# Data transfer between LibTorch C++ and Eigen

## 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:

(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).

What great contribution.
A question: I did not see any example of cv2::imread(). Is that because you had issues with ABI compatibility between OpenCV and libtorch?

Thanks,

Thank you @dambo,
Yes, I am still working on it to add more functions and examples.
Cheers,
Andrews

I am working on this issue for several days now. I will share my docker if I am successful.

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I shall upload the code to this repo once I am done:

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Hey,

I also needed to transfer data from Eigen to libtorch, so I inspected your example. For a square matrix, it seems that it is a transpose porblem, but if you try the same thing with a non-square matrix, the result is chaotic

After a bit of thinking, I came out with this solution (I tested on square and non square matrix).

``````torch::Tensor eigenVectorToTorchTensor(VectorXd e){
auto t = torch::rand({e.rows()});
float* data = t.data_ptr<float>();

Map<VectorXf> ef(data,t.size(0),1);
ef = e.cast<float>();

return t;
}

torch::Tensor eigenMatrixToTorchTensor(MatrixXd e){
auto t = torch::rand({e.cols(),e.rows()});
float* data = t.data_ptr<float>();

Map<MatrixXf> ef(data,t.size(1),t.size(0));
ef = e.cast<float>();
return t.transpose(0,1);
}
``````

NB: Here I am interested into converting an Eigen object to a torch object, but I think that you can easily get the reverse function from this code.

Hope it helps!
A+

/jeremy

2 Likes

Hello all, I’d like to ask whether this issue has been resolved or not?

@999999999 the code from @jmaceiras works for me, and I think that’s a good solution. The one change I would make is to use `torch::empty` instead of `torch::rand` so you don’t have to compute random values that are immediately overwritten.

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The default memory order of Eigen is ColMajor which each column is stored sequentially. But libtorch use row major so that data stores row by row. You need to use
`Matrix<double, -1, -1, RowMajor>`
to define a row major matrix.

1 Like