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382de964
编写于
8月 08, 2017
作者:
C
Cao Ying
提交者:
GitHub
8月 08, 2017
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差异文件
Merge pull request #3249 from lcy-seso/kmax_score_layer
Add a Kmax sequence score layer.
上级
d9f97b02
92b2b1bd
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
423 addition
and
2 deletion
+423
-2
doc/api/v2/config/layer.rst
doc/api/v2/config/layer.rst
+5
-0
paddle/gserver/layers/KmaxSeqScoreLayer.cpp
paddle/gserver/layers/KmaxSeqScoreLayer.cpp
+117
-0
paddle/gserver/tests/CMakeLists.txt
paddle/gserver/tests/CMakeLists.txt
+10
-0
paddle/gserver/tests/test_KmaxSeqScore.cpp
paddle/gserver/tests/test_KmaxSeqScore.cpp
+160
-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
+43
-1
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_kmax_seq_socre_layer.protostr
...tests/configs/protostr/test_kmax_seq_socre_layer.protostr
+66
-0
python/paddle/trainer_config_helpers/tests/configs/test_kmax_seq_socre_layer.py
...config_helpers/tests/configs/test_kmax_seq_socre_layer.py
+11
-0
未找到文件。
doc/api/v2/config/layer.rst
浏览文件 @
382de964
...
...
@@ -257,6 +257,11 @@ seq_concat
.. autoclass:: paddle.v2.layer.seq_concat
:noindex:
kmax_sequence_score
-------------------
.. autoclass:: paddle.v2.layer.kmax_sequence_score
:noindex:
sub_nested_seq
--------------
.. autoclass:: paddle.v2.layer.sub_nested_seq
...
...
paddle/gserver/layers/KmaxSeqScoreLayer.cpp
0 → 100644
浏览文件 @
382de964
/* 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
{
class
KmaxSeqScoreLayer
:
public
Layer
{
private:
MatrixPtr
scores_
;
size_t
beamSize_
;
void
kmaxScorePerSeq
(
const
real
*
score
,
real
*
sortedRes
,
const
ICpuGpuVectorPtr
seqStartPos
);
public:
explicit
KmaxSeqScoreLayer
(
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
(
kmax_seq_score
,
KmaxSeqScoreLayer
);
bool
KmaxSeqScoreLayer
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
bool
ret
=
Layer
::
init
(
layerMap
,
parameterMap
);
CHECK_EQ
(
1U
,
inputLayers_
.
size
());
beamSize_
=
config_
.
beam_size
();
CHECK_GE
(
beamSize_
,
1U
);
setNeedSequenceInfo
(
false
);
setNeedGradient
(
false
);
return
ret
;
}
void
KmaxSeqScoreLayer
::
kmaxScorePerSeq
(
const
real
*
scores
,
real
*
sortedIds
,
const
ICpuGpuVectorPtr
seqStartPos
)
{
int
*
starts
=
seqStartPos
->
getMutableData
(
false
);
std
::
vector
<
real
>
indices
;
for
(
size_t
i
=
0
;
i
<
seqStartPos
->
getSize
()
-
1
;
++
i
)
{
int
seqLen
=
starts
[
i
+
1
]
-
starts
[
i
];
int
k
=
std
::
min
(
static_cast
<
int
>
(
beamSize_
),
seqLen
);
indices
.
resize
(
seqLen
,
0
);
std
::
iota
(
begin
(
indices
),
end
(
indices
),
0.
);
std
::
vector
<
real
>
tmpScore
(
scores
+
starts
[
i
],
scores
+
starts
[
i
+
1
]);
std
::
partial_sort
(
begin
(
indices
),
begin
(
indices
)
+
k
,
end
(
indices
),
[
&
](
size_t
a
,
size_t
b
)
{
return
tmpScore
[
a
]
>
tmpScore
[
b
];
});
memcpy
(
sortedIds
+
(
i
*
beamSize_
),
indices
.
data
(),
k
*
sizeof
(
real
));
}
}
void
KmaxSeqScoreLayer
::
forward
(
PassType
passType
)
{
Layer
::
forward
(
passType
);
const
Argument
&
input
=
getInput
(
0
);
const
MatrixPtr
inputScore
=
getInputValue
(
0
);
CHECK
(
input
.
hasSeq
()
||
input
.
hasSubseq
())
<<
"input of "
<<
getName
()
<<
" must be a sequence or a nested sequence."
;
CHECK_EQ
(
input
.
value
->
getWidth
(),
1UL
)
<<
"input of "
<<
getName
()
<<
" is score over a sequence or a nested sequence, so its width "
<<
" must be 1."
;
if
(
useGpu_
)
{
// this Layer runs only in CPU, if the model is runing on GPU,
// then copy the input to this layer from GPU to CPU.
Matrix
::
resizeOrCreate
(
scores_
,
inputScore
->
getHeight
(),
1
,
false
/* trans */
,
false
/* useGpu */
);
scores_
->
copyFrom
(
*
inputScore
);
}
else
{
scores_
=
inputScore
;
}
Matrix
::
resizeOrCreate
(
output_
.
value
,
input
.
hasSubseq
()
?
input
.
getNumSubSequences
()
:
input
.
getNumSequences
(),
beamSize_
,
false
,
false
);
output_
.
value
->
one
();
output_
.
value
->
mulScalar
(
-
1.
);
kmaxScorePerSeq
(
scores_
->
getData
(),
output_
.
value
->
getData
(),
input
.
hasSubseq
()
?
input
.
subSequenceStartPositions
:
input
.
sequenceStartPositions
);
}
void
KmaxSeqScoreLayer
::
backward
(
const
UpdateCallback
&
callback
)
{}
}
// namespace paddle
paddle/gserver/tests/CMakeLists.txt
浏览文件 @
382de964
...
...
@@ -66,6 +66,16 @@ add_unittest_without_exec(test_BatchNorm
add_test
(
NAME test_BatchNorm
COMMAND test_BatchNorm
)
################# test_KmaxSeqScore #######################
add_unittest_without_exec
(
test_KmaxSeqScore
test_KmaxSeqScore.cpp
LayerGradUtil.cpp
)
add_test
(
NAME test_KmaxSeqScore
COMMAND test_KmaxSeqScore
)
################## test_Evaluator #######################
add_unittest
(
test_Evaluator
test_Evaluator.cpp
)
...
...
paddle/gserver/tests/test_KmaxSeqScore.cpp
0 → 100644
浏览文件 @
382de964
/* 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 <gtest/gtest.h>
#include <algorithm>
#include <string>
#include <vector>
#include "ModelConfig.pb.h"
#include "paddle/gserver/layers/DataLayer.h"
#include "paddle/trainer/Trainer.h"
#include "paddle/utils/GlobalConstants.h"
#include "LayerGradUtil.h"
#include "paddle/testing/TestUtil.h"
using
namespace
paddle
;
// NOLINT
using
namespace
std
;
// NOLINT
DECLARE_bool
(
use_gpu
);
DECLARE_int32
(
gpu_id
);
DECLARE_bool
(
thread_local_rand_use_global_seed
);
vector
<
int
>
randSampling
(
int
range
,
int
n
)
{
CHECK_GE
(
range
,
n
);
vector
<
int
>
num
(
range
);
iota
(
begin
(
num
),
end
(
num
),
0
);
if
(
range
==
n
)
return
num
;
random_shuffle
(
begin
(
num
),
end
(
num
));
num
.
resize
(
n
);
return
num
;
}
void
genRandomSeqInfo
(
vector
<
int
>&
seqStartPosition
,
vector
<
int
>&
subSeqStartPosition
)
{
const
int
maxSeqNum
=
100
;
// generate random start position information
int
seqNum
=
1
+
(
rand
()
%
maxSeqNum
);
seqStartPosition
.
resize
(
seqNum
+
1
,
0
);
subSeqStartPosition
.
resize
(
1
,
0
);
for
(
int
i
=
0
;
i
<
seqNum
;
++
i
)
{
int
subSeqLen
=
1
+
(
rand
()
%
maxSeqNum
);
for
(
int
j
=
0
;
j
<
subSeqLen
;
++
j
)
subSeqStartPosition
.
push_back
(
subSeqStartPosition
.
back
()
+
subSeqLen
);
seqStartPosition
[
i
+
1
]
=
subSeqStartPosition
.
back
();
}
}
void
genRandomGroundTruth
(
real
*
values
,
vector
<
vector
<
int
>>&
groundTruth
,
vector
<
int
>&
startPos
,
size_t
beamSize
)
{
groundTruth
.
resize
(
startPos
.
size
()
-
1
,
vector
<
int
>
(
beamSize
,
-
1
));
for
(
size_t
i
=
0
;
i
<
startPos
.
size
()
-
1
;
++
i
)
{
int
seqLen
=
startPos
[
i
+
1
]
-
startPos
[
i
];
vector
<
int
>
pos
=
randSampling
(
seqLen
,
min
(
static_cast
<
int
>
(
beamSize
),
seqLen
));
for
(
size_t
j
=
0
;
j
<
pos
.
size
();
++
j
)
{
groundTruth
[
i
][
j
]
=
pos
[
j
];
values
[
startPos
[
i
]
+
pos
[
j
]]
=
1.
;
}
}
}
void
checkLayerOut
(
vector
<
vector
<
int
>>
groundTruth
,
real
*
layerOut
,
size_t
beamSize
)
{
for
(
size_t
i
=
0
;
i
<
groundTruth
.
size
();
++
i
)
{
int
begPos
=
i
*
beamSize
;
vector
<
real
>
tmp
(
layerOut
+
begPos
,
layerOut
+
begPos
+
beamSize
);
sort
(
begin
(
tmp
),
end
(
tmp
));
sort
(
begin
(
groundTruth
[
i
]),
end
(
groundTruth
[
i
]));
for
(
size_t
j
=
0
;
j
<
beamSize
;
++
j
)
CHECK_EQ
(
tmp
[
j
],
groundTruth
[
i
][
j
]);
}
}
TEST
(
Layer
,
kmaxSeqScoreLayer
)
{
const
size_t
maxBeamSize
=
100
;
int
beamSize
=
1
+
(
rand
()
%
maxBeamSize
);
vector
<
int
>
seqStartPosition
;
vector
<
int
>
subSeqStartPosition
;
genRandomSeqInfo
(
seqStartPosition
,
subSeqStartPosition
);
MatrixPtr
inValue
=
Matrix
::
create
(
subSeqStartPosition
.
back
(),
1
,
false
,
false
);
for
(
auto
hasSubseq
:
{
false
,
true
})
{
vector
<
vector
<
int
>>
groundTruth
;
inValue
->
randomizeUniform
();
genRandomGroundTruth
(
inValue
->
getData
(),
groundTruth
,
hasSubseq
?
subSeqStartPosition
:
seqStartPosition
,
beamSize
);
for
(
auto
useGpu
:
{
false
,
true
})
{
TestConfig
config
;
config
.
layerConfig
.
set_type
(
"kmax_seq_score"
);
config
.
layerConfig
.
set_beam_size
(
beamSize
);
if
(
hasSubseq
)
{
config
.
inputDefs
.
push_back
({
INPUT_SELF_DEFINE_DATA
,
"scores"
,
inValue
,
seqStartPosition
,
subSeqStartPosition
});
}
else
{
config
.
inputDefs
.
push_back
(
{
INPUT_SELF_DEFINE_DATA
,
"scores"
,
inValue
,
seqStartPosition
});
}
config
.
layerConfig
.
add_inputs
();
// data layer initialize
std
::
vector
<
DataLayerPtr
>
dataLayers
;
LayerMap
layerMap
;
vector
<
Argument
>
datas
;
initDataLayer
(
config
,
&
dataLayers
,
&
datas
,
&
layerMap
,
"kmax_seq_score"
,
100
/* actually this parameter is unused in self-defined input*/
,
false
,
useGpu
);
// test layer initialize
std
::
vector
<
ParameterPtr
>
parameters
;
LayerPtr
kmaxSeqScoreLayer
;
FLAGS_use_gpu
=
useGpu
;
initTestLayer
(
config
,
&
layerMap
,
&
parameters
,
&
kmaxSeqScoreLayer
);
kmaxSeqScoreLayer
->
forward
(
PASS_TRAIN
);
const
MatrixPtr
outValue
=
kmaxSeqScoreLayer
->
getOutputValue
();
CHECK_EQ
(
outValue
->
getHeight
(),
hasSubseq
?
subSeqStartPosition
.
size
()
-
1
:
seqStartPosition
.
size
()
-
1
);
CHECK_EQ
(
outValue
->
getWidth
(),
beamSize
);
checkLayerOut
(
groundTruth
,
outValue
->
getData
(),
beamSize
);
}
}
}
int
main
(
int
argc
,
char
**
argv
)
{
testing
::
InitGoogleTest
(
&
argc
,
argv
);
initMain
(
argc
,
argv
);
FLAGS_thread_local_rand_use_global_seed
=
true
;
srand
((
size_t
)(
time
(
NULL
)));
return
RUN_ALL_TESTS
();
}
python/paddle/trainer/config_parser.py
浏览文件 @
382de964
...
...
@@ -3248,6 +3248,16 @@ class CTCLayer(LayerBase):
config_assert
(
len
(
self
.
inputs
)
==
2
,
'CTCLayer must have 2 inputs'
)
@
config_layer
(
'kmax_seq_score'
)
class
KmaxSeqScoreLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
,
beam_size
,
**
xargs
):
super
(
KmaxSeqScoreLayer
,
self
).
__init__
(
name
,
'kmax_seq_score'
,
0
,
inputs
=
inputs
,
**
xargs
)
config_assert
(
len
(
self
.
inputs
)
==
1
,
'KmaxSeqScoreLayer has only one input.'
)
self
.
config
.
beam_size
=
beam_size
@
config_layer
(
'warp_ctc'
)
class
WarpCTCLayer
(
LayerBase
):
def
__init__
(
self
,
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
382de964
...
...
@@ -132,6 +132,7 @@ __all__ = [
'sub_nested_seq_layer'
,
'clip_layer'
,
'slice_projection'
,
'kmax_sequence_score_layer'
,
]
...
...
@@ -228,6 +229,8 @@ class LayerType(object):
SUB_NESTED_SEQ
=
'sub_nested_seq'
CLIP_LAYER
=
'clip'
KMAX_SEQ_SCORE
=
'kmax_seq_score'
@
staticmethod
def
is_layer_type
(
type_name
):
"""
...
...
@@ -6158,7 +6161,8 @@ def clip_layer(input, min, max, name=None):
:type min: double
:param max: The upper threshold for clipping.
:type max: double
:return: LayerOutput
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer
(
name
=
name
,
...
...
@@ -6168,3 +6172,41 @@ def clip_layer(input, min, max, name=None):
max
=
max
)
return
LayerOutput
(
name
,
LayerType
.
CLIP_LAYER
,
parents
=
[
input
],
size
=
input
.
size
)
@
wrap_name_default
()
@
layer_support
()
def
kmax_sequence_score_layer
(
input
,
name
=
None
,
beam_size
=
1
):
"""
This layer accepts one input which are scores over a sequence or a nested
sequence, and returns indices of beam_size sequences with highest scores.
.. code-block:: python
kmax_indices = kmax_sequence_score_layer(input=input_layer, beam_size)
:param name: The Layer Name.
:type name: basestring
:param input: The input layer. It stores scores over a sequence or a nested
sequence and its size must be 1.
:type input: LayerOutput.
:param beam_size: squence indices with top beam_size scores are returned.
:type beam_size: double
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert
isinstance
(
input
,
LayerOutput
),
(
"kmax_sequence_score_layer "
"accepts only one input."
)
assert
input
.
size
==
1
,
(
"input of kmax_sequence_score_layer is a score"
"over a sequence or a nested sequence, so its width must be 1."
)
Layer
(
name
=
name
,
type
=
LayerType
.
KMAX_SEQ_SCORE
,
inputs
=
[
input
.
name
],
beam_size
=
beam_size
)
return
LayerOutput
(
name
,
LayerType
.
KMAX_SEQ_SCORE
,
parents
=
[
input
],
size
=
input
.
size
)
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
浏览文件 @
382de964
...
...
@@ -8,6 +8,6 @@ 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_clip_layer test_row_l2_norm_layer
test_seq_select_layers
)
test_
kmax_seq_socre_layer test_
seq_select_layers
)
export
whole_configs
=(
test_split_datasource
)
python/paddle/trainer_config_helpers/tests/configs/protostr/test_kmax_seq_socre_layer.protostr
0 → 100644
浏览文件 @
382de964
type: "nn"
layers {
name: "input"
type: "data"
size: 300
active_type: ""
}
layers {
name: "data"
type: "data"
size: 128
active_type: ""
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 1
active_type: "exponential"
inputs {
input_layer_name: "data"
input_parameter_name: "___fc_layer_0__.w0"
}
bias_parameter_name: "___fc_layer_0__.wbias"
}
layers {
name: "__kmax_sequence_score_layer_0__"
type: "kmax_seq_score"
active_type: ""
inputs {
input_layer_name: "__fc_layer_0__"
}
beam_size: 5
}
parameters {
name: "___fc_layer_0__.w0"
size: 128
initial_mean: 0.0
initial_std: 0.0883883476483
dims: 128
dims: 1
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___fc_layer_0__.wbias"
size: 1
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 1
initial_strategy: 0
initial_smart: false
}
input_layer_names: "data"
output_layer_names: "__kmax_sequence_score_layer_0__"
sub_models {
name: "root"
layer_names: "input"
layer_names: "data"
layer_names: "__fc_layer_0__"
layer_names: "__kmax_sequence_score_layer_0__"
input_layer_names: "data"
output_layer_names: "__kmax_sequence_score_layer_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/test_kmax_seq_socre_layer.py
0 → 100644
浏览文件 @
382de964
#!/usr/bin/env python
#coding=utf-8
from
paddle.trainer_config_helpers
import
*
data
=
data_layer
(
name
=
'input'
,
size
=
300
)
data
=
data_layer
(
name
=
"data"
,
size
=
128
)
scores
=
fc_layer
(
input
=
data
,
size
=
1
,
act
=
ExpActivation
())
kmax_seq_id
=
kmax_sequence_score_layer
(
input
=
scores
,
beam_size
=
5
)
outputs
(
kmax_seq_id
)
编辑
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