Jenson nano部署yolov5+Tensor Rt加速
前言
在工作中接触到了jenson nano 部署，在网上找了很多资料也入了很多坑，经过不断的努力终于整出来啦，决定写下这篇文章
一、准备工具
1、读卡器
2、SD卡
3、螺丝刀
二、 烧录
1、下载烧录镜像，可以去官网下载，根据自己所需的版本，我下载的是4.4.1地址如下：
JetPack SDK 4.4.1 archive | NVIDIA Developerr
2、下载好后进行解压
3、下载SD卡格式化工具，网址如下
Get Started With Jetson Nano Developer Kit | NVIDIA Developer
同时下载烧录工具
4、开始格式化SD卡
5、 开始烧录镜像 （大概二十分钟左右）
6、烧录完成后，插卡开机
7、开机后，简单设置一下（密码，时区，语言，输入法等）就会这样的页面
三、搭建环境
1、配置cuda
打开终端（ctrl+alt+t）
sudo gedit ~/.bashrc
打开文档在最后加入以下代码，ctrl+S保存后关闭文档
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验证是否配置成功
nvcc -V2、配置conda
因为它得架构与windows和liunx不同，所以这里我们要安装archiconda
在终端输入下载命令
wget https://github.com/Archiconda/build-tools/releases/download/0.2.3/Archiconda3-0.2.3-Linux-aarch64.sh
下载好后执行以下命令
bash Archiconda3-0.2.3-Linux-aarch64.sh然后就是傻瓜式安装（过程与anaconda一样）
配置环境变量
sudo gedit ~/.bashrc
在文档最后加入以下命令，保存退出
export PATH=~/archiconda3/bin:$PATH
查看版本号
conda -V3、创建虚拟环境(我这里创建的是以nano命名的，你可以改成自己喜欢的名字)
conda create -n nano python=3.6这里可以进入一下刚创建的虚拟环境（可以省略）
conda activate nano退出虚拟环境
conda deactivate4、换源
先备份source.list文件
sudo cp /etc/apt/sources.list /etc/apt/sources.list.bak 进入source.list文件
sudo gedit /etc/apt/sources.list按 i 开始输入，然后ctrl+a删除内容，之后将以下内容复制进去，Esc 结束输入，然后保存退出
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更新软件列表，保存到本地
sudo apt-get update更新软件
sudo apt-get upgrade有可能会出现如下错误
在dpkg文件下打开终端用如下命令可以解决
sudo mkdir info5、安装pip
升级所有已安装的软件包，并解决依赖关系
sudo apt-get dist-upgrade之后就是安装pip
sudo apt-get install python3-pip libopenblas-base libopenmpi-dev更新pip到最新版本
pip3 install --upgrade pip6、下载torch和torchvision
去英伟达官网下载所需版本，我这里是1.8.0，网址如下：
PyTorch for Jetson - version 1.10 now available - Jetson Nano - NVIDIA Developer Forums
如果官网进不去的话可以在网盘里直接下载
链接: https://pan.baidu.com/s/1DpKXfMRcLV4TSJpvH7iCmA?pwd=2yxf
提取码: 2yxf
下载好后，这里推荐一个工具（MobaXterm）用于连接板子进行互传，当然你也可以直接下载到U盘，用U盘实现互传
具体操作可以看这个博主的博客：MobaXterm（终端工具）下载&安装&使用教程_蜗牛也不慢......的博客-CSDN博客
连接成功后把网盘里的东西解压后，传过去，建议传到home目录下
7、安装torch
在文件目录下打开终端，然后进入你创建的虚拟环境，输入以下命令
sudo apt-get install python3-pip libopenblas-base libopenmpi-dev pip install torch-1.8.0-cp36-cp36m-linux_aarch64.whl这时需要安装numpy，不然测试torch时不显示安装成功
sudo apt install python3-numpy测试torch，是否安装成功
import torch
print(torch.__version__)这是会报错，继续执行以下命令
export OPENBLAS_CORETYPE=ARMV88、安装torchvision，逐个执行以下命令（如果执行第三个命令报错，继续执行第四第五行命令，如果不报错就直接cd ..）
cd torchvision
export BUILD_VERSION=0.9.0
sudo python setup.py install
python setup.py build
python setup.py install
cd ..如果执行完第五个命令有这个错，可以不用管，接着往下走
测试torchvision
python
import torchvision
print(torchvision.__version__)在测试时如果报错PIL，就安装pillow，命令如下（在第二步如果报权限错误，就在开头加sudo，或在结尾加--user）
sudo apt-get install libjpeg8 libjpeg62-dev libfreetype6 libfreetype6-dev
python3 -m pip install -i https://mirrors.aliyun.com/pypi/simple pillow到次环境就搭建好啦，下面开始部署yolov5
四、试跑Yolov5
1、去官网上下载需要的版本，我这里下载的是5.0版本，下载的时候要把对应的权重也要下载下来网址如下：
ultralytics/yolov5 at v5.0 (github.com)
这里可以选择版本
权重网址如下：
Releases · ultralytics/yolov5 (github.com)
2、通过我们之前介绍的工具将v5和模型拉到home目录下，然后打开终端执行以下命令
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple这时应该会报错，可以查看你得numpy版本，将版本降到1.19.4就OK了，命令如下
pip install numpy==1.19.4 -i https://pypi.tuna.tsinghua.edu.cn/simple解决完报错后，会进行安装相关依赖包，当安装opencv时会出现Building wheel for opencv-python (pyroject.toml)... 这种情况等就可以啦
3、安装好所有依赖包后，执行以下命令
python3 detect.py --weights yolov5s.pt 
如果遇到这个错误，在common.py中再添加一个类就可以
import warnings
class SPPF(nn.Module):
# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
super().__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_ * 4, c2, 1, 1)
self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
def forward(self, x):
x = self.cv1(x)
with warnings.catch_warnings():
warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
y1 = self.m(x)
y2 = self.m(y1)
return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
下面我们开始部署yolov5
五、tensorrt部署yolov5
1、tensorrt官网下载v5，网址如下，确定下载是5.0版本
https://gitcode.net/mirrors/wang-xinyu/tensorrtx?utm_source=csdn_github_accelerator
2、通过之前的工具传到home目录下
传完之后在文件夹中找到gen_wts.py,复制到我们刚才试跑的v5文件夹中，在文件中打开终端，运行如下命令生成.wts文件
python3 gen_wts.py --w yolov5s.pt把生成的.wts文件复制到tensorrtx-yolov5-v5.0\yolov5中
3、然后找到yololayer.h文件，打开之后可以修改类别数量及图片大小（根据自己的需求而定）
4、在当前目录下创建文件build，命令如下
mkdir build #创建build文件夹
cd build #进入build
cmake .. #构建项目
#将我们上面生成的.wts文件复制到build文件夹中
make5、在当前终端运行如下命令，目地：将yolov5s.wts文件转为.engine(引擎文件)
sudo ./yolov5 -s yolov5s.wts yolov5s.engine s6、在tensorrtx-yolov5-v5.0\yolov5下新建sample文件夹，在里面放一张需要测试的图片进行测试，命令如下
sudo ./yolov5 -d yolov5s.engine ../sample但是看不出效果，这个我们交付时可用用的都是摄像头，下面我们开始调用摄像头
7、调用摄像头
修改yolov5.cpp文件,将里面内容替换成如下代码
#include
#include
#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;
//修改为自己的类别
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) * divisor
if (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;
}
}
//#创建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_NAME
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
assert(data);
std::map 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 0
IConvolutionLayer* 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 1
IConvolutionLayer* 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{det0, det1, det2});
yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
network->markOutput(*yolo->getOutput(0));
// Build engine
builder->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);
#endif
std::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 more
network->destroy();
// Release host memory
for (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_NAME
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
assert(data);
std::map 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{det0, det1, det2, det3});
yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
network->markOutput(*yolo->getOutput(0));
// Build engine
builder->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);
#endif
std::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 more
network->destroy();
// Release host memory
for (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 builder
IBuilder* builder = createInferBuilder(gLogger);
IBuilderConfig* config = builder->createBuilderConfig();
// Create model to populate the network, then set the outputs and create an engine
ICudaEngine* engine = build_engine(maxBatchSize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
assert(engine != nullptr);
// Serialize the engine
(*modelStream) = engine->serialize();
// Close everything down
engine->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 host
CUDA_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 device
CUDA_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 stream
cudaStream_t stream;
CUDA_CHECK(cudaStreamCreate(&stream));
//#读取本地视频
//cv::VideoCapture capture("/home/nano/Videos/video.mp4");
//#调用本地usb摄像头,我的默认参数为1,如果1报错,可修改为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 RGB
int 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 inference
auto start = std::chrono::system_clock::now();//#获取模型推理开始时间
doInference(*context, stream, buffers, data, prob, BATCH_SIZE);
auto end = std::chrono::system_clock::now();//#结束时间
//std::cout << std::chrono::duration_cast(end - start).count() << "ms" << std::endl;
int fps = 1000.0 / std::chrono::duration_cast(end - start).count();
std::vector> 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 buffers
cudaStreamDestroy(stream);
CUDA_CHECK(cudaFree(buffers[inputIndex]));
CUDA_CHECK(cudaFree(buffers[outputIndex]));
// Destroy the engine
context->destroy();
engine->destroy();
runtime->destroy();
return 0;
}
改完之后执行如下命令，调用摄像头成功
cd build
make
sudo ./yolov5 -v yolov5s.engine
如图
到此就大功告成啦