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449 lines
18 KiB
C++
449 lines
18 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
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#define EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H
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namespace Eigen {
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/** \class TensorImagePatch
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Patch extraction specialized for image processing.
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* This assumes that the input has a least 3 dimensions ordered as follow:
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* 1st dimension: channels (of size d)
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* 2nd dimension: rows (of size r)
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* 3rd dimension: columns (of size c)
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* There can be additional dimensions such as time (for video) or batch (for
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* bulk processing after the first 3.
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* Calling the image patch code with patch_rows and patch_cols is equivalent
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* to calling the regular patch extraction code with parameters d, patch_rows,
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* patch_cols, and 1 for all the additional dimensions.
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*/
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namespace internal {
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template<DenseIndex Rows, DenseIndex Cols, typename XprType>
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struct traits<TensorImagePatchOp<Rows, Cols, XprType> > : public traits<XprType>
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{
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typedef typename XprType::Scalar Scalar;
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typedef traits<XprType> XprTraits;
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typedef typename packet_traits<Scalar>::type Packet;
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typedef typename XprTraits::StorageKind StorageKind;
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typedef typename XprTraits::Index Index;
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typedef typename XprType::Nested Nested;
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typedef typename remove_reference<Nested>::type _Nested;
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static const int NumDimensions = XprTraits::NumDimensions + 1;
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static const int Layout = XprTraits::Layout;
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};
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template<DenseIndex Rows, DenseIndex Cols, typename XprType>
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struct eval<TensorImagePatchOp<Rows, Cols, XprType>, Eigen::Dense>
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{
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typedef const TensorImagePatchOp<Rows, Cols, XprType>& type;
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};
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template<DenseIndex Rows, DenseIndex Cols, typename XprType>
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struct nested<TensorImagePatchOp<Rows, Cols, XprType>, 1, typename eval<TensorImagePatchOp<Rows, Cols, XprType> >::type>
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{
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typedef TensorImagePatchOp<Rows, Cols, XprType> type;
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};
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} // end namespace internal
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template<DenseIndex Rows, DenseIndex Cols, typename XprType>
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class TensorImagePatchOp : public TensorBase<TensorImagePatchOp<Rows, Cols, XprType>, ReadOnlyAccessors>
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{
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public:
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typedef typename Eigen::internal::traits<TensorImagePatchOp>::Scalar Scalar;
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typedef typename Eigen::internal::traits<TensorImagePatchOp>::Packet Packet;
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typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename XprType::PacketReturnType PacketReturnType;
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typedef typename Eigen::internal::nested<TensorImagePatchOp>::type Nested;
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typedef typename Eigen::internal::traits<TensorImagePatchOp>::StorageKind StorageKind;
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typedef typename Eigen::internal::traits<TensorImagePatchOp>::Index Index;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorImagePatchOp(const XprType& expr, DenseIndex patch_rows, DenseIndex patch_cols,
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DenseIndex row_strides, DenseIndex col_strides,
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PaddingType padding_type)
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: m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
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m_row_strides(row_strides), m_col_strides(col_strides),
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m_padding_type(padding_type) {}
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EIGEN_DEVICE_FUNC
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DenseIndex patch_rows() const { return m_patch_rows; }
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EIGEN_DEVICE_FUNC
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DenseIndex patch_cols() const { return m_patch_cols; }
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EIGEN_DEVICE_FUNC
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DenseIndex row_strides() const { return m_row_strides; }
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EIGEN_DEVICE_FUNC
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DenseIndex col_strides() const { return m_col_strides; }
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EIGEN_DEVICE_FUNC
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PaddingType padding_type() const { return m_padding_type; }
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EIGEN_DEVICE_FUNC
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const typename internal::remove_all<typename XprType::Nested>::type&
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expression() const { return m_xpr; }
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protected:
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typename XprType::Nested m_xpr;
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const DenseIndex m_patch_rows;
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const DenseIndex m_patch_cols;
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const DenseIndex m_row_strides;
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const DenseIndex m_col_strides;
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const PaddingType m_padding_type;
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};
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// Eval as rvalue
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template<DenseIndex Rows, DenseIndex Cols, typename ArgType, typename Device>
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struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
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{
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typedef TensorImagePatchOp<Rows, Cols, ArgType> XprType;
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typedef typename XprType::Index Index;
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static const int NumInputDims = internal::array_size<typename TensorEvaluator<ArgType, Device>::Dimensions>::value;
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static const int NumDims = NumInputDims + 1;
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typedef DSizes<Index, NumDims> Dimensions;
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typedef typename XprType::Scalar Scalar;
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enum {
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IsAligned = false,
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PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
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Layout = TensorEvaluator<ArgType, Device>::Layout,
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CoordAccess = NumDims == 5,
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};
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
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: m_impl(op.expression(), device)
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{
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EIGEN_STATIC_ASSERT(NumDims >= 4, YOU_MADE_A_PROGRAMMING_MISTAKE);
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const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
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// Caches a few variables.
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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m_inputRows = input_dims[1];
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m_inputCols = input_dims[2];
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} else {
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m_inputRows = input_dims[NumInputDims-2];
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m_inputCols = input_dims[NumInputDims-3];
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}
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m_row_strides = op.row_strides();
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m_col_strides = op.col_strides();
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// We only support same strides for both dimensions and square patches.
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eigen_assert(m_row_strides == m_col_strides);
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switch (op.padding_type()) {
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case PADDING_VALID:
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<<<<<<< local
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m_outputRows = std::ceil((m_inputRows - op.patch_rows() + 1.f) / static_cast<float>(m_row_strides));
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m_outputCols = std::ceil((m_inputCols - op.patch_cols() + 1.f) / static_cast<float>(m_col_strides));
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=======
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m_outputRows = numext::ceil((m_inputRows - op.patch_rows() + 1.f) / static_cast<float>(m_row_strides));
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m_outputCols = numext::ceil((m_inputCols - op.patch_cols() + 1.f) / static_cast<float>(m_col_strides));
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>>>>>>> other
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// Calculate the padding
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m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + op.patch_rows() - m_inputRows) / 2;
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m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + op.patch_cols() - m_inputCols) / 2;
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break;
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case PADDING_SAME:
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<<<<<<< local
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m_outputRows = std::ceil(m_inputRows / static_cast<float>(m_row_strides));
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m_outputCols = std::ceil(m_inputCols / static_cast<float>(m_col_strides));
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=======
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m_outputRows = numext::ceil(m_inputRows / static_cast<float>(m_row_strides));
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m_outputCols = numext::ceil(m_inputCols / static_cast<float>(m_col_strides));
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>>>>>>> other
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// Calculate the padding
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m_rowPaddingTop = ((m_outputRows - 1) * m_row_strides + op.patch_rows() - m_inputRows) / 2;
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m_colPaddingLeft = ((m_outputCols - 1) * m_col_strides + op.patch_cols() - m_inputCols) / 2;
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break;
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default:
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eigen_assert(false && "unexpected padding");
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}
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// Dimensions for result of extraction.
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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// ColMajor
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// 0: depth
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// 1: patch_rows
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// 2: patch_cols
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// 3: number of patches
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// 4 and beyond: anything else (such as batch).
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m_dimensions[0] = input_dims[0];
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m_dimensions[1] = op.patch_rows();
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m_dimensions[2] = op.patch_cols();
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m_dimensions[3] = m_outputRows * m_outputCols;
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for (int i = 4; i < NumDims; ++i) {
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m_dimensions[i] = input_dims[i-1];
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}
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} else {
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// RowMajor
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// NumDims-1: depth
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// NumDims-2: patch_rows
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// NumDims-3: patch_cols
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// NumDims-4: number of patches
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// NumDims-5 and beyond: anything else (such as batch).
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m_dimensions[NumDims-1] = input_dims[NumInputDims-1];
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m_dimensions[NumDims-2] = op.patch_rows();
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m_dimensions[NumDims-3] = op.patch_cols();
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m_dimensions[NumDims-4] = m_outputRows * m_outputCols;
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for (int i = NumDims-5; i >= 0; --i) {
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m_dimensions[i] = input_dims[i];
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}
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}
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// Strides for moving the patch in various dimensions.
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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m_colStride = m_dimensions[1];
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m_patchStride = m_colStride * m_dimensions[2] * m_dimensions[0];
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m_otherStride = m_patchStride * m_dimensions[3];
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} else {
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m_colStride = m_dimensions[NumDims-2];
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m_patchStride = m_colStride * m_dimensions[NumDims-3] * m_dimensions[NumDims-1];
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m_otherStride = m_patchStride * m_dimensions[NumDims-4];
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}
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// Strides for navigating through the input tensor.
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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m_rowInputStride = input_dims[0];
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m_colInputStride = input_dims[0] * input_dims[1];
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m_patchInputStride = input_dims[0] * input_dims[1] * input_dims[2];
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} else {
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m_rowInputStride = input_dims[NumInputDims-1];
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m_colInputStride = input_dims[NumInputDims-1] * input_dims[NumInputDims-2];
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m_patchInputStride = input_dims[NumInputDims-1] * input_dims[NumInputDims-2] * input_dims[NumInputDims-3];
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}
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// Fast representations of different variables.
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m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
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m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
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m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
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// Number of patches in the width dimension.
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m_fastOutputRows = internal::TensorIntDivisor<Index>(m_outputRows);
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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m_fastDimZero = internal::TensorIntDivisor<Index>(m_dimensions[0]);
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} else {
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m_fastDimZero = internal::TensorIntDivisor<Index>(m_dimensions[NumDims-1]);
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}
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}
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename XprType::PacketReturnType PacketReturnType;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
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m_impl.evalSubExprsIfNeeded(NULL);
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return true;
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
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m_impl.cleanup();
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
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{
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// Patch index corresponding to the passed in index.
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const Index patchIndex = index / m_fastPatchStride;
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// Find the offset of the element wrt the location of the first element.
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const Index patchOffset = (index - patchIndex * m_patchStride) / m_fastDimZero;
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// Other ways to index this element.
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const Index otherIndex = (NumDims == 4) ? 0 : index / m_fastOtherStride;
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const Index patch2DIndex = (NumDims == 4) ? patchIndex : (index - otherIndex * m_otherStride) / m_fastPatchStride;
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const Index colIndex = patch2DIndex / m_fastOutputRows;
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const Index colOffset = patchOffset / m_fastColStride;
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// Calculate col index in the input original tensor.
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const Index inputCol = colIndex * m_col_strides + colOffset - m_colPaddingLeft;
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if (inputCol < 0 || inputCol >= m_inputCols) {
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return Scalar(0);
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}
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const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
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const Index rowOffset = patchOffset - colOffset * m_colStride;
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// Calculate row index in the original input tensor.
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const Index inputRow = rowIndex * m_row_strides + rowOffset - m_rowPaddingTop;
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if (inputRow < 0 || inputRow >= m_inputRows) {
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return Scalar(0);
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}
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const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
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const Index depth = index - (index / m_fastDimZero) * m_dimensions[depth_index];
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const Index inputIndex = depth + inputRow * m_rowInputStride + inputCol * m_colInputStride + otherIndex * m_patchInputStride;
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return m_impl.coeff(inputIndex);
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}
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template<int LoadMode>
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
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{
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const Index packetSize = internal::unpacket_traits<PacketReturnType>::size;
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EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
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eigen_assert(index+packetSize-1 < dimensions().TotalSize());
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const Index indices[2] = {index, index + packetSize - 1};
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const Index patchIndex = indices[0] / m_fastPatchStride;
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if (patchIndex != indices[1] / m_fastPatchStride) {
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return packetWithPossibleZero(index);
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}
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const Index otherIndex = (NumDims == 4) ? 0 : indices[0] / m_fastOtherStride;
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eigen_assert(otherIndex == indices[1] / m_fastOtherStride);
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// Find the offset of the element wrt the location of the first element.
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const Index patchOffsets[2] = {(indices[0] - patchIndex * m_patchStride) / m_fastDimZero,
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(indices[1] - patchIndex * m_patchStride) / m_fastDimZero};
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const Index patch2DIndex = (NumDims == 4) ? patchIndex : (indices[0] - otherIndex * m_otherStride) / m_fastPatchStride;
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eigen_assert(patch2DIndex == (indices[1] - otherIndex * m_otherStride) / m_fastPatchStride);
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const Index colIndex = patch2DIndex / m_fastOutputRows;
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const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, patchOffsets[1] / m_fastColStride};
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// Calculate col indices in the original input tensor.
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const Index inputCols[2] = {colIndex * m_col_strides + colOffsets[0] -
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m_colPaddingLeft, colIndex * m_col_strides + colOffsets[1] - m_colPaddingLeft};
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if (inputCols[1] < 0 || inputCols[0] >= m_inputCols) {
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// all zeros
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return internal::pset1<PacketReturnType>(Scalar(0));
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}
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if (inputCols[0] == inputCols[1]) {
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const Index rowIndex = patch2DIndex - colIndex * m_outputRows;
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const Index rowOffsets[2] = {patchOffsets[0] - colOffsets[0]*m_colStride, patchOffsets[1] - colOffsets[1]*m_colStride};
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eigen_assert(rowOffsets[0] <= rowOffsets[1]);
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// Calculate col indices in the original input tensor.
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const Index inputRows[2] = {rowIndex * m_row_strides + rowOffsets[0] -
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m_rowPaddingTop, rowIndex * m_row_strides + rowOffsets[1] - m_rowPaddingTop};
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if (inputRows[1] < 0 || inputRows[0] >= m_inputRows) {
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// all zeros
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return internal::pset1<PacketReturnType>(Scalar(0));
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}
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if (inputRows[0] >= 0 && inputRows[1] < m_inputRows) {
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// no padding
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const int depth_index = static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
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const Index depth = index - (index / m_fastDimZero) * m_dimensions[depth_index];
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const Index inputIndex = depth + inputRows[0] * m_rowInputStride + inputCols[0] * m_colInputStride + otherIndex * m_patchInputStride;
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return m_impl.template packet<Unaligned>(inputIndex);
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}
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}
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return packetWithPossibleZero(index);
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}
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EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
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const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
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Index rowPaddingTop() const { return m_rowPaddingTop; }
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Index colPaddingLeft() const { return m_colPaddingLeft; }
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Index outputRows() const { return m_outputRows; }
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Index outputCols() const { return m_outputCols; }
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Index userRowStride() const { return m_row_strides; }
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Index userColStride() const { return m_col_strides; }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<Index, NumDims>& coords) const
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{
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// Location of the first element of the patch.
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// ColMajor
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// 0: d, 1: patch_rows, 2: patch_cols, 3: number of patches, 4: number of batches
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// RowMajor
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// 0: number of batches, 1: number of patches, 2: patch_cols , 3: patch_rows, 4: d
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const Index patchIndex = coords[static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 3 : 1];
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array<Index, NumDims-1> inputCoords;
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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inputCoords[0] = coords[0]; // depth
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inputCoords[1] = patchIndex / m_inputCols + coords[1] - m_rowPaddingTop;
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inputCoords[2] = patchIndex - patchIndex / m_inputCols * m_inputCols + coords[2] - m_colPaddingLeft;
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inputCoords[3] = coords[4]; // batch
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} else {
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inputCoords[3] = coords[4]; // depth
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inputCoords[2] = patchIndex / m_inputCols + coords[3] - m_rowPaddingTop;
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inputCoords[1] = patchIndex - patchIndex / m_inputCols * m_inputCols + coords[2] - m_colPaddingLeft;
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inputCoords[0] = coords[0]; // batch
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}
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// If the computed coordinates are outside the original image perimeter, return 0.
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if (inputCoords[1] < 0 || inputCoords[1] >= m_inputRows ||
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inputCoords[2] < 0 || inputCoords[2] >= m_inputCols) {
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return Scalar(0);
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}
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if (TensorEvaluator<ArgType, Device>::CoordAccess) {
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return m_impl.coeff(inputCoords);
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} else {
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Index inputIndex;
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if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
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inputIndex =
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inputCoords[3] * m_patchInputStride +
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inputCoords[2] * m_colInputStride +
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inputCoords[1] * m_rowInputStride +
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inputCoords[0];
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} else {
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inputIndex =
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inputCoords[1] * m_patchInputStride +
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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;
|
|
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
|