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Added support for extraction of patches from images

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Benoit Steiner
2014-11-13 09:28:54 -08:00
parent eeabf7975e
commit ec785b0180
6 changed files with 587 additions and 0 deletions
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1
unsupported/Eigen/CXX11/Tensor
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@@ -59,6 +59,7 @@
#include "unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorPatch.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h"
#include "unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h"

13
unsupported/Eigen/CXX11/src/Tensor/TensorBase.h
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@@ -255,6 +255,19 @@ class TensorBase<Derived, ReadOnlyAccessors>
return TensorPatchOp<const PatchDims, const Derived>(derived(), patch_dims);
}
template <Index Rows, Index Cols> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorImagePatchOp<Rows, Cols, const Derived>
extract_image_patches() const {
return TensorImagePatchOp<Rows, Cols, const Derived>(derived(), Rows, Cols, 1, 1);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorImagePatchOp<Dynamic, Dynamic, const Derived>
extract_image_patches(const Index patch_rows, const Index patch_cols,
const Index row_stride = 1, const Index col_stride = 1) const {
return TensorImagePatchOp<Dynamic, Dynamic, const Derived>(derived(), patch_rows, patch_cols, row_stride, col_stride);
}
// Morphing operators.
template <typename NewDimensions> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const TensorReshapingOp<const NewDimensions, const Derived>

1
unsupported/Eigen/CXX11/src/Tensor/TensorForwardDeclarations.h
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@@ -27,6 +27,7 @@ template<typename Axis, typename LeftXprType, typename RightXprType> class Tenso
template<typename Dimensions, typename LeftXprType, typename RightXprType> class TensorContractionOp;
template<typename Dimensions, typename InputXprType, typename KernelXprType> class TensorConvolutionOp;
template<typename PatchDim, typename XprType> class TensorPatchOp;
template<DenseIndex Rows, DenseIndex Cols, typename XprType> class TensorImagePatchOp;
template<typename Broadcast, typename XprType> class TensorBroadcastingOp;
template<std::size_t DimId, typename XprType> class TensorChippingOp;
template<typename NewDimensions, typename XprType> class TensorReshapingOp;

291
unsupported/Eigen/CXX11/src/Tensor/TensorImagePatch.h Normal file
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@@ -0,0 +1,291 @@
// 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;
};
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)
: m_xpr(expr), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
m_row_strides(row_strides), m_col_strides(col_strides){}
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
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;
};
// 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,
};
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);
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
m_dimensions[0] = input_dims[0];
m_dimensions[1] = op.patch_rows();
m_dimensions[2] = op.patch_cols();
m_dimensions[3] = ceilf(static_cast<float>(input_dims[1]) / op.row_strides()) *
ceilf(static_cast<float>(input_dims[2]) / op.col_strides());
for (int i = 4; i < NumDims; ++i) {
m_dimensions[i] = input_dims[i-1];
}
m_colStride = m_dimensions[1];
m_patchStride = m_colStride * m_dimensions[2] * m_dimensions[0];
m_otherStride = m_patchStride * m_dimensions[3];
m_inputRows = input_dims[1];
m_inputCols = input_dims[2];
m_rowInputStride = input_dims[0] * op.row_strides();
m_colInputStride = input_dims[0] * input_dims[1] * op.col_strides();
m_patchInputStride = input_dims[0] * input_dims[1] * input_dims[2];
m_rowPaddingTop = op.patch_rows() / 2;
m_colPaddingLeft = op.patch_cols() / 2;
m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
m_fastInputRows = internal::TensorIntDivisor<Index>(m_inputRows);
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
{
// Find the location of the first element of the patch.
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;
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_fastInputRows;
const Index colOffset = patchOffset / m_fastColStride;
const Index inputCol = colIndex + colOffset - m_colPaddingLeft;
if (inputCol < 0 || inputCol >= m_inputCols) {
return Scalar(0);
}
const Index rowIndex = patch2DIndex - colIndex * m_inputRows; // m_rowStride is always 1
const Index rowOffset = patchOffset - colOffset * m_colStride;
const Index inputRow = rowIndex + 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_fastInputRows;
const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, patchOffsets[1] / m_fastColStride};
const Index inputCols[2] = {colIndex + colOffsets[0] - m_colPaddingLeft, colIndex + 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_inputRows;
const Index rowOffsets[2] = {patchOffsets[0] - colOffsets[0]*m_colStride, patchOffsets[1] - colOffsets[1]*m_colStride};
eigen_assert(rowOffsets[0] <= rowOffsets[1]);
const Index inputRows[2] = {rowIndex + rowOffsets[0] - m_rowPaddingTop, rowIndex + 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);
}
Scalar* data() const { return NULL; }
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;
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_rowPaddingTop;
Index m_colPaddingLeft;
internal::TensorIntDivisor<Index> m_fastInputRows;
internal::TensorIntDivisor<Index> m_fastDimZero;
TensorEvaluator<ArgType, Device> m_impl;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_IMAGE_PATCH_H