C++Pytorch- libTorch 示例

#include <torch/script.h>
#include <torch/torch.h> 
#include <torch/Tensor.h> 
#include <opencv2/opencv.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include <iostream>
#include <memory>
#include <string>
#include <vector>

/* main */
int main(int argc, const char* argv[]) {
  if (argc < 4) {
    std::cerr << "usage: example-app <path-to-exported-script-module> "
      << "<path-to-image>  <path-to-category-text>\n";
    return -1;
  }

  // Deserialize the ScriptModule from a file using torch::jit::load().
  std::shared_ptr<torch::jit::script::Module> module = torch::jit::load(argv[1]);

  assert(module != nullptr);
  std::cout << "load model ok\n";

  // Create a vector of inputs.
  std::vector<torch::jit::IValue> inputs;
  inputs.push_back(torch::rand({64, 3, 224, 224}));

  // evalute time
  double t = (double)cv::getTickCount();
  module->forward(inputs).toTensor();
  t = (double)cv::getTickCount() - t;
  printf("execution time = %gs\n", t / cv::getTickFrequency());
  inputs.pop_back();

  // load image with opencv and transform
  cv::Mat image;
  image = cv::imread(argv[2], 1);
  cv::cvtColor(image, image, CV_BGR2RGB);
  cv::Mat img_float;
  image.convertTo(img_float, CV_32F, 1.0/255);
  cv::resize(img_float, img_float, cv::Size(224, 224));
  //std::cout << img_float.at<cv::Vec3f>(56,34)[1] << std::endl;
  auto img_tensor = torch::CPU(torch::kFloat32).tensorFromBlob(img_float.data, {1, 224, 224, 3});
  img_tensor = img_tensor.permute({0,3,1,2});
  img_tensor[0][0] = img_tensor[0][0].sub_(0.485).div_(0.229);
  img_tensor[0][1] = img_tensor[0][1].sub_(0.456).div_(0.224);
  img_tensor[0][2] = img_tensor[0][2].sub_(0.406).div_(0.225);
  auto img_var = torch::autograd::make_variable(img_tensor, false);
  inputs.push_back(img_var);
  
  // Execute the model and turn its output into a tensor.
  torch::Tensor out_tensor = module->forward(inputs).toTensor();
  std::cout << out_tensor.slice(/*dim=*/1, /*start=*/0, /*end=*/10) << '\n';

  // Load labels
  std::string label_file = argv[3];
  std::ifstream rf(label_file.c_str());
  CHECK(rf) << "Unable to open labels file " << label_file;
  std::string line;
  std::vector<std::string> labels;
  while (std::getline(rf, line))
    labels.push_back(line);

  // print predicted top-5 labels
  std::tuple<torch::Tensor,torch::Tensor> result = out_tensor.sort(-1, true);
  torch::Tensor top_scores = std::get<0>(result)[0];
  torch::Tensor top_idxs = std::get<1>(result)[0].toType(torch::kInt32);
  
  auto top_scores_a = top_scores.accessor<float,1>();
  auto top_idxs_a = top_idxs.accessor<int,1>();

  for (int i = 0; i < 5; ++i) {
    int idx = top_idxs_a[i];
    std::cout << "top-" << i+1 << " label: ";
    std::cout << labels[idx] << ", score: " << top_scores_a[i] << std::endl;
  }

  return 0;
}

　　

主要要求：PyTroch 1.0，opencv，cmake

步骤1
转换您的PYTORCH模型以编写脚本并将其脚本化为文件

运行python tracing.py并获取model.pt
第2步
用C ++加载脚本模块并执行

在此处下载LibTorch并解压缩

麦克马

mkdir build
cd build
cmake -DCMAKE_PREFIX_PATH=/Users/hankai/code/cpp-pytorch/libtorch ..
make
Run demo
./example-app ../model.pt ../dog.png ../synset_words.txt
输入图像和预测标签