Semi-automated OpenVINO benchmark_app with variable parameters
Description
This program allows the users to specify variable parameters in the OpenVINO benchmark_app and run the benchmark with all combinations of the given parameters automatically.
The program will generate the report file in the CSV format with coded date and time file name ('result_DDmm-HHMMSS.csv'). You can analyze or visualize the benchmark result with MS Excel or a spreadsheet application.
The program is just a front-end for the OpenVINO official benchmark_app.
This program utilizes the benchmark_app as the benchmark core logic. So the performance result measured by this program must be consistent with the one measured by the benchmark_app.
Also, the command line parameters and their meaning are compatible with the benchmark_app.
Requirements
- OpenVINO 2022.1 or higher
This program is not compatible with OpenVINO 2021.
How to run
- Install required Python modules.
python -m pip install --upgrade pip setuptools
python -m pip install -r requirements.txt
- Run the auto benchmark (command line example)
python auto_benchmark_app.py -m resnet.xml -niter 100 -nthreads %1,2,4,8 -nstreams %1,2 -d %CPU,GPU -cdir cache
With this command line, -nthreads has 4 options (1,2,4,8), -nstreams has 2 options (1,2), and -d option has 2 options (CPU,GPU). As the result, 16 (4x2x2) benchmarks will be performed in total.
Parameter options
You can specify variable parameters by adding following prefix to the parameters.
| Prefix | Type | Description/Example |
|---|---|---|
| $ | range | $1,8,2 == range(1,8,2) => [1,3,5,7]All range() compatible expressions are possible. e.g. $1,5 or $5,1,-1 |
| % | list | %CPU,GPU => ['CPU', 'GPU'], %1,2,4,8 => [1,2,4,8] |
| @ | ir-models | @models == IR models in the './models' dir => ['resnet.xml', 'googlenet.xml', ...]This option will recursively search the '.xml' files in the specified directory. |
Examples of command line
python auto_benchmark_app.py -cdir cache -m resnet.xml -nthreads $1,5 -nstreams %1,2,4,8 -d %CPU,GPU
- Run benchmark with
-nthreads=range(1,5)=[1,2,3,4],-nstreams=[1,2,4,8],-d=['CPU','GPU']. Total 32 combinations.
python auto_benchmark_app.py -m @models -niter 100 -nthreads %1,2,4,8 -nstreams %1,2 -d CPU -cdir cache
- Run benchmark with
-m=[all .xml files inmodelsdirectory],-nthreads= [1,2,4,8],-nstreams=[1,2].
Example of a result file
The last 4 items in each line are the performance data in the order of 'count', 'duration (ms)', 'latency AVG (ms)', and 'throughput (fps)'.
#CPU: Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz
#MEM: 33947893760
#OS: Windows-10-10.0.22000-SP0
#OpenVINO: 2022.1.0-7019-cdb9bec7210-releases/2022/1
#Last 4 items in the lines : test count, duration (ms), latency AVG (ms), and throughput (fps)
benchmark_app.py,-m,models\FP16\googlenet-v1.xml,-niter,100,-nthreads,1,-nstreams,1,-d,CPU,-cdir,cache,100,772.55,30.20,129.44
benchmark_app.py,-m,models\FP16\resnet-50-tf.xml,-niter,100,-nthreads,1,-nstreams,1,-d,CPU,-cdir,cache,100,1917.62,75.06,52.15
benchmark_app.py,-m,models\FP16\squeezenet1.1.xml,-niter,100,-nthreads,1,-nstreams,1,-d,CPU,-cdir,cache,100,195.28,7.80,512.10
benchmark_app.py,-m,models\FP16-INT8\googlenet-v1.xml,-niter,100,-nthreads,1,-nstreams,1,-d,CPU,-cdir,cache,104,337.09,24.75,308.53
benchmark_app.py,-m,models\FP16-INT8\resnet-50-tf.xml,-niter,100,-nthreads,1,-nstreams,1,-d,CPU,-cdir,cache,100,1000.39,38.85,99.96
benchmark_app.py,-m,models\FP16-INT8\squeezenet1.1.xml,-niter,100,-nthreads,1,-nstreams,1,-d,CPU,-cdir,cache,104,64.22,4.69,1619.38
benchmark_app.py,-m,models\FP32\googlenet-v1.xml,-niter,100,-nthreads,1,-nstreams,1,-d,CPU,-cdir,cache,100,778.90,30.64,128.39
benchmark_app.py,-m,models\FP32\resnet-50-tf.xml,-niter,100,-nthreads,1,-nstreams,1,-d,CPU,-cdir,cache,100,1949.73,76.91,51.29
benchmark_app.py,-m,models\FP32\squeezenet1.1.xml,-niter,100,-nthreads,1,-nstreams,1,-d,CPU,-cdir,cache,100,182.59,7.58,547.69
benchmark_app.py,-m,models\FP32-INT8\googlenet-v1.xml,-niter,100,-nthreads,1,-nstreams,1,-d,CPU,-cdir,cache,104,331.73,24.90,313.51
benchmark_app.py,-m,models\FP32-INT8\resnet-50-tf.xml,-niter,100,-nthreads,1,-nstreams,1,-d,CPU,-cdir,cache,100,968.38,38.45,103.27
benchmark_app.py,-m,models\FP32-INT8\squeezenet1.1.xml,-niter,100,-nthreads,1,-nstreams,1,-d,CPU,-cdir,cache,104,67.70,5.04,1536.23
benchmark_app.py,-m,models\FP16\googlenet-v1.xml,-niter,100,-nthreads,2,-nstreams,1,-d,CPU,-cdir,cache,100,1536.14,15.30,65.10
benchmark_app.py,-m,models\FP16\resnet-50-tf.xml,-niter,100,-nthreads,2,-nstreams,1,-d,CPU,-cdir,cache,100,3655.59,36.50,27.36
benchmark_app.py,-m,models\FP16\squeezenet1.1.xml,-niter,100,-nthreads,2,-nstreams,1,-d,CPU,-cdir,cache,100,366.73,3.68,272.68
benchmark_app.py,-m,models\FP16-INT8\googlenet-v1.xml,-niter,100,-nthreads,2,-nstreams,1,-d,CPU,-cdir,cache,100,872.87,8.66,114.56
benchmark_app.py,-m,models\FP16-INT8\resnet-50-tf.xml,-niter,100,-nthreads,2,-nstreams,1,-d,CPU,-cdir,cache,100,1963.67,19.54,50.93
benchmark_app.py,-m,models\FP16-INT8\squeezenet1.1.xml,-niter,100,-nthreads,2,-nstreams,1,-d,CPU,-cdir,cache,100,242.28,2.34,412.74
benchmark_app.py,-m,models\FP32\googlenet-v1.xml,-niter,100,-nthreads,2,-nstreams,1,-d,CPU,-cdir,cache,100,1506.14,14.96,66.39
benchmark_app.py,-m,models\FP32\resnet-50-tf.xml,-niter,100,-nthreads,2,-nstreams,1,-d,CPU,-cdir,cache,100,3593.88,35.88,27.83
benchmark_app.py,-m,models\FP32\squeezenet1.1.xml,-niter,100,-nthreads,2,-nstreams,1,-d,CPU,-cdir,cache,100,366.28,3.56,273.01
benchmark_app.py,-m,models\FP32-INT8\googlenet-v1.xml,-niter,100,-nthreads,2,-nstreams,1,-d,CPU,-cdir,cache,100,876.52,8.69,114.09
benchmark_app.py,-m,models\FP32-INT8\resnet-50-tf.xml,-niter,100,-nthreads,2,-nstreams,1,-d,CPU,-cdir,cache,100,1934.72,19.25,51.69
END
14 Sep 01, 2022
67 Nov 25, 2022
70 Dec 07, 2022
186 Dec 18, 2022
145 Dec 13, 2022
4.8k Jan 07, 2023
464 Dec 19, 2022
387 Jan 04, 2023
142 Dec 24, 2022
33 Dec 08, 2022
116 Dec 26, 2022
208 Dec 30, 2022
31 Nov 25, 2022
2.4k Dec 25, 2022
36 Dec 09, 2022
1.5k Jan 02, 2023
89 Nov 09, 2022
134 Dec 23, 2022
127 Dec 12, 2022