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656c672c
编写于
12月 26, 2018
作者:
T
tensor-tang
提交者:
GitHub
12月 26, 2018
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差异文件
Merge pull request #15051 from tensor-tang/test/seq_pool1
Add seq pool1 inference test
上级
ef2d292b
d46a140d
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
148 addition
and
20 deletion
+148
-20
paddle/fluid/inference/tests/api/CMakeLists.txt
paddle/fluid/inference/tests/api/CMakeLists.txt
+4
-0
paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc
...le/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc
+117
-0
paddle/fluid/inference/tests/api/tester_helper.h
paddle/fluid/inference/tests/api/tester_helper.h
+27
-20
未找到文件。
paddle/fluid/inference/tests/api/CMakeLists.txt
浏览文件 @
656c672c
...
...
@@ -108,6 +108,10 @@ inference_analysis_api_test_with_refer_result(test_analyzer_mobilenet_transpose
inference_analysis_api_test_with_fake_data
(
test_analyzer_resnet50
"
${
INFERENCE_DEMO_INSTALL_DIR
}
/resnet50"
analyzer_resnet50_tester.cc
"resnet50_model.tar.gz"
)
# seq_pool1
inference_analysis_api_test_with_fake_data
(
test_analyzer_seq_pool1
"
${
INFERENCE_DEMO_INSTALL_DIR
}
/seq_pool1"
analyzer_seq_pool1_tester.cc
"seq_pool1.tar.gz"
)
# mobilenet with depthwise_conv op
inference_analysis_api_test_with_fake_data
(
test_analyzer_mobilenet_depthwise_conv
"
${
INFERENCE_DEMO_INSTALL_DIR
}
/mobilenet_depthwise_conv"
analyzer_resnet50_tester.cc
"mobilenet_model.tar.gz"
)
...
...
paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc
0 → 100644
浏览文件 @
656c672c
/* 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 <fstream>
#include <iostream>
#include "paddle/fluid/inference/tests/api/tester_helper.h"
namespace
paddle
{
namespace
inference
{
namespace
analysis
{
void
SetConfig
(
AnalysisConfig
*
cfg
)
{
cfg
->
param_file
=
FLAGS_infer_model
+
"/params"
;
cfg
->
prog_file
=
FLAGS_infer_model
+
"/model"
;
cfg
->
use_gpu
=
false
;
cfg
->
device
=
0
;
cfg
->
enable_ir_optim
=
true
;
cfg
->
specify_input_name
=
true
;
cfg
->
SetCpuMathLibraryNumThreads
(
FLAGS_paddle_num_threads
);
}
void
SetInput
(
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
*
inputs
)
{
std
::
vector
<
std
::
string
>
feed_names
=
{
"slot10000_embed"
,
"slot10001_embed"
,
"slot10004_embed"
,
"slot10005_embed"
,
"slot10008_embed"
,
"slot10009_embed"
,
"slot10012_embed"
,
"slot10013_embed"
,
"slot10108_embed"
,
"slot13324_embed"
,
"slot13325_embed"
,
"slot13326_embed"
,
"slot13327_embed"
,
"slot13328_embed"
,
"slot13329_embed"
,
"slot13330_embed"
,
"slot13331_embed"
,
"slot15501_embed"
,
"slot15502_embed"
,
"slot15503_embed"
,
"slot15504_embed"
,
"slot15505_embed"
,
"slot15506_embed"
,
"slot15507_embed"
,
"slot15508_embed"
,
"slot15516_embed"
,
"slot15519_embed"
,
"slot15523_embed"
,
"slot15531_embed"
,
"slot15533_embed"
,
"slot15548_embed"
,
"slot15564_embed"
,
"slot15565_embed"
,
"slot15566_embed"
,
"slot15570_embed"
,
"slot15571_embed"
,
"slot15572_embed"
,
"slot15573_embed"
,
"slot15574_embed"
,
"slot15575_embed"
,
"slot15576_embed"
,
"slot15577_embed"
,
"slot15579_embed"
,
"slot15581_embed"
,
"slot15582_embed"
,
"slot15583_embed"
,
"slot15584_embed"
,
"slot5016_embed"
,
"slot5021_embed"
,
"slot6002_embed"
,
"slot6003_embed"
,
"slot6004_embed"
,
"slot6005_embed"
,
"slot6006_embed"
,
"slot6007_embed"
,
"slot6008_embed"
,
"slot6009_embed"
,
"slot6011_embed"
,
"slot6014_embed"
,
"slot6015_embed"
,
"slot6023_embed"
,
"slot6024_embed"
,
"slot6025_embed"
,
"slot6027_embed"
,
"slot6029_embed"
,
"slot6031_embed"
,
"slot6034_embed"
,
"slot6035_embed"
,
"slot6036_embed"
,
"slot6037_embed"
,
"slot6039_embed"
,
"slot6048_embed"
,
"slot6050_embed"
,
"slot6058_embed"
,
"slot6059_embed"
,
"slot6060_embed"
,
"slot6066_embed"
,
"slot6067_embed"
,
"slot6068_embed"
,
"slot6069_embed"
,
"slot6070_embed"
,
"slot6071_embed"
,
"slot6072_embed"
,
"slot6073_embed"
,
"slot6182_embed"
,
"slot6183_embed"
,
"slot6184_embed"
,
"slot6185_embed"
,
"slot6186_embed"
,
"slot6188_embed"
,
"slot6189_embed"
,
"slot6190_embed"
,
"slot6201_embed"
,
"slot6202_embed"
,
"slot6203_embed"
,
"slot6247_embed"
,
"slot6248_embed"
,
"slot6250_embed"
,
"slot6251_embed"
,
"slot6807_embed"
,
"slot6808_embed"
,
"slot6809_embed"
,
"slot6810_embed"
,
"slot6811_embed"
,
"slot6812_embed"
,
"slot6813_embed"
,
"slot6814_embed"
,
"slot6815_embed"
,
"slot6816_embed"
,
"slot6817_embed"
,
"slot6818_embed"
,
"slot6819_embed"
,
"slot6820_embed"
,
"slot6822_embed"
,
"slot6823_embed"
,
"slot6826_embed"
,
"slot7002_embed"
,
"slot7003_embed"
,
"slot7004_embed"
,
"slot7005_embed"
,
"slot7006_embed"
,
"slot7008_embed"
,
"slot7009_embed"
,
"slot7010_embed"
,
"slot7011_embed"
,
"slot7013_embed"
,
"slot7014_embed"
,
"slot7015_embed"
,
"slot7016_embed"
,
"slot7017_embed"
,
"slot7019_embed"
,
"slot7100_embed"
,
"slot7506_embed"
,
"slot7507_embed"
,
"slot7514_embed"
,
"slot7515_embed"
,
"slot7516_embed"
};
SetFakeImageInput
(
inputs
,
FLAGS_infer_model
,
true
,
"model"
,
"params"
,
&
feed_names
);
}
// Easy for profiling independently.
void
profile
(
bool
use_mkldnn
=
false
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
if
(
use_mkldnn
)
{
cfg
.
EnableMKLDNN
();
}
std
::
vector
<
PaddleTensor
>
outputs
;
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
input_slots_all
;
SetInput
(
&
input_slots_all
);
TestPrediction
(
reinterpret_cast
<
const
PaddlePredictor
::
Config
*>
(
&
cfg
),
input_slots_all
,
&
outputs
,
FLAGS_num_threads
);
}
TEST
(
Analyzer_seq_pool1
,
profile
)
{
profile
();
}
// Check the fuse status
TEST
(
Analyzer_seq_pool1
,
fuse_statis
)
{
AnalysisConfig
cfg
;
SetConfig
(
&
cfg
);
int
num_ops
;
auto
predictor
=
CreatePaddlePredictor
<
AnalysisConfig
>
(
cfg
);
auto
fuse_statis
=
GetFuseStatis
(
static_cast
<
AnalysisPredictor
*>
(
predictor
.
get
()),
&
num_ops
);
LOG
(
INFO
)
<<
"num_ops: "
<<
num_ops
;
EXPECT_EQ
(
num_ops
,
314
);
}
}
// namespace analysis
}
// namespace inference
}
// namespace paddle
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @
656c672c
...
...
@@ -132,7 +132,8 @@ std::unordered_map<std::string, int> GetFuseStatis(PaddlePredictor *predictor,
void
SetFakeImageInput
(
std
::
vector
<
std
::
vector
<
PaddleTensor
>>
*
inputs
,
const
std
::
string
&
dirname
,
bool
is_combined
=
true
,
std
::
string
model_filename
=
"model"
,
std
::
string
params_filename
=
"params"
)
{
std
::
string
params_filename
=
"params"
,
const
std
::
vector
<
std
::
string
>
*
feed_names
=
nullptr
)
{
// Set fake_image_data
PADDLE_ENFORCE_EQ
(
FLAGS_test_all_data
,
0
,
"Only have single batch of data."
);
std
::
vector
<
std
::
vector
<
int64_t
>>
feed_target_shapes
=
GetFeedTargetShapes
(
...
...
@@ -146,26 +147,32 @@ void SetFakeImageInput(std::vector<std::vector<PaddleTensor>> *inputs,
os
<<
"}
\n
"
;
}
LOG
(
INFO
)
<<
os
.
str
();
int
dim1
=
feed_target_shapes
[
0
][
1
];
int
dim2
=
feed_target_shapes
[
0
][
2
];
int
dim3
=
feed_target_shapes
[
0
][
3
];
PaddleTensor
input
;
std
::
vector
<
int
>
shape
({
FLAGS_batch_size
,
dim1
,
dim2
,
dim3
});
input
.
shape
=
shape
;
input
.
dtype
=
PaddleDType
::
FLOAT32
;
// fill input data, for profile easily, do not use random data here.
size_t
size
=
FLAGS_batch_size
*
dim1
*
dim2
*
dim3
;
input
.
data
.
Resize
(
size
*
sizeof
(
float
));
float
*
input_data
=
static_cast
<
float
*>
(
input
.
data
.
data
());
for
(
size_t
i
=
0
;
i
<
size
;
i
++
)
{
*
(
input_data
+
i
)
=
static_cast
<
float
>
(
i
)
/
size
;
if
(
feed_names
)
{
PADDLE_ENFORCE_EQ
(
feed_names
->
size
(),
feed_target_shapes
.
size
());
}
std
::
vector
<
PaddleTensor
>
input_slots
(
feed_target_shapes
.
size
());
for
(
size_t
i
=
0
;
i
<
feed_target_shapes
.
size
();
++
i
)
{
const
auto
&
feed_shape
=
feed_target_shapes
[
i
];
auto
&
input
=
input_slots
[
i
];
std
::
vector
<
int
>
shape
({
FLAGS_batch_size
});
for
(
size_t
s
=
1
;
s
<
feed_shape
.
size
();
++
s
)
{
shape
.
push_back
(
static_cast
<
int
>
(
feed_shape
[
s
]));
}
if
(
feed_names
)
{
input
.
name
=
(
*
feed_names
)[
i
];
}
input
.
shape
=
shape
;
input
.
dtype
=
PaddleDType
::
FLOAT32
;
size_t
len
=
std
::
accumulate
(
shape
.
begin
(),
shape
.
end
(),
1
,
[](
int
a
,
int
b
)
{
return
a
*
b
;
});
input
.
data
.
Resize
(
len
*
sizeof
(
float
));
input
.
lod
.
assign
({{
0
,
static_cast
<
size_t
>
(
FLAGS_batch_size
)}});
float
*
input_data
=
static_cast
<
float
*>
(
input
.
data
.
data
());
// fill input data, for profile easily, do not use random data here.
for
(
size_t
j
=
0
;
j
<
len
;
++
j
)
{
*
(
input_data
+
j
)
=
static_cast
<
float
>
(
j
)
/
len
;
}
}
std
::
vector
<
PaddleTensor
>
input_slots
;
input_slots
.
assign
({
input
});
(
*
inputs
).
emplace_back
(
input_slots
);
}
...
...
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