| Current File : //usr/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h |
// 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 {
// FIXME use proper doxygen documentation (e.g. \tparam MakePointer_)
/** \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)
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.");
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