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体验新版 GitCode,发现更多精彩内容 >>
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提交
5023530a
编写于
9月 10, 2018
作者:
Y
Yan Chunwei
提交者:
GitHub
9月 10, 2018
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Refactor/remove sensitive (#13314)
上级
478a4e85
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
21 addition
and
35 deletion
+21
-35
paddle/fluid/inference/analysis/CMakeLists.txt
paddle/fluid/inference/analysis/CMakeLists.txt
+8
-8
paddle/fluid/inference/analysis/analyzer_tester.cc
paddle/fluid/inference/analysis/analyzer_tester.cc
+13
-27
未找到文件。
paddle/fluid/inference/analysis/CMakeLists.txt
浏览文件 @
5023530a
...
...
@@ -48,18 +48,18 @@ function (inference_download_and_uncompress install_dir url gz_filename)
message
(
STATUS
"finish downloading
${
gz_filename
}
"
)
endfunction
(
inference_download_and_uncompress
)
set
(
DITU_RNN_MODEL_URL
"http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid
%2Fmodel.tar.gz"
)
set
(
DITU_RNN_DATA_URL
"http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid
%2Fdata.txt.tar.gz"
)
set
(
DITU_INSTALL_DIR
"
${
THIRD_PARTY_PATH
}
/inference_demo/ditu_rnn"
CACHE PATH
"Ditu RNN
model and data root."
FORCE
)
if
(
NOT EXISTS
${
DITU
_INSTALL_DIR
}
AND WITH_TESTING
)
inference_download_and_uncompress
(
${
DITU_INSTALL_DIR
}
${
DITU_RNN_MODEL_URL
}
"ditu_rnn_fluid
%2Fmodel.tar.gz"
)
inference_download_and_uncompress
(
${
DITU_INSTALL_DIR
}
${
DITU_RNN_DATA_URL
}
"ditu_rnn_fluid
%2Fdata.txt.tar.gz"
)
set
(
RNN1_MODEL_URL
"http://paddle-inference-dist.bj.bcebos.com/rnn1
%2Fmodel.tar.gz"
)
set
(
RNN1_DATA_URL
"http://paddle-inference-dist.bj.bcebos.com/rnn1
%2Fdata.txt.tar.gz"
)
set
(
RNN1_INSTALL_DIR
"
${
THIRD_PARTY_PATH
}
/inference_demo/rnn1"
CACHE PATH
"RNN1
model and data root."
FORCE
)
if
(
NOT EXISTS
${
RNN1
_INSTALL_DIR
}
AND WITH_TESTING
)
inference_download_and_uncompress
(
${
RNN1_INSTALL_DIR
}
${
RNN1_MODEL_URL
}
"rnn1
%2Fmodel.tar.gz"
)
inference_download_and_uncompress
(
${
RNN1_INSTALL_DIR
}
${
RNN1_DATA_URL
}
"rnn1
%2Fdata.txt.tar.gz"
)
endif
()
inference_analysis_test
(
test_analyzer SRCS analyzer_tester.cc
EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis_predictor
ARGS --infer_
ditu_rnn_model=
${
DITU
_INSTALL_DIR
}
/model
--infer_d
itu_rnn_data=
${
DITU
_INSTALL_DIR
}
/data.txt
)
ARGS --infer_
model=
${
RNN1
_INSTALL_DIR
}
/model
--infer_d
ata=
${
RNN1
_INSTALL_DIR
}
/data.txt
)
inference_analysis_test
(
test_data_flow_graph SRCS data_flow_graph_tester.cc
)
inference_analysis_test
(
test_data_flow_graph_to_fluid_pass SRCS data_flow_graph_to_fluid_pass_tester.cc
)
...
...
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @
5023530a
...
...
@@ -26,8 +26,8 @@
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
#include "paddle/fluid/inference/utils/singleton.h"
DEFINE_string
(
infer_
ditu_rnn_model
,
""
,
"model path for ditu RNN
"
);
DEFINE_string
(
infer_d
itu_rnn_data
,
""
,
"data path for ditu RNN
"
);
DEFINE_string
(
infer_
model
,
""
,
"model path
"
);
DEFINE_string
(
infer_d
ata
,
""
,
"data path
"
);
DEFINE_int32
(
batch_size
,
10
,
"batch size."
);
DEFINE_int32
(
repeat
,
1
,
"Running the inference program repeat times."
);
DEFINE_int32
(
num_threads
,
1
,
"Running the inference program in multi-threads."
);
...
...
@@ -223,17 +223,6 @@ void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
}
// namespace
const
float
ditu_rnn_target_data
[]
=
{
104.711
,
11.2431
,
1.35422
,
0
,
0
,
0
,
0
,
0
,
27.7039
,
1.41486
,
7.09526
,
0
,
0
,
0
,
0
,
0
,
7.6481
,
6.5324
,
56.383
,
2.88018
,
8.92918
,
132.007
,
4.27429
,
2.02934
,
14.1727
,
10.7461
,
25.0616
,
16.0197
,
14.4163
,
16.9199
,
6.75517
,
0
,
80.0249
,
4.77739
,
0
,
0
,
0
,
0
,
0
,
0
,
47.5643
,
2.67029
,
8.76252
,
0
,
0
,
0
,
0
,
0
,
51.8822
,
4.4411
,
0
,
0
,
0
,
0
,
0
,
0
,
10.7286
,
12.0595
,
10.6672
,
0
,
0
,
0
,
0
,
0
,
93.5771
,
3.84641
,
0
,
0
,
0
,
0
,
0
,
0
,
169.426
,
0
,
0
,
0
,
0
,
0
,
0
,
0
};
void
CompareResult
(
const
std
::
vector
<
PaddleTensor
>
&
outputs
,
const
std
::
vector
<
PaddleTensor
>
&
base_outputs
)
{
PADDLE_ENFORCE_GT
(
outputs
.
size
(),
0
);
...
...
@@ -255,11 +244,10 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
}
}
// Test with a really complicate model.
void
TestDituRNNPrediction
(
bool
use_analysis
,
bool
activate_ir
,
int
num_threads
)
{
void
TestRNN1Prediction
(
bool
use_analysis
,
bool
activate_ir
,
int
num_threads
)
{
AnalysisConfig
config
;
config
.
prog_file
=
FLAGS_infer_
ditu_rnn_
model
+
"/__model__"
;
config
.
param_file
=
FLAGS_infer_
ditu_rnn_
model
+
"/param"
;
config
.
prog_file
=
FLAGS_infer_model
+
"/__model__"
;
config
.
param_file
=
FLAGS_infer_model
+
"/param"
;
config
.
use_gpu
=
false
;
config
.
device
=
0
;
config
.
specify_input_name
=
true
;
...
...
@@ -277,7 +265,7 @@ void TestDituRNNPrediction(bool use_analysis, bool activate_ir,
CreatePaddlePredictor
<
AnalysisConfig
,
PaddleEngineKind
::
kAnalysis
>
(
config
);
std
::
vector
<
PaddleTensor
>
input_slots
;
DataRecord
data
(
FLAGS_infer_d
itu_rnn_d
ata
,
batch_size
);
DataRecord
data
(
FLAGS_infer_data
,
batch_size
);
// Prepare inputs.
PrepareInputs
(
&
input_slots
,
&
data
,
batch_size
);
std
::
vector
<
PaddleTensor
>
outputs
,
base_outputs
;
...
...
@@ -307,7 +295,7 @@ void TestDituRNNPrediction(bool use_analysis, bool activate_ir,
threads
.
emplace_back
([
&
,
tid
]()
{
// Each thread should have local input_slots and outputs.
std
::
vector
<
PaddleTensor
>
input_slots
;
DataRecord
data
(
FLAGS_infer_d
itu_rnn_d
ata
,
batch_size
);
DataRecord
data
(
FLAGS_infer_data
,
batch_size
);
PrepareInputs
(
&
input_slots
,
&
data
,
batch_size
);
std
::
vector
<
PaddleTensor
>
outputs
;
Timer
timer
;
...
...
@@ -354,24 +342,22 @@ void TestDituRNNPrediction(bool use_analysis, bool activate_ir,
}
// Inference with analysis and IR, easy for profiling independently.
TEST
(
Analyzer
,
DituRNN
)
{
TestDituRNNPrediction
(
true
,
true
,
FLAGS_num_threads
);
}
TEST
(
Analyzer
,
rnn1
)
{
TestRNN1Prediction
(
true
,
true
,
FLAGS_num_threads
);
}
// Other unit-tests of
DituRNN
, test different options of use_analysis,
// Other unit-tests of
RNN1
, test different options of use_analysis,
// activate_ir and multi-threads.
TEST
(
Analyzer
,
Ditu
RNN_tests
)
{
TEST
(
Analyzer
,
RNN_tests
)
{
int
num_threads
[
2
]
=
{
1
,
4
};
for
(
auto
i
:
num_threads
)
{
// Directly infer with the original model.
Test
DituRNN
Prediction
(
false
,
false
,
i
);
Test
RNN1
Prediction
(
false
,
false
,
i
);
// Inference with the original model with the analysis turned on, the
// analysis
// module will transform the program to a data flow graph.
Test
DituRNN
Prediction
(
true
,
false
,
i
);
Test
RNN1
Prediction
(
true
,
false
,
i
);
// Inference with analysis and IR. The IR module will fuse some large
// kernels.
Test
DituRNN
Prediction
(
true
,
true
,
i
);
Test
RNN1
Prediction
(
true
,
true
,
i
);
}
}
...
...
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