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体验新版 GitCode,发现更多精彩内容 >>
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5961b52b
编写于
3月 24, 2017
作者:
T
Tao Luo
提交者:
GitHub
3月 24, 2017
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差异文件
Merge pull request #1653 from Noplz/normalize-layer
CrossChannelNorm Layer for SSD
上级
bfc33108
21b7f4a6
变更
11
隐藏空白更改
内联
并排
Showing
11 changed file
with
279 addition
and
16 deletion
+279
-16
doc/api/v2/config/layer.rst
doc/api/v2/config/layer.rst
+6
-0
paddle/gserver/layers/CrossChannelNormLayer.cpp
paddle/gserver/layers/CrossChannelNormLayer.cpp
+122
-0
paddle/gserver/layers/NormLayer.cpp
paddle/gserver/layers/NormLayer.cpp
+12
-0
paddle/gserver/layers/NormLayer.h
paddle/gserver/layers/NormLayer.h
+31
-0
paddle/gserver/layers/PriorBox.cpp
paddle/gserver/layers/PriorBox.cpp
+17
-13
paddle/gserver/tests/test_LayerGrad.cpp
paddle/gserver/tests/test_LayerGrad.cpp
+19
-0
paddle/math/BaseMatrix.cu
paddle/math/BaseMatrix.cu
+18
-0
paddle/math/BaseMatrix.h
paddle/math/BaseMatrix.h
+3
-0
paddle/math/tests/test_BaseMatrix.cpp
paddle/math/tests/test_BaseMatrix.cpp
+2
-0
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+8
-3
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+41
-0
未找到文件。
doc/api/v2/config/layer.rst
浏览文件 @
5961b52b
...
...
@@ -109,6 +109,12 @@ sum_to_one_norm
:members: sum_to_one_norm
:noindex:
cross_channel_norm
------------------
.. automodule:: paddle.v2.layer
:members: cross_channel_norm
:noindex:
Recurrent Layers
================
...
...
paddle/gserver/layers/CrossChannelNormLayer.cpp
0 → 100644
浏览文件 @
5961b52b
/* Copyright (c) 2016 PaddlePaddle Authors. 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 "Layer.h"
#include "NormLayer.h"
#include "paddle/math/BaseMatrix.h"
#include "paddle/math/Matrix.h"
namespace
paddle
{
MatrixPtr
CrossChannelNormLayer
::
createSampleMatrix
(
MatrixPtr
data
,
size_t
iter
,
size_t
spatialDim
)
{
return
Matrix
::
create
(
data
->
getData
()
+
iter
*
channels_
*
spatialDim
,
channels_
,
spatialDim
,
false
,
useGpu_
);
}
MatrixPtr
CrossChannelNormLayer
::
createSpatialMatrix
(
MatrixPtr
data
,
size_t
iter
,
size_t
spatialDim
)
{
return
Matrix
::
create
(
data
->
getData
()
+
iter
*
spatialDim
,
1
,
spatialDim
,
false
,
useGpu_
);
}
void
CrossChannelNormLayer
::
forward
(
PassType
passType
)
{
Layer
::
forward
(
passType
);
MatrixPtr
inV
=
getInputValue
(
0
);
size_t
batchSize
=
inV
->
getHeight
();
size_t
dataDim
=
inV
->
getWidth
();
CHECK_EQ
(
getSize
(),
dataDim
);
reserveOutput
(
batchSize
,
dataDim
);
MatrixPtr
outV
=
getOutputValue
();
size_t
spatialDim
=
dataDim
/
channels_
;
Matrix
::
resizeOrCreate
(
dataBuffer_
,
batchSize
,
dataDim
,
false
,
useGpu_
);
Matrix
::
resizeOrCreate
(
spatialBuffer_
,
1
,
spatialDim
,
false
,
useGpu_
);
Matrix
::
resizeOrCreate
(
normBuffer_
,
batchSize
,
spatialDim
,
false
,
useGpu_
);
normBuffer_
->
zeroMem
();
// add eps to avoid overflow
normBuffer_
->
addScalar
(
*
normBuffer_
,
1e-6
);
inV
->
square2
(
*
dataBuffer_
);
for
(
size_t
i
=
0
;
i
<
batchSize
;
i
++
)
{
const
MatrixPtr
inVTmp
=
createSampleMatrix
(
inV
,
i
,
spatialDim
);
const
MatrixPtr
dataTmp
=
createSampleMatrix
(
dataBuffer_
,
i
,
spatialDim
);
MatrixPtr
outVTmp
=
createSampleMatrix
(
outV
,
i
,
spatialDim
);
MatrixPtr
normTmp
=
createSpatialMatrix
(
normBuffer_
,
i
,
spatialDim
);
// compute norm.
spatialBuffer_
->
sumCols
(
*
dataTmp
,
1
,
0
);
spatialBuffer_
->
sqrt2
(
*
spatialBuffer_
);
normTmp
->
copyFrom
(
*
spatialBuffer_
);
outVTmp
->
copyFrom
(
*
inVTmp
);
outVTmp
->
divRowVector
(
*
spatialBuffer_
);
// scale the layer.
outVTmp
->
mulColVector
(
*
scale_
->
getW
());
}
}
void
CrossChannelNormLayer
::
backward
(
const
UpdateCallback
&
callback
)
{
MatrixPtr
inG
=
getInputGrad
(
0
);
MatrixPtr
inV
=
getInputValue
(
0
);
MatrixPtr
outG
=
getOutputGrad
();
MatrixPtr
outV
=
getOutputValue
();
size_t
batchSize
=
inG
->
getHeight
();
size_t
dataDim
=
inG
->
getWidth
();
size_t
spatialDim
=
dataDim
/
channels_
;
dataBuffer_
->
dotMul
(
*
outG
,
*
outV
);
Matrix
::
resizeOrCreate
(
scaleDiff_
,
channels_
,
1
,
false
,
useGpu_
);
Matrix
::
resizeOrCreate
(
channelBuffer_
,
channels_
,
1
,
false
,
useGpu_
);
Matrix
::
resizeOrCreate
(
sampleBuffer_
,
channels_
,
spatialDim
,
false
,
useGpu_
);
scaleDiff_
->
zeroMem
();
for
(
size_t
i
=
0
;
i
<
batchSize
;
i
++
)
{
MatrixPtr
outGTmp
=
createSampleMatrix
(
outG
,
i
,
spatialDim
);
const
MatrixPtr
dataTmp
=
createSampleMatrix
(
dataBuffer_
,
i
,
spatialDim
);
const
MatrixPtr
inVTmp
=
createSampleMatrix
(
inV
,
i
,
spatialDim
);
const
MatrixPtr
inGTmp
=
createSampleMatrix
(
inG
,
i
,
spatialDim
);
const
MatrixPtr
normTmp
=
createSpatialMatrix
(
normBuffer_
,
i
,
spatialDim
);
channelBuffer_
->
sumRows
(
*
dataTmp
,
1
,
0
);
channelBuffer_
->
dotDiv
(
*
channelBuffer_
,
*
(
scale_
->
getW
()));
// store a / scale[i] in scaleDiff_ temporary
scaleDiff_
->
add
(
*
channelBuffer_
,
1.
);
sampleBuffer_
->
dotMul
(
*
inVTmp
,
*
outGTmp
);
spatialBuffer_
->
sumCols
(
*
sampleBuffer_
,
1.
,
1.
);
// scale the grad
inGTmp
->
copyFrom
(
*
inVTmp
);
inGTmp
->
mulRowVector
(
*
spatialBuffer_
);
// divide by square of norm
spatialBuffer_
->
dotMul
(
*
normTmp
,
*
normTmp
);
inGTmp
->
divRowVector
(
*
spatialBuffer_
);
// subtract
inGTmp
->
add
(
*
outGTmp
,
-
1
,
1
);
// divide by norm
inGTmp
->
divRowVector
(
*
normTmp
);
// scale the diff
inGTmp
->
mulColVector
(
*
scale_
->
getW
());
}
// updata scale
if
(
scale_
->
getWGrad
())
scale_
->
getWGrad
()
->
copyFrom
(
*
scaleDiff_
);
scale_
->
getParameterPtr
()
->
incUpdate
(
callback
);
}
}
// namespace paddle
paddle/gserver/layers/NormLayer.cpp
浏览文件 @
5961b52b
...
...
@@ -26,6 +26,8 @@ Layer* NormLayer::create(const LayerConfig& config) {
return
new
ResponseNormLayer
(
config
);
}
else
if
(
norm
==
"cmrnorm-projection"
)
{
return
new
CMRProjectionNormLayer
(
config
);
}
else
if
(
norm
==
"cross-channel-norm"
)
{
return
new
CrossChannelNormLayer
(
config
);
}
else
{
LOG
(
FATAL
)
<<
"Unknown norm type: "
<<
norm
;
return
nullptr
;
...
...
@@ -54,4 +56,14 @@ bool ResponseNormLayer::init(const LayerMap& layerMap,
return
true
;
}
bool
CrossChannelNormLayer
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
Layer
::
init
(
layerMap
,
parameterMap
);
CHECK
(
parameters_
[
0
]);
const
NormConfig
&
conf
=
config_
.
inputs
(
0
).
norm_conf
();
channels_
=
conf
.
channels
();
scale_
.
reset
(
new
Weight
(
channels_
,
1
,
parameters_
[
0
]));
return
true
;
}
}
// namespace paddle
paddle/gserver/layers/NormLayer.h
浏览文件 @
5961b52b
...
...
@@ -65,4 +65,35 @@ public:
}
};
/**
* This layer applys normalization across the channels of each sample to a
* conv layer's output, and scales the output by a group of trainable factors
* whose dimensions equal to the number of channels.
* - Input: One and only one input layer are accepted.
* - Output: The normalized data of the input data.
* Reference:
* Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed,
* Cheng-Yang Fu, Alexander C. Berg. SSD: Single Shot MultiBox Detector
*/
class
CrossChannelNormLayer
:
public
NormLayer
{
public:
explicit
CrossChannelNormLayer
(
const
LayerConfig
&
config
)
:
NormLayer
(
config
)
{}
bool
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
);
void
forward
(
PassType
passType
);
void
backward
(
const
UpdateCallback
&
callback
);
MatrixPtr
createSampleMatrix
(
MatrixPtr
data
,
size_t
iter
,
size_t
spatialDim
);
MatrixPtr
createSpatialMatrix
(
MatrixPtr
data
,
size_t
iter
,
size_t
spatialDim
);
protected:
size_t
channels_
;
std
::
unique_ptr
<
Weight
>
scale_
;
MatrixPtr
scaleDiff_
;
MatrixPtr
normBuffer_
;
MatrixPtr
dataBuffer_
;
MatrixPtr
channelBuffer_
;
MatrixPtr
spatialBuffer_
;
MatrixPtr
sampleBuffer_
;
};
}
// namespace paddle
paddle/gserver/layers/PriorBox.cpp
浏览文件 @
5961b52b
...
...
@@ -20,7 +20,7 @@ namespace paddle {
/**
* @brief A layer for generating priorbox locations and variances.
* - Input: Two and only two input layer are accepted. The input layer must be
* be a data output layer and a convolution output layer.
*
be a data output layer and a convolution output layer.
* - Output: The priorbox locations and variances of the input data.
* Reference:
* Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed,
...
...
@@ -45,27 +45,32 @@ protected:
MatrixPtr
buffer_
;
};
REGISTER_LAYER
(
priorbox
,
PriorBoxLayer
);
bool
PriorBoxLayer
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
Layer
::
init
(
layerMap
,
parameterMap
);
auto
pbConf
=
config_
.
inputs
(
0
).
priorbox_conf
();
std
::
vector
<
real
>
tmp
;
aspectRatio_
.
push_back
(
1.
);
std
::
copy
(
pbConf
.
min_size
().
begin
(),
pbConf
.
min_size
().
end
(),
std
::
back_inserter
(
minSize_
));
std
::
copy
(
pbConf
.
max_size
().
begin
(),
pbConf
.
max_size
().
end
(),
std
::
back_inserter
(
maxSize_
));
std
::
copy
(
pbConf
.
aspect_ratio
().
begin
(),
pbConf
.
aspect_ratio
().
end
(),
std
::
back_inserter
(
aspectRatio_
));
std
::
copy
(
pbConf
.
variance
().
begin
(),
pbConf
.
variance
().
end
(),
std
::
back_inserter
(
variance_
));
std
::
copy
(
pbConf
.
aspect_ratio
().
begin
(),
pbConf
.
aspect_ratio
().
end
(),
std
::
back_inserter
(
tmp
));
// flip
int
inputRatioLength
=
aspectRatio_
.
size
();
for
(
int
index
=
0
;
index
<
inputRatioLength
;
index
++
)
aspectRatio_
.
push_back
(
1
/
aspectRatio_
[
index
]);
aspectRatio_
.
push_back
(
1.
);
int
inputRatioLength
=
tmp
.
size
();
for
(
int
index
=
0
;
index
<
inputRatioLength
;
index
++
)
{
aspectRatio_
.
push_back
(
tmp
[
index
]);
aspectRatio_
.
push_back
(
1
/
tmp
[
index
]);
}
numPriors_
=
aspectRatio_
.
size
();
if
(
maxSize_
.
size
()
>
0
)
numPriors_
++
;
return
true
;
...
...
@@ -94,12 +99,12 @@ void PriorBoxLayer::forward(PassType passType) {
for
(
int
w
=
0
;
w
<
layerWidth
;
++
w
)
{
real
centerX
=
(
w
+
0.5
)
*
stepW
;
real
centerY
=
(
h
+
0.5
)
*
stepH
;
int
minSize
=
0
;
real
minSize
=
0
;
for
(
size_t
s
=
0
;
s
<
minSize_
.
size
();
s
++
)
{
// first prior.
minSize
=
minSize_
[
s
];
int
boxWidth
=
minSize
;
int
boxHeight
=
minSize
;
real
boxWidth
=
minSize
;
real
boxHeight
=
minSize
;
// xmin, ymin, xmax, ymax.
tmpPtr
[
idx
++
]
=
(
centerX
-
boxWidth
/
2.
)
/
imageWidth
;
tmpPtr
[
idx
++
]
=
(
centerY
-
boxHeight
/
2.
)
/
imageHeight
;
...
...
@@ -112,7 +117,7 @@ void PriorBoxLayer::forward(PassType passType) {
CHECK_EQ
(
minSize_
.
size
(),
maxSize_
.
size
());
// second prior.
for
(
size_t
s
=
0
;
s
<
maxSize_
.
size
();
s
++
)
{
int
maxSize
=
maxSize_
[
s
];
real
maxSize
=
maxSize_
[
s
];
boxWidth
=
boxHeight
=
sqrt
(
minSize
*
maxSize
);
tmpPtr
[
idx
++
]
=
(
centerX
-
boxWidth
/
2.
)
/
imageWidth
;
tmpPtr
[
idx
++
]
=
(
centerY
-
boxHeight
/
2.
)
/
imageHeight
;
...
...
@@ -145,6 +150,5 @@ void PriorBoxLayer::forward(PassType passType) {
MatrixPtr
outV
=
getOutputValue
();
outV
->
copyFrom
(
buffer_
->
data_
,
dim
*
2
);
}
REGISTER_LAYER
(
priorbox
,
PriorBoxLayer
);
}
// namespace paddle
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @
5961b52b
...
...
@@ -1642,6 +1642,25 @@ TEST(Layer, PadLayer) {
}
}
TEST
(
Layer
,
CrossChannelNormLayer
)
{
TestConfig
config
;
config
.
layerConfig
.
set_type
(
"norm"
);
config
.
layerConfig
.
set_size
(
100
);
LayerInputConfig
*
input
=
config
.
layerConfig
.
add_inputs
();
NormConfig
*
norm
=
input
->
mutable_norm_conf
();
norm
->
set_norm_type
(
"cross-channel-norm"
);
norm
->
set_channels
(
10
);
norm
->
set_size
(
100
);
norm
->
set_scale
(
0
);
norm
->
set_pow
(
0
);
norm
->
set_blocked
(
0
);
config
.
inputDefs
.
push_back
({
INPUT_DATA
,
"layer_0"
,
100
,
10
});
for
(
auto
useGpu
:
{
false
,
true
})
{
testLayerGrad
(
config
,
"cross-channel-norm"
,
10
,
false
,
useGpu
,
false
,
5
);
}
}
TEST
(
Layer
,
smooth_l1
)
{
TestConfig
config
;
config
.
layerConfig
.
set_type
(
"smooth_l1"
);
...
...
paddle/math/BaseMatrix.cu
浏览文件 @
5961b52b
...
...
@@ -1453,6 +1453,24 @@ void BaseMatrixT<T>::divRowVector(BaseMatrixT& b) {
true_type
()
/* bAsRowVector */
,
false_type
());
}
template
<
class
T
>
void
BaseMatrixT
<
T
>::
mulColVector
(
BaseMatrixT
&
b
)
{
MatrixOffset
offset
(
0
,
0
,
0
,
0
);
int
numRows
=
height_
;
int
numCols
=
width_
;
applyBinary
(
binary
::
DotMul
<
T
>
(),
b
,
numRows
,
numCols
,
offset
,
false_type
(),
true_type
()
/* bAsColVector */
);
}
template
<
class
T
>
void
BaseMatrixT
<
T
>::
divColVector
(
BaseMatrixT
&
b
)
{
MatrixOffset
offset
(
0
,
0
,
0
,
0
);
int
numRows
=
height_
;
int
numCols
=
width_
;
applyBinary
(
binary
::
DotDiv
<
T
>
(),
b
,
numRows
,
numCols
,
offset
,
false_type
(),
true_type
()
/* bAsColVector */
);
}
template
<
>
template
<
class
Agg
>
int
BaseMatrixT
<
real
>::
applyRow
(
Agg
agg
,
BaseMatrixT
&
b
)
{
...
...
paddle/math/BaseMatrix.h
浏览文件 @
5961b52b
...
...
@@ -545,6 +545,9 @@ public:
void
mulRowVector
(
BaseMatrixT
&
b
);
void
divRowVector
(
BaseMatrixT
&
b
);
void
mulColVector
(
BaseMatrixT
&
b
);
void
divColVector
(
BaseMatrixT
&
b
);
void
addP2P
(
BaseMatrixT
&
b
);
/**
...
...
paddle/math/tests/test_BaseMatrix.cpp
浏览文件 @
5961b52b
...
...
@@ -110,6 +110,8 @@ TEST(BaseMatrix, BaseMatrix) {
compare
(
&
BaseMatrix
::
addRowVector
);
compare
(
&
BaseMatrix
::
mulRowVector
);
compare
(
&
BaseMatrix
::
divRowVector
);
compare
(
&
BaseMatrix
::
mulColVector
);
compare
(
&
BaseMatrix
::
divColVector
);
compare
(
&
BaseMatrix
::
addP2P
);
compare
(
&
BaseMatrix
::
invSqrt
);
}
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
5961b52b
...
...
@@ -1220,9 +1220,11 @@ def parse_image(image, input_layer_name, image_conf):
def
parse_norm
(
norm
,
input_layer_name
,
norm_conf
):
norm_conf
.
norm_type
=
norm
.
norm_type
config_assert
(
norm
.
norm_type
in
[
'rnorm'
,
'cmrnorm-projection'
],
"norm-type %s is not in [rnorm, 'cmrnorm-projection']"
%
norm
.
norm_type
)
config_assert
(
norm
.
norm_type
in
[
'rnorm'
,
'cmrnorm-projection'
,
'cross-channel-norm'
],
"norm-type %s is not in [rnorm, cmrnorm-projection, cross-channel-norm]"
%
norm
.
norm_type
)
norm_conf
.
channels
=
norm
.
channels
norm_conf
.
size
=
norm
.
size
norm_conf
.
scale
=
norm
.
scale
...
...
@@ -1898,6 +1900,9 @@ class NormLayer(LayerBase):
norm_conf
)
self
.
set_cnn_layer
(
name
,
norm_conf
.
output_y
,
norm_conf
.
output_x
,
norm_conf
.
channels
,
False
)
if
norm_conf
.
norm_type
==
"cross-channel-norm"
:
self
.
create_input_parameter
(
0
,
norm_conf
.
channels
,
[
norm_conf
.
channels
,
1
])
@
config_layer
(
'pool'
)
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
5961b52b
...
...
@@ -112,6 +112,7 @@ __all__ = [
'out_prod_layer'
,
'print_layer'
,
'priorbox_layer'
,
'cross_channel_norm_layer'
,
'spp_layer'
,
'pad_layer'
,
'eos_layer'
,
...
...
@@ -1008,6 +1009,46 @@ def priorbox_layer(input,
size
=
size
)
@
wrap_name_default
(
"cross_channel_norm"
)
def
cross_channel_norm_layer
(
input
,
name
=
None
,
param_attr
=
None
):
"""
Normalize a layer's output. This layer is necessary for ssd.
This layer applys normalize across the channels of each sample to
a conv layer's output and scale the output by a group of trainable
factors which dimensions equal to the channel's number.
:param name: The Layer Name.
:type name: basestring
:param input: The input layer.
:type input: LayerOutput
:param param_attr: The Parameter Attribute|list.
:type param_attr: ParameterAttribute
:return: LayerOutput
"""
assert
input
.
num_filters
is
not
None
Layer
(
name
=
name
,
type
=
LayerType
.
NORM_LAYER
,
inputs
=
[
Input
(
input
.
name
,
norm
=
Norm
(
norm_type
=
"cross-channel-norm"
,
channels
=
input
.
num_filters
,
size
=
input
.
size
,
scale
=
0
,
pow
=
0
,
blocked
=
0
),
**
param_attr
.
attr
)
])
return
LayerOutput
(
name
,
LayerType
.
NORM_LAYER
,
parents
=
input
,
num_filters
=
input
.
num_filters
,
size
=
input
.
size
)
@
wrap_name_default
(
"seq_pooling"
)
@
wrap_bias_attr_default
(
has_bias
=
False
)
@
wrap_param_default
([
'pooling_type'
],
default_factory
=
lambda
_
:
MaxPooling
())
...
...
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