利用yolov5和TensorRT从0到1实现目标检测的模型训练到模型部署全过程

Related tags

Deep Learningyolov5-TensorRT
Overview

写在前面

利用TensorRT加速推理速度是以时间换取精度的做法，意味着在推理速度上升的同时将会有精度的下降，不过不用太担心，精度下降微乎其微。此外，要有NVIDIA显卡，经测试，CUDA10.2可以支持20系列显卡及以下，30系列显卡需要CUDA11.x的支持，并且目前有bug。

默认你已经完成了 yolov5的训练过程并得到了.pt模型权值文件。

本文目的仅是带着走通流程。

注意要对应yolov5和tensorrtx的版本。

  • ./yolov5包含yolov5训练以及模型初转化阶段的代码
  • ./model_process是将.wts模型转化为.engine模型的代码
  • ./detector是利用.engine模型进行前向推理阶段的代码

我的运行环境(注意OpenCV要选择适合你的visual studio的版本等问题)：

win10

Visual Studio 2019

NVIDIA GeForce RTX 2060

opencv-3.4.3-vc14_vc15

cuda_10.2.89_441.22_win10

cudnn-10.2-windows10-x64-v7.6.5.32

TensorRT-7.0.0.11.Windows10.x86_64.cuda-10.2.cudnn7.6

cmake-3.21.2-windows-x86_64

上述环境的百度云(测试10、20系列可用)：

链接：https://pan.baidu.com/s/1AyaloTzLap8X2hsJBvyeBw
提取码：dwr7

其他版本下载地址：

CUDA cudnn TensorRT CMake OpenCV

环境安装：

1、安装OpenCV并配置好环境变量

2、安装CUDA

一路默认。一般的安装路径为：C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2

3、安装cudnn和TensorRT

cudnn和TensorRT的安装仅是将下载的对应版本的压缩包解压并复制*.h、*.lib、*.dll到CUDA的安装路径。

1 将cuDNN压缩包解压

2 将cuda\bin中的文件复制到 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin

3 将cuda\include中的文件复制到 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\include

4 将cuda\lib中的文件复制到 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\lib

另外，

1 将TensorRT压缩包解压

2 将 TensorRT-7.0.0.11\include中头文件复制到C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\include

3 将TensorRT-7.0.0.11\lib中所有lib文件复制到C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\lib\x64

4 将TensorRT-7.0.0.11\lib中所有dll文件复制到C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin

4、安装CMake软件备用

一、将训练阶段得到的.pt模型转化为.wts中间模型

把tensorrtx里面的yolov5\gen_wts.py加入到yolov5里面，执行

python gen_wts.py -w [.pt权值文件路径]

runs\train\exp\weights\best.pt为训练过程生成的.pt模型，生成的best.wts会保存到同目录下，此best.wts待会会用到。

cuda版本每个电脑不一样

配置好的tensorrtx，包括Cmakelist.txt的设定以及dirent.h的配置。

若使用原作者的请参照tensorrtx源码https://github.com/wang-xinyu/tensorrtx ，配置过程中会遇到一些问题，挨个解决，问题不大。

1、在yolov5目录下新建build文件夹

2、修改CMakelist.txt

add_definitions(-DAPI_EXPORTS)

3、打开CMake

​​ generate后关闭

4、yolov5/include/dirent.h

​​ 也可使用我的配置好的

二、利用Cmake软件创建VS工程

修改CMakeLists.txt中此处为你的opencv安装路径。

配置好上方两个目录之后，点击Configure，根据你的环境选择配置，

点击Gnerate，警告可忽视，

现在关闭Cmake即可。

三、wts转化为engine

VS打开刚刚在bulid目录下创建的工程。

build处vs打开，生成

问题:我的模型只识别一个类，需要更改


cd {tensorrtx}/yolov5/

// update CLASS_NUM in yololayer.h if your model is trained on custom dataset

为1

生成项目。

把之前生成的best.wts复制到build\release目录里面

cmd里面运行：

.\test.exe -s .\best.wts best.engine s

运行成功在同文件夹下面会得到best.engine转换后的文件。之后的推理过程使用的都是这个文件。

测试：

.\yolov5.exe -d best.engine .\samples

至此，流程走完。

如果想要进一步封装，可以按照我的示例。

注释掉yolov5.cpp，并取消 几个文件的注释。重新生成项目。按照你的需求更改。

Owner
Helium
Helium
GitHub Repository
A Python package to process & model ChEMBL data.

insilico: A Python package to process & model ChEMBL data. ChEMBL is a manually curated chemical database of bioactive molecules with drug-like proper

Steven Newton 0 Dec 09, 2021
Solve a Rubiks Cube using Python Opencv and Kociemba module

Rubiks_Cube_Solver Solve a Rubiks Cube using Python Opencv and Kociemba module Main Steps Get the countours of the cube check whether there are tota

Adarsh Badagala 176 Jan 01, 2023
This repository contains the code for "Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP".

Self-Diagnosis and Self-Debiasing This repository contains the source code for Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based

Timo Schick 62 Dec 12, 2022
MXNet implementation for: Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution

Octave Convolution MXNet implementation for: Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution Imag

Meta Research 549 Dec 28, 2022
Some bravo or inspiring research works on the topic of curriculum learning.

Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN Official code for NeurIPS 2021 paper "Towards Scalable Unpaired Virtu

131 Jan 07, 2023
Code for AutoNL on ImageNet (CVPR2020)

Neural Architecture Search for Lightweight Non-Local Networks This repository contains the code for CVPR 2020 paper Neural Architecture Search for Lig

Yingwei Li 104 Aug 31, 2022
A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).

Attention Walk ⠀⠀ A PyTorch Implementation of Watch Your Step: Learning Node Embeddings via Graph Attention (NIPS 2018). Abstract Graph embedding meth

Benedek Rozemberczki 303 Dec 09, 2022
This project is used for the paper Differentiable Programming of Isometric Tensor Network

This project is used for the paper "Differentiable Programming of Isometric Tensor Network". (arXiv:2110.03898)

Chenhua Geng 15 Dec 13, 2022
Multivariate Time Series Forecasting with efficient Transformers. Code for the paper "Long-Range Transformers for Dynamic Spatiotemporal Forecasting."

Spacetimeformer Multivariate Forecasting This repository contains the code for the paper, "Long-Range Transformers for Dynamic Spatiotemporal Forecast

QData 440 Jan 02, 2023
Multi agent DDPG algorithm written in Python + Pytorch

Multi agent DDPG algorithm written in Python + Pytorch. It also includes a Jupyter notebook, Tennis.ipynb, as a showcase.

Rogier Wachters 2 Feb 26, 2022
ObjectDrawer-ToolBox: a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system

ObjectDrawer-ToolBox is a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system, Object Drawer.

77 Jan 05, 2023
Tensorflow Implementation for "Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition"

Tensorflow Implementation for "Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition" Pre-trained Deep Convo

Ankush Malaker 5 Nov 11, 2022
DTCN IJCAI - Sequential prediction learning framework and algorithm

DTCN This is the implementation of our paper "Sequential Prediction of Social Me

Bobby 2 Jan 24, 2022
Alphabetical Letter Recognition

DecisionTrees-Image-Classification Alphabetical Letter Recognition In these demo we are using "Decision Trees" Our database is composed by Learning Im

Mohammed Firass 4 Nov 30, 2021
Code related to the manuscript "Averting A Crisis In Simulation-Based Inference"

Abstract We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms are inadequate for the falsificat

Montefiore Artificial Intelligence Research 3 Nov 14, 2022
Code for the paper "Curriculum Dropout", ICCV 2017

Curriculum Dropout Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability dis

Pietro Morerio 21 Jan 02, 2022
Iranian Cars Detection using Yolov5s, PyTorch

Iranian Cars Detection using Yolov5 Train 1- git clone https://github.com/ultralytics/yolov5 cd yolov5 pip install -r requirements.txt 2- Dataset ../

Nahid Ebrahimian 22 Dec 05, 2022
Neural Network to colorize grayscale images

#colornet Neural Network to colorize grayscale images Results Grayscale Prediction Ground Truth Eiji K used colornet for anime colorization Sources Au

Pavel Hanchar 3.6k Dec 24, 2022
Generative Adversarial Text-to-Image Synthesis

###Generative Adversarial Text-to-Image Synthesis Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee This is the

Scott Ellison Reed 883 Dec 31, 2022
Pretty Tensor - Fluent Neural Networks in TensorFlow

Pretty Tensor provides a high level builder API for TensorFlow. It provides thin wrappers on Tensors so that you can easily build multi-layer neural networks.

Google 1.2k Dec 29, 2022