编译安装飞桨fastdeploy@FreeBSD(失败)

news/2024/4/29 2:17:17/文章来源:https://blog.csdn.net/skywalk8163/article/details/136992862

FastDeploy是一款全场景易用灵活极致高效的AI推理部署工具, 支持云边端部署。提供超过 🔥160+ TextVisionSpeech跨模态模型📦开箱即用的部署体验,并实现🔚端到端的推理性能优化。包括 物体检测、字符识别(OCR)、人脸、人像扣图、多目标跟踪系统、NLP、Stable Diffusion文图生成、TTS 等几十种任务场景,满足开发者多场景、多硬件、多平台的产业部署需求。官网:GitHub - PaddlePaddle/FastDeploy: ⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end optimization, multi-platform and multi-framework support.

遗憾的是在FreeBSD下没有装成。

发现fastdeploy需要opencv-python,所以花了很大的精力来安装,但是也没有装上。opencv可以用Pillow代替,但是后面还是碰到没法解决的问题。

编译安装opencv-python

编译安装没完成,估计还是用pkg install opencv-python装成的。

需要安装opencv-pyhton

安装opencv-python

pip install opencv-python,但是装不上,所以选择源代码编译安装

先安装pip install scikit-build

然后下载opencv-python源代码

可以用git clone https://github.com/opencv/opencv-python

也可以在pip安装的时候拿到下载链接,然后wget下载

https://mirror.baidu.com/pypi/packages/25/72/da7c69a3542071bf1e8f65336721b8b2659194425438d988f79bc14ed9cc/opencv-python-4.9.0.80.tar.gz

解压源代码:

tar -xzvf opencv-python-4.9.0.80.tar.gz

设置编译多线程:

set MAX_JOBS=8
export MAX_JOBS=8

开始编译

进入 opencv-python-4.9.0.80 目录并编译

cd  opencv-python-4.9.0.80
python setup.py install 

编译失败,见后面的记录。

安装FastDeploy

标准流程是cpu安装:pip install numpy opencv-python fastdeploy-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html

我们使用命令

pip install fastdeploy-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html

这样跳过了opencv部分,先把fastdeploy装好了。

推理

Python 推理示例

准备模型和图片

wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
tar xvf ppyoloe_crn_l_300e_coco.tgz
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

 python推理

# GPU/TensorRT部署参考 examples/vision/detection/paddledetection/python
import cv2
import fastdeploy.vision as visionmodel = vision.detection.PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel","ppyoloe_crn_l_300e_coco/model.pdiparams","ppyoloe_crn_l_300e_coco/infer_cfg.yml")
im = cv2.imread("000000014439.jpg")
result = model.predict(im)
print(result)vis_im = vision.vis_detection(im, result, score_threshold=0.5)
cv2.imwrite("vis_image.jpg", vis_im)

opencv-python这里实在装不上, 用Pillow代替,但是报错:

# GPU/TensorRT部署参考 examples/vision/detection/paddledetection/python
# import cv2
from PIL import Image
import fastdeploy.vision as visionmodel = vision.detection.PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel","ppyoloe_crn_l_300e_coco/model.pdiparams","ppyoloe_crn_l_300e_coco/infer_cfg.yml")
# im = cv2.imread("000000014439.jpg")
im =   Image.open("000000014439.jpg")
result = model.predict(im)
print(result)vis_im = vision.vis_detection(im, result, score_threshold=0.5)
# cv2.imwrite("vis_image.jpg", vis_im)
vis_im.save("vis_image.jpg")

结论:python推理失败

C++推理

预编译环境

Release版本

平台文件说明
Linux x64fastdeploy-linux-x64-1.0.7.tgzg++ 8.2编译产出
Windows x64fastdeploy-win-x64-1.0.7.zipVisual Studio 16 2019编译产出
Mac OSX x64fastdeploy-osx-x86_64-1.0.7.tgzclang++ 10.0.0编译产出

没有FreeBSD的,所以我们要自己编译。

进入FastDeploy目录进行编译:

cd FastDeploy
mkdri build && cd build 
cmake ..
make 

可以根据自己cpu的核数x,加上-j 2*x参数 ,如4核cpu  make -j 8

老规矩,编译好之后加入PATH路径,而不是放入/usr/bin目录,以免污染整个系统。

发现目录结构远比想像的要复杂,还是用make install 安装吧 。切换root账户,

cmake .. -DCMAKE_INSTALL_PREFIX=/home/xxx/work/fd
make -j 8 
make install 

最终使用的语句是在root账户下,在FastDeploy目录执行:

mkdir build
cd build/
cmake .. -DCMAKE_INSTALL_PREFIX=/home/skywalk/work/fd -DWITH_CAPI=ON
make -j 8
make install # 第一次运行报错,所以把下面的patch库挪到install
mkdir third_libs/install
cp -rf third_libs/patchelf/ third_libs/install/
make install

这里参数漏掉一个D ,加上之后编译不过去,也是就是DWITH_CAPI=ON编译不过去,ENABLE_PADDLE_BACKEND和ENABLE_ORT_BACKEND也都过不去。

把参数全删掉可以过去,但那样就没有用了啊!

结论:编译环境失败

准备图片

wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
tar xvf ppyoloe_crn_l_300e_coco.tgz
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

推理

源码

// GPU/TensorRT部署参考 examples/vision/detection/paddledetection/cpp
#include "fastdeploy/vision.h"int main(int argc, char* argv[]) {namespace vision = fastdeploy::vision;auto model = vision::detection::PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel","ppyoloe_crn_l_300e_coco/model.pdiparams","ppyoloe_crn_l_300e_coco/infer_cfg.yml");auto im = cv::imread("000000014439.jpg");vision::DetectionResult res;model.Predict(im, &res);auto vis_im = vision::VisDetection(im, res, 0.5);cv::imwrite("vis_image.jpg", vis_im);return 0;
}

把文件保存为infer_demo.c, 用gcc编译报错。

到FastDeploy/examples/runtime/cpp 目录,编译

mkdir build && cd build
cmake .. -DFASTDEPLOY_INSTALL_DIR=/home/skywalk/work/fd
make -j 8

(在没有任何参数的编译出来的环境下)编译出来一个runtime_demo文件,执行直接崩了。

结论

目前fastdeploy在FreeBSD没有调通。当然在linux下是极其好用的。

调试

pip install opencv-python报错

搞不定,下载源代码手动编译安装python setup.py install

编译时报错 No module named 'skbuild'

  File "/usr/home/skywalk/work/opencv-python-headless-4.9.0.80/setup.py", line 10, in <module>
    from skbuild import cmaker, setup
ModuleNotFoundError: No module named 'skbuild'

pip install scikit-build

编译安装时报错

[ 31%] Building CXX object modules/dnn/CMakeFiles/opencv_dnn.dir/misc/caffe/opencv-caffe.pb.cc.o
In file included from /usr/home/skywalk/work/opencv-python-4.9.0.80/opencv/modules/dnn/misc/caffe/opencv-caffe.pb.cc:4:
In file included from /usr/home/skywalk/work/opencv-python-4.9.0.80/opencv/modules/dnn/misc/caffe/opencv-caffe.pb.h:10:
/usr/local/include/google/protobuf/port_def.inc:210:1: error: static_assert failed due to requirement '201103L >= 201402L' "Protobuf only supports C++14 and newer."
static_assert(PROTOBUF_CPLUSPLUS_MIN(201402L), "Protobuf only supports C++14 and newer.");
^             ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In file included from /usr/home/skywalk/work/opencv-python-4.9.0.80/opencv/modules/dnn/misc/caffe/opencv-caffe.pb.cc:4:
/usr/home/skywalk/work/opencv-python-4.9.0.80/opencv/modules/dnn/misc/caffe/opencv-caffe.pb.h:17:2: error: This file was generated by an older version of protoc which is
#error This file was generated by an older version of protoc which is
 ^
/usr/home/skywalk/work/opencv-python-4.9.0.80/opencv/modules/dnn/misc/caffe/opencv-caffe.pb.h:18:2: error: incompatible with your Protocol Buffer headers. Please
#error incompatible with your Protocol Buffer headers. Please
  pip install protobuf==3.20试试

不行,用opencv-python4.4.

到openv目录 手工cmke .. make -j 报错

/usr/local/include/absl/strings/internal/has_absl_stringify.h:46:8: error: no template named 'enable_if_t' in namespace 'std'; did you mean simply 'enable_if_t'?
    T, std::enable_if_t<std::is_void<decltype(AbslStringify(
       ^~~~~
[  2%] Built target gen_opencv_python_source
/usr/local/include/absl/meta/type_traits.h:656:1: note: 'enable_if_t' declared here
using enable_if_t = typename std::enable_if<B, T>::type;
^
[  2%] Building CXX object 3rdparty/protobuf/CMakeFiles/libprotobuf.dir/src/google/protobuf/generated_message_table_driven_lite.cc.o
In file included from /home/skywalk/work/opencv-python-4.4.0.42/opencv/3rdparty/protobuf/src/google/protobuf/arena.cc:31:
In file included from /usr/local/include/google/protobuf/arena.h:53:
In file included from /usr/local/include/google/protobuf/arena_align.h:85:
/usr/local/include/google/protobuf/port_def.inc:210:1: error: static_assert failed due to requirement '201103L >= 201402L' "Protobuf only supports C++14 and newer."
static_assert(PROTOBUF_CPLUSPLUS_MIN(201402L), "Protobuf only supports C++14 and newer.");

尝试升级gcc:pkg upgrade gcc,但是也只升级到gcc13 还是不到14

尝试使用opencv-python3.4.17版本

报错

[ 45%] Building CXX object modules/dnn/CMakeFiles/opencv_dnn.dir/misc/caffe/opencv-caffe.pb.cc.o
In file included from /usr/home/skywalk/work/opencv-python-3.4.17.63/opencv/modules/dnn/misc/caffe/opencv-caffe.pb.cc:4:
/usr/home/skywalk/work/opencv-python-3.4.17.63/opencv/modules/dnn/misc/caffe/opencv-caffe.pb.h:17:2: error: This file was generated by an older version of protoc which is
#error This file was generated by an older version of protoc which is
 ^
/usr/home/skywalk/work/opencv-python-3.4.17.63/opencv/modules/dnn/misc/caffe/opencv-caffe.pb.h:18:2: error: incompatible with your Protocol Buffer headers.  Please
#error incompatible with your Protocol Buffer headers.  Please
 ^
/usr/home/skywalk/work/opencv-python-3.4.17.63/opencv/modules/dnn/misc/caffe/opencv-caffe.pb.h:19:2: error: regenerate this file with a newer version of protoc.
#error regenerate this file with a newer version of protoc.

搞不定。

不过可喜的是,

import cv2没有报错,也就是opencv可以用啊!

后来测试,发现不行

编译opencv-python-headless报错

In file included from /usr/home/skywalk/work/opencv-python-headless-4.9.0.80/opencv/modules/dnn/misc/caffe/opencv-caffe.pb.cc:4:
In file included from /usr/home/skywalk/work/opencv-python-headless-4.9.0.80/opencv/modules/dnn/misc/caffe/opencv-caffe.pb.h:10:
/usr/local/include/google/protobuf/port_def.inc:210:1: error: static_assert failed due to requirement '201103L >= 201402L' "Protobuf only supports C++14 and newer."
static_assert(PROTOBUF_CPLUSPLUS_MIN(201402L), "Protobuf only supports C++14 and newer.");
^             ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

在root账户下fastdeploy c编译,make install 报错

-- Installing: /root/work/fd/include/fastdeploy/utils/path.h
CMake Error at cmake_install.cmake:81 (file):
  file INSTALL cannot find
  "/home/skywalk/github/FastDeploy/build/third_libs/install": No such file or
  directory.

尝试

cd ~/github/FastDeploy/build/third_libs

mkdir install
cp -rf patchelf/ install/
然后再make install

c推理例子编译报错:

skywalk@x250:~/github/FastDeploy/examples/runtime/cpp % gcc infer_demo.cc
In file included from /usr/local/include/fastdeploy/vision/visualize/visualize.h:17,
                 from /usr/local/include/fastdeploy/vision.h:78,
                 from infer_demo.cc:2:
/usr/local/include/fastdeploy/vision/common/result.h:16:10: fatal error: opencv2/core/core.hpp: No such file or directory
   16 | #include "opencv2/core/core.hpp"
      |          ^~~~~~~~~~~~~~~~~~~~~~~
compilation terminated.
skywalk@x250:~/github/FastDeploy/examples/runtime/cpp %

examples/runtime/cpp目录编译生成的demo 文件runtime_demo执行报错:

./runtime_demo
[ERROR] fastdeploy/runtime/runtime.cc(105)::AutoSelectBackend    The candiate backends for ModelFormat::PADDLE & Device::CPU are [ Backend::PDINFER ,Backend::PDLITE ,Backend::ORT ,Backend::OPENVINO ], but both of them have not been compiled with current FastDeploy yet.
Assertion failed: (runtime.Init(runtime_option)), function main, file /home/skywalk/github/FastDeploy/examples/runtime/cpp/infer_paddle_paddle_inference.cc, line 37.
Abort (core dumped)

fastdeploy编译报错'opencv2/imgcodecs.hpp' file not found

type.cc.o
/home/skywalk/github/FastDeploy/c_api/fastdeploy_capi/core/fd_type.cc:17:10: fatal error: 'opencv2/imgcodecs.hpp' file not found
#include <opencv2/imgcodecs.hpp>
         ^~~~~~~~~~~~~~~~~~~~~~~

用Pillow替代opencv推理报错

python inf.py
Traceback (most recent call last):
  File "/usr/home/skywalk/py310/lib/python3.10/site-packages/fastdeploy_python-0.0.0-py3.10-freebsd-13.2-RELEASE-amd64.egg/fastdeploy/c_lib_wrap.py", line 164, in <module>
    from .libs.fastdeploy_main import *
ImportError: Shared object "libdl.so.2" not found, required by "libonnxruntime.so.1.12.0"

During handling of the above exception, another exception occurred:

先搁置。

本文来自互联网用户投稿,该文观点仅代表作者本人,不代表本站立场。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如若转载,请注明出处:http://www.luyixian.cn/news_show_1027276.aspx

如若内容造成侵权/违法违规/事实不符,请联系dt猫网进行投诉反馈email:809451989@qq.com,一经查实,立即删除!

相关文章

samba实现linux共享文件夹

一、samba安装 sudo apt install samba 二、配置Samba 编辑Samba配置文件sudo vi /etc/samba/smb.conf 在文件末尾添加以下内容&#xff0c;设置一个简单的共享目录&#xff08;替换path_to_share为实际的共享目录路径&#xff09;&#xff1a; [Share] path /path_to_sha…

上位机图像处理和嵌入式模块部署(qmacvisual图像修复)

【 声明&#xff1a;版权所有&#xff0c;欢迎转载&#xff0c;请勿用于商业用途。 联系信箱&#xff1a;feixiaoxing 163.com】 qmacvisual提供了一个图像修复的功能。所谓的图像修复&#xff0c;就是对图像中缺省的部分进行修补&#xff0c;它的操作&#xff0c;其实分成两个…

Unity---ToLua 逻辑热更新

13.2 逻辑热更新——Lua3-1_哔哩哔哩_bilibili ulua https://github.com/topameng/tolua

每日一题 --- 链表相交[力扣][Go]

链表相交 题目&#xff1a;面试题 02.07. 链表相交 给你两个单链表的头节点 headA 和 headB &#xff0c;请你找出并返回两个单链表相交的起始节点。如果两个链表没有交点&#xff0c;返回 null 。 图示两个链表在节点 c1 开始相交**&#xff1a;** 题目数据 保证 整个链式结…

什么?Postman也能测WebSocket接口了?

01、WebSocket 简介 WebSocket是一种在单个TCP连接上进行全双工通信的协议。 WebSocket使得客户端和服务器之间的数据交换变得更加简单&#xff0c;允许服务端主动向客户端推送数据。在WebSocket API中&#xff0c;浏览器和服务器只需要完成一次握手&#xff0c;两者之间就直…

超级好用的Linux系统远程连接工具FinalShell

FinalShell是一体化的的服务器,网络管理软件,不仅是ssh客户端,还是功能强大的开发,运维工具,充分满足开发,运维需求&#xff0c;现在可以免费激活了&#xff01;&#xff01;&#xff01; 介绍 特色功能: 1、多平台支持Windows,macOS,Linux 2、多标签,批量服务器管理 3、漂…

南京观海微电子---Vitis HLS的工作机制——Vitis HLS教程

1. 前言 Vitis HLS&#xff08;原VivadoHLS&#xff09;是一个高级综合工具。用户可以通过该工具直接将C、 C编写的函数翻译成HDL硬件描述语言&#xff0c;最终再映射成FPGA内部的LUT、DSP资源以及RAM资源等。 用户通过Vitis HLS&#xff0c;使用C/C代码来开发RTL IP核&#x…

Web框架开发-Django中间件

一、中间件的概念 中间件顾名思义,是介于request与response处理之间的一道处理过程,相对比较轻量级,并且在全局上改变django的输入与输出。因为改变的是全局,所以需要谨慎实用,用不好会影响到性能。 Django的中间件的定义: Middleware is a framework of hooks into Dj…

如何利用webpack来优化前端性能

当涉及前端性能优化时&#xff0c;Webpack 是一款不可或缺的工具。它不仅仅是一个模块打包工具&#xff0c;还提供了各种功能和插件&#xff0c;可以帮助开发人员优化前端应用程序的性能。在这篇文章中&#xff0c;我们将深入探讨如何有效地利用 Webpack 来优化前端性能&#x…

echarts 3D示例 echart, echarts-gl

echarts官网有很多的炫酷的3D模型 来尝试实现下&#xff0c;使用原本的柱状图或者折线图代码创建echarts示例,使用cdn的方式引入echarts <!DOCTYPE html> <html lang"en"><head><meta charset"UTF-8" /><meta name"viewp…

Spring高级面试题-2024

Spring 框架中都用到了哪些设计模式&#xff1f; 1. 简单工厂&#xff1a; ○ BeanFactory&#xff1a;Spring的BeanFactory充当工厂&#xff0c;负责根据配置信息创建Bean实例。它是一种工厂模式的应用&#xff0c;根据指定的类名或ID创建Bean对象。2. 工厂方法&#xff…

vue实现把Ox格式颜色值转换成rgb渐变颜色值(开箱即用)

图示&#xff1a; 核心代码&#xff1a; //将0x格式的颜色转换为Hex格式&#xff0c;并计算插值返回rgb颜色 Vue.prototype.$convertToHex function (colorCode1, colorCode2, amount) {// 确保输入是字符串&#xff0c;并检查是否以0x开头let newCode1 let newCode2 if (t…

独立站攻略|如何使用SEO代理优化网站排名?

每天&#xff0c;互联网上都会生成和共享大量信息&#xff0c;这使得预测哪个关键字或主题将成为趋势变得很有挑战性&#xff0c;因此人们可以预测和优化他们的搜索引擎排名。但使用“SEO 代理”&#xff0c;就会使得SEO优化更加有效且精准。 一、什么是SEO&#xff1f; 简而言…

2024 年排名前 5 的 Node.js 后端框架

自 2009 年以来&#xff0c;Node.js 一直是人们谈论的话题&#xff0c;大多数后端开发人员都倾向于使用 Node.js。在过去的几年里&#xff0c;它的受欢迎程度有所增加。它被认为是美国最受欢迎的网络开发工具&#xff0c;包括 Netflix 和 PayPal 等客户。 受欢迎程度增加的原因…

Swagger添加JWT验证(ASP.NET)

文章目录 JWT1、解析2、配置JWT JWT 1、解析 1&#xff09;客户端向授权服务系统发起请求&#xff0c;申请获取“令牌”。 2&#xff09;授权服务根据用户身份&#xff0c;生成一张专属“令牌”&#xff0c;并将该“令牌”以JWT规范返回给客户端 3&#xff09;客户端将获取到的…

Machine Learning机器学习之贝叶斯网络(BayesianNetwork)

目录 前言 算法提出背景&#xff1a; 贝叶斯算法特点&#xff1a; 一、贝叶斯定理 二、朴素贝叶斯分类模型 1、朴素贝叶斯分类模型&#xff08;Naive Bayes Classifier&#xff09; 2、原理 2.1 朴素贝叶斯假设 2.2条件独立性假设 2.3后验概率计算 2.4类别预测 2.5小结 3、建模…

力扣热门算法题 135. 分发糖果,146. LRU 缓存,148. 排序链表

135. 分发糖果&#xff0c;146. LRU 缓存&#xff0c;148. 排序链表&#xff0c;每题做详细思路梳理&#xff0c;配套Python&Java双语代码&#xff0c; 2024.03.28 可通过leetcode所有测试用例。 目录 135. 分发糖果 解题思路 完整代码 Python Java 146. LRU 缓存 …

北斗短报文+4G应急广播系统:实时监控 自动预警 保护校园安全的新力量

安全无小事&#xff0c;生命重如山。学生是祖国的未来&#xff0c;校园安全是全社会安全工作的一个重要的组成部分。它直接关系到青少年学生能否安健康地成长&#xff0c;关系到千千万万个家庭的幸福安宁和社会稳定。 灾害事故和突发事件频频发生&#xff0c;给学生、教职员工…

XSS学习(cookie远程登录演示)

1.HTTP特点&#xff1a; 1.请求应答模式。 2.灵活可扩展 3.可靠传输 4.无状态。 这里给大家举一个例子&#xff1a; HTTP是无状态的&#xff0c;所按理来说我每进行一次会话&#xff0c;比如我在CSDN发一个帖子&#xff0c;好像按理来以说我都要进行一次重新登陆&#xff0…

Vue 04 Vue 中的 Ajax、slot 插槽

Vue学习 Vue 0401 Vue中的Ajax服务器准备axios使用跨域问题解决Vue-CLI 配置代理1Vue-CLI 配置代理2案例: 用户搜索vue-resource 02 slot插槽默认插槽具名插槽作用域插槽slot总结 Vue 04 B站 Vue全家桶&#xff08;BV1Zy4y1K7SH&#xff09; 学习笔记 Vue 中的 ajax 01 Vue中的…