename
Place
,
typename
T
>
struct
LRNGradFunctor
{
void
operator
()(
const
framework
::
ExecutionContext
&
ctx
,
const
framework
::
Tensor
&
x
,
const
framework
::
Tensor
&
out
,
const
framework
::
Tensor
&
mid
,
framework
::
Tensor
*
x_g
,
const
framework
::
Tensor
&
out_g
,
int
N
,
int
C
,
int
H
,
int
W
,
int
n
,
T
alpha
,
T
beta
);
};
/**
* \brief Backward calculation for normalization with across maps.
*
...
...
@@ -97,7 +87,7 @@ class LRNKernel : public framework::OpKernel<T> {
* The implementation of this Function is derived from the
* CrossMapNormalFunc implementation.
*
* InputGrad = OutputGrad *
denoms
^ (-beta)
* InputGrad = OutputGrad *
MidOut
^ (-beta)
* -- upper
* + > (OutputGrad * OutputValue * (-2 * alpha * beta) / MidOut) * InputValue
* -- lower
...
...
@@ -113,18 +103,15 @@ class LRNGradKernel : public framework::OpKernel<T> {
public:
using
Tensor
=
framework
::
Tensor
;
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
const
Tensor
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
const
Tensor
*
out
=
ctx
.
Input
<
Tensor
>
(
"Out"
);
const
Tensor
*
out_g
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
const
Tensor
*
mid
=
ctx
.
Input
<
Tensor
>
(
"MidOut"
);
const
Tensor
&
x
=
*
ctx
.
Input
<
Tensor
>
(
"X"
);
const
Tensor
&
out
=
*
ctx
.
Input
<
Tensor
>
(
"Out"
);
const
Tensor
&
out_g
=
*
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
const
Tensor
&
mid
=
*
ctx
.
Input
<
Tensor
>
(
"MidOut"
);
auto
x_g
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
x_g
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
x_g_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
x_g
);
x_g_e
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
=
x_g_e
.
constant
(
0.0
);
auto
x_dims
=
x
->
dims
();
auto
x_dims
=
x
.
dims
();
int
N
=
x_dims
[
0
];
int
C
=
x_dims
[
1
];
int
H
=
x_dims
[
2
];
...
...
@@ -133,51 +120,9 @@ class LRNGradKernel : public framework::OpKernel<T> {
int
n
=
ctx
.
Attr
<
int
>
(
"n"
);
T
alpha
=
ctx
.
Attr
<
T
>
(
"alpha"
);
T
beta
=
ctx
.
Attr
<
T
>
(
"beta"
);
T
ratio
=
-
2
*
alpha
*
beta
;
auto
e_x
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
*
x
);
auto
e_x_g
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
*
x_g
);
auto
e_out
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
*
out
);
auto
e_out_g
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
*
out_g
);
auto
e_mid
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
*
mid
);
const
int
start
=
-
(
n
-
1
)
/
2
;
const
int
end
=
start
+
n
;
for
(
int
m
=
0
;
m
<
N
;
m
++
)
{
for
(
int
i
=
0
;
i
<
C
;
i
++
)
{
auto
i_x
=
e_x
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
i
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
auto
i_x_g
=
e_x_g
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
i
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
auto
i_out_g
=
e_out_g
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
i
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
auto
i_mid
=
e_mid
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
i
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
i_x_g
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
=
i_mid
.
pow
(
-
beta
)
*
i_out_g
;
for
(
int
c
=
start
;
c
<=
end
;
c
++
)
{
int
ch
=
i
+
c
;
if
(
ch
<
0
||
ch
>=
C
)
{
continue
;
}
auto
c_out
=
e_out
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
ch
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
auto
c_mid
=
e_mid
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
ch
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
auto
c_out_g
=
e_out_g
.
slice
(
Eigen
::
array
<
int
,
4
>
({{
m
,
ch
,
0
,
0
}}),
Eigen
::
array
<
int
,
4
>
({{
1
,
1
,
H
,
W
}}));
i_x_g
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
+=
ratio
*
c_out_g
*
c_out
*
i_x
/
c_mid
;
}
}
}
LRNGradFunctor
<
Place
,
T
>
f
;
f
(
ctx
,
x
,
out
,
mid
,
x_g
,
out_g
,
N
,
C
,
H
,
W
,
n
,
alpha
,
beta
);
}
};
...
...
python/paddle/v2/fluid/tests/test_lrn_op.py
浏览文件 @
c7e739f5
...
...
@@ -23,7 +23,7 @@ class TestLRNOp(OpTest):
start
=
-
(
self
.
n
-
1
)
/
2
end
=
start
+
self
.
n
mid
=
np
.
empty
((
self
.
N
,
self
.
C
,
self
.
H
,
self
.
W
)
,
dtype
=
float
)
mid
=
np
.
empty
((
self
.
N
,
self
.
C
,
self
.
H
,
self
.
W
)
).
astype
(
"float32"
)
mid
.
fill
(
self
.
k
)
for
m
in
range
(
0
,
self
.
N
):
for
i
in
range
(
0
,
self
.
C
):
...
...
@@ -74,5 +74,4 @@ class TestLRNOp(OpTest):
if
__name__
==
"__main__"
:
exit
(
0
)
# LRN grad implement wrong
unittest
.
main
()
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