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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_IMAGE_PATCH_H
11 #define EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
12 
13 namespace Eigen {
14 
15 /** \class TensorImagePatch
16   * \ingroup CXX11_Tensor_Module
17   *
18   * \brief Patch extraction specialized for image processing.
19   * This assumes that the input has a least 3 dimensions ordered as follow:
20   *  1st dimension: channels (of size d)
21   *  2nd dimension: rows (of size r)
22   *  3rd dimension: columns (of size c)
23   *  There can be additional dimensions such as time (for video) or batch (for
24   * bulk processing after the first 3.
25   * Calling the image patch code with patch_rows and patch_cols is equivalent
26   * to calling the regular patch extraction code with parameters d, patch_rows,
27   * patch_cols, and 1 for all the additional dimensions.
28   */
29 namespace internal {
30 template<DenseIndex Rows, DenseIndex Cols, typename XprType>
31 struct traits<TensorImagePatchOp<Rows, Cols, XprType> > : public traits<XprType>
32 {
33   typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
34   typedef traits<XprType> XprTraits;
35   typedef typename XprTraits::StorageKind StorageKind;
36   typedef typename XprTraits::Index Index;
37   typedef typename XprType::Nested Nested;
38   typedef typename remove_reference<Nested>::type _Nested;
39   static const int NumDimensions = XprTraits::NumDimensions + 1;
40   static const int Layout = XprTraits::Layout;
41 };
42 
43 template<DenseIndex Rows, DenseIndex Cols, typename XprType>
44 struct eval<TensorImagePatchOp<Rows, Cols, XprType>, Eigen::Dense>
45 {
46   typedef const TensorImagePatchOp<Rows, Cols, XprType>& type;
47 };
48 
49 template<DenseIndex Rows, DenseIndex Cols, typename XprType>
50 struct nested<TensorImagePatchOp<Rows, Cols, XprType>, 1, typename eval<TensorImagePatchOp<Rows, Cols, XprType> >::type>
51 {
52   typedef TensorImagePatchOp<Rows, Cols, XprType> type;
53 };
54 
55 }  // end namespace internal
56 
57 template<DenseIndex Rows, DenseIndex Cols, typename XprType>
58 class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprType>, ReadOnlyAccessors>
59 {
60   public:
61   typedef typename Eigen::internal::traits<TensorImagePatchOp>::Scalar Scalar;
62   typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
63   typedef typename XprType::CoeffReturnType CoeffReturnType;
64   typedef typename Eigen::internal::nested<TensorImagePatchOp>::type Nested;
65   typedef typename Eigen::internal::traits<TensorImagePatchOp>::StorageKind StorageKind;
66   typedef typename Eigen::internal::traits<TensorImagePatchOp>::Index Index;
67 
68   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols,
69                                                            DenseIndex row_strides, DenseIndex col_strides,
70                                                            DenseIndex in_row_strides, DenseIndex in_col_strides,
71                                                            DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
72                                                            PaddingType padding_type, Scalar padding_value)
73       : m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
74         m_row_strides(row_strides), m_col_strides(col_strides),
75         m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
76         m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
77         m_padding_explicit(false), m_padding_top(0), m_padding_bottom(0), m_padding_left(0), m_padding_right(0),
78         m_padding_type(padding_type), m_padding_value(padding_value) {}
79 
80   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols,
81                                                            DenseIndex row_strides, DenseIndex col_strides,
82                                                            DenseIndex in_row_strides, DenseIndex in_col_strides,
83                                                            DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
84                                                            DenseIndex padding_top, DenseIndex padding_bottom,
85                                                            DenseIndex padding_left, DenseIndex padding_right,
86                                                            Scalar padding_value)
87       : m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
88         m_row_strides(row_strides), m_col_strides(col_strides),
89         m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
90         m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
91         m_padding_explicit(true), m_padding_top(padding_top), m_padding_bottom(padding_bottom),
92         m_padding_left(padding_left), m_padding_right(padding_right),
93         m_padding_type(PADDING_VALID), m_padding_value(padding_value) {}
94 
95     EIGEN_DEVICE_FUNC
96     DenseIndex patch_rows() const { return m_patch_rows; }
97     EIGEN_DEVICE_FUNC
98     DenseIndex patch_cols() const { return m_patch_cols; }
99     EIGEN_DEVICE_FUNC
100     DenseIndex row_strides() const { return m_row_strides; }
101     EIGEN_DEVICE_FUNC
102     DenseIndex col_strides() const { return m_col_strides; }
103     EIGEN_DEVICE_FUNC
104     DenseIndex in_row_strides() const { return m_in_row_strides; }
105     EIGEN_DEVICE_FUNC
106     DenseIndex in_col_strides() const { return m_in_col_strides; }
107     EIGEN_DEVICE_FUNC
108     DenseIndex row_inflate_strides() const { return m_row_inflate_strides; }
109     EIGEN_DEVICE_FUNC
110     DenseIndex col_inflate_strides() const { return m_col_inflate_strides; }
111     EIGEN_DEVICE_FUNC
112     bool padding_explicit() const { return m_padding_explicit; }
113     EIGEN_DEVICE_FUNC
114     DenseIndex padding_top() const { return m_padding_top; }
115     EIGEN_DEVICE_FUNC
116     DenseIndex padding_bottom() const { return m_padding_bottom; }
117     EIGEN_DEVICE_FUNC
118     DenseIndex padding_left() const { return m_padding_left; }
119     EIGEN_DEVICE_FUNC
120     DenseIndex padding_right() const { return m_padding_right; }
121     EIGEN_DEVICE_FUNC
122     PaddingType padding_type() const { return m_padding_type; }
123     EIGEN_DEVICE_FUNC
124     Scalar padding_value() const { return m_padding_value; }
125 
126     EIGEN_DEVICE_FUNC
127     const typename internal::remove_all<typename XprType::Nested>::type&
128     expression() const { return m_xpr; }
129 
130   protected:
131     typename XprType::Nested m_xpr;
132     const DenseIndex m_patch_rows;
133     const DenseIndex m_patch_cols;
134     const DenseIndex m_row_strides;
135     const DenseIndex m_col_strides;
136     const DenseIndex m_in_row_strides;
137     const DenseIndex m_in_col_strides;
138     const DenseIndex m_row_inflate_strides;
139     const DenseIndex m_col_inflate_strides;
140     const bool m_padding_explicit;
141     const DenseIndex m_padding_top;
142     const DenseIndex m_padding_bottom;
143     const DenseIndex m_padding_left;
144     const DenseIndex m_padding_right;
145     const PaddingType m_padding_type;
146     const Scalar m_padding_value;
147 };
148 
149 // Eval as rvalue
150 template<DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device>
151 struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
152 {
153   typedef TensorImagePatchOp<Rows, Cols, ArgType> XprType;
154   typedef typename XprType::Index Index;
155   static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
156   static const int NumDims = NumInputDims + 1;
157   typedef DSizes<Index, NumDims> Dimensions;
158   typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
159   typedef TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>,
160                           Device> Self;
161   typedef TensorEvaluator<ArgType, Device> Impl;
162   typedef typename XprType::CoeffReturnType CoeffReturnType;
163   typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
164   static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
165 
166   enum {
167     IsAligned = false,
168     PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
169     Layout = TensorEvaluator<ArgType, Device>::Layout,
170     CoordAccess = false,
171     RawAccess = false
172   };
173 
174   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
175       : m_impl(op.expression(), device)
176   {
177     EIGEN_STATIC_ASSERT((NumDims >= 4), YOU_MADE_A_PROGRAMMING_MISTAKE);
178 
179     m_paddingValue = op.padding_value();
180 
181     const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
182 
183     // Caches a few variables.
184     if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
185       m_inputDepth = input_dims[0];
186       m_inputRows = input_dims[1];
187       m_inputCols = input_dims[2];
188     } else {
189       m_inputDepth = input_dims[NumInputDims-1];
190       m_inputRows = input_dims[NumInputDims-2];
191       m_inputCols = input_dims[NumInputDims-3];
192     }
193 
194     m_row_strides = op.row_strides();
195     m_col_strides = op.col_strides();
196 
197     // Input strides and effective input/patch size
198     m_in_row_strides = op.in_row_strides();
199     m_in_col_strides = op.in_col_strides();
200     m_row_inflate_strides = op.row_inflate_strides();
201     m_col_inflate_strides = op.col_inflate_strides();
202     // The "effective" input rows and input cols are the input rows and cols
203     // after inflating them with zeros.
204     // For examples, a 2x3 matrix with row_inflate_strides and
205     // col_inflate_strides of 2 comes from:
206     //   A B C
207     //   D E F
208     //
209     // to a matrix is 3 x 5:
210     //
211     //   A . B . C
212     //   . . . . .
213     //   D . E . F
214 
215     m_input_rows_eff = (m_inputRows - 1) * m_row_inflate_strides + 1;
216     m_input_cols_eff = (m_inputCols - 1) * m_col_inflate_strides + 1;
217     m_patch_rows_eff = op.patch_rows() + (op.patch_rows() - 1) * (m_in_row_strides - 1);
218     m_patch_cols_eff = op.patch_cols() + (op.patch_cols() - 1) * (m_in_col_strides - 1);
219 
220     if (op.padding_explicit()) {
221       m_outputRows = numext::ceil((m_input_rows_eff + op.padding_top() + op.padding_bottom() - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
222       m_outputCols = numext::ceil((m_input_cols_eff + op.padding_left() + op.padding_right() - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
223       m_rowPaddingTop = op.padding_top();
224       m_colPaddingLeft = op.padding_left();
225     } else {
226       // Computing padding from the type
227       switch (op.padding_type()) {
228         case PADDING_VALID:
229           m_outputRows = numext::ceil((m_input_rows_eff - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides));
230           m_outputCols = numext::ceil((m_input_cols_eff - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides));
231           // Calculate the padding
232           m_rowPaddingTop = numext::maxi<Index>(0, ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2);
233           m_colPaddingLeft = numext::maxi<Index>(0, ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2);
234           break;
235         case PADDING_SAME:
236           m_outputRows = numext::ceil(m_input_rows_eff / static_cast<float>(m_row_strides));
237           m_outputCols = numext::ceil(m_input_cols_eff / static_cast<float>(m_col_strides));
238           // Calculate the padding
239           m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2;
240           m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2;
241           break;
242         default:
243           eigen_assert(false && "unexpected padding");
244       }
245     }
246     eigen_assert(m_outputRows > 0);
247     eigen_assert(m_outputCols > 0);
248 
249     // Dimensions for result of extraction.
250     if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
251       // ColMajor
252       // 0: depth
253       // 1: patch_rows
254       // 2: patch_cols
255       // 3: number of patches
256       // 4 and beyond: anything else (such as batch).
257       m_dimensions[0] = input_dims[0];
258       m_dimensions[1] = op.patch_rows();
259       m_dimensions[2] = op.patch_cols();
260       m_dimensions[3] = m_outputRows * m_outputCols;
261       for (int i = 4; i < NumDims; ++i) {
262         m_dimensions[i] = input_dims[i-1];
263       }
264     } else {
265       // RowMajor
266       // NumDims-1: depth
267       // NumDims-2: patch_rows
268       // NumDims-3: patch_cols
269       // NumDims-4: number of patches
270       // NumDims-5 and beyond: anything else (such as batch).
271       m_dimensions[NumDims-1] = input_dims[NumInputDims-1];
272       m_dimensions[NumDims-2] = op.patch_rows();
273       m_dimensions[NumDims-3] = op.patch_cols();
274       m_dimensions[NumDims-4] = m_outputRows * m_outputCols;
275       for (int i = NumDims-5; i >= 0; --i) {
276         m_dimensions[i] = input_dims[i];
277       }
278     }
279 
280     // Strides for moving the patch in various dimensions.
281     if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
282       m_colStride = m_dimensions[1];
283       m_patchStride = m_colStride * m_dimensions[2] * m_dimensions[0];
284       m_otherStride = m_patchStride * m_dimensions[3];
285     } else {
286       m_colStride = m_dimensions[NumDims-2];
287       m_patchStride = m_colStride * m_dimensions[NumDims-3] * m_dimensions[NumDims-1];
288       m_otherStride = m_patchStride * m_dimensions[NumDims-4];
289     }
290 
291     // Strides for navigating through the input tensor.
292     m_rowInputStride = m_inputDepth;
293     m_colInputStride = m_inputDepth * m_inputRows;
294     m_patchInputStride = m_inputDepth * m_inputRows * m_inputCols;
295 
296     // Fast representations of different variables.
297     m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
298     m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
299     m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
300     m_fastInflateRowStride = internal::TensorIntDivisor<Index>(m_row_inflate_strides);
301     m_fastInflateColStride = internal::TensorIntDivisor<Index>(m_col_inflate_strides);
302     m_fastInputColsEff = internal::TensorIntDivisor<Index>(m_input_cols_eff);
303 
304     // Number of patches in the width dimension.
305     m_fastOutputRows = internal::TensorIntDivisor<Index>(m_outputRows);
306     if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
307       m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[0]);
308     } else {
309       m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims-1]);
310     }
311   }
312 
313   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
314 
315   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
316     m_impl.evalSubExprsIfNeeded(NULL);
317     return true;
318   }
319 
320   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
321     m_impl.cleanup();
322   }
323 
324   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
325   {
326     // Patch index corresponding to the passed in index.
327     const Index patchIndex = index / m_fastPatchStride;
328     // Find the offset of the element wrt the location of the first element.
329     const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastOutputDepth;
330 
331     // Other ways to index this element.
332     const Index otherIndex = (NumDims == 4) ? 0 : index / m_fastOtherStride;
333     const Index patch2DIndex = (NumDims == 4) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride;
334 
335     // Calculate col index in the input original tensor.
336     const Index colIndex = patch2DIndex / m_fastOutputRows;
337     const Index colOffset = patchOffset / m_fastColStride;
338     const Index inputCol = colIndex * m_col_strides + colOffset * m_in_col_strides - m_colPaddingLeft;
339     const Index origInputCol = (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInflateColStride) : 0);
340     if (inputCol < 0 || inputCol >= m_input_cols_eff ||
341         ((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides))) {
342       return Scalar(m_paddingValue);
343     }
344 
345     // Calculate row index in the original input tensor.
346     const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
347     const Index rowOffset = patchOffset - colOffset * m_colStride;
348     const Index inputRow = rowIndex * m_row_strides + rowOffset * m_in_row_strides - m_rowPaddingTop;
349     const Index origInputRow = (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInflateRowStride) : 0);
350     if (inputRow < 0 || inputRow >= m_input_rows_eff ||
351         ((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) {
352       return Scalar(m_paddingValue);
353     }
354 
355     const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
356     const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
357 
358     const Index inputIndex = depth + origInputRow * m_rowInputStride + origInputCol * m_colInputStride + otherIndex * m_patchInputStride;
359     return m_impl.coeff(inputIndex);
360   }
361 
362   template<int LoadMode>
363   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
364   {
365     EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
366     eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
367 
368     if (m_in_row_strides != 1 || m_in_col_strides != 1 || m_row_inflate_strides != 1 || m_col_inflate_strides != 1) {
369       return packetWithPossibleZero(index);
370     }
371 
372     const Index indices[2] = {index, index + PacketSize - 1};
373     const Index patchIndex = indices[0] / m_fastPatchStride;
374     if (patchIndex != indices[1] / m_fastPatchStride) {
375       return packetWithPossibleZero(index);
376     }
377     const Index otherIndex = (NumDims == 4) ? 0 : indices[0] / m_fastOtherStride;
378     eigen_assert(otherIndex == indices[1] / m_fastOtherStride);
379 
380     // Find the offset of the element wrt the location of the first element.
381     const Index patchOffsets[2] = {(indices[0] - patchIndex * m_patchStride) / m_fastOutputDepth,
382                                    (indices[1] - patchIndex * m_patchStride) / m_fastOutputDepth};
383 
384     const Index patch2DIndex = (NumDims == 4) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride;
385     eigen_assert(patch2DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride);
386 
387     const Index colIndex = patch2DIndex / m_fastOutputRows;
388     const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, patchOffsets[1] / m_fastColStride};
389 
390     // Calculate col indices in the original input tensor.
391     const Index inputCols[2] = {colIndex * m_col_strides + colOffsets[0] -
392       m_colPaddingLeft, colIndex * m_col_strides + colOffsets[1] - m_colPaddingLeft};
393     if (inputCols[1] < 0 || inputCols[0] >= m_inputCols) {
394       return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
395     }
396 
397     if (inputCols[0] == inputCols[1]) {
398       const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
399       const Index rowOffsets[2] = {patchOffsets[0] - colOffsets[0]*m_colStride, patchOffsets[1] - colOffsets[1]*m_colStride};
400       eigen_assert(rowOffsets[0] <= rowOffsets[1]);
401       // Calculate col indices in the original input tensor.
402       const Index inputRows[2] = {rowIndex * m_row_strides + rowOffsets[0] -
403         m_rowPaddingTop, rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop};
404 
405       if (inputRows[1] < 0 || inputRows[0] >= m_inputRows) {
406         return internal::pset1<PacketReturnType>(Scalar(m_paddingValue));
407       }
408 
409       if (inputRows[0] >= 0 && inputRows[1] < m_inputRows) {
410         // no padding
411         const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
412         const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index];
413         const Index inputIndex = depth + inputRows[0] * m_rowInputStride + inputCols[0] * m_colInputStride + otherIndex * m_patchInputStride;
414         return m_impl.template packet<Unaligned>(inputIndex);
415       }
416     }
417 
418     return packetWithPossibleZero(index);
419   }
420 
421   EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
422 
423   const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
424 
425   Index rowPaddingTop() const { return m_rowPaddingTop; }
426   Index colPaddingLeft() const { return m_colPaddingLeft; }
427   Index outputRows() const { return m_outputRows; }
428   Index outputCols() const { return m_outputCols; }
429   Index userRowStride() const { return m_row_strides; }
430   Index userColStride() const { return m_col_strides; }
431   Index userInRowStride() const { return m_in_row_strides; }
432   Index userInColStride() const { return m_in_col_strides; }
433   Index rowInflateStride() const { return m_row_inflate_strides; }
434   Index colInflateStride() const { return m_col_inflate_strides; }
435 
436   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
437   costPerCoeff(bool vectorized) const {
438     // We conservatively estimate the cost for the code path where the computed
439     // index is inside the original image and
440     // TensorEvaluator<ArgType, Device>::CoordAccess is false.
441     const double compute_cost = 3 * TensorOpCost::DivCost<Index>() +
442                                 6 * TensorOpCost::MulCost<Index>() +
443                                 8 * TensorOpCost::MulCost<Index>();
444     return m_impl.costPerCoeff(vectorized) +
445            TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
446   }
447 
448  protected:
449   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
450   {
451     EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
452     for (int i = 0; i < PacketSize; ++i) {
453       values[i] = coeff(index+i);
454     }
455     PacketReturnType rslt = internal::pload<PacketReturnType>(values);
456     return rslt;
457   }
458 
459   Dimensions m_dimensions;
460 
461   Index m_otherStride;
462   Index m_patchStride;
463   Index m_colStride;
464   Index m_row_strides;
465   Index m_col_strides;
466 
467   Index m_in_row_strides;
468   Index m_in_col_strides;
469   Index m_row_inflate_strides;
470   Index m_col_inflate_strides;
471 
472   Index m_input_rows_eff;
473   Index m_input_cols_eff;
474   Index m_patch_rows_eff;
475   Index m_patch_cols_eff;
476 
477   internal::TensorIntDivisor<Index> m_fastOtherStride;
478   internal::TensorIntDivisor<Index> m_fastPatchStride;
479   internal::TensorIntDivisor<Index> m_fastColStride;
480   internal::TensorIntDivisor<Index> m_fastInflateRowStride;
481   internal::TensorIntDivisor<Index> m_fastInflateColStride;
482   internal::TensorIntDivisor<Index> m_fastInputColsEff;
483 
484   Index m_rowInputStride;
485   Index m_colInputStride;
486   Index m_patchInputStride;
487 
488   Index m_inputDepth;
489   Index m_inputRows;
490   Index m_inputCols;
491 
492   Index m_outputRows;
493   Index m_outputCols;
494 
495   Index m_rowPaddingTop;
496   Index m_colPaddingLeft;
497 
498   internal::TensorIntDivisor<Index> m_fastOutputRows;
499   internal::TensorIntDivisor<Index> m_fastOutputDepth;
500 
501   Scalar m_paddingValue;
502 
503   TensorEvaluator<ArgType, Device> m_impl;
504 };
505 
506 
507 } // end namespace Eigen
508 
509 #endif // EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
510