289 lines
10 KiB
C++
289 lines
10 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 Navdeep Jaitly <ndjaitly@google.com>
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// 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_REVERSE_H
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#define EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
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namespace Eigen {
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/** \class TensorReverse
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Tensor reverse elements class.
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*
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*/
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namespace internal {
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template<typename ReverseDimensions, typename XprType>
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struct traits<TensorReverseOp<ReverseDimensions,
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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 ReverseDimensions, typename XprType>
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struct eval<TensorReverseOp<ReverseDimensions, XprType>, Eigen::Dense>
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{
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typedef const TensorReverseOp<ReverseDimensions, XprType>& type;
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};
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template<typename ReverseDimensions, typename XprType>
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struct nested<TensorReverseOp<ReverseDimensions, XprType>, 1,
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typename eval<TensorReverseOp<ReverseDimensions, XprType> >::type>
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{
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typedef TensorReverseOp<ReverseDimensions, XprType> type;
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};
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} // end namespace internal
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template<typename ReverseDimensions, typename XprType>
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class TensorReverseOp : public TensorBase<TensorReverseOp<ReverseDimensions,
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XprType>, WriteAccessors>
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{
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public:
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typedef typename Eigen::internal::traits<TensorReverseOp>::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<TensorReverseOp>::type Nested;
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typedef typename Eigen::internal::traits<TensorReverseOp>::StorageKind
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StorageKind;
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typedef typename Eigen::internal::traits<TensorReverseOp>::Index Index;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReverseOp(
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const XprType& expr, const ReverseDimensions& reverse_dims)
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: m_xpr(expr), m_reverse_dims(reverse_dims) { }
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EIGEN_DEVICE_FUNC
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const ReverseDimensions& reverse() const { return m_reverse_dims; }
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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 TensorReverseOp& operator = (const TensorReverseOp& other)
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{
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typedef TensorAssignOp<TensorReverseOp, const TensorReverseOp> 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 TensorReverseOp& operator = (const OtherDerived& other)
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{
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typedef TensorAssignOp<TensorReverseOp, 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 ReverseDimensions m_reverse_dims;
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};
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// Eval as rvalue
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template<typename ReverseDimensions, typename ArgType, typename Device>
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struct TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, Device>
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{
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typedef TensorReverseOp<ReverseDimensions, ArgType> XprType;
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typedef typename XprType::Index Index;
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static const int NumDims = internal::array_size<ReverseDimensions>::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 = TensorEvaluator<ArgType, Device>::PacketAccess,
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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,
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const Device& device)
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: m_impl(op.expression(), device), m_reverse(op.reverse())
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{
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// Reversing a scalar isn't supported yet. It would be a no-op anyway.
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EIGEN_STATIC_ASSERT((NumDims > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
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// Compute strides
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m_dimensions = m_impl.dimensions();
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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m_strides[0] = 1;
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for (int i = 1; i < NumDims; ++i) {
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m_strides[i] = m_strides[i-1] * m_dimensions[i-1];
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}
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} else {
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m_strides[NumDims-1] = 1;
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for (int i = NumDims - 2; i >= 0; --i) {
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m_strides[i] = m_strides[i+1] * m_dimensions[i+1];
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}
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}
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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const Dimensions& dimensions() const { return m_dimensions; }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
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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 Index reverseIndex(
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Index index) const {
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eigen_assert(index < dimensions().TotalSize());
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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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Index idx = index / m_strides[i];
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index -= idx * m_strides[i];
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if (m_reverse[i]) {
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idx = m_dimensions[i] - idx - 1;
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}
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inputIndex += idx * m_strides[i] ;
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}
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if (m_reverse[0]) {
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inputIndex += (m_dimensions[0] - index - 1);
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} else {
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inputIndex += index;
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}
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} else {
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for (int i = 0; i < NumDims - 1; ++i) {
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Index idx = index / m_strides[i];
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index -= idx * m_strides[i];
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if (m_reverse[i]) {
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idx = m_dimensions[i] - idx - 1;
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}
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inputIndex += idx * m_strides[i] ;
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}
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if (m_reverse[NumDims-1]) {
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inputIndex += (m_dimensions[NumDims-1] - index - 1);
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} else {
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inputIndex += index;
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}
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}
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return inputIndex;
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(
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Index index) const {
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return m_impl.coeff(reverseIndex(index));
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}
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template<int LoadMode>
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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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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// TODO(ndjaitly): write a better packing routine that uses
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// local structure.
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EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type
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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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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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for (int i = 0; i < NumDims; ++i) {
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if (m_reverse[i]) {
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compute_cost += 2 * TensorOpCost::AddCost<Index>();
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}
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}
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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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Dimensions m_dimensions;
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array<Index, NumDims> m_strides;
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TensorEvaluator<ArgType, Device> m_impl;
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ReverseDimensions m_reverse;
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};
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// Eval as lvalue
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template <typename ReverseDimensions, typename ArgType, typename Device>
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struct TensorEvaluator<TensorReverseOp<ReverseDimensions, ArgType>, Device>
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: public TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>,
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Device> {
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typedef TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>,
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Device> Base;
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typedef TensorReverseOp<ReverseDimensions, ArgType> XprType;
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typedef typename XprType::Index Index;
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static const int NumDims = internal::array_size<ReverseDimensions>::value;
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typedef DSizes<Index, NumDims> Dimensions;
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enum {
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IsAligned = false,
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PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
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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,
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const Device& device)
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: Base(op, device) {}
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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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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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const Dimensions& dimensions() const { return this->m_dimensions; }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) {
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return this->m_impl.coeffRef(this->reverseIndex(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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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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// This code is pilfered from TensorMorphing.h
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EIGEN_ALIGN_MAX CoeffReturnType 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_REVERSE_H
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