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Eigen-unsupported
3.3.3
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00001 // This file is part of Eigen, a lightweight C++ template library 00002 // for linear algebra. 00003 // 00004 // Copyright (C) 2015 Ke Yang <yangke@gmail.com> 00005 // 00006 // This Source Code Form is subject to the terms of the Mozilla 00007 // Public License v. 2.0. If a copy of the MPL was not distributed 00008 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 00009 00010 #ifndef EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H 00011 #define EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H 00012 00013 namespace Eigen { 00014 00022 namespace internal { 00023 template<typename Strides, typename XprType> 00024 struct traits<TensorInflationOp<Strides, XprType> > : public traits<XprType> 00025 { 00026 typedef typename XprType::Scalar Scalar; 00027 typedef traits<XprType> XprTraits; 00028 typedef typename XprTraits::StorageKind StorageKind; 00029 typedef typename XprTraits::Index Index; 00030 typedef typename XprType::Nested Nested; 00031 typedef typename remove_reference<Nested>::type _Nested; 00032 static const int NumDimensions = XprTraits::NumDimensions; 00033 static const int Layout = XprTraits::Layout; 00034 }; 00035 00036 template<typename Strides, typename XprType> 00037 struct eval<TensorInflationOp<Strides, XprType>, Eigen::Dense> 00038 { 00039 typedef const TensorInflationOp<Strides, XprType>& type; 00040 }; 00041 00042 template<typename Strides, typename XprType> 00043 struct nested<TensorInflationOp<Strides, XprType>, 1, typename eval<TensorInflationOp<Strides, XprType> >::type> 00044 { 00045 typedef TensorInflationOp<Strides, XprType> type; 00046 }; 00047 00048 } // end namespace internal 00049 00050 template<typename Strides, typename XprType> 00051 class TensorInflationOp : public TensorBase<TensorInflationOp<Strides, XprType>, ReadOnlyAccessors> 00052 { 00053 public: 00054 typedef typename Eigen::internal::traits<TensorInflationOp>::Scalar Scalar; 00055 typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; 00056 typedef typename XprType::CoeffReturnType CoeffReturnType; 00057 typedef typename Eigen::internal::nested<TensorInflationOp>::type Nested; 00058 typedef typename Eigen::internal::traits<TensorInflationOp>::StorageKind StorageKind; 00059 typedef typename Eigen::internal::traits<TensorInflationOp>::Index Index; 00060 00061 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorInflationOp(const XprType& expr, const Strides& strides) 00062 : m_xpr(expr), m_strides(strides) {} 00063 00064 EIGEN_DEVICE_FUNC 00065 const Strides& strides() const { return m_strides; } 00066 00067 EIGEN_DEVICE_FUNC 00068 const typename internal::remove_all<typename XprType::Nested>::type& 00069 expression() const { return m_xpr; } 00070 00071 protected: 00072 typename XprType::Nested m_xpr; 00073 const Strides m_strides; 00074 }; 00075 00076 // Eval as rvalue 00077 template<typename Strides, typename ArgType, typename Device> 00078 struct TensorEvaluator<const TensorInflationOp<Strides, ArgType>, Device> 00079 { 00080 typedef TensorInflationOp<Strides, ArgType> XprType; 00081 typedef typename XprType::Index Index; 00082 static const int NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; 00083 typedef DSizes<Index, NumDims> Dimensions; 00084 typedef typename XprType::Scalar Scalar; 00085 typedef typename XprType::CoeffReturnType CoeffReturnType; 00086 typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; 00087 static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size; 00088 00089 enum { 00090 IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false, 00091 PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, 00092 BlockAccess = false, 00093 Layout = TensorEvaluator<ArgType, Device>::Layout, 00094 CoordAccess = false, // to be implemented 00095 RawAccess = false 00096 }; 00097 00098 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) 00099 : m_impl(op.expression(), device), m_strides(op.strides()) 00100 { 00101 m_dimensions = m_impl.dimensions(); 00102 // Expand each dimension to the inflated dimension. 00103 for (int i = 0; i < NumDims; ++i) { 00104 m_dimensions[i] = (m_dimensions[i] - 1) * op.strides()[i] + 1; 00105 } 00106 00107 // Remember the strides for fast division. 00108 for (int i = 0; i < NumDims; ++i) { 00109 m_fastStrides[i] = internal::TensorIntDivisor<Index>(m_strides[i]); 00110 } 00111 00112 const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); 00113 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { 00114 m_outputStrides[0] = 1; 00115 m_inputStrides[0] = 1; 00116 for (int i = 1; i < NumDims; ++i) { 00117 m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1]; 00118 m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1]; 00119 } 00120 } else { // RowMajor 00121 m_outputStrides[NumDims-1] = 1; 00122 m_inputStrides[NumDims-1] = 1; 00123 for (int i = NumDims - 2; i >= 0; --i) { 00124 m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1]; 00125 m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1]; 00126 } 00127 } 00128 } 00129 00130 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } 00131 00132 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { 00133 m_impl.evalSubExprsIfNeeded(NULL); 00134 return true; 00135 } 00136 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { 00137 m_impl.cleanup(); 00138 } 00139 00140 // Computes the input index given the output index. Returns true if the output 00141 // index doesn't fall into a hole. 00142 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool getInputIndex(Index index, Index* inputIndex) const 00143 { 00144 eigen_assert(index < dimensions().TotalSize()); 00145 *inputIndex = 0; 00146 if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { 00147 for (int i = NumDims - 1; i > 0; --i) { 00148 const Index idx = index / m_outputStrides[i]; 00149 if (idx != idx / m_fastStrides[i] * m_strides[i]) { 00150 return false; 00151 } 00152 *inputIndex += idx / m_strides[i] * m_inputStrides[i]; 00153 index -= idx * m_outputStrides[i]; 00154 } 00155 if (index != index / m_fastStrides[0] * m_strides[0]) { 00156 return false; 00157 } 00158 *inputIndex += index / m_strides[0]; 00159 return true; 00160 } else { 00161 for (int i = 0; i < NumDims - 1; ++i) { 00162 const Index idx = index / m_outputStrides[i]; 00163 if (idx != idx / m_fastStrides[i] * m_strides[i]) { 00164 return false; 00165 } 00166 *inputIndex += idx / m_strides[i] * m_inputStrides[i]; 00167 index -= idx * m_outputStrides[i]; 00168 } 00169 if (index != index / m_fastStrides[NumDims-1] * m_strides[NumDims-1]) { 00170 return false; 00171 } 00172 *inputIndex += index / m_strides[NumDims - 1]; 00173 } 00174 return true; 00175 } 00176 00177 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const 00178 { 00179 Index inputIndex = 0; 00180 if (getInputIndex(index, &inputIndex)) { 00181 return m_impl.coeff(inputIndex); 00182 } else { 00183 return Scalar(0); 00184 } 00185 } 00186 00187 // TODO(yangke): optimize this function so that we can detect and produce 00188 // all-zero packets 00189 template<int LoadMode> 00190 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const 00191 { 00192 EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE) 00193 eigen_assert(index+PacketSize-1 < dimensions().TotalSize()); 00194 00195 EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize]; 00196 for (int i = 0; i < PacketSize; ++i) { 00197 values[i] = coeff(index+i); 00198 } 00199 PacketReturnType rslt = internal::pload<PacketReturnType>(values); 00200 return rslt; 00201 } 00202 00203 EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { 00204 const double compute_cost = NumDims * (3 * TensorOpCost::DivCost<Index>() + 00205 3 * TensorOpCost::MulCost<Index>() + 00206 2 * TensorOpCost::AddCost<Index>()); 00207 const double input_size = m_impl.dimensions().TotalSize(); 00208 const double output_size = m_dimensions.TotalSize(); 00209 if (output_size == 0) 00210 return TensorOpCost(); 00211 return m_impl.costPerCoeff(vectorized) + 00212 TensorOpCost(sizeof(CoeffReturnType) * input_size / output_size, 0, 00213 compute_cost, vectorized, PacketSize); 00214 } 00215 00216 EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } 00217 00218 protected: 00219 Dimensions m_dimensions; 00220 array<Index, NumDims> m_outputStrides; 00221 array<Index, NumDims> m_inputStrides; 00222 TensorEvaluator<ArgType, Device> m_impl; 00223 const Strides m_strides; 00224 array<internal::TensorIntDivisor<Index>, NumDims> m_fastStrides; 00225 }; 00226 00227 } // end namespace Eigen 00228 00229 #endif // EIGEN_CXX11_TENSOR_TENSOR_INFLATION_H