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Added ability to swap the layout of a tensor
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Benoit Steiner
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198
unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h
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198
unsupported/Eigen/CXX11/src/Tensor/TensorLayoutSwap.h
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
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#define EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
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namespace Eigen {
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/** \class TensorLayoutSwap
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Swap the layout from col-major to row-major, or row-major
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* to col-major, and invert the order of the dimensions.
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*
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* Beware: the dimensions are reversed by this operation. If you want to
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* preserve the ordering of the dimensions, you need to combine this
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* operation with a shuffle.
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*
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* \example:
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* Tensor<float, 2, ColMajor> input(2, 4);
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* Tensor<float, 2, RowMajor> output = input.swap_layout();
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* eigen_assert(output.dimension(0) == 4);
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* eigen_assert(output.dimension(1) == 2);
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*
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* array<int, 2> shuffle(1, 0);
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* output = input.swap_layout().shuffle(shuffle);
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* eigen_assert(output.dimension(0) == 2);
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* eigen_assert(output.dimension(1) == 4);
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*
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*/
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namespace internal {
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template<typename XprType>
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struct traits<TensorLayoutSwapOp<XprType> > : public traits<XprType>
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{
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typedef typename XprType::Scalar Scalar;
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typedef traits<XprType> XprTraits;
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typedef typename packet_traits<Scalar>::type Packet;
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typedef typename XprTraits::StorageKind StorageKind;
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typedef typename XprTraits::Index Index;
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typedef typename XprType::Nested Nested;
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typedef typename remove_reference<Nested>::type _Nested;
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static const int NumDimensions = traits<XprType>::NumDimensions;
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static const int Layout = (traits<XprType>::Layout == ColMajor) ? RowMajor : ColMajor;
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};
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template<typename XprType>
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struct eval<TensorLayoutSwapOp<XprType>, Eigen::Dense>
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{
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typedef const TensorLayoutSwapOp<XprType>& type;
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};
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template<typename XprType>
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struct nested<TensorLayoutSwapOp<XprType>, 1, typename eval<TensorLayoutSwapOp<XprType> >::type>
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{
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typedef TensorLayoutSwapOp<XprType> type;
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};
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} // end namespace internal
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template<typename XprType>
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class TensorLayoutSwapOp : public TensorBase<TensorLayoutSwapOp<XprType>, WriteAccessors>
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{
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public:
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typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Scalar Scalar;
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typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Packet Packet;
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typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
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typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
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typedef typename internal::remove_const<typename XprType::PacketReturnType>::type PacketReturnType;
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typedef typename Eigen::internal::nested<TensorLayoutSwapOp>::type Nested;
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typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::StorageKind StorageKind;
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typedef typename Eigen::internal::traits<TensorLayoutSwapOp>::Index Index;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorLayoutSwapOp(const XprType& expr)
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: m_xpr(expr) {}
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EIGEN_DEVICE_FUNC
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const typename internal::remove_all<typename XprType::Nested>::type&
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expression() const { return m_xpr; }
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template<typename OtherDerived>
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE TensorLayoutSwapOp& operator = (const OtherDerived& other)
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{
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typedef TensorAssignOp<TensorLayoutSwapOp, const OtherDerived> Assign;
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Assign assign(*this, other);
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internal::TensorExecutor<const Assign, DefaultDevice, false>::run(assign, DefaultDevice());
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return *this;
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}
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protected:
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typename XprType::Nested m_xpr;
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};
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// Eval as rvalue
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template<typename ArgType, typename Device>
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struct TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device>
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{
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typedef TensorLayoutSwapOp<ArgType> XprType;
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typedef typename XprType::Index Index;
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static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
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typedef DSizes<Index, NumDims> Dimensions;
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enum {
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IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
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PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
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Layout = (TensorEvaluator<ArgType, Device>::Layout == ColMajor) ? RowMajor : ColMajor,
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CoordAccess = false, // to be implemented
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};
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
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: m_impl(op.expression(), device)
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{
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for(int i = 0; i < NumDims; ++i) {
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m_dimensions[i] = m_impl.dimensions()[NumDims-1-i];
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}
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}
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typedef typename XprType::Scalar Scalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename XprType::PacketReturnType PacketReturnType;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(CoeffReturnType* data) {
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return m_impl.evalSubExprsIfNeeded(data);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
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m_impl.cleanup();
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
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{
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return m_impl.coeff(index);
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}
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template<int LoadMode>
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
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{
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return m_impl.template packet<LoadMode>(index);
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}
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CoeffReturnType* data() const { return m_impl.data(); }
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const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
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protected:
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TensorEvaluator<ArgType, Device> m_impl;
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Dimensions m_dimensions;
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};
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// Eval as lvalue
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template<typename ArgType, typename Device>
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struct TensorEvaluator<TensorLayoutSwapOp<ArgType>, Device>
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: public TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device>
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{
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typedef TensorEvaluator<const TensorLayoutSwapOp<ArgType>, Device> Base;
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typedef TensorLayoutSwapOp<ArgType> XprType;
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enum {
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IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
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PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
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Layout = (TensorEvaluator<ArgType, Device>::Layout == ColMajor) ? RowMajor : ColMajor,
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CoordAccess = false, // to be implemented
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};
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
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: Base(op, device)
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{ }
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typedef typename XprType::Index Index;
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typedef typename XprType::Scalar Scalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename XprType::PacketReturnType PacketReturnType;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
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{
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return this->m_impl.coeffRef(index);
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}
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template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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void writePacket(Index index, const PacketReturnType& x)
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{
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this->m_impl.template writePacket<StoreMode>(index, x);
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}
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};
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} // end namespace Eigen
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#endif // EIGEN_CXX11_TENSOR_TENSOR_LAYOUT_SWAP_H
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61
unsupported/test/cxx11_tensor_layout_swap.cpp
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61
unsupported/test/cxx11_tensor_layout_swap.cpp
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@ -0,0 +1,61 @@
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#include "main.h"
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#include <Eigen/CXX11/Tensor>
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using Eigen::Tensor;
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static void test_simple_swap()
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{
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Tensor<float, 3, ColMajor> tensor(2,3,7);
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tensor.setRandom();
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Tensor<float, 3, RowMajor> tensor2 = tensor.swap_layout();
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VERIFY_IS_EQUAL(tensor.dimension(0), tensor2.dimension(2));
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VERIFY_IS_EQUAL(tensor.dimension(1), tensor2.dimension(1));
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VERIFY_IS_EQUAL(tensor.dimension(2), tensor2.dimension(0));
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 7; ++k) {
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VERIFY_IS_EQUAL(tensor(i,j,k), tensor2(k,j,i));
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}
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}
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}
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}
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static void test_swap_as_lvalue()
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{
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Tensor<float, 3, ColMajor> tensor(2,3,7);
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tensor.setRandom();
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Tensor<float, 3, RowMajor> tensor2(7,3,2);
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tensor2.swap_layout() = tensor;
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VERIFY_IS_EQUAL(tensor.dimension(0), tensor2.dimension(2));
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VERIFY_IS_EQUAL(tensor.dimension(1), tensor2.dimension(1));
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VERIFY_IS_EQUAL(tensor.dimension(2), tensor2.dimension(0));
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 7; ++k) {
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VERIFY_IS_EQUAL(tensor(i,j,k), tensor2(k,j,i));
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}
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}
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}
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}
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void test_cxx11_tensor_layout_swap()
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{
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CALL_SUBTEST(test_simple_swap());
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CALL_SUBTEST(test_swap_as_lvalue());
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}
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