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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>
// Copyright (C) 2015 Jianwei Cui <thucjw@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_CONVOLUTIONBYFFT_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTIONBYFFT_H
namespace Eigen {
/** \class TensorConvolutionByFFT
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor convolution class.
*
*
*/
namespace internal {
template<typename Dimensions, typename InputXprType, typename KernelXprType>
struct traits<TensorConvolutionByFFTOp<Dimensions, InputXprType, KernelXprType> >
{
// Type promotion to handle the case where the types of the lhs and the rhs are different.
typedef typename promote_storage_type<typename InputXprType::Scalar,
typename KernelXprType::Scalar>::ret Scalar;
typedef typename packet_traits<Scalar>::type Packet;
typedef typename promote_storage_type<typename traits<InputXprType>::StorageKind,
typename traits<KernelXprType>::StorageKind>::ret StorageKind;
typedef typename promote_index_type<typename traits<InputXprType>::Index,
typename traits<KernelXprType>::Index>::type Index;
typedef typename InputXprType::Nested LhsNested;
typedef typename KernelXprType::Nested RhsNested;
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
static const int NumDimensions = traits<InputXprType>::NumDimensions;
static const int Layout = traits<InputXprType>::Layout;
enum {
Flags = 0,
};
};
template<typename Dimensions, typename InputXprType, typename KernelXprType>
struct eval<TensorConvolutionByFFTOp<Dimensions, InputXprType, KernelXprType>, Eigen::Dense>
{
typedef const TensorConvolutionByFFTOp<Dimensions, InputXprType, KernelXprType>& type;
};
template<typename Dimensions, typename InputXprType, typename KernelXprType>
struct nested<TensorConvolutionByFFTOp<Dimensions, InputXprType, KernelXprType>, 1, typename eval<TensorConvolutionByFFTOp<Dimensions, InputXprType, KernelXprType> >::type>
{
typedef TensorConvolutionByFFTOp<Dimensions, InputXprType, KernelXprType> type;
};
} // end namespace internal
template<typename Indices, typename InputXprType, typename KernelXprType>
class TensorConvolutionByFFTOp : public TensorBase<TensorConvolutionByFFTOp<Indices, InputXprType, KernelXprType> >
{
public:
typedef typename Eigen::internal::traits<TensorConvolutionByFFTOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorConvolutionByFFTOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename internal::promote_storage_type<typename InputXprType::CoeffReturnType,
typename KernelXprType::CoeffReturnType>::ret CoeffReturnType;
typedef typename internal::promote_storage_type<typename InputXprType::PacketReturnType,
typename KernelXprType::PacketReturnType>::ret PacketReturnType;
typedef typename Eigen::internal::nested<TensorConvolutionByFFTOp>::type Nested;
typedef typename Eigen::internal::traits<TensorConvolutionByFFTOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorConvolutionByFFTOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConvolutionByFFTOp(const InputXprType& input, const KernelXprType& kernel, const Indices& dims)
: m_input_xpr(input), m_kernel_xpr(kernel), m_indices(dims) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const Indices& indices() const { return m_indices; }
/** \returns the nested expressions */
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const typename internal::remove_all<typename InputXprType::Nested>::type&
inputExpression() const { return m_input_xpr; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
const typename internal::remove_all<typename KernelXprType::Nested>::type&
kernelExpression() const { return m_kernel_xpr; }
protected:
typename InputXprType::Nested m_input_xpr;
typename KernelXprType::Nested m_kernel_xpr;
const Indices m_indices;
};
template<typename Indices, typename InputArgType, typename KernelArgType, typename Device>
struct TensorEvaluator<const TensorConvolutionByFFTOp<Indices, InputArgType, KernelArgType>, Device>
{
typedef TensorConvolutionByFFTOp<Indices, InputArgType, KernelArgType> XprType;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
static const int NumDims = internal::array_size<typename TensorEvaluator<InputArgType, Device>::Dimensions>::value;
static const int NumKernelDims = internal::array_size<Indices>::value;
typedef typename XprType::Index Index;
typedef DSizes<Index, NumDims> Dimensions;
enum {
IsAligned = TensorEvaluator<InputArgType, Device>::IsAligned &
TensorEvaluator<KernelArgType, Device>::IsAligned,
PacketAccess = false,
BlockAccess = false,
Layout = TensorEvaluator<InputArgType, Device>::Layout,
CoordAccess = false, // to be implemented
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_inputImpl(op.inputExpression(), device), m_kernelImpl(op.kernelExpression(), device), m_kernelArg(op.kernelExpression()), m_kernel(NULL), m_local_kernel(false), m_device(device)
{
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<KernelArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
const typename TensorEvaluator<InputArgType, Device>::Dimensions& input_dims = m_inputImpl.dimensions();
const typename TensorEvaluator<KernelArgType, Device>::Dimensions& kernel_dims = m_kernelImpl.dimensions();
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
m_inputStride[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_inputStride[i] = m_inputStride[i - 1] * input_dims[i - 1];
}
} else {
m_inputStride[NumDims - 1] = 1;
for (int i = NumDims - 2; i >= 0; --i) {
m_inputStride[i] = m_inputStride[i + 1] * input_dims[i + 1];
}
}
m_dimensions = m_inputImpl.dimensions();
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
for (int i = 0; i < NumKernelDims; ++i) {
const Index index = op.indices()[i];
const Index input_dim = input_dims[index];
const Index kernel_dim = kernel_dims[i];
const Index result_dim = input_dim - kernel_dim + 1;
m_dimensions[index] = result_dim;
if (i > 0) {
m_kernelStride[i] = m_kernelStride[i - 1] * kernel_dims[i - 1];
} else {
m_kernelStride[0] = 1;
}
m_indexStride[i] = m_inputStride[index];
}
m_outputStride[0] = 1;
for (int i = 1; i < NumDims; ++i) {
m_outputStride[i] = m_outputStride[i - 1] * m_dimensions[i - 1];
}
} else {
for (int i = NumKernelDims - 1; i >= 0; --i) {
const Index index = op.indices()[i];
const Index input_dim = input_dims[index];
const Index kernel_dim = kernel_dims[i];
const Index result_dim = input_dim - kernel_dim + 1;
m_dimensions[index] = result_dim;
if (i < NumKernelDims - 1) {
m_kernelStride[i] = m_kernelStride[i + 1] * kernel_dims[i + 1];
} else {
m_kernelStride[NumKernelDims - 1] = 1;
}
m_indexStride[i] = m_inputStride[index];
}
m_outputStride[NumDims - 1] = 1;
for (int i = NumDims - 2; i >= 0; --i) {
m_outputStride[i] = m_outputStride[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_inputImpl.evalSubExprsIfNeeded(NULL);
m_kernelImpl.evalSubExprsIfNeeded(NULL);
typedef typename internal::traits<InputArgType>::Index TensorIndex;
Tensor<Scalar, NumDims, Layout, TensorIndex> input(m_inputImpl.dimensions());
for (int i = 0; i < m_inputImpl.dimensions().TotalSize(); ++i) {
input.data()[i] = m_inputImpl.coeff(i);
}
Tensor<Scalar, NumDims, Layout, TensorIndex> kernel(m_kernelImpl.dimensions());
for (int i = 0; i < m_kernelImpl.dimensions().TotalSize(); ++i) {
kernel.data()[i] = m_kernelImpl.coeff(i);
}
array<std::pair<ptrdiff_t, ptrdiff_t>, NumDims> paddings;
for (int i = 0; i < NumDims; ++i) {
paddings[i] = std::make_pair(0, m_inputImpl.dimensions()[i] - m_kernelImpl.dimensions()[i]);
}
Eigen::array<bool, NumKernelDims> reverse;
for (int i = 0; i < NumKernelDims; ++i) {
reverse[i] = true;
}
Eigen::array<bool, NumDims> fft;
for (int i = 0; i < NumDims; ++i) {
fft[i] = i;
}
Eigen::DSizes<TensorIndex, NumDims> slice_offsets;
for (int i = 0; i < NumDims; ++i) {
slice_offsets[i] = m_kernelImpl.dimensions()[i] - 1;
}
Eigen::DSizes<TensorIndex, NumDims> slice_extents;
for (int i = 0; i < NumDims; ++i) {
slice_extents[i] = m_inputImpl.dimensions()[i] - m_kernelImpl.dimensions()[i] + 1;
}
Tensor<Scalar, NumDims, Layout, TensorIndex> kernel_variant = kernel.reverse(reverse).pad(paddings);
Tensor<std::complex<Scalar>, NumDims, Layout, TensorIndex> kernel_fft = kernel_variant.template fft<Eigen::BothParts, FFT_FORWARD>(fft);
//Tensor<std::complex<Scalar>, NumDims, Layout|IndexType> kernel_fft = kernel.reverse(reverse).pad(paddings).template fft<2>(fft);
Tensor<std::complex<Scalar>, NumDims, Layout, TensorIndex> input_fft = input.template fft<Eigen::BothParts, FFT_FORWARD>(fft);
Tensor<std::complex<Scalar>, NumDims, Layout, TensorIndex> prod = (input_fft * kernel_fft).template fft<Eigen::BothParts, FFT_REVERSE>(fft);
Tensor<std::complex<Scalar>, NumDims, Layout, TensorIndex> tensor_result = prod.slice(slice_offsets, slice_extents);
for (int i = 0; i < tensor_result.size(); ++i) {
data[i] = std::real(tensor_result.data()[i]);
}
return false;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_inputImpl.cleanup();
if (m_local_kernel) {
m_device.deallocate((void*)m_kernel);
m_local_kernel = false;
}
m_kernel = NULL;
}
void evalTo(typename XprType::Scalar* buffer) {
evalSubExprsIfNeeded(NULL);
for (int i = 0; i < dimensions().TotalSize(); ++i) {
buffer[i] += coeff(i);
}
cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
CoeffReturnType result = CoeffReturnType(0);
return result;
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
private:
array<Index, NumDims> m_inputStride;
array<Index, NumDims> m_outputStride;
array<Index, NumKernelDims> m_indexStride;
array<Index, NumKernelDims> m_kernelStride;
TensorEvaluator<InputArgType, Device> m_inputImpl;
TensorEvaluator<KernelArgType, Device> m_kernelImpl;
Dimensions m_dimensions;
KernelArgType m_kernelArg;
const Scalar* m_kernel;
bool m_local_kernel;
const Device& m_device;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTIONBYFFT_H
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