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体验新版 GitCode,发现更多精彩内容 >>
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6da00da7
编写于
11月 20, 2017
作者:
X
xzl
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
code format check
上级
7dc584f5
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
192 addition
and
182 deletion
+192
-182
paddle/cuda/include/hl_cnn.h
paddle/cuda/include/hl_cnn.h
+18
-16
paddle/cuda/include/stub/hl_cnn_stub.h
paddle/cuda/include/stub/hl_cnn_stub.h
+19
-17
paddle/gserver/layers/UpsampleLayer.cpp
paddle/gserver/layers/UpsampleLayer.cpp
+1
-0
paddle/gserver/layers/UpsampleLayer.h
paddle/gserver/layers/UpsampleLayer.h
+0
-1
paddle/math/Matrix.cpp
paddle/math/Matrix.cpp
+110
-110
paddle/math/Matrix.h
paddle/math/Matrix.h
+36
-36
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+6
-2
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+2
-0
未找到文件。
paddle/cuda/include/hl_cnn.h
浏览文件 @
6da00da7
...
...
@@ -378,14 +378,15 @@ extern void hl_maxout_backward(real* inGrad,
* @param[in] outputW the output widht.
* @param[out] outputData output data.
*/
extern
void
hl_upsample_forward
(
real
*
inputData
,
real
*
maskData
,
size_t
batchSize
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
,
real
*
outputData
);
extern
void
hl_upsample_forward
(
real
*
inputData
,
real
*
maskData
,
size_t
batchSize
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
,
real
*
outputData
);
/**
* @brief Upsample backward.
...
...
@@ -399,13 +400,14 @@ extern void hl_upsample_forward(real *inputData, real *maskData,
* @param[in] outputW the output widht.
* @param[out] inputGradData the input grad data.
*/
extern
void
hl_upsample_backward
(
real
*
outputGradData
,
real
*
maskData
,
size_t
batchSize
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
,
real
*
inputGradData
);
extern
void
hl_upsample_backward
(
real
*
outputGradData
,
real
*
maskData
,
size_t
batchSize
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
,
real
*
inputGradData
);
#endif // HL_CNN_H_
paddle/cuda/include/stub/hl_cnn_stub.h
浏览文件 @
6da00da7
...
...
@@ -222,22 +222,24 @@ inline void hl_maxout_backward(real* inGrad,
size_t
featLen
,
size_t
group
)
{}
inline
void
hl_upsample_forward
(
real
*
inputData
,
real
*
maskData
,
size_t
batchSize
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
,
real
*
outputData
)
{}
inline
void
hl_upsample_backward
(
real
*
outputGradData
,
real
*
maskData
,
size_t
batchSize
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
,
real
*
inputGradData
)
{}
inline
void
hl_upsample_forward
(
real
*
inputData
,
real
*
maskData
,
size_t
batchSize
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
,
real
*
outputData
)
{}
inline
void
hl_upsample_backward
(
real
*
outputGradData
,
real
*
maskData
,
size_t
batchSize
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
,
real
*
inputGradData
)
{}
#endif // HL_CNN_STUB_H_
paddle/gserver/layers/UpsampleLayer.cpp
浏览文件 @
6da00da7
...
...
@@ -30,6 +30,7 @@ size_t UpsampleLayer::getOutputSize() {
bool
UpsampleLayer
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
Layer
::
init
(
layerMap
,
parameterMap
);
CHECK_EQ
(
inputLayers_
.
size
(),
2U
);
CHECK_EQ
(
config_
.
inputs_size
(),
2
);
const
auto
&
conf
=
config_
.
inputs
(
0
).
upsample_conf
();
...
...
paddle/gserver/layers/UpsampleLayer.h
浏览文件 @
6da00da7
...
...
@@ -32,7 +32,6 @@ namespace paddle {
class
UpsampleLayer
:
public
Layer
{
public:
explicit
UpsampleLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
{}
~
UpsampleLayer
()
{}
bool
init
(
const
LayerMap
&
layerMap
,
...
...
paddle/math/Matrix.cpp
浏览文件 @
6da00da7
...
...
@@ -1024,61 +1024,63 @@ void GpuMatrix::check(std::ostream& os, Matrix& refMat, bool printDiff) {
}
void
GpuMatrix
::
upsampleForward
(
Matrix
&
input
,
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
)
{
CHECK
(
input
.
useGpu_
==
true
)
<<
"Matrix type are not equal"
;
CHECK
(
mask
.
useGpu_
==
true
)
<<
"Matrix type are not equal"
;
real
*
inputData
=
input
.
getData
();
real
*
maskData
=
mask
.
getData
();
real
*
outData
=
data_
;
size_t
batch
=
input
.
getHeight
();
CHECK
(
imgSizeH
*
imgSizeW
*
channels
==
input
.
getWidth
());
CHECK
(
imgSizeH
*
imgSizeW
*
channels
==
mask
.
getWidth
());
CHECK_EQ
(
batch
,
this
->
getHeight
());
CHECK
(
width_
==
outputH
*
outputW
*
channels
);
hl_upsample_forward
(
inputData
,
maskData
,
batch
,
imgSizeH
,
imgSizeW
,
channels
,
outputH
,
outputW
,
outData
);
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
)
{
CHECK
(
input
.
useGpu_
==
true
)
<<
"Matrix type are not equal"
;
CHECK
(
mask
.
useGpu_
==
true
)
<<
"Matrix type are not equal"
;
real
*
inputData
=
input
.
getData
();
real
*
maskData
=
mask
.
getData
();
real
*
outData
=
data_
;
size_t
batch
=
input
.
getHeight
();
CHECK
(
imgSizeH
*
imgSizeW
*
channels
==
input
.
getWidth
());
CHECK
(
imgSizeH
*
imgSizeW
*
channels
==
mask
.
getWidth
());
CHECK_EQ
(
batch
,
this
->
getHeight
());
CHECK
(
width_
==
outputH
*
outputW
*
channels
);
hl_upsample_forward
(
inputData
,
maskData
,
batch
,
imgSizeH
,
imgSizeW
,
channels
,
outputH
,
outputW
,
outData
);
}
void
GpuMatrix
::
upsampleBackward
(
Matrix
&
outputGrad
,
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
)
{
CHECK
(
outputGrad
.
useGpu_
==
true
)
<<
"Matrix type are not equal"
;
CHECK
(
mask
.
useGpu_
==
true
)
<<
"Matrix type are not equal"
;
real
*
outputGradData
=
outputGrad
.
getData
();
real
*
maskData
=
mask
.
getData
();
real
*
inputGradData
=
data_
;
size_t
batch
=
outputGrad
.
getHeight
();
CHECK
(
imgSizeH
*
imgSizeW
==
this
->
getWidth
()
/
channels
);
CHECK_EQ
(
batch
,
this
->
getHeight
());
CHECK_EQ
(
channels
*
outputH
*
outputW
,
outputGrad
.
getWidth
());
hl_upsample_backward
(
outputGradData
,
maskData
,
batch
,
imgSizeH
,
imgSizeW
,
channels
,
outputH
,
outputW
,
inputGradData
);
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
)
{
CHECK
(
outputGrad
.
useGpu_
==
true
)
<<
"Matrix type are not equal"
;
CHECK
(
mask
.
useGpu_
==
true
)
<<
"Matrix type are not equal"
;
real
*
outputGradData
=
outputGrad
.
getData
();
real
*
maskData
=
mask
.
getData
();
real
*
inputGradData
=
data_
;
size_t
batch
=
outputGrad
.
getHeight
();
CHECK
(
imgSizeH
*
imgSizeW
==
this
->
getWidth
()
/
channels
);
CHECK_EQ
(
batch
,
this
->
getHeight
());
CHECK_EQ
(
channels
*
outputH
*
outputW
,
outputGrad
.
getWidth
());
hl_upsample_backward
(
outputGradData
,
maskData
,
batch
,
imgSizeH
,
imgSizeW
,
channels
,
outputH
,
outputW
,
inputGradData
);
}
void
GpuMatrix
::
maxPoolForward
(
Matrix
&
inputMat
,
...
...
@@ -2040,71 +2042,69 @@ void CpuMatrix::inverse(MatrixPtr& matInv, bool memAlloc) {
}
void
CpuMatrix
::
upsampleForward
(
Matrix
&
input
,
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
)
{
real
*
inputData
=
input
.
getData
();
real
*
maskData
=
mask
.
getData
();
real
*
outData
=
data_
;
size_t
inLength
=
imgSizeH
*
imgSizeW
;
size_t
outLength
=
outputH
*
outputW
;
size_t
batch
=
input
.
getHeight
();
CHECK
(
inLength
==
input
.
getWidth
()
/
channels
);
CHECK_EQ
(
batch
,
this
->
getHeight
());
CHECK_EQ
(
channels
*
outLength
,
this
->
getWidth
());
for
(
size_t
k
=
0
;
k
<
batch
;
k
++
)
{
for
(
size_t
c
=
0
;
c
<
channels
;
c
++
)
{
for
(
size_t
i
=
0
;
i
<
inLength
;
i
++
)
{
size_t
out_index
=
static_cast
<
int
>
(
maskData
[
i
]);
if
(
out_index
>=
outLength
)
{
LOG
(
FATAL
)
<<
"upsample index "
<<
out_index
<<
" out of range."
;
}
outData
[
out_index
]
=
inputData
[
i
];
}
inputData
+=
inLength
;
maskData
+=
inLength
;
outData
+=
outLength
;
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
)
{
real
*
inputData
=
input
.
getData
();
real
*
maskData
=
mask
.
getData
();
real
*
outData
=
data_
;
size_t
inLength
=
imgSizeH
*
imgSizeW
;
size_t
outLength
=
outputH
*
outputW
;
size_t
batch
=
input
.
getHeight
();
CHECK
(
inLength
==
input
.
getWidth
()
/
channels
);
CHECK_EQ
(
batch
,
this
->
getHeight
());
CHECK_EQ
(
channels
*
outLength
,
this
->
getWidth
());
for
(
size_t
k
=
0
;
k
<
batch
;
k
++
)
{
for
(
size_t
c
=
0
;
c
<
channels
;
c
++
)
{
for
(
size_t
i
=
0
;
i
<
inLength
;
i
++
)
{
size_t
out_index
=
static_cast
<
int
>
(
maskData
[
i
]);
if
(
out_index
>=
outLength
)
{
LOG
(
FATAL
)
<<
"upsample index "
<<
out_index
<<
" out of range."
;
}
outData
[
out_index
]
=
inputData
[
i
];
}
inputData
+=
inLength
;
maskData
+=
inLength
;
outData
+=
outLength
;
}
}
}
void
CpuMatrix
::
upsampleBackward
(
Matrix
&
outputGrad
,
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
)
{
real
*
outputGradData
=
outputGrad
.
getData
();
real
*
maskData
=
mask
.
getData
();
real
*
inputGradData
=
data_
;
size_t
inLength
=
imgSizeH
*
imgSizeW
;
size_t
outLength
=
outputH
*
outputW
;
size_t
batch
=
outputGrad
.
getHeight
();
CHECK
(
inLength
==
this
->
getWidth
()
/
channels
);
CHECK_EQ
(
batch
,
this
->
getHeight
());
CHECK_EQ
(
channels
*
outLength
,
outputGrad
.
getWidth
());
for
(
size_t
k
=
0
;
k
<
batch
;
k
++
)
{
for
(
size_t
c
=
0
;
c
<
channels
;
c
++
)
{
for
(
size_t
i
=
0
;
i
<
inLength
;
i
++
)
{
size_t
out_index
=
static_cast
<
int
>
(
maskData
[
i
]);
if
(
out_index
>=
outLength
)
{
LOG
(
FATAL
)
<<
"upsample index "
<<
out_index
<<
" out of range."
;
}
inputGradData
[
i
]
=
outputGradData
[
out_index
];
}
inputGradData
+=
inLength
;
maskData
+=
inLength
;
outputGradData
+=
outLength
;
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
)
{
real
*
outputGradData
=
outputGrad
.
getData
();
real
*
maskData
=
mask
.
getData
();
real
*
inputGradData
=
data_
;
size_t
inLength
=
imgSizeH
*
imgSizeW
;
size_t
outLength
=
outputH
*
outputW
;
size_t
batch
=
outputGrad
.
getHeight
();
CHECK
(
inLength
==
this
->
getWidth
()
/
channels
);
CHECK_EQ
(
batch
,
this
->
getHeight
());
CHECK_EQ
(
channels
*
outLength
,
outputGrad
.
getWidth
());
for
(
size_t
k
=
0
;
k
<
batch
;
k
++
)
{
for
(
size_t
c
=
0
;
c
<
channels
;
c
++
)
{
for
(
size_t
i
=
0
;
i
<
inLength
;
i
++
)
{
size_t
out_index
=
static_cast
<
int
>
(
maskData
[
i
]);
if
(
out_index
>=
outLength
)
{
LOG
(
FATAL
)
<<
"upsample index "
<<
out_index
<<
" out of range."
;
}
inputGradData
[
i
]
=
outputGradData
[
out_index
];
}
inputGradData
+=
inLength
;
maskData
+=
inLength
;
outputGradData
+=
outLength
;
}
}
}
void
CpuMatrix
::
maxPoolForward
(
Matrix
&
inputMat
,
...
...
paddle/math/Matrix.h
浏览文件 @
6da00da7
...
...
@@ -860,22 +860,22 @@ public:
}
virtual
void
upsampleForward
(
Matrix
&
input
,
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
)
{
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
)
{
LOG
(
FATAL
)
<<
"Not implemeted"
;
}
virtual
void
upsampleBackward
(
Matrix
&
outputGrad
,
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
)
{
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
)
{
LOG
(
FATAL
)
<<
"Not implemeted"
;
}
...
...
@@ -1438,20 +1438,20 @@ public:
void
classificationError
(
Matrix
&
output
,
IVector
&
label
,
size_t
topkSize
=
1
);
void
upsampleForward
(
Matrix
&
input
,
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
);
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
);
void
upsampleBackward
(
Matrix
&
outputGrad
,
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
);
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
);
void
maxPoolForward
(
Matrix
&
inputMat
,
size_t
imgSizeH
,
...
...
@@ -1726,20 +1726,20 @@ public:
MatrixPtr
clone
(
size_t
height
,
size_t
width
,
bool
useGpu
=
false
);
void
upsampleForward
(
Matrix
&
input
,
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
);
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
);
void
upsampleBackward
(
Matrix
&
outputGrad
,
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
);
Matrix
&
mask
,
size_t
imgSizeH
,
size_t
imgSizeW
,
size_t
channels
,
size_t
outputH
,
size_t
outputW
);
void
maxPoolForward
(
Matrix
&
inputMat
,
size_t
imgSizeH
,
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
6da00da7
...
...
@@ -978,12 +978,14 @@ class Pad(Cfg):
def
__init__
(
self
,
channels
,
pad_c
,
pad_h
,
pad_w
):
self
.
add_keys
(
locals
())
@
config_class
class
Upsample
(
Cfg
):
def
__init__
(
self
,
scale
,
scale_y
,
pad_out_x
,
pad_out_y
,
upsample_size
,
upsample_size_y
):
self
.
add_keys
(
locals
())
@
config_class
class
Norm
(
Cfg
):
def
__init__
(
self
,
...
...
@@ -2393,6 +2395,7 @@ class SpatialPyramidPoolLayer(LayerBase):
output_x
=
(
pow
(
4
,
spp_conf
.
pyramid_height
)
-
1
)
/
(
4
-
1
)
self
.
set_cnn_layer
(
name
,
1
,
output_x
,
spp_conf
.
image_conf
.
channels
)
@
config_layer
(
'upsample'
)
class
UpsampleLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
,
**
xargs
):
...
...
@@ -2407,9 +2410,10 @@ class UpsampleLayer(LayerBase):
input_layer
.
height
)
upsample
=
self
.
inputs
[
0
].
upsample
output_x
=
0
output_x
=
0
output_y
=
0
output_size
=
0
if
upsample
.
scale
:
self
.
config
.
inputs
[
0
].
upsample_conf
.
scale
=
upsample
.
scale
self
.
config
.
inputs
[
0
].
upsample_conf
.
scale_y
=
upsample
.
scale_y
...
...
@@ -2427,11 +2431,11 @@ class UpsampleLayer(LayerBase):
output_size
=
image_conf
.
channels
*
output_x
*
output_y
self
.
set_layer_height_width
(
output_y
,
output_x
)
self
.
set_layer_depth
(
input_layer
.
depth
)
self
.
set_layer_size
(
output_size
)
@
config_layer
(
'pad'
)
class
PadLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
,
**
xargs
):
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
6da00da7
...
...
@@ -2881,6 +2881,7 @@ def img_pool3d_layer(input,
num_filters
=
num_channels
,
size
=
l
.
config
.
size
)
@
wrap_name_default
(
"upsample"
)
@
layer_support
()
def
upsample_layer
(
input
,
...
...
@@ -2930,6 +2931,7 @@ def upsample_layer(input,
'scale or upsample_size, there must be one to be designated'
assert
len
(
input
)
==
2
,
'layer input size must be 2'
assert
input
[
1
].
layer_type
==
LayerType
.
POOL_LAYER
,
\
'the second input should be the MaxPoolWithMaskLayer'
...
...
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