Libtorch：Linux系统中使用libtorch【下载、解压】【下载的libtorch是编译好的库文件，可直接使用】

末蓝、 2023-09-25 12:55 14阅读 0赞

这几天学习C++部署[深度][Link 1]模型，看到libtorch是现在人人都可以用普遍方法

## 1 安装[opencv][]版本一定要3.4.12，否则与libtorch共同编译产生错误 ##

linux上安装库或者程序，一个是编译安装、一个是apt安装，这里选择固定版本用编译安装  
[Tags · opencv/opencv · GitHub][Tags _ opencv_opencv _ GitHub]  
[Download | CMake][Download _ CMake]

1 先下载个cmake，我用clion要求cmake20一下才行，直接调用bin下的cmake到clion就行  
2下载opencv3.4.12源码  
3 mkdir build，先安装一些依赖项，网上有，但是有两个库安装冲突，可以试试apitude install  
4 cmake-gui 配置一下，再配置一下，再generate  
5 make -j16 \#我是8核心，一开始用j32直接死机  
6 sudo make install \# 这里将build好的文件安装到/usr/local/include share之类的，但是调用链接还得加到cmakelist  
7 卸载进入build文件夹，make uninstall

## 2 安装libtorch ##

1 我按pytorch官方示例的wget的cpu版本,加压就行，因为是直接编译好的库文件  
https://pytorch.org/cppdocs/installing.html

https://download.pytorch.org/libtorch/cpu/libtorch-shared-with-deps-1.9.1%2Bcpu.zip

![3eb2e4551c234f36811db433224294a4.png][]

2 用的代码是之前看到知乎上的分类推理resnet18.pt  
https://link.zhihu.com/?target=https%3A//github.com/BIGBALLON/PyTorch-CPP  
https://blog.csdn.net/weixin\_44523062/article/details/120132110

将下面代码在[clion][]中打开，配置cmakelist 和调试参数 restnet18.pt label.txt  
生成可执行程序加入参数运行 就 ok 啦

### C++代码 ###

// One-stop header.
    #include <torch/script.h>
    
    #include <cmath>
    #include <iostream>
    #include <memory>
    #include <opencv2/highgui/highgui.hpp>
    #include <opencv2/imgproc/imgproc.hpp>
    #include <opencv2/opencv.hpp>
    #include <string>
    #include <vector>
    
    #define kIMAGE_SIZE 224
    #define kCHANNELS 3
    #define kTOP_K 3
    
    bool LoadImage(std::string file_name, cv::Mat &image) {
      image = cv::imread(file_name);  // CV_8UC3
      if (image.empty() || !image.data) {
        return false;
      }
      cv::namedWindow("d");
      cv::imshow("d",image);
      cv::waitKey();
      cv::destroyAllWindows();
      cv::cvtColor(image, image, cv::COLOR_BGR2RGB);
      std::cout << "== image size: " << image.size() << " ==" << std::endl;
    
      // scale image to fit
      cv::Size scale(kIMAGE_SIZE, kIMAGE_SIZE);
      cv::resize(image, image, scale);
      std::cout << "== simply resize: " << image.size() << " ==" << std::endl;
    
      // convert [unsigned int] to [float]
      image.convertTo(image, CV_32FC3, 1.0f / 255.0f);
    
      return true;
    }
    
    bool LoadImageNetLabel(std::string file_name,
                           std::vector<std::string> &labels) {
      std::ifstream ifs(file_name);
      if (!ifs) {
        return false;
      }
      std::string line;
      while (std::getline(ifs, line)) {
        labels.push_back(line);
      }
      return true;
    }
    
    int main(int argc, const char *argv[]) {
      if (argc != 3) {
        std::cerr << "Usage: classifier <path-to-exported-script-module> "
                     "<path-to-lable-file>"
                  << std::endl;
        return -1;
      }
    
      torch::jit::script::Module module = torch::jit::load(argv[1]);
      std::cout << "== Switch to GPU mode" << std::endl;
      // to GPU
    //    module.to(at::kCUDA);
        module.to(at::kCPU);
    
      std::cout << "== Model [" << argv[1] << "] loaded!\n";
      std::vector<std::string> labels;
      if (LoadImageNetLabel(argv[2], labels)) {
        std::cout << "== Label loaded! Let's try it\n";
      } else {
        std::cerr << "Please check your label file path." << std::endl;
        return -1;
      }
    
      std::string file_name = "";
      cv::Mat image;
      while (true) {
        std::cout << "== Input image path: [enter Q to exit]" << std::endl;
        std::cin >> file_name;
        if (file_name == "Q") {
          break;
        }
        if (LoadImage(file_name, image)) {
          auto input_tensor = torch::from_blob(
              image.data, {1, kIMAGE_SIZE, kIMAGE_SIZE, kCHANNELS});
          input_tensor = input_tensor.permute({0, 3, 1, 2});
          input_tensor[0][0] = input_tensor[0][0].sub_(0.485).div_(0.229);
          input_tensor[0][1] = input_tensor[0][1].sub_(0.456).div_(0.224);
          input_tensor[0][2] = input_tensor[0][2].sub_(0.406).div_(0.225);
    
          // to GPU
    //      input_tensor = input_tensor.to(at::kCUDA);
          input_tensor = input_tensor.to(at::kCPU);
    
          torch::Tensor out_tensor = module.forward({input_tensor}).toTensor();
    
          auto results = out_tensor.sort(-1, true);
          auto softmaxs = std::get<0>(results)[0].softmax(0);
          auto indexs = std::get<1>(results)[0];
    
          for (int i = 0; i < kTOP_K; ++i) {
            auto idx = indexs[i].item<int>();
            std::cout << "    ============= Top-" << i + 1
                      << " =============" << std::endl;
            std::cout << "    Label:  " << labels[idx] << std::endl;
            std::cout << "    With Probability:  "
                      << softmaxs[i].item<float>() * 100.0f << "%" << std::endl;
          }
    
        } else {
          std::cout << "Can't load the image, please check your path." << std::endl;
        }
      }
      return 0;
    }

### cmakelists ###

cmake_minimum_required(VERSION 3.20)
    project(testlibtorch)
    
    set(CMAKE_CXX_STANDARD 14)
    set(Torch_DIR /home2/libtorch_apk/libtorch/share/cmake/Torch)
    
    # 寻找OpenCV库
    find_package( OpenCV 3 REQUIRED )
    # 添加头文件
    include_directories( ${OpenCV_INCLUDE_DIRS} )
    
    find_package(Torch REQUIRED)
    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}")
    
    add_executable(testlibtorch
            prediction.cpp)
    target_link_libraries(testlibtorch "${TORCH_LIBRARIES}")
    target_link_libraries( testlibtorch ${OpenCV_LIBS} )
    
    set_property(TARGET testlibtorch PROPERTY CXX_STANDARD 14)

### python导出模型程序 ###

import torch
    import torchvision
    
    model = torchvision.models.resnet18(pretrained=True)
    
    # Don't forget change model to eval mode
    model.eval()
    example = torch.rand(1, 3, 224, 224)
    traced_script_module = torch.jit.trace(model, example)
    traced_script_module.save("resnet18.pt")

[2021-09-07libtorch在linux下使用\_AIchiNiurou的博客-CSDN博客\_libtorch linux][2021-09-07libtorch_linux_AIchiNiurou_-CSDN_libtorch linux]

[Libtorch各类版本下载---持续更新\_枫呱呱的博客-CSDN博客\_libtorch下载][Libtorch_---_-CSDN_libtorch]

[Link 1]: https://so.csdn.net/so/search?q=%E6%B7%B1%E5%BA%A6&spm=1001.2101.3001.7020
[opencv]: https://so.csdn.net/so/search?q=opencv&spm=1001.2101.3001.7020
[Tags _ opencv_opencv _ GitHub]: https://github.com/opencv/opencv/tags?after=4.5.0
[Download _ CMake]: https://cmake.org/download/
[3eb2e4551c234f36811db433224294a4.png]: https://img-blog.csdnimg.cn/3eb2e4551c234f36811db433224294a4.png
[clion]: https://so.csdn.net/so/search?q=clion&spm=1001.2101.3001.7020
[2021-09-07libtorch_linux_AIchiNiurou_-CSDN_libtorch linux]: https://blog.csdn.net/weixin_44523062/article/details/120168549
[Libtorch_---_-CSDN_libtorch]: https://blog.csdn.net/qq_35275007/article/details/116592120