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
28 Dec 16, 2022
7 Dec 27, 2022
5 May 26, 2022
173 Dec 30, 2022
3 Apr 09, 2022
139 Jan 01, 2023
1.5k Jan 03, 2023
27 Oct 06, 2022
3 Feb 22, 2022
145 Sep 28, 2022
57 Dec 15, 2022
165 Jan 09, 2023
10 Oct 06, 2022
606 Dec 21, 2022
3 Sep 23, 2022
72 Dec 28, 2022
1.8k Jan 07, 2023
62 Dec 28, 2022
366 Nov 15, 2022
90 Dec 22, 2022