RK3576 LINUX RKNN SDK 测试

Lary_Rock2024-11-08 5:04

安装Conda工具

bash 复制代码 
安装 Miniforge Conda
wget -c https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
chmod 777 Miniforge3-Linux-x86_64.sh
bash Miniforge3-Linux-x86_64.sh

source ~/miniforge3/bin/activate # Miniforge 安装的目录


# 创建名称为 toolkit2 的 Python 3.8 环境
conda create -n toolkit2 python=3.8

# 激活toolkit2环境
conda activate toolkit2

工程代码下载

bash 复制代码 
# 新建 Projects 文件夹
mkdir Projects
# 进入该目录
cd Projects
# 下载 RKNN-Toolkit2 仓库
git clone https://github.com/airockchip/rknn-toolkit2.git --depth 1
# 下载 RKNN Model Zoo 仓库
git clone https://github.com/airockchip/rknn_model_zoo.git --depth 1
# 下载 rknn-llm 仓库
git clone https://github.com/airockchip/rknn-llm.git --depth 1

确认板端环境

安装依赖环境以及YOLOv5运行

bash 复制代码 
# 安装依赖库和 RKNN-Toolkit2
# 进入 rknn-toolkit2 目录
cd Projects/rknn-toolkit2/rknn-toolkit2
# 请根据不同的 python 版本，选择不同的 requirements 文件
# 例如 python3.8 对应 requirements_cp38.txt
pip install -r packages/requirements_cpxx.txt
# 安装 RKNN-Toolkit2
# 请根据不同的 python 版本及处理器架构，选择不同的 wheel 安装包文件：
# 其中 x.x.x 是 RKNN-Toolkit2 版本号，cpxx 是 python 版本号，请根据实际数值进行替换
pip install packages/rknn_toolkit2--x.x.x-cpxx-cpxx-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

# 验证是否安装成功
# 进入 Python 交互模式
python
# 导入 RKNN 类
from rknn.api import RKNN

# 更新包列表
sudo apt update
# 安装 cmake
sudo apt install cmake


 # 进入 rknpu2 目录
cd Projects/rknn-toolkit2/rknpu2
# 推送 rknn_server 到板端
# 注：在64位Linux系统中，BOARD_ARCH对应a
# adb push runtime/Linux/rknn_server/${BOARD_ARC
adb push  runtime\Linux\rknn_server\aarch64\usr\bin\rknn_server  /usr/bin
adb push  runtime\Linux\rknn_server\aarch64\usr\bin\start_rknn.sh  /usr/bin
adb push  runtime\Linux\rknn_server\aarch64\usr\bin\restart_r knn.sh  /usr/bin
# 推送 librknnrt.so
# adb push runtime/Linux/librknn_api/${BOARD_ARC
adb push runtime\Linux\rlibrknn_api\aarch64\librknnrt.so   / usr/lib
# 进入板端
adb shell
# 赋予可执行权限
chmod +x /usr/bin/rknn_server
chmod +x /usr/bin/start_rknn.sh
chmod +x /usr/bin/restart_rknn.sh
# 重启 rknn_server 服务
restart_rknn.sh



 RKNN C Demo 使用方法
 
准备模型
# 进入 rknn_model_zoo/examples/yolov5/model 目录
cd Projects/rknn_model_zoo/examples/yolov5/model
# 运行 download_model.sh 脚本，下载 yolov5 onnx 模型
# 例如，下载好的 onnx 模型存放路径为 model/yolov5s_relu.onnx
./download_model.sh



模型转换
# 进入 rknn_model_zoo/examples/yolov5/python 目录
cd Projects/rknn_model_zoo/examples/yolov5/python
# 运行 convert.py 脚本，将原始的 ONNX 模型转成 RKNN 模型
# 用法: python convert.py model_path [rk3566|rk3588|rk3562] [i8/fp] [output_path]
python convert.py ../model/yolov5s_relu.onnx rk3576 i8 ../model/yolov5s_relu.rknn    
    
# 添加到 build-linux.sh 脚本的开头位置即可
GCC_COMPILER=Projects/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu

# 用的是sdk自带的gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu 
# 路径 linux/prebuilts/gcc/linux-x86/aarch64/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu
--- a/build-linux.sh
+++ b/build-linux.sh
@@ -1,5 +1,7 @@
 #!/bin/bash

+GCC_COMPILER=Projects/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-rockchip1031-linux-gnu
+
 set -e

 echo "$0 $@"
 # 编译之前修改 库文件
   cd 3rdparty/mpp/Linux/aarch64/
   git status  ./
   rm librockchip_mpp.so
   mv librockchip_mpp.so.0  librockchip_mpp.so
   cd ..
   cd rknn_yolov5_demo/

 
 # 编译
 ./build-linux.sh -t rk3576 -a aarch64 -b Release
 (toolkit2) ip3@ip3-MOMENTPLUS-M2:~/work/linux/Projects/rknn-toolkit2/rknpu2/examples/rknn_yolov5_demo$ ./build-linux.sh -t rk3576 -a aarch64 -b Release
./build-linux.sh -t rk3576 -a aarch64 -b Release
/home/ip3/work/linux/Projects/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-rockchip1031-linux-gnu
===================================
TARGET_SOC=RK3576
TARGET_ARCH=aarch64
BUILD_TYPE=Release
BUILD_DIR=/home/ip3/work/linux/Projects/rknn-toolkit2/rknpu2/examples/rknn_yolov5_demo/build/build_RK3576_linux_aarch64_Release
CC=/home/ip3/work/linux/Projects/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-rockchip1031-linux-gnu-gcc
CXX=/home/ip3/work/linux/Projects/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-rockchip1031-linux-gnu-g++
===================================
-- Configuring done
-- Generating done
-- Build files have been written to: /home/ip3/work/linux/Projects/rknn-toolkit2/rknpu2/examples/rknn_yolov5_demo/build/build_RK3576_linux_aarch64_Release
Consolidate compiler generated dependencies of target rknn_yolov5_video_demo
Consolidate compiler generated dependencies of target rknn_yolov5_demo
[ 60%] Built target rknn_yolov5_video_demo
[100%] Built target rknn_yolov5_demo
[ 40%] Built target rknn_yolov5_demo
[100%] Built target rknn_yolov5_video_demo
Install the project...
-- Install configuration: "Release"
-- Up-to-date: /home/ip3/work/linux/Projects/rknn-toolkit2/rknpu2/examples/rknn_yolov5_demo/install/rknn_yolov5_demo_Linux/./rknn_yolov5_demo
-- Up-to-date: /home/ip3/work/linux/Projects/rknn-toolkit2/rknpu2/examples/rknn_yolov5_demo/install/rknn_yolov5_demo_Linux/lib/librknnrt.so
-- Up-to-date: /home/ip3/work/linux/Projects/rknn-toolkit2/rknpu2/examples/rknn_yolov5_demo/install/rknn_yolov5_demo_Linux/lib/librga.so
-- Up-to-date: /home/ip3/work/linux/Projects/rknn-toolkit2/rknpu2/examples/rknn_yolov5_demo/install/rknn_yolov5_demo_Linux/./model/RK3576
-- Up-to-date: /home/ip3/work/linux/Projects/rknn-toolkit2/rknpu2/examples/rknn_yolov5_demo/install/rknn_yolov5_demo_Linux/./model/RK3576/yolov5s-640-640.rknn
-- Up-to-date: /home/ip3/work/linux/Projects/rknn-toolkit2/rknpu2/examples/rknn_yolov5_demo/install/rknn_yolov5_demo_Linux/./model/bus.jpg
-- Up-to-date: /home/ip3/work/linux/Projects/rknn-toolkit2/rknpu2/examples/rknn_yolov5_demo/install/rknn_yolov5_demo_Linux/./model/coco_80_labels_list.txt
-- Up-to-date: /home/ip3/work/linux/Projects/rknn-toolkit2/rknpu2/examples/rknn_yolov5_demo/install/rknn_yolov5_demo_Linux/./rknn_yolov5_video_demo
-- Up-to-date: /home/ip3/work/linux/Projects/rknn-toolkit2/rknpu2/examples/rknn_yolov5_demo/install/rknn_yolov5_demo_Linux/lib/librockchip_mpp.so
-- Up-to-date: /home/ip3/work/linux/Projects/rknn-toolkit2/rknpu2/examples/rknn_yolov5_demo/install/rknn_yolov5_demo_Linux/lib/libmk_api.so


  
 
 # 进入 rknn_model_zoo 目录
cd Projects/rknn_model_zoo
# 运行 build-linux.sh 脚本
# 用法:./build-linux.sh -t <target> -a <arch> -d <build_demo_name> [-b <build_type>] [-m]
# -t : target (rk356x/rk3588) # 平台类型，rk3568/rk3566 都统一为rk356x
# -a : arch (aarch64/armhf) # 板端系统架构
# -d : demo name # 对应 examples 目录下子文件夹的名称，如yolov5、mobilenet
# -b : build_type(Debug/Release)
# -m : enable address sanitizer, build_type need set to Debug
chmod +x build-linux.sh
./build-linux.sh -t rk3576 -a aarch64 -d yolov5

(toolkit2) ip3@ip3-MOMENTPLUS-M2:~/work/linux/Projects/rknn_model_zoo$ ./build-linux.sh -t rk3576 -a aarch64 -d yolov5
./build-linux.sh -t rk3576 -a aarch64 -d yolov5
/home/ip3/work/linux/Projects/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-rockchip1031-linux-gnu
===================================
BUILD_DEMO_NAME=yolov5
BUILD_DEMO_PATH=examples/yolov5/cpp
TARGET_SOC=rk3576
TARGET_ARCH=aarch64
BUILD_TYPE=Release
ENABLE_ASAN=OFF
DISABLE_RGA=OFF
INSTALL_DIR=/home/ip3/work/linux/Projects/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov5_demo
BUILD_DIR=/home/ip3/work/linux/Projects/rknn_model_zoo/build/build_rknn_yolov5_demo_rk3576_linux_aarch64_Release
CC=/home/ip3/work/linux/Projects/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-rockchip1031-linux-gnu-gcc
CXX=/home/ip3/work/linux/Projects/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-rockchip1031-linux-gnu-g++
===================================
-- Configuring done
-- Generating done
-- Build files have been written to: /home/ip3/work/linux/Projects/rknn_model_zoo/build/build_rknn_yolov5_demo_rk3576_linux_aarch64_Release
Consolidate compiler generated dependencies of target imageutils
Consolidate compiler generated dependencies of target fileutils
Consolidate compiler generated dependencies of target imagedrawing
Consolidate compiler generated dependencies of target audioutils
[ 25%] Built target imagedrawing
[ 41%] Built target fileutils
[ 50%] Built target audioutils
[ 66%] Built target imageutils
Consolidate compiler generated dependencies of target rknn_yolov5_demo
[100%] Built target rknn_yolov5_demo
[ 16%] Built target imageutils
[ 33%] Built target fileutils
[ 50%] Built target imagedrawing
[ 83%] Built target rknn_yolov5_demo
[100%] Built target audioutils
Install the project...
-- Install configuration: "Release"
-- Installing: /home/ip3/work/linux/Projects/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov5_demo/./rknn_yolov5_demo
-- Set runtime path of "/home/ip3/work/linux/Projects/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov5_demo/./rknn_yolov5_demo" to "$ORIGIN/lib"
-- Installing: /home/ip3/work/linux/Projects/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov5_demo/./model/bus.jpg
-- Installing: /home/ip3/work/linux/Projects/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov5_demo/./model/coco_80_labels_list.txt
-- Installing: /home/ip3/work/linux/Projects/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov5_demo/model/yolov5s_relu.rknn
-- Installing: /home/ip3/work/linux/Projects/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov5_demo/lib/librknnrt.so
-- Installing: /home/ip3/work/linux/Projects/rknn_model_zoo/install/rk3576_linux_aarch64/rknn_yolov5_demo/lib/librga.so

#编译之后的文件
(toolkit2) ip3@ip3-MOMENTPLUS-M2:~/work/linux/Projects/rknn_model_zoo/install$ tree
.
└── rk3576_linux_aarch64
    └── rknn_yolov5_demo
        ├── lib
        │   ├── librga.so
        │   └── librknnrt.so
        ├── model
        │   ├── bus.jpg
        │   ├── coco_80_labels_list.txt
        │   └── yolov5s_relu.rknn
        └── rknn_yolov5_demo



# 推送文件到板端
# 进入 rknn_model_zoo 目录
cd Projects/rknn_model_zoo
# 推送整个 rknn_yolov5_demo 文件夹到板端
# 注：rknn_yolov5_demo 文件夹下有一个同名的可执行文件 rknn_yolov5_demo
# 注：使用不同的模型和平台时，建议直接在 install 下找对应的路径
adb push install/rk3576_linux_aarch64/rknn_yolov5_demo /data/



# 板端运行 Demo
# 进入板端
adb shell
# 进入 rknn_yolov5_demo 目录
cd /data/rknn_yolov5_demo/
# 设置依赖库环境
export LD_LIBRARY_PATH=./lib
# 运行可执行文件
# 用法: ./rknn_yolov5_demo <model_path> <input_path>
./rknn_yolov5_demo model/yolov5s_relu.rknn  model/bus.jpg


# 查看结果
# 拉取到本地当前目录
adb pull /data/rknn_yolov5_demo/out.png .

RKLLM部署

bash 复制代码 
# 代码 rknn-llm
# 环境 激活toolkit2环境
conda activate toolkit2

# 切换到rkllm-toolkit目录
cd rknn-llm/rkllm-toolkit/

# 安装rkllm_toolkit(文件请根据具体版本修改)，会自动下载RKLLM-Toolkit工具所需要的相关依赖包。
pip install packages/rkllm_toolkit-1.0.1-cp38-cp38-linux_x86_64.whl

#模型下载
cd Projects 
mkdir modem

Qwen-1_8B-Chat_downloader.py
from modelscope import snapshot_download
#model_dir = snapshot_download('qwen/Qwen-7B-Chat',cache_dir='./modem')
model_dir = snapshot_download('qwen/Qwen-1_8B-Chat',cache_dir='./modem')

运行Qwen-1_8B-Chat_downloader.py
python Qwen-1_8B-Chat_downloader.py



# 模型转换
# 在rknn-llm/rkllm-toolkit/examples/下新建 huggingface目录
# 新建download.py 代码如下:
from rkllm.api import RKLLM
# 模型路径
modelpath = '/home/ip3/work/linux/Projects/modem/qwen/Qwen-1_8B-Chat'
# 初始化RKLLM对象
llm = RKLLM()
# 模型加载
ret = llm.load_huggingface(model = modelpath)
if ret != 0:
    print('Load model failed!')
    exit(ret)
# 模型的量化构建
ret = llm.build(do_quantization=True, optimization_level=1, quantized_dtype='w8a8', target_platform='rk3576',num_npu_core=2)
if ret != 0:
    print('Build model failed!')
    exit(ret)
# 导出rkllm模型
ret = llm.export_rkllm("./qwen.rkllm")
if ret != 0:
    print('Export model failed!')
    exit(ret)



# RKLLM Runtime 编译
cd  Projects/rknn-llm/rkllm-runtime/examples/rkllm_api_demo

#设置编译工具连接

+++ b/rkllm-runtime/examples/rkllm_api_demo/build-linux.sh
@@ -1,10 +1,12 @@
 #!/bin/bash
+
 # Debug / Release / RelWithDebInfo
 if [[ -z ${BUILD_TYPE} ]];then
     BUILD_TYPE=Release
 fi

-GCC_COMPILER_PATH=~/gcc-arm-10.2-2020.11-x86_64-aarch64-none-linux-gnu/bin/aarch64-none-linux-gnu
+GCC_COMPILER_PATH=~/work/linux/Projects/gcc-arm-10.3-2021.07-x86_64-aarch64-none-linux-gnu/bin/aarch64-none-linux-gnu

(toolkit2) ip3@ip3-MOMENTPLUS-M2:~/work/linux/Projects/rknn-llm/rkllm-runtime/examples/rkllm_api_demo$ ./build-linux.sh
-- Configuring done
-- Generating done
-- Build files have been written to: /home/ip3/work/linux/Projects/rknn-llm/rkllm-runtime/examples/rkllm_api_demo/build/build_linux_aarch64_Release
Consolidate compiler generated dependencies of target llm_demo
Consolidate compiler generated dependencies of target multimodel_demo
[ 50%] Built target llm_demo
[100%] Built target multimodel_demo


(toolkit2) ip3@ip3-MOMENTPLUS-M2:~/work/linux/Projects/rknn-llm/rkllm-runtime/examples/rkllm_api_demo/build$ tree  -L 2
.
└── build_linux_aarch64_Release
    ├── CMakeCache.txt
    ├── CMakeFiles
    ├── cmake_install.cmake
    ├── llm_demo
    ├── Makefile
    └── multimodel_demo

2 directories, 5 files


# 推送板端

# 注意：要去到llm_demo 和 librkllmrt.so所在的目录下去执行
adb push llm_demo /data  # 将llm_demo使用ssh传输到开发板的家目录
adb push ./rkllm-runtime/runtime/Linux/librkllm_api/aarch64/librkllmrt.so /data/lib
adb push  Projects/rknn-llm/rkllm-toolkit/examples/huggingface/qwen.rkllm  /data

运行
adb shell  && cd data
cd librkllmrt.so所在的目录
export LD_LIBRARY_PATH=./lib

# 修改最大可打开文件的数量
ulimit -HSn 10240

root@linaro-alip:/data# ./llm_demo qwen.rkllm  128 4080
rkllm init start
I rkllm: rkllm-runtime version: 1.1.2, rknpu driver version: 0.9.8, platform: RK3576

# 或者运行绑定cpu运行测试
# taskset f0 ./llm_demo /path/qwen.rkllm
相关推荐
pk_xz12345627 分钟前
Shell 脚本中变量和字符串的入门介绍
linux·运维·服务器
小珑也要变强30 分钟前
Linux之sed命令详解
linux·运维·服务器
海绵波波10736 分钟前
Webserver(4.3)TCP通信实现
服务器·网络·tcp/ip
九河云2 小时前
AWS账号注册费用详解：新用户是否需要付费？
服务器·云计算·aws
幺零九零零3 小时前
【计算机网络】TCP协议面试常考（一）
服务器·tcp/ip·计算机网络
云飞云共享云桌面4 小时前
8位机械工程师如何共享一台图形工作站算力？
linux·服务器·网络
Peter_chq5 小时前
【操作系统】基于环形队列的生产消费模型
linux·c语言·开发语言·c++·后端
一坨阿亮6 小时前
Linux 使用中的问题
linux·运维
dsywws7 小时前
Linux学习笔记之vim入门
linux·笔记·学习
热门推荐
01【HarmonyOS】HUAWEI DevEco Studio 下载地址汇总02(欧拉)openEuler系统添加网卡文件配置流程、(欧拉)openEuler系统手动配置ipv6地址流程、(欧拉)openEuler系统网络管理说明03组基轨迹建模 GBTM的介绍与实现（Stata 或 R）04【AIGC】重塑未来的科技巨轮05全面解析：构建基于深度学习的安全帽检测系统（UI界面+YOLO代码+数据集）06【经验分享】Ubuntu22.04安装微信(linux官方版)07基于YOLOv10深度学习的CT扫描图像肾结石智能检测系统【python源码+Pyqt5界面+数据集+训练代码】深度学习实战、目标检测08Ubuntu 20.04使用Livox mid 360 测试 FAST_LIO09RAG 实践- Ollama+RagFlow 部署本地知识库10红米手机使用google play