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体验新版 GitCode,发现更多精彩内容 >>
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b571a414
编写于
4月 13, 2017
作者:
Q
qijun
浏览文件
操作
浏览文件
下载
差异文件
Merge remote-tracking branch 'baidu/develop' into feature/add_v2_api_doc
上级
1e29b124
b25c5124
变更
24
隐藏空白更改
内联
并排
Showing
24 changed file
with
370 addition
and
84 deletion
+370
-84
demo/seqToseq/api_train_v2.py
demo/seqToseq/api_train_v2.py
+46
-40
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
paddle/py_paddle/dataprovider_converter.py
paddle/py_paddle/dataprovider_converter.py
+7
-0
paddle/py_paddle/util.py
paddle/py_paddle/util.py
+5
-1
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
+25
-1
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
python/paddle/v2/data_feeder.py
python/paddle/v2/data_feeder.py
+29
-7
python/paddle/v2/dataset/wmt14.py
python/paddle/v2/dataset/wmt14.py
+7
-1
python/paddle/v2/inference.py
python/paddle/v2/inference.py
+11
-4
python/paddle/v2/trainer.py
python/paddle/v2/trainer.py
+1
-1
未找到文件。
demo/seqToseq/api_train_v2.py
浏览文件 @
b571a414
...
...
@@ -126,51 +126,57 @@ def seqToseq_net(source_dict_dim, target_dict_dim, is_generating=False):
def
main
():
paddle
.
init
(
use_gpu
=
False
,
trainer_count
=
1
)
is_generating
=
True
# source and target dict dim.
dict_size
=
30000
source_dict_dim
=
target_dict_dim
=
dict_size
# define network topology
cost
=
seqToseq_net
(
source_dict_dim
,
target_dict_dim
)
parameters
=
paddle
.
parameters
.
create
(
cost
)
# define optimize method and trainer
optimizer
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
5e-5
,
regularization
=
paddle
.
optimizer
.
L2Regularization
(
rate
=
1e-3
))
trainer
=
paddle
.
trainer
.
SGD
(
cost
=
cost
,
parameters
=
parameters
,
update_equation
=
optimizer
)
# define data reader
feeding
=
{
'source_language_word'
:
0
,
'target_language_word'
:
1
,
'target_language_next_word'
:
2
}
wmt14_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
wmt14
.
train
(
dict_size
=
dict_size
),
buf_size
=
8192
),
batch_size
=
5
)
# define event_handler callback
def
event_handler
(
event
):
if
isinstance
(
event
,
paddle
.
event
.
EndIteration
):
if
event
.
batch_id
%
10
==
0
:
print
"
\n
Pass %d, Batch %d, Cost %f, %s"
%
(
event
.
pass_id
,
event
.
batch_id
,
event
.
cost
,
event
.
metrics
)
else
:
sys
.
stdout
.
write
(
'.'
)
sys
.
stdout
.
flush
()
# start to train
trainer
.
train
(
reader
=
wmt14_reader
,
event_handler
=
event_handler
,
num_passes
=
10000
,
feeding
=
feeding
)
# train the network
if
not
is_generating
:
cost
=
seqToseq_net
(
source_dict_dim
,
target_dict_dim
)
parameters
=
paddle
.
parameters
.
create
(
cost
)
# define optimize method and trainer
optimizer
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
5e-5
,
regularization
=
paddle
.
optimizer
.
L2Regularization
(
rate
=
8e-4
))
trainer
=
paddle
.
trainer
.
SGD
(
cost
=
cost
,
parameters
=
parameters
,
update_equation
=
optimizer
)
# define data reader
wmt14_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
wmt14
.
train
(
dict_size
),
buf_size
=
8192
),
batch_size
=
5
)
# define event_handler callback
def
event_handler
(
event
):
if
isinstance
(
event
,
paddle
.
event
.
EndIteration
):
if
event
.
batch_id
%
10
==
0
:
print
"
\n
Pass %d, Batch %d, Cost %f, %s"
%
(
event
.
pass_id
,
event
.
batch_id
,
event
.
cost
,
event
.
metrics
)
else
:
sys
.
stdout
.
write
(
'.'
)
sys
.
stdout
.
flush
()
# start to train
trainer
.
train
(
reader
=
wmt14_reader
,
event_handler
=
event_handler
,
num_passes
=
2
)
# generate a english sequence to french
else
:
gen_creator
=
paddle
.
dataset
.
wmt14
.
test
(
dict_size
)
gen_data
=
[]
for
item
in
gen_creator
():
gen_data
.
append
((
item
[
0
],
))
if
len
(
gen_data
)
==
3
:
break
beam_gen
=
seqToseq_net
(
source_dict_dim
,
target_dict_dim
,
is_generating
)
parameters
=
paddle
.
dataset
.
wmt14
.
model
()
trg_dict
=
paddle
.
dataset
.
wmt14
.
trg_dict
(
dict_size
)
if
__name__
==
'__main__'
:
...
...
paddle/gserver/layers/SequenceLastInstanceLayer.cpp
浏览文件 @
b571a414
...
...
@@ -25,6 +25,11 @@ namespace paddle {
* Input: a sequence
* If SequenceLevel = kNonseq:
* 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:
* Check input sequence must has sub-sequence
* Output: a sequence containing only the last instance of each sub-sequence
...
...
@@ -37,6 +42,7 @@ class SequenceLastInstanceLayer : public SequencePoolLayer {
protected:
MatrixPtr
tmpSrc_
;
MatrixPtr
tmpDest_
;
std
::
vector
<
int
>
instanceIds_
;
public:
explicit
SequenceLastInstanceLayer
(
const
LayerConfig
&
config
)
...
...
@@ -54,6 +60,7 @@ REGISTER_LAYER(seqlastins, SequenceLastInstanceLayer);
bool
SequenceLastInstanceLayer
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
SequencePoolLayer
::
init
(
layerMap
,
parameterMap
);
reversed_
=
config_
.
select_first
();
tmpSrc_
=
Matrix
::
create
(
nullptr
,
/* height= */
1
,
1
,
/* trans= */
false
,
useGpu_
);
...
...
@@ -66,7 +73,8 @@ bool SequenceLastInstanceLayer::init(const LayerMap& layerMap,
void
SequenceLastInstanceLayer
::
forward
(
PassType
passType
)
{
SequencePoolLayer
::
forward
(
passType
);
const
int
*
starts
=
startPositions_
->
getData
(
false
);
auto
starts
=
(
stride_
>
0
)
?
stridePositions_
->
getData
()
:
startPositions_
->
getData
(
false
);
MatrixPtr
inputValue
=
getInputValue
(
0
);
MatrixPtr
outputValue
=
getOutputValue
();
...
...
@@ -74,9 +82,10 @@ void SequenceLastInstanceLayer::forward(PassType passType) {
AsyncGpuBlock
asyncGpuBlock
;
REGISTER_TIMER_INFO
(
"SequenceLastInstanceLayerForward"
,
getName
().
c_str
());
instanceIds_
.
clear
();
for
(
size_t
seqId
=
0
;
seqId
<
newBatchSize_
;
++
seqId
)
{
int
insId
=
config_
.
select_first
()
?
starts
[
seqId
]
:
starts
[
seqId
+
1
]
-
1
;
int
insId
=
reversed_
?
starts
[
seqId
]
:
starts
[
seqId
+
1
]
-
1
;
instanceIds_
.
push_back
(
insId
)
;
outputValue
->
subMatrix
(
seqId
,
1
,
tmpDest_
)
->
assign
(
*
(
inputValue
->
subMatrix
(
insId
,
1
,
tmpSrc_
)));
...
...
@@ -96,18 +105,13 @@ void SequenceLastInstanceLayer::backward(const UpdateCallback& callback) {
MatrixPtr
inputGrad
=
getInputGrad
(
0
);
MatrixPtr
outputGrad
=
getOutputGrad
();
const
int
*
starts
=
startPositions_
->
getData
(
false
);
size_t
numSequences
=
startPositions_
->
getSize
()
-
1
;
if
(
inputGrad
)
{
AsyncGpuBlock
asyncGpuBlock
;
REGISTER_TIMER_INFO
(
"SequenceLastInstanceLayerBackward"
,
getName
().
c_str
());
for
(
size_t
seqId
=
0
;
seqId
<
numSequences
;
++
seqId
)
{
int
insId
=
config_
.
select_first
()
?
starts
[
seqId
]
:
starts
[
seqId
+
1
]
-
1
;
inputGrad
->
subMatrix
(
insId
,
1
,
tmpDest_
)
for
(
size_t
seqId
=
0
;
seqId
<
newBatchSize_
;
++
seqId
)
{
inputGrad
->
subMatrix
(
instanceIds_
[
seqId
],
1
,
tmpDest_
)
->
add
(
*
(
outputGrad
->
subMatrix
(
seqId
,
1
,
tmpSrc_
)));
}
}
...
...
paddle/gserver/layers/SequencePoolLayer.cpp
浏览文件 @
b571a414
...
...
@@ -37,6 +37,7 @@ bool SequencePoolLayer::init(const LayerMap& layerMap,
}
else
{
LOG
(
FATAL
)
<<
"Unknown trans_type: "
<<
config_
.
trans_type
();
}
stride_
=
config_
.
seq_pool_stride
();
setNeedSequenceInfo
(
false
);
return
true
;
}
...
...
@@ -55,8 +56,6 @@ void SequencePoolLayer::forward(PassType passType) {
CHECK_EQ
(
starts
->
getData
()[
newBatchSize_
],
input
.
getBatchSize
());
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,
* thus, in this case, output_ has no sequenceStartPositions.
* If type_ = kSeq, seq has sub-seq degrades to a seq, thus, only in this
...
...
@@ -67,6 +66,15 @@ void SequencePoolLayer::forward(PassType passType) {
<<
"when trans_type = seq, input must hasSubseq"
;
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
)
{
...
...
paddle/gserver/layers/SequencePoolLayer.h
浏览文件 @
b571a414
...
...
@@ -26,6 +26,10 @@ namespace paddle {
* Output: output size is the number of input sequences (NOT input instances)
* output[i] = seqlastin/average/max_{for each instance in this
* 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:
* Check input sequence must has sub-sequence
* Output: output size is the number of input sub-sequences
...
...
@@ -42,6 +46,11 @@ protected:
enum
SequenceLevel
{
kNonSeq
=
0
,
kSeq
=
1
};
size_t
newBatchSize_
;
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:
explicit
SequencePoolLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
{}
...
...
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @
b571a414
...
...
@@ -804,10 +804,14 @@ TEST(Layer, ExpandLayer) {
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
;
config
.
layerConfig
.
set_type
(
layer_type
);
config
.
layerConfig
.
set_size
(
10
);
config
.
layerConfig
.
set_seq_pool_stride
(
stride
);
config
.
biasSize
=
0
;
config
.
inputDefs
.
push_back
(
...
...
@@ -827,36 +831,46 @@ void testDegradeLayer(bool hasSubseq, string layer_type, string trans_type) {
if
(
layer_type
==
"average"
)
{
for
(
auto
strategy
:
{
"average"
,
"sum"
,
"squarerootn"
})
{
LOG
(
INFO
)
<<
" hasSubseq="
<<
hasSubseq
<<
" trans_type="
<<
trans_type
<<
" average_strategy="
<<
strategy
;
<<
" average_strategy="
<<
strategy
<<
" seq_pool_stride="
<<
stride
;
config
.
layerConfig
.
set_average_strategy
(
strategy
);
testDegradeLayerGrad
(
config
,
layer_type
);
}
}
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
);
}
}
TEST
(
Layer
,
MaxLayer
)
{
testDegradeLayer
(
false
,
"max"
,
"non-seq"
);
// seq max to non-seq
testDegradeLayer
(
true
,
"max"
,
"non-seq"
);
// hasSubseq max to non-seq
testDegradeLayer
(
true
,
"max"
,
"seq"
);
// hasSubseq max to seq
testDegradeLayer
(
false
,
"max"
,
"non-seq"
,
-
1
);
// seq max to non-seq
testDegradeLayer
(
true
,
"max"
,
"non-seq"
,
-
1
);
// hasSubseq max to non-seq
testDegradeLayer
(
true
,
"max"
,
"seq"
,
-
1
);
// hasSubseq max to seq
}
TEST
(
Layer
,
SequenceLastInstanceLayer
)
{
testDegradeLayer
(
false
,
"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
,
"seqlastins"
,
"non-seq"
);
// hasSubseq seqlastins to non-seq
testDegradeLayer
(
true
,
"seqlastins"
,
"seq"
);
// hasSubseq seqlastins to seq
"non-seq"
,
-
1
);
// hasSubseq seqlastins to non-seq
testDegradeLayer
(
true
,
"seqlastins"
,
"seq"
,
-
1
);
// hasSubseq seqlastins to seq
}
TEST
(
Layer
,
AverageLayer
)
{
testDegradeLayer
(
false
,
"average"
,
"non-seq"
);
// seq average to non-seq
testDegradeLayer
(
true
,
"average"
,
"non-seq"
);
// hasSubseq average to non-seq
testDegradeLayer
(
true
,
"average"
,
"seq"
);
// hasSubseq average to seq
testDegradeLayer
(
false
,
"average"
,
"non-seq"
,
-
1
);
// seq average to non-seq
testDegradeLayer
(
true
,
"average"
,
"non-seq"
,
-
1
);
// hasSubseq average to non-seq
testDegradeLayer
(
true
,
"average"
,
"seq"
,
-
1
);
// hasSubseq average to seq
}
TEST
(
Layer
,
SequenceConcatLayer
)
{
...
...
paddle/parameter/Argument.cpp
浏览文件 @
b571a414
...
...
@@ -559,6 +559,49 @@ void Argument::degradeSequence(const Argument& input) {
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
(
std
::
unordered_map
<
std
::
string
,
std
::
string
>*
out
)
const
{
if
(
value
)
{
...
...
paddle/parameter/Argument.h
浏览文件 @
b571a414
...
...
@@ -291,6 +291,15 @@ struct Argument {
*/
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
* are several kinds of output. The keys of output dictionary are 'value',
...
...
paddle/parameter/tests/CMakeLists.txt
浏览文件 @
b571a414
add_simple_unittest
(
test_common
)
add_simple_unittest
(
test_argument
)
paddle/parameter/tests/test_argument.cpp
0 → 100644
浏览文件 @
b571a414
/* 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
();
}
paddle/py_paddle/dataprovider_converter.py
浏览文件 @
b571a414
...
...
@@ -187,6 +187,13 @@ class SequenceScanner(IScanner):
self
.
__inner_scanner__
=
inner_scanner
self
.
__setter__
=
setter
def
pre_scan
(
self
,
dat
):
for
each
in
dat
:
self
.
__inner_scanner__
.
pre_scan
(
each
)
def
finish_pre_scan
(
self
,
argument
):
self
.
__inner_scanner__
.
finish_pre_scan
(
argument
)
def
scan
(
self
,
dat
):
self
.
__seq__
.
append
(
self
.
__seq__
[
-
1
]
+
self
.
get_size
(
dat
))
for
each
in
dat
:
...
...
paddle/py_paddle/util.py
浏览文件 @
b571a414
...
...
@@ -83,13 +83,17 @@ def __arguments_to_numpy__(i, arg):
assert
isinstance
(
arg
,
swig_paddle
.
Arguments
)
value
=
arg
.
getSlotValue
(
i
)
ids
=
arg
.
getSlotIds
(
i
)
prob
=
arg
.
getSlotIn
(
i
)
if
value
is
not
None
:
assert
isinstance
(
value
,
swig_paddle
.
Matrix
)
value
=
value
.
copyToNumpyMat
()
if
ids
is
not
None
:
assert
isinstance
(
ids
,
swig_paddle
.
IVector
)
ids
=
ids
.
copyToNumpyArray
()
return
{
"value"
:
value
,
"id"
:
ids
}
if
prob
is
not
None
:
assert
isinstance
(
prob
,
swig_paddle
.
Matrix
)
prob
=
prob
.
copyToNumpyMat
()
return
{
"value"
:
value
,
"id"
:
ids
,
"prob"
:
prob
}
def
__monkeypatch_gradient_machine__
():
...
...
proto/ModelConfig.proto
浏览文件 @
b571a414
...
...
@@ -441,6 +441,11 @@ message LayerConfig {
// blank label used in ctc loss
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
{
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
b571a414
...
...
@@ -2485,6 +2485,7 @@ class SequenceLastInstanceLayer(LayerBase):
active_type
=
'linear'
,
trans_type
=
'non-seq'
,
bias
=
False
,
stride
=-
1
,
**
xargs
):
super
(
SequenceLastInstanceLayer
,
self
).
__init__
(
name
,
...
...
@@ -2495,10 +2496,11 @@ class SequenceLastInstanceLayer(LayerBase):
**
xargs
)
config_assert
(
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
for
input_index
in
xrange
(
len
(
self
.
inputs
)):
input_layer
=
self
.
get_input_layer
(
input_index
)
self
.
set_layer_size
(
input_layer
.
size
)
self
.
config
.
seq_pool_stride
=
stride
self
.
set_layer_size
(
self
.
get_input_layer
(
0
).
size
)
self
.
create_bias_parameter
(
bias
,
self
.
config
.
size
)
...
...
@@ -2510,10 +2512,16 @@ class SequenceFirstInstanceLayer(SequenceLastInstanceLayer):
active_type
=
'linear'
,
trans_type
=
'non-seq'
,
bias
=
False
,
stride
=-
1
,
**
xargs
):
super
(
SequenceFirstInstanceLayer
,
self
).
__init__
(
name
,
inputs
=
inputs
,
active_type
=
active_type
,
bias
=
bias
,
**
xargs
)
self
.
config
.
trans_type
=
trans_type
name
,
inputs
=
inputs
,
active_type
=
active_type
,
trans_type
=
trans_type
,
bias
=
bias
,
stride
=
stride
,
**
xargs
)
self
.
config
.
select_first
=
True
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
b571a414
...
...
@@ -1342,10 +1342,16 @@ def grumemory(input,
def
last_seq
(
input
,
name
=
None
,
agg_level
=
AggregateLevel
.
EACH_TIMESTEP
,
stride
=-
1
,
layer_attr
=
None
):
"""
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:
.. code-block:: python
...
...
@@ -1357,6 +1363,8 @@ def last_seq(input,
:type name: basestring
:param input: Input layer name.
:type input: LayerOutput
:param stride: window size.
:type stride: Int
:param layer_attr: extra layer attributes.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
...
...
@@ -1368,11 +1376,15 @@ def last_seq(input,
" series information at all. Maybe you want to use"
" first_seq instead."
)
if
agg_level
==
AggregateLevel
.
EACH_SEQUENCE
:
assert
stride
==
-
1
Layer
(
name
=
name
,
type
=
LayerType
.
SEQUENCE_LAST_INSTANCE
,
inputs
=
[
input
.
name
],
trans_type
=
agg_level
,
stride
=
stride
,
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
))
return
LayerOutput
(
name
,
...
...
@@ -1386,10 +1398,16 @@ def last_seq(input,
def
first_seq
(
input
,
name
=
None
,
agg_level
=
AggregateLevel
.
EACH_TIMESTEP
,
stride
=-
1
,
layer_attr
=
None
):
"""
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:
.. code-block:: python
...
...
@@ -1401,6 +1419,8 @@ def first_seq(input,
:type name: basestring
:param input: Input layer name.
:type input: LayerOutput
:param stride: window size.
:type stride: Int
:param layer_attr: extra layer attributes.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
...
...
@@ -1413,11 +1433,15 @@ def first_seq(input,
' time series information at all. Maybe you want to use'
' last_seq instead.'
)
if
agg_level
==
AggregateLevel
.
EACH_SEQUENCE
:
assert
stride
==
-
1
Layer
(
name
=
name
,
type
=
LayerType
.
SEQUENCE_FIRST_INSTANCE
,
inputs
=
[
input
.
name
],
trans_type
=
agg_level
,
stride
=
stride
,
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
))
return
LayerOutput
(
name
,
...
...
@@ -4873,7 +4897,7 @@ def nce_layer(input,
if
neg_distribution
is
not
None
:
assert
isinstance
(
neg_distribution
,
collections
.
Sequence
)
assert
len
(
neg_distribution
)
==
num_classes
assert
sum
(
neg_distribution
)
==
1
assert
abs
(
sum
(
neg_distribution
)
-
1.0
)
<
1e-5
if
not
isinstance
(
act
,
BaseActivation
):
raise
TypeError
()
...
...
python/paddle/trainer_config_helpers/tests/configs/last_first_seq.py
浏览文件 @
b571a414
...
...
@@ -14,4 +14,7 @@ for op in seq_op:
for
al
in
agg_level
:
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
)
python/paddle/trainer_config_helpers/tests/configs/protostr/last_first_seq.protostr
浏览文件 @
b571a414
...
...
@@ -15,6 +15,7 @@ layers {
}
select_first: true
trans_type: "seq"
seq_pool_stride: -1
}
layers {
name: "__first_seq_1__"
...
...
@@ -26,6 +27,7 @@ layers {
}
select_first: true
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__last_seq_0__"
...
...
@@ -36,6 +38,7 @@ layers {
input_layer_name: "data"
}
trans_type: "seq"
seq_pool_stride: -1
}
layers {
name: "__last_seq_1__"
...
...
@@ -46,12 +49,38 @@ layers {
input_layer_name: "data"
}
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"
output_layer_names: "__first_seq_0__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__last_seq_0__"
output_layer_names: "__last_seq_1__"
output_layer_names: "__first_seq_2__"
output_layer_names: "__last_seq_2__"
sub_models {
name: "root"
layer_names: "data"
...
...
@@ -59,11 +88,15 @@ sub_models {
layer_names: "__first_seq_1__"
layer_names: "__last_seq_0__"
layer_names: "__last_seq_1__"
layer_names: "__first_seq_2__"
layer_names: "__last_seq_2__"
input_layer_names: "data"
output_layer_names: "__first_seq_0__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__last_seq_0__"
output_layer_names: "__last_seq_1__"
output_layer_names: "__first_seq_2__"
output_layer_names: "__last_seq_2__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/shared_gru.protostr
浏览文件 @
b571a414
...
...
@@ -128,6 +128,7 @@ layers {
input_layer_name: "__simple_gru_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__last_seq_1__"
...
...
@@ -138,6 +139,7 @@ layers {
input_layer_name: "__simple_gru_1__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__fc_layer_0__"
...
...
python/paddle/trainer_config_helpers/tests/configs/protostr/shared_lstm.protostr
浏览文件 @
b571a414
...
...
@@ -210,6 +210,7 @@ layers {
input_layer_name: "__lstm_group_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__last_seq_1__"
...
...
@@ -220,6 +221,7 @@ layers {
input_layer_name: "__lstm_group_1__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__fc_layer_0__"
...
...
python/paddle/trainer_config_helpers/tests/configs/protostr/simple_rnn_layers.protostr
浏览文件 @
b571a414
...
...
@@ -143,6 +143,7 @@ layers {
input_layer_name: "__recurrent_layer_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__first_seq_0__"
...
...
@@ -154,6 +155,7 @@ layers {
}
select_first: true
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__last_seq_1__"
...
...
@@ -164,6 +166,7 @@ layers {
input_layer_name: "__lstmemory_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__first_seq_1__"
...
...
@@ -175,6 +178,7 @@ layers {
}
select_first: true
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__last_seq_2__"
...
...
@@ -185,6 +189,7 @@ layers {
input_layer_name: "__gru_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__first_seq_2__"
...
...
@@ -196,6 +201,7 @@ layers {
}
select_first: true
trans_type: "non-seq"
seq_pool_stride: -1
}
parameters {
name: "___fc_layer_0__.w0"
...
...
python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr
浏览文件 @
b571a414
...
...
@@ -96,6 +96,7 @@ layers {
input_layer_name: "rnn_forward"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__recurrent_group_1__"
...
...
@@ -145,6 +146,7 @@ layers {
}
select_first: true
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__recurrent_group_2__"
...
...
@@ -193,6 +195,7 @@ layers {
input_layer_name: "rnn_subseq_forward"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__lstm_group_0___recurrent_group"
...
...
@@ -282,6 +285,7 @@ layers {
input_layer_name: "__lstm_group_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__gru_group_0___recurrent_group"
...
...
@@ -330,6 +334,7 @@ layers {
input_layer_name: "__gru_group_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
layers {
name: "__recurrent_group_3__"
...
...
@@ -378,6 +383,7 @@ layers {
input_layer_name: "__fc_layer_0__"
}
trans_type: "non-seq"
seq_pool_stride: -1
}
parameters {
name: "___mixed_0__.w0"
...
...
python/paddle/v2/data_feeder.py
浏览文件 @
b571a414
...
...
@@ -13,7 +13,7 @@
# limitations under the License.
from
py_paddle
import
DataProviderConverter
import
collections
import
paddle.trainer.PyDataProvider2
as
pydp2
__all__
=
[
'DataFeeder'
]
...
...
@@ -35,15 +35,30 @@ class DataFeeder(DataProviderConverter):
DataFeeder converts this mini-batch data entries into Arguments in order
to feed it to C++ interface.
The example usage:
The simple usage shows below
.. code-block:: python
feeding = ['image', 'label']
data_types = enumerate_data_types_of_data_layers(topology)
feeder = DataFeeder(data_types=data_types, feeding=feeding)
minibatch_data = [([1.0, 2.0, 3.0, ...], 5)]
arg = feeder(minibatch_data)
If mini-batch data and data layers are not one to one mapping, we
could pass a dictionary to feeding parameter to represent the mapping
relationship.
.. code-block:: python
data_types = [('image', paddle.data_type.dense_vector(784)),
('label', paddle.data_type.integer_value(10))]
reader_dict
= {'image':0, 'label':1}
feeder = DataFeeder(data_types=data_types,
reader_dict=reader_dict
)
feeding
= {'image':0, 'label':1}
feeder = DataFeeder(data_types=data_types,
feeding=feeding
)
minibatch_data = [
( [1.0,2.0,3.0,4.0], 5, [6,7,8] ), # first sample
( [1.0,2.0,3.0,4.0], 5, [6,7,8] ) # second sample
...
...
@@ -65,9 +80,9 @@ class DataFeeder(DataProviderConverter):
a tuple of (data_name, data_type).
:type data_types: list
:param
reader_dict: A dictionary to specify the position of each data
in the input data.
:type feeding: dict
:param
feeding: A dictionary or a sequence to specify the position of each
data
in the input data.
:type feeding: dict
|collections.Sequence|None
"""
def
__init__
(
self
,
data_types
,
feeding
=
None
):
...
...
@@ -75,6 +90,13 @@ class DataFeeder(DataProviderConverter):
input_types
=
[]
if
feeding
is
None
:
feeding
=
default_feeding_map
(
data_types
)
elif
isinstance
(
feeding
,
collections
.
Sequence
):
feed_list
=
feeding
feeding
=
dict
()
for
i
,
name
in
enumerate
(
feed_list
):
feeding
[
name
]
=
i
elif
not
isinstance
(
feeding
,
dict
):
raise
TypeError
(
"Feeding should be dict or sequence or None."
)
self
.
feeding
=
feeding
for
each
in
data_types
:
...
...
python/paddle/v2/dataset/wmt14.py
浏览文件 @
b571a414
...
...
@@ -34,7 +34,7 @@ URL_TRAIN = 'http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz
MD5_TRAIN
=
'a755315dd01c2c35bde29a744ede23a6'
# this is the pretrained model, whose bleu = 26.92
URL_MODEL
=
'http://paddlepaddle.bj.bcebos.com/demo/wmt_14/wmt14_model.tar.gz'
MD5_MODEL
=
'
6b097d23e15654608c6f74923e975535
'
MD5_MODEL
=
'
4ce14a26607fb8a1cc23bcdedb1895e4
'
START
=
"<s>"
END
=
"<e>"
...
...
@@ -140,6 +140,12 @@ def model():
return
parameters
def
trg_dict
(
dict_size
):
tar_file
=
download
(
URL_TRAIN
,
'wmt14'
,
MD5_TRAIN
)
src_dict
,
trg_dict
=
__read_to_dict__
(
tar_file
,
dict_size
)
return
trg_dict
def
fetch
():
download
(
URL_TRAIN
,
'wmt14'
,
MD5_TRAIN
)
download
(
URL_MODEL
,
'wmt14'
,
MD5_MODEL
)
python/paddle/v2/inference.py
浏览文件 @
b571a414
...
...
@@ -48,8 +48,13 @@ class Inference(object):
self
.
__gradient_machine__
.
finish
()
def
iter_infer_field
(
self
,
field
,
**
kwargs
):
if
not
isinstance
(
field
,
list
)
and
not
isinstance
(
field
,
tuple
):
field
=
[
field
]
for
result
in
self
.
iter_infer
(
**
kwargs
):
yield
[
each_result
[
field
]
for
each_result
in
result
]
for
each_result
in
result
:
item
=
[
each_result
[
each_field
]
for
each_field
in
field
]
yield
item
def
infer
(
self
,
field
=
'value'
,
**
kwargs
):
retv
=
None
...
...
@@ -87,9 +92,11 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
:type input: collections.Iterable
:param feeding: Reader dictionary. Default could generate from input
value.
:param field: The prediction field. It should in [`value`, `ids`]. `value`
means return the prediction probabilities, `ids` means return
the prediction labels. Default is `value`
:param field: The prediction field. It should in [`value`, `id`, `prob`].
`value` and `prob` mean return the prediction probabilities,
`id` means return the prediction labels. Default is `value`.
Note that `prob` only used when output_layer is beam_search
or max_id.
:type field: str
:return: a numpy array
:rtype: numpy.ndarray
...
...
python/paddle/v2/trainer.py
浏览文件 @
b571a414
...
...
@@ -83,7 +83,7 @@ class SGD(object):
:type event_handler: (BaseEvent) => None
:param feeding: Feeding is a map of neural network input name and array
index that reader returns.
:type feeding: dict
:type feeding: dict
|list
:return:
"""
if
event_handler
is
None
:
...
...
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