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Diffstat (limited to 'unsupported/Eigen/CXX11/src/Tensor/Tensor.h')
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diff --git a/unsupported/Eigen/CXX11/src/Tensor/Tensor.h b/unsupported/Eigen/CXX11/src/Tensor/Tensor.h new file mode 100644 index 000000000..1940a9692 --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/Tensor.h @@ -0,0 +1,527 @@ +// 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_ \anchor tensor_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_ \anchor tensor_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 TopicCustomizingEigen 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 developement 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 |