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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 (at) 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