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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_CONVOLUTION_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
namespace Eigen {
/** \class TensorConvolution
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor convolution class.
*
*
*/
namespace internal {
template<typename Dimensions, typename InputXprType, typename KernelXprType>
struct traits<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> >
{
// Type promotion to handle the case where the types of the lhs and the rhs are different.
typedef typename internal::promote_storage_type<typename InputXprType::Scalar,
typename KernelXprType::Scalar>::ret Scalar;
typedef typename internal::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;
enum {
Flags = 0,
};
};
template<typename Dimensions, typename InputXprType, typename KernelXprType>
struct eval<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>, Eigen::Dense>
{
typedef const TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>& type;
};
template<typename Dimensions, typename InputXprType, typename KernelXprType>
struct nested<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType>, 1, typename eval<TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> >::type>
{
typedef TensorConvolutionOp<Dimensions, InputXprType, KernelXprType> type;
};
} // end namespace internal
template<typename Indices, typename InputXprType, typename KernelXprType>
class TensorConvolutionOp : public TensorBase<TensorConvolutionOp<Indices, InputXprType, KernelXprType> >
{
public:
typedef typename Eigen::internal::traits<TensorConvolutionOp>::Scalar Scalar;
typedef typename Eigen::internal::traits<TensorConvolutionOp>::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<TensorConvolutionOp>::type Nested;
typedef typename Eigen::internal::traits<TensorConvolutionOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorConvolutionOp>::Index Index;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConvolutionOp(const InputXprType& input, const KernelXprType& kernel, const Indices& dims)
: m_input_xpr(input), m_kernel_xpr(kernel), m_indices(dims) {}
EIGEN_DEVICE_FUNC
const Indices& indices() const { return m_indices; }
/** \returns the nested expressions */
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename InputXprType::Nested>::type&
inputExpression() const { return m_input_xpr; }
EIGEN_DEVICE_FUNC
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 TensorConvolutionOp<Indices, InputArgType, KernelArgType>, Device>
{
typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType;
static const int NumDims = TensorEvaluator<InputArgType, Device>::Dimensions::count;
static const int KernelDims = 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 = /*TensorEvaluator<InputArgType>::PacketAccess & TensorEvaluator<KernelArgType>::PacketAccess */
false,
};
TensorEvaluator(const XprType& op, const Device& device)
: m_inputImpl(op.inputExpression(), device), m_kernelImpl(op.kernelExpression(), device), m_dimensions(op.inputExpression().dimensions())
{
const typename TensorEvaluator<InputArgType, Device>::Dimensions& input_dims = m_inputImpl.dimensions();
const typename TensorEvaluator<KernelArgType, Device>::Dimensions& kernel_dims = m_kernelImpl.dimensions();
for (int i = 0; i < NumDims; ++i) {
if (i > 0) {
m_inputStride[i] = m_inputStride[i-1] * input_dims[i-1];
} else {
m_inputStride[0] = 1;
}
}
for (int i = 0; i < KernelDims; ++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];
}
for (int i = 0; i < NumDims; ++i) {
if (i > 0) {
m_outputStride[i] = m_outputStride[i-1] * m_dimensions[i-1];
} else {
m_outputStride[0] = 1;
}
}
}
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
const Dimensions& dimensions() const { return m_dimensions; }
void evalTo(typename XprType::Scalar* buffer) const {
for (int i = 0; i < dimensions().TotalSize(); ++i) {
buffer[i] += coeff(i);
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
Index startInput = 0;
for (int i = NumDims - 1; i >= 0; --i) {
const Index idx = index / m_outputStride[i];
startInput += idx * m_inputStride[i];
index -= idx * m_outputStride[i];
}
CoeffReturnType result = CoeffReturnType(0);
convolve(startInput, 0, 0, result);
return result;
}
/* TODO: vectorization
template<int LoadMode>
EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
{
assert(false);
}*/
EIGEN_DEVICE_FUNC void convolve(Index firstIndex, Index firstKernel, int DimIndex, CoeffReturnType& accum) const {
for (int j = 0; j < m_kernelImpl.dimensions()[DimIndex]; ++j) {
const Index input = firstIndex + j * m_indexStride[DimIndex];
const Index kernel = firstKernel + j * m_kernelStride[DimIndex];
if (DimIndex < KernelDims-1) {
convolve(input, kernel, DimIndex+1, accum);
} else {
accum += m_inputImpl.coeff(input) * m_kernelImpl.coeff(kernel);
}
}
}
private:
array<Index, NumDims> m_inputStride;
array<Index, NumDims> m_outputStride;
array<Index, KernelDims> m_indexStride;
array<Index, KernelDims> m_kernelStride;
Dimensions m_dimensions;
TensorEvaluator<InputArgType, Device> m_inputImpl;
TensorEvaluator<KernelArgType, Device> m_kernelImpl;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
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