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体验新版 GitCode,发现更多精彩内容 >>
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5cb29a8f
编写于
8月 02, 2017
作者:
G
Guo Sheng
提交者:
GitHub
8月 02, 2017
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差异文件
Merge pull request #3083 from guoshengCS/add-L2NormLayer
add RowL2NormLayer
上级
691a00e3
3fe9e48f
变更
8
显示空白变更内容
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并排
Showing
8 changed file
with
198 addition
and
1 deletion
+198
-1
doc/api/v2/config/layer.rst
doc/api/v2/config/layer.rst
+5
-0
paddle/gserver/layers/RowL2NormLayer.cpp
paddle/gserver/layers/RowL2NormLayer.cpp
+98
-0
paddle/gserver/tests/test_LayerGrad.cpp
paddle/gserver/tests/test_LayerGrad.cpp
+13
-0
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+10
-0
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+38
-0
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
.../paddle/trainer_config_helpers/tests/configs/file_list.sh
+1
-1
python/paddle/trainer_config_helpers/tests/configs/protostr/test_row_l2_norm_layer.protostr
...rs/tests/configs/protostr/test_row_l2_norm_layer.protostr
+27
-0
python/paddle/trainer_config_helpers/tests/configs/test_row_l2_norm_layer.py
...er_config_helpers/tests/configs/test_row_l2_norm_layer.py
+6
-0
未找到文件。
doc/api/v2/config/layer.rst
浏览文件 @
5cb29a8f
...
...
@@ -105,6 +105,11 @@ cross_channel_norm
.. autoclass:: paddle.v2.layer.cross_channel_norm
:noindex:
row_l2_norm
-----------
.. autoclass:: paddle.v2.layer.row_l2_norm
:noindex:
Recurrent Layers
================
...
...
paddle/gserver/layers/RowL2NormLayer.cpp
0 → 100644
浏览文件 @
5cb29a8f
/* 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"
namespace
paddle
{
/**
* A layer for L2 normalization in each row,
* \f[
* out[i] = \frac{in[i]}{\sqrt{\sum_{k=1}^N in[k]^{2}}}
* \f]
* where the size of \f$in\f$ is (batchSize x dataDim),
* and the size of \f$out\f$ is (batchSize x dataDim).
*/
class
RowL2NormLayer
:
public
Layer
{
protected:
MatrixPtr
inSquare_
;
MatrixPtr
l2NormReciprocal_
;
MatrixPtr
dotSum_
;
public:
explicit
RowL2NormLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
{}
bool
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
override
;
void
forward
(
PassType
passType
)
override
;
void
backward
(
const
UpdateCallback
&
callback
=
nullptr
)
override
;
};
REGISTER_LAYER
(
row_l2_norm
,
RowL2NormLayer
);
bool
RowL2NormLayer
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
Layer
::
init
(
layerMap
,
parameterMap
);
CHECK_EQ
(
inputLayers_
.
size
(),
1U
);
return
true
;
}
void
RowL2NormLayer
::
forward
(
PassType
passType
)
{
Layer
::
forward
(
passType
);
MatrixPtr
inV
=
getInputValue
(
0
);
/* malloc memory for the output_ if necessary */
size_t
batchSize
=
inV
->
getHeight
();
size_t
dataDim
=
getSize
();
CHECK_EQ
(
dataDim
,
inV
->
getWidth
());
resetOutput
(
batchSize
,
dataDim
);
MatrixPtr
outV
=
getOutputValue
();
Matrix
::
resizeOrCreate
(
inSquare_
,
batchSize
,
dataDim
,
false
,
useGpu_
);
inV
->
square2
(
*
inSquare_
);
Matrix
::
resizeOrCreate
(
l2NormReciprocal_
,
batchSize
,
1
,
false
,
useGpu_
);
inSquare_
->
rowSum
(
*
l2NormReciprocal_
);
l2NormReciprocal_
->
sqrt2
(
*
l2NormReciprocal_
);
l2NormReciprocal_
->
scalarDiv
(
*
l2NormReciprocal_
,
1.0
);
outV
->
rowScale
(
0
,
*
inV
,
*
l2NormReciprocal_
);
}
void
RowL2NormLayer
::
backward
(
const
UpdateCallback
&
callback
)
{
MatrixPtr
inV
=
getInputValue
(
0
);
MatrixPtr
inG
=
getInputGrad
(
0
);
MatrixPtr
outV
=
getOutputValue
();
MatrixPtr
outG
=
getOutputGrad
();
size_t
batchSize
=
inV
->
getHeight
();
// inG[ij] += outG[ij] / l2NormReciprocal
// inG[ij] += -inV[ij] * l2NormReciprocal * l2NormReciprocal * DotMul(outG[i],
// inV[i])
if
(
inG
)
{
Matrix
::
resizeOrCreate
(
dotSum_
,
batchSize
,
1
,
false
,
useGpu_
);
dotSum_
->
zeroMem
();
dotSum_
->
rowDotMul
(
0
,
*
outG
,
*
outV
);
dotSum_
->
dotMul
(
*
dotSum_
,
*
l2NormReciprocal_
);
dotSum_
->
dotMul
(
*
dotSum_
,
*
l2NormReciprocal_
);
inSquare_
->
rowScale
(
0
,
*
inV
,
*
dotSum_
);
inG
->
sub
(
*
inSquare_
);
inG
->
addRowScale
(
0
,
*
outG
,
*
l2NormReciprocal_
);
}
}
}
// namespace paddle
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @
5cb29a8f
...
...
@@ -1899,6 +1899,19 @@ TEST(Layer, CropLayer) {
}
}
TEST
(
Layer
,
RowL2NormLayer
)
{
const
size_t
batchSize
=
128
;
const
size_t
size
=
512
;
TestConfig
config
;
config
.
layerConfig
.
set_type
(
"row_l2_norm"
);
config
.
layerConfig
.
set_size
(
size
);
config
.
inputDefs
.
push_back
({
INPUT_DATA
,
"input"
,
size
,
0
});
config
.
layerConfig
.
add_inputs
();
for
(
auto
useGpu
:
{
false
,
true
})
{
testLayerGrad
(
config
,
"row_l2_norm"
,
batchSize
,
false
,
useGpu
,
false
);
}
}
int
main
(
int
argc
,
char
**
argv
)
{
testing
::
InitGoogleTest
(
&
argc
,
argv
);
initMain
(
argc
,
argv
);
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
5cb29a8f
...
...
@@ -2754,6 +2754,16 @@ class SumToOneNormLayer(LayerBase):
self
.
set_layer_size
(
input_layer0
.
size
)
@
config_layer
(
'row_l2_norm'
)
class
RowL2NormLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
,
**
xargs
):
super
(
RowL2NormLayer
,
self
).
__init__
(
name
,
'row_l2_norm'
,
0
,
inputs
=
inputs
,
**
xargs
)
config_assert
(
len
(
self
.
inputs
)
==
1
,
'RowL2NormLayer must have 1 input'
)
input_layer
=
self
.
get_input_layer
(
0
)
self
.
set_layer_size
(
input_layer
.
size
)
@
config_layer
(
'cos_vm'
)
class
CosSimVecMatLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
size
,
inputs
,
cos_scale
=
1.0
,
device
=
None
):
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
5cb29a8f
...
...
@@ -76,6 +76,7 @@ __all__ = [
'trans_layer'
,
'rotate_layer'
,
'sum_to_one_norm_layer'
,
'row_l2_norm_layer'
,
'get_output_layer'
,
'LayerType'
,
'context_projection'
,
...
...
@@ -160,6 +161,7 @@ class LayerType(object):
BATCH_NORM_LAYER
=
'batch_norm'
NORM_LAYER
=
'norm'
SUM_TO_ONE_NORM_LAYER
=
'sum_to_one_norm'
ROW_L2_NORM_LAYER
=
'row_l2_norm'
ADDTO_LAYER
=
'addto'
CONCAT_LAYER
=
'concat'
...
...
@@ -2889,6 +2891,42 @@ def sum_to_one_norm_layer(input, name=None, layer_attr=None):
name
,
LayerType
.
SUM_TO_ONE_NORM_LAYER
,
parents
=
[
input
],
size
=
input
.
size
)
@
wrap_name_default
()
@
layer_support
()
def
row_l2_norm_layer
(
input
,
name
=
None
,
layer_attr
=
None
):
"""
A layer for L2-normalization in each row.
.. math::
out[i] =
\f
rac{in[i]}{\sqrt{\sum_{k=1}^N in[k]^{2}}}
where the size of :math:`in` is (batchSize x dataDim) ,
and the size of :math:`out` is a (batchSize x dataDim) .
The example usage is:
.. code-block:: python
row_l2_norm_layer = row_l2_norm_layer(input=layer)
:param input: Input layer.
:type input: LayerOutput
:param name: Layer name.
:type name: basestring
:param layer_attr: extra layer attributes.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer
(
name
=
name
,
type
=
LayerType
.
ROW_L2_NORM_LAYER
,
inputs
=
[
input
.
name
],
**
ExtraAttr
.
to_kwargs
(
layer_attr
))
return
LayerOutput
(
name
,
LayerType
.
ROW_L2_NORM_LAYER
,
parents
=
[
input
],
size
=
input
.
size
)
@
wrap_name_default
(
"addto"
)
@
wrap_act_default
(
act
=
LinearActivation
())
@
wrap_bias_attr_default
(
has_bias
=
False
)
...
...
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
浏览文件 @
5cb29a8f
...
...
@@ -7,6 +7,6 @@ test_rnn_group shared_fc shared_lstm shared_gru test_cost_layers_with_weight
test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops
test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer
test_recursive_topology test_gated_unit_layer
)
test_recursive_topology test_gated_unit_layer
test_row_l2_norm_layer
)
export
whole_configs
=(
test_split_datasource
)
python/paddle/trainer_config_helpers/tests/configs/protostr/test_row_l2_norm_layer.protostr
0 → 100644
浏览文件 @
5cb29a8f
type: "nn"
layers {
name: "input"
type: "data"
size: 300
active_type: ""
}
layers {
name: "__row_l2_norm_layer_0__"
type: "row_l2_norm"
size: 300
active_type: ""
inputs {
input_layer_name: "input"
}
}
input_layer_names: "input"
output_layer_names: "__row_l2_norm_layer_0__"
sub_models {
name: "root"
layer_names: "input"
layer_names: "__row_l2_norm_layer_0__"
input_layer_names: "input"
output_layer_names: "__row_l2_norm_layer_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/test_row_l2_norm_layer.py
0 → 100644
浏览文件 @
5cb29a8f
from
paddle.trainer_config_helpers
import
*
data
=
data_layer
(
name
=
'input'
,
size
=
300
)
row_l2_norm
=
row_l2_norm_layer
(
input
=
data
)
outputs
(
row_l2_norm
)
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