Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
PaddleDetection
提交
05a97ab5
P
PaddleDetection
项目概览
PaddlePaddle
/
PaddleDetection
大约 1 年 前同步成功
通知
695
Star
11112
Fork
2696
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
184
列表
看板
标记
里程碑
合并请求
40
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
PaddleDetection
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
184
Issue
184
列表
看板
标记
里程碑
合并请求
40
合并请求
40
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
提交
05a97ab5
编写于
9月 14, 2016
作者:
X
xuwei06
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Further fix the memory for Hierarchical RNN
Sequences should be sorted according to the number of subsequences they have.
上级
a9d327bd
变更
12
隐藏空白更改
内联
并排
Showing
12 changed file
with
206 addition
and
128 deletion
+206
-128
paddle/cuda/src/hl_cuda_matrix.cu
paddle/cuda/src/hl_cuda_matrix.cu
+1
-0
paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp
paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp
+59
-56
paddle/gserver/gradientmachines/RecurrentGradientMachine.h
paddle/gserver/gradientmachines/RecurrentGradientMachine.h
+1
-5
paddle/gserver/layers/PrintLayer.cpp
paddle/gserver/layers/PrintLayer.cpp
+58
-0
paddle/gserver/tests/sequence_nest_rnn.conf
paddle/gserver/tests/sequence_nest_rnn.conf
+4
-3
paddle/gserver/tests/sequence_rnn.conf
paddle/gserver/tests/sequence_rnn.conf
+3
-3
paddle/gserver/tests/test_RecurrentGradientMachine.cpp
paddle/gserver/tests/test_RecurrentGradientMachine.cpp
+8
-5
paddle/parameter/Argument.cpp
paddle/parameter/Argument.cpp
+24
-41
paddle/parameter/Argument.h
paddle/parameter/Argument.h
+22
-14
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+8
-0
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+16
-1
python/paddle/trainer_config_helpers/tests/layers_test_config.py
...paddle/trainer_config_helpers/tests/layers_test_config.py
+2
-0
未找到文件。
paddle/cuda/src/hl_cuda_matrix.cu
浏览文件 @
05a97ab5
...
...
@@ -19,6 +19,7 @@ limitations under the License. */
#include "hl_matrix_apply.cuh"
#include "hl_sequence.h"
#include "paddle/utils/Logging.h"
#include "hl_device_functions.cuh"
DEFINE_MATRIX_UNARY_OP
(
Zero
,
a
=
0
);
DEFINE_MATRIX_TERNARY_PARAMETER_OP
(
_add
,
TWO_PARAMETER
,
c
=
p1
*
a
+
p2
*
b
);
...
...
paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp
浏览文件 @
05a97ab5
...
...
@@ -434,23 +434,25 @@ void RecurrentGradientMachine::forward(const std::vector<Argument>& inArgs,
}
}
seqLengthAndStart_
.
clear
();
info_
.
clear
();
info_
.
resize
(
inFrameLines_
.
size
());
seqLengthAndStart_
.
resize
(
inFrameLines_
.
size
());
seqInfos_
.
clear
();
seqInfos_
.
resize
(
inFrameLines_
.
size
());
{
AsyncGpuBlock
asyncGpuBlock
;
// if shareInlinkInfo, only calculate info of the first inlink
// else, calculate info for each inlink
if
(
shareInlinkInfo
)
{
input
.
getSeqLengthAndStart
(
&
seqLengthAndStart_
[
0
],
&
maxSequenceLength_
);
input
.
getSeqInfo
(
&
seqInfos_
[
0
]);
maxSequenceLength_
=
seqInfos_
[
0
][
0
].
topLevelLength
;
createInFrameInfo
(
0
,
input
,
passType
);
}
else
{
for
(
size_t
i
=
0
;
i
<
inFrameLines_
.
size
();
i
++
)
{
const
Argument
&
input1
=
inFrameLines_
[
i
].
inLayer
->
getOutput
();
input1
.
getSeq
LengthAndStart
(
&
seqLengthAndStart_
[
i
],
&
maxSequenceLength_
)
;
input1
.
getSeq
Info
(
&
seqInfos_
[
i
]);
maxSequenceLength_
=
seqInfos_
[
i
][
0
].
topLevelLength
;
createInFrameInfo
(
i
,
input1
,
passType
);
}
}
...
...
@@ -614,7 +616,7 @@ void RecurrentGradientMachine::removeBeamSearchStatisticsCallbacks() {
* for all realLayer of inFrameLines one time.
*/
void
RecurrentGradientMachine
::
createInFrameInfo
(
int
inlink
s_i
d
,
void
RecurrentGradientMachine
::
createInFrameInfo
(
int
inlink
I
d
,
const
Argument
&
input
,
PassType
passType
)
{
bool
hasSubseq
=
input
.
hasSubseq
();
...
...
@@ -622,66 +624,67 @@ void RecurrentGradientMachine::createInFrameInfo(int inlinks_id,
size_t
numSequences
=
input
.
getNumSequences
();
std
::
vector
<
int
>
allIds
;
auto
&
seqInfo
=
seqInfos_
[
inlinkId
];
numSeqs_
.
clear
();
Info
*
inlink_info
=
&
info_
[
inlinks_id
];
inlink_info
->
idIndex
.
clear
();
inlink_info
->
idIndex
.
push_back
(
0
);
// first idIndex = 0
Info
*
inlinkInfo
=
&
info_
[
inlinkId
];
inlinkInfo
->
idIndex
.
clear
();
inlinkInfo
->
idIndex
.
push_back
(
0
);
// first idIndex = 0
std
::
vector
<
int
>
sequenceStartPositions
;
const
int
*
subSequenceStartPositions
=
nullptr
;
if
(
hasSubseq
)
{
// for sequenceScatterAgentLayer
// numSubSequences : all sentences within all samples(batch)
size_t
numSubSequences
=
input
.
getNumSubSequences
();
std
::
vector
<
int
>
sequenceStartPositions
;
inlink_info
->
seqStartPosIndex
.
clear
();
inlink_info
->
seqStartPosIndex
.
push_back
(
0
);
// first seqStartPosIndex = 0
// maxSequenceLength_: max number of sentences(subseq) in allsamples
for
(
int
i
=
0
;
i
<
maxSequenceLength_
;
++
i
)
{
subSequenceStartPositions
=
input
.
subSequenceStartPositions
->
getData
(
false
);
inlinkInfo
->
seqStartPosIndex
.
clear
();
inlinkInfo
->
seqStartPosIndex
.
push_back
(
0
);
// first seqStartPosIndex = 0
}
// maxSequenceLength_: max topLevelLength in allsamples
for
(
int
i
=
0
;
i
<
maxSequenceLength_
;
++
i
)
{
if
(
hasSubseq
)
{
sequenceStartPositions
.
push_back
(
0
);
// first element = 0
int
numSeqs
=
0
;
for
(
size_t
j
=
0
;
j
<
numSubSequences
;
++
j
)
{
// for each sentence
// seqLengthAndStart_[inlinks_id][j]:
// a 4-tuple including <subseqlen, subseqstart, seqid, subseqid>
if
(
std
::
get
<
3
>
(
seqLengthAndStart_
[
inlinks_id
][
j
])
==
i
)
{
++
numSeqs
;
// subseqstart: the cpuSubSequenceStartPositions of this subseq
int
subSeqStart
=
std
::
get
<
1
>
(
seqLengthAndStart_
[
inlinks_id
][
j
]);
int
subSeqLength
=
std
::
get
<
0
>
(
seqLengthAndStart_
[
inlinks_id
][
j
]);
for
(
int
k
=
subSeqStart
;
k
<
subSeqStart
+
subSeqLength
;
++
k
)
{
allIds
.
push_back
(
k
);
}
sequenceStartPositions
.
push_back
(
sequenceStartPositions
.
back
()
+
subSeqLength
);
}
}
inlink_info
->
idIndex
.
push_back
(
allIds
.
size
());
inlink_info
->
seqStartPosIndex
.
push_back
(
sequenceStartPositions
.
size
());
numSeqs_
.
push_back
(
numSeqs
);
}
// inFrameLine create sequenceStartPositions one time
CHECK_EQ
(
sequenceStartPositions
.
size
(),
maxSequenceLength_
+
numSubSequences
)
;
CHECK_EQ
(
inlink_info
->
seqStartPosIndex
.
size
(),
static_cast
<
size_t
>
(
maxSequenceLength_
+
1
))
;
createSeqPos
(
sequenceStartPositions
,
&
inlink_info
->
sequenceStartPositions
);
}
else
{
// for scatterAgentLayer
for
(
int
i
=
0
;
i
<
maxSequenceLength_
;
++
i
)
{
int
numSeqs
=
0
;
for
(
size_t
j
=
0
;
j
<
numSequences
;
++
j
)
{
int
seqLength
=
std
::
get
<
0
>
(
seqLengthAndStart_
[
inlinks_id
][
j
])
;
if
(
i
>=
seqLength
)
{
break
;
int
numSeqs
=
0
;
for
(
size_t
j
=
0
;
j
<
numSequences
;
++
j
)
{
int
seqLength
=
seqInfo
[
j
].
topLevelLength
;
if
(
i
>=
seqLength
)
{
break
;
}
++
numSeqs
;
if
(
hasSubseq
)
{
int
subSeqStart
=
subSequenceStartPositions
[
seqInfo
[
j
].
subSeqStart
+
i
]
;
int
subSeqEnd
=
subSequenceStartPositions
[
seqInfo
[
j
].
subSeqStart
+
i
+
1
]
;
for
(
int
k
=
subSeqStart
;
k
<
subSeqEnd
;
++
k
)
{
allIds
.
push_back
(
k
)
;
}
++
numSeqs
;
int
seqStart
=
std
::
get
<
1
>
(
seqLengthAndStart_
[
inlinks_id
][
j
]);
sequenceStartPositions
.
push_back
(
sequenceStartPositions
.
back
()
+
subSeqEnd
-
subSeqStart
);
}
else
{
int
seqStart
=
seqInfo
[
j
].
seqStart
;
allIds
.
push_back
(
reversed_
?
(
seqStart
+
seqLength
-
1
-
i
)
:
(
seqStart
+
i
));
}
inlink_info
->
idIndex
.
push_back
(
allIds
.
size
());
numSeqs_
.
push_back
(
numSeqs
);
}
inlinkInfo
->
idIndex
.
push_back
(
allIds
.
size
());
numSeqs_
.
push_back
(
numSeqs
);
if
(
hasSubseq
)
{
inlinkInfo
->
seqStartPosIndex
.
push_back
(
sequenceStartPositions
.
size
());
}
}
if
(
hasSubseq
)
{
// inFrameLine create sequenceStartPositions one time
CHECK_EQ
(
sequenceStartPositions
.
size
(),
maxSequenceLength_
+
input
.
getNumSubSequences
());
CHECK_EQ
(
inlinkInfo
->
seqStartPosIndex
.
size
(),
static_cast
<
size_t
>
(
maxSequenceLength_
+
1
));
createSeqPos
(
sequenceStartPositions
,
&
inlinkInfo
->
sequenceStartPositions
);
}
// copy and check scatterId
copyScattedId
(
allIds
,
&
inlink
_i
nfo
->
allIds
,
input
.
getBatchSize
());
CHECK_EQ
(
inlink
_i
nfo
->
idIndex
.
size
(),
copyScattedId
(
allIds
,
&
inlink
I
nfo
->
allIds
,
input
.
getBatchSize
());
CHECK_EQ
(
inlink
I
nfo
->
idIndex
.
size
(),
static_cast
<
size_t
>
(
maxSequenceLength_
+
1
));
}
...
...
@@ -701,7 +704,7 @@ void RecurrentGradientMachine::createMemoryFrameInfo(
const
int
*
starts
=
input
.
sequenceStartPositions
->
getData
(
false
);
for
(
size_t
i
=
0
;
i
<
numSequences
;
++
i
)
{
// memory info adopt info of inlinks[0]
int
seqId
=
s
td
::
get
<
2
>
(
seqLengthAndStart_
[
0
][
i
])
;
int
seqId
=
s
eqInfos_
[
0
][
i
].
seqId
;
for
(
int
k
=
starts
[
seqId
];
k
<
starts
[
seqId
+
1
];
++
k
)
{
allIds
.
push_back
(
k
);
}
...
...
@@ -713,7 +716,7 @@ void RecurrentGradientMachine::createMemoryFrameInfo(
}
else
{
// for scatterAgentLayer
for
(
size_t
i
=
0
;
i
<
numSequences
;
++
i
)
{
allIds
.
push_back
(
s
td
::
get
<
2
>
(
seqLengthAndStart_
[
0
][
i
])
);
allIds
.
push_back
(
s
eqInfos_
[
0
][
i
].
seqId
);
}
}
// copy and check scatterId
...
...
paddle/gserver/gradientmachines/RecurrentGradientMachine.h
浏览文件 @
05a97ab5
...
...
@@ -337,11 +337,7 @@ protected:
// data) or has more than i subsequences (for subsequence data)
std
::
vector
<
int
>
numSeqs_
;
// each inlinks has a "std::vector<std::tuple<int, int, int, int>>" denotes
// its sequence info:
// if hasSubSeq, tuple of (subSeqLength, subSeqStart, seqIndex, subSeqIndex)
// else, tuple of (seqLength, seqStart, seqIndex, seqIndex)
std
::
vector
<
std
::
vector
<
std
::
tuple
<
int
,
int
,
int
,
int
>>>
seqLengthAndStart_
;
std
::
vector
<
std
::
vector
<
Argument
::
SeqInfo
>>
seqInfos_
;
// the id of inlink which share info with outlinks
int
targetInfoInlinkId_
;
...
...
paddle/gserver/layers/PrintLayer.cpp
0 → 100644
浏览文件 @
05a97ab5
/* Copyright (c) 2016 Baidu, Inc. 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
PrintLayer
:
public
Layer
{
public:
explicit
PrintLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
{}
void
forward
(
PassType
passType
);
void
backward
(
const
UpdateCallback
&
callback
)
{}
};
void
PrintLayer
::
forward
(
PassType
passType
)
{
Layer
::
forward
(
passType
);
for
(
size_t
i
=
0
;
i
!=
inputLayers_
.
size
();
++
i
)
{
const
auto
&
argu
=
getInput
(
i
);
const
std
::
string
&
name
=
inputLayers_
[
i
]
->
getName
();
if
(
argu
.
value
)
{
std
::
ostringstream
os
;
argu
.
value
->
print
(
os
);
LOG
(
INFO
)
<<
"layer="
<<
name
<<
" value matrix:
\n
"
<<
os
.
str
();
}
if
(
argu
.
ids
)
{
std
::
ostringstream
os
;
argu
.
ids
->
print
(
os
,
argu
.
ids
->
getSize
());
LOG
(
INFO
)
<<
"layer="
<<
name
<<
" ids vector:
\n
"
<<
os
.
str
();
}
if
(
auto
startPos
=
argu
.
sequenceStartPositions
)
{
std
::
ostringstream
os
;
startPos
->
getVector
(
false
)
->
print
(
os
,
startPos
->
getSize
());
LOG
(
INFO
)
<<
"layer="
<<
name
<<
" sequence pos vector:
\n
"
<<
os
.
str
();
}
if
(
auto
subStartPos
=
argu
.
subSequenceStartPositions
)
{
std
::
ostringstream
os
;
subStartPos
->
getVector
(
false
)
->
print
(
os
,
subStartPos
->
getSize
());
LOG
(
INFO
)
<<
"layer="
<<
name
<<
" sub-sequence pos vector:
\n
"
<<
os
.
str
();
}
}
}
REGISTER_LAYER
(
print
,
PrintLayer
);
}
// namespace paddle
paddle/gserver/tests/sequence_nest_rnn.conf
浏览文件 @
05a97ab5
...
...
@@ -42,14 +42,16 @@ def outer_step(x):
inner_mem
=
memory
(
name
=
"inner_rnn_state"
,
size
=
hidden_dim
,
boot_layer
=
outer_mem
)
return
fc_layer
(
input
=[
y
,
inner_mem
],
out
=
fc_layer
(
input
=[
y
,
inner_mem
],
size
=
hidden_dim
,
act
=
TanhActivation
(),
bias_attr
=
True
,
name
=
"inner_rnn_state"
)
return
out
inner_rnn_output
=
recurrent_group
(
step
=
inner_step
,
name
=
"inner"
,
input
=
x
)
last
=
last_seq
(
input
=
inner_rnn_output
,
name
=
"outer_rnn_state"
)
...
...
@@ -60,11 +62,10 @@ def outer_step(x):
return
inner_rnn_output
out
=
recurrent_group
(
name
=
"outer"
,
step
=
outer_step
,
input
=
SubsequenceInput
(
emb
))
value_printer_evaluator
(
input
=
out
)
rep
=
last_seq
(
input
=
out
)
prob
=
fc_layer
(
size
=
label_dim
,
input
=
rep
,
...
...
paddle/gserver/tests/sequence_rnn.conf
浏览文件 @
05a97ab5
...
...
@@ -35,18 +35,18 @@ emb = embedding_layer(input=data, size=word_dim)
def
step
(
y
):
mem
=
memory
(
name
=
"rnn_state"
,
size
=
hidden_dim
)
return
fc_layer
(
input
=[
y
,
mem
],
out
=
fc_layer
(
input
=[
y
,
mem
],
size
=
hidden_dim
,
act
=
TanhActivation
(),
bias_attr
=
True
,
name
=
"rnn_state"
)
return
out
out
=
recurrent_group
(
name
=
"rnn"
,
step
=
step
,
input
=
emb
)
value_printer_evaluator
(
input
=
out
)
rep
=
last_seq
(
input
=
out
)
prob
=
fc_layer
(
size
=
label_dim
,
input
=
rep
,
...
...
paddle/gserver/tests/test_RecurrentGradientMachine.cpp
浏览文件 @
05a97ab5
...
...
@@ -92,7 +92,7 @@ void CalCost(const string& conf, const string& dir, real* cost,
rmDir
(
dir
.
c_str
());
}
void
test
(
const
string
&
conf1
,
const
string
&
conf2
)
{
void
test
(
const
string
&
conf1
,
const
string
&
conf2
,
double
eps
)
{
int
num_passes
=
5
;
real
*
cost1
=
new
real
[
num_passes
];
const
string
dir1
=
"gserver/tests/t1"
;
...
...
@@ -104,8 +104,9 @@ void test(const string& conf1, const string& conf2) {
for
(
int
i
=
0
;
i
<
num_passes
;
i
++
)
{
LOG
(
INFO
)
<<
"num_passes: "
<<
i
<<
", cost1="
<<
cost1
[
i
]
<<
", cost2="
<<
cost2
[
i
];
ASSERT_NEAR
(
cost1
[
i
],
cost2
[
i
],
1e-3
);
<<
", cost2="
<<
cost2
[
i
]
<<
", diff="
<<
std
::
abs
(
cost1
[
i
]
-
cost2
[
i
]);
ASSERT_NEAR
(
cost1
[
i
],
cost2
[
i
],
eps
);
}
delete
[]
cost1
;
delete
[]
cost2
;
...
...
@@ -113,12 +114,14 @@ void test(const string& conf1, const string& conf2) {
TEST
(
RecurrentGradientMachine
,
HasSubSequence
)
{
test
(
"gserver/tests/sequence_layer_group.conf"
,
"gserver/tests/sequence_nest_layer_group.conf"
);
"gserver/tests/sequence_nest_layer_group.conf"
,
1e-5
);
}
TEST
(
RecurrentGradientMachine
,
rnn
)
{
test
(
"gserver/tests/sequence_rnn.conf"
,
"gserver/tests/sequence_nest_rnn.conf"
);
"gserver/tests/sequence_nest_rnn.conf"
,
0
);
}
...
...
paddle/parameter/Argument.cpp
浏览文件 @
05a97ab5
...
...
@@ -477,51 +477,34 @@ void Argument::splitByDataId(const std::vector<Argument>& argus,
}
}
void
Argument
::
getSeqLengthAndStart
(
std
::
vector
<
std
::
tuple
<
int
,
int
,
int
,
int
>>*
seqLengthAndStart
,
int
*
maxSequenceLength
)
const
{
void
Argument
::
getSeqInfo
(
std
::
vector
<
SeqInfo
>*
seqInfo
)
const
{
const
int
*
starts
=
sequenceStartPositions
->
getData
(
false
);
if
(
hasSubseq
())
{
size_t
numSubSequences
=
getNumSubSequences
();
(
*
seqLengthAndStart
).
reserve
(
numSubSequences
);
const
int
*
subStarts
=
subSequenceStartPositions
->
getData
(
false
);
int
seqIndex
=
0
;
int
subSeqIndex
=
0
;
*
maxSequenceLength
=
0
;
for
(
size_t
i
=
0
;
i
<
numSubSequences
;
++
i
)
{
if
(
subStarts
[
i
]
==
starts
[
seqIndex
])
{
subSeqIndex
=
0
;
(
*
seqLengthAndStart
)
.
push_back
(
std
::
make_tuple
<
int
,
int
,
int
,
int
>
(
subStarts
[
i
+
1
]
-
subStarts
[
i
],
(
int
)
subStarts
[
i
],
(
int
)
seqIndex
,
(
int
)
subSeqIndex
));
++
subSeqIndex
;
++
seqIndex
;
}
else
if
(
subStarts
[
i
]
<
starts
[
seqIndex
])
{
(
*
seqLengthAndStart
)
.
push_back
(
std
::
make_tuple
<
int
,
int
,
int
,
int
>
(
subStarts
[
i
+
1
]
-
subStarts
[
i
],
(
int
)
subStarts
[
i
],
(
int
)
seqIndex
-
1
,
(
int
)
subSeqIndex
));
++
subSeqIndex
;
const
int
*
subStarts
=
hasSubseq
()
?
subSequenceStartPositions
->
getData
(
false
)
:
nullptr
;
size_t
numSequences
=
getNumSequences
();
seqInfo
->
reserve
(
numSequences
);
int
subSeqEnd
=
0
;
for
(
size_t
i
=
0
;
i
<
numSequences
;
++
i
)
{
SeqInfo
info
;
info
.
seqStart
=
starts
[
i
];
info
.
subLevelLength
=
starts
[
i
+
1
]
-
starts
[
i
];
info
.
seqId
=
i
;
if
(
hasSubseq
())
{
info
.
subSeqStart
=
subSeqEnd
;
while
(
subStarts
[
subSeqEnd
]
<
starts
[
i
+
1
])
{
++
subSeqEnd
;
}
// maxSequenceLength_ = 1 + max(subSeqIndex) in each Seq.
if
(
*
maxSequenceLength
<
std
::
get
<
3
>
((
*
seqLengthAndStart
)[
i
]))
*
maxSequenceLength
=
std
::
get
<
3
>
((
*
seqLengthAndStart
)[
i
]);
}
*
maxSequenceLength
+=
1
;
}
else
{
size_t
numSequences
=
getNumSequences
();
(
*
seqLengthAndStart
).
reserve
(
numSequences
);
for
(
size_t
i
=
0
;
i
<
numSequences
;
++
i
)
{
(
*
seqLengthAndStart
)
.
push_back
(
std
::
make_tuple
<
int
,
int
,
int
,
int
>
(
starts
[
i
+
1
]
-
starts
[
i
],
(
int
)
starts
[
i
],
(
int
)
i
,
(
int
)
i
));
info
.
topLevelLength
=
subSeqEnd
-
info
.
subSeqStart
;
}
else
{
info
.
topLevelLength
=
info
.
subLevelLength
;
info
.
subSeqStart
=
0
;
// not used
}
std
::
sort
((
*
seqLengthAndStart
).
begin
(),
(
*
seqLengthAndStart
).
end
(),
std
::
greater
<
std
::
tuple
<
int
,
int
,
int
,
int
>>
());
*
maxSequenceLength
=
std
::
get
<
0
>
((
*
seqLengthAndStart
)[
0
]);
seqInfo
->
push_back
(
info
);
}
std
::
sort
(
seqInfo
->
begin
(),
seqInfo
->
end
(),
[](
const
SeqInfo
&
a
,
const
SeqInfo
&
b
)
{
return
a
.
topLevelLength
>
b
.
topLevelLength
;
});
}
void
Argument
::
checkSubset
()
const
{
...
...
paddle/parameter/Argument.h
浏览文件 @
05a97ab5
...
...
@@ -253,21 +253,29 @@ struct Argument {
static
void
splitByDataId
(
const
std
::
vector
<
Argument
>&
argus
,
std
::
vector
<
std
::
vector
<
Argument
>>*
arguGroups
);
struct
SeqInfo
{
// Equal to sequence length for sequence data
// Equal to number of subsequences for subsequence data
int
topLevelLength
;
int
seqStart
;
int
seqId
;
// Equal to topLevelLength for sequence data
// Equal to sum of the length of subsequences for subsequence data
int
subLevelLength
;
// Only used for subsequence data, start position of this sequence
// is subSequenceStartPositions, i.e.
// subSequenceStartPositions[subSeqStart] == seqStart
int
subSeqStart
;
};
/*
Get Sequence Length, startPositions and max Length according to input
1. For sequence data:
Each tuple is (seq_length, seq_start, seq_id, seq_id)
The tuples are sorted according to seq_length or subseq_length
*maxSequenceLength is the maximal sequence length
2. For subsequence data:
Each tuple is (subseq_length, subseq_start, seq_id, subseq_id)
The tuples are not sorted. They are in the original order.
*maxSequenceLenth is the maximal number of subsequences in each sequence.
*/
void
getSeqLengthAndStart
(
std
::
vector
<
std
::
tuple
<
int
,
int
,
int
,
int
>>*
seqLengthAndStart
,
int
*
maxSequenceLength
)
const
;
Get SeqInfo for each sequence of this argument
Elements in *seqInfo are sorted by topLevelLength in descending order
*/
void
getSeqInfo
(
std
::
vector
<
SeqInfo
>*
segInfo
)
const
;
/*
Check Whether sequenceStartPositions is subset of
subSequenceStartPositions.
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
05a97ab5
...
...
@@ -1408,6 +1408,14 @@ class SelectiveFCLayer(LayerBase):
input_index
,
psize
,
dims
,
sparse
,
format
)
self
.
create_bias_parameter
(
bias
,
self
.
config
.
size
)
@
config_layer
(
'print'
)
class
PrintLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
):
super
(
PrintLayer
,
self
).
__init__
(
name
,
'print'
,
0
,
inputs
)
@
config_layer
(
'data'
)
class
DataLayer
(
LayerBase
):
def
__init__
(
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
05a97ab5
...
...
@@ -52,7 +52,7 @@ __all__ = ["full_matrix_projection", "AggregateLevel", "ExpandLevel",
'cross_entropy_with_selfnorm'
,
'cross_entropy'
,
'multi_binary_label_cross_entropy'
,
'rank_cost'
,
'lambda_cost'
,
'huber_cost'
,
'block_expand_layer'
,
'out_prod_layer'
,
'block_expand_layer'
,
'out_prod_layer'
,
'print_layer'
]
...
...
@@ -108,6 +108,8 @@ class LayerType(object):
LINEAR_COMBINATION_LAYER
=
"convex_comb"
BLOCK_EXPAND
=
"blockexpand"
PRINT_LAYER
=
"print"
CTC_LAYER
=
"ctc"
CRF_LAYER
=
"crf"
CRF_DECODING_LAYER
=
"crf_decoding"
...
...
@@ -729,6 +731,19 @@ def fc_layer(input, size, act=None, name=None,
return
LayerOutput
(
name
,
LayerType
.
FC_LAYER
,
input
,
activation
=
act
,
size
=
size
)
@
wrap_name_default
(
"print"
)
def
print_layer
(
input
,
name
=
None
):
"""
Print the output value of input layers. This layer is useful for debugging.
"""
assert
isinstance
(
input
,
list
)
Layer
(
name
=
name
,
type
=
LayerType
.
PRINT_LAYER
,
inputs
=
[
l
.
name
for
l
in
input
],
)
return
LayerOutput
(
name
,
LayerType
.
PRINT_LAYER
,
input
)
@
wrap_name_default
(
"seq_pooling"
)
@
wrap_bias_attr_default
(
has_bias
=
False
)
...
...
python/paddle/trainer_config_helpers/tests/layers_test_config.py
浏览文件 @
05a97ab5
...
...
@@ -34,6 +34,8 @@ out = fc_layer(input=[cos1, cos3, linear_comb, z],
size
=
num_classes
,
act
=
SoftmaxActivation
())
print_layer
(
input
=
[
out
])
outputs
(
classification_cost
(
out
,
data_layer
(
name
=
"label"
,
size
=
num_classes
)))
# for ctc
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录