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dd10fd82
编写于
5月 23, 2020
作者:
D
Dong Daxiang
提交者:
GitHub
5月 23, 2020
浏览文件
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差异文件
Merge pull request #581 from MRXLT/lod-numpy
lod tensor feed support numpy array input
上级
5f123651
d00169c0
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
64 addition
and
39 deletion
+64
-39
core/general-server/op/general_reader_op.cpp
core/general-server/op/general_reader_op.cpp
+19
-5
core/general-server/op/general_response_op.cpp
core/general-server/op/general_response_op.cpp
+37
-17
python/paddle_serving_client/__init__.py
python/paddle_serving_client/__init__.py
+8
-17
未找到文件。
core/general-server/op/general_reader_op.cpp
浏览文件 @
dd10fd82
...
...
@@ -131,7 +131,7 @@ int GeneralReaderOp::inference() {
lod_tensor
.
dtype
=
paddle
::
PaddleDType
::
FLOAT32
;
}
if
(
req
->
insts
(
0
).
tensor_array
(
i
).
shape
(
0
)
==
-
1
)
{
if
(
model_config
->
_is_lod_feed
[
i
]
)
{
lod_tensor
.
lod
.
resize
(
1
);
lod_tensor
.
lod
[
0
].
push_back
(
0
);
VLOG
(
2
)
<<
"var["
<<
i
<<
"] is lod_tensor"
;
...
...
@@ -153,6 +153,7 @@ int GeneralReaderOp::inference() {
// specify the memory needed for output tensor_vector
for
(
int
i
=
0
;
i
<
var_num
;
++
i
)
{
if
(
out
->
at
(
i
).
lod
.
size
()
==
1
)
{
int
tensor_size
=
0
;
for
(
int
j
=
0
;
j
<
batch_size
;
++
j
)
{
const
Tensor
&
tensor
=
req
->
insts
(
j
).
tensor_array
(
i
);
int
data_len
=
0
;
...
...
@@ -162,15 +163,28 @@ int GeneralReaderOp::inference() {
data_len
=
tensor
.
float_data_size
();
}
VLOG
(
2
)
<<
"tensor size for var["
<<
i
<<
"]: "
<<
data_len
;
tensor_size
+=
data_len
;
int
cur_len
=
out
->
at
(
i
).
lod
[
0
].
back
();
VLOG
(
2
)
<<
"current len: "
<<
cur_len
;
out
->
at
(
i
).
lod
[
0
].
push_back
(
cur_len
+
data_len
);
VLOG
(
2
)
<<
"new len: "
<<
cur_len
+
data_len
;
int
sample_len
=
0
;
if
(
tensor
.
shape_size
()
==
1
)
{
sample_len
=
data_len
;
}
else
{
sample_len
=
tensor
.
shape
(
0
);
}
out
->
at
(
i
).
lod
[
0
].
push_back
(
cur_len
+
sample_len
);
VLOG
(
2
)
<<
"new len: "
<<
cur_len
+
sample_len
;
}
out
->
at
(
i
).
data
.
Resize
(
tensor_size
*
elem_size
[
i
]);
out
->
at
(
i
).
shape
=
{
out
->
at
(
i
).
lod
[
0
].
back
()};
for
(
int
j
=
1
;
j
<
req
->
insts
(
0
).
tensor_array
(
i
).
shape_size
();
++
j
)
{
out
->
at
(
i
).
shape
.
push_back
(
req
->
insts
(
0
).
tensor_array
(
i
).
shape
(
j
));
}
if
(
out
->
at
(
i
).
shape
.
size
()
==
1
)
{
out
->
at
(
i
).
shape
.
push_back
(
1
);
}
out
->
at
(
i
).
data
.
Resize
(
out
->
at
(
i
).
lod
[
0
].
back
()
*
elem_size
[
i
]);
out
->
at
(
i
).
shape
=
{
out
->
at
(
i
).
lod
[
0
].
back
(),
1
};
VLOG
(
2
)
<<
"var["
<<
i
<<
"] is lod_tensor and len="
<<
out
->
at
(
i
).
lod
[
0
].
back
();
}
else
{
...
...
core/general-server/op/general_response_op.cpp
浏览文件 @
dd10fd82
...
...
@@ -15,8 +15,10 @@
#include "core/general-server/op/general_response_op.h"
#include <algorithm>
#include <iostream>
#include <map>
#include <memory>
#include <sstream>
#include <utility>
#include "core/general-server/op/general_infer_helper.h"
#include "core/predictor/framework/infer.h"
#include "core/predictor/framework/memory.h"
...
...
@@ -86,37 +88,51 @@ int GeneralResponseOp::inference() {
// To get the order of model return values
output
->
set_engine_name
(
pre_name
);
FetchInst
*
fetch_inst
=
output
->
add_insts
();
std
::
map
<
std
::
string
,
int
>
fetch_index_map
;
for
(
int
i
=
0
;
i
<
in
->
size
();
++
i
)
{
VLOG
(
2
)
<<
"index "
<<
i
<<
" var "
<<
in
->
at
(
i
).
name
;
fetch_index_map
.
insert
(
std
::
pair
<
std
::
string
,
int
>
(
in
->
at
(
i
).
name
,
i
));
}
for
(
auto
&
idx
:
fetch_index
)
{
Tensor
*
tensor
=
fetch_inst
->
add_tensor_array
();
tensor
->
set_elem_type
(
1
);
int
true_idx
=
fetch_index_map
[
model_config
->
_fetch_name
[
idx
]];
if
(
model_config
->
_is_lod_fetch
[
idx
])
{
VLOG
(
2
)
<<
"out["
<<
idx
<<
"] is lod_tensor"
;
for
(
int
k
=
0
;
k
<
in
->
at
(
idx
).
shape
.
size
();
++
k
)
{
VLOG
(
2
)
<<
"out["
<<
idx
<<
"] "
<<
model_config
->
_fetch_name
[
idx
]
<<
" is lod_tensor"
;
for
(
int
k
=
0
;
k
<
in
->
at
(
true_idx
).
shape
.
size
();
++
k
)
{
VLOG
(
2
)
<<
"shape["
<<
k
<<
"]: "
<<
in
->
at
(
idx
).
shape
[
k
];
tensor
->
add_shape
(
in
->
at
(
idx
).
shape
[
k
]);
tensor
->
add_shape
(
in
->
at
(
true_
idx
).
shape
[
k
]);
}
}
else
{
VLOG
(
2
)
<<
"out["
<<
idx
<<
"] is tensor"
;
for
(
int
k
=
0
;
k
<
in
->
at
(
idx
).
shape
.
size
();
++
k
)
{
VLOG
(
2
)
<<
"shape["
<<
k
<<
"]: "
<<
in
->
at
(
idx
).
shape
[
k
];
tensor
->
add_shape
(
in
->
at
(
idx
).
shape
[
k
]);
VLOG
(
2
)
<<
"out["
<<
idx
<<
"] "
<<
model_config
->
_fetch_name
[
idx
]
<<
" is tensor"
;
for
(
int
k
=
0
;
k
<
in
->
at
(
true_idx
).
shape
.
size
();
++
k
)
{
VLOG
(
2
)
<<
"shape["
<<
k
<<
"]: "
<<
in
->
at
(
true_idx
).
shape
[
k
];
tensor
->
add_shape
(
in
->
at
(
true_idx
).
shape
[
k
]);
}
}
}
int
var_idx
=
0
;
for
(
auto
&
idx
:
fetch_index
)
{
int
true_idx
=
fetch_index_map
[
model_config
->
_fetch_name
[
idx
]];
int
cap
=
1
;
for
(
int
j
=
0
;
j
<
in
->
at
(
idx
).
shape
.
size
();
++
j
)
{
cap
*=
in
->
at
(
idx
).
shape
[
j
];
for
(
int
j
=
0
;
j
<
in
->
at
(
true_
idx
).
shape
.
size
();
++
j
)
{
cap
*=
in
->
at
(
true_
idx
).
shape
[
j
];
}
if
(
in
->
at
(
idx
).
dtype
==
paddle
::
PaddleDType
::
INT64
)
{
int64_t
*
data_ptr
=
static_cast
<
int64_t
*>
(
in
->
at
(
idx
).
data
.
data
());
if
(
in
->
at
(
true_idx
).
dtype
==
paddle
::
PaddleDType
::
INT64
)
{
VLOG
(
2
)
<<
"Prepare float var ["
<<
model_config
->
_fetch_name
[
idx
]
<<
"]."
;
int64_t
*
data_ptr
=
static_cast
<
int64_t
*>
(
in
->
at
(
true_idx
).
data
.
data
());
if
(
model_config
->
_is_lod_fetch
[
idx
])
{
FetchInst
*
fetch_p
=
output
->
mutable_insts
(
0
);
for
(
int
j
=
0
;
j
<
in
->
at
(
idx
).
lod
[
0
].
size
();
++
j
)
{
for
(
int
j
=
0
;
j
<
in
->
at
(
true_
idx
).
lod
[
0
].
size
();
++
j
)
{
fetch_p
->
mutable_tensor_array
(
var_idx
)
->
add_lod
(
in
->
at
(
idx
).
lod
[
0
][
j
]);
in
->
at
(
true_
idx
).
lod
[
0
][
j
]);
}
for
(
int
j
=
0
;
j
<
cap
;
++
j
)
{
fetch_p
->
mutable_tensor_array
(
var_idx
)
->
add_int64_data
(
data_ptr
[
j
]);
...
...
@@ -127,14 +143,17 @@ int GeneralResponseOp::inference() {
fetch_p
->
mutable_tensor_array
(
var_idx
)
->
add_int64_data
(
data_ptr
[
j
]);
}
}
VLOG
(
2
)
<<
"fetch var ["
<<
model_config
->
_fetch_name
[
idx
]
<<
"] ready"
;
var_idx
++
;
}
else
if
(
in
->
at
(
idx
).
dtype
==
paddle
::
PaddleDType
::
FLOAT32
)
{
float
*
data_ptr
=
static_cast
<
float
*>
(
in
->
at
(
idx
).
data
.
data
());
}
else
if
(
in
->
at
(
true_idx
).
dtype
==
paddle
::
PaddleDType
::
FLOAT32
)
{
VLOG
(
2
)
<<
"Prepare float var ["
<<
model_config
->
_fetch_name
[
idx
]
<<
"]."
;
float
*
data_ptr
=
static_cast
<
float
*>
(
in
->
at
(
true_idx
).
data
.
data
());
if
(
model_config
->
_is_lod_fetch
[
idx
])
{
FetchInst
*
fetch_p
=
output
->
mutable_insts
(
0
);
for
(
int
j
=
0
;
j
<
in
->
at
(
idx
).
lod
[
0
].
size
();
++
j
)
{
for
(
int
j
=
0
;
j
<
in
->
at
(
true_
idx
).
lod
[
0
].
size
();
++
j
)
{
fetch_p
->
mutable_tensor_array
(
var_idx
)
->
add_lod
(
in
->
at
(
idx
).
lod
[
0
][
j
]);
in
->
at
(
true_
idx
).
lod
[
0
][
j
]);
}
for
(
int
j
=
0
;
j
<
cap
;
++
j
)
{
fetch_p
->
mutable_tensor_array
(
var_idx
)
->
add_float_data
(
data_ptr
[
j
]);
...
...
@@ -145,6 +164,7 @@ int GeneralResponseOp::inference() {
fetch_p
->
mutable_tensor_array
(
var_idx
)
->
add_float_data
(
data_ptr
[
j
]);
}
}
VLOG
(
2
)
<<
"fetch var ["
<<
model_config
->
_fetch_name
[
idx
]
<<
"] ready"
;
var_idx
++
;
}
}
...
...
python/paddle_serving_client/__init__.py
浏览文件 @
dd10fd82
...
...
@@ -203,7 +203,12 @@ class Client(object):
def
shape_check
(
self
,
feed
,
key
):
if
key
in
self
.
lod_tensor_set
:
return
if
len
(
feed
[
key
])
!=
self
.
feed_tensor_len
[
key
]:
if
isinstance
(
feed
[
key
],
list
)
and
len
(
feed
[
key
])
!=
self
.
feed_tensor_len
[
key
]:
raise
SystemExit
(
"The shape of feed tensor {} not match."
.
format
(
key
))
if
type
(
feed
[
key
]).
__module__
==
np
.
__name__
and
np
.
size
(
feed
[
key
])
!=
self
.
feed_tensor_len
[
key
]:
raise
SystemExit
(
"The shape of feed tensor {} not match."
.
format
(
key
))
...
...
@@ -254,23 +259,16 @@ class Client(object):
for
key
in
feed_i
:
if
key
not
in
self
.
feed_names_
:
raise
ValueError
(
"Wrong feed name: {}."
.
format
(
key
))
if
not
isinstance
(
feed_i
[
key
],
np
.
ndarray
):
self
.
shape_check
(
feed_i
,
key
)
#
if not isinstance(feed_i[key], np.ndarray):
self
.
shape_check
(
feed_i
,
key
)
if
self
.
feed_types_
[
key
]
==
int_type
:
if
i
==
0
:
int_feed_names
.
append
(
key
)
if
isinstance
(
feed_i
[
key
],
np
.
ndarray
):
if
key
in
self
.
lod_tensor_set
:
raise
ValueError
(
"LodTensor var can not be ndarray type."
)
int_shape
.
append
(
list
(
feed_i
[
key
].
shape
))
else
:
int_shape
.
append
(
self
.
feed_shapes_
[
key
])
if
isinstance
(
feed_i
[
key
],
np
.
ndarray
):
if
key
in
self
.
lod_tensor_set
:
raise
ValueError
(
"LodTensor var can not be ndarray type."
)
#int_slot.append(np.reshape(feed_i[key], (-1)).tolist())
int_slot
.
append
(
feed_i
[
key
])
self
.
has_numpy_input
=
True
else
:
...
...
@@ -280,17 +278,10 @@ class Client(object):
if
i
==
0
:
float_feed_names
.
append
(
key
)
if
isinstance
(
feed_i
[
key
],
np
.
ndarray
):
if
key
in
self
.
lod_tensor_set
:
raise
ValueError
(
"LodTensor var can not be ndarray type."
)
float_shape
.
append
(
list
(
feed_i
[
key
].
shape
))
else
:
float_shape
.
append
(
self
.
feed_shapes_
[
key
])
if
isinstance
(
feed_i
[
key
],
np
.
ndarray
):
if
key
in
self
.
lod_tensor_set
:
raise
ValueError
(
"LodTensor var can not be ndarray type."
)
#float_slot.append(np.reshape(feed_i[key], (-1)).tolist())
float_slot
.
append
(
feed_i
[
key
])
self
.
has_numpy_input
=
True
else
:
...
...
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