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
Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model in Tensorflow Lite.

TFLite-msg_chn_wacv20-depth-completion Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model

Ibai Gorordo 2 Oct 04, 2021
PyTorch code for training MM-DistillNet for multimodal knowledge distillation

There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge MM-DistillNet is a

51 Dec 20, 2022
Implementation of Nyström Self-attention, from the paper Nyströmformer

Nyström Attention Implementation of Nyström Self-attention, from the paper Nyströmformer. Yannic Kilcher video Install $ pip install nystrom-attention

Phil Wang 95 Jan 02, 2023
Neural network chess engine trained on Gary Kasparov's games.

Neural Chess It's not the best chess engine, but it is a chess engine. Proof of concept neural network chess engine (feed-forward multi-layer perceptr

3 Jun 22, 2022
Notebooks, slides and dataset of the CorrelAid Machine Learning Winter School

CorrelAid Machine Learning Winter School Welcome to the CorrelAid ML Winter School! Task The problem we want to solve is to classify trees in Roosevel

CorrelAid 12 Nov 23, 2022
Self-Regulated Learning for Egocentric Video Activity Anticipation

Self-Regulated Learning for Egocentric Video Activity Anticipation Introduction This is a Pytorch implementation of the model described in our paper:

qzhb 13 Sep 23, 2022
Faster RCNN pytorch windows

Faster-RCNN-pytorch-windows Faster RCNN implementation with pytorch for windows Open cmd, compile this comands: cd lib python setup.py build develop T

Hwa-Rang Kim 1 Nov 11, 2022
1st ranked 'driver careless behavior detection' for AI Online Competition 2021, hosted by MSIT Korea.

2021AICompetition-03 본 repo 는 mAy-I Inc. 팀으로 참가한 2021 인공지능 온라인 경진대회 중 [이미지] 운전 사고 예방을 위한 운전자 부주의 행동 검출 모델] 태스크 수행을 위한 레포지토리입니다. mAy-I 는 과학기술정보통신부가 주최하

Junhyuk Park 9 Dec 01, 2022
Transparent Transformer Segmentation

Transparent Transformer Segmentation Introduction This repository contains the data and code for IJCAI 2021 paper Segmenting transparent object in the

谢恩泽 140 Jan 02, 2023
Advancing mathematics by guiding human intuition with AI

Advancing mathematics by guiding human intuition with AI This repo contains two colab notebooks which accompany the paper, available online at https:/

DeepMind 315 Dec 26, 2022
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks

DECAF (DEbiasing CAusal Fairness) Code Author: Trent Kyono This repository contains the code used for the "DECAF: Generating Fair Synthetic Data Using

van_der_Schaar \LAB 7 Nov 24, 2022
Interactive web apps created using geemap and streamlit

geemap-apps Introduction This repo demostrates how to build a multi-page Earth Engine App using streamlit and geemap. You can deploy the app on variou

Qiusheng Wu 27 Dec 23, 2022
Implementation of the GBST block from the Charformer paper, in Pytorch

Charformer - Pytorch Implementation of the GBST (gradient-based subword tokenization) module from the Charformer paper, in Pytorch. The paper proposes

Phil Wang 105 Dec 26, 2022
SegNet-Basic with Keras

SegNet-Basic: What is Segnet? Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-wise Image Segmentation Segnet = (Encoder + Decoder)

Yad Konrad 81 Jun 30, 2022
QHack—the quantum machine learning hackathon

Official repo for QHack—the quantum machine learning hackathon

Xanadu 72 Dec 21, 2022
On the adaptation of recurrent neural networks for system identification

On the adaptation of recurrent neural networks for system identification This repository contains the Python code to reproduce the results of the pape

Marco Forgione 3 Jan 13, 2022
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

[ICLR 2022] The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training The Unreasonable Effectiveness of

VITA 44 Dec 23, 2022
A PyTorch implementation of DenseNet.

A PyTorch Implementation of DenseNet This is a PyTorch implementation of the DenseNet-BC architecture as described in the paper Densely Connected Conv

Brandon Amos 771 Dec 15, 2022
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Super Resolution Examples We run this script under TensorFlow 2.0 and the TensorLayer2.0+. For TensorLayer 1.4 version, please check release. 🚀 🚀 🚀

TensorLayer Community 2.9k Jan 08, 2023
InsCLR: Improving Instance Retrieval with Self-Supervision

InsCLR: Improving Instance Retrieval with Self-Supervision This is an official PyTorch implementation of the InsCLR paper. Download Dataset Dataset Im

Zelu Deng 25 Aug 30, 2022