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体验新版 GitCode,发现更多精彩内容 >>
提交
ecae157e
编写于
12月 26, 2018
作者:
T
Tao Luo
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
simplify some data record in analyzer_tester
test=develop
上级
05f1b65d
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
45 addition
and
99 deletion
+45
-99
paddle/fluid/inference/tests/api/analyzer_mm_dnn_tester.cc
paddle/fluid/inference/tests/api/analyzer_mm_dnn_tester.cc
+9
-26
paddle/fluid/inference/tests/api/analyzer_ner_tester.cc
paddle/fluid/inference/tests/api/analyzer_ner_tester.cc
+10
-23
paddle/fluid/inference/tests/api/analyzer_seq_conv1_tester.cc
...le/fluid/inference/tests/api/analyzer_seq_conv1_tester.cc
+14
-50
paddle/fluid/inference/tests/api/tester_helper.h
paddle/fluid/inference/tests/api/tester_helper.h
+12
-0
未找到文件。
paddle/fluid/inference/tests/api/analyzer_mm_dnn_tester.cc
浏览文件 @
ecae157e
...
...
@@ -19,11 +19,9 @@ namespace inference {
using
contrib
::
AnalysisConfig
;
struct
DataRecord
{
std
::
vector
<
std
::
vector
<
int64_t
>>
query
_data_all
,
title_data_all
;
std
::
vector
<
std
::
vector
<
int64_t
>>
query
,
title
;
std
::
vector
<
size_t
>
lod1
,
lod2
;
size_t
batch_iter
{
0
};
size_t
batch_size
{
1
};
size_t
num_samples
;
// total number of samples
size_t
batch_iter
{
0
},
batch_size
{
1
},
num_samples
;
// total number of samples
DataRecord
()
=
default
;
explicit
DataRecord
(
const
std
::
string
&
path
,
int
batch_size
=
1
)
:
batch_size
(
batch_size
)
{
...
...
@@ -33,22 +31,9 @@ struct DataRecord {
DataRecord
data
;
size_t
batch_end
=
batch_iter
+
batch_size
;
// NOTE skip the final batch, if no enough data is provided.
if
(
batch_end
<=
query_data_all
.
size
())
{
data
.
query_data_all
.
assign
(
query_data_all
.
begin
()
+
batch_iter
,
query_data_all
.
begin
()
+
batch_end
);
data
.
title_data_all
.
assign
(
title_data_all
.
begin
()
+
batch_iter
,
title_data_all
.
begin
()
+
batch_end
);
// Prepare LoDs
data
.
lod1
.
push_back
(
0
);
data
.
lod2
.
push_back
(
0
);
CHECK
(
!
data
.
query_data_all
.
empty
());
CHECK
(
!
data
.
title_data_all
.
empty
());
CHECK_EQ
(
data
.
query_data_all
.
size
(),
data
.
title_data_all
.
size
());
for
(
size_t
j
=
0
;
j
<
data
.
query_data_all
.
size
();
j
++
)
{
// calculate lod
data
.
lod1
.
push_back
(
data
.
lod1
.
back
()
+
data
.
query_data_all
[
j
].
size
());
data
.
lod2
.
push_back
(
data
.
lod2
.
back
()
+
data
.
title_data_all
[
j
].
size
());
}
if
(
batch_end
<=
query
.
size
())
{
GetInputPerBatch
(
query
,
&
data
.
query
,
&
data
.
lod1
,
batch_iter
,
batch_end
);
GetInputPerBatch
(
title
,
&
data
.
title
,
&
data
.
lod2
,
batch_iter
,
batch_end
);
}
batch_iter
+=
batch_size
;
return
data
;
...
...
@@ -67,8 +52,8 @@ struct DataRecord {
// load title data
std
::
vector
<
int64_t
>
title_data
;
split_to_int64
(
data
[
1
],
' '
,
&
title_data
);
query
_data_all
.
push_back
(
std
::
move
(
query_data
));
title
_data_all
.
push_back
(
std
::
move
(
title_data
));
query
.
push_back
(
std
::
move
(
query_data
));
title
.
push_back
(
std
::
move
(
title_data
));
}
num_samples
=
num_lines
;
}
...
...
@@ -81,10 +66,8 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
lod_title_tensor
.
name
=
"right"
;
auto
one_batch
=
data
->
NextBatch
();
// assign data
TensorAssignData
<
int64_t
>
(
&
lod_query_tensor
,
one_batch
.
query_data_all
,
one_batch
.
lod1
);
TensorAssignData
<
int64_t
>
(
&
lod_title_tensor
,
one_batch
.
title_data_all
,
one_batch
.
lod2
);
TensorAssignData
<
int64_t
>
(
&
lod_query_tensor
,
one_batch
.
query
,
one_batch
.
lod1
);
TensorAssignData
<
int64_t
>
(
&
lod_title_tensor
,
one_batch
.
title
,
one_batch
.
lod2
);
// Set inputs.
input_slots
->
assign
({
lod_query_tensor
,
lod_title_tensor
});
for
(
auto
&
tensor
:
*
input_slots
)
{
...
...
paddle/fluid/inference/tests/api/analyzer_ner_tester.cc
浏览文件 @
ecae157e
...
...
@@ -19,11 +19,9 @@ namespace inference {
using
contrib
::
AnalysisConfig
;
struct
DataRecord
{
std
::
vector
<
std
::
vector
<
int64_t
>>
word
_data_all
,
mention_data_all
;
std
::
vector
<
std
::
vector
<
int64_t
>>
word
,
mention
;
std
::
vector
<
size_t
>
lod
;
// two inputs have the same lod info.
size_t
batch_iter
{
0
};
size_t
batch_size
{
1
};
size_t
num_samples
;
// total number of samples
size_t
batch_iter
{
0
},
batch_size
{
1
},
num_samples
;
// total number of samples
DataRecord
()
=
default
;
explicit
DataRecord
(
const
std
::
string
&
path
,
int
batch_size
=
1
)
:
batch_size
(
batch_size
)
{
...
...
@@ -33,20 +31,10 @@ struct DataRecord {
DataRecord
data
;
size_t
batch_end
=
batch_iter
+
batch_size
;
// NOTE skip the final batch, if no enough data is provided.
if
(
batch_end
<=
word_data_all
.
size
())
{
data
.
word_data_all
.
assign
(
word_data_all
.
begin
()
+
batch_iter
,
word_data_all
.
begin
()
+
batch_end
);
data
.
mention_data_all
.
assign
(
mention_data_all
.
begin
()
+
batch_iter
,
mention_data_all
.
begin
()
+
batch_end
);
// Prepare LoDs
data
.
lod
.
push_back
(
0
);
CHECK
(
!
data
.
word_data_all
.
empty
());
CHECK
(
!
data
.
mention_data_all
.
empty
());
CHECK_EQ
(
data
.
word_data_all
.
size
(),
data
.
mention_data_all
.
size
());
for
(
size_t
j
=
0
;
j
<
data
.
word_data_all
.
size
();
j
++
)
{
// calculate lod
data
.
lod
.
push_back
(
data
.
lod
.
back
()
+
data
.
word_data_all
[
j
].
size
());
}
if
(
batch_end
<=
word
.
size
())
{
GetInputPerBatch
(
word
,
&
data
.
word
,
&
data
.
lod
,
batch_iter
,
batch_end
);
GetInputPerBatch
(
mention
,
&
data
.
mention
,
&
data
.
lod
,
batch_iter
,
batch_end
);
}
batch_iter
+=
batch_size
;
return
data
;
...
...
@@ -65,8 +53,8 @@ struct DataRecord {
// load mention data
std
::
vector
<
int64_t
>
mention_data
;
split_to_int64
(
data
[
3
],
' '
,
&
mention_data
);
word
_data_all
.
push_back
(
std
::
move
(
word_data
));
mention
_data_all
.
push_back
(
std
::
move
(
mention_data
));
word
.
push_back
(
std
::
move
(
word_data
));
mention
.
push_back
(
std
::
move
(
mention_data
));
}
num_samples
=
num_lines
;
}
...
...
@@ -79,9 +67,8 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
lod_mention_tensor
.
name
=
"mention"
;
auto
one_batch
=
data
->
NextBatch
();
// assign data
TensorAssignData
<
int64_t
>
(
&
lod_word_tensor
,
one_batch
.
word_data_all
,
one_batch
.
lod
);
TensorAssignData
<
int64_t
>
(
&
lod_mention_tensor
,
one_batch
.
mention_data_all
,
TensorAssignData
<
int64_t
>
(
&
lod_word_tensor
,
one_batch
.
word
,
one_batch
.
lod
);
TensorAssignData
<
int64_t
>
(
&
lod_mention_tensor
,
one_batch
.
mention
,
one_batch
.
lod
);
// Set inputs.
input_slots
->
assign
({
lod_word_tensor
,
lod_mention_tensor
});
...
...
paddle/fluid/inference/tests/api/analyzer_seq_conv1_tester.cc
浏览文件 @
ecae157e
...
...
@@ -18,12 +18,9 @@ namespace paddle {
namespace
inference
{
struct
DataRecord
{
std
::
vector
<
std
::
vector
<
int64_t
>>
title1_all
,
title2_all
,
title3_all
,
l1_all
;
std
::
vector
<
std
::
vector
<
int64_t
>>
title1
,
title2
,
title3
,
l1
;
std
::
vector
<
size_t
>
title1_lod
,
title2_lod
,
title3_lod
,
l1_lod
;
size_t
batch_iter
{
0
};
size_t
batch_size
{
1
};
size_t
num_samples
;
// total number of samples
std
::
vector
<
size_t
>
lod1
,
lod2
,
lod3
,
l1_lod
;
size_t
batch_iter
{
0
},
batch_size
{
1
},
num_samples
;
// total number of samples
DataRecord
()
=
default
;
explicit
DataRecord
(
const
std
::
string
&
path
,
int
batch_size
=
1
)
:
batch_size
(
batch_size
)
{
...
...
@@ -33,41 +30,11 @@ struct DataRecord {
DataRecord
data
;
size_t
batch_end
=
batch_iter
+
batch_size
;
// NOTE skip the final batch, if no enough data is provided.
if
(
batch_end
<=
title1_all
.
size
())
{
data
.
title1_all
.
assign
(
title1_all
.
begin
()
+
batch_iter
,
title1_all
.
begin
()
+
batch_end
);
data
.
title2_all
.
assign
(
title2_all
.
begin
()
+
batch_iter
,
title2_all
.
begin
()
+
batch_end
);
data
.
title3_all
.
assign
(
title3_all
.
begin
()
+
batch_iter
,
title3_all
.
begin
()
+
batch_end
);
data
.
l1_all
.
assign
(
l1_all
.
begin
()
+
batch_iter
,
l1_all
.
begin
()
+
batch_end
);
// Prepare LoDs
data
.
title1_lod
.
push_back
(
0
);
data
.
title2_lod
.
push_back
(
0
);
data
.
title3_lod
.
push_back
(
0
);
data
.
l1_lod
.
push_back
(
0
);
CHECK
(
!
data
.
title1_all
.
empty
());
CHECK
(
!
data
.
title2_all
.
empty
());
CHECK
(
!
data
.
title3_all
.
empty
());
CHECK
(
!
data
.
l1_all
.
empty
());
CHECK_EQ
(
data
.
title1_all
.
size
(),
data
.
title2_all
.
size
());
CHECK_EQ
(
data
.
title1_all
.
size
(),
data
.
title3_all
.
size
());
CHECK_EQ
(
data
.
title1_all
.
size
(),
data
.
l1_all
.
size
());
for
(
size_t
j
=
0
;
j
<
data
.
title1_all
.
size
();
j
++
)
{
data
.
title1
.
push_back
(
data
.
title1_all
[
j
]);
data
.
title2
.
push_back
(
data
.
title2_all
[
j
]);
data
.
title3
.
push_back
(
data
.
title3_all
[
j
]);
data
.
l1
.
push_back
(
data
.
l1_all
[
j
]);
// calculate lod
data
.
title1_lod
.
push_back
(
data
.
title1_lod
.
back
()
+
data
.
title1_all
[
j
].
size
());
data
.
title2_lod
.
push_back
(
data
.
title2_lod
.
back
()
+
data
.
title2_all
[
j
].
size
());
data
.
title3_lod
.
push_back
(
data
.
title3_lod
.
back
()
+
data
.
title3_all
[
j
].
size
());
data
.
l1_lod
.
push_back
(
data
.
l1_lod
.
back
()
+
data
.
l1_all
[
j
].
size
());
}
if
(
batch_end
<=
title1
.
size
())
{
GetInputPerBatch
(
title1
,
&
data
.
title1
,
&
data
.
lod1
,
batch_iter
,
batch_end
);
GetInputPerBatch
(
title2
,
&
data
.
title2
,
&
data
.
lod2
,
batch_iter
,
batch_end
);
GetInputPerBatch
(
title3
,
&
data
.
title3
,
&
data
.
lod3
,
batch_iter
,
batch_end
);
GetInputPerBatch
(
l1
,
&
data
.
l1
,
&
data
.
l1_lod
,
batch_iter
,
batch_end
);
}
batch_iter
+=
batch_size
;
return
data
;
...
...
@@ -92,10 +59,10 @@ struct DataRecord {
// load l1 data
std
::
vector
<
int64_t
>
l1_data
;
split_to_int64
(
data
[
3
],
' '
,
&
l1_data
);
title1
_all
.
push_back
(
std
::
move
(
title1_data
));
title2
_all
.
push_back
(
std
::
move
(
title2_data
));
title3
_all
.
push_back
(
std
::
move
(
title3_data
));
l1
_all
.
push_back
(
std
::
move
(
l1_data
));
title1
.
push_back
(
std
::
move
(
title1_data
));
title2
.
push_back
(
std
::
move
(
title2_data
));
title3
.
push_back
(
std
::
move
(
title3_data
));
l1
.
push_back
(
std
::
move
(
l1_data
));
}
num_samples
=
num_lines
;
}
...
...
@@ -110,12 +77,9 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
l1_tensor
.
name
=
"l1"
;
auto
one_batch
=
data
->
NextBatch
();
// assign data
TensorAssignData
<
int64_t
>
(
&
title1_tensor
,
one_batch
.
title1
,
one_batch
.
title1_lod
);
TensorAssignData
<
int64_t
>
(
&
title2_tensor
,
one_batch
.
title2
,
one_batch
.
title2_lod
);
TensorAssignData
<
int64_t
>
(
&
title3_tensor
,
one_batch
.
title3
,
one_batch
.
title3_lod
);
TensorAssignData
<
int64_t
>
(
&
title1_tensor
,
one_batch
.
title1
,
one_batch
.
lod1
);
TensorAssignData
<
int64_t
>
(
&
title2_tensor
,
one_batch
.
title2
,
one_batch
.
lod2
);
TensorAssignData
<
int64_t
>
(
&
title3_tensor
,
one_batch
.
title3
,
one_batch
.
lod3
);
TensorAssignData
<
int64_t
>
(
&
l1_tensor
,
one_batch
.
l1
,
one_batch
.
l1_lod
);
// Set inputs.
input_slots
->
assign
({
title1_tensor
,
title2_tensor
,
title3_tensor
,
l1_tensor
});
...
...
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @
ecae157e
...
...
@@ -169,6 +169,18 @@ void SetFakeImageInput(std::vector<std::vector<PaddleTensor>> *inputs,
(
*
inputs
).
emplace_back
(
input_slots
);
}
void
GetInputPerBatch
(
const
std
::
vector
<
std
::
vector
<
int64_t
>>
&
in
,
std
::
vector
<
std
::
vector
<
int64_t
>>
*
out
,
std
::
vector
<
size_t
>
*
lod
,
size_t
batch_iter
,
size_t
batch_end
)
{
lod
->
clear
();
lod
->
push_back
(
0
);
for
(
auto
it
=
in
.
begin
()
+
batch_iter
;
it
<
in
.
begin
()
+
batch_end
;
it
++
)
{
out
->
push_back
(
*
it
);
lod
->
push_back
(
lod
->
back
()
+
(
*
it
).
size
());
// calculate lod
}
}
void
TestOneThreadPrediction
(
const
PaddlePredictor
::
Config
*
config
,
const
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
&
inputs
,
...
...
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