From 8bb2ebd70373d237ad8622fc8b9f9f4b3e658aab Mon Sep 17 00:00:00 2001
From: Carl Philipp Klemm <philipp@uvos.xyz>
Date: Mon, 15 May 2023 16:41:04 +0200
Subject: [PATCH] add eigen to torch conversions
---
eigentorchconversions.h | 85 +++++++++++++++++++++++++++++++++++++++++
1 file changed, 85 insertions(+)
create mode 100644 eigentorchconversions.h
diff --git a/eigentorchconversions.h b/eigentorchconversions.h
new file mode 100644
index 0000000..79b78fe
--- /dev/null
+++ b/eigentorchconversions.h
@@ -0,0 +1,85 @@
+#include <climits>
+#include <sys/types.h>
+#include <torch/torch.h>
+#include <Eigen/Dense>
+#include <torch/types.h>
+#include <vector>
+
+#include "tensoroptions.h"
+
+template <typename V>
+bool checkTorchType(const torch::Tensor& tensor)
+{
+ static_assert(std::is_same<V, float>::value || std::is_same<V, double>::value ||
+ std::is_same<V, int64_t>::value || std::is_same<V, int32_t>::value || std::is_same<V, int8_t>::value,
+ "This function dose not work with this type");
+ if constexpr(std::is_same<V, float>::value)
+ return tensor.dtype() == torch::kFloat32;
+ else if constexpr(std::is_same<V, double>::value)
+ return tensor.dtype() == torch::kFloat64;
+ else if constexpr(std::is_same<V, int64_t>::value)
+ return tensor.dtype() == torch::kInt64;
+ else if constexpr(std::is_same<V, int32_t>::value)
+ return tensor.dtype() == torch::kInt32;
+ else if constexpr(std::is_same<V, int8_t>::value)
+ return tensor.dtype() == torch::kInt8;
+}
+
+template <typename V>
+using MatrixXrm = typename Eigen::Matrix<V, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
+
+template <typename V>
+torch::Tensor eigen2libtorch(Eigen::MatrixX<V> &M)
+{
+ Eigen::Matrix<V, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> E(M);
+ std::vector<int64_t> dims = {E.rows(), E.cols()};
+ auto T = torch::from_blob(E.data(), dims, tensorOptCpu<V>(false)).clone();
+ return T;
+}
+
+template <typename V>
+torch::Tensor eigen2libtorch(MatrixXrm<V> &E, bool copydata = true)
+{
+ std::vector<int64_t> dims = {E.rows(), E.cols()};
+ auto T = torch::from_blob(E.data(), dims, tensorOptCpu<V>(false));
+ if (copydata)
+ return T.clone();
+ else
+ return T;
+}
+
+template <typename V>
+torch::Tensor eigenVector2libtorch(Eigen::Vector<V, Eigen::Dynamic> &E, bool copydata = true)
+{
+ std::vector<int64_t> dims = {E.rows()};
+ auto T = torch::from_blob(E.data(), dims, tensorOptCpu<V>(false));
+ if (copydata)
+ return T.clone();
+ else
+ return T;
+}
+
+template<typename V>
+Eigen::Matrix<V, Eigen::Dynamic, Eigen::Dynamic> libtorch2eigenMaxtrix(torch::Tensor &Tin)
+{
+ /*
+ LibTorch is Row-major order and Eigen is Column-major order.
+ MatrixXrm uses Eigen::RowMajor for compatibility.
+ */
+ assert(checkTorchType<V>(Tin));
+ Tin = Tin.contiguous();
+ auto T = Tin.to(torch::kCPU);
+ Eigen::Map<MatrixXrm<V>> E(T.data_ptr<V>(), T.size(0), T.size(1));
+ return E;
+}
+
+template<typename V>
+Eigen::Vector<V, Eigen::Dynamic> libtorch2eigenVector(torch::Tensor &Tin)
+{
+ assert(Tin.sizes().size() == 1);
+ assert(checkTorchType<V>(Tin));
+ Tin = Tin.contiguous();
+ auto T = Tin.to(torch::kCPU);
+ Eigen::Map<Eigen::Vector<V, Eigen::Dynamic>> E(T.data_ptr<V>(), T.numel());
+ return E;
+}
--
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