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// 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_FIXED_SIZE_H
#define EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_H
namespace Eigen {
/** \class TensorFixedSize
* \ingroup CXX11_Tensor_Module
*
* \brief The fixed sized version of the tensor class.
*
* The fixed sized equivalent of
* Eigen::Tensor<float, 3> t(3, 5, 7);
* is
* Eigen::TensorFixedSize<float, Sizes<3,5,7>> t;
*/
template<typename Scalar_, typename Dimensions_, int Options_, typename IndexType>
class TensorFixedSize : public TensorBase<TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> >
{
public:
typedef TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> Self;
typedef TensorBase<TensorFixedSize<Scalar_, Dimensions_, Options_, IndexType> > Base;
typedef typename Eigen::internal::nested<Self>::type Nested;
typedef typename internal::traits<Self>::StorageKind StorageKind;
typedef typename internal::traits<Self>::Index Index;
typedef Scalar_ Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef typename Base::CoeffReturnType CoeffReturnType;
static const int Options = Options_;
enum {
IsAligned = bool(EIGEN_MAX_ALIGN_BYTES>0),
PacketAccess = (internal::packet_traits<Scalar>::size > 1),
BlockAccess = false,
PreferBlockAccess = false,
Layout = Options_ & RowMajor ? RowMajor : ColMajor,
CoordAccess = true,
RawAccess = true
};
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockNotImplemented TensorBlock;
//===--------------------------------------------------------------------===//
typedef Dimensions_ Dimensions;
static const std::size_t NumIndices = Dimensions::count;
protected:
TensorStorage<Scalar, Dimensions, Options> m_storage;
public:
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rank() const { return NumIndices; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index dimension(std::size_t n) const { return m_storage.dimensions()[n]; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_storage.dimensions(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_storage.size(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar *data() { return m_storage.data(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar *data() const { return m_storage.data(); }
// This makes EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
// work, because that uses base().coeffRef() - and we don't yet
// implement a similar class hierarchy
inline Self& base() { return *this; }
inline const Self& base() const { return *this; }
#if EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(Index firstIndex, IndexTypes... otherIndices) const
{
// The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return coeff(array<Index, NumIndices>{{firstIndex, otherIndices...}});
}
#endif
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeff(const array<Index, NumIndices>& indices) const
{
eigen_internal_assert(checkIndexRange(indices));
return m_storage.data()[linearizedIndex(indices)];
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
{
eigen_internal_assert(index >= 0 && index < size());
return m_storage.data()[index];
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& coeff() const
{
EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
return m_storage.data()[0];
}
#if EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index firstIndex, IndexTypes... otherIndices)
{
// The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return coeffRef(array<Index, NumIndices>{{firstIndex, otherIndices...}});
}
#endif
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices)
{
eigen_internal_assert(checkIndexRange(indices));
return m_storage.data()[linearizedIndex(indices)];
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
eigen_internal_assert(index >= 0 && index < size());
return m_storage.data()[index];
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& coeffRef()
{
EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
return m_storage.data()[0];
}
#if EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(Index firstIndex, IndexTypes... otherIndices) const
{
// The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return this->operator()(array<Index, NumIndices>{{firstIndex, otherIndices...}});
}
#else
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1) const
{
if (Options&RowMajor) {
const Index index = i1 + i0 * m_storage.dimensions()[1];
return m_storage.data()[index];
} else {
const Index index = i0 + i1 * m_storage.dimensions()[0];
return m_storage.data()[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2) const
{
if (Options&RowMajor) {
const Index index = i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0);
return m_storage.data()[index];
} else {
const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * i2);
return m_storage.data()[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3) const
{
if (Options&RowMajor) {
const Index index = i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0));
return m_storage.data()[index];
} else {
const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * i3));
return m_storage.data()[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
{
if (Options&RowMajor) {
const Index index = i4 + m_storage.dimensions()[4] * (i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0)));
return m_storage.data()[index];
} else {
const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * (i3 + m_storage.dimensions()[3] * i4)));
return m_storage.data()[index];
}
}
#endif
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const
{
eigen_assert(checkIndexRange(indices));
return coeff(indices);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const
{
eigen_internal_assert(index >= 0 && index < size());
return coeff(index);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()() const
{
EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
return coeff();
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator[](Index index) const
{
// The bracket operator is only for vectors, use the parenthesis operator instead.
EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE);
return coeff(index);
}
#if EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(Index firstIndex, IndexTypes... otherIndices)
{
// The number of indices used to access a tensor coefficient must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return operator()(array<Index, NumIndices>{{firstIndex, otherIndices...}});
}
#else
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1)
{
if (Options&RowMajor) {
const Index index = i1 + i0 * m_storage.dimensions()[1];
return m_storage.data()[index];
} else {
const Index index = i0 + i1 * m_storage.dimensions()[0];
return m_storage.data()[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2)
{
if (Options&RowMajor) {
const Index index = i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0);
return m_storage.data()[index];
} else {
const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * i2);
return m_storage.data()[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3)
{
if (Options&RowMajor) {
const Index index = i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0));
return m_storage.data()[index];
} else {
const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * i3));
return m_storage.data()[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4)
{
if (Options&RowMajor) {
const Index index = i4 + m_storage.dimensions()[4] * (i3 + m_storage.dimensions()[3] * (i2 + m_storage.dimensions()[2] * (i1 + m_storage.dimensions()[1] * i0)));
return m_storage.data()[index];
} else {
const Index index = i0 + m_storage.dimensions()[0] * (i1 + m_storage.dimensions()[1] * (i2 + m_storage.dimensions()[2] * (i3 + m_storage.dimensions()[3] * i4)));
return m_storage.data()[index];
}
}
#endif
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices)
{
eigen_assert(checkIndexRange(indices));
return coeffRef(indices);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index index)
{
eigen_assert(index >= 0 && index < size());
return coeffRef(index);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()()
{
EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
return coeffRef();
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator[](Index index)
{
// The bracket operator is only for vectors, use the parenthesis operator instead
EIGEN_STATIC_ASSERT(NumIndices == 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
return coeffRef(index);
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorFixedSize()
: m_storage()
{
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorFixedSize(const Self& other)
: m_storage(other.m_storage)
{
}
#if EIGEN_HAS_RVALUE_REFERENCES
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorFixedSize(Self&& other)
: m_storage(other.m_storage)
{
}
#endif
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorFixedSize(const TensorBase<OtherDerived, ReadOnlyAccessors>& other)
{
typedef TensorAssignOp<TensorFixedSize, const OtherDerived> Assign;
Assign assign(*this, other.derived());
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorFixedSize(const TensorBase<OtherDerived, WriteAccessors>& other)
{
typedef TensorAssignOp<TensorFixedSize, const OtherDerived> Assign;
Assign assign(*this, other.derived());
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
}
// FIXME: check that the dimensions of other match the dimensions of *this.
// Unfortunately this isn't possible yet when the rhs is an expression.
EIGEN_TENSOR_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(TensorFixedSize)
protected:
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE bool checkIndexRange(const array<Index, NumIndices>& /*indices*/) const
{
using internal::array_apply_and_reduce;
using internal::array_zip_and_reduce;
using internal::greater_equal_zero_op;
using internal::logical_and_op;
using internal::lesser_op;
return true;
// check whether the indices are all >= 0
/* array_apply_and_reduce<logical_and_op, greater_equal_zero_op>(indices) &&
// check whether the indices fit in the dimensions
array_zip_and_reduce<logical_and_op, lesser_op>(indices, m_storage.dimensions());*/
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Index linearizedIndex(const array<Index, NumIndices>& indices) const
{
if (Options&RowMajor) {
return m_storage.dimensions().IndexOfRowMajor(indices);
} else {
return m_storage.dimensions().IndexOfColMajor(indices);
}
}
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_FIXED_SIZE_H
|
