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体验新版 GitCode,发现更多精彩内容 >>
提交
df5a95dc
编写于
4月 13, 2017
作者:
C
Cao Ying
提交者:
GitHub
4月 13, 2017
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操作
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差异文件
Merge pull request #1701 from luotao1/stride
stride pooling for seqlastin and seqfirstin.
上级
892cc82d
e6366e34
变更
17
隐藏空白更改
内联
并排
Showing
17 changed file
with
263 addition
and
29 deletion
+263
-29
paddle/gserver/layers/SequenceLastInstanceLayer.cpp
paddle/gserver/layers/SequenceLastInstanceLayer.cpp
+14
-10
paddle/gserver/layers/SequencePoolLayer.cpp
paddle/gserver/layers/SequencePoolLayer.cpp
+10
-2
paddle/gserver/layers/SequencePoolLayer.h
paddle/gserver/layers/SequencePoolLayer.h
+9
-0
paddle/gserver/tests/test_LayerGrad.cpp
paddle/gserver/tests/test_LayerGrad.cpp
+26
-12
paddle/parameter/Argument.cpp
paddle/parameter/Argument.cpp
+43
-0
paddle/parameter/Argument.h
paddle/parameter/Argument.h
+9
-0
paddle/parameter/tests/CMakeLists.txt
paddle/parameter/tests/CMakeLists.txt
+1
-0
paddle/parameter/tests/test_argument.cpp
paddle/parameter/tests/test_argument.cpp
+57
-0
proto/ModelConfig.proto
proto/ModelConfig.proto
+5
-0
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+13
-5
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+24
-0
python/paddle/trainer_config_helpers/tests/configs/last_first_seq.py
...le/trainer_config_helpers/tests/configs/last_first_seq.py
+3
-0
python/paddle/trainer_config_helpers/tests/configs/protostr/last_first_seq.protostr
...ig_helpers/tests/configs/protostr/last_first_seq.protostr
+33
-0
python/paddle/trainer_config_helpers/tests/configs/protostr/shared_gru.protostr
...config_helpers/tests/configs/protostr/shared_gru.protostr
+2
-0
python/paddle/trainer_config_helpers/tests/configs/protostr/shared_lstm.protostr
...onfig_helpers/tests/configs/protostr/shared_lstm.protostr
+2
-0
python/paddle/trainer_config_helpers/tests/configs/protostr/simple_rnn_layers.protostr
...helpers/tests/configs/protostr/simple_rnn_layers.protostr
+6
-0
python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr
...ig_helpers/tests/configs/protostr/test_rnn_group.protostr
+6
-0
未找到文件。
paddle/gserver/layers/SequenceLastInstanceLayer.cpp
浏览文件 @
df5a95dc
...
@@ -25,6 +25,11 @@ namespace paddle {
...
@@ -25,6 +25,11 @@ namespace paddle {
* Input: a sequence
* Input: a sequence
* If SequenceLevel = kNonseq:
* If SequenceLevel = kNonseq:
* Output: a sequence containing only the last instance of the input sequence
* Output: a sequence containing only the last instance of the input sequence
* If stride_ > 0:
* Output: a shorten sequence. The operation of getting last instance of a
* sequence is independently performed on every slice of the input
* sequence, which is obtained by sliding a window with the window
* size set to stride_.
* If SequenceLevel = kSeq:
* If SequenceLevel = kSeq:
* Check input sequence must has sub-sequence
* Check input sequence must has sub-sequence
* Output: a sequence containing only the last instance of each sub-sequence
* Output: a sequence containing only the last instance of each sub-sequence
...
@@ -37,6 +42,7 @@ class SequenceLastInstanceLayer : public SequencePoolLayer {
...
@@ -37,6 +42,7 @@ class SequenceLastInstanceLayer : public SequencePoolLayer {
protected:
protected:
MatrixPtr
tmpSrc_
;
MatrixPtr
tmpSrc_
;
MatrixPtr
tmpDest_
;
MatrixPtr
tmpDest_
;
std
::
vector
<
int
>
instanceIds_
;
public:
public:
explicit
SequenceLastInstanceLayer
(
const
LayerConfig
&
config
)
explicit
SequenceLastInstanceLayer
(
const
LayerConfig
&
config
)
...
@@ -54,6 +60,7 @@ REGISTER_LAYER(seqlastins, SequenceLastInstanceLayer);
...
@@ -54,6 +60,7 @@ REGISTER_LAYER(seqlastins, SequenceLastInstanceLayer);
bool
SequenceLastInstanceLayer
::
init
(
const
LayerMap
&
layerMap
,
bool
SequenceLastInstanceLayer
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
const
ParameterMap
&
parameterMap
)
{
SequencePoolLayer
::
init
(
layerMap
,
parameterMap
);
SequencePoolLayer
::
init
(
layerMap
,
parameterMap
);
reversed_
=
config_
.
select_first
();
tmpSrc_
=
tmpSrc_
=
Matrix
::
create
(
nullptr
,
/* height= */
1
,
1
,
/* trans= */
false
,
useGpu_
);
Matrix
::
create
(
nullptr
,
/* height= */
1
,
1
,
/* trans= */
false
,
useGpu_
);
...
@@ -66,7 +73,8 @@ bool SequenceLastInstanceLayer::init(const LayerMap& layerMap,
...
@@ -66,7 +73,8 @@ bool SequenceLastInstanceLayer::init(const LayerMap& layerMap,
void
SequenceLastInstanceLayer
::
forward
(
PassType
passType
)
{
void
SequenceLastInstanceLayer
::
forward
(
PassType
passType
)
{
SequencePoolLayer
::
forward
(
passType
);
SequencePoolLayer
::
forward
(
passType
);
const
int
*
starts
=
startPositions_
->
getData
(
false
);
auto
starts
=
(
stride_
>
0
)
?
stridePositions_
->
getData
()
:
startPositions_
->
getData
(
false
);
MatrixPtr
inputValue
=
getInputValue
(
0
);
MatrixPtr
inputValue
=
getInputValue
(
0
);
MatrixPtr
outputValue
=
getOutputValue
();
MatrixPtr
outputValue
=
getOutputValue
();
...
@@ -74,9 +82,10 @@ void SequenceLastInstanceLayer::forward(PassType passType) {
...
@@ -74,9 +82,10 @@ void SequenceLastInstanceLayer::forward(PassType passType) {
AsyncGpuBlock
asyncGpuBlock
;
AsyncGpuBlock
asyncGpuBlock
;
REGISTER_TIMER_INFO
(
"SequenceLastInstanceLayerForward"
,
getName
().
c_str
());
REGISTER_TIMER_INFO
(
"SequenceLastInstanceLayerForward"
,
getName
().
c_str
());
instanceIds_
.
clear
();
for
(
size_t
seqId
=
0
;
seqId
<
newBatchSize_
;
++
seqId
)
{
for
(
size_t
seqId
=
0
;
seqId
<
newBatchSize_
;
++
seqId
)
{
int
insId
=
int
insId
=
reversed_
?
starts
[
seqId
]
:
starts
[
seqId
+
1
]
-
1
;
config_
.
select_first
()
?
starts
[
seqId
]
:
starts
[
seqId
+
1
]
-
1
;
instanceIds_
.
push_back
(
insId
)
;
outputValue
->
subMatrix
(
seqId
,
1
,
tmpDest_
)
outputValue
->
subMatrix
(
seqId
,
1
,
tmpDest_
)
->
assign
(
*
(
inputValue
->
subMatrix
(
insId
,
1
,
tmpSrc_
)));
->
assign
(
*
(
inputValue
->
subMatrix
(
insId
,
1
,
tmpSrc_
)));
...
@@ -96,18 +105,13 @@ void SequenceLastInstanceLayer::backward(const UpdateCallback& callback) {
...
@@ -96,18 +105,13 @@ void SequenceLastInstanceLayer::backward(const UpdateCallback& callback) {
MatrixPtr
inputGrad
=
getInputGrad
(
0
);
MatrixPtr
inputGrad
=
getInputGrad
(
0
);
MatrixPtr
outputGrad
=
getOutputGrad
();
MatrixPtr
outputGrad
=
getOutputGrad
();
const
int
*
starts
=
startPositions_
->
getData
(
false
);
size_t
numSequences
=
startPositions_
->
getSize
()
-
1
;
if
(
inputGrad
)
{
if
(
inputGrad
)
{
AsyncGpuBlock
asyncGpuBlock
;
AsyncGpuBlock
asyncGpuBlock
;
REGISTER_TIMER_INFO
(
"SequenceLastInstanceLayerBackward"
,
getName
().
c_str
());
REGISTER_TIMER_INFO
(
"SequenceLastInstanceLayerBackward"
,
getName
().
c_str
());
for
(
size_t
seqId
=
0
;
seqId
<
numSequences
;
++
seqId
)
{
for
(
size_t
seqId
=
0
;
seqId
<
newBatchSize_
;
++
seqId
)
{
int
insId
=
inputGrad
->
subMatrix
(
instanceIds_
[
seqId
],
1
,
tmpDest_
)
config_
.
select_first
()
?
starts
[
seqId
]
:
starts
[
seqId
+
1
]
-
1
;
inputGrad
->
subMatrix
(
insId
,
1
,
tmpDest_
)
->
add
(
*
(
outputGrad
->
subMatrix
(
seqId
,
1
,
tmpSrc_
)));
->
add
(
*
(
outputGrad
->
subMatrix
(
seqId
,
1
,
tmpSrc_
)));
}
}
}
}
...
...
paddle/gserver/layers/SequencePoolLayer.cpp
浏览文件 @
df5a95dc
...
@@ -37,6 +37,7 @@ bool SequencePoolLayer::init(const LayerMap& layerMap,
...
@@ -37,6 +37,7 @@ bool SequencePoolLayer::init(const LayerMap& layerMap,
}
else
{
}
else
{
LOG
(
FATAL
)
<<
"Unknown trans_type: "
<<
config_
.
trans_type
();
LOG
(
FATAL
)
<<
"Unknown trans_type: "
<<
config_
.
trans_type
();
}
}
stride_
=
config_
.
seq_pool_stride
();
setNeedSequenceInfo
(
false
);
setNeedSequenceInfo
(
false
);
return
true
;
return
true
;
}
}
...
@@ -55,8 +56,6 @@ void SequencePoolLayer::forward(PassType passType) {
...
@@ -55,8 +56,6 @@ void SequencePoolLayer::forward(PassType passType) {
CHECK_EQ
(
starts
->
getData
()[
newBatchSize_
],
input
.
getBatchSize
());
CHECK_EQ
(
starts
->
getData
()[
newBatchSize_
],
input
.
getBatchSize
());
CHECK_EQ
(
newBatchSize_
,
starts
->
getSize
()
-
1
);
CHECK_EQ
(
newBatchSize_
,
starts
->
getSize
()
-
1
);
resetOutput
(
newBatchSize_
,
dim
);
/* If type_ = kNonSeq, both seq has or not has sub-seq degrade to a non-seq,
/* If type_ = kNonSeq, both seq has or not has sub-seq degrade to a non-seq,
* thus, in this case, output_ has no sequenceStartPositions.
* thus, in this case, output_ has no sequenceStartPositions.
* If type_ = kSeq, seq has sub-seq degrades to a seq, thus, only in this
* If type_ = kSeq, seq has sub-seq degrades to a seq, thus, only in this
...
@@ -67,6 +66,15 @@ void SequencePoolLayer::forward(PassType passType) {
...
@@ -67,6 +66,15 @@ void SequencePoolLayer::forward(PassType passType) {
<<
"when trans_type = seq, input must hasSubseq"
;
<<
"when trans_type = seq, input must hasSubseq"
;
output_
.
degradeSequence
(
input
);
output_
.
degradeSequence
(
input
);
}
}
if
(
stride_
>
0
)
{
CHECK_EQ
(
input
.
hasSubseq
(),
0UL
)
<<
"sequence stride pooling is invalid for hasSubseq now"
;
output_
.
poolSequenceWithStride
(
input
,
stride_
,
&
stridePositions_
,
reversed_
);
newBatchSize_
=
stridePositions_
->
getSize
()
-
1
;
}
resetOutput
(
newBatchSize_
,
dim
);
}
}
void
SequencePoolLayer
::
backward
(
const
UpdateCallback
&
callback
)
{
void
SequencePoolLayer
::
backward
(
const
UpdateCallback
&
callback
)
{
...
...
paddle/gserver/layers/SequencePoolLayer.h
浏览文件 @
df5a95dc
...
@@ -26,6 +26,10 @@ namespace paddle {
...
@@ -26,6 +26,10 @@ namespace paddle {
* Output: output size is the number of input sequences (NOT input instances)
* Output: output size is the number of input sequences (NOT input instances)
* output[i] = seqlastin/average/max_{for each instance in this
* output[i] = seqlastin/average/max_{for each instance in this
* sequence}{input[i]}
* sequence}{input[i]}
* If stride_ > 0:
* Check input sequence must not have sub-sequence
* Output: a shorten sequence, pooling is performed upon a small local
* area
* If SequenceLevel = kSeq:
* If SequenceLevel = kSeq:
* Check input sequence must has sub-sequence
* Check input sequence must has sub-sequence
* Output: output size is the number of input sub-sequences
* Output: output size is the number of input sub-sequences
...
@@ -42,6 +46,11 @@ protected:
...
@@ -42,6 +46,11 @@ protected:
enum
SequenceLevel
{
kNonSeq
=
0
,
kSeq
=
1
};
enum
SequenceLevel
{
kNonSeq
=
0
,
kSeq
=
1
};
size_t
newBatchSize_
;
size_t
newBatchSize_
;
ICpuGpuVectorPtr
startPositions_
;
ICpuGpuVectorPtr
startPositions_
;
int
stride_
;
// Store the start position of each window.
IVectorPtr
stridePositions_
;
// Whether the input sequence is reversed or not.
bool
reversed_
=
false
;
public:
public:
explicit
SequencePoolLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
{}
explicit
SequencePoolLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
{}
...
...
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @
df5a95dc
...
@@ -804,10 +804,14 @@ TEST(Layer, ExpandLayer) {
...
@@ -804,10 +804,14 @@ TEST(Layer, ExpandLayer) {
testExpandLayer
(
"seq"
,
true
);
// seq expand to hasSubseq
testExpandLayer
(
"seq"
,
true
);
// seq expand to hasSubseq
}
}
void
testDegradeLayer
(
bool
hasSubseq
,
string
layer_type
,
string
trans_type
)
{
void
testDegradeLayer
(
bool
hasSubseq
,
string
layer_type
,
string
trans_type
,
int
stride
)
{
TestConfig
config
;
TestConfig
config
;
config
.
layerConfig
.
set_type
(
layer_type
);
config
.
layerConfig
.
set_type
(
layer_type
);
config
.
layerConfig
.
set_size
(
10
);
config
.
layerConfig
.
set_size
(
10
);
config
.
layerConfig
.
set_seq_pool_stride
(
stride
);
config
.
biasSize
=
0
;
config
.
biasSize
=
0
;
config
.
inputDefs
.
push_back
(
config
.
inputDefs
.
push_back
(
...
@@ -827,36 +831,46 @@ void testDegradeLayer(bool hasSubseq, string layer_type, string trans_type) {
...
@@ -827,36 +831,46 @@ void testDegradeLayer(bool hasSubseq, string layer_type, string trans_type) {
if
(
layer_type
==
"average"
)
{
if
(
layer_type
==
"average"
)
{
for
(
auto
strategy
:
{
"average"
,
"sum"
,
"squarerootn"
})
{
for
(
auto
strategy
:
{
"average"
,
"sum"
,
"squarerootn"
})
{
LOG
(
INFO
)
<<
" hasSubseq="
<<
hasSubseq
<<
" trans_type="
<<
trans_type
LOG
(
INFO
)
<<
" hasSubseq="
<<
hasSubseq
<<
" trans_type="
<<
trans_type
<<
" average_strategy="
<<
strategy
;
<<
" average_strategy="
<<
strategy
<<
" seq_pool_stride="
<<
stride
;
config
.
layerConfig
.
set_average_strategy
(
strategy
);
config
.
layerConfig
.
set_average_strategy
(
strategy
);
testDegradeLayerGrad
(
config
,
layer_type
);
testDegradeLayerGrad
(
config
,
layer_type
);
}
}
}
else
{
}
else
{
LOG
(
INFO
)
<<
" hasSubseq="
<<
hasSubseq
<<
" trans_type="
<<
trans_type
;
LOG
(
INFO
)
<<
" hasSubseq="
<<
hasSubseq
<<
" trans_type="
<<
trans_type
<<
" seq_pool_stride="
<<
stride
;
testDegradeLayerGrad
(
config
,
layer_type
);
testDegradeLayerGrad
(
config
,
layer_type
);
}
}
}
}
TEST
(
Layer
,
MaxLayer
)
{
TEST
(
Layer
,
MaxLayer
)
{
testDegradeLayer
(
false
,
"max"
,
"non-seq"
);
// seq max to non-seq
testDegradeLayer
(
false
,
"max"
,
"non-seq"
,
-
1
);
// seq max to non-seq
testDegradeLayer
(
true
,
"max"
,
"non-seq"
);
// hasSubseq max to non-seq
testDegradeLayer
(
true
,
"max"
,
"non-seq"
,
-
1
);
// hasSubseq max to non-seq
testDegradeLayer
(
true
,
"max"
,
"seq"
);
// hasSubseq max to seq
testDegradeLayer
(
true
,
"max"
,
"seq"
,
-
1
);
// hasSubseq max to seq
}
}
TEST
(
Layer
,
SequenceLastInstanceLayer
)
{
TEST
(
Layer
,
SequenceLastInstanceLayer
)
{
testDegradeLayer
(
false
,
testDegradeLayer
(
false
,
"seqlastins"
,
"seqlastins"
,
"non-seq"
);
// seq seqlastins to non-seq
"non-seq"
,
-
1
);
// seq seqlastins to non-seq
testDegradeLayer
(
false
,
"seqlastins"
,
"non-seq"
,
5
);
// seq seqlastins to a shorten seq, stride window = 5
testDegradeLayer
(
true
,
testDegradeLayer
(
true
,
"seqlastins"
,
"seqlastins"
,
"non-seq"
);
// hasSubseq seqlastins to non-seq
"non-seq"
,
testDegradeLayer
(
true
,
"seqlastins"
,
"seq"
);
// hasSubseq seqlastins to seq
-
1
);
// hasSubseq seqlastins to non-seq
testDegradeLayer
(
true
,
"seqlastins"
,
"seq"
,
-
1
);
// hasSubseq seqlastins to seq
}
}
TEST
(
Layer
,
AverageLayer
)
{
TEST
(
Layer
,
AverageLayer
)
{
testDegradeLayer
(
false
,
"average"
,
"non-seq"
);
// seq average to non-seq
testDegradeLayer
(
false
,
"average"
,
"non-seq"
,
-
1
);
// seq average to non-seq
testDegradeLayer
(
true
,
"average"
,
"non-seq"
);
// hasSubseq average to non-seq
testDegradeLayer
(
testDegradeLayer
(
true
,
"average"
,
"seq"
);
// hasSubseq average to seq
true
,
"average"
,
"non-seq"
,
-
1
);
// hasSubseq average to non-seq
testDegradeLayer
(
true
,
"average"
,
"seq"
,
-
1
);
// hasSubseq average to seq
}
}
TEST
(
Layer
,
SequenceConcatLayer
)
{
TEST
(
Layer
,
SequenceConcatLayer
)
{
...
...
paddle/parameter/Argument.cpp
浏览文件 @
df5a95dc
...
@@ -559,6 +559,49 @@ void Argument::degradeSequence(const Argument& input) {
...
@@ -559,6 +559,49 @@ void Argument::degradeSequence(const Argument& input) {
tgtBuf
[
numSequences
]
=
numSubSequences
;
tgtBuf
[
numSequences
]
=
numSubSequences
;
}
}
void
Argument
::
poolSequenceWithStride
(
const
Argument
&
input
,
size_t
stride
,
IVectorPtr
*
stridePostions
,
bool
reversed
)
{
// If input.sequenceStartPositions = [0, 9, 14, 17, 30] and stride = 5,
// then sequenceStartPositions = [0, 2, 3, 4, 7].
// If reversed = false, stridePostions = [0, 5, 9, 14, 17, 22, 27, 30];
// else reversed = true, stridePostions = [0, 4, 9, 14, 17, 20, 25, 30]
CHECK
(
input
.
sequenceStartPositions
);
CHECK_EQ
(
input
.
hasSubseq
(),
0UL
);
CHECK_GT
(
stride
,
0
)
<<
"stride must larger than 0"
;
size_t
numSequences
=
input
.
getNumSequences
();
ICpuGpuVector
::
resizeOrCreate
(
sequenceStartPositions
,
numSequences
+
1
,
false
);
const
int
*
starts
=
input
.
sequenceStartPositions
->
getData
(
false
);
int
*
tgtBuf
=
sequenceStartPositions
->
getMutableData
(
false
);
// first index of target sequence and stride positions are both 0
tgtBuf
[
0
]
=
0
;
std
::
vector
<
int
>
stridePos
;
for
(
size_t
seqId
=
0
;
seqId
<
numSequences
;
++
seqId
)
{
size_t
seqLength
=
starts
[
seqId
+
1
]
-
starts
[
seqId
];
stridePos
.
emplace_back
(
starts
[
seqId
]);
if
(
seqLength
==
0
)
{
// empty sequence
tgtBuf
[
seqId
+
1
]
=
tgtBuf
[
seqId
];
}
else
{
int
size
=
ceil
((
float
)
seqLength
/
stride
);
tgtBuf
[
seqId
+
1
]
=
tgtBuf
[
seqId
]
+
size
;
for
(
int
i
=
0
;
i
<
size
-
1
;
++
i
)
{
int
cur
=
reversed
?
starts
[
seqId
+
1
]
-
(
size
-
1
-
i
)
*
stride
:
stridePos
.
back
()
+
stride
;
stridePos
.
emplace_back
(
cur
);
}
}
}
stridePos
.
emplace_back
(
starts
[
numSequences
]);
int
size
=
stridePos
.
size
();
CHECK_EQ
(
size
-
1
,
tgtBuf
[
numSequences
]);
IVector
::
resizeOrCreate
(
*
stridePostions
,
size
,
false
);
(
*
stridePostions
)
->
copyFrom
(
stridePos
.
data
(),
size
);
}
void
Argument
::
getValueString
(
void
Argument
::
getValueString
(
std
::
unordered_map
<
std
::
string
,
std
::
string
>*
out
)
const
{
std
::
unordered_map
<
std
::
string
,
std
::
string
>*
out
)
const
{
if
(
value
)
{
if
(
value
)
{
...
...
paddle/parameter/Argument.h
浏览文件 @
df5a95dc
...
@@ -291,6 +291,15 @@ struct Argument {
...
@@ -291,6 +291,15 @@ struct Argument {
*/
*/
void
degradeSequence
(
const
Argument
&
input
);
void
degradeSequence
(
const
Argument
&
input
);
/*
After pooling with stride n (n is smaller than sequence length),
a long sequence will be shorten.
This function is invalid for sequence having sub-sequence.
*/
void
poolSequenceWithStride
(
const
Argument
&
input
,
size_t
stride
,
IVectorPtr
*
stridePositions
,
bool
reversed
=
false
);
/**
/**
* @brief getValueString will return the argument's output in string. There
* @brief getValueString will return the argument's output in string. There
* are several kinds of output. The keys of output dictionary are 'value',
* are several kinds of output. The keys of output dictionary are 'value',
...
...
paddle/parameter/tests/CMakeLists.txt
浏览文件 @
df5a95dc
add_simple_unittest
(
test_common
)
add_simple_unittest
(
test_common
)
add_simple_unittest
(
test_argument
)
paddle/parameter/tests/test_argument.cpp
0 → 100644
浏览文件 @
df5a95dc
/* 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 <paddle/parameter/Argument.h>
using
namespace
paddle
;
// NOLINT
TEST
(
Argument
,
poolSequenceWithStride
)
{
Argument
input
,
output
;
ICpuGpuVector
::
resizeOrCreate
(
input
.
sequenceStartPositions
,
5
,
false
);
int
*
inStart
=
input
.
sequenceStartPositions
->
getMutableData
(
false
);
inStart
[
0
]
=
0
;
inStart
[
1
]
=
9
;
inStart
[
2
]
=
14
;
inStart
[
3
]
=
17
;
inStart
[
4
]
=
30
;
int
strideResult
[]
=
{
0
,
5
,
9
,
14
,
17
,
22
,
27
,
30
};
int
strideResultReversed
[]
=
{
0
,
4
,
9
,
14
,
17
,
20
,
25
,
30
};
for
(
auto
reversed
:
{
false
,
true
})
{
IVectorPtr
stridePositions
;
output
.
poolSequenceWithStride
(
input
,
5
/* stride */
,
&
stridePositions
,
reversed
);
const
int
*
outStart
=
output
.
sequenceStartPositions
->
getData
(
false
);
CHECK_EQ
(
outStart
[
0
],
0
);
CHECK_EQ
(
outStart
[
1
],
2
);
CHECK_EQ
(
outStart
[
2
],
3
);
CHECK_EQ
(
outStart
[
3
],
4
);
CHECK_EQ
(
outStart
[
4
],
7
);
CHECK_EQ
(
stridePositions
->
getSize
(),
8
);
auto
result
=
reversed
?
strideResultReversed
:
strideResult
;
for
(
int
i
=
0
;
i
<
8
;
i
++
)
{
CHECK_EQ
(
stridePositions
->
getData
()[
i
],
result
[
i
]);
}
}
}
int
main
(
int
argc
,
char
**
argv
)
{
testing
::
InitGoogleTest
(
&
argc
,
argv
);
initMain
(
argc
,
argv
);
return
RUN_ALL_TESTS
();
}
proto/ModelConfig.proto
浏览文件 @
df5a95dc
...
@@ -441,6 +441,11 @@ message LayerConfig {
...
@@ -441,6 +441,11 @@ message LayerConfig {
// blank label used in ctc loss
// blank label used in ctc loss
optional
uint32
blank
=
52
[
default
=
0
];
optional
uint32
blank
=
52
[
default
=
0
];
// stride parameter for seqlastins layer, AverageLayer, MaxLayer, which
// controls the scope of pooling operation. can be set > 0.
// leave empty or set to -1 to disable this stride pooling.
optional
int32
seq_pool_stride
=
53
[
default
=
-
1
];
}
}
message
EvaluatorConfig
{
message
EvaluatorConfig
{
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
df5a95dc
...
@@ -2485,6 +2485,7 @@ class SequenceLastInstanceLayer(LayerBase):
...
@@ -2485,6 +2485,7 @@ class SequenceLastInstanceLayer(LayerBase):
active_type
=
'linear'
,
active_type
=
'linear'
,
trans_type
=
'non-seq'
,
trans_type
=
'non-seq'
,
bias
=
False
,
bias
=
False
,
stride
=-
1
,
**
xargs
):
**
xargs
):
super
(
SequenceLastInstanceLayer
,
self
).
__init__
(
super
(
SequenceLastInstanceLayer
,
self
).
__init__
(
name
,
name
,
...
@@ -2495,10 +2496,11 @@ class SequenceLastInstanceLayer(LayerBase):
...
@@ -2495,10 +2496,11 @@ class SequenceLastInstanceLayer(LayerBase):
**
xargs
)
**
xargs
)
config_assert
(
config_assert
(
len
(
inputs
)
==
1
,
'SequenceLastInstanceLayer must have 1 input'
)
len
(
inputs
)
==
1
,
'SequenceLastInstanceLayer must have 1 input'
)
if
trans_type
==
'seq'
:
config_assert
(
stride
==
-
1
,
'subseq does not support stride window'
)
self
.
config
.
trans_type
=
trans_type
self
.
config
.
trans_type
=
trans_type
for
input_index
in
xrange
(
len
(
self
.
inputs
)):
self
.
config
.
seq_pool_stride
=
stride
input_layer
=
self
.
get_input_layer
(
input_index
)
self
.
set_layer_size
(
self
.
get_input_layer
(
0
).
size
)
self
.
set_layer_size
(
input_layer
.
size
)
self
.
create_bias_parameter
(
bias
,
self
.
config
.
size
)
self
.
create_bias_parameter
(
bias
,
self
.
config
.
size
)
...
@@ -2510,10 +2512,16 @@ class SequenceFirstInstanceLayer(SequenceLastInstanceLayer):
...
@@ -2510,10 +2512,16 @@ class SequenceFirstInstanceLayer(SequenceLastInstanceLayer):
active_type
=
'linear'
,
active_type
=
'linear'
,
trans_type
=
'non-seq'
,
trans_type
=
'non-seq'
,
bias
=
False
,
bias
=
False
,
stride
=-
1
,
**
xargs
):
**
xargs
):
super
(
SequenceFirstInstanceLayer
,
self
).
__init__
(
super
(
SequenceFirstInstanceLayer
,
self
).
__init__
(
name
,
inputs
=
inputs
,
active_type
=
active_type
,
bias
=
bias
,
**
xargs
)
name
,
self
.
config
.
trans_type
=
trans_type
inputs
=
inputs
,
active_type
=
active_type
,
trans_type
=
trans_type
,
bias
=
bias
,
stride
=
stride
,
**
xargs
)
self
.
config
.
select_first
=
True
self
.
config
.
select_first
=
True
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
df5a95dc
...
@@ -1342,10 +1342,16 @@ def grumemory(input,
...
@@ -1342,10 +1342,16 @@ def grumemory(input,
def
last_seq
(
input
,
def
last_seq
(
input
,
name
=
None
,
name
=
None
,
agg_level
=
AggregateLevel
.
EACH_TIMESTEP
,
agg_level
=
AggregateLevel
.
EACH_TIMESTEP
,
stride
=-
1
,
layer_attr
=
None
):
layer_attr
=
None
):
"""
"""
Get Last Timestamp Activation of a sequence.
Get Last Timestamp Activation of a sequence.
If stride > 0, this layer slides a window whose size is determined by stride,
and return the last value of the window as the output. Thus, a long sequence
will be shorten. Note that for sequence with sub-sequence, the default value
of stride is -1.
The simple usage is:
The simple usage is:
.. code-block:: python
.. code-block:: python
...
@@ -1357,6 +1363,8 @@ def last_seq(input,
...
@@ -1357,6 +1363,8 @@ def last_seq(input,
:type name: basestring
:type name: basestring
:param input: Input layer name.
:param input: Input layer name.
:type input: LayerOutput
:type input: LayerOutput
:param stride: window size.
:type stride: Int
:param layer_attr: extra layer attributes.
:param layer_attr: extra layer attributes.
:type layer_attr: ExtraLayerAttribute.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:return: LayerOutput object.
...
@@ -1368,11 +1376,15 @@ def last_seq(input,
...
@@ -1368,11 +1376,15 @@ def last_seq(input,
" series information at all. Maybe you want to use"
" series information at all. Maybe you want to use"
" first_seq instead."
)
" first_seq instead."
)
if
agg_level
==
AggregateLevel
.
EACH_SEQUENCE
:
assert
stride
==
-
1
Layer
(
Layer
(
name
=
name
,
name
=
name
,
type
=
LayerType
.
SEQUENCE_LAST_INSTANCE
,
type
=
LayerType
.
SEQUENCE_LAST_INSTANCE
,
inputs
=
[
input
.
name
],
inputs
=
[
input
.
name
],
trans_type
=
agg_level
,
trans_type
=
agg_level
,
stride
=
stride
,
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
))
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
))
return
LayerOutput
(
return
LayerOutput
(
name
,
name
,
...
@@ -1386,10 +1398,16 @@ def last_seq(input,
...
@@ -1386,10 +1398,16 @@ def last_seq(input,
def
first_seq
(
input
,
def
first_seq
(
input
,
name
=
None
,
name
=
None
,
agg_level
=
AggregateLevel
.
EACH_TIMESTEP
,
agg_level
=
AggregateLevel
.
EACH_TIMESTEP
,
stride
=-
1
,
layer_attr
=
None
):
layer_attr
=
None
):
"""
"""
Get First Timestamp Activation of a sequence.
Get First Timestamp Activation of a sequence.
If stride > 0, this layer slides a window whose size is determined by stride,
and return the first value of the window as the output. Thus, a long sequence
will be shorten. Note that for sequence with sub-sequence, the default value
of stride is -1.
The simple usage is:
The simple usage is:
.. code-block:: python
.. code-block:: python
...
@@ -1401,6 +1419,8 @@ def first_seq(input,
...
@@ -1401,6 +1419,8 @@ def first_seq(input,
:type name: basestring
:type name: basestring
:param input: Input layer name.
:param input: Input layer name.
:type input: LayerOutput
:type input: LayerOutput
:param stride: window size.
:type stride: Int
:param layer_attr: extra layer attributes.
:param layer_attr: extra layer attributes.
:type layer_attr: ExtraLayerAttribute.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:return: LayerOutput object.
...
@@ -1413,11 +1433,15 @@ def first_seq(input,
...
@@ -1413,11 +1433,15 @@ def first_seq(input,
' time series information at all. Maybe you want to use'
' time series information at all. Maybe you want to use'
' last_seq instead.'
)
' last_seq instead.'
)
if
agg_level
==
AggregateLevel
.
EACH_SEQUENCE
:
assert
stride
==
-
1
Layer
(
Layer
(
name
=
name
,
name
=
name
,
type
=
LayerType
.
SEQUENCE_FIRST_INSTANCE
,
type
=
LayerType
.
SEQUENCE_FIRST_INSTANCE
,
inputs
=
[
input
.
name
],
inputs
=
[
input
.
name
],
trans_type
=
agg_level
,
trans_type
=
agg_level
,
stride
=
stride
,
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
))
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
))
return
LayerOutput
(
return
LayerOutput
(
name
,
name
,
...
...
python/paddle/trainer_config_helpers/tests/configs/last_first_seq.py
浏览文件 @
df5a95dc
...
@@ -14,4 +14,7 @@ for op in seq_op:
...
@@ -14,4 +14,7 @@ for op in seq_op:
for
al
in
agg_level
:
for
al
in
agg_level
:
opts
.
append
(
op
(
input
=
din
,
agg_level
=
al
))
opts
.
append
(
op
(
input
=
din
,
agg_level
=
al
))
for
op
in
seq_op
:
opts
.
append
(
op
(
input
=
din
,
agg_level
=
AggregateLevel
.
EACH_TIMESTEP
,
stride
=
5
))
outputs
(
opts
)
outputs
(
opts
)
python/paddle/trainer_config_helpers/tests/configs/protostr/last_first_seq.protostr
浏览文件 @
df5a95dc
...
@@ -15,6 +15,7 @@ layers {
...
@@ -15,6 +15,7 @@ layers {
}
}
select_first: true
select_first: true
trans_type: "seq"
trans_type: "seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__first_seq_1__"
name: "__first_seq_1__"
...
@@ -26,6 +27,7 @@ layers {
...
@@ -26,6 +27,7 @@ layers {
}
}
select_first: true
select_first: true
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__last_seq_0__"
name: "__last_seq_0__"
...
@@ -36,6 +38,7 @@ layers {
...
@@ -36,6 +38,7 @@ layers {
input_layer_name: "data"
input_layer_name: "data"
}
}
trans_type: "seq"
trans_type: "seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__last_seq_1__"
name: "__last_seq_1__"
...
@@ -46,12 +49,38 @@ layers {
...
@@ -46,12 +49,38 @@ layers {
input_layer_name: "data"
input_layer_name: "data"
}
}
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__first_seq_2__"
type: "seqlastins"
size: 30
active_type: "linear"
inputs {
input_layer_name: "data"
}
select_first: true
trans_type: "non-seq"
seq_pool_stride: 5
}
layers {
name: "__last_seq_2__"
type: "seqlastins"
size: 30
active_type: "linear"
inputs {
input_layer_name: "data"
}
trans_type: "non-seq"
seq_pool_stride: 5
}
}
input_layer_names: "data"
input_layer_names: "data"
output_layer_names: "__first_seq_0__"
output_layer_names: "__first_seq_0__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__last_seq_0__"
output_layer_names: "__last_seq_0__"
output_layer_names: "__last_seq_1__"
output_layer_names: "__last_seq_1__"
output_layer_names: "__first_seq_2__"
output_layer_names: "__last_seq_2__"
sub_models {
sub_models {
name: "root"
name: "root"
layer_names: "data"
layer_names: "data"
...
@@ -59,11 +88,15 @@ sub_models {
...
@@ -59,11 +88,15 @@ sub_models {
layer_names: "__first_seq_1__"
layer_names: "__first_seq_1__"
layer_names: "__last_seq_0__"
layer_names: "__last_seq_0__"
layer_names: "__last_seq_1__"
layer_names: "__last_seq_1__"
layer_names: "__first_seq_2__"
layer_names: "__last_seq_2__"
input_layer_names: "data"
input_layer_names: "data"
output_layer_names: "__first_seq_0__"
output_layer_names: "__first_seq_0__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__last_seq_0__"
output_layer_names: "__last_seq_0__"
output_layer_names: "__last_seq_1__"
output_layer_names: "__last_seq_1__"
output_layer_names: "__first_seq_2__"
output_layer_names: "__last_seq_2__"
is_recurrent_layer_group: false
is_recurrent_layer_group: false
}
}
python/paddle/trainer_config_helpers/tests/configs/protostr/shared_gru.protostr
浏览文件 @
df5a95dc
...
@@ -128,6 +128,7 @@ layers {
...
@@ -128,6 +128,7 @@ layers {
input_layer_name: "__simple_gru_0__"
input_layer_name: "__simple_gru_0__"
}
}
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__last_seq_1__"
name: "__last_seq_1__"
...
@@ -138,6 +139,7 @@ layers {
...
@@ -138,6 +139,7 @@ layers {
input_layer_name: "__simple_gru_1__"
input_layer_name: "__simple_gru_1__"
}
}
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__fc_layer_0__"
name: "__fc_layer_0__"
...
...
python/paddle/trainer_config_helpers/tests/configs/protostr/shared_lstm.protostr
浏览文件 @
df5a95dc
...
@@ -210,6 +210,7 @@ layers {
...
@@ -210,6 +210,7 @@ layers {
input_layer_name: "__lstm_group_0__"
input_layer_name: "__lstm_group_0__"
}
}
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__last_seq_1__"
name: "__last_seq_1__"
...
@@ -220,6 +221,7 @@ layers {
...
@@ -220,6 +221,7 @@ layers {
input_layer_name: "__lstm_group_1__"
input_layer_name: "__lstm_group_1__"
}
}
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__fc_layer_0__"
name: "__fc_layer_0__"
...
...
python/paddle/trainer_config_helpers/tests/configs/protostr/simple_rnn_layers.protostr
浏览文件 @
df5a95dc
...
@@ -143,6 +143,7 @@ layers {
...
@@ -143,6 +143,7 @@ layers {
input_layer_name: "__recurrent_layer_0__"
input_layer_name: "__recurrent_layer_0__"
}
}
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__first_seq_0__"
name: "__first_seq_0__"
...
@@ -154,6 +155,7 @@ layers {
...
@@ -154,6 +155,7 @@ layers {
}
}
select_first: true
select_first: true
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__last_seq_1__"
name: "__last_seq_1__"
...
@@ -164,6 +166,7 @@ layers {
...
@@ -164,6 +166,7 @@ layers {
input_layer_name: "__lstmemory_0__"
input_layer_name: "__lstmemory_0__"
}
}
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__first_seq_1__"
name: "__first_seq_1__"
...
@@ -175,6 +178,7 @@ layers {
...
@@ -175,6 +178,7 @@ layers {
}
}
select_first: true
select_first: true
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__last_seq_2__"
name: "__last_seq_2__"
...
@@ -185,6 +189,7 @@ layers {
...
@@ -185,6 +189,7 @@ layers {
input_layer_name: "__gru_0__"
input_layer_name: "__gru_0__"
}
}
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__first_seq_2__"
name: "__first_seq_2__"
...
@@ -196,6 +201,7 @@ layers {
...
@@ -196,6 +201,7 @@ layers {
}
}
select_first: true
select_first: true
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
parameters {
parameters {
name: "___fc_layer_0__.w0"
name: "___fc_layer_0__.w0"
...
...
python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr
浏览文件 @
df5a95dc
...
@@ -96,6 +96,7 @@ layers {
...
@@ -96,6 +96,7 @@ layers {
input_layer_name: "rnn_forward"
input_layer_name: "rnn_forward"
}
}
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__recurrent_group_1__"
name: "__recurrent_group_1__"
...
@@ -145,6 +146,7 @@ layers {
...
@@ -145,6 +146,7 @@ layers {
}
}
select_first: true
select_first: true
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__recurrent_group_2__"
name: "__recurrent_group_2__"
...
@@ -193,6 +195,7 @@ layers {
...
@@ -193,6 +195,7 @@ layers {
input_layer_name: "rnn_subseq_forward"
input_layer_name: "rnn_subseq_forward"
}
}
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__lstm_group_0___recurrent_group"
name: "__lstm_group_0___recurrent_group"
...
@@ -282,6 +285,7 @@ layers {
...
@@ -282,6 +285,7 @@ layers {
input_layer_name: "__lstm_group_0__"
input_layer_name: "__lstm_group_0__"
}
}
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__gru_group_0___recurrent_group"
name: "__gru_group_0___recurrent_group"
...
@@ -330,6 +334,7 @@ layers {
...
@@ -330,6 +334,7 @@ layers {
input_layer_name: "__gru_group_0__"
input_layer_name: "__gru_group_0__"
}
}
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
layers {
layers {
name: "__recurrent_group_3__"
name: "__recurrent_group_3__"
...
@@ -378,6 +383,7 @@ layers {
...
@@ -378,6 +383,7 @@ layers {
input_layer_name: "__fc_layer_0__"
input_layer_name: "__fc_layer_0__"
}
}
trans_type: "non-seq"
trans_type: "non-seq"
seq_pool_stride: -1
}
}
parameters {
parameters {
name: "___mixed_0__.w0"
name: "___mixed_0__.w0"
...
...
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