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539b3f30
编写于
9月 12, 2018
作者:
T
tensor-tang
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add ocr analysis ut
上级
62a98210
变更
2
隐藏空白更改
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Showing
2 changed file
with
181 addition
and
0 deletion
+181
-0
paddle/fluid/inference/tests/api/CMakeLists.txt
paddle/fluid/inference/tests/api/CMakeLists.txt
+11
-0
paddle/fluid/inference/tests/api/analyzer_vis_tester.cc
paddle/fluid/inference/tests/api/analyzer_vis_tester.cc
+170
-0
未找到文件。
paddle/fluid/inference/tests/api/CMakeLists.txt
浏览文件 @
539b3f30
...
...
@@ -55,3 +55,14 @@ inference_analysis_test(test_text_classification SRCS analyzer_text_classificati
--infer_data=
${
TEXT_CLASSIFICATION_INSTALL_DIR
}
/data.txt
--topn=1
# Just run top 1 batch.
)
# ocr
set
(
OCR_MODEL_URL
"http://paddlemodels.cdn.bcebos.com/inference-vis-demos%2Focr.tar.gz"
)
set
(
OCR_INSTALL_DIR
"
${
THIRD_PARTY_PATH
}
/inference_demo/ocr"
)
if
(
NOT EXISTS
${
OCR_INSTALL_DIR
}
AND WITH_INFERENCE
)
inference_download_and_uncompress
(
${
OCR_INSTALL_DIR
}
${
OCR_MODEL_URL
}
)
endif
()
inference_analysis_test
(
test_analyzer_ocr SRCS analyzer_vis_tester.cc
EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis_predictor
ARGS --infer_model=
${
OCR_INSTALL_DIR
}
/model
--infer_data=
${
OCR_INSTALL_DIR
}
/data.txt
)
paddle/fluid/inference/tests/api/analyzer_vis_tester.cc
0 → 100644
浏览文件 @
539b3f30
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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 "paddle/fluid/inference/analysis/analyzer.h"
#include <gflags/gflags.h>
#include <glog/logging.h>
#include <gtest/gtest.h>
#include <fstream>
#include <iostream>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
#include "paddle/fluid/inference/api/analysis_predictor.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
DEFINE_string
(
infer_model
,
""
,
"model path for LAC"
);
DEFINE_string
(
infer_data
,
""
,
"data file for LAC"
);
DEFINE_int32
(
batch_size
,
1
,
"batch size."
);
DEFINE_int32
(
repeat
,
1
,
"Running the inference program repeat times."
);
namespace
paddle
{
namespace
inference
{
namespace
analysis
{
struct
Record
{
std
::
vector
<
float
>
data
;
std
::
vector
<
int32_t
>
shape
;
};
Record
ProcessALine
(
const
std
::
string
&
line
)
{
VLOG
(
3
)
<<
"process a line"
;
std
::
vector
<
std
::
string
>
columns
;
split
(
line
,
'\t'
,
&
columns
);
CHECK_EQ
(
columns
.
size
(),
2UL
)
<<
"data format error, should be <data>
\t
<shape>"
;
Record
record
;
std
::
vector
<
std
::
string
>
data_strs
;
split
(
columns
[
0
],
' '
,
&
data_strs
);
for
(
auto
&
d
:
data_strs
)
{
record
.
data
.
push_back
(
std
::
stof
(
d
));
}
std
::
vector
<
std
::
string
>
shape_strs
;
split
(
columns
[
1
],
' '
,
&
shape_strs
);
for
(
auto
&
s
:
shape_strs
)
{
record
.
shape
.
push_back
(
std
::
stoi
(
s
));
}
VLOG
(
3
)
<<
"data size "
<<
record
.
data
.
size
();
VLOG
(
3
)
<<
"data shape size "
<<
record
.
shape
.
size
();
return
record
;
}
/*
* Use the native and analysis fluid engine to inference the demo.
* ocr, mobilenet and se_resnext50
*/
void
TestVisualPrediction
()
{
std
::
unique_ptr
<
PaddlePredictor
>
predictor
;
AnalysisConfig
cfg
;
cfg
.
param_file
=
FLAGS_infer_model
+
"/__params__"
;
cfg
.
prog_file
=
FLAGS_infer_model
+
"/__model__"
;
cfg
.
use_gpu
=
false
;
cfg
.
device
=
0
;
// cfg.specify_input_name = true;
cfg
.
enable_ir_optim
=
true
;
predictor
=
CreatePaddlePredictor
<
AnalysisConfig
,
PaddleEngineKind
::
kAnalysis
>
(
cfg
);
// Only have single batch of data.
std
::
string
line
;
std
::
ifstream
file
(
FLAGS_infer_data
);
std
::
getline
(
file
,
line
);
auto
record
=
ProcessALine
(
line
);
file
.
close
();
// Inference.
PaddleTensor
input
;
input
.
shape
=
record
.
shape
;
input
.
data
=
PaddleBuf
(
record
.
data
.
data
(),
record
.
data
.
size
()
*
sizeof
(
float
));
input
.
dtype
=
PaddleDType
::
FLOAT32
;
std
::
vector
<
PaddleTensor
>
outputs_slots
;
Timer
timer
;
timer
.
tic
();
for
(
int
i
=
0
;
i
<
FLAGS_repeat
;
i
++
)
{
predictor
->
Run
({
input
},
&
outputs_slots
);
}
PrintTime
(
/*batch size*/
1
,
FLAGS_repeat
,
/*num threads*/
1
,
/*thread id*/
0
,
timer
.
toc
()
/
FLAGS_repeat
);
VLOG
(
3
)
<<
"output.size "
<<
outputs_slots
.
size
();
// run native as reference
NativeConfig
config
;
config
.
param_file
=
FLAGS_infer_model
+
"/__params__"
;
config
.
prog_file
=
FLAGS_infer_model
+
"/__model__"
;
config
.
use_gpu
=
false
;
config
.
device
=
0
;
// config.specify_input_name = true;
auto
ref_predictor
=
CreatePaddlePredictor
<
NativeConfig
,
PaddleEngineKind
::
kNative
>
(
config
);
std
::
vector
<
PaddleTensor
>
ref_outputs_slots
;
ref_predictor
->
Run
({
input
},
&
ref_outputs_slots
);
EXPECT_EQ
(
ref_outputs_slots
.
size
(),
outputs_slots
.
size
());
for
(
size_t
i
=
0
;
i
<
outputs_slots
.
size
();
++
i
)
{
auto
&
ref_out
=
ref_outputs_slots
[
i
];
auto
&
out
=
outputs_slots
[
i
];
size_t
ref_size
=
std
::
accumulate
(
ref_out
.
shape
.
begin
(),
ref_out
.
shape
.
end
(),
1
,
[](
int
a
,
int
b
)
{
return
a
*
b
;
});
size_t
size
=
std
::
accumulate
(
out
.
shape
.
begin
(),
out
.
shape
.
end
(),
1
,
[](
int
a
,
int
b
)
{
return
a
*
b
;
});
EXPECT_EQ
(
size
,
ref_size
);
EXPECT_EQ
(
out
.
dtype
,
ref_out
.
dtype
);
switch
(
out
.
dtype
)
{
case
PaddleDType
::
INT64
:
{
int64_t
*
pdata
=
static_cast
<
int64_t
*>
(
out
.
data
.
data
());
int64_t
*
pdata_ref
=
static_cast
<
int64_t
*>
(
ref_out
.
data
.
data
());
for
(
size_t
j
=
0
;
j
<
size
;
++
j
)
{
EXPECT_EQ
(
pdata_ref
[
j
],
pdata
[
j
]);
}
break
;
}
case
PaddleDType
::
FLOAT32
:
{
float
*
pdata
=
static_cast
<
float
*>
(
out
.
data
.
data
());
float
*
pdata_ref
=
static_cast
<
float
*>
(
ref_out
.
data
.
data
());
for
(
size_t
j
=
0
;
j
<
size
;
++
j
)
{
EXPECT_NEAR
(
pdata_ref
[
j
],
pdata
[
j
],
1e-3
);
}
break
;
}
}
// print what are fused
AnalysisPredictor
*
analysis_predictor
=
dynamic_cast
<
AnalysisPredictor
*>
(
predictor
.
get
());
auto
&
fuse_statis
=
analysis_predictor
->
analysis_argument
()
.
Get
<
std
::
unordered_map
<
std
::
string
,
int
>>
(
framework
::
ir
::
kFuseStatisAttr
);
for
(
auto
&
item
:
fuse_statis
)
{
LOG
(
INFO
)
<<
"fused "
<<
item
.
first
<<
" "
<<
item
.
second
;
}
int
num_ops
=
0
;
for
(
auto
&
node
:
analysis_predictor
->
analysis_argument
().
main_dfg
->
nodes
.
nodes
())
{
if
(
node
->
IsFunction
())
{
++
num_ops
;
}
}
LOG
(
INFO
)
<<
"has num ops: "
<<
num_ops
;
}
}
TEST
(
Analyzer_vis
,
analysis
)
{
TestVisualPrediction
();
}
}
// namespace analysis
}
// namespace inference
}
// namespace paddle
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