? ? ? ? 一、準備工具

??二、燒錄

????????三、搭配環境

????????四、試跑Yolov5

????????五、tensorRT部署yolov5

---

前言：

在工作或學習中我們需要進行部署，下面這篇文章是我親自部署jetson nano之后做出的總結，包括自己遇到一些報錯和踩坑，希望對你們有所幫助 :?）

一、準備工具

讀卡器

SD卡?

小螺絲刀

網線（更改語言需要網絡）

二、燒錄

燒錄鏡像就是要把SD卡里的東西給完全清除，好比我們電腦重裝系統一樣，把SD卡格式化。

?插上讀卡器后會自動識別U盤，我的電腦會識別很多，彈出很多個U盤選項，這個是正常現象，只格式化一個就可以了。

1. 在本地的電腦上下載燒錄的鏡像，可以去官網自行下載，下載的時候需要看好版本，不然會出問題，我下載的版本是4.4.1，下載的網址如下：

??JetPack SDK 4.4.1 archive | NVIDIA Developer

2. 下載好之后進行解壓：

3.?然后我們需要下載燒錄SD卡的工具，網址如下：

Get Started With Jetson Nano Developer Kit | NVIDIA Developer

下載完之后是一個.exe可執行文件，運行安裝就可以了。

4. 開始燒錄鏡像

運行上面下載好的Etcher之后是這樣的，選擇完鏡像之后會自動識別SD卡，讓后點擊flash

?這個是正在燒錄的界面，會自動燒錄2遍（大約20分鐘左右）

5.燒錄完成，彈出讀卡器，插卡開機。

6.開機之后，簡單設置一下（密碼、地區、時間），然后會就是和我們本地電腦Ubuntu界面差不多。

三、搭配環境

1.??配置CUDA

首先打開終端（ctrl+alt+t），輸入以下命令：

sudo gedit ~/.bashrc

輸入密碼后進入文檔：

鼠標滾輪滾到文檔最下面，輸入下面命令，然后按ctrl+s保存：

export CUDA_HOME=/usr/local/cuda-10.2 export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH export PATH=/usr/local/cuda-10.2/bin:$PATH

驗證CUDA是否安裝配置成功 ：

nvcc -V

出現這個就證明配置成功了。

?*報錯*

我輸出之后會出現讀取異常，或者不允許保存的提示，然后運行nvcc -V的時候顯示命令不可執行，這個多輸入幾次或者關閉終端再輸入幾次命令

2.? 配置conda

jetson nanoB01的架構是aarch64,與windows和liunx不同不同,所以不能安裝Anaconda，可以安裝一個替代它的archiconda。

在終端輸入以下下載命令：

wget https://github.com/Archiconda/build-tools/releases/download/0.2.3/Archiconda3-0.2.3-Linux-aarch64.sh

下載成功。?

* 報錯*

遇到下載一半或者下載到99%之后報錯的問題。

這個問題是因為堆棧小的原因，改一下堆棧大小就可以了。

先查一以下自己的系統堆棧大小，用這個命令：

ulimit -a

stack size是堆棧的大小，我原來是8192，需要改成102400

?

直接用代碼 ulimit -s 102400 修改，改完之后：

下載好之后運行這個命令：

bash Archiconda3-0.2.3-Linux-aarch64.sh

接下來就是傻瓜式的安裝：

安裝完之后配置環境變量：?

sudo gedit ~/.bashrc

在最初打開的文檔里的最后一行加上這個命令：?

export PATH=~/archiconda3/bin:$PATH

查看conda的版本號：

conda -V

3. 創建你自己的虛擬環境：

conda create -n xxx（虛擬環境名） python=3.6 #創建一個python3.6的虛擬環境 conda activate xxx #進入虛擬環境 conda deactivate #（退出虛擬環境）

4. 換源

首先需要備份一下sources.list文件，執行后終端沒有響應

sudo cp /etc/apt/sources.list /etc/apt/sources.list.bak

進入sources.list內部

sudo gedit /etc/apt/sources.list

進入之后，ctrl+a刪除所有內容，之后將以下內容復制進去保存退出。

deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic main multiverse restricted universe deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-security main multiverse restricted universe deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-updates main multiverse restricted universe deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-backports main multiverse restricted universe deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic main multiverse restricted universe deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-security main multiverse restricted universe deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-updates main multiverse restricted universe deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-backports main multiverse restricted universe

?然后更新軟件列表，保存到本地：

sudo apt-get update

?更新軟件：

sudo apt-get upgrade

升級所有的安裝包，并解決依賴關系

sudo apt-get dist-upgrade

5. 安裝pip

sudo apt-get install python3-pip libopenblas-base libopenmpi-dev

更新pip到最新版本

pip3 install --upgrade pip #如果pip已是最新，可不執行

?6. 下載torch和torchvision

nano上安裝需要去英偉達官網下載所需版本，我這里是1.8.0，網址如下：

PyTorch for Jetson - version 1.10 now available - Jetson Nano - NVIDIA Developer Forums

下載好后，這里推薦一個工具（MobaXterm）用于連接板子進行互傳，當然你也可以直接下載到U盤，用U盤實現互傳。

具體操作可以看這個博主的博客：MobaXterm（終端工具）下載&安裝&使用教程_蝸牛也不慢......的博客-CSDN博客

連接成功后把可以在本地下載的東西解壓后，傳過去，建議傳到home目錄下。
?

7. 安裝torch

在torchvision文件夾的目錄下右鍵，進入終端，然后進入你創建的虛擬環境，輸入以下命令:

sudo apt-get install python3-pip libopenblas-base libopenmpi-dev

pip install torch-1.8.0-cp36-cp36m-linux_aarch64.whl

?如果遇到網絡問題下載不了，就在第二個命令下載后面加上清華下載鏡像：

-i https://pypi.tuna.tsinghua.edu.cn/simple

安裝torch時,一定要安裝numpy，不然的話顯示你安裝torch成功,但是你conda list是找不到的。

sudo apt install python3-numpy

?測試torch是否安裝成功：

import torch print(torch.__version__)

*報錯*

在測試torch是否安裝成功時報錯：非法指令(核心已轉儲)。

export OPENBLAS_CORETYPE=ARMV8

8. 安裝torchvision，逐個執行以下命令（如果執行第三個命令報錯，繼續執行第四第五行命令，如果不報錯就直接cd ..）

cd torchvisionexport BUILD_VERSION=0.9.0sudo python setup.py install python setup.py buildpython setup.py installcd .. #(中間有個空格)

*報錯*

在測試時如果報錯PIL，就安裝pillow，命令如下（在第二步如果報權限錯誤，就在開頭加sudo，或在結尾加--user）

sudo apt-get install libjpeg8 libjpeg62-dev libfreetype6 libfreetype6-devpython3 -m pip install -i https://mirrors.aliyun.com/pypi/simple pillow

??

?到這里為止環境就全部搭建完畢了，下面開始部署Yolo5。

?四、試跑Yolov5

1. 去官網上下載需要的版本，我這里下載的是5.0版本，下載的時候要把對應的權重也要下載。網址如下：

ultralytics/yolov5 at v5.0 (github.com)

在這里選擇版本：

權重網址如下：

Releases · ultralytics/yolov5 (github.com)

2. 下載完或者使用工具將yolo5和權重文件拖到主目錄之后，使用cd進入yolo5文件目錄下，在終端下載依賴項：

pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

?*報錯*

遇到下面的問題是因為numpy版本過高導致的，降低以下版本就可以了。

把版本降低到1.19.4：

pip install numpy==1.19.4 -i https://pypi.tuna.tsinghua.edu.cn/simple

?解決完報錯之后，在運行上面的命令，會自動下載需要的安裝包，其他的都是很快的，但是到opencv的時候需要花費很長很長很長很長的時間......，當安裝opencv時會出現Building wheel for opencv-python (pyroject.toml)...? 這種情況正常現象，是opencv在編譯，繼續等就可以了，我編譯了2個多小時...

3. 經過N長時間的等待，安裝好所有依賴包后，將權重文件拖到yolov5文件夾根目錄下，在yolov5的根目錄下打開終端，執行以下命令：

python3 detect.py --weights yolov5s.pt

測試一下是沒問題的，接下來我們開始部署yolov5。

五、tensorRT部署yolov5

1. tensorRT官網下載yolov5，網址如下，確定下載是v5.0版本:

mirrors / wang-xinyu / tensorrtx · GitCode

mirrors / wang-xinyu / tensorrtx · GitCodeImplementation of popular deep learning networks with TensorRT network definition API 🚀 Github 鏡像倉庫 🚀 源項目地址https://gitcode.net/mirrors/wang-xinyu/tensorrtx?utm_source=csdn_github_accelerator

2. 下載完畢之后在文件夾里找到gen_wts.py，然后復制到yolov5文件夾下，右鍵打開終端，執行命令后生成yolov5.wts文件:

python3 gen_wts.py --w yolov5s.pt

注：每個人的文件路徑不同，這個根據自己的途權重路徑而改變，一般路徑都在tensorrtx-yolov5-v5.0 -> yolov5 -> gen_wts.pt。

?3. 找到yololayer.h文件,打開修改類別數量(根據自己的情況而定),和輸入圖片大小(修改是盡量是32的倍數)

4. 在當前目錄下創建文件build，命令如下：

maker build #創建build文件夾cd build #進入buildcmake .. #構建項目#將我們上面生成的.wts文件復制到build文件夾中make

5. 將上面生成的yolov5.wts文件拖到tensortx/yolov5下，右鍵打開終端：

sudo ./yolov5 -s yolov5s.wts yolov5s.engine s

6. 在tensorrtx-yolov5-v5.0\yolov5下新建sample文件夾，在里面放一張需要測試的圖片(盡量放人的圖片)進行測試，命令如下?:

sudo ./yolov5 -d yolov5s.engine ../sample

注：運行之后 ，在build文件夾下會生成一張檢測過的圖片，但是效果不是很好，這個是正常現象。

7.?測試圖片看不出效果,并且真正部署到生產環境,交付給用戶使用.是通過調用攝像頭.所以要改一下tensorrtx-yolov5-v5.0 -> yolov5.cpp，可以參考網上大神的教程：

#include <iostream> #include <chrono> #include "cuda_utils.h" #include "logging.h" #include "common.hpp" #include "utils.h" #include "calibrator.h"#define USE_FP16 // set USE_INT8 or USE_FP16 or USE_FP32 #define DEVICE 0 // GPU id #define NMS_THRESH 0.4 #define CONF_THRESH 0.5 #define BATCH_SIZE 1// stuff we know about the network and the input/output blobs static const int INPUT_H = Yolo::INPUT_H; static const int INPUT_W = Yolo::INPUT_W; static const int CLASS_NUM = Yolo::CLASS_NUM; static const int OUTPUT_SIZE = Yolo::MAX_OUTPUT_BBOX_COUNT * sizeof(Yolo::Detection) / sizeof(float) + 1; // we assume the yololayer outputs no more than MAX_OUTPUT_BBOX_COUNT boxes that conf >= 0.1 const char* INPUT_BLOB_NAME = "data"; const char* OUTPUT_BLOB_NAME = "prob"; static Logger gLogger;//修改為自己的類別 char *my_classes[]={ "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light","fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow","elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee","skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard","surfboard","tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple","sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch","potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone","microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear","hair drier", "toothbrush" };static int get_width(int x, float gw, int divisor = 8) {//return math.ceil(x / divisor) * divisorif (int(x * gw) % divisor == 0) {return int(x * gw);}return (int(x * gw / divisor) + 1) * divisor; }static int get_depth(int x, float gd) {if (x == 1) {return 1;}else {return round(x * gd) > 1 ? round(x * gd) : 1;} }//#創建engine和network ICudaEngine* build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) {INetworkDefinition* network = builder->createNetworkV2(0U);// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAMEITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });assert(data);std::map<std::string, Weights> weightMap = loadWeights(wts_name);/* ------ yolov5 backbone------ */auto focus0 = focus(network, weightMap, *data, 3, get_width(64, gw), 3, "model.0");auto conv1 = convBlock(network, weightMap, *focus0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");auto bottleneck_CSP2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2");auto conv3 = convBlock(network, weightMap, *bottleneck_CSP2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");auto bottleneck_csp4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(9, gd), true, 1, 0.5, "model.4");auto conv5 = convBlock(network, weightMap, *bottleneck_csp4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");auto bottleneck_csp6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6");auto conv7 = convBlock(network, weightMap, *bottleneck_csp6->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.7");auto spp8 = SPP(network, weightMap, *conv7->getOutput(0), get_width(1024, gw), get_width(1024, gw), 5, 9, 13, "model.8");/* ------ yolov5 head ------ */auto bottleneck_csp9 = C3(network, weightMap, *spp8->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.9");auto conv10 = convBlock(network, weightMap, *bottleneck_csp9->getOutput(0), get_width(512, gw), 1, 1, 1, "model.10");auto upsample11 = network->addResize(*conv10->getOutput(0));assert(upsample11);upsample11->setResizeMode(ResizeMode::kNEAREST);upsample11->setOutputDimensions(bottleneck_csp6->getOutput(0)->getDimensions());ITensor* inputTensors12[] = { upsample11->getOutput(0), bottleneck_csp6->getOutput(0) };auto cat12 = network->addConcatenation(inputTensors12, 2);auto bottleneck_csp13 = C3(network, weightMap, *cat12->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.13");auto conv14 = convBlock(network, weightMap, *bottleneck_csp13->getOutput(0), get_width(256, gw), 1, 1, 1, "model.14");auto upsample15 = network->addResize(*conv14->getOutput(0));assert(upsample15);upsample15->setResizeMode(ResizeMode::kNEAREST);upsample15->setOutputDimensions(bottleneck_csp4->getOutput(0)->getDimensions());ITensor* inputTensors16[] = { upsample15->getOutput(0), bottleneck_csp4->getOutput(0) };auto cat16 = network->addConcatenation(inputTensors16, 2);auto bottleneck_csp17 = C3(network, weightMap, *cat16->getOutput(0), get_width(512, gw), get_width(256, gw), get_depth(3, gd), false, 1, 0.5, "model.17");// yolo layer 0IConvolutionLayer* det0 = network->addConvolutionNd(*bottleneck_csp17->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.0.weight"], weightMap["model.24.m.0.bias"]);auto conv18 = convBlock(network, weightMap, *bottleneck_csp17->getOutput(0), get_width(256, gw), 3, 2, 1, "model.18");ITensor* inputTensors19[] = { conv18->getOutput(0), conv14->getOutput(0) };auto cat19 = network->addConcatenation(inputTensors19, 2);auto bottleneck_csp20 = C3(network, weightMap, *cat19->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.20");//yolo layer 1IConvolutionLayer* det1 = network->addConvolutionNd(*bottleneck_csp20->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.1.weight"], weightMap["model.24.m.1.bias"]);auto conv21 = convBlock(network, weightMap, *bottleneck_csp20->getOutput(0), get_width(512, gw), 3, 2, 1, "model.21");ITensor* inputTensors22[] = { conv21->getOutput(0), conv10->getOutput(0) };auto cat22 = network->addConcatenation(inputTensors22, 2);auto bottleneck_csp23 = C3(network, weightMap, *cat22->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.23");IConvolutionLayer* det2 = network->addConvolutionNd(*bottleneck_csp23->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.2.weight"], weightMap["model.24.m.2.bias"]);auto yolo = addYoLoLayer(network, weightMap, "model.24", std::vector<IConvolutionLayer*>{det0, det1, det2});yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);network->markOutput(*yolo->getOutput(0));// Build enginebuilder->setMaxBatchSize(maxBatchSize);config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB #if defined(USE_FP16)config->setFlag(BuilderFlag::kFP16); #elif defined(USE_INT8)std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;assert(builder->platformHasFastInt8());config->setFlag(BuilderFlag::kINT8);Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, INPUT_W, INPUT_H, "./coco_calib/", "int8calib.table", INPUT_BLOB_NAME);config->setInt8Calibrator(calibrator); #endifstd::cout << "Building engine, please wait for a while..." << std::endl;ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);std::cout << "Build engine successfully!" << std::endl;// Don't need the network any morenetwork->destroy();// Release host memoryfor (auto& mem : weightMap){free((void*)(mem.second.values));}return engine; }ICudaEngine* build_engine_p6(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) {INetworkDefinition* network = builder->createNetworkV2(0U);// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAMEITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });assert(data);std::map<std::string, Weights> weightMap = loadWeights(wts_name);/* ------ yolov5 backbone------ */auto focus0 = focus(network, weightMap, *data, 3, get_width(64, gw), 3, "model.0");auto conv1 = convBlock(network, weightMap, *focus0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");auto c3_2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2");auto conv3 = convBlock(network, weightMap, *c3_2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");auto c3_4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(9, gd), true, 1, 0.5, "model.4");auto conv5 = convBlock(network, weightMap, *c3_4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");auto c3_6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6");auto conv7 = convBlock(network, weightMap, *c3_6->getOutput(0), get_width(768, gw), 3, 2, 1, "model.7");auto c3_8 = C3(network, weightMap, *conv7->getOutput(0), get_width(768, gw), get_width(768, gw), get_depth(3, gd), true, 1, 0.5, "model.8");auto conv9 = convBlock(network, weightMap, *c3_8->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.9");auto spp10 = SPP(network, weightMap, *conv9->getOutput(0), get_width(1024, gw), get_width(1024, gw), 3, 5, 7, "model.10");auto c3_11 = C3(network, weightMap, *spp10->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.11");/* ------ yolov5 head ------ */auto conv12 = convBlock(network, weightMap, *c3_11->getOutput(0), get_width(768, gw), 1, 1, 1, "model.12");auto upsample13 = network->addResize(*conv12->getOutput(0));assert(upsample13);upsample13->setResizeMode(ResizeMode::kNEAREST);upsample13->setOutputDimensions(c3_8->getOutput(0)->getDimensions());ITensor* inputTensors14[] = { upsample13->getOutput(0), c3_8->getOutput(0) };auto cat14 = network->addConcatenation(inputTensors14, 2);auto c3_15 = C3(network, weightMap, *cat14->getOutput(0), get_width(1536, gw), get_width(768, gw), get_depth(3, gd), false, 1, 0.5, "model.15");auto conv16 = convBlock(network, weightMap, *c3_15->getOutput(0), get_width(512, gw), 1, 1, 1, "model.16");auto upsample17 = network->addResize(*conv16->getOutput(0));assert(upsample17);upsample17->setResizeMode(ResizeMode::kNEAREST);upsample17->setOutputDimensions(c3_6->getOutput(0)->getDimensions());ITensor* inputTensors18[] = { upsample17->getOutput(0), c3_6->getOutput(0) };auto cat18 = network->addConcatenation(inputTensors18, 2);auto c3_19 = C3(network, weightMap, *cat18->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.19");auto conv20 = convBlock(network, weightMap, *c3_19->getOutput(0), get_width(256, gw), 1, 1, 1, "model.20");auto upsample21 = network->addResize(*conv20->getOutput(0));assert(upsample21);upsample21->setResizeMode(ResizeMode::kNEAREST);upsample21->setOutputDimensions(c3_4->getOutput(0)->getDimensions());ITensor* inputTensors21[] = { upsample21->getOutput(0), c3_4->getOutput(0) };auto cat22 = network->addConcatenation(inputTensors21, 2);auto c3_23 = C3(network, weightMap, *cat22->getOutput(0), get_width(512, gw), get_width(256, gw), get_depth(3, gd), false, 1, 0.5, "model.23");auto conv24 = convBlock(network, weightMap, *c3_23->getOutput(0), get_width(256, gw), 3, 2, 1, "model.24");ITensor* inputTensors25[] = { conv24->getOutput(0), conv20->getOutput(0) };auto cat25 = network->addConcatenation(inputTensors25, 2);auto c3_26 = C3(network, weightMap, *cat25->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.26");auto conv27 = convBlock(network, weightMap, *c3_26->getOutput(0), get_width(512, gw), 3, 2, 1, "model.27");ITensor* inputTensors28[] = { conv27->getOutput(0), conv16->getOutput(0) };auto cat28 = network->addConcatenation(inputTensors28, 2);auto c3_29 = C3(network, weightMap, *cat28->getOutput(0), get_width(1536, gw), get_width(768, gw), get_depth(3, gd), false, 1, 0.5, "model.29");auto conv30 = convBlock(network, weightMap, *c3_29->getOutput(0), get_width(768, gw), 3, 2, 1, "model.30");ITensor* inputTensors31[] = { conv30->getOutput(0), conv12->getOutput(0) };auto cat31 = network->addConcatenation(inputTensors31, 2);auto c3_32 = C3(network, weightMap, *cat31->getOutput(0), get_width(2048, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.32");/* ------ detect ------ */IConvolutionLayer* det0 = network->addConvolutionNd(*c3_23->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.0.weight"], weightMap["model.33.m.0.bias"]);IConvolutionLayer* det1 = network->addConvolutionNd(*c3_26->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.1.weight"], weightMap["model.33.m.1.bias"]);IConvolutionLayer* det2 = network->addConvolutionNd(*c3_29->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.2.weight"], weightMap["model.33.m.2.bias"]);IConvolutionLayer* det3 = network->addConvolutionNd(*c3_32->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.3.weight"], weightMap["model.33.m.3.bias"]);auto yolo = addYoLoLayer(network, weightMap, "model.33", std::vector<IConvolutionLayer*>{det0, det1, det2, det3});yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);network->markOutput(*yolo->getOutput(0));// Build enginebuilder->setMaxBatchSize(maxBatchSize);config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB #if defined(USE_FP16)config->setFlag(BuilderFlag::kFP16); #elif defined(USE_INT8)std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;assert(builder->platformHasFastInt8());config->setFlag(BuilderFlag::kINT8);Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, INPUT_W, INPUT_H, "./coco_calib/", "int8calib.table", INPUT_BLOB_NAME);config->setInt8Calibrator(calibrator); #endifstd::cout << "Building engine, please wait for a while..." << std::endl;ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);std::cout << "Build engine successfully!" << std::endl;// Don't need the network any morenetwork->destroy();// Release host memoryfor (auto& mem : weightMap){free((void*)(mem.second.values));}return engine; }void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream, float& gd, float& gw, std::string& wts_name) {// Create builderIBuilder* builder = createInferBuilder(gLogger);IBuilderConfig* config = builder->createBuilderConfig();// Create model to populate the network, then set the outputs and create an engineICudaEngine* engine = build_engine(maxBatchSize, builder, config, DataType::kFLOAT, gd, gw, wts_name);assert(engine != nullptr);// Serialize the engine(*modelStream) = engine->serialize();// Close everything downengine->destroy();builder->destroy();config->destroy(); }void doInference(IExecutionContext& context, cudaStream_t& stream, void** buffers, float* input, float* output, int batchSize) {// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to hostCUDA_CHECK(cudaMemcpyAsync(buffers[0], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));context.enqueue(batchSize, buffers, stream, nullptr);CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));cudaStreamSynchronize(stream); }bool parse_args(int argc, char** argv, std::string& engine) {if (argc < 3) return false;if (std::string(argv[1]) == "-v" && argc == 3) {engine = std::string(argv[2]);}else {return false;}return true; }int main(int argc, char** argv) {cudaSetDevice(DEVICE);//std::string wts_name = "";std::string engine_name = "";//float gd = 0.0f, gw = 0.0f;//std::string img_dir;if (!parse_args(argc, argv, engine_name)) {std::cerr << "arguments not right!" << std::endl;std::cerr << "./yolov5 -v [.engine] // run inference with camera" << std::endl;return -1;}std::ifstream file(engine_name, std::ios::binary);if (!file.good()) {std::cerr << " read " << engine_name << " error! " << std::endl;return -1;}char* trtModelStream{ nullptr };size_t size = 0;file.seekg(0, file.end);size = file.tellg();file.seekg(0, file.beg);trtModelStream = new char[size];assert(trtModelStream);file.read(trtModelStream, size);file.close();// prepare input data ---------------------------static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W];//for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)// data[i] = 1.0;static float prob[BATCH_SIZE * OUTPUT_SIZE];IRuntime* runtime = createInferRuntime(gLogger);assert(runtime != nullptr);ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);assert(engine != nullptr);IExecutionContext* context = engine->createExecutionContext();assert(context != nullptr);delete[] trtModelStream;assert(engine->getNbBindings() == 2);void* buffers[2];// In order to bind the buffers, we need to know the names of the input and output tensors.// Note that indices are guaranteed to be less than IEngine::getNbBindings()const int inputIndex = engine->getBindingIndex(INPUT_BLOB_NAME);const int outputIndex = engine->getBindingIndex(OUTPUT_BLOB_NAME);assert(inputIndex == 0);assert(outputIndex == 1);// Create GPU buffers on deviceCUDA_CHECK(cudaMalloc(&buffers[inputIndex], BATCH_SIZE * 3 * INPUT_H * INPUT_W * sizeof(float)));CUDA_CHECK(cudaMalloc(&buffers[outputIndex], BATCH_SIZE * OUTPUT_SIZE * sizeof(float)));// Create streamcudaStream_t stream;CUDA_CHECK(cudaStreamCreate(&stream));//#讀取本地視頻//cv::VideoCapture capture("/home/nano/Videos/video.mp4");//#調用本地usb攝像頭,我的默認參數為1,如果1報錯,可修改為0.cv::VideoCapture capture(1);if (!capture.isOpened()) {std::cout << "Error opening video stream or file" << std::endl;return -1;}int key;int fcount = 0;while (1){cv::Mat frame;capture >> frame;if (frame.empty()){std::cout << "Fail to read image from camera!" << std::endl;break;}fcount++;//if (fcount < BATCH_SIZE && f + 1 != (int)file_names.size()) continue;for (int b = 0; b < fcount; b++) {//cv::Mat img = cv::imread(img_dir + "/" + file_names[f - fcount + 1 + b]);cv::Mat img = frame;if (img.empty()) continue;cv::Mat pr_img = preprocess_img(img, INPUT_W, INPUT_H); // letterbox BGR to RGBint i = 0;for (int row = 0; row < INPUT_H; ++row) {uchar* uc_pixel = pr_img.data + row * pr_img.step;for (int col = 0; col < INPUT_W; ++col) {data[b * 3 * INPUT_H * INPUT_W + i] = (float)uc_pixel[2] / 255.0;data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = (float)uc_pixel[1] / 255.0;data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = (float)uc_pixel[0] / 255.0;uc_pixel += 3;++i;}}}// Run inferenceauto start = std::chrono::system_clock::now();//#獲取模型推理開始時間doInference(*context, stream, buffers, data, prob, BATCH_SIZE);auto end = std::chrono::system_clock::now();//#結束時間//std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;int fps = 1000.0 / std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count();std::vector<std::vector<Yolo::Detection>> batch_res(fcount);for (int b = 0; b < fcount; b++) {auto& res = batch_res[b];nms(res, &prob[b * OUTPUT_SIZE], CONF_THRESH, NMS_THRESH);}for (int b = 0; b < fcount; b++) {auto& res = batch_res[b];//std::cout << res.size() << std::endl;//cv::Mat img = cv::imread(img_dir + "/" + file_names[f - fcount + 1 + b]);for (size_t j = 0; j < res.size(); j++) {cv::Rect r = get_rect(frame, res[j].bbox);cv::rectangle(frame, r, cv::Scalar(0x27, 0xC1, 0x36), 2);std::string label = my_classes[(int)res[j].class_id];cv::putText(frame, label, cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2);std::string jetson_fps = "FPS: " + std::to_string(fps);cv::putText(frame, jetson_fps, cv::Point(11, 80), cv::FONT_HERSHEY_PLAIN, 3, cv::Scalar(0, 0, 255), 2, cv::LINE_AA);}//cv::imwrite("_" + file_names[f - fcount + 1 + b], img);}cv::imshow("yolov5", frame);key = cv::waitKey(1);if (key == 'q') {break;}fcount = 0;}capture.release();// Release stream and bufferscudaStreamDestroy(stream);CUDA_CHECK(cudaFree(buffers[inputIndex]));CUDA_CHECK(cudaFree(buffers[outputIndex]));// Destroy the enginecontext->destroy();engine->destroy();runtime->destroy();return 0; }

修改完.cpp代碼之后，tensorrtx-yolov5-v5.0 -> yolov5 - > build文件夾下，運行以下命令：

make sudo ./yolov5 -v yolov5s.engine

運行之后攝像頭調用成功，效果如下：

到此我們就大功告成了，以上就是Jeston nano完整的部署過程，希望對你們有所幫助，請大家多多支持。創作不易，希望得到你們的鼓勵～

總結

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