Data transfer between LibTorch C++ and Eigen - opencv

Question

Data transfer between LibTorch C++ and Eigen (Questions and Help)
Hello all,
I'm developing a Data Transfer Tools for C++ Linear Algebra Libraries, as you can see here:
https://github.com/andrewssobral/dtt
(considering bi-dimensional arrays or matrices)
and I'm wondering if you can help me on the following code for data transfer between LibTorch and Eigen:
std::cout << "Testing LibTorch to Eigen:" << std::endl;
// LibTorch
torch::Device device(torch::cuda::is_available() ? torch::kCUDA : torch::kCPU);
torch::Tensor T = torch::rand({3, 3});
std::cout << "LibTorch:" << std::endl;
std::cout << T << std::endl;
// Eigen
float* data = T.data_ptr<float>();
Eigen::Map<Eigen::MatrixXf> E(data, T.size(0), T.size(1));
std::cout << "EigenMat:\n" << E << std::endl;
// re-check after changes
E(0,0) = 0;
std::cout << "EigenMat:\n" << E << std::endl;
std::cout << "LibTorch:" << std::endl;
std::cout << T << std::endl;
This is the output of the code:
--------------------------------------------------
Testing LibTorch to Eigen:
LibTorch:
0.6232 0.5574 0.6925
0.7996 0.9860 0.1471
0.4431 0.5914 0.8361
[ Variable[CPUFloatType]{3,3} ]
EigenMat (after data transfer):
0.6232 0.7996 0.4431
0.5574 0.986 0.5914
0.6925 0.1471 0.8361
# Modifying EigenMat, set element at (0,0) = 0
EigenMat:
0 0.7996 0.4431
0.5574 0.986 0.5914
0.6925 0.1471 0.8361
# Now, the LibTorch matrix was also modified (OK), but the rows and columns were switched.
LibTorch:
0.0000 0.5574 0.6925
0.7996 0.9860 0.1471
0.4431 0.5914 0.8361
[ Variable[CPUFloatType]{3,3} ]
Do someone knows what's happening ?
There's a better way to do that?
I need also to do the same for Armadillo, ArrayFire and OpenCV (cv::Mat).
Thanks in advance!

Answer 1

The reason for the switched rows and columns is that LibTorch (apparently) uses row-major storage, while Eigen by default uses column-major storage. I don't know if you can change the behavior of LibTorch, but with Eigen you can also use row-major storage, like so:
typedef Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> MatrixXf_rm; // same as MatrixXf, but with row-major memory layout
and then use it like this:
Eigen::Map<MatrixXf_rm> E(data, T.size(0), T.size(1));