Sharing memory between Eigen::Matrix and fdeep::tensor3

eigen_fdeep_share_memory.cpp

// Sharing memory between Eigen::Matrix and fdeep::tensor5

#include <iostream>
#include <eigen3/Eigen/Dense>
#include <fdeep/fdeep.hpp>

int main()
{
    // use row major storage order for eigen matrix, since fdeep uses it too
    using RowMajorMatrixXf = Eigen::Matrix<fdeep::float_type, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;

    // dimensions of the eigen matrix
    const int rows = 640;
    const int cols = 480;

    // initialize memory shared between matrix and tensor
    fdeep::shared_float_vec data_vec = fplus::make_shared_ref<fdeep::float_vec>();
    data_vec->resize(static_cast<std::size_t>(rows * cols));

    // create eigen matrix using the memory block from the vector above
    Eigen::Map<RowMajorMatrixXf, Eigen::Unaligned> mapped_matrix(
        data_vec->data(),
        rows, cols);

    // populate mapped_matrix some way
    mapped_matrix(0, 0) = 4.0f;
    mapped_matrix(1, 1) = 5.0f;
    mapped_matrix(4, 2) = 6.0f;

    // create fdeep::tensor5 also using the memory block of the vector
    const int tensor5_channels = rows;
    const int tensor5_rows = 1;
    const int tensor5_cols = cols;
    fdeep::shape5 tensor5_shape(1, 1, tensor5_rows, tensor5_cols, tensor5_channels);
    fdeep::tensor5 t(tensor5_shape, data_vec);

    // print some values to make sure the mapping is correct
    std::cout << t.get(0, 0, 0, 0, 0) << std::endl;
    std::cout << t.get(0, 0, 0, 1, 1) << std::endl;
    std::cout << t.get(0, 0, 0, 4, 2) << std::endl;
}