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// 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_PATCH_H
#define EIGEN_CXX11_TENSOR_TENSOR_PATCH_H
namespace Eigen {
/** \class TensorPatch
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor patch class.
*
*
*/
namespace internal {
template<typename PatchDim, typename XprType>
struct traits<TensorPatchOp<PatchDim, XprType> > : public traits<XprType>
{
typedef typename XprType::Scalar Scalar;
typedef traits<XprType> XprTraits;
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<typename PatchDim, typename XprType>
struct eval<TensorPatchOp<PatchDim, XprType>, Eigen::Dense>
{
typedef const TensorPatchOp<PatchDim, XprType>& type;
};
template<typename PatchDim, typename XprType>
struct nested<TensorPatchOp<PatchDim, XprType>, 1, typename eval<TensorPatchOp<PatchDim, XprType> >::type>
{
typedef TensorPatchOp<PatchDim, XprType> type;
};
} // end namespace internal
template<typename PatchDim, typename XprType>
class TensorPatchOp : public TensorBase<TensorPatchOp<PatchDim, XprType>, ReadOnlyAccessors>
{
public:
typedef typename Eigen::internal::traits<TensorPatchOp>::Scalar Scalar;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename Eigen::internal::nested<TensorPatchOp>::type Nested;
typedef typename Eigen::internal::traits<TensorPatchOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorPatchOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorPatchOp(const XprType& expr, const PatchDim& patch_dims)
: m_xpr(expr), m_patch_dims(patch_dims) {}
EIGEN_DEVICE_FUNC
const PatchDim& patch_dims() const { return m_patch_dims; }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
const PatchDim m_patch_dims;
};
// Eval as rvalue
template<typename PatchDim, typename ArgType, typename Device>
struct TensorEvaluator<const TensorPatchOp<PatchDim, ArgType>, Device>
{
typedef TensorPatchOp<PatchDim, 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;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
enum {
IsAligned = false,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false,
RawAccess = false
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device)
{
Index num_patches = 1;
const typename TensorEvaluator<ArgType, Device>::Dimensions& input_dims = m_impl.dimensions();
const PatchDim& patch_dims = op.patch_dims();
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = 0; i < NumDims-1; ++i) {
m_dimensions[i] = patch_dims[i];
num_patches *= (input_dims[i] - patch_dims[i] + 1);
}
m_dimensions[NumDims-1] = num_patches;
m_inputStrides[0] = 1;
m_patchStrides[0] = 1;
for (int i = 1; i < NumDims-1; ++i) {
m_inputStrides[i] = m_inputStrides[i-1] * input_dims[i-1];
m_patchStrides[i] = m_patchStrides[i-1] * (input_dims[i-1] - patch_dims[i-1] + 1);
}
m_outputStrides[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
}
} else {
for (int i = 0; i < NumDims-1; ++i) {
m_dimensions[i+1] = patch_dims[i];
num_patches *= (input_dims[i] - patch_dims[i] + 1);
}
m_dimensions[0] = num_patches;
m_inputStrides[NumDims-2] = 1;
m_patchStrides[NumDims-2] = 1;
for (int i = NumDims-3; i >= 0; --i) {
m_inputStrides[i] = m_inputStrides[i+1] * input_dims[i+1];
m_patchStrides[i] = m_patchStrides[i+1] * (input_dims[i+1] - patch_dims[i+1] + 1);
}
m_outputStrides[NumDims-1] = 1;
for (int i = NumDims-2; i >= 0; --i) {
m_outputStrides[i] = m_outputStrides[i+1] * m_dimensions[i+1];
}
}
}
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
{
Index output_stride_index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? NumDims - 1 : 0;
// Find the location of the first element of the patch.
Index patchIndex = index / m_outputStrides[output_stride_index];
// Find the offset of the element wrt the location of the first element.
Index patchOffset = index - patchIndex * m_outputStrides[output_stride_index];
Index inputIndex = 0;
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 2; i > 0; --i) {
const Index patchIdx = patchIndex / m_patchStrides[i];
patchIndex -= patchIdx * m_patchStrides[i];
const Index offsetIdx = patchOffset / m_outputStrides[i];
patchOffset -= offsetIdx * m_outputStrides[i];
inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i];
}
} else {
for (int i = 0; i < NumDims - 2; ++i) {
const Index patchIdx = patchIndex / m_patchStrides[i];
patchIndex -= patchIdx * m_patchStrides[i];
const Index offsetIdx = patchOffset / m_outputStrides[i+1];
patchOffset -= offsetIdx * m_outputStrides[i+1];
inputIndex += (patchIdx + offsetIdx) * m_inputStrides[i];
}
}
inputIndex += (patchIndex + patchOffset);
return m_impl.coeff(inputIndex);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
EIGEN_STATIC_ASSERT((PacketSize > 1), YOU_MADE_A_PROGRAMMING_MISTAKE)
eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
Index output_stride_index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? NumDims - 1 : 0;
Index indices[2] = {index, index + PacketSize - 1};
Index patchIndices[2] = {indices[0] / m_outputStrides[output_stride_index],
indices[1] / m_outputStrides[output_stride_index]};
Index patchOffsets[2] = {indices[0] - patchIndices[0] * m_outputStrides[output_stride_index],
indices[1] - patchIndices[1] * m_outputStrides[output_stride_index]};
Index inputIndices[2] = {0, 0};
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = NumDims - 2; i > 0; --i) {
const Index patchIdx[2] = {patchIndices[0] / m_patchStrides[i],
patchIndices[1] / m_patchStrides[i]};
patchIndices[0] -= patchIdx[0] * m_patchStrides[i];
patchIndices[1] -= patchIdx[1] * m_patchStrides[i];
const Index offsetIdx[2] = {patchOffsets[0] / m_outputStrides[i],
patchOffsets[1] / m_outputStrides[i]};
patchOffsets[0] -= offsetIdx[0] * m_outputStrides[i];
patchOffsets[1] -= offsetIdx[1] * m_outputStrides[i];
inputIndices[0] += (patchIdx[0] + offsetIdx[0]) * m_inputStrides[i];
inputIndices[1] += (patchIdx[1] + offsetIdx[1]) * m_inputStrides[i];
}
} else {
for (int i = 0; i < NumDims - 2; ++i) {
const Index patchIdx[2] = {patchIndices[0] / m_patchStrides[i],
patchIndices[1] / m_patchStrides[i]};
patchIndices[0] -= patchIdx[0] * m_patchStrides[i];
patchIndices[1] -= patchIdx[1] * m_patchStrides[i];
const Index offsetIdx[2] = {patchOffsets[0] / m_outputStrides[i+1],
patchOffsets[1] / m_outputStrides[i+1]};
patchOffsets[0] -= offsetIdx[0] * m_outputStrides[i+1];
patchOffsets[1] -= offsetIdx[1] * m_outputStrides[i+1];
inputIndices[0] += (patchIdx[0] + offsetIdx[0]) * m_inputStrides[i];
inputIndices[1] += (patchIdx[1] + offsetIdx[1]) * m_inputStrides[i];
}
}
inputIndices[0] += (patchIndices[0] + patchOffsets[0]);
inputIndices[1] += (patchIndices[1] + patchOffsets[1]);
if (inputIndices[1] - inputIndices[0] == PacketSize - 1) {
PacketReturnType rslt = m_impl.template packet<Unaligned>(inputIndices[0]);
return rslt;
}
else {
EIGEN_ALIGN_MAX CoeffReturnType values[PacketSize];
values[0] = m_impl.coeff(inputIndices[0]);
values[PacketSize-1] = m_impl.coeff(inputIndices[1]);
for (int i = 1; i < PacketSize-1; ++i) {
values[i] = coeff(index+i);
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
const double compute_cost = NumDims * (TensorOpCost::DivCost<Index>() +
TensorOpCost::MulCost<Index>() +
2 * TensorOpCost::AddCost<Index>());
return m_impl.costPerCoeff(vectorized) +
TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected:
Dimensions m_dimensions;
array<Index, NumDims> m_outputStrides;
array<Index, NumDims-1> m_inputStrides;
array<Index, NumDims-1> m_patchStrides;
TensorEvaluator<ArgType, Device> m_impl;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_PATCH_H