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Added ability to reverse the order of the coefficients in a tensor
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Benoit Steiner
parent
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207
unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h
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207
unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.com>
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// Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
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#define EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
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namespace Eigen {
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/** \class TensorReverse
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Tensor reverse elements class.
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*
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*/
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namespace internal {
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template<typename ReverseDimensions, typename XprType>
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struct traits<TensorReverseOp<ReverseDimensions,
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XprType> > : public traits<XprType>
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{
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typedef typename XprType::Scalar Scalar;
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typedef traits<XprType> XprTraits;
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typedef typename packet_traits<Scalar>::type Packet;
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typedef typename XprTraits::StorageKind StorageKind;
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typedef typename XprTraits::Index Index;
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typedef typename XprType::Nested Nested;
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typedef typename remove_reference<Nested>::type _Nested;
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static const int NumDimensions = XprTraits::NumDimensions;
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static const int Layout = XprTraits::Layout;
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};
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template<typename ReverseDimensions, typename XprType>
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struct eval<TensorReverseOp<ReverseDimensions, XprType>, Eigen::Dense>
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{
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typedef const TensorReverseOp<ReverseDimensions, XprType>& type;
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};
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template<typename ReverseDimensions, typename XprType>
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struct nested<TensorReverseOp<ReverseDimensions, XprType>, 1,
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typename eval<TensorReverseOp<ReverseDimensions, XprType> >::type>
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{
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typedef TensorReverseOp<ReverseDimensions, XprType> type;
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};
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} // end namespace internal
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template<typename ReverseDimensions, typename XprType>
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class TensorReverseOp : public TensorBase<TensorReverseOp<ReverseDimensions,
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XprType>, ReadOnlyAccessors>
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{
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public:
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typedef typename Eigen::internal::traits<TensorReverseOp>::Scalar Scalar;
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typedef typename Eigen::internal::traits<TensorReverseOp>::Packet Packet;
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typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename XprType::PacketReturnType PacketReturnType;
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typedef typename Eigen::internal::nested<TensorReverseOp>::type Nested;
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typedef typename Eigen::internal::traits<TensorReverseOp>::StorageKind
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StorageKind;
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typedef typename Eigen::internal::traits<TensorReverseOp>::Index Index;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorReverseOp(const XprType& expr,
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const ReverseDimensions& reverse_dims)
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: m_xpr(expr), m_reverse_dims(reverse_dims) {}
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EIGEN_DEVICE_FUNC
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const ReverseDimensions& reverse() const { return m_reverse_dims; }
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EIGEN_DEVICE_FUNC
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const typename internal::remove_all<typename XprType::Nested>::type&
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expression() const { return m_xpr; }
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protected:
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typename XprType::Nested m_xpr;
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const ReverseDimensions m_reverse_dims;
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};
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// Eval as rvalue
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template<typename ReverseDimensions, typename ArgType, typename Device>
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struct TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, Device>
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{
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typedef TensorReverseOp<ReverseDimensions, ArgType> XprType;
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typedef typename XprType::Index Index;
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static const int NumDims = internal::array_size<ReverseDimensions>::value;
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typedef DSizes<Index, NumDims> Dimensions;
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enum {
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IsAligned = false,
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PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
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Layout = TensorEvaluator<ArgType, Device>::Layout,
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CoordAccess = false, // to be implemented
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};
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op,
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const Device& device)
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: m_impl(op.expression(), device), m_reverse(op.reverse())
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{
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// Compute strides
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m_dimensions = m_impl.dimensions();
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if (Layout == ColMajor) {
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m_strides[0] = 1;
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for (int i = 1; i < NumDims; ++i) {
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m_strides[i] = m_strides[i-1] * m_dimensions[i-1];
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}
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} else {
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m_strides[NumDims-1] = 1;
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for (int i = NumDims - 2; i >= 0; --i) {
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m_strides[i] = m_strides[i+1] * m_dimensions[i+1];
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}
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}
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}
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typedef typename XprType::Scalar Scalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename XprType::PacketReturnType PacketReturnType;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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const Dimensions& dimensions() const { return m_dimensions; }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
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m_impl.evalSubExprsIfNeeded(NULL);
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return true;
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
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m_impl.cleanup();
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
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{
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eigen_assert(index < dimensions().TotalSize());
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Index inputIndex = 0;
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if (Layout == ColMajor) {
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for (int i = NumDims - 1; i > 0; --i) {
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Index idx = index / m_strides[i];
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index -= idx * m_strides[i];
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if (m_reverse[i]) {
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idx = m_dimensions[i] - idx - 1;
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}
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inputIndex += idx * m_strides[i] ;
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}
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if (m_reverse[0]) {
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inputIndex += (m_dimensions[0] - index - 1);
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} else {
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inputIndex += index;
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}
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return m_impl.coeff(inputIndex);
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} else {
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for (int i = 0; i < NumDims - 1; ++i) {
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Index idx = index / m_strides[i];
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index -= idx * m_strides[i];
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if (m_reverse[i]) {
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idx = m_dimensions[i] - idx - 1;
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}
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inputIndex += idx * m_strides[i] ;
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}
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if (m_reverse[NumDims-1]) {
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inputIndex += (m_dimensions[NumDims-1] - index - 1);
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} else {
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inputIndex += index;
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}
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return m_impl.coeff(inputIndex);
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}
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}
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template<int LoadMode>
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
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PacketReturnType packet(Index index) const
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{
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const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
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EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
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eigen_assert(index+packetSize-1 < dimensions().TotalSize());
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// TODO(ndjaitly): write a better packing routine that uses
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// local structure.
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EIGEN_ALIGN_DEFAULT typename internal::remove_const<CoeffReturnType>::type
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values[packetSize];
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for (int i = 0; i < packetSize; ++i) {
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values[i] = coeff(index+i);
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}
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PacketReturnType rslt = internal::pload<PacketReturnType>(values);
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return rslt;
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}
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Scalar* data() const { return NULL; }
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protected:
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Dimensions m_dimensions;
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array<Index, NumDims> m_strides;
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TensorEvaluator<ArgType, Device> m_impl;
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ReverseDimensions m_reverse;
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};
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} // end namespace Eigen
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#endif // EIGEN_CXX11_TENSOR_TENSOR_REVERSE_H
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167
unsupported/test/cxx11_tensor_reverse.cpp
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167
unsupported/test/cxx11_tensor_reverse.cpp
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@ -0,0 +1,167 @@
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.com and
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// Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#include "main.h"
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#include <Eigen/CXX11/Tensor>
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using Eigen::Tensor;
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using Eigen::array;
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template <int DataLayout>
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static void test_simple_reverse()
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{
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Tensor<float, 4, DataLayout> tensor(2,3,5,7);
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tensor.setRandom();
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array<bool, 4> dim_rev;
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dim_rev[0] = false;
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dim_rev[1] = true;
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dim_rev[2] = true;
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dim_rev[3] = false;
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Tensor<float, 4, DataLayout> reversed_tensor;
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reversed_tensor = tensor.reverse(dim_rev);
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VERIFY_IS_EQUAL(reversed_tensor.dimension(0), 2);
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VERIFY_IS_EQUAL(reversed_tensor.dimension(1), 3);
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VERIFY_IS_EQUAL(reversed_tensor.dimension(2), 5);
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VERIFY_IS_EQUAL(reversed_tensor.dimension(3), 7);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(i,2-j,4-k,l));
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}
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}
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}
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}
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dim_rev[0] = true;
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dim_rev[1] = false;
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dim_rev[2] = false;
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dim_rev[3] = false;
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reversed_tensor = tensor.reverse(dim_rev);
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VERIFY_IS_EQUAL(reversed_tensor.dimension(0), 2);
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VERIFY_IS_EQUAL(reversed_tensor.dimension(1), 3);
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VERIFY_IS_EQUAL(reversed_tensor.dimension(2), 5);
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VERIFY_IS_EQUAL(reversed_tensor.dimension(3), 7);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(1-i,j,k,l));
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}
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}
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}
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}
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dim_rev[0] = true;
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dim_rev[1] = false;
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dim_rev[2] = false;
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dim_rev[3] = true;
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reversed_tensor = tensor.reverse(dim_rev);
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VERIFY_IS_EQUAL(reversed_tensor.dimension(0), 2);
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VERIFY_IS_EQUAL(reversed_tensor.dimension(1), 3);
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VERIFY_IS_EQUAL(reversed_tensor.dimension(2), 5);
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VERIFY_IS_EQUAL(reversed_tensor.dimension(3), 7);
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 3; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(1-i,j,k,6-l));
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}
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}
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}
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}
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}
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template <int DataLayout>
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static void test_expr_reverse()
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{
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Tensor<float, 4, DataLayout> tensor(2,3,5,7);
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tensor.setRandom();
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array<bool, 4> dim_rev;
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dim_rev[0] = false;
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dim_rev[1] = true;
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dim_rev[2] = false;
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dim_rev[3] = true;
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Tensor<float, 4, DataLayout> expected;
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expected = tensor.reverse(dim_rev);
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Tensor<float, 4, DataLayout> result(2,3,5,7);
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array<ptrdiff_t, 4> src_slice_dim{{2,3,1,7}};
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array<ptrdiff_t, 4> src_slice_start{{0,0,0,0}};
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array<ptrdiff_t, 4> dst_slice_dim{{2,3,1,7}};
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array<ptrdiff_t, 4> dst_slice_start{{0,0,0,0}};
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for (int i = 0; i < 5; ++i) {
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result.slice(dst_slice_start, dst_slice_dim) =
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tensor.slice(src_slice_start, src_slice_dim).reverse(dim_rev);
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src_slice_start[2] += 1;
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dst_slice_start[2] += 1;
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}
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VERIFY_IS_EQUAL(result.dimension(0), 2);
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VERIFY_IS_EQUAL(result.dimension(1), 3);
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VERIFY_IS_EQUAL(result.dimension(2), 5);
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VERIFY_IS_EQUAL(result.dimension(3), 7);
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for (int i = 0; i < expected.dimension(0); ++i) {
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for (int j = 0; j < expected.dimension(1); ++j) {
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for (int k = 0; k < expected.dimension(2); ++k) {
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for (int l = 0; l < expected.dimension(3); ++l) {
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VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,j,k,l));
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}
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}
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}
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}
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dst_slice_start[2] = 0;
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result.setRandom();
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for (int i = 0; i < 5; ++i) {
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result.slice(dst_slice_start, dst_slice_dim) =
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tensor.reverse(dim_rev).slice(dst_slice_start, dst_slice_dim);
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dst_slice_start[2] += 1;
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}
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for (int i = 0; i < expected.dimension(0); ++i) {
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for (int j = 0; j < expected.dimension(1); ++j) {
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for (int k = 0; k < expected.dimension(2); ++k) {
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for (int l = 0; l < expected.dimension(3); ++l) {
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VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,j,k,l));
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}
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}
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}
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}
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}
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void test_cxx11_tensor_reverse()
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{
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CALL_SUBTEST(test_simple_reverse<ColMajor>());
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CALL_SUBTEST(test_simple_reverse<RowMajor>());
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CALL_SUBTEST(test_expr_reverse<ColMajor>());
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CALL_SUBTEST(test_expr_reverse<RowMajor>());
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}
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