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Diffstat (limited to 'third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h')
| -rw-r--r-- | third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h | 757 |
1 files changed, 757 insertions, 0 deletions
diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h new file mode 100644 index 0000000000..a1d33d964e --- /dev/null +++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h @@ -0,0 +1,757 @@ +// 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 + +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 typename internal::remove_const<typename XprType::Scalar>::type Scalar; + typedef traits<XprType> XprTraits; + typedef typename packet_traits<Scalar>::type Packet; + typedef typename XprTraits::StorageKind StorageKind; + typedef typename XprTraits::Index Index; + typedef typename XprType::Nested Nested; + typedef typename remove_reference<Nested>::type _Nested; + static const int NumDimensions = XprTraits::NumDimensions + 1; + static const int Layout = XprTraits::Layout; +}; + +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::internal::traits<TensorImagePatchOp>::Packet Packet; + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename XprType::PacketReturnType PacketReturnType; + 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 typename internal::remove_all<typename XprType::Nested>::type& + 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 const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value; + static const int NumDims = NumInputDims + 1; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar; + typedef TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, + Device> Self; + typedef TensorEvaluator<ArgType, Device> Impl; + + enum { + IsAligned = false, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + BlockAccess = true, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = NumDims == 5, + }; + + typedef typename internal::TensorBlock<Index, Scalar, NumDims, Layout> + OutputTensorBlock; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const 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 = 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 = 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 = ceil((m_input_rows_eff - m_patch_rows_eff + 1.f) / static_cast<float>(m_row_strides)); + m_outputCols = ceil((m_input_cols_eff - m_patch_cols_eff + 1.f) / 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; + break; + case PADDING_SAME: + m_outputRows = ceil(m_input_rows_eff / static_cast<float>(m_row_strides)); + m_outputCols = 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; + break; + default: + eigen_assert(false && "unexpected padding"); + } + } + 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_fastInputRowStride = internal::TensorIntDivisor<Index>(m_row_inflate_strides); + m_fastInputColStride = 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]); + } + + m_block_total_size_max = numext::maxi(static_cast<std::size_t>(1), + device.lastLevelCacheSize() / + sizeof(Scalar)); + } + + typedef typename XprType::CoeffReturnType CoeffReturnType; + typedef typename XprType::PacketReturnType PacketReturnType; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) { + m_impl.evalSubExprsIfNeeded(NULL); + return true; + } + + EIGEN_DEVICE_FUNC 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_fastInputColStride) : 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_fastInputRowStride) : 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 + { + const Index packetSize = internal::unpacket_traits<PacketReturnType>::size; + EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE) + 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 EIGEN_STRONG_INLINE void getResourceRequirements( + std::vector<internal::TensorOpResourceRequirements>* resources) const { + resources->push_back(internal::TensorOpResourceRequirements( + internal::kSkewedInnerDims, m_block_total_size_max)); + } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void block( + OutputTensorBlock* output_block) const { + typedef typename internal::ImagePatchCopyOp<Self, PacketAccess> + ImagePatchCopyOp; + typedef typename internal::ImagePatchPaddingOp<Self> ImagePatchPaddingOp; + + // Calculate loop limits and various input/output dim sizes. + const DSizes<Index, NumDims>& block_sizes = output_block->block_sizes(); + const bool col_major = + static_cast<int>(Layout) == static_cast<int>(ColMajor); + const Index depth_dim_size = block_sizes[col_major ? 0 : NumDims - 1]; + const Index output_depth_dim_size = m_dimensions[ + col_major ? 0 : NumDims - 1]; + const Index row_dim_size = block_sizes[col_major ? 1 : NumDims - 2]; + const Index output_row_dim_size = m_dimensions[col_major ? 1 : NumDims - 2]; + const Index col_dim_size = block_sizes[col_major ? 2 : NumDims - 3]; + const Index block_col_stride = row_dim_size * depth_dim_size; + const Index patch_index_dim_size = block_sizes[col_major ? 3 : NumDims - 4]; + const Index outer_dim_size = block_sizes.TotalSize() / + (depth_dim_size * row_dim_size * col_dim_size * patch_index_dim_size); + + const Index patch_size = row_dim_size * col_dim_size * depth_dim_size; + const Index batch_size = patch_size * patch_index_dim_size; + + Index output_index = output_block->first_coeff_index(); + + // Loop through outer dimensions. + for (Index outer_dim_index = 0; + outer_dim_index < outer_dim_size; + ++outer_dim_index) { + const Index outer_output_base_index = outer_dim_index * batch_size; + // Find the offset of the element wrt the location of the first element. + const Index patchIndexStart = output_index / m_fastPatchStride; + const Index patchOffset = + (output_index - patchIndexStart * m_patchStride) / m_fastOutputDepth; + const Index colOffsetStart = patchOffset / m_fastColStride; + // Other ways to index this element. + const Index otherIndex = (NumDims == 4) ? + 0 : output_index / m_fastOtherStride; + const Index patch2DIndexStart = (NumDims == 4) ? + 0 : (output_index - otherIndex * m_otherStride) / m_fastPatchStride; + // Calculate starting depth index. + const Index depth = output_index - (output_index / m_fastOutputDepth) * + output_depth_dim_size; + const Index patch_input_base_index = depth + otherIndex * + m_patchInputStride; + + // Loop through patches. + for (Index patch_index_dim_index = 0; + patch_index_dim_index < patch_index_dim_size; + ++patch_index_dim_index) { + const Index patch_output_base_index = outer_output_base_index + + patch_index_dim_index * patch_size; + // Patch index corresponding to the passed in index. + const Index patchIndex = patchIndexStart + patch_index_dim_index; + const Index patch2DIndex = (NumDims == 4) ? + patchIndex : patch2DIndexStart + patch_index_dim_index; + const Index colIndex = patch2DIndex / m_fastOutputRows; + const Index input_col_base = colIndex * m_col_strides; + const Index row_offset_base = (patch2DIndex - colIndex * m_outputRows) * + m_row_strides - m_rowPaddingTop; + + // Loop through columns. + for (Index col_dim_index = 0; + col_dim_index < col_dim_size; + ++col_dim_index) { + const Index col_output_base_index = patch_output_base_index + + col_dim_index * block_col_stride; + + // Calculate col index in the input original tensor. + Index colOffset = colOffsetStart + col_dim_index; + Index inputCol = input_col_base + colOffset * m_in_col_strides - + m_colPaddingLeft; + Index origInputCol = (m_col_inflate_strides == 1) ? + inputCol : ((inputCol >= 0) ? + (inputCol / m_fastInputColStride) : 0); + + bool pad_column = false; + if (inputCol < 0 || inputCol >= m_input_cols_eff || + ((m_col_inflate_strides != 1) && + (inputCol != origInputCol * m_col_inflate_strides))) { + pad_column = true; + } + + const Index col_input_base_index = patch_input_base_index + + origInputCol * m_colInputStride; + const Index input_row_base = row_offset_base + + ((patchOffset + col_dim_index * output_row_dim_size) - + colOffset * m_colStride) * m_in_row_strides; + // Loop through rows. + for (Index row_dim_index = 0; + row_dim_index < row_dim_size; + ++row_dim_index) { + const Index output_base_index = col_output_base_index + + row_dim_index * depth_dim_size; + bool pad_row = false; + Index inputIndex; + if (!pad_column) { + Index inputRow = input_row_base + row_dim_index * + m_in_row_strides; + Index origInputRow = (m_row_inflate_strides == 1) ? + inputRow : ((inputRow >= 0) ? + (inputRow / m_fastInputRowStride) : 0); + if (inputRow < 0 || inputRow >= m_input_rows_eff || + ((m_row_inflate_strides != 1) && + (inputRow != origInputRow * m_row_inflate_strides))) { + pad_row = true; + } else { + inputIndex = col_input_base_index + origInputRow * + m_rowInputStride; + } + } + // Copy (or pad) along depth dimension. + if (pad_column || pad_row) { + ImagePatchPaddingOp::Run(depth_dim_size, Scalar(m_paddingValue), + output_base_index, output_block->data()); + } else { + ImagePatchCopyOp::Run(*this, depth_dim_size, + output_base_index, output_block->data(), + inputIndex); + } + } + } + } + output_index += m_otherStride; + } + } + + EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + + const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; } + + Index rowPaddingTop() const { return m_rowPaddingTop; } + Index colPaddingLeft() const { return m_colPaddingLeft; } + Index outputRows() const { return m_outputRows; } + Index outputCols() const { return m_outputCols; } + Index userRowStride() const { return m_row_strides; } + Index userColStride() const { return m_col_strides; } + Index userInRowStride() const { return m_in_row_strides; } + Index userInColStride() const { return m_in_col_strides; } + Index rowInflateStride() const { return m_row_inflate_strides; } + Index colInflateStride() const { return m_col_inflate_strides; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<Index, NumDims>& coords) const + { + // Location of the first element of the patch. + // ColMajor + // 0: d, 1: patch_rows, 2: patch_cols, 3: number of patches, 4: number of batches + // RowMajor + // 0: number of batches, 1: number of patches, 2: patch_cols , 3: patch_rows, 4: d + const Index patch2DIndex = coords[static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 3 : 1]; + + array<Index, NumDims-1> inputCoords; + Index input_col_idx = patch2DIndex / m_fastInputColsEff; + Index inputCol = input_col_idx + coords[1] * m_in_row_strides - m_rowPaddingTop; + Index inputRow = patch2DIndex - input_col_idx * m_input_cols_eff + coords[2] * m_in_col_strides - m_colPaddingLeft; + const Index origInputCol = (m_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInputColStride) : 0); + const Index origInputRow = (m_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInputRowStride) : 0); + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + inputCoords[0] = coords[0]; // depth + inputCoords[1] = origInputCol; + inputCoords[2] = origInputRow; + inputCoords[3] = coords[4]; // batch + } else { + inputCoords[3] = coords[4]; // depth + inputCoords[2] = origInputCol; + inputCoords[1] = origInputRow; + inputCoords[0] = coords[0]; // batch + } + // If the computed coordinates are outside the original image perimeter, return 0. + if (inputCol < 0 || inputCol >= m_input_cols_eff || inputRow < 0 || inputRow >= m_input_rows_eff || + ((m_col_inflate_strides != 1) && (inputCol != origInputCol * m_col_inflate_strides)) || + ((m_row_inflate_strides != 1) && (inputRow != origInputRow * m_row_inflate_strides))) { + return Scalar(m_paddingValue); + } + if (TensorEvaluator<ArgType, Device>::CoordAccess) { + return m_impl.coeff(inputCoords); + } else { + Index inputIndex; + if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { + inputIndex = + inputCoords[3] * m_patchInputStride + + inputCoords[2] * m_colInputStride + + inputCoords[1] * m_rowInputStride + + inputCoords[0]; + } else { + inputIndex = + inputCoords[1] * m_patchInputStride + + inputCoords[2] * m_colInputStride + + inputCoords[3] * m_rowInputStride + + inputCoords[4]; + } + return m_impl.coeff(inputIndex); + } + } + + protected: + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const + { + const int packetSize = internal::unpacket_traits<PacketReturnType>::size; + EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type values[packetSize]; + 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_fastInputRowStride; + internal::TensorIntDivisor<Index> m_fastInputColStride; + 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; + std::size_t m_block_total_size_max; + + TensorEvaluator<ArgType, Device> m_impl; +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H |