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22c23483
编写于
6月 01, 2021
作者:
H
HexToString
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix pybind bug
上级
f270c38d
变更
2
显示空白变更内容
内联
并排
Showing
2 changed file
with
292 addition
and
18 deletion
+292
-18
core/general-client/src/general_model.cpp
core/general-client/src/general_model.cpp
+288
-16
python/paddle_serving_client/client.py
python/paddle_serving_client/client.py
+4
-2
未找到文件。
core/general-client/src/general_model.cpp
100644 → 100755
浏览文件 @
22c23483
...
...
@@ -18,7 +18,6 @@
#include "core/sdk-cpp/include/common.h"
#include "core/sdk-cpp/include/predictor_sdk.h"
#include "core/util/include/timer.h"
DEFINE_bool
(
profile_client
,
false
,
""
);
DEFINE_bool
(
profile_server
,
false
,
""
);
...
...
@@ -46,7 +45,7 @@ void PredictorClient::init_gflags(std::vector<std::string> argv) {
int
argc
=
argv
.
size
();
char
**
arr
=
new
char
*
[
argv
.
size
()];
std
::
string
line
;
for
(
size_t
i
=
0
;
i
<
argv
.
size
();
i
++
)
{
for
(
size_t
i
=
0
;
i
<
argv
.
size
();
++
i
)
{
arr
[
i
]
=
&
argv
[
i
][
0
];
line
+=
argv
[
i
];
line
+=
' '
;
...
...
@@ -146,6 +145,59 @@ int PredictorClient::create_predictor() {
return
0
;
}
/*Determine whether the memory structure can be copied directly
if the memory offset stored in rows == the actual memory offset
if means the structure of memory is not changed by numpy(newaxis,numpy) or
numpy(1:numpy)
so you can directly copy the memory.
*/
template
<
typename
T
>
bool
isCopyLegal
(
py
::
array_t
<
T
>
*
feed_array
)
{
const
ssize_t
*
shape
=
feed_array
->
shape
();
ssize_t
dims
=
feed_array
->
ndim
();
ssize_t
item_size
=
feed_array
->
itemsize
();
ssize_t
*
middle
=
new
ssize_t
[
dims
];
// Calculates the memory offset stored in rows
int64_t
memory_offset
=
0
;
for
(
int16_t
i
=
dims
-
1
;
i
>=
0
;
--
i
)
{
middle
[
i
]
=
i
==
0
?
(
ssize_t
)(
shape
[
i
]
/
3
)
:
(
ssize_t
)(
shape
[
i
]
/
2
);
int64_t
one_dim_offset
=
middle
[
i
];
for
(
int16_t
j
=
i
+
1
;
j
<
dims
;
++
j
)
{
one_dim_offset
=
one_dim_offset
*
shape
[
j
];
}
memory_offset
+=
item_size
*
one_dim_offset
;
}
// Calculate the actual memory offset
int64_t
feed_offset
=
0
;
switch
(
dims
)
{
case
6
:
{
feed_offset
=
feed_array
->
offset_at
(
middle
[
0
],
middle
[
1
],
middle
[
2
],
middle
[
3
],
middle
[
4
],
middle
[
5
]);
break
;
}
case
5
:
{
feed_offset
=
feed_array
->
offset_at
(
middle
[
0
],
middle
[
1
],
middle
[
2
],
middle
[
3
],
middle
[
4
]);
break
;
}
case
4
:
{
feed_offset
=
feed_array
->
offset_at
(
middle
[
0
],
middle
[
1
],
middle
[
2
],
middle
[
3
]);
break
;
}
case
3
:
{
feed_offset
=
feed_array
->
offset_at
(
middle
[
0
],
middle
[
1
],
middle
[
2
]);
break
;
}
case
2
:
{
feed_offset
=
feed_array
->
offset_at
(
middle
[
0
],
middle
[
1
]);
break
;
}
}
delete
[]
middle
;
return
memory_offset
==
feed_offset
;
}
int
PredictorClient
::
numpy_predict
(
const
std
::
vector
<
std
::
vector
<
py
::
array_t
<
float
>>>
&
float_feed_batch
,
const
std
::
vector
<
std
::
string
>
&
float_feed_name
,
...
...
@@ -189,7 +241,6 @@ int PredictorClient::numpy_predict(
}
int
vec_idx
=
0
;
for
(
int
bi
=
0
;
bi
<
batch_size
;
bi
++
)
{
VLOG
(
2
)
<<
"prepare batch "
<<
bi
;
std
::
vector
<
Tensor
*>
tensor_vec
;
...
...
@@ -220,11 +271,10 @@ int PredictorClient::numpy_predict(
return
-
1
;
}
int
nbytes
=
float_feed
[
vec_idx
].
nbytes
();
// int ndims = float_feed[vec_idx].ndim();
void
*
rawdata_ptr
=
(
void
*
)
float_feed
[
vec_idx
].
data
(
0
);
void
*
rawdata_ptr
=
reinterpret_cast
<
void
*>
float_feed
[
vec_idx
].
data
(
0
);
int
total_number
=
float_feed
[
vec_idx
].
size
();
// float* end_ptr = (rawdata_ptr + total_number);
Tensor
*
tensor
=
tensor_vec
[
idx
];
VLOG
(
2
)
<<
"prepare float feed "
<<
name
<<
" shape size "
<<
float_shape
[
vec_idx
].
size
();
for
(
uint32_t
j
=
0
;
j
<
float_shape
[
vec_idx
].
size
();
++
j
)
{
...
...
@@ -234,8 +284,120 @@ int PredictorClient::numpy_predict(
tensor
->
add_lod
(
float_lod_slot_batch
[
vec_idx
][
j
]);
}
tensor
->
set_elem_type
(
P_FLOAT32
);
if
(
isCopyLegal
(
&
float_feed
[
vec_idx
]))
{
tensor
->
mutable_float_data
()
->
Resize
(
total_number
,
0
);
memcpy
(
tensor
->
mutable_float_data
()
->
mutable_data
(),
rawdata_ptr
,
nbytes
);
memcpy
(
tensor
->
mutable_float_data
()
->
mutable_data
(),
rawdata_ptr
,
nbytes
);
vec_idx
++
;
continue
;
}
tensor
->
mutable_float_data
()
->
Reserve
(
total_number
);
const
int
float_shape_size
=
float_shape
[
vec_idx
].
size
();
switch
(
float_shape_size
)
{
case
6
:
{
auto
float_array
=
float_feed
[
vec_idx
].
unchecked
<
6
>
();
for
(
ssize_t
i
=
0
;
i
<
float_array
.
shape
(
0
);
++
i
)
{
for
(
ssize_t
j
=
0
;
j
<
float_array
.
shape
(
1
);
++
j
)
{
for
(
ssize_t
k
=
0
;
k
<
float_array
.
shape
(
2
);
++
k
)
{
for
(
ssize_t
l
=
0
;
l
<
float_array
.
shape
(
3
);
++
l
)
{
for
(
ssize_t
m
=
0
;
m
<
float_array
.
shape
(
4
);
++
m
)
{
for
(
ssize_t
n
=
0
;
n
<
float_array
.
shape
(
5
);
++
n
)
{
tensor
->
add_float_data
(
float_array
(
i
,
j
,
k
,
l
,
m
,
n
));
}
}
}
}
}
}
break
;
}
case
5
:
{
auto
float_array
=
float_feed
[
vec_idx
].
unchecked
<
5
>
();
for
(
ssize_t
i
=
0
;
i
<
float_array
.
shape
(
0
);
++
i
)
{
for
(
ssize_t
j
=
0
;
j
<
float_array
.
shape
(
1
);
++
j
)
{
for
(
ssize_t
k
=
0
;
k
<
float_array
.
shape
(
2
);
++
k
)
{
for
(
ssize_t
l
=
0
;
l
<
float_array
.
shape
(
3
);
++
l
)
{
for
(
ssize_t
m
=
0
;
m
<
float_array
.
shape
(
4
);
++
m
)
{
tensor
->
add_float_data
(
float_array
(
i
,
j
,
k
,
l
,
m
));
}
}
}
}
}
break
;
}
case
4
:
{
auto
float_array
=
float_feed
[
vec_idx
].
unchecked
<
4
>
();
for
(
ssize_t
i
=
0
;
i
<
float_array
.
shape
(
0
);
++
i
)
{
for
(
ssize_t
j
=
0
;
j
<
float_array
.
shape
(
1
);
++
j
)
{
for
(
ssize_t
k
=
0
;
k
<
float_array
.
shape
(
2
);
++
k
)
{
for
(
ssize_t
l
=
0
;
l
<
float_array
.
shape
(
3
);
++
l
)
{
tensor
->
add_float_data
(
float_array
(
i
,
j
,
k
,
l
));
}
}
}
}
break
;
}
case
3
:
{
auto
float_array
=
float_feed
[
vec_idx
].
unchecked
<
3
>
();
for
(
ssize_t
i
=
0
;
i
<
float_array
.
shape
(
0
);
++
i
)
{
for
(
ssize_t
j
=
0
;
j
<
float_array
.
shape
(
1
);
++
j
)
{
for
(
ssize_t
k
=
0
;
k
<
float_array
.
shape
(
2
);
++
k
)
{
tensor
->
add_float_data
(
float_array
(
i
,
j
,
k
));
}
}
}
break
;
}
case
2
:
{
auto
float_array
=
float_feed
[
vec_idx
].
unchecked
<
2
>
();
for
(
ssize_t
i
=
0
;
i
<
float_array
.
shape
(
0
);
++
i
)
{
for
(
ssize_t
j
=
0
;
j
<
float_array
.
shape
(
1
);
++
j
)
{
tensor
->
add_float_data
(
float_array
(
i
,
j
));
}
}
break
;
}
case
1
:
{
auto
float_array
=
float_feed
[
vec_idx
].
unchecked
<
1
>
();
for
(
ssize_t
i
=
0
;
i
<
float_array
.
shape
(
0
);
i
++
)
{
tensor
->
add_float_data
(
float_array
(
i
));
}
break
;
}
}
/*
// this is for debug.
std::cout << std::endl;
std::cout << "origin " <<std::endl;
std::cout << "tensor->float_data_size() = " << tensor->float_data_size()
<< std::endl;
std::cout << "&tensor->first = " <<
tensor->mutable_float_data()->mutable_data() << std::endl;
std::cout << "tensor->first = " <<
*tensor->mutable_float_data()->mutable_data() << std::endl;
std::cout << "&tensor->last = " <<
(tensor->mutable_float_data()->mutable_data()+total_number-1) <<
std::endl;
std::cout << "tensor->last = " <<
*(tensor->mutable_float_data()->mutable_data()+total_number-1) <<
std::endl;
std::cout << "&tensor->middle = " <<
(tensor->mutable_float_data()->mutable_data()+int(total_number/7)) <<
std::endl;
std::cout << "tensor->middle = " <<
*(tensor->mutable_float_data()->mutable_data()+int(total_number/7)) <<
std::endl;
for(int my =0; my <total_number/1000; my++){
std::cout << my << " : " <<
*(tensor->mutable_float_data()->mutable_data()+my) << " ";
}
std::cout << std::endl;
std::cout << std::endl;
*/
vec_idx
++
;
}
...
...
@@ -251,7 +413,7 @@ int PredictorClient::numpy_predict(
}
Tensor
*
tensor
=
tensor_vec
[
idx
];
int
nbytes
=
int_feed
[
vec_idx
].
nbytes
();
void
*
rawdata_ptr
=
(
void
*
)
int_feed
[
vec_idx
].
data
(
0
);
void
*
rawdata_ptr
=
reinterpret_cast
<
void
*>
int_feed
[
vec_idx
].
data
(
0
);
int
total_number
=
int_feed
[
vec_idx
].
size
();
for
(
uint32_t
j
=
0
;
j
<
int_shape
[
vec_idx
].
size
();
++
j
)
{
...
...
@@ -261,22 +423,132 @@ int PredictorClient::numpy_predict(
tensor
->
add_lod
(
int_lod_slot_batch
[
vec_idx
][
j
]);
}
tensor
->
set_elem_type
(
_type
[
idx
]);
if
(
isCopyLegal
(
&
int_feed
[
vec_idx
]))
{
if
(
_type
[
idx
]
==
P_INT64
)
{
VLOG
(
2
)
<<
"prepare int feed "
<<
name
<<
" shape size "
<<
int_shape
[
vec_idx
].
size
();
tensor
->
mutable_int64_data
()
->
Resize
(
total_number
,
0
);
memcpy
(
tensor
->
mutable_int64_data
()
->
mutable_data
(),
rawdata_ptr
,
nbytes
);
vec_idx
++
;
}
else
{
tensor
->
mutable_int_data
()
->
Resize
(
total_number
,
0
);
memcpy
(
tensor
->
mutable_int64
_data
()
->
mutable_data
(),
rawdata_ptr
,
nbytes
);
tensor
->
mutable_int
_data
()
->
mutable_data
(),
rawdata_ptr
,
nbytes
);
vec_idx
++
;
}
continue
;
}
if
(
_type
[
idx
]
==
P_INT64
)
{
VLOG
(
2
)
<<
"prepare int feed "
<<
name
<<
" shape size "
<<
int_shape
[
vec_idx
].
size
();
tensor
->
mutable_int64_data
()
->
Reserve
(
total_number
);
}
else
{
VLOG
(
2
)
<<
"prepare int32 feed "
<<
name
<<
" shape size "
<<
int_shape
[
vec_idx
].
size
();
tensor
->
mutable_int_data
()
->
Resize
(
total_number
,
0
);
memcpy
(
tensor
->
mutable_int_data
()
->
mutable_data
(),
rawdata_ptr
,
nbytes
);
vec_idx
++
;
tensor
->
mutable_int_data
()
->
Reserve
(
total_number
);
}
const
int
int_shape_size
=
int_shape
[
vec_idx
].
size
();
switch
(
int_shape_size
)
{
case
6
:
{
auto
int_array
=
int_feed
[
vec_idx
].
unchecked
<
6
>
();
for
(
ssize_t
i
=
0
;
i
<
int_array
.
shape
(
0
);
++
i
)
{
for
(
ssize_t
j
=
0
;
j
<
int_array
.
shape
(
1
);
++
j
)
{
for
(
ssize_t
k
=
0
;
k
<
int_array
.
shape
(
2
);
++
k
)
{
for
(
ssize_t
l
=
0
;
k
<
int_array
.
shape
(
3
);
++
l
)
{
for
(
ssize_t
m
=
0
;
k
<
int_array
.
shape
(
4
);
++
m
)
{
for
(
ssize_t
n
=
0
;
k
<
int_array
.
shape
(
5
);
++
n
)
{
if
(
_type
[
idx
]
==
P_INT64
)
{
tensor
->
add_int64_data
(
int_array
(
i
,
j
,
k
,
l
,
m
,
n
));
}
else
{
tensor
->
add_int_data
(
int_array
(
i
,
j
,
k
,
l
,
m
,
n
));
}
}
}
}
}
}
}
break
;
}
case
5
:
{
auto
int_array
=
int_feed
[
vec_idx
].
unchecked
<
5
>
();
for
(
ssize_t
i
=
0
;
i
<
int_array
.
shape
(
0
);
++
i
)
{
for
(
ssize_t
j
=
0
;
j
<
int_array
.
shape
(
1
);
++
j
)
{
for
(
ssize_t
k
=
0
;
k
<
int_array
.
shape
(
2
);
++
k
)
{
for
(
ssize_t
l
=
0
;
k
<
int_array
.
shape
(
3
);
++
l
)
{
for
(
ssize_t
m
=
0
;
k
<
int_array
.
shape
(
4
);
++
m
)
{
if
(
_type
[
idx
]
==
P_INT64
)
{
tensor
->
add_int64_data
(
int_array
(
i
,
j
,
k
,
l
,
m
));
}
else
{
tensor
->
add_int_data
(
int_array
(
i
,
j
,
k
,
l
,
m
));
}
}
}
}
}
}
break
;
}
case
4
:
{
auto
int_array
=
int_feed
[
vec_idx
].
unchecked
<
4
>
();
for
(
ssize_t
i
=
0
;
i
<
int_array
.
shape
(
0
);
++
i
)
{
for
(
ssize_t
j
=
0
;
j
<
int_array
.
shape
(
1
);
++
j
)
{
for
(
ssize_t
k
=
0
;
k
<
int_array
.
shape
(
2
);
++
k
)
{
for
(
ssize_t
l
=
0
;
k
<
int_array
.
shape
(
3
);
++
l
)
{
if
(
_type
[
idx
]
==
P_INT64
)
{
tensor
->
add_int64_data
(
int_array
(
i
,
j
,
k
,
l
));
}
else
{
tensor
->
add_int_data
(
int_array
(
i
,
j
,
k
,
l
));
}
}
}
}
}
break
;
}
case
3
:
{
auto
int_array
=
int_feed
[
vec_idx
].
unchecked
<
3
>
();
for
(
ssize_t
i
=
0
;
i
<
int_array
.
shape
(
0
);
++
i
)
{
for
(
ssize_t
j
=
0
;
j
<
int_array
.
shape
(
1
);
++
j
)
{
for
(
ssize_t
k
=
0
;
k
<
int_array
.
shape
(
2
);
++
k
)
{
if
(
_type
[
idx
]
==
P_INT64
)
{
tensor
->
add_int64_data
(
int_array
(
i
,
j
,
k
));
}
else
{
tensor
->
add_int_data
(
int_array
(
i
,
j
,
k
));
}
}
}
}
break
;
}
case
2
:
{
auto
int_array
=
int_feed
[
vec_idx
].
unchecked
<
2
>
();
for
(
ssize_t
i
=
0
;
i
<
int_array
.
shape
(
0
);
++
i
)
{
for
(
ssize_t
j
=
0
;
j
<
int_array
.
shape
(
1
);
++
j
)
{
if
(
_type
[
idx
]
==
P_INT64
)
{
tensor
->
add_int64_data
(
int_array
(
i
,
j
));
}
else
{
tensor
->
add_int_data
(
int_array
(
i
,
j
));
}
}
}
break
;
}
case
1
:
{
auto
int_array
=
int_feed
[
vec_idx
].
unchecked
<
1
>
();
for
(
ssize_t
i
=
0
;
i
<
int_array
.
shape
(
0
);
i
++
)
{
if
(
_type
[
idx
]
==
P_INT64
)
{
tensor
->
add_int64_data
(
int_array
(
i
));
}
else
{
tensor
->
add_int_data
(
int_array
(
i
));
}
}
break
;
}
}
vec_idx
++
;
}
VLOG
(
2
)
<<
"batch ["
<<
bi
<<
"] "
<<
"int feed value prepared"
;
...
...
python/paddle_serving_client/client.py
浏览文件 @
22c23483
...
...
@@ -356,7 +356,8 @@ class Client(object):
int_feed_names
.
append
(
key
)
shape_lst
=
[]
if
batch
==
False
:
feed_i
[
key
]
=
feed_i
[
key
][
np
.
newaxis
,
:]
feed_i
[
key
]
=
np
.
expand_dims
(
feed_i
[
key
],
0
).
repeat
(
1
,
axis
=
0
)
if
isinstance
(
feed_i
[
key
],
np
.
ndarray
):
shape_lst
.
extend
(
list
(
feed_i
[
key
].
shape
))
int_shape
.
append
(
shape_lst
)
...
...
@@ -380,7 +381,8 @@ class Client(object):
float_feed_names
.
append
(
key
)
shape_lst
=
[]
if
batch
==
False
:
feed_i
[
key
]
=
feed_i
[
key
][
np
.
newaxis
,
:]
feed_i
[
key
]
=
np
.
expand_dims
(
feed_i
[
key
],
0
).
repeat
(
1
,
axis
=
0
)
if
isinstance
(
feed_i
[
key
],
np
.
ndarray
):
shape_lst
.
extend
(
list
(
feed_i
[
key
].
shape
))
float_shape
.
append
(
shape_lst
)
...
...
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