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体验新版 GitCode,发现更多精彩内容 >>
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93af8d73
编写于
1月 17, 2020
作者:
M
MRXLT
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
change batch predict inferface
上级
21e1cc08
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
25 addition
and
23 deletion
+25
-23
core/general-client/include/general_model.h
core/general-client/include/general_model.h
+3
-3
core/general-client/src/general_model.cpp
core/general-client/src/general_model.cpp
+9
-4
core/general-client/src/pybind_general_model.cpp
core/general-client/src/pybind_general_model.cpp
+7
-9
python/examples/imdb/test_client_batch.py
python/examples/imdb/test_client_batch.py
+3
-3
python/paddle_serving/serving_client/__init__.py
python/paddle_serving/serving_client/__init__.py
+3
-4
未找到文件。
core/general-client/include/general_model.h
浏览文件 @
93af8d73
...
...
@@ -17,6 +17,7 @@
#include <sys/types.h>
#include <unistd.h>
#include <algorithm>
#include <fstream>
#include <map>
#include <string>
...
...
@@ -58,13 +59,12 @@ class PredictorClient {
const
std
::
vector
<
std
::
string
>&
int_feed_name
,
const
std
::
vector
<
std
::
string
>&
fetch_name
);
std
::
vector
<
std
::
vector
<
std
::
vector
<
float
>>>
predict_for_batch
(
std
::
vector
<
std
::
vector
<
std
::
vector
<
float
>>>
batch_predict
(
const
std
::
vector
<
std
::
vector
<
std
::
vector
<
float
>>>&
float_feed_batch
,
const
std
::
vector
<
std
::
string
>&
float_feed_name
,
const
std
::
vector
<
std
::
vector
<
std
::
vector
<
int64_t
>>>&
int_feed_batch
,
const
std
::
vector
<
std
::
string
>&
int_feed_name
,
const
std
::
vector
<
std
::
string
>&
fetch_name
,
const
int64_t
&
batch_size
);
const
std
::
vector
<
std
::
string
>&
fetch_name
);
std
::
vector
<
std
::
vector
<
float
>>
predict_with_profile
(
const
std
::
vector
<
std
::
vector
<
float
>>&
float_feed
,
...
...
core/general-client/src/general_model.cpp
浏览文件 @
93af8d73
...
...
@@ -171,13 +171,13 @@ std::vector<std::vector<float>> PredictorClient::predict(
return
fetch_result
;
}
std
::
vector
<
std
::
vector
<
std
::
vector
<
float
>>>
PredictorClient
::
predict_for_batch
(
std
::
vector
<
std
::
vector
<
std
::
vector
<
float
>>>
PredictorClient
::
batch_predict
(
const
std
::
vector
<
std
::
vector
<
std
::
vector
<
float
>>>
&
float_feed_batch
,
const
std
::
vector
<
std
::
string
>
&
float_feed_name
,
const
std
::
vector
<
std
::
vector
<
std
::
vector
<
int64_t
>>>
&
int_feed_batch
,
const
std
::
vector
<
std
::
string
>
&
int_feed_name
,
const
std
::
vector
<
std
::
string
>
&
fetch_name
,
const
int64_t
&
batch_size
)
{
const
std
::
vector
<
std
::
string
>
&
fetch_name
)
{
int
batch_size
=
std
::
max
(
float_feed_batch
.
size
(),
int_feed_batch
.
size
());
std
::
vector
<
std
::
vector
<
std
::
vector
<
float
>>>
fetch_result_batch
;
if
(
fetch_name
.
size
()
==
0
)
{
return
fetch_result_batch
;
...
...
@@ -229,6 +229,8 @@ std::vector<std::vector<std::vector<float>>> PredictorClient::predict_for_batch(
tensor
->
add_shape
(
_shape
[
idx
][
j
]);
}
tensor
->
set_elem_type
(
0
);
VLOG
(
3
)
<<
"feed var name "
<<
name
<<
" index "
<<
vec_idx
<<
"first data "
<<
int_feed
[
vec_idx
][
0
];
for
(
int
j
=
0
;
j
<
int_feed
[
vec_idx
].
size
();
++
j
)
{
tensor
->
add_data
(
const_cast
<
char
*>
(
reinterpret_cast
<
const
char
*>
(
&
(
int_feed
[
vec_idx
][
j
]))),
...
...
@@ -248,10 +250,13 @@ std::vector<std::vector<std::vector<float>>> PredictorClient::predict_for_batch(
for
(
int
bi
=
0
;
bi
<
batch_size
;
bi
++
)
{
for
(
auto
&
name
:
fetch_name
)
{
int
idx
=
_fetch_name_to_idx
[
name
];
int
len
=
res
.
insts
(
0
).
tensor_array
(
idx
).
data_size
();
int
len
=
res
.
insts
(
bi
).
tensor_array
(
idx
).
data_size
();
VLOG
(
3
)
<<
"fetch name: "
<<
name
;
VLOG
(
3
)
<<
"tensor data size: "
<<
len
;
fetch_result_batch
[
bi
][
idx
].
resize
(
len
);
VLOG
(
3
)
<<
"fetch name "
<<
name
<<
" index "
<<
idx
<<
" first data "
<<
*
(
const
float
*
)
res
.
insts
(
bi
).
tensor_array
(
idx
).
data
(
0
).
c_str
();
for
(
int
i
=
0
;
i
<
len
;
++
i
)
{
fetch_result_batch
[
bi
][
idx
][
i
]
=
*
(
const
float
*
)
res
.
insts
(
bi
).
tensor_array
(
idx
).
data
(
i
).
c_str
();
...
...
core/general-client/src/pybind_general_model.cpp
浏览文件 @
93af8d73
...
...
@@ -57,7 +57,7 @@ PYBIND11_MODULE(serving_client, m) {
fetch_name
);
})
.
def
(
"
predict_for_batch
"
,
.
def
(
"
batch_predict
"
,
[](
PredictorClient
&
self
,
const
std
::
vector
<
std
::
vector
<
std
::
vector
<
float
>>>
&
float_feed_batch
,
...
...
@@ -65,14 +65,12 @@ PYBIND11_MODULE(serving_client, m) {
const
std
::
vector
<
std
::
vector
<
std
::
vector
<
int64_t
>>>
&
int_feed_batch
,
const
std
::
vector
<
std
::
string
>
&
int_feed_name
,
const
std
::
vector
<
std
::
string
>
&
fetch_name
,
const
int64_t
&
batch_size
)
{
return
self
.
predict_for_batch
(
float_feed_batch
,
float_feed_name
,
int_feed_batch
,
int_feed_name
,
fetch_name
,
batch_size
);
const
std
::
vector
<
std
::
string
>
&
fetch_name
)
{
return
self
.
batch_predict
(
float_feed_batch
,
float_feed_name
,
int_feed_batch
,
int_feed_name
,
fetch_name
);
});
}
...
...
python/examples/imdb/test_client_batch.py
浏览文件 @
93af8d73
...
...
@@ -19,7 +19,7 @@ from multiprocessing import Pool
import
time
def
predict_for_batch
(
batch_size
=
4
):
def
batch_predict
(
batch_size
=
4
):
client
=
Client
()
client
.
load_client_config
(
conf_file
)
client
.
connect
([
"127.0.0.1:8010"
])
...
...
@@ -33,7 +33,7 @@ def predict_for_batch(batch_size=4):
fetch
=
[
"acc"
,
"cost"
,
"prediction"
]
feed_batch
.
append
(
feed
)
if
len
(
feed_batch
)
==
batch_size
:
fetch_batch
=
client
.
predict_for_batch
(
fetch_batch
=
client
.
batch_predict
(
feed_batch
=
feed_batch
,
fetch
=
fetch
)
for
i
in
range
(
batch_size
):
print
(
"{} {}"
.
format
(
fetch_batch
[
i
][
"prediction"
][
1
],
...
...
@@ -47,4 +47,4 @@ def predict_for_batch(batch_size=4):
if
__name__
==
'__main__'
:
conf_file
=
sys
.
argv
[
1
]
batch_size
=
int
(
sys
.
argv
[
2
])
predict_for_batch
(
batch_size
)
batch_predict
(
batch_size
)
python/paddle_serving/serving_client/__init__.py
浏览文件 @
93af8d73
...
...
@@ -154,8 +154,7 @@ class Client(object):
return
result_map
def
predict_for_batch
(
self
,
feed_batch
=
[],
fetch
=
[]):
batch_size
=
len
(
feed_batch
)
def
batch_predict
(
self
,
feed_batch
=
[],
fetch
=
[]):
int_slot_batch
=
[]
float_slot_batch
=
[]
int_feed_names
=
[]
...
...
@@ -184,9 +183,9 @@ class Client(object):
if
key
in
self
.
fetch_names_
:
fetch_names
.
append
(
key
)
result_batch
=
self
.
client_handle_
.
predict_for_batch
(
result_batch
=
self
.
client_handle_
.
batch_predict
(
float_slot_batch
,
float_feed_names
,
int_slot_batch
,
int_feed_names
,
fetch_names
,
batch_size
)
fetch_names
)
result_map_batch
=
[]
for
result
in
result_batch
:
...
...
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