通过StupUpToo/CMAKESList.txt生成和调用Python脚本中的自定义C++ LBKARK函数

Question

通常，我需要生成一个共享库文件（.so），我可以使用<torch.ops.load_库（）>；函数，该函数来自python脚本

在Windows 10:64位操作系统上工作

尝试了两种方法-都不生成此文件

1>CMakesList.txt-构建成功，我得到了一个dll和.lib文件，我尝试使用python ctypes-cdll.load_library（）加载该文件，但没有函数，通过vs代码使用dumpbin进行交叉检查。

cmake_minimum_required (VERSION 3.8)
find_package(Torch REQUIRED)
project(reductionResNet VERSION 1.0 DESCRIPTION "Deep_Learning")

# add_executable (customOperator "customOperator.cpp" "customOperator.h")
# Define our library target
add_library(customOperator SHARED customOperator.cpp)
# Enable C++14
target_compile_features(customOperator PRIVATE cxx_std_14)
# Link against LibTorch
target_link_libraries(customOperator "${TORCH_LIBRARIES}")

if (MSVC)
    file(GLOB TORCH_DLLS "${TORCH_INSTALL_PREFIX}/lib/*.dll")
    add_custom_command(TARGET customOperator
                        POST_BUILD
                        COMMAND ${CMAKE_COMMAND} -E copy_if_different
                        ${TORCH_DLLS}
                        $<TARGET_FILE_DIR:customOperator>)
endif (MSVC)

2>Python Setuptools.py-抛出错误->；链接：错误LNK2001:未解析的外部符号PyInit_缩减

from setuptools import setup, Extension
from torch.utils import cpp_extension

setup(name='customOperator',
      ext_modules=[cpp_extension.CppExtension('customOperator', ['customOperator.cpp'],
      include_dirs = ['C:/Libtorch/libtorch-win-shared-with-deps-1.8.1+cpu/libtorch'])],
      cmdclass={'build_ext':cpp_extension.BuildExtension.with_options(no_python_abi_suffix=True)}  )  

这些是我一直关注的教程Pytorch_Docs&Git_tutorial

这是Tested.cpp文件，其中包含两个函数-reduce和repeatInterleave

#include "customOperator.h"
#include <torch/torch.h>

using namespace std;

torch::Tensor repeatInterleave(
    torch::Tensor input,
    int val
) {
    auto output_ = torch::repeat_interleave(input, val);
    return output_;
}

torch::Tensor reduction(
    torch::Tensor layerOne,
    torch::Tensor layerTwo,
    torch::Tensor layerThree,
    torch::Tensor layerFour) {


    auto layerOne_ = repeatInterleave(layerOne, 8);
    auto layerTwo_ = repeatInterleave(layerTwo, 4);
    auto layerThree_ = repeatInterleave(layerThree, 2);

    int len = layerFour.sizes()[0];
    //cout << len << endl;

    torch::Tensor arr[512] = {};

    //torch::Tensor* arr = new torch::Tensor[len];
    //std::vector<std::string> x = { "a", "b", "c" };
    //x.push_back("d");
    //std::vector <torch::Tensor> arr = {};

    for (int i = 0; i < len; i += 1) {
        arr[i] = (layerOne_[i] + layerTwo_[i] + layerThree_[i] + layerFour[i]) / 4;

        //arr.push_back((layerOne_[i] + layerTwo_[i] + layerThree_[i] + layerFour[i]) / 4);
        //cout << arr[i] << endl;
    }

    //cout << arr << endl;
    //torch::Tensor output = torch::zeros(layerFour.sizes());
    //delete[] arr;

    auto ouput = torch::stack(arr);
    return ouput;
}

int main()
{
    cout << "Hello CMake." << endl;
    return 0;
}