Question

我可以使用pybind1将三维numpy数组传递给接受Eigen::Tensor作为参数的c ++函数。例如，考虑以下c ++函数：

Eigen::Tensor<double, 3> addition_tensor(Eigen::Tensor<double, 3> a,
                                         Eigen::Tensor<double, 3> b) {
    return a + b;
}

编译函数，将其导入python并将numpy数组np.ones((1, 2, 2))传递给它后，我收到以下错误消息：

TypeError: addition_tensor(): incompatible function arguments. The following argument types are supported:
    1. (arg0: Eigen::Tensor<double, 3, 0, long>, arg1: Eigen::Tensor<double, 3, 0, long>) -> Eigen::Tensor<double, 3, 0, long>

由于无法将三维numpy array传递给接受Eigen::MatrixXd的函数，我感到特别惊讶，因为它无法传递三维numpy数组，例如：

Eigen::MatrixXd addition(Eigen::MatrixXd a, Eigen::MatrixXd b) { return a + b; }

此示例中使用的整个代码是：

#include <eigen-git-mirror/Eigen/Dense>
#include <eigen-git-mirror/unsupported/Eigen/CXX11/Tensor>
#include "pybind11/include/pybind11/eigen.h"
#include "pybind11/include/pybind11/pybind11.h"

Eigen::MatrixXd addition(Eigen::MatrixXd a, Eigen::MatrixXd b) { return a + b; }

Eigen::Tensor<double, 3> addition_tensor(Eigen::Tensor<double, 3> a,
                                         Eigen::Tensor<double, 3> b) {
    return a + b;
}

PYBIND11_MODULE(example, m) {
    m.def("addition", &addition, "A function which adds two numbers");
    m.def("addition_tensor", &addition_tensor,
          "A function which adds two numbers");
}

我用g++ -shared -fPIC `python3 -m pybind11 --includes` example.cpp -o example`python3-config --extension-suffix`编译了上面的代码。有人知道如何将三维numpy数组转换为接受三维Eigen::Tensor的函数吗？

Answer 1

不直接支持它，这里是一些讨论（如果您想将其添加到项目中，则包括一些执行映射的代码）：https://github.com/pybind/pybind11/issues/1377

Answer 2

感谢@John Zwinck的回答，我可以实现我想要的东西。如果有人感兴趣，这是复制品：

#include <eigen-git-mirror/Eigen/Dense>
#include <eigen-git-mirror/unsupported/Eigen/CXX11/Tensor>
#include "pybind11/include/pybind11/eigen.h"
#include "pybind11/include/pybind11/numpy.h"
#include "pybind11/include/pybind11/pybind11.h"

Eigen::Tensor<double, 3, Eigen::RowMajor> getTensor(
    pybind11::array_t<double> inArray) {
    // request a buffer descriptor from Python
    pybind11::buffer_info buffer_info = inArray.request();

    // extract data an shape of input array
    double *data = static_cast<double *>(buffer_info.ptr);
    std::vector<ssize_t> shape = buffer_info.shape;

    // wrap ndarray in Eigen::Map:
    // the second template argument is the rank of the tensor and has to be
    // known at compile time
    Eigen::TensorMap<Eigen::Tensor<double, 3, Eigen::RowMajor>> in_tensor(
        data, shape[0], shape[1], shape[2]);
    return in_tensor;
}

pybind11::array_t<double> return_array(
    Eigen::Tensor<double, 3, Eigen::RowMajor> inp) {
    std::vector<ssize_t> shape(3);
    shape[0] = inp.dimension(0);
    shape[1] = inp.dimension(1);
    shape[2] = inp.dimension(2);
    return pybind11::array_t<double>(
        shape,  // shape
        {shape[1] * shape[2] * sizeof(double), shape[2] * sizeof(double),
         sizeof(double)},  // strides
        inp.data());       // data pointer
}

pybind11::array_t<double> addition(pybind11::array_t<double> a,
                                   pybind11::array_t<double> b) {
    Eigen::Tensor<double, 3, Eigen::RowMajor> a_t = getTensor(a);
    Eigen::Tensor<double, 3, Eigen::RowMajor> b_t = getTensor(b);
    Eigen::Tensor<double, 3, Eigen::RowMajor> res = a_t + b_t;
    return return_array(res);
}

PYBIND11_MODULE(example, m) {
    m.def("addition", &addition, "A function which adds two numbers");
}

与约翰提到的链接中的建议相反，我不介意为RowMajor使用Eigen::Tensor的存储顺序。我还发现tensorflow代码中也多次使用了此存储顺序。我不知道上面的代码是否不必要地复制了数据。

我可以使用pybind11将numpy数组传递给接受Eigen :: Tensor的函数吗？

2 个答案: