配置步骤

分四步

第一步：正确下载软件

要素：

1. 在官网下载pytorch和libtorch，版本注意保持一致，如果是GPU版注意保持CUDA版本一致
2. libtorch还分为debug版和release版，这点也要注意，笔者就因为用debug调用release的库，导致模型序列化错误，但编译时并不会报错

第二步：VS2022配置

包含目录或附加包含目录：
E:\\libtorch-win-shared-with-deps-1.11.0+cpu\\libtorch\\include
E:\\libtorch-win-shared-with-deps-1.11.0+cpu\\libtorch\\include\\torch\\csrc\\api\\include
库目录或附加库目录：
E:\\libtorch-win-shared-with-deps-1.11.0+cpu\\libtorch\\lib
附加依赖项：
c10.lib
fbgemm.lib
asmjit.lib
torch.lib
torch_cpu.lib

第三步：生成序列化模型

def test(weight_file):
    example = torch.rand(1,3, 224, 224)
    model = HandRecNet()
    if os.path.exists(weight_file):
        model.load_state_dict(torch.load(weight_file, map_location=\'cpu\'))
    model.eval()
    with torch.no_grad():
        traced_script_module =  torch.jit.trace(model, example)
    traced_script_module.save(\"traced_model.pt\"）

第四步：调用模型

#include  //one-stop header
#include 

#include 
#include 
#include 
#include 

#include 
#include 

int main(int argc, const char* argv[]){
    if (argc != 2){
        std::cerr << \"usage: example-app \\n\";
	    return -1;
    }
    torch::jit::script::Module module;
    try{
        // Deserialize the scriptmodule from a file using torch::jit::load().
        module = torch::jit::load(argv[1]);
    }
    catch(const c10::Error& e){
        std::cerr << \"error loading the model\\n\";
	return -1;
    }
    std::cout << \"model load ok\\n\";
    // load image with opencv and transform.
    // 1. read image
    cv::Mat image;
    image = cv::imread(\"../dog2.JPEG\", CV_LOAD_IMAGE_COLOR);
    // 2. convert color space, opencv read the image in BGR
    cv::cvtColor(image, image, CV_BGR2RGB);
    cv::Mat img_float;
    // convert to float format
    image.convertTo(img_float, CV_32F, 1.0/255);
    // 3. resize the image for resnet101 model
    cv::resize(img_float, img_float, cv::Size(224, 224),cv::INTER_AREA);
    // 4. transform to tensor
    auto img_tensor = torch::from_blob(img_float.data, {1,224,224,3},torch::kFloat32);
    // in pytorch, batch first, then channel
    img_tensor = img_tensor.permute({0,3,1,2}); 
    // 5. Removing mean values of the RGB channels
    // the values are from following link.
    // https://github.com/pytorch/examples/blob/master/imagenet/main.py#L202
    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);
    
    // Create vectors of inputs.
    std::vector inputs1, inputs2;
    inputs1.push_back(torch::ones({1,3,224,224}));
    inputs2.push_back(img_tensor);
    
    // 6. Execute the model and turn its output into a tensor
    at::Tensor output = module.forward(inputs2).toTensor();
    std::cout << output.sizes() << std::endl;
    std::cout << output.slice(/*dim=*/1,/*start=*/0,/*end=*/3) << \'\\n\';

    // 7. Load labels
    std::string label_file = \"../synset_words.txt\";
    std::ifstream rf(label_file.c_str());
    CHECK(rf) << \"Unable to open labels file\" << label_file;
    std::string line;
    std::vector labels;
    while(std::getline(rf, line)){labels.push_back(line);}
    
    // 8. print predicted top-3 labels
    std::tuple result = output.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();
    auto top_idxs_a = top_idxs.accessor();
    for(int i=0; i<3;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;
}
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参考

[1]https://blog.csdn.net/weixin_44278406/article/details/103637160
[2]https://blog.csdn.net/Challovactor/article/details/104793002
[3]https://pytorch.org/tutorials/advanced/cpp_export.html
[4]https://pytorch.org/docs/stable/jit.html#
[5]https://zhuanlan.zhihu.com/p/146453159
[6]https://zhuanlan.zhihu.com/p/141401062
[7]http://zhaoxuhui.top/blog/2021/04/13/libtorch-installation-and-use.html