265 lines
9.3 KiB
C++
265 lines
9.3 KiB
C++
// 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 the mozilla.org home page
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#ifndef EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
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#define EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
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namespace Eigen {
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/** \class TensorShuffling
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Tensor shuffling class.
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*
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*
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*/
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namespace internal {
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template<typename Shuffle, typename XprType>
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struct traits<TensorShufflingOp<Shuffle, 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 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 = XprTraits::NumDimensions;
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static const int Layout = XprTraits::Layout;
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};
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template<typename Shuffle, typename XprType>
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struct eval<TensorShufflingOp<Shuffle, XprType>, Eigen::Dense>
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{
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typedef const TensorShufflingOp<Shuffle, XprType>& type;
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};
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template<typename Shuffle, typename XprType>
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struct nested<TensorShufflingOp<Shuffle, XprType>, 1, typename eval<TensorShufflingOp<Shuffle, XprType> >::type>
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{
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typedef TensorShufflingOp<Shuffle, XprType> type;
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};
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} // end namespace internal
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template<typename Shuffle, typename XprType>
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class TensorShufflingOp : public TensorBase<TensorShufflingOp<Shuffle, XprType> >
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{
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public:
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typedef typename Eigen::internal::traits<TensorShufflingOp>::Scalar Scalar;
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typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename Eigen::internal::nested<TensorShufflingOp>::type Nested;
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typedef typename Eigen::internal::traits<TensorShufflingOp>::StorageKind StorageKind;
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typedef typename Eigen::internal::traits<TensorShufflingOp>::Index Index;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorShufflingOp(const XprType& expr, const Shuffle& shuffle)
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: m_xpr(expr), m_shuffle(shuffle) {}
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EIGEN_DEVICE_FUNC
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const Shuffle& shufflePermutation() const { return m_shuffle; }
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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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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE TensorShufflingOp& operator = (const TensorShufflingOp& other)
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{
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typedef TensorAssignOp<TensorShufflingOp, const TensorShufflingOp> Assign;
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Assign assign(*this, other);
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internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
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return *this;
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}
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template<typename OtherDerived>
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE TensorShufflingOp& operator = (const OtherDerived& other)
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{
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typedef TensorAssignOp<TensorShufflingOp, const OtherDerived> Assign;
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Assign assign(*this, other);
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internal::TensorExecutor<const Assign, DefaultDevice>::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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const Shuffle m_shuffle;
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};
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// Eval as rvalue
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template<typename Shuffle, typename ArgType, typename Device>
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struct TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device>
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{
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typedef TensorShufflingOp<Shuffle, 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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typedef typename XprType::Scalar Scalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
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static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
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enum {
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IsAligned = false,
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PacketAccess = (internal::packet_traits<Scalar>::size > 1),
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Layout = TensorEvaluator<ArgType, Device>::Layout,
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CoordAccess = false, // to be implemented
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RawAccess = false
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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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const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
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const Shuffle& shuffle = op.shufflePermutation();
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for (int i = 0; i < NumDims; ++i) {
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m_dimensions[i] = input_dims[shuffle[i]];
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}
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array<Index, NumDims> inputStrides;
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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inputStrides[0] = 1;
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m_outputStrides[0] = 1;
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for (int i = 1; i < NumDims; ++i) {
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inputStrides[i] = inputStrides[i - 1] * input_dims[i - 1];
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m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
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}
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} else {
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inputStrides[NumDims - 1] = 1;
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m_outputStrides[NumDims - 1] = 1;
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for (int i = NumDims - 2; i >= 0; --i) {
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inputStrides[i] = inputStrides[i + 1] * input_dims[i + 1];
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m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
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}
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}
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for (int i = 0; i < NumDims; ++i) {
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m_inputStrides[i] = inputStrides[shuffle[i]];
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}
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}
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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(Scalar* /*data*/) {
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m_impl.evalSubExprsIfNeeded(NULL);
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return true;
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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(srcCoeff(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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EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
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eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
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EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
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for (int i = 0; i < PacketSize; ++i) {
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values[i] = coeff(index+i);
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}
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PacketReturnType rslt = internal::pload<PacketReturnType>(values);
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return rslt;
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
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const double compute_cost = NumDims * (2 * TensorOpCost::AddCost<Index>() +
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2 * TensorOpCost::MulCost<Index>() +
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TensorOpCost::DivCost<Index>());
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return m_impl.costPerCoeff(vectorized) +
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TensorOpCost(0, 0, compute_cost, false /* vectorized */, PacketSize);
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}
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EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
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protected:
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const {
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Index inputIndex = 0;
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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for (int i = NumDims - 1; i > 0; --i) {
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const Index idx = index / m_outputStrides[i];
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inputIndex += idx * m_inputStrides[i];
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index -= idx * m_outputStrides[i];
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}
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return inputIndex + index * m_inputStrides[0];
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} else {
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for (int i = 0; i < NumDims - 1; ++i) {
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const Index idx = index / m_outputStrides[i];
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inputIndex += idx * m_inputStrides[i];
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index -= idx * m_outputStrides[i];
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}
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return inputIndex + index * m_inputStrides[NumDims - 1];
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}
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}
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Dimensions m_dimensions;
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array<Index, NumDims> m_outputStrides;
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array<Index, NumDims> m_inputStrides;
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TensorEvaluator<ArgType, Device> m_impl;
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};
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// Eval as lvalue
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template<typename Shuffle, typename ArgType, typename Device>
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struct TensorEvaluator<TensorShufflingOp<Shuffle, ArgType>, Device>
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: public TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device>
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{
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typedef TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device> Base;
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typedef TensorShufflingOp<Shuffle, 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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typedef typename XprType::Scalar Scalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
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static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
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enum {
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IsAligned = false,
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PacketAccess = (internal::packet_traits<Scalar>::size > 1),
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RawAccess = false
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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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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index)
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{
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return this->m_impl.coeffRef(this->srcCoeff(index));
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}
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template <int StoreMode> EIGEN_STRONG_INLINE
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void writePacket(Index index, const PacketReturnType& x)
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{
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EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
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EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
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internal::pstore<CoeffReturnType, PacketReturnType>(values, x);
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for (int i = 0; i < PacketSize; ++i) {
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this->coeffRef(index+i) = values[i];
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}
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}
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};
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} // end namespace Eigen
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#endif // EIGEN_CXX11_TENSOR_TENSOR_SHUFFLING_H
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