diff options
Diffstat (limited to 'unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h')
| -rw-r--r-- | unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h | 382 |
1 files changed, 382 insertions, 0 deletions
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h b/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h new file mode 100644 index 000000000..bf0e7edfb --- /dev/null +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h @@ -0,0 +1,382 @@ +// 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 XprType::Scalar 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; +}; + +} // 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, + PaddingType padding_type) + : m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols), + m_row_strides(row_strides), m_col_strides(col_strides), + m_padding_type(padding_type) {} + + 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 + PaddingType padding_type() const { return m_padding_type; } + + 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 PaddingType m_padding_type; +}; + + +// 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 NumDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value + 1; + typedef DSizes<Index, NumDims> Dimensions; + typedef typename XprType::Scalar Scalar; + + enum { + IsAligned = false, + PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess, + Layout = TensorEvaluator<ArgType, Device>::Layout, + CoordAccess = NumDims == 5, + }; + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) + : m_impl(op.expression(), device) + { + // Only column major tensors are supported for now. + EIGEN_STATIC_ASSERT((static_cast<int>(Layout) == static_cast<int>(ColMajor)), YOU_MADE_A_PROGRAMMING_MISTAKE); + + EIGEN_STATIC_ASSERT(NumDims >= 4, YOU_MADE_A_PROGRAMMING_MISTAKE); + + const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions(); + + // Caches a few variables. + m_inputRows = input_dims[1]; + m_inputCols = input_dims[2]; + + m_row_strides = op.row_strides(); + m_col_strides = op.col_strides(); + + // We only support same strides for both dimensions and square patches. + eigen_assert(m_row_strides == m_col_strides); + + switch (op.padding_type()) { + case PADDING_VALID: + m_outputRows = ceil((m_inputRows - op.patch_rows() + 1.f) / static_cast<float>(m_row_strides)); + m_outputCols = ceil((m_inputCols - op.patch_cols() + 1.f) / static_cast<float>(m_col_strides)); + // Calculate the padding + m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + op.patch_rows() - m_inputRows) / 2; + m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + op.patch_cols() - m_inputCols) / 2; + break; + case PADDING_SAME: + m_outputRows = ceil(m_inputRows / static_cast<float>(m_row_strides)); + m_outputCols = ceil(m_inputCols / static_cast<float>(m_col_strides)); + // Calculate the padding + m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + op.patch_rows() - m_inputRows) / 2; + m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + op.patch_cols() - m_inputCols) / 2; + break; + default: + eigen_assert(false && "unexpected padding"); + } + + // Dimensions for result of extraction. + // 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]; + } + + // Strides for moving the patch in various dimensions. + m_colStride = m_dimensions[1]; + m_patchStride = m_colStride * m_dimensions[2] * m_dimensions[0]; + m_otherStride = m_patchStride * m_dimensions[3]; + + // Strides for navigating through the input tensor. + m_rowInputStride = input_dims[0]; + m_colInputStride = input_dims[0] * input_dims[1]; + m_patchInputStride = input_dims[0] * input_dims[1] * input_dims[2]; + + // 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); + // Number of patches in the width dimension. + m_fastOutputRows = internal::TensorIntDivisor<Index>(m_outputRows); + m_fastDimZero = internal::TensorIntDivisor<Index>(m_dimensions[0]); + } + + 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_fastDimZero; + + // 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; + + const Index colIndex = patch2DIndex / m_fastOutputRows; + const Index colOffset = patchOffset / m_fastColStride; + + // Calculate col index in the input original tensor. + const Index inputCol = colIndex * m_col_strides + colOffset - m_colPaddingLeft; + if (inputCol < 0 || inputCol >= m_inputCols) { + return Scalar(0); + } + const Index rowIndex = patch2DIndex - colIndex * m_outputRows; + const Index rowOffset = patchOffset - colOffset * m_colStride; + + // Calculate row index in the original input tensor. + const Index inputRow = rowIndex * m_row_strides + rowOffset - m_rowPaddingTop; + if (inputRow < 0 || inputRow >= m_inputRows) { + return Scalar(0); + } + + const Index depth = index - (index / m_fastDimZero) * m_dimensions[0]; + + const Index inputIndex = depth + inputRow * m_rowInputStride + inputCol * 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()); + + 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_fastDimZero, + (indices[1] - patchIndex * m_patchStride) / m_fastDimZero}; + + 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) { + // all zeros + return internal::pset1<PacketReturnType>(Scalar(0)); + } + + 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) { + // all zeros + return internal::pset1<PacketReturnType>(Scalar(0)); + } + + if (inputRows[0] >= 0 && inputRows[1] < m_inputRows) { + // no padding + const Index depth = index - (index / m_fastDimZero) * m_dimensions[0]; + 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 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; } + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<Index, NumDims>& coords) const + { + // Location of the first element of the patch. + // 0: d, 1: patch_rows, 2: patch_cols, 3: number of patches, 4: number of batches + const Index patchIndex = coords[3]; + + array<Index, NumDims-1> inputCoords; + inputCoords[0] = coords[0]; // depth + inputCoords[1] = patchIndex / m_inputCols + coords[1] - m_rowPaddingTop; + inputCoords[2] = patchIndex - patchIndex / m_inputCols * m_inputCols + coords[2] - m_colPaddingLeft; + inputCoords[3] = coords[4]; // batch + // If the computed coordinates are outside the original image perimeter, return 0. + if (inputCoords[1] < 0 || inputCoords[1] >= m_inputRows || + inputCoords[2] < 0 || inputCoords[2] >= m_inputCols) { + return Scalar(0); + } + if (TensorEvaluator<ArgType, Device>::CoordAccess) { + return m_impl.coeff(inputCoords); + } else { + Index inputIndex = + inputCoords[3] * m_patchInputStride + + inputCoords[2] * m_colInputStride + + inputCoords[1] * m_rowInputStride + + inputCoords[0]; + 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; + internal::TensorIntDivisor<Index> m_fastOtherStride; + internal::TensorIntDivisor<Index> m_fastPatchStride; + internal::TensorIntDivisor<Index> m_fastColStride; + + Index m_rowInputStride; + Index m_colInputStride; + Index m_patchInputStride; + + 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_fastDimZero; + + TensorEvaluator<ArgType, Device> m_impl; +}; + + +} // end namespace Eigen + +#endif // EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H |