385 lines
14 KiB
C++
385 lines
14 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_CHIPPING_H
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#define EIGEN_CXX11_TENSOR_TENSOR_CHIPPING_H
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namespace Eigen {
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/** \class TensorKChippingReshaping
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* \ingroup CXX11_Tensor_Module
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*
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* \brief A chip is a thin slice, corresponding to a column or a row in a 2-d tensor.
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*
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*
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*/
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namespace internal {
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template<DenseIndex DimId, typename XprType>
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struct traits<TensorChippingOp<DimId, 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 - 1;
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static const int Layout = XprTraits::Layout;
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};
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template<DenseIndex DimId, typename XprType>
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struct eval<TensorChippingOp<DimId, XprType>, Eigen::Dense>
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{
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typedef const TensorChippingOp<DimId, XprType>& type;
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};
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template<DenseIndex DimId, typename XprType>
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struct nested<TensorChippingOp<DimId, XprType>, 1, typename eval<TensorChippingOp<DimId, XprType> >::type>
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{
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typedef TensorChippingOp<DimId, XprType> type;
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};
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template <DenseIndex DimId>
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struct DimensionId
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{
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DimensionId(DenseIndex dim) {
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eigen_assert(dim == DimId);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const {
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return DimId;
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}
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};
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template <>
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struct DimensionId<Dynamic>
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{
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DimensionId(DenseIndex dim) : actual_dim(dim) {
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eigen_assert(dim >= 0);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex actualDim() const {
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return actual_dim;
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}
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private:
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const DenseIndex actual_dim;
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};
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} // end namespace internal
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template<DenseIndex DimId, typename XprType>
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class TensorChippingOp : public TensorBase<TensorChippingOp<DimId, XprType> >
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{
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public:
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typedef typename Eigen::internal::traits<TensorChippingOp>::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<TensorChippingOp>::type Nested;
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typedef typename Eigen::internal::traits<TensorChippingOp>::StorageKind StorageKind;
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typedef typename Eigen::internal::traits<TensorChippingOp>::Index Index;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorChippingOp(const XprType& expr, const Index offset, const Index dim)
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: m_xpr(expr), m_offset(offset), m_dim(dim) {
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}
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EIGEN_DEVICE_FUNC
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const Index offset() const { return m_offset; }
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EIGEN_DEVICE_FUNC
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const Index dim() const { return m_dim.actualDim(); }
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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 TensorChippingOp& operator = (const TensorChippingOp& other)
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{
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typedef TensorAssignOp<TensorChippingOp, const TensorChippingOp> 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 TensorChippingOp& operator = (const OtherDerived& other)
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{
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typedef TensorAssignOp<TensorChippingOp, 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 Index m_offset;
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const internal::DimensionId<DimId> m_dim;
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};
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// Eval as rvalue
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template<DenseIndex DimId, typename ArgType, typename Device>
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struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
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{
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typedef TensorChippingOp<DimId, ArgType> XprType;
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static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
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static const int NumDims = NumInputDims-1;
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typedef typename XprType::Index Index;
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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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// Alignment can't be guaranteed at compile time since it depends on the
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// slice offsets.
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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, const Device& device)
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: m_impl(op.expression(), device), m_dim(op.dim()), m_device(device)
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{
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EIGEN_STATIC_ASSERT((NumInputDims >= 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
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eigen_assert(NumInputDims > m_dim.actualDim());
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const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
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eigen_assert(op.offset() < input_dims[m_dim.actualDim()]);
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int j = 0;
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for (int i = 0; i < NumInputDims; ++i) {
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if (i != m_dim.actualDim()) {
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m_dimensions[j] = input_dims[i];
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++j;
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}
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}
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m_stride = 1;
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m_inputStride = 1;
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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for (int i = 0; i < m_dim.actualDim(); ++i) {
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m_stride *= input_dims[i];
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m_inputStride *= input_dims[i];
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}
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} else {
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for (int i = NumInputDims-1; i > m_dim.actualDim(); --i) {
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m_stride *= input_dims[i];
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m_inputStride *= input_dims[i];
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}
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}
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m_inputStride *= input_dims[m_dim.actualDim()];
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m_inputOffset = m_stride * op.offset();
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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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if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == 0) ||
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(static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == NumInputDims-1)) {
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// m_stride is equal to 1, so let's avoid the integer division.
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eigen_assert(m_stride == 1);
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Index inputIndex = index * m_inputStride + m_inputOffset;
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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] = m_impl.coeff(inputIndex);
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inputIndex += m_inputStride;
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}
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PacketReturnType rslt = internal::pload<PacketReturnType>(values);
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return rslt;
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} else if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == NumInputDims - 1) ||
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(static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == 0)) {
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// m_stride is aways greater than index, so let's avoid the integer division.
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eigen_assert(m_stride > index);
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return m_impl.template packet<LoadMode>(index + m_inputOffset);
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} else {
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const Index idx = index / m_stride;
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const Index rem = index - idx * m_stride;
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if (rem + PacketSize <= m_stride) {
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Index inputIndex = idx * m_inputStride + m_inputOffset + rem;
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return m_impl.template packet<LoadMode>(inputIndex);
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} else {
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// Cross the stride boundary. Fallback to slow path.
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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);
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++index;
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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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}
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
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costPerCoeff(bool vectorized) const {
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double cost = 0;
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if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) &&
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m_dim.actualDim() == 0) ||
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(static_cast<int>(Layout) == static_cast<int>(RowMajor) &&
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m_dim.actualDim() == NumInputDims - 1)) {
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cost += TensorOpCost::MulCost<Index>() + TensorOpCost::AddCost<Index>();
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} else if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) &&
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m_dim.actualDim() == NumInputDims - 1) ||
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(static_cast<int>(Layout) == static_cast<int>(RowMajor) &&
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m_dim.actualDim() == 0)) {
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cost += TensorOpCost::AddCost<Index>();
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} else {
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cost += 3 * TensorOpCost::MulCost<Index>() + TensorOpCost::DivCost<Index>() +
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3 * TensorOpCost::AddCost<Index>();
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}
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return m_impl.costPerCoeff(vectorized) +
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TensorOpCost(0, 0, cost, vectorized, PacketSize);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType* data() const {
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CoeffReturnType* result = const_cast<CoeffReturnType*>(m_impl.data());
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if (((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == NumDims) ||
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(static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == 0)) &&
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result) {
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return result + m_inputOffset;
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} else {
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return NULL;
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}
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}
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protected:
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index srcCoeff(Index index) const
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{
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Index inputIndex;
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if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == 0) ||
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(static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == NumInputDims-1)) {
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// m_stride is equal to 1, so let's avoid the integer division.
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eigen_assert(m_stride == 1);
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inputIndex = index * m_inputStride + m_inputOffset;
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} else if ((static_cast<int>(Layout) == static_cast<int>(ColMajor) && m_dim.actualDim() == NumInputDims-1) ||
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(static_cast<int>(Layout) == static_cast<int>(RowMajor) && m_dim.actualDim() == 0)) {
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// m_stride is aways greater than index, so let's avoid the integer division.
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eigen_assert(m_stride > index);
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inputIndex = index + m_inputOffset;
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} else {
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const Index idx = index / m_stride;
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inputIndex = idx * m_inputStride + m_inputOffset;
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index -= idx * m_stride;
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inputIndex += index;
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}
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return inputIndex;
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}
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Dimensions m_dimensions;
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Index m_stride;
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Index m_inputOffset;
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Index m_inputStride;
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TensorEvaluator<ArgType, Device> m_impl;
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const internal::DimensionId<DimId> m_dim;
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const Device& m_device;
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};
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// Eval as lvalue
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template<DenseIndex DimId, typename ArgType, typename Device>
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struct TensorEvaluator<TensorChippingOp<DimId, ArgType>, Device>
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: public TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
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{
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typedef TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device> Base;
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typedef TensorChippingOp<DimId, ArgType> XprType;
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static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
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static const int NumDims = NumInputDims-1;
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typedef typename XprType::Index Index;
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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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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_DEVICE_FUNC 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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if ((static_cast<int>(this->Layout) == static_cast<int>(ColMajor) && this->m_dim.actualDim() == 0) ||
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(static_cast<int>(this->Layout) == static_cast<int>(RowMajor) && this->m_dim.actualDim() == NumInputDims-1)) {
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// m_stride is equal to 1, so let's avoid the integer division.
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eigen_assert(this->m_stride == 1);
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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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Index inputIndex = index * this->m_inputStride + this->m_inputOffset;
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for (int i = 0; i < PacketSize; ++i) {
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this->m_impl.coeffRef(inputIndex) = values[i];
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inputIndex += this->m_inputStride;
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}
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} else if ((static_cast<int>(this->Layout) == static_cast<int>(ColMajor) && this->m_dim.actualDim() == NumInputDims-1) ||
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(static_cast<int>(this->Layout) == static_cast<int>(RowMajor) && this->m_dim.actualDim() == 0)) {
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// m_stride is aways greater than index, so let's avoid the integer division.
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eigen_assert(this->m_stride > index);
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this->m_impl.template writePacket<StoreMode>(index + this->m_inputOffset, x);
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} else {
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const Index idx = index / this->m_stride;
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const Index rem = index - idx * this->m_stride;
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if (rem + PacketSize <= this->m_stride) {
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const Index inputIndex = idx * this->m_inputStride + this->m_inputOffset + rem;
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this->m_impl.template writePacket<StoreMode>(inputIndex, x);
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} else {
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// Cross stride boundary. Fallback to slow path.
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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) = values[i];
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++index;
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}
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}
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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_CHIPPING_H
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