mirror of
https://gitlab.com/libeigen/eigen.git
synced 2026-04-10 11:34:33 +08:00
Added support for convolution and reshaping of tensors.
This commit is contained in:
Benoit Steiner
206
unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h
Normal file
206
unsupported/Eigen/CXX11/src/Tensor/TensorConvolution.h
Normal file
@@ -0,0 +1,206 @@
|
||||
// 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;
|
||||
};
|
||||
|
||||
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>
|
||||
struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType> >
|
||||
{
|
||||
typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType;
|
||||
|
||||
static const int NumDims = TensorEvaluator<InputArgType>::Dimensions::count;
|
||||
static const int KernelDims = Indices::size;
|
||||
typedef typename XprType::Index Index;
|
||||
typedef DSizes<Index, NumDims> Dimensions;
|
||||
|
||||
enum {
|
||||
IsAligned = TensorEvaluator<InputArgType>::IsAligned & TensorEvaluator<KernelArgType>::IsAligned,
|
||||
PacketAccess = /*TensorEvaluator<InputArgType>::PacketAccess & TensorEvaluator<KernelArgType>::PacketAccess */
|
||||
false,
|
||||
};
|
||||
|
||||
TensorEvaluator(const XprType& op)
|
||||
: m_inputImpl(op.inputExpression()), m_kernelImpl(op.kernelExpression()), m_dimensions(op.inputExpression().dimensions())
|
||||
{
|
||||
const typename TensorEvaluator<InputArgType>::Dimensions& input_dims = m_inputImpl.dimensions();
|
||||
const typename TensorEvaluator<KernelArgType>::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> m_inputImpl;
|
||||
TensorEvaluator<KernelArgType> m_kernelImpl;
|
||||
};
|
||||
|
||||
|
||||
} // end namespace Eigen
|
||||
|
||||
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
|
||||
Reference in New Issue
Block a user