mirror of
https://gitlab.com/libeigen/eigen.git
synced 2025-04-29 23:34:12 +08:00
324 lines
13 KiB
C++
324 lines
13 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
|
|
// for linear algebra.
|
|
//
|
|
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
|
|
//
|
|
// This Source Code Form is subject to the terms of the Mozilla
|
|
// Public License v. 2.0. If a copy of the MPL was not distributed
|
|
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
|
|
|
|
#ifndef EIGEN_CXX11_TENSOR_TENSOR_MAP_H
|
|
#define EIGEN_CXX11_TENSOR_TENSOR_MAP_H
|
|
|
|
namespace Eigen {
|
|
|
|
/** \class TensorMap
|
|
* \ingroup CXX11_Tensor_Module
|
|
*
|
|
* \brief A tensor expression mapping an existing array of data.
|
|
*
|
|
*/
|
|
/// template <class> class MakePointer_ is added to convert the host pointer to the device pointer.
|
|
/// It is added due to the fact that for our device compiler T* is not allowed.
|
|
/// If we wanted to use the same Evaluator functions we have to convert that type to our pointer T.
|
|
/// This is done through our MakePointer_ class. By default the Type in the MakePointer_<T> is T* .
|
|
/// Therefore, by adding the default value, we managed to convert the type and it does not break any
|
|
/// existing code as its default value is T*.
|
|
template<typename PlainObjectType, int Options_, template <class> class MakePointer_> class TensorMap : public TensorBase<TensorMap<PlainObjectType, Options_, MakePointer_> >
|
|
{
|
|
public:
|
|
typedef TensorMap<PlainObjectType, Options_, MakePointer_> Self;
|
|
typedef typename PlainObjectType::Base Base;
|
|
typedef typename Eigen::internal::nested<Self>::type Nested;
|
|
typedef typename internal::traits<PlainObjectType>::StorageKind StorageKind;
|
|
typedef typename internal::traits<PlainObjectType>::Index Index;
|
|
typedef typename internal::traits<PlainObjectType>::Scalar Scalar;
|
|
typedef typename NumTraits<Scalar>::Real RealScalar;
|
|
typedef typename Base::CoeffReturnType CoeffReturnType;
|
|
|
|
/* typedef typename internal::conditional<
|
|
bool(internal::is_lvalue<PlainObjectType>::value),
|
|
Scalar *,
|
|
const Scalar *>::type
|
|
PointerType;*/
|
|
typedef typename MakePointer_<Scalar>::Type PointerType;
|
|
typedef PointerType PointerArgType;
|
|
|
|
static const int Options = Options_;
|
|
|
|
static const Index NumIndices = PlainObjectType::NumIndices;
|
|
typedef typename PlainObjectType::Dimensions Dimensions;
|
|
|
|
enum {
|
|
IsAligned = ((int(Options_)&Aligned)==Aligned),
|
|
Layout = PlainObjectType::Layout,
|
|
CoordAccess = true,
|
|
RawAccess = true
|
|
};
|
|
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr) : m_data(dataPtr), m_dimensions() {
|
|
// The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
|
|
EIGEN_STATIC_ASSERT((0 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
|
|
}
|
|
|
|
#if EIGEN_HAS_VARIADIC_TEMPLATES
|
|
template<typename... IndexTypes> EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index firstDimension, IndexTypes... otherDimensions) : m_data(dataPtr), m_dimensions(firstDimension, otherDimensions...) {
|
|
// The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
|
|
EIGEN_STATIC_ASSERT((sizeof...(otherDimensions) + 1 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
|
|
}
|
|
#else
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index firstDimension) : m_data(dataPtr), m_dimensions(firstDimension) {
|
|
// The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
|
|
EIGEN_STATIC_ASSERT((1 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
|
|
}
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index dim1, Index dim2) : m_data(dataPtr), m_dimensions(dim1, dim2) {
|
|
EIGEN_STATIC_ASSERT(2 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
|
|
}
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index dim1, Index dim2, Index dim3) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3) {
|
|
EIGEN_STATIC_ASSERT(3 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
|
|
}
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index dim1, Index dim2, Index dim3, Index dim4) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3, dim4) {
|
|
EIGEN_STATIC_ASSERT(4 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
|
|
}
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index dim1, Index dim2, Index dim3, Index dim4, Index dim5) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3, dim4, dim5) {
|
|
EIGEN_STATIC_ASSERT(5 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
|
|
}
|
|
#endif
|
|
|
|
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, const array<Index, NumIndices>& dimensions)
|
|
: m_data(dataPtr), m_dimensions(dimensions)
|
|
{ }
|
|
|
|
template <typename Dimensions>
|
|
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, const Dimensions& dimensions)
|
|
: m_data(dataPtr), m_dimensions(dimensions)
|
|
{ }
|
|
|
|
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(PlainObjectType& tensor)
|
|
: m_data(tensor.data()), m_dimensions(tensor.dimensions())
|
|
{ }
|
|
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE Index rank() const { return m_dimensions.rank(); }
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE Index dimension(Index n) const { return m_dimensions[n]; }
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE Index size() const { return m_dimensions.TotalSize(); }
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE PointerType data() { return m_data; }
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE const PointerType data() const { return m_data; }
|
|
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const
|
|
{
|
|
// eigen_assert(checkIndexRange(indices));
|
|
if (PlainObjectType::Options&RowMajor) {
|
|
const Index index = m_dimensions.IndexOfRowMajor(indices);
|
|
return m_data[index];
|
|
} else {
|
|
const Index index = m_dimensions.IndexOfColMajor(indices);
|
|
return m_data[index];
|
|
}
|
|
}
|
|
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE const Scalar& operator()() const
|
|
{
|
|
EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE)
|
|
return m_data[0];
|
|
}
|
|
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const
|
|
{
|
|
eigen_internal_assert(index >= 0 && index < size());
|
|
return m_data[index];
|
|
}
|
|
|
|
#if EIGEN_HAS_VARIADIC_TEMPLATES
|
|
template<typename... IndexTypes> EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE const Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const
|
|
{
|
|
EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
|
|
eigen_assert(internal::all((Eigen::NumTraits<Index>::highest() >= otherIndices)...));
|
|
if (PlainObjectType::Options&RowMajor) {
|
|
const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
|
|
return m_data[index];
|
|
} else {
|
|
const Index index = m_dimensions.IndexOfColMajor(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
|
|
return m_data[index];
|
|
}
|
|
}
|
|
#else
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1) const
|
|
{
|
|
if (PlainObjectType::Options&RowMajor) {
|
|
const Index index = i1 + i0 * m_dimensions[1];
|
|
return m_data[index];
|
|
} else {
|
|
const Index index = i0 + i1 * m_dimensions[0];
|
|
return m_data[index];
|
|
}
|
|
}
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2) const
|
|
{
|
|
if (PlainObjectType::Options&RowMajor) {
|
|
const Index index = i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0);
|
|
return m_data[index];
|
|
} else {
|
|
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2);
|
|
return m_data[index];
|
|
}
|
|
}
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3) const
|
|
{
|
|
if (PlainObjectType::Options&RowMajor) {
|
|
const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0));
|
|
return m_data[index];
|
|
} else {
|
|
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3));
|
|
return m_data[index];
|
|
}
|
|
}
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
|
|
{
|
|
if (PlainObjectType::Options&RowMajor) {
|
|
const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)));
|
|
return m_data[index];
|
|
} else {
|
|
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4)));
|
|
return m_data[index];
|
|
}
|
|
}
|
|
#endif
|
|
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices)
|
|
{
|
|
// eigen_assert(checkIndexRange(indices));
|
|
if (PlainObjectType::Options&RowMajor) {
|
|
const Index index = m_dimensions.IndexOfRowMajor(indices);
|
|
return m_data[index];
|
|
} else {
|
|
const Index index = m_dimensions.IndexOfColMajor(indices);
|
|
return m_data[index];
|
|
}
|
|
}
|
|
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE Scalar& operator()()
|
|
{
|
|
EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE)
|
|
return m_data[0];
|
|
}
|
|
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE Scalar& operator()(Index index)
|
|
{
|
|
eigen_internal_assert(index >= 0 && index < size());
|
|
return m_data[index];
|
|
}
|
|
|
|
#if EIGEN_HAS_VARIADIC_TEMPLATES
|
|
template<typename... IndexTypes> EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE Scalar& operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices)
|
|
{
|
|
static_assert(sizeof...(otherIndices) + 2 == NumIndices || NumIndices == Dynamic, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
|
|
eigen_assert(internal::all((Eigen::NumTraits<Index>::highest() >= otherIndices)...));
|
|
const std::size_t NumDims = sizeof...(otherIndices) + 2;
|
|
if (PlainObjectType::Options&RowMajor) {
|
|
const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumDims>{{firstIndex, secondIndex, otherIndices...}});
|
|
return m_data[index];
|
|
} else {
|
|
const Index index = m_dimensions.IndexOfColMajor(array<Index, NumDims>{{firstIndex, secondIndex, otherIndices...}});
|
|
return m_data[index];
|
|
}
|
|
}
|
|
#else
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1)
|
|
{
|
|
if (PlainObjectType::Options&RowMajor) {
|
|
const Index index = i1 + i0 * m_dimensions[1];
|
|
return m_data[index];
|
|
} else {
|
|
const Index index = i0 + i1 * m_dimensions[0];
|
|
return m_data[index];
|
|
}
|
|
}
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2)
|
|
{
|
|
if (PlainObjectType::Options&RowMajor) {
|
|
const Index index = i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0);
|
|
return m_data[index];
|
|
} else {
|
|
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2);
|
|
return m_data[index];
|
|
}
|
|
}
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3)
|
|
{
|
|
if (PlainObjectType::Options&RowMajor) {
|
|
const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0));
|
|
return m_data[index];
|
|
} else {
|
|
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3));
|
|
return m_data[index];
|
|
}
|
|
}
|
|
EIGEN_DEVICE_FUNC
|
|
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4)
|
|
{
|
|
if (PlainObjectType::Options&RowMajor) {
|
|
const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)));
|
|
return m_data[index];
|
|
} else {
|
|
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4)));
|
|
return m_data[index];
|
|
}
|
|
}
|
|
#endif
|
|
|
|
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Self& operator=(const Self& other)
|
|
{
|
|
typedef TensorAssignOp<Self, const Self> Assign;
|
|
Assign assign(*this, other);
|
|
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
|
|
return *this;
|
|
}
|
|
|
|
template<typename OtherDerived>
|
|
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
|
|
Self& operator=(const OtherDerived& other)
|
|
{
|
|
typedef TensorAssignOp<Self, const OtherDerived> Assign;
|
|
Assign assign(*this, other);
|
|
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
|
|
return *this;
|
|
}
|
|
|
|
private:
|
|
typename MakePointer_<Scalar>::Type m_data;
|
|
Dimensions m_dimensions;
|
|
};
|
|
|
|
} // end namespace Eigen
|
|
|
|
#endif // EIGEN_CXX11_TENSOR_TENSOR_MAP_H
|