Semi-automated OpenVINO benchmark_app with variable parameters

Overview

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

  1. Install required Python modules.
python -m pip install --upgrade pip setuptools
python -m pip install -r requirements.txt
  1. 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 in models directory], -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

Owner
Yasunori Shimura
Yasunori Shimura
This is the official Pytorch implementation of the paper "Diverse Motion Stylization for Multiple Style Domains via Spatial-Temporal Graph-Based Generative Model"

Diverse Motion Stylization (Official) This is the official Pytorch implementation of this paper. Diverse Motion Stylization for Multiple Style Domains

Soomin Park 28 Dec 16, 2022
PyTorch implementation for OCT-GAN Neural ODE-based Conditional Tabular GANs (WWW 2021)

OCT-GAN: Neural ODE-based Conditional Tabular GANs (OCT-GAN) Code for reproducing the experiments in the paper: Jayoung Kim*, Jinsung Jeon*, Jaehoon L

BigDyL 7 Dec 27, 2022
HarDNeXt: Official HarDNeXt repository

HarDNeXt-Pytorch HarDNeXt: A Stage Receptive Field and Connectivity Aware Convolution Neural Network HarDNeXt-MSEG for Medical Image Segmentation in 0

5 May 26, 2022
[CVPR 2021] Official PyTorch Implementation for "Iterative Filter Adaptive Network for Single Image Defocus Deblurring"

IFAN: Iterative Filter Adaptive Network for Single Image Defocus Deblurring Checkout for the demo (GUI/Google Colab)! The GUI version might occasional

Junyong Lee 173 Dec 30, 2022
Gif-caption - A straightforward GIF Captioner written in Python

Broksy's GIF Captioner Have you ever wanted to easily caption a GIF without havi

3 Apr 09, 2022
Deep Image Search is an AI-based image search engine that includes deep transfor learning features Extraction and tree-based vectorized search.

Deep Image Search - AI-Based Image Search Engine Deep Image Search is an AI-based image search engine that includes deep transfer learning features Ex

139 Jan 01, 2023
This repository contains the code for the paper "PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization"

PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization News: [2020/05/04] Added EGL rendering option for training data g

Shunsuke Saito 1.5k Jan 03, 2023
An image classification app boilerplate to serve your deep learning models asap!

Image 🖼 Classification App Boilerplate Have you been puzzled by tons of videos, blogs and other resources on the internet and don't know where and ho

Smaranjit Ghose 27 Oct 06, 2022
The aim of this project is to build an AI bot that can play the Wordle game, or more generally Squabble

Wordle RL The aim of this project is to build an AI bot that can play the Wordle game, or more generally Squabble I know there are more deterministic

Aditya Arora 3 Feb 22, 2022
Learning to Segment Instances in Videos with Spatial Propagation Network

Learning to Segment Instances in Videos with Spatial Propagation Network This paper is available at the 2017 DAVIS Challenge website. Check our result

Jingchun Cheng 145 Sep 28, 2022
This is a official repository of SimViT.

SimViT This is a official repository of SimViT. We will open our models and codes about object detection and semantic segmentation soon. Our code refe

ligang 57 Dec 15, 2022
[CVPR'2020] DeepDeform: Learning Non-rigid RGB-D Reconstruction with Semi-supervised Data

DeepDeform (CVPR'2020) DeepDeform is an RGB-D video dataset containing over 390,000 RGB-D frames in 400 videos, with 5,533 optical and scene flow imag

Aljaz Bozic 165 Jan 09, 2023
A neuroanatomy-based augmented reality experience powered by computer vision. Features 3D visuals of the Atlas Brain Map slices.

Brain Augmented Reality (AR) A neuroanatomy-based augmented reality experience powered by computer vision that features 3D visuals of the Atlas Brain

Yasmeen Brain 10 Oct 06, 2022
A PyTorch implementation for V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

A PyTorch implementation of V-Net Vnet is a PyTorch implementation of the paper V-Net: Fully Convolutional Neural Networks for Volumetric Medical Imag

Matthew Macy 606 Dec 21, 2022
Julia package for multiway (inverse) covariance estimation.

TensorGraphicalModels TensorGraphicalModels.jl is a suite of Julia tools for estimating high-dimensional multiway (tensor-variate) covariance and inve

Wayne Wang 3 Sep 23, 2022
An official PyTorch Implementation of Boundary-aware Self-supervised Learning for Video Scene Segmentation (BaSSL)

An official PyTorch Implementation of Boundary-aware Self-supervised Learning for Video Scene Segmentation (BaSSL)

Kakao Brain 72 Dec 28, 2022
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

Benedek Rozemberczki 1.8k Jan 07, 2023
Winning solution of the Indoor Location & Navigation Kaggle competition

This repository contains the code to generate the winning solution of the Kaggle competition on indoor location and navigation organized by Microsoft

Tom Van de Wiele 62 Dec 28, 2022
PyTorch implementation of Trust Region Policy Optimization

PyTorch implementation of TRPO Try my implementation of PPO (aka newer better variant of TRPO), unless you need to you TRPO for some specific reasons.

Ilya Kostrikov 366 Nov 15, 2022
Codes for paper "KNAS: Green Neural Architecture Search"

KNAS Codes for paper "KNAS: Green Neural Architecture Search" KNAS is a green (energy-efficient) Neural Architecture Search (NAS) approach. It contain

90 Dec 22, 2022