| Current File : //usr/include/eigen3/unsupported/Eigen/CXX11/src/Tensor/Tensor.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>
// Copyright (C) 2013 Christian Seiler <christian@iwakd.de>
//
// 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_H
#define EIGEN_CXX11_TENSOR_TENSOR_H
namespace Eigen {
/** \class Tensor
* \ingroup CXX11_Tensor_Module
*
* \brief The tensor class.
*
* The %Tensor class is the work-horse for all \em dense tensors within Eigen.
*
* The %Tensor class encompasses only dynamic-size objects so far.
*
* The first two template parameters are required:
* \tparam Scalar_ Numeric type, e.g. float, double, int or `std::complex<float>`.
* User defined scalar types are supported as well (see \ref user_defined_scalars "here").
* \tparam NumIndices_ Number of indices (i.e. rank of the tensor)
*
* The remaining template parameters are optional -- in most cases you don't have to worry about them.
* \tparam Options_ A combination of either \b #RowMajor or \b #ColMajor, and of either
* \b #AutoAlign or \b #DontAlign.
* The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required
* for vectorization. It defaults to aligning tensors. Note that tensors currently do not support any operations that profit from vectorization.
* Support for such operations (i.e. adding two tensors etc.) is planned.
*
* You can access elements of tensors using normal subscripting:
*
* \code
* Eigen::Tensor<double, 4> t(10, 10, 10, 10);
* t(0, 1, 2, 3) = 42.0;
* \endcode
*
* This class can be extended with the help of the plugin mechanism described on the page
* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_TENSOR_PLUGIN.
*
* <i><b>Some notes:</b></i>
*
* <dl>
* <dt><b>Relation to other parts of Eigen:</b></dt>
* <dd>The midterm development goal for this class is to have a similar hierarchy as Eigen uses for matrices, so that
* taking blocks or using tensors in expressions is easily possible, including an interface with the vector/matrix code
* by providing .asMatrix() and .asVector() (or similar) methods for rank 2 and 1 tensors. However, currently, the %Tensor
* class does not provide any of these features and is only available as a stand-alone class that just allows for
* coefficient access. Also, when fixed-size tensors are implemented, the number of template arguments is likely to
* change dramatically.</dd>
* </dl>
*
* \ref TopicStorageOrders
*/
template<typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
class Tensor : public TensorBase<Tensor<Scalar_, NumIndices_, Options_, IndexType_> >
{
public:
typedef Tensor<Scalar_, NumIndices_, Options_, IndexType_> Self;
typedef TensorBase<Tensor<Scalar_, NumIndices_, 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;
enum {
IsAligned = bool(EIGEN_MAX_ALIGN_BYTES>0) & !(Options_&DontAlign),
Layout = Options_ & RowMajor ? RowMajor : ColMajor,
CoordAccess = true,
RawAccess = true
};
static const int Options = Options_;
static const int NumIndices = NumIndices_;
typedef DSizes<Index, NumIndices_> Dimensions;
protected:
TensorStorage<Scalar, Dimensions, Options> m_storage;
#ifdef EIGEN_HAS_SFINAE
template<typename CustomIndices>
struct isOfNormalIndex{
static const bool is_array = internal::is_base_of<array<Index, NumIndices>, CustomIndices>::value;
static const bool is_int = NumTraits<CustomIndices>::IsInteger;
static const bool value = is_array | is_int;
};
#endif
public:
// Metadata
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 inline const Scalar& coeff(Index firstIndex, Index secondIndex, 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) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return coeff(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
}
#endif
// normal indices
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)];
}
// custom indices
#ifdef EIGEN_HAS_SFINAE
template<typename CustomIndices,
EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) )
>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(CustomIndices& indices) const
{
return coeff(internal::customIndices2Array<Index,NumIndices>(indices));
}
#endif
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];
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
{
eigen_internal_assert(index >= 0 && index < size());
return m_storage.data()[index];
}
#if EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
inline Scalar& coeffRef(Index firstIndex, Index secondIndex, 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) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return coeffRef(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
}
#endif
// normal indices
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(const array<Index, NumIndices>& indices)
{
eigen_internal_assert(checkIndexRange(indices));
return m_storage.data()[linearizedIndex(indices)];
}
// custom indices
#ifdef EIGEN_HAS_SFINAE
template<typename CustomIndices,
EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) )
>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(CustomIndices& indices)
{
return coeffRef(internal::customIndices2Array<Index,NumIndices>(indices));
}
#endif
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef()
{
EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
return m_storage.data()[0];
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& coeffRef(Index index)
{
eigen_internal_assert(index >= 0 && index < size());
return m_storage.data()[index];
}
#if EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
inline const Scalar& operator()(Index firstIndex, Index secondIndex, 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) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return this->operator()(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
}
#else
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1) const
{
return coeff(array<Index, 2>(i0, i1));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2) const
{
return coeff(array<Index, 3>(i0, i1, i2));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3) const
{
return coeff(array<Index, 4>(i0, i1, i2, i3));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
{
return coeff(array<Index, 5>(i0, i1, i2, i3, i4));
}
#endif
// custom indices
#ifdef EIGEN_HAS_SFINAE
template<typename CustomIndices,
EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) )
>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(CustomIndices& indices) const
{
return coeff(internal::customIndices2Array<Index,NumIndices>(indices));
}
#endif
// normal indices
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, NumIndices>& indices) const
{
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>
inline Scalar& operator()(Index firstIndex, Index secondIndex, 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) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
return operator()(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
}
#else
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1)
{
return coeffRef(array<Index, 2>(i0, i1));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2)
{
return coeffRef(array<Index, 3>(i0, i1, i2));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3)
{
return coeffRef(array<Index, 4>(i0, i1, i2, i3));
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4)
{
return coeffRef(array<Index, 5>(i0, i1, i2, i3, i4));
}
#endif
// normal indices
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, NumIndices>& indices)
{
return coeffRef(indices);
}
// custom indices
#ifdef EIGEN_HAS_SFINAE
template<typename CustomIndices,
EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomIndices>::value) )
>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar& operator()(CustomIndices& indices)
{
return coeffRef(internal::customIndices2Array<Index,NumIndices>(indices));
}
#endif
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 Tensor()
: m_storage()
{
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Tensor(const Self& other)
: m_storage(other.m_storage)
{
}
#if EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index firstDimension, IndexTypes... otherDimensions)
: m_storage(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, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
#else
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Tensor(Index dim1)
: m_storage(dim1, array<Index, 1>(dim1))
{
EIGEN_STATIC_ASSERT(1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2)
: m_storage(dim1*dim2, array<Index, 2>(dim1, dim2))
{
EIGEN_STATIC_ASSERT(2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2, Index dim3)
: m_storage(dim1*dim2*dim3, array<Index, 3>(dim1, dim2, dim3))
{
EIGEN_STATIC_ASSERT(3 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2, Index dim3, Index dim4)
: m_storage(dim1*dim2*dim3*dim4, array<Index, 4>(dim1, dim2, dim3, dim4))
{
EIGEN_STATIC_ASSERT(4 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Tensor(Index dim1, Index dim2, Index dim3, Index dim4, Index dim5)
: m_storage(dim1*dim2*dim3*dim4*dim5, array<Index, 5>(dim1, dim2, dim3, dim4, dim5))
{
EIGEN_STATIC_ASSERT(5 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
}
#endif
/** Normal Dimension */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Tensor(const array<Index, NumIndices>& dimensions)
: m_storage(internal::array_prod(dimensions), dimensions)
{
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Tensor(const TensorBase<OtherDerived, ReadOnlyAccessors>& other)
{
typedef TensorAssignOp<Tensor, const OtherDerived> Assign;
Assign assign(*this, other.derived());
resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Tensor(const TensorBase<OtherDerived, WriteAccessors>& other)
{
typedef TensorAssignOp<Tensor, const OtherDerived> Assign;
Assign assign(*this, other.derived());
resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Tensor& operator=(const Tensor& other)
{
typedef TensorAssignOp<Tensor, const Tensor> Assign;
Assign assign(*this, other);
resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
return *this;
}
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Tensor& operator=(const OtherDerived& other)
{
typedef TensorAssignOp<Tensor, const OtherDerived> Assign;
Assign assign(*this, other);
resize(TensorEvaluator<const Assign, DefaultDevice>(assign, DefaultDevice()).dimensions());
internal::TensorExecutor<const Assign, DefaultDevice>::run(assign, DefaultDevice());
return *this;
}
#if EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes> EIGEN_DEVICE_FUNC
void resize(Index firstDimension, IndexTypes... otherDimensions)
{
// The number of dimensions used to resize a tensor must be equal to the rank of the tensor.
EIGEN_STATIC_ASSERT(sizeof...(otherDimensions) + 1 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
resize(array<Index, NumIndices>{{firstDimension, otherDimensions...}});
}
#endif
/** Normal Dimension */
EIGEN_DEVICE_FUNC void resize(const array<Index, NumIndices>& dimensions)
{
int i;
Index size = Index(1);
for (i = 0; i < NumIndices; i++) {
internal::check_rows_cols_for_overflow<Dynamic>::run(size, dimensions[i]);
size *= dimensions[i];
}
#ifdef EIGEN_INITIALIZE_COEFFS
bool size_changed = size != this->size();
m_storage.resize(size, dimensions);
if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
#else
m_storage.resize(size, dimensions);
#endif
}
// Why this overload, DSizes is derived from array ??? //
EIGEN_DEVICE_FUNC void resize(const DSizes<Index, NumIndices>& dimensions) {
array<Index, NumIndices> dims;
for (int i = 0; i < NumIndices; ++i) {
dims[i] = dimensions[i];
}
resize(dims);
}
EIGEN_DEVICE_FUNC
void resize()
{
EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE);
// Nothing to do: rank 0 tensors have fixed size
}
/** Custom Dimension */
#ifdef EIGEN_HAS_SFINAE
template<typename CustomDimension,
EIGEN_SFINAE_ENABLE_IF( !(isOfNormalIndex<CustomDimension>::value) )
>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(CustomDimension& dimensions)
{
resize(internal::customIndices2Array<Index,NumIndices>(dimensions));
}
#endif
#ifndef EIGEN_EMULATE_CXX11_META_H
template <typename std::ptrdiff_t... Indices>
EIGEN_DEVICE_FUNC
void resize(const Sizes<Indices...>& dimensions) {
array<Index, NumIndices> dims;
for (int i = 0; i < NumIndices; ++i) {
dims[i] = static_cast<Index>(dimensions[i]);
}
resize(dims);
}
#else
template <std::size_t V1, std::size_t V2, std::size_t V3, std::size_t V4, std::size_t V5>
EIGEN_DEVICE_FUNC
void resize(const Sizes<V1, V2, V3, V4, V5>& dimensions) {
array<Index, NumIndices> dims;
for (int i = 0; i < NumIndices; ++i) {
dims[i] = static_cast<Index>(dimensions[i]);
}
resize(dims);
}
#endif
protected:
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
// 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_H