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cdac60f6
编写于
11月 01, 2016
作者:
Q
qijun
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add SpatialPyramidPoolLayer c++ support
上级
46bd5f53
变更
11
展开全部
隐藏空白更改
内联
并排
Showing
11 changed file
with
562 addition
and
182 deletion
+562
-182
paddle/cuda/include/hl_cnn.h
paddle/cuda/include/hl_cnn.h
+10
-4
paddle/cuda/include/stub/hl_cnn_stub.h
paddle/cuda/include/stub/hl_cnn_stub.h
+6
-4
paddle/cuda/src/hl_cuda_cnn.cu
paddle/cuda/src/hl_cuda_cnn.cu
+23
-17
paddle/gserver/layers/PoolProjection.cpp
paddle/gserver/layers/PoolProjection.cpp
+81
-0
paddle/gserver/layers/PoolProjection.h
paddle/gserver/layers/PoolProjection.h
+72
-0
paddle/gserver/layers/Projection.h
paddle/gserver/layers/Projection.h
+9
-4
paddle/gserver/layers/SpatialPyramidPoolLayer.cpp
paddle/gserver/layers/SpatialPyramidPoolLayer.cpp
+128
-0
paddle/gserver/layers/SpatialPyramidPoolLayer.h
paddle/gserver/layers/SpatialPyramidPoolLayer.h
+54
-0
paddle/gserver/tests/test_LayerGrad.cpp
paddle/gserver/tests/test_LayerGrad.cpp
+29
-3
paddle/math/Matrix.cpp
paddle/math/Matrix.cpp
+138
-150
proto/ModelConfig.proto.m4
proto/ModelConfig.proto.m4
+12
-0
未找到文件。
paddle/cuda/include/hl_cnn.h
浏览文件 @
cdac60f6
...
...
@@ -91,6 +91,7 @@ extern void hl_expand_feature2col(
* @param[in] paddingH padding height.
* @param[in] paddingW padding width.
* @param[out] tgtData output data.
* @param[in] tgtStride output data stride.
*
*/
extern
void
hl_maxpool_forward
(
...
...
@@ -100,7 +101,8 @@ extern void hl_maxpool_forward(
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
);
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
,
const
int
tgtStride
);
/**
* @brief Maximum pool backward.
...
...
@@ -123,6 +125,7 @@ extern void hl_maxpool_forward(
* @param[in] paddingH padding height.
* @param[in] paddingW padding width.
* @param[out] targetGrad output grad.
* @param[in] outStride output grad data stride.
*
*/
extern
void
hl_maxpool_backward
(
...
...
@@ -135,7 +138,7 @@ extern void hl_maxpool_backward(
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
scaleA
,
real
scaleB
,
real
*
targetGrad
);
real
*
targetGrad
,
const
int
outStride
);
/**
* @brief Averge pool forward.
...
...
@@ -154,6 +157,7 @@ extern void hl_maxpool_backward(
* @param[in] paddingH padding height.
* @param[in] paddingW padding width.
* @param[out] tgtData output data.
* @param[in] tgtStride output data stride.
*
*/
extern
void
hl_avgpool_forward
(
...
...
@@ -163,7 +167,8 @@ extern void hl_avgpool_forward(
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
);
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
,
const
int
tgtStride
);
/**
* @brief Maximum pool backward.
...
...
@@ -184,6 +189,7 @@ extern void hl_avgpool_forward(
* @param[in] scaleA scale.
* @param[in] scaleB scale.
* @param[out] backGrad output grad.
* @param[in] outStride output grad data stride.
*
*/
extern
void
hl_avgpool_backward
(
...
...
@@ -195,7 +201,7 @@ extern void hl_avgpool_backward(
const
int
strideH
,
const
int
strideW
,
int
paddingH
,
int
paddingW
,
real
scaleA
,
real
scaleB
,
real
*
backGrad
);
real
*
backGrad
,
const
int
outStride
);
/**
* @brief Cross-map-respose normalize forward.
...
...
paddle/cuda/include/stub/hl_cnn_stub.h
浏览文件 @
cdac60f6
...
...
@@ -44,7 +44,8 @@ inline void hl_maxpool_forward(
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
)
{}
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
,
const
int
tgtStride
)
{}
inline
void
hl_maxpool_backward
(
const
int
frameCnt
,
const
real
*
inputData
,
...
...
@@ -56,7 +57,7 @@ inline void hl_maxpool_backward(
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
scaleA
,
real
scaleB
,
real
*
targetGrad
)
{}
real
*
targetGrad
,
const
int
outStride
)
{}
inline
void
hl_avgpool_forward
(
const
int
frameCnt
,
const
real
*
inputData
,
...
...
@@ -65,7 +66,8 @@ inline void hl_avgpool_forward(
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
)
{}
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
,
const
int
tgtStride
)
{}
inline
void
hl_avgpool_backward
(
const
int
frameCnt
,
const
real
*
outGrad
,
...
...
@@ -76,7 +78,7 @@ inline void hl_avgpool_backward(
const
int
strideH
,
const
int
strideW
,
int
paddingH
,
int
paddingW
,
real
scaleA
,
real
scaleB
,
real
*
backGrad
)
{}
real
*
backGrad
,
const
int
outStride
)
{}
inline
void
hl_CMRNorm_forward
(
size_t
frameCnt
,
const
real
*
in
,
real
*
scale
,
real
*
out
,
...
...
paddle/cuda/src/hl_cuda_cnn.cu
浏览文件 @
cdac60f6
...
...
@@ -152,7 +152,7 @@ __global__ void KeMaxPoolForward(const int nthreads, const real* inputData,
const
int
ksizeW
,
const
int
ksizeH
,
const
int
strideH
,
const
int
strideW
,
const
int
offsetH
,
const
int
offsetW
,
real
*
tgtData
)
{
real
*
tgtData
,
const
int
tgtStride
)
{
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
index
<
nthreads
)
{
int
pw
=
index
%
pooledW
;
...
...
@@ -173,7 +173,9 @@ __global__ void KeMaxPoolForward(const int nthreads, const real* inputData,
maxval
=
inputData
[
h
*
width
+
w
];
}
}
tgtData
[
index
]
=
maxval
;
int
tgtIndex
=
index
%
(
pooledW
*
pooledH
*
channels
)
+
frameNum
*
tgtStride
;
tgtData
[
tgtIndex
]
=
maxval
;
}
}
...
...
@@ -184,7 +186,7 @@ void hl_maxpool_forward(const int frameCnt, const real* inputData,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
)
{
real
*
tgtData
,
const
int
tgtStride
)
{
int
num_kernels
=
pooledH
*
pooledW
*
channels
*
frameCnt
;
int
blocks
=
(
num_kernels
+
1024
-
1
)
/
1024
;
...
...
@@ -194,7 +196,7 @@ void hl_maxpool_forward(const int frameCnt, const real* inputData,
KeMaxPoolForward
<<<
grid
,
threads
,
0
,
STREAM_DEFAULT
>>>
(
num_kernels
,
inputData
,
channels
,
height
,
width
,
pooledH
,
pooledW
,
sizeX
,
sizeY
,
strideH
,
strideW
,
paddingH
,
paddingW
,
tgtData
);
paddingH
,
paddingW
,
tgtData
,
tgtStride
);
CHECK_SYNC
(
"hl_maxpool_forward failed"
);
}
...
...
@@ -207,7 +209,7 @@ __global__ void KeMaxPoolBackward(const int nthreads, const real* inputData,
const
int
strideH
,
const
int
strideW
,
const
int
padH
,
const
int
padW
,
real
scaleA
,
real
scaleB
,
real
*
targetGrad
)
{
real
*
targetGrad
,
const
int
outStride
)
{
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
index
<
nthreads
)
{
// find out the local index
...
...
@@ -223,8 +225,8 @@ __global__ void KeMaxPoolBackward(const int nthreads, const real* inputData,
int
pwend
=
offsetW
>=
0
?
min
(
offsetW
/
strideW
+
1
,
pooledW
)
:
0
;
real
gradient
=
0
;
real
input
=
inputData
[
index
];
outData
+=
(
frameNum
*
channels
+
offsetC
)
*
pooledH
*
pooledW
;
outGrad
+=
(
frameNum
*
channels
+
offsetC
)
*
pooledH
*
pooledW
;
outData
+=
(
frameNum
*
outStride
+
offsetC
*
pooledH
*
pooledW
)
;
outGrad
+=
(
frameNum
*
outStride
+
offsetC
*
pooledH
*
pooledW
)
;
for
(
int
ph
=
phstart
;
ph
<
phend
;
++
ph
)
{
for
(
int
pw
=
pwstart
;
pw
<
pwend
;
++
pw
)
{
if
(
input
==
outData
[
ph
*
pooledW
+
pw
])
{
...
...
@@ -246,7 +248,7 @@ void hl_maxpool_backward(const int frameCnt, const real* inputData,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
scaleA
,
real
scaleB
,
real
*
targetGrad
)
{
real
*
targetGrad
,
const
int
outStride
)
{
int
num_kernels
=
height
*
width
*
channels
*
frameCnt
;
int
blocks
=
(
num_kernels
+
1024
-
1
)
/
1024
;
...
...
@@ -257,7 +259,7 @@ void hl_maxpool_backward(const int frameCnt, const real* inputData,
strideH
,
strideW
,
paddingH
,
paddingW
,
scaleA
,
scaleB
,
targetGrad
);
targetGrad
,
outStride
);
CHECK_SYNC
(
"hl_maxpool_backward"
);
}
...
...
@@ -268,7 +270,7 @@ __global__ void KeAvgPoolForward(const int nthreads, const real* inputData,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
padH
,
const
int
padW
,
real
*
tgtData
)
{
real
*
tgtData
,
const
int
tgtStride
)
{
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
index
<
nthreads
)
{
int
pw
=
index
%
pooledW
;
...
...
@@ -293,7 +295,9 @@ __global__ void KeAvgPoolForward(const int nthreads, const real* inputData,
aveval
+=
inputData
[
h
*
width
+
w
];
}
}
tgtData
[
index
]
=
aveval
/
pool_size
;
int
tgtIndex
=
index
%
(
pooledW
*
pooledH
*
channels
)
+
frameNum
*
tgtStride
;
tgtData
[
tgtIndex
]
=
aveval
/
pool_size
;
}
}
...
...
@@ -303,14 +307,15 @@ void hl_avgpool_forward(const int frameCnt, const real* inputData,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
)
{
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
,
const
int
tgtStride
)
{
int
num_kernels
=
pooledH
*
pooledW
*
channels
*
frameCnt
;
int
blocks
=
(
num_kernels
+
1024
-
1
)
/
1024
;
KeAvgPoolForward
<<<
blocks
,
1024
,
0
,
STREAM_DEFAULT
>>>
(
num_kernels
,
inputData
,
channels
,
height
,
width
,
pooledH
,
pooledW
,
sizeX
,
sizeY
,
strideH
,
strideW
,
paddingH
,
paddingW
,
tgtData
);
paddingH
,
paddingW
,
tgtData
,
tgtStride
);
CHECK_SYNC
(
"hl_avgpool_forward failed"
);
}
...
...
@@ -322,7 +327,7 @@ __global__ void KeAvgPoolBackward(const int nthreads, const real* outGrad,
const
int
strideH
,
const
int
strideW
,
const
int
padH
,
const
int
padW
,
real
scaleA
,
real
scaleB
,
real
*
tgtGrad
)
{
real
*
tgtGrad
,
const
int
outStride
)
{
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
index
<
nthreads
)
{
int
offsetW
=
index
%
width
+
padW
;
...
...
@@ -335,7 +340,8 @@ __global__ void KeAvgPoolBackward(const int nthreads, const real* outGrad,
int
phend
=
offsetH
>=
0
?
min
(
offsetH
/
strideH
+
1
,
pooledH
)
:
0
;
int
pwend
=
offsetW
>=
0
?
min
(
offsetW
/
strideW
+
1
,
pooledW
)
:
0
;
real
gradient
=
0
;
outGrad
+=
(
frameNum
*
channels
+
offsetC
)
*
pooledH
*
pooledW
;
outGrad
+=
(
frameNum
*
outStride
+
offsetC
*
pooledH
*
pooledW
);
for
(
int
ph
=
phstart
;
ph
<
phend
;
++
ph
)
{
for
(
int
pw
=
pwstart
;
pw
<
pwend
;
++
pw
)
{
...
...
@@ -360,7 +366,7 @@ void hl_avgpool_backward(const int frameCnt, const real* outGrad,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
scaleA
,
real
scaleB
,
real
*
backGrad
)
{
real
*
backGrad
,
const
int
outStride
)
{
int
num_kernels
=
height
*
width
*
channels
*
frameCnt
;
int
blocks
=
(
num_kernels
+
1024
-
1
)
/
1024
;
...
...
@@ -370,7 +376,7 @@ void hl_avgpool_backward(const int frameCnt, const real* outGrad,
strideH
,
strideW
,
paddingH
,
paddingW
,
scaleA
,
scaleB
,
backGrad
);
backGrad
,
outStride
);
CHECK_SYNC
(
"hl_avgpool_backward failed"
);
}
...
...
paddle/gserver/layers/PoolProjection.cpp
0 → 100644
浏览文件 @
cdac60f6
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "PoolProjection.h"
namespace
paddle
{
REGISTER_PROJECTION_CREATE_FUNC
(
pool2
,
&
PoolProjection
::
create
);
PoolProjection
*
PoolProjection
::
create
(
const
ProjectionConfig
&
config
,
ParameterPtr
parameter
,
bool
useGpu
)
{
const
std
::
string
&
pool
=
config
.
pool_conf
().
pool_type
();
if
(
pool
==
"max"
)
{
return
new
MaxPoolProjection
(
config
,
parameter
,
useGpu
);
}
else
if
(
pool
==
"avg"
)
{
return
new
AvgPoolProjection
(
config
,
parameter
,
useGpu
);
}
else
{
LOG
(
FATAL
)
<<
"Unknown pool type: "
<<
pool
;
return
nullptr
;
}
}
void
MaxPoolProjection
::
forward
()
{
MatrixPtr
inputV
=
in_
->
value
;
MatrixPtr
outV
=
out_
->
value
;
outV
->
maxPoolForward
(
*
inputV
,
imgSizeY_
,
imgSize_
,
channels_
,
sizeX_
,
sizeY_
,
strideY_
,
stride_
,
outputY_
,
outputX_
,
confPaddingY_
,
confPadding_
);
}
void
MaxPoolProjection
::
backward
(
const
UpdateCallback
&
callback
)
{
(
void
)
callback
;
MatrixPtr
outGrad
=
out_
->
grad
;
MatrixPtr
inputV
=
in_
->
value
;
MatrixPtr
outV
=
out_
->
value
;
MatrixPtr
inputGrad
=
in_
->
grad
;
if
(
NULL
==
inputGrad
)
{
return
;
}
inputGrad
->
maxPoolBackward
(
*
inputV
,
imgSizeY_
,
imgSize_
,
*
outGrad
,
*
outV
,
sizeX_
,
sizeY_
,
strideY_
,
stride_
,
outputY_
,
outputX_
,
1
,
1
,
confPaddingY_
,
confPadding_
);
}
void
AvgPoolProjection
::
forward
()
{
MatrixPtr
inputV
=
in_
->
value
;
MatrixPtr
outV
=
out_
->
value
;
outV
->
avgPoolForward
(
*
inputV
,
imgSizeY_
,
imgSize_
,
channels_
,
sizeX_
,
sizeY_
,
strideY_
,
stride_
,
outputY_
,
outputX_
,
confPaddingY_
,
confPadding_
);
}
void
AvgPoolProjection
::
backward
(
const
UpdateCallback
&
callback
)
{
(
void
)
callback
;
MatrixPtr
outputGrad
=
out_
->
grad
;
MatrixPtr
inputGrad
=
in_
->
grad
;
if
(
NULL
==
inputGrad
)
{
return
;
}
inputGrad
->
avgPoolBackward
(
*
outputGrad
,
imgSizeY_
,
imgSize_
,
sizeX_
,
sizeY_
,
strideY_
,
stride_
,
outputY_
,
outputX_
,
1
,
1
,
confPaddingY_
,
confPadding_
);
}
}
// namespace paddle
paddle/gserver/layers/PoolProjection.h
0 → 100644
浏览文件 @
cdac60f6
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "Projection.h"
namespace
paddle
{
class
PoolProjection
:
public
Projection
{
protected:
size_t
imgSizeY_
,
imgSize_
;
size_t
outputY_
,
outputX_
;
size_t
strideY_
,
stride_
;
size_t
sizeY_
,
sizeX_
;
int
confPaddingY_
,
confPadding_
;
size_t
channels_
;
std
::
string
poolType_
;
public:
PoolProjection
(
const
ProjectionConfig
&
config
,
ParameterPtr
parameter
,
bool
useGpu
)
:
Projection
(
config
,
parameter
,
useGpu
)
{
const
PoolConfig
&
conf
=
config_
.
pool_conf
();
poolType_
=
conf
.
pool_type
();
channels_
=
conf
.
channels
();
sizeX_
=
conf
.
size_x
();
stride_
=
conf
.
stride
();
outputX_
=
conf
.
output_x
();
imgSize_
=
conf
.
img_size
();
confPadding_
=
conf
.
padding
();
sizeY_
=
conf
.
has_size_y
()
?
conf
.
size_y
()
:
conf
.
size_x
();
imgSizeY_
=
conf
.
has_img_size_y
()
?
conf
.
img_size_y
()
:
conf
.
img_size
();
strideY_
=
conf
.
has_stride_y
()
?
conf
.
stride_y
()
:
conf
.
stride
();
confPaddingY_
=
conf
.
has_padding_y
()
?
conf
.
padding_y
()
:
conf
.
padding
();
outputY_
=
conf
.
has_output_y
()
?
conf
.
output_y
()
:
conf
.
output_x
();
}
static
PoolProjection
*
create
(
const
ProjectionConfig
&
config
,
ParameterPtr
parameter
,
bool
useGpu
);
const
std
::
string
&
getPoolType
()
const
{
return
poolType_
;
}
};
class
MaxPoolProjection
:
public
PoolProjection
{
public:
MaxPoolProjection
(
const
ProjectionConfig
&
config
,
ParameterPtr
parameter
,
bool
useGpu
)
:
PoolProjection
(
config
,
parameter
,
useGpu
)
{}
virtual
void
forward
();
virtual
void
backward
(
const
UpdateCallback
&
callback
=
nullptr
);
};
class
AvgPoolProjection
:
public
PoolProjection
{
public:
AvgPoolProjection
(
const
ProjectionConfig
&
config
,
ParameterPtr
parameter
,
bool
useGpu
)
:
PoolProjection
(
config
,
parameter
,
useGpu
)
{}
virtual
void
forward
();
virtual
void
backward
(
const
UpdateCallback
&
callback
=
nullptr
);
};
}
// namespace paddle
paddle/gserver/layers/Projection.h
浏览文件 @
cdac60f6
...
...
@@ -12,12 +12,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/parameter/Parameter.h"
#include "ModelConfig.pb.h"
#include "Layer.h"
#include "ModelConfig.pb.h"
#include "paddle/parameter/Parameter.h"
namespace
paddle
{
...
...
@@ -28,6 +27,11 @@ namespace paddle {
Projection::registrar_.registerClass<__class_name>(#__type_name); \
})
#define REGISTER_PROJECTION_CREATE_FUNC(__type_name, createFunction) \
static InitFunction __reg_type_##__type_name([]() { \
Projection::registrar_.registerClass(#__type_name, createFunction); \
})
/**
* A projection takes one Argument as input, calculate the result and add it
* to output Argument.
...
...
@@ -50,7 +54,8 @@ public:
registrar_
;
/**
* Forward propagation. If backward() will be called, in and out must be kept valid until then.
* Forward propagation. If backward() will be called, in and out must be kept
* valid until then.
* @param in input of projection
* @param out output of projection
* @param passType PASS_TRAIN of PASS_TEST
...
...
paddle/gserver/layers/SpatialPyramidPoolLayer.cpp
0 → 100644
浏览文件 @
cdac60f6
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "SpatialPyramidPoolLayer.h"
namespace
paddle
{
REGISTER_LAYER
(
spp
,
SpatialPyramidPoolLayer
);
ProjectionConfig
SpatialPyramidPoolLayer
::
getConfig
(
size_t
imgSizeW
,
size_t
imgSizeH
,
size_t
channels
,
size_t
pyramidLevel
,
std
::
string
&
poolType
)
{
ProjectionConfig
config
;
config
.
set_type
(
"pool2"
);
PoolConfig
*
conf
=
config
.
mutable_pool_conf
();
conf
->
set_channels
(
channels
);
conf
->
set_img_size
(
imgSizeW
);
conf
->
set_img_size_y
(
imgSizeH
);
conf
->
set_pool_type
(
poolType
);
int
numBins
=
std
::
pow
(
2
,
pyramidLevel
);
int
sizeH
=
std
::
ceil
(
imgSizeH
/
static_cast
<
double
>
(
numBins
));
int
remainderH
=
sizeH
*
numBins
-
imgSizeH
;
int
paddingH
=
(
remainderH
+
1
)
/
2
;
int
outSizeH
=
outputSize
(
imgSizeH
,
sizeH
,
paddingH
,
sizeH
);
int
sizeW
=
std
::
ceil
(
imgSizeW
/
static_cast
<
double
>
(
numBins
));
int
remainderW
=
sizeW
*
numBins
-
imgSizeW
;
int
paddingW
=
(
remainderW
+
1
)
/
2
;
int
outSizeW
=
outputSize
(
imgSizeW
,
sizeW
,
paddingW
,
sizeW
);
conf
->
set_stride
(
sizeW
);
conf
->
set_stride_y
(
sizeH
);
conf
->
set_size_x
(
sizeW
);
conf
->
set_size_y
(
sizeH
);
conf
->
set_padding
(
paddingW
);
conf
->
set_padding_y
(
paddingH
);
conf
->
set_output_x
(
outSizeW
);
conf
->
set_output_y
(
outSizeH
);
config
.
set_output_size
(
outSizeH
*
outSizeW
*
channels
);
return
config
;
}
void
SpatialPyramidPoolLayer
::
splitInput
(
Argument
&
input
,
size_t
height
,
size_t
width
,
bool
useGpu
)
{
input
.
value
=
getInput
(
0
).
value
;
if
(
passType_
!=
PASS_TEST
&&
needGradient
())
{
Matrix
::
resizeOrCreate
(
input
.
grad
,
height
,
width
,
/* trans */
false
,
useGpu
);
input
.
grad
->
zeroMem
();
}
}
bool
SpatialPyramidPoolLayer
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
Layer
::
init
(
layerMap
,
parameterMap
);
CHECK_EQ
(
config_
.
inputs_size
(),
1
);
const
SppConfig
&
sppConf
=
config_
.
inputs
(
0
).
spp_conf
();
pyramidHeight_
=
sppConf
.
pyramid_height
();
poolType_
=
sppConf
.
pool_type
();
channels_
=
sppConf
.
channels
();
imgSizeW_
=
sppConf
.
img_size
();
imgSizeH_
=
sppConf
.
has_img_size_y
()
?
sppConf
.
img_size_y
()
:
imgSizeW_
;
poolProjections_
.
reserve
(
pyramidHeight_
);
projCol_
.
reserve
(
pyramidHeight_
);
projInput_
.
reserve
(
pyramidHeight_
);
projOutput_
.
resize
(
pyramidHeight_
);
size_t
startCol
=
0
;
size_t
endCol
=
0
;
for
(
size_t
i
=
0
;
i
<
pyramidHeight_
;
i
++
)
{
poolProjections_
.
emplace_back
(
PoolProjection
::
create
(
getConfig
(
imgSizeW_
,
imgSizeH_
,
channels_
,
i
,
poolType_
),
nullptr
,
useGpu_
));
endCol
+=
poolProjections_
[
i
]
->
getOutputSize
();
projCol_
.
push_back
(
std
::
make_pair
(
startCol
,
endCol
));
startCol
=
endCol
;
projInput_
.
emplace_back
(
Argument
());
}
outputSize_
=
endCol
;
return
true
;
}
void
SpatialPyramidPoolLayer
::
forward
(
PassType
passType
)
{
Layer
::
forward
(
passType
);
int
batchSize
=
getInput
(
0
).
getBatchSize
();
resetOutput
(
batchSize
,
outputSize_
);
for
(
size_t
i
=
0
;
i
<
pyramidHeight_
;
i
++
)
{
size_t
startCol
=
projCol_
[
i
].
first
;
size_t
endCol
=
projCol_
[
i
].
second
;
projOutput_
[
i
].
value
=
output_
.
value
->
subColMatrix
(
startCol
,
endCol
);
projOutput_
[
i
].
grad
=
output_
.
grad
->
subColMatrix
(
startCol
,
endCol
);
splitInput
(
projInput_
[
i
],
getInput
(
0
).
value
->
getHeight
(),
getInput
(
0
).
value
->
getWidth
(),
useGpu_
);
}
for
(
size_t
i
=
0
;
i
<
pyramidHeight_
;
i
++
)
{
poolProjections_
[
i
]
->
forward
(
&
projInput_
[
i
],
&
projOutput_
[
i
],
passType
);
}
}
void
SpatialPyramidPoolLayer
::
backward
(
const
UpdateCallback
&
callback
)
{
for
(
size_t
i
=
0
;
i
<
pyramidHeight_
;
i
++
)
{
if
(
poolProjections_
[
i
])
{
poolProjections_
[
i
]
->
backward
(
callback
);
getInput
(
0
).
grad
->
add
(
*
projInput_
[
i
].
grad
);
}
}
}
}
// namespace paddle
paddle/gserver/layers/SpatialPyramidPoolLayer.h
0 → 100644
浏览文件 @
cdac60f6
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "Layer.h"
#include "PoolProjection.h"
#include "paddle/utils/Logging.h"
namespace
paddle
{
class
SpatialPyramidPoolLayer
:
public
Layer
{
protected:
size_t
channels_
;
size_t
imgSizeW_
;
size_t
imgSizeH_
;
size_t
pyramidHeight_
;
size_t
outputSize_
;
std
::
string
poolType_
;
std
::
vector
<
std
::
unique_ptr
<
PoolProjection
>>
poolProjections_
;
std
::
vector
<
Argument
>
projInput_
;
std
::
vector
<
Argument
>
projOutput_
;
std
::
vector
<
std
::
pair
<
size_t
,
size_t
>>
projCol_
;
public:
explicit
SpatialPyramidPoolLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
{}
~
SpatialPyramidPoolLayer
()
{}
virtual
bool
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
);
ProjectionConfig
getConfig
(
size_t
sizeX_
,
size_t
sizeY_
,
size_t
channels
,
size_t
pyamidLevel_
,
std
::
string
&
poolType_
);
int
outputSize
(
int
imageSize
,
int
windowSize
,
int
padding
,
int
stride
)
{
return
(
imageSize
-
windowSize
+
2
*
padding
)
/
stride
+
1
;
}
virtual
void
forward
(
PassType
passType
);
virtual
void
backward
(
const
UpdateCallback
&
callback
=
nullptr
);
void
splitInput
(
Argument
&
input
,
size_t
height
,
size_t
width
,
bool
useGpu
);
};
}
// namespace paddle
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @
cdac60f6
...
...
@@ -13,14 +13,14 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include <vector>
#include <string>
#include
"paddle/gserver/layers/DataLayer.h"
#include
<vector>
#include "ModelConfig.pb.h"
#include "paddle/gserver/layers/DataLayer.h"
#include "paddle/trainer/Trainer.h"
#include "TestUtil.h"
#include "LayerGradUtil.h"
#include "TestUtil.h"
using
namespace
paddle
;
// NOLINT
using
namespace
std
;
// NOLINT
...
...
@@ -880,6 +880,32 @@ TEST(Layer, PoolLayer) {
#endif
}
void
testSppLayer
(
const
string
&
poolType
,
const
int
pyramidHeight
,
bool
trans
,
bool
useGpu
)
{
TestConfig
config
;
config
.
layerConfig
.
set_type
(
"spp"
);
config
.
inputDefs
.
push_back
({
INPUT_DATA
,
"layer_0"
,
3200
,
0
});
LayerInputConfig
*
input
=
config
.
layerConfig
.
add_inputs
();
SppConfig
*
sppConfig
=
input
->
mutable_spp_conf
();
sppConfig
->
set_pool_type
(
poolType
);
sppConfig
->
set_pyramid_height
(
pyramidHeight
);
sppConfig
->
set_channels
(
16
);
sppConfig
->
set_img_size
(
10
);
sppConfig
->
set_img_size_y
(
20
);
testLayerGrad
(
config
,
"spp"
,
100
,
trans
,
useGpu
);
}
TEST
(
Layer
,
SpatialPyramidPoolLayer
)
{
for
(
auto
useGpu
:
{
false
,
true
})
{
testSppLayer
(
"avg"
,
1
,
false
,
useGpu
);
testSppLayer
(
"avg"
,
3
,
false
,
useGpu
);
testSppLayer
(
"avg"
,
5
,
false
,
useGpu
);
testSppLayer
(
"max"
,
1
,
false
,
useGpu
);
testSppLayer
(
"max"
,
3
,
false
,
useGpu
);
testSppLayer
(
"avg"
,
5
,
false
,
useGpu
);
}
}
TEST
(
Layer
,
rankCostLayer
)
{
TestConfig
config
;
config
.
layerConfig
.
set_type
(
"rank-cost"
);
...
...
paddle/math/Matrix.cpp
浏览文件 @
cdac60f6
此差异已折叠。
点击以展开。
proto/ModelConfig.proto.m4
浏览文件 @
cdac60f6
...
...
@@ -120,6 +120,14 @@ message PoolConfig {
optional uint32 padding_y = 13 [default = 0];
}
message SppConfig {
required string pool_type = 1;
required uint32 pyramid_height = 2;
required uint32 channels = 3;
required uint32 img_size = 4;
optional uint32 img_size_y = 5;
}
message NormConfig {
// rnorm or cmrnorm
required string norm_type = 1;
...
...
@@ -194,6 +202,9 @@ message ProjectionConfig {
optional ConvConfig conv_conf = 8;
optional int32 num_filters = 9;
// For pool
optional PoolConfig pool_conf = 10;
// For IdentityOffsetProjection
optional uint64 offset = 11 [default = 0];
}
...
...
@@ -235,6 +246,7 @@ message LayerInputConfig {
// Set the argument name.
optional string input_layer_argument = 9;
optional MaxOutConfig maxout_conf = 10;
optional SppConfig spp_conf = 11;
}
message LayerConfig {
...
...
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