| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> |
| // |
| // 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_IMAGE_PATCH_H |
| #define EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H |
| |
| // IWYU pragma: private |
| #include "./InternalHeaderCheck.h" |
| |
| namespace Eigen { |
| |
| /** \class TensorImagePatch |
| * \ingroup CXX11_Tensor_Module |
| * |
| * \brief Patch extraction specialized for image processing. |
| * This assumes that the input has a least 3 dimensions ordered as follow: |
| * 1st dimension: channels (of size d) |
| * 2nd dimension: rows (of size r) |
| * 3rd dimension: columns (of size c) |
| * There can be additional dimensions such as time (for video) or batch (for |
| * bulk processing after the first 3. |
| * Calling the image patch code with patch_rows and patch_cols is equivalent |
| * to calling the regular patch extraction code with parameters d, patch_rows, |
| * patch_cols, and 1 for all the additional dimensions. |
| */ |
| namespace internal { |
| |
| template <DenseIndex Rows, DenseIndex Cols, typename XprType> |
| struct traits<TensorImagePatchOp<Rows, Cols, XprType> > : public traits<XprType> { |
| typedef std::remove_const_t<typename XprType::Scalar> Scalar; |
| typedef traits<XprType> XprTraits; |
| typedef typename XprTraits::StorageKind StorageKind; |
| typedef typename XprTraits::Index Index; |
| typedef typename XprType::Nested Nested; |
| typedef std::remove_reference_t<Nested> Nested_; |
| static constexpr int NumDimensions = XprTraits::NumDimensions + 1; |
| static constexpr int Layout = XprTraits::Layout; |
| typedef typename XprTraits::PointerType PointerType; |
| }; |
| |
| template <DenseIndex Rows, DenseIndex Cols, typename XprType> |
| struct eval<TensorImagePatchOp<Rows, Cols, XprType>, Eigen::Dense> { |
| typedef const TensorImagePatchOp<Rows, Cols, XprType>& type; |
| }; |
| |
| template <DenseIndex Rows, DenseIndex Cols, typename XprType> |
| struct nested<TensorImagePatchOp<Rows, Cols, XprType>, 1, |
| typename eval<TensorImagePatchOp<Rows, Cols, XprType> >::type> { |
| typedef TensorImagePatchOp<Rows, Cols, XprType> type; |
| }; |
| |
| template <typename Self, bool Vectorizable> |
| struct ImagePatchCopyOp { |
| typedef typename Self::Index Index; |
| typedef typename Self::Scalar Scalar; |
| typedef typename Self::Impl Impl; |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const Self& self, const Index num_coeff_to_copy, |
| const Index dst_index, Scalar* dst_data, |
| const Index src_index) { |
| const Impl& impl = self.impl(); |
| for (Index i = 0; i < num_coeff_to_copy; ++i) { |
| dst_data[dst_index + i] = impl.coeff(src_index + i); |
| } |
| } |
| }; |
| |
| template <typename Self> |
| struct ImagePatchCopyOp<Self, true> { |
| typedef typename Self::Index Index; |
| typedef typename Self::Scalar Scalar; |
| typedef typename Self::Impl Impl; |
| typedef typename packet_traits<Scalar>::type Packet; |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const Self& self, const Index num_coeff_to_copy, |
| const Index dst_index, Scalar* dst_data, |
| const Index src_index) { |
| const Impl& impl = self.impl(); |
| const Index packet_size = internal::unpacket_traits<Packet>::size; |
| const Index vectorized_size = (num_coeff_to_copy / packet_size) * packet_size; |
| for (Index i = 0; i < vectorized_size; i += packet_size) { |
| Packet p = impl.template packet<Unaligned>(src_index + i); |
| internal::pstoret<Scalar, Packet, Unaligned>(dst_data + dst_index + i, p); |
| } |
| for (Index i = vectorized_size; i < num_coeff_to_copy; ++i) { |
| dst_data[dst_index + i] = impl.coeff(src_index + i); |
| } |
| } |
| }; |
| |
| template <typename Self> |
| struct ImagePatchPaddingOp { |
| typedef typename Self::Index Index; |
| typedef typename Self::Scalar Scalar; |
| typedef typename packet_traits<Scalar>::type Packet; |
| static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void Run(const Index num_coeff_to_pad, const Scalar padding_value, |
| const Index dst_index, Scalar* dst_data) { |
| const Index packet_size = internal::unpacket_traits<Packet>::size; |
| const Packet padded_packet = internal::pset1<Packet>(padding_value); |
| const Index vectorized_size = (num_coeff_to_pad / packet_size) * packet_size; |
| for (Index i = 0; i < vectorized_size; i += packet_size) { |
| internal::pstoret<Scalar, Packet, Unaligned>(dst_data + dst_index + i, padded_packet); |
| } |
| for (Index i = vectorized_size; i < num_coeff_to_pad; ++i) { |
| dst_data[dst_index + i] = padding_value; |
| } |
| } |
| }; |
| |
| } // end namespace internal |
| |
| template <DenseIndex Rows, DenseIndex Cols, typename XprType> |
| class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprType>, ReadOnlyAccessors> { |
| public: |
| typedef typename Eigen::internal::traits<TensorImagePatchOp>::Scalar Scalar; |
| typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename Eigen::internal::nested<TensorImagePatchOp>::type Nested; |
| typedef typename Eigen::internal::traits<TensorImagePatchOp>::StorageKind StorageKind; |
| typedef typename Eigen::internal::traits<TensorImagePatchOp>::Index Index; |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, |
| DenseIndex patch_cols, DenseIndex row_strides, |
| DenseIndex col_strides, DenseIndex in_row_strides, |
| DenseIndex in_col_strides, DenseIndex row_inflate_strides, |
| DenseIndex col_inflate_strides, PaddingType padding_type, |
| Scalar padding_value) |
| : m_xpr(expr), |
| m_patch_rows(patch_rows), |
| m_patch_cols(patch_cols), |
| m_row_strides(row_strides), |
| m_col_strides(col_strides), |
| m_in_row_strides(in_row_strides), |
| m_in_col_strides(in_col_strides), |
| m_row_inflate_strides(row_inflate_strides), |
| m_col_inflate_strides(col_inflate_strides), |
| m_padding_explicit(false), |
| m_padding_top(0), |
| m_padding_bottom(0), |
| m_padding_left(0), |
| m_padding_right(0), |
| m_padding_type(padding_type), |
| m_padding_value(padding_value) {} |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, |
| DenseIndex patch_cols, DenseIndex row_strides, |
| DenseIndex col_strides, DenseIndex in_row_strides, |
| DenseIndex in_col_strides, DenseIndex row_inflate_strides, |
| DenseIndex col_inflate_strides, DenseIndex padding_top, |
| DenseIndex padding_bottom, DenseIndex padding_left, |
| DenseIndex padding_right, Scalar padding_value) |
| : m_xpr(expr), |
| m_patch_rows(patch_rows), |
| m_patch_cols(patch_cols), |
| m_row_strides(row_strides), |
| m_col_strides(col_strides), |
| m_in_row_strides(in_row_strides), |
| m_in_col_strides(in_col_strides), |
| m_row_inflate_strides(row_inflate_strides), |
| m_col_inflate_strides(col_inflate_strides), |
| m_padding_explicit(true), |
| m_padding_top(padding_top), |
| m_padding_bottom(padding_bottom), |
| m_padding_left(padding_left), |
| m_padding_right(padding_right), |
| m_padding_type(PADDING_VALID), |
| m_padding_value(padding_value) {} |
| |
| EIGEN_DEVICE_FUNC DenseIndex patch_rows() const { return m_patch_rows; } |
| EIGEN_DEVICE_FUNC DenseIndex patch_cols() const { return m_patch_cols; } |
| EIGEN_DEVICE_FUNC DenseIndex row_strides() const { return m_row_strides; } |
| EIGEN_DEVICE_FUNC DenseIndex col_strides() const { return m_col_strides; } |
| EIGEN_DEVICE_FUNC DenseIndex in_row_strides() const { return m_in_row_strides; } |
| EIGEN_DEVICE_FUNC DenseIndex in_col_strides() const { return m_in_col_strides; } |
| EIGEN_DEVICE_FUNC DenseIndex row_inflate_strides() const { return m_row_inflate_strides; } |
| EIGEN_DEVICE_FUNC DenseIndex col_inflate_strides() const { return m_col_inflate_strides; } |
| EIGEN_DEVICE_FUNC bool padding_explicit() const { return m_padding_explicit; } |
| EIGEN_DEVICE_FUNC DenseIndex padding_top() const { return m_padding_top; } |
| EIGEN_DEVICE_FUNC DenseIndex padding_bottom() const { return m_padding_bottom; } |
| EIGEN_DEVICE_FUNC DenseIndex padding_left() const { return m_padding_left; } |
| EIGEN_DEVICE_FUNC DenseIndex padding_right() const { return m_padding_right; } |
| EIGEN_DEVICE_FUNC PaddingType padding_type() const { return m_padding_type; } |
| EIGEN_DEVICE_FUNC Scalar padding_value() const { return m_padding_value; } |
| |
| EIGEN_DEVICE_FUNC const internal::remove_all_t<typename XprType::Nested>& expression() const { return m_xpr; } |
| |
| protected: |
| typename XprType::Nested m_xpr; |
| const DenseIndex m_patch_rows; |
| const DenseIndex m_patch_cols; |
| const DenseIndex m_row_strides; |
| const DenseIndex m_col_strides; |
| const DenseIndex m_in_row_strides; |
| const DenseIndex m_in_col_strides; |
| const DenseIndex m_row_inflate_strides; |
| const DenseIndex m_col_inflate_strides; |
| const bool m_padding_explicit; |
| const DenseIndex m_padding_top; |
| const DenseIndex m_padding_bottom; |
| const DenseIndex m_padding_left; |
| const DenseIndex m_padding_right; |
| const PaddingType m_padding_type; |
| const Scalar m_padding_value; |
| }; |
| |
| // Eval as rvalue |
| template <DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device> |
| struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device> { |
| typedef TensorImagePatchOp<Rows, Cols, ArgType> XprType; |
| typedef typename XprType::Index Index; |
| static constexpr int NumInputDims = |
| internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; |
| static constexpr int NumDims = NumInputDims + 1; |
| typedef DSizes<Index, NumDims> Dimensions; |
| typedef std::remove_const_t<typename XprType::Scalar> Scalar; |
| typedef TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device> Self; |
| typedef TensorEvaluator<ArgType, Device> Impl; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType; |
| static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size; |
| typedef StorageMemory<CoeffReturnType, Device> Storage; |
| typedef typename Storage::Type EvaluatorPointerType; |
| |
| static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout; |
| enum { |
| IsAligned = false, |
| PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, |
| BlockAccess = false, |
| PreferBlockAccess = true, |
| CoordAccess = false, |
| RawAccess = false |
| }; |
| |
| //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===// |
| typedef internal::TensorBlockNotImplemented TensorBlock; |
| //===--------------------------------------------------------------------===// |
| |
| EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) |
| : m_device(device), m_impl(op.expression(), device) { |
| EIGEN_STATIC_ASSERT((NumDims >= 4), YOU_MADE_A_PROGRAMMING_MISTAKE); |
| |
| m_paddingValue = op.padding_value(); |
| |
| const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); |
| |
| // Caches a few variables. |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| m_inputDepth = input_dims[0]; |
| m_inputRows = input_dims[1]; |
| m_inputCols = input_dims[2]; |
| } else { |
| m_inputDepth = input_dims[NumInputDims - 1]; |
| m_inputRows = input_dims[NumInputDims - 2]; |
| m_inputCols = input_dims[NumInputDims - 3]; |
| } |
| |
| m_row_strides = op.row_strides(); |
| m_col_strides = op.col_strides(); |
| |
| // Input strides and effective input/patch size |
| m_in_row_strides = op.in_row_strides(); |
| m_in_col_strides = op.in_col_strides(); |
| m_row_inflate_strides = op.row_inflate_strides(); |
| m_col_inflate_strides = op.col_inflate_strides(); |
| // The "effective" input rows and input cols are the input rows and cols |
| // after inflating them with zeros. |
| // For examples, a 2x3 matrix with row_inflate_strides and |
| // col_inflate_strides of 2 comes from: |
| // A B C |
| // D E F |
| // |
| // to a matrix is 3 x 5: |
| // |
| // A . B . C |
| // . . . . . |
| // D . E . F |
| |
| m_input_rows_eff = (m_inputRows - 1) * m_row_inflate_strides + 1; |
| m_input_cols_eff = (m_inputCols - 1) * m_col_inflate_strides + 1; |
| m_patch_rows_eff = op.patch_rows() + (op.patch_rows() - 1) * (m_in_row_strides - 1); |
| m_patch_cols_eff = op.patch_cols() + (op.patch_cols() - 1) * (m_in_col_strides - 1); |
| |
| if (op.padding_explicit()) { |
| 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)); |
| 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)); |
| m_rowPaddingTop = op.padding_top(); |
| m_colPaddingLeft = op.padding_left(); |
| } else { |
| // Computing padding from the type |
| switch (op.padding_type()) { |
| case PADDING_VALID: |
| m_outputRows = numext::ceil((m_input_rows_eff - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides)); |
| m_outputCols = numext::ceil((m_input_cols_eff - m_patch_cols_eff + 1.f) / static_cast<float>(m_col_strides)); |
| // Calculate the padding |
| m_rowPaddingTop = |
| numext::maxi<Index>(0, ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2); |
| m_colPaddingLeft = |
| numext::maxi<Index>(0, ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2); |
| break; |
| case PADDING_SAME: |
| m_outputRows = numext::ceil(m_input_rows_eff / static_cast<float>(m_row_strides)); |
| m_outputCols = numext::ceil(m_input_cols_eff / static_cast<float>(m_col_strides)); |
| // Calculate the padding |
| m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + m_patch_rows_eff - m_input_rows_eff) / 2; |
| m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + m_patch_cols_eff - m_input_cols_eff) / 2; |
| // The padding size calculation for PADDING_SAME has been updated to |
| // be consistent with how TensorFlow extracts its paddings. |
| m_rowPaddingTop = numext::maxi<Index>(0, m_rowPaddingTop); |
| m_colPaddingLeft = numext::maxi<Index>(0, m_colPaddingLeft); |
| break; |
| default: |
| eigen_assert(false && "unexpected padding"); |
| m_outputCols = 0; // silence the uninitialised warning; |
| m_outputRows = 0; //// silence the uninitialised warning; |
| } |
| } |
| eigen_assert(m_outputRows > 0); |
| eigen_assert(m_outputCols > 0); |
| |
| // Dimensions for result of extraction. |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| // ColMajor |
| // 0: depth |
| // 1: patch_rows |
| // 2: patch_cols |
| // 3: number of patches |
| // 4 and beyond: anything else (such as batch). |
| m_dimensions[0] = input_dims[0]; |
| m_dimensions[1] = op.patch_rows(); |
| m_dimensions[2] = op.patch_cols(); |
| m_dimensions[3] = m_outputRows * m_outputCols; |
| for (int i = 4; i < NumDims; ++i) { |
| m_dimensions[i] = input_dims[i - 1]; |
| } |
| } else { |
| // RowMajor |
| // NumDims-1: depth |
| // NumDims-2: patch_rows |
| // NumDims-3: patch_cols |
| // NumDims-4: number of patches |
| // NumDims-5 and beyond: anything else (such as batch). |
| m_dimensions[NumDims - 1] = input_dims[NumInputDims - 1]; |
| m_dimensions[NumDims - 2] = op.patch_rows(); |
| m_dimensions[NumDims - 3] = op.patch_cols(); |
| m_dimensions[NumDims - 4] = m_outputRows * m_outputCols; |
| for (int i = NumDims - 5; i >= 0; --i) { |
| m_dimensions[i] = input_dims[i]; |
| } |
| } |
| |
| // Strides for moving the patch in various dimensions. |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| m_colStride = m_dimensions[1]; |
| m_patchStride = m_colStride * m_dimensions[2] * m_dimensions[0]; |
| m_otherStride = m_patchStride * m_dimensions[3]; |
| } else { |
| m_colStride = m_dimensions[NumDims - 2]; |
| m_patchStride = m_colStride * m_dimensions[NumDims - 3] * m_dimensions[NumDims - 1]; |
| m_otherStride = m_patchStride * m_dimensions[NumDims - 4]; |
| } |
| |
| // Strides for navigating through the input tensor. |
| m_rowInputStride = m_inputDepth; |
| m_colInputStride = m_inputDepth * m_inputRows; |
| m_patchInputStride = m_inputDepth * m_inputRows * m_inputCols; |
| |
| // Fast representations of different variables. |
| m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride); |
| m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride); |
| m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride); |
| m_fastInflateRowStride = internal::TensorIntDivisor<Index>(m_row_inflate_strides); |
| m_fastInflateColStride = internal::TensorIntDivisor<Index>(m_col_inflate_strides); |
| m_fastInputColsEff = internal::TensorIntDivisor<Index>(m_input_cols_eff); |
| |
| // Number of patches in the width dimension. |
| m_fastOutputRows = internal::TensorIntDivisor<Index>(m_outputRows); |
| if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { |
| m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[0]); |
| } else { |
| m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims - 1]); |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } |
| |
| EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType /*data*/) { |
| m_impl.evalSubExprsIfNeeded(NULL); |
| return true; |
| } |
| |
| #ifdef EIGEN_USE_THREADS |
| template <typename EvalSubExprsCallback> |
| EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(EvaluatorPointerType, EvalSubExprsCallback done) { |
| m_impl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); }); |
| } |
| #endif // EIGEN_USE_THREADS |
| |
| EIGEN_STRONG_INLINE void cleanup() { m_impl.cleanup(); } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { |
| // Patch index corresponding to the passed in index. |
| const Index patchIndex = index / m_fastPatchStride; |
| // Find the offset of the element wrt the location of the first element. |
| const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastOutputDepth; |
| |
| // Other ways to index this element. |
| const Index otherIndex = (NumDims == 4) ? 0 : index / m_fastOtherStride; |
| const Index patch2DIndex = (NumDims == 4) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride; |
| |
| // Calculate col index in the input original tensor. |
| const Index colIndex = patch2DIndex / m_fastOutputRows; |
| const Index colOffset = patchOffset / m_fastColStride; |
| const Index inputCol = colIndex * m_col_strides + colOffset * m_in_col_strides - m_colPaddingLeft; |
| const Index origInputCol = |
| (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInflateColStride) : 0); |
| if (inputCol < 0 || inputCol >= m_input_cols_eff || |
| ((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides))) { |
| return Scalar(m_paddingValue); |
| } |
| |
| // Calculate row index in the original input tensor. |
| const Index rowIndex = patch2DIndex - colIndex * m_outputRows; |
| const Index rowOffset = patchOffset - colOffset * m_colStride; |
| const Index inputRow = rowIndex * m_row_strides + rowOffset * m_in_row_strides - m_rowPaddingTop; |
| const Index origInputRow = |
| (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInflateRowStride) : 0); |
| if (inputRow < 0 || inputRow >= m_input_rows_eff || |
| ((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) { |
| return Scalar(m_paddingValue); |
| } |
| |
| const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1; |
| const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index]; |
| |
| const Index inputIndex = |
| depth + origInputRow * m_rowInputStride + origInputCol * m_colInputStride + otherIndex * m_patchInputStride; |
| return m_impl.coeff(inputIndex); |
| } |
| |
| template <int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const { |
| eigen_assert(index + PacketSize - 1 < dimensions().TotalSize()); |
| |
| if (m_in_row_strides != 1 || m_in_col_strides != 1 || m_row_inflate_strides != 1 || m_col_inflate_strides != 1) { |
| return packetWithPossibleZero(index); |
| } |
| |
| const Index indices[2] = {index, index + PacketSize - 1}; |
| const Index patchIndex = indices[0] / m_fastPatchStride; |
| if (patchIndex != indices[1] / m_fastPatchStride) { |
| return packetWithPossibleZero(index); |
| } |
| const Index otherIndex = (NumDims == 4) ? 0 : indices[0] / m_fastOtherStride; |
| eigen_assert(otherIndex == indices[1] / m_fastOtherStride); |
| |
| // Find the offset of the element wrt the location of the first element. |
| const Index patchOffsets[2] = {(indices[0] - patchIndex * m_patchStride) / m_fastOutputDepth, |
| (indices[1] - patchIndex * m_patchStride) / m_fastOutputDepth}; |
| |
| const Index patch2DIndex = |
| (NumDims == 4) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride; |
| eigen_assert(patch2DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride); |
| |
| const Index colIndex = patch2DIndex / m_fastOutputRows; |
| const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, patchOffsets[1] / m_fastColStride}; |
| |
| // Calculate col indices in the original input tensor. |
| const Index inputCols[2] = {colIndex * m_col_strides + colOffsets[0] - m_colPaddingLeft, |
| colIndex * m_col_strides + colOffsets[1] - m_colPaddingLeft}; |
| if (inputCols[1] < 0 || inputCols[0] >= m_inputCols) { |
| return internal::pset1<PacketReturnType>(Scalar(m_paddingValue)); |
| } |
| |
| if (inputCols[0] == inputCols[1]) { |
| const Index rowIndex = patch2DIndex - colIndex * m_outputRows; |
| const Index rowOffsets[2] = {patchOffsets[0] - colOffsets[0] * m_colStride, |
| patchOffsets[1] - colOffsets[1] * m_colStride}; |
| eigen_assert(rowOffsets[0] <= rowOffsets[1]); |
| // Calculate col indices in the original input tensor. |
| const Index inputRows[2] = {rowIndex * m_row_strides + rowOffsets[0] - m_rowPaddingTop, |
| rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop}; |
| |
| if (inputRows[1] < 0 || inputRows[0] >= m_inputRows) { |
| return internal::pset1<PacketReturnType>(Scalar(m_paddingValue)); |
| } |
| |
| if (inputRows[0] >= 0 && inputRows[1] < m_inputRows) { |
| // no padding |
| const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1; |
| const Index depth = index - (index / m_fastOutputDepth) * m_dimensions[depth_index]; |
| const Index inputIndex = |
| depth + inputRows[0] * m_rowInputStride + inputCols[0] * m_colInputStride + otherIndex * m_patchInputStride; |
| return m_impl.template packet<Unaligned>(inputIndex); |
| } |
| } |
| |
| return packetWithPossibleZero(index); |
| } |
| |
| EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowPaddingTop() const { return m_rowPaddingTop; } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colPaddingLeft() const { return m_colPaddingLeft; } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputRows() const { return m_outputRows; } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index outputCols() const { return m_outputCols; } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userRowStride() const { return m_row_strides; } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userColStride() const { return m_col_strides; } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInRowStride() const { return m_in_row_strides; } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index userInColStride() const { return m_in_col_strides; } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rowInflateStride() const { return m_row_inflate_strides; } |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index colInflateStride() const { return m_col_inflate_strides; } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { |
| // We conservatively estimate the cost for the code path where the computed |
| // index is inside the original image and |
| // TensorEvaluator<ArgType, Device>::CoordAccess is false. |
| const double compute_cost = |
| 3 * TensorOpCost::DivCost<Index>() + 6 * TensorOpCost::MulCost<Index>() + 8 * TensorOpCost::MulCost<Index>(); |
| return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, compute_cost, vectorized, PacketSize); |
| } |
| |
| protected: |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const { |
| EIGEN_ALIGN_MAX std::remove_const_t<CoeffReturnType> values[PacketSize]; |
| EIGEN_UNROLL_LOOP |
| for (int i = 0; i < PacketSize; ++i) { |
| values[i] = coeff(index + i); |
| } |
| PacketReturnType rslt = internal::pload<PacketReturnType>(values); |
| return rslt; |
| } |
| |
| Dimensions m_dimensions; |
| |
| Index m_otherStride; |
| Index m_patchStride; |
| Index m_colStride; |
| Index m_row_strides; |
| Index m_col_strides; |
| |
| Index m_in_row_strides; |
| Index m_in_col_strides; |
| Index m_row_inflate_strides; |
| Index m_col_inflate_strides; |
| |
| Index m_input_rows_eff; |
| Index m_input_cols_eff; |
| Index m_patch_rows_eff; |
| Index m_patch_cols_eff; |
| |
| internal::TensorIntDivisor<Index> m_fastOtherStride; |
| internal::TensorIntDivisor<Index> m_fastPatchStride; |
| internal::TensorIntDivisor<Index> m_fastColStride; |
| internal::TensorIntDivisor<Index> m_fastInflateRowStride; |
| internal::TensorIntDivisor<Index> m_fastInflateColStride; |
| internal::TensorIntDivisor<Index> m_fastInputColsEff; |
| |
| Index m_rowInputStride; |
| Index m_colInputStride; |
| Index m_patchInputStride; |
| |
| Index m_inputDepth; |
| Index m_inputRows; |
| Index m_inputCols; |
| |
| Index m_outputRows; |
| Index m_outputCols; |
| |
| Index m_rowPaddingTop; |
| Index m_colPaddingLeft; |
| |
| internal::TensorIntDivisor<Index> m_fastOutputRows; |
| internal::TensorIntDivisor<Index> m_fastOutputDepth; |
| |
| Scalar m_paddingValue; |
| |
| const Device EIGEN_DEVICE_REF m_device; |
| TensorEvaluator<ArgType, Device> m_impl; |
| }; |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H |
