TensorMap.h
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1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
5 //
6 // This Source Code Form is subject to the terms of the Mozilla
7 // Public License v. 2.0. If a copy of the MPL was not distributed
8 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9 
10 #ifndef EIGEN_CXX11_TENSOR_TENSOR_MAP_H
11 #define EIGEN_CXX11_TENSOR_TENSOR_MAP_H
12 
13 namespace Eigen {
14 
15 // FIXME use proper doxygen documentation (e.g. \tparam MakePointer_)
16 
23 template<typename PlainObjectType, int Options_, template <class> class MakePointer_> class TensorMap : public TensorBase<TensorMap<PlainObjectType, Options_, MakePointer_> >
30 {
31  public:
32  typedef TensorMap<PlainObjectType, Options_, MakePointer_> Self;
33  typedef TensorBase<TensorMap<PlainObjectType, Options_, MakePointer_> > Base;
34  #ifdef EIGEN_USE_SYCL
35  typedef typename Eigen::internal::remove_reference<typename Eigen::internal::nested<Self>::type>::type Nested;
36  #else
37  typedef typename Eigen::internal::nested<Self>::type Nested;
38  #endif
39  typedef typename internal::traits<PlainObjectType>::StorageKind StorageKind;
40  typedef typename internal::traits<PlainObjectType>::Index Index;
41  typedef typename internal::traits<PlainObjectType>::Scalar Scalar;
42  typedef typename NumTraits<Scalar>::Real RealScalar;
43  typedef typename PlainObjectType::Base::CoeffReturnType CoeffReturnType;
44 
45  typedef typename MakePointer_<Scalar>::Type PointerType;
46  typedef typename MakePointer_<Scalar>::ConstType PointerConstType;
47 
48  // WARN: PointerType still can be a pointer to const (const Scalar*), for
49  // example in TensorMap<Tensor<const Scalar, ...>> expression. This type of
50  // expression should be illegal, but adding this restriction is not possible
51  // in practice (see https://bitbucket.org/eigen/eigen/pull-requests/488).
52  typedef typename internal::conditional<
53  bool(internal::is_lvalue<PlainObjectType>::value),
54  PointerType, // use simple pointer in lvalue expressions
55  PointerConstType // use const pointer in rvalue expressions
56  >::type StoragePointerType;
57 
58  // If TensorMap was constructed over rvalue expression (e.g. const Tensor),
59  // we should return a reference to const from operator() (and others), even
60  // if TensorMap itself is not const.
61  typedef typename internal::conditional<
62  bool(internal::is_lvalue<PlainObjectType>::value),
63  Scalar&,
64  const Scalar&
65  >::type StorageRefType;
66 
67  static const int Options = Options_;
68 
69  static const Index NumIndices = PlainObjectType::NumIndices;
70  typedef typename PlainObjectType::Dimensions Dimensions;
71 
72  enum {
73  IsAligned = ((int(Options_)&Aligned)==Aligned),
74  Layout = PlainObjectType::Layout,
75  CoordAccess = true,
76  RawAccess = true
77  };
78 
79  EIGEN_DEVICE_FUNC
80  EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr) : m_data(dataPtr), m_dimensions() {
81  // The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
82  EIGEN_STATIC_ASSERT((0 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
83  }
84 
85 #if EIGEN_HAS_VARIADIC_TEMPLATES
86  template<typename... IndexTypes> EIGEN_DEVICE_FUNC
87  EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index firstDimension, IndexTypes... otherDimensions) : m_data(dataPtr), m_dimensions(firstDimension, otherDimensions...) {
88  // The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
89  EIGEN_STATIC_ASSERT((sizeof...(otherDimensions) + 1 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
90  }
91 #else
92  EIGEN_DEVICE_FUNC
93  EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index firstDimension) : m_data(dataPtr), m_dimensions(firstDimension) {
94  // The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
95  EIGEN_STATIC_ASSERT((1 == NumIndices || NumIndices == Dynamic), YOU_MADE_A_PROGRAMMING_MISTAKE)
96  }
97  EIGEN_DEVICE_FUNC
98  EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2) : m_data(dataPtr), m_dimensions(dim1, dim2) {
99  EIGEN_STATIC_ASSERT(2 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
100  }
101  EIGEN_DEVICE_FUNC
102  EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2, Index dim3) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3) {
103  EIGEN_STATIC_ASSERT(3 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
104  }
105  EIGEN_DEVICE_FUNC
106  EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2, Index dim3, Index dim4) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3, dim4) {
107  EIGEN_STATIC_ASSERT(4 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
108  }
109  EIGEN_DEVICE_FUNC
110  EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, Index dim1, Index dim2, Index dim3, Index dim4, Index dim5) : m_data(dataPtr), m_dimensions(dim1, dim2, dim3, dim4, dim5) {
111  EIGEN_STATIC_ASSERT(5 == NumIndices || NumIndices == Dynamic, YOU_MADE_A_PROGRAMMING_MISTAKE)
112  }
113 #endif
114 
115  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, const array<Index, NumIndices>& dimensions)
116  : m_data(dataPtr), m_dimensions(dimensions)
117  { }
118 
119  template <typename Dimensions>
120  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(StoragePointerType dataPtr, const Dimensions& dimensions)
121  : m_data(dataPtr), m_dimensions(dimensions)
122  { }
123 
124  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorMap(PlainObjectType& tensor)
125  : m_data(tensor.data()), m_dimensions(tensor.dimensions())
126  { }
127 
128  EIGEN_DEVICE_FUNC
129  EIGEN_STRONG_INLINE Index rank() const { return m_dimensions.rank(); }
130  EIGEN_DEVICE_FUNC
131  EIGEN_STRONG_INLINE Index dimension(Index n) const { return m_dimensions[n]; }
132  EIGEN_DEVICE_FUNC
133  EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
134  EIGEN_DEVICE_FUNC
135  EIGEN_STRONG_INLINE Index size() const { return m_dimensions.TotalSize(); }
136  EIGEN_DEVICE_FUNC
137  EIGEN_STRONG_INLINE StoragePointerType data() { return m_data; }
138  EIGEN_DEVICE_FUNC
139  EIGEN_STRONG_INLINE StoragePointerType data() const { return m_data; }
140 
141  EIGEN_DEVICE_FUNC
142  EIGEN_STRONG_INLINE StorageRefType operator()(const array<Index, NumIndices>& indices) const
143  {
144  // eigen_assert(checkIndexRange(indices));
145  if (PlainObjectType::Options&RowMajor) {
146  const Index index = m_dimensions.IndexOfRowMajor(indices);
147  return m_data[index];
148  } else {
149  const Index index = m_dimensions.IndexOfColMajor(indices);
150  return m_data[index];
151  }
152  }
153 
154  EIGEN_DEVICE_FUNC
155  EIGEN_STRONG_INLINE StorageRefType operator()() const
156  {
157  EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE)
158  return m_data[0];
159  }
160 
161  EIGEN_DEVICE_FUNC
162  EIGEN_STRONG_INLINE StorageRefType operator()(Index index) const
163  {
164  eigen_internal_assert(index >= 0 && index < size());
165  return m_data[index];
166  }
167 
168 #if EIGEN_HAS_VARIADIC_TEMPLATES
169  template<typename... IndexTypes> EIGEN_DEVICE_FUNC
170  EIGEN_STRONG_INLINE StorageRefType operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices) const
171  {
172  EIGEN_STATIC_ASSERT(sizeof...(otherIndices) + 2 == NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
173  eigen_assert(internal::all((Eigen::NumTraits<Index>::highest() >= otherIndices)...));
174  if (PlainObjectType::Options&RowMajor) {
175  const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
176  return m_data[index];
177  } else {
178  const Index index = m_dimensions.IndexOfColMajor(array<Index, NumIndices>{{firstIndex, secondIndex, otherIndices...}});
179  return m_data[index];
180  }
181  }
182 #else
183  EIGEN_DEVICE_FUNC
184  EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1) const
185  {
186  if (PlainObjectType::Options&RowMajor) {
187  const Index index = i1 + i0 * m_dimensions[1];
188  return m_data[index];
189  } else {
190  const Index index = i0 + i1 * m_dimensions[0];
191  return m_data[index];
192  }
193  }
194  EIGEN_DEVICE_FUNC
195  EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2) const
196  {
197  if (PlainObjectType::Options&RowMajor) {
198  const Index index = i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0);
199  return m_data[index];
200  } else {
201  const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2);
202  return m_data[index];
203  }
204  }
205  EIGEN_DEVICE_FUNC
206  EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3) const
207  {
208  if (PlainObjectType::Options&RowMajor) {
209  const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0));
210  return m_data[index];
211  } else {
212  const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3));
213  return m_data[index];
214  }
215  }
216  EIGEN_DEVICE_FUNC
217  EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
218  {
219  if (PlainObjectType::Options&RowMajor) {
220  const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)));
221  return m_data[index];
222  } else {
223  const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4)));
224  return m_data[index];
225  }
226  }
227 #endif
228 
229  EIGEN_DEVICE_FUNC
230  EIGEN_STRONG_INLINE StorageRefType operator()(const array<Index, NumIndices>& indices)
231  {
232  // eigen_assert(checkIndexRange(indices));
233  if (PlainObjectType::Options&RowMajor) {
234  const Index index = m_dimensions.IndexOfRowMajor(indices);
235  return m_data[index];
236  } else {
237  const Index index = m_dimensions.IndexOfColMajor(indices);
238  return m_data[index];
239  }
240  }
241 
242  EIGEN_DEVICE_FUNC
243  EIGEN_STRONG_INLINE StorageRefType operator()()
244  {
245  EIGEN_STATIC_ASSERT(NumIndices == 0, YOU_MADE_A_PROGRAMMING_MISTAKE)
246  return m_data[0];
247  }
248 
249  EIGEN_DEVICE_FUNC
250  EIGEN_STRONG_INLINE StorageRefType operator()(Index index)
251  {
252  eigen_internal_assert(index >= 0 && index < size());
253  return m_data[index];
254  }
255 
256 #if EIGEN_HAS_VARIADIC_TEMPLATES
257  template<typename... IndexTypes> EIGEN_DEVICE_FUNC
258  EIGEN_STRONG_INLINE StorageRefType operator()(Index firstIndex, Index secondIndex, IndexTypes... otherIndices)
259  {
260  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.");
261  eigen_assert(internal::all((Eigen::NumTraits<Index>::highest() >= otherIndices)...));
262  const std::size_t NumDims = sizeof...(otherIndices) + 2;
263  if (PlainObjectType::Options&RowMajor) {
264  const Index index = m_dimensions.IndexOfRowMajor(array<Index, NumDims>{{firstIndex, secondIndex, otherIndices...}});
265  return m_data[index];
266  } else {
267  const Index index = m_dimensions.IndexOfColMajor(array<Index, NumDims>{{firstIndex, secondIndex, otherIndices...}});
268  return m_data[index];
269  }
270  }
271 #else
272  EIGEN_DEVICE_FUNC
273  EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1)
274  {
275  if (PlainObjectType::Options&RowMajor) {
276  const Index index = i1 + i0 * m_dimensions[1];
277  return m_data[index];
278  } else {
279  const Index index = i0 + i1 * m_dimensions[0];
280  return m_data[index];
281  }
282  }
283  EIGEN_DEVICE_FUNC
284  EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2)
285  {
286  if (PlainObjectType::Options&RowMajor) {
287  const Index index = i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0);
288  return m_data[index];
289  } else {
290  const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2);
291  return m_data[index];
292  }
293  }
294  EIGEN_DEVICE_FUNC
295  EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3)
296  {
297  if (PlainObjectType::Options&RowMajor) {
298  const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0));
299  return m_data[index];
300  } else {
301  const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3));
302  return m_data[index];
303  }
304  }
305  EIGEN_DEVICE_FUNC
306  EIGEN_STRONG_INLINE StorageRefType operator()(Index i0, Index i1, Index i2, Index i3, Index i4)
307  {
308  if (PlainObjectType::Options&RowMajor) {
309  const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)));
310  return m_data[index];
311  } else {
312  const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4)));
313  return m_data[index];
314  }
315  }
316 #endif
317 
318  EIGEN_TENSOR_INHERIT_ASSIGNMENT_OPERATORS(TensorMap)
319 
320  private:
321  StoragePointerType m_data;
322  Dimensions m_dimensions;
323 };
324 
325 } // end namespace Eigen
326 
327 #endif // EIGEN_CXX11_TENSOR_TENSOR_MAP_H
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rank() const
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