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Added support for argmax/argmin
This commit is contained in:
Benoit Steiner
288
unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.h
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288
unsupported/Eigen/CXX11/src/Tensor/TensorArgMax.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) 2015 Eugene Brevdo <ebrevdo@gmail.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_ARG_MAX_H
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#define EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
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namespace Eigen {
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namespace internal {
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/** \class TensorIndexTuple
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Tensor + Index Tuple class.
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*
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*
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*/
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template<typename XprType>
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struct traits<TensorIndexTupleOp<XprType> > : public traits<XprType>
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{
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typedef traits<XprType> XprTraits;
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typedef typename XprTraits::StorageKind StorageKind;
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typedef typename XprTraits::Index Index;
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typedef Tuple<Index, typename XprTraits::Scalar> Scalar;
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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 XprType>
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struct eval<TensorIndexTupleOp<XprType>, Eigen::Dense>
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{
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typedef const TensorIndexTupleOp<XprType>& type;
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};
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template<typename XprType>
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struct nested<TensorIndexTupleOp<XprType>, 1,
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typename eval<TensorIndexTupleOp<XprType> >::type>
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{
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typedef TensorIndexTupleOp<XprType> type;
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};
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} // end namespace internal
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template<typename XprType>
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class TensorIndexTupleOp : public TensorBase<TensorIndexTupleOp<XprType>, ReadOnlyAccessors>
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{
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public:
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typedef typename Eigen::internal::traits<TensorIndexTupleOp>::Scalar Scalar;
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typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
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typedef typename Eigen::internal::nested<TensorIndexTupleOp>::type Nested;
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typedef typename Eigen::internal::traits<TensorIndexTupleOp>::StorageKind StorageKind;
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typedef typename Eigen::internal::traits<TensorIndexTupleOp>::Index Index;
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typedef Tuple<Index, typename XprType::CoeffReturnType> CoeffReturnType;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorIndexTupleOp(const XprType& expr)
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: m_xpr(expr) {}
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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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};
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// Eval as rvalue
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template<typename ArgType, typename Device>
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struct TensorEvaluator<const TensorIndexTupleOp<ArgType>, Device>
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{
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typedef TensorIndexTupleOp<ArgType> XprType;
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typedef typename XprType::Index Index;
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typedef typename XprType::Scalar Scalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
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static const int NumDims = internal::array_size<Dimensions>::value;
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enum {
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IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
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PacketAccess = /*TensorEvaluator<ArgType, Device>::PacketAccess*/ false,
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BlockAccess = false,
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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, const Device& device)
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: m_impl(op.expression(), device) { }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
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return m_impl.dimensions();
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
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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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return CoeffReturnType(index, m_impl.coeff(index));
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}
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EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
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protected:
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TensorEvaluator<ArgType, Device> m_impl;
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};
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namespace internal {
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/** \class TensorTupleIndex
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Converts to Tensor<Tuple<Index, Scalar> > and reduces to Tensor<Index>.
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*
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*/
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template<typename ReduceOp, typename Dims, typename XprType>
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struct traits<TensorTupleReducerOp<ReduceOp, Dims, XprType> > : public traits<XprType>
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{
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typedef traits<XprType> XprTraits;
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typedef typename XprTraits::StorageKind StorageKind;
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typedef typename XprTraits::Index Index;
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typedef Index Scalar;
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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 ReduceOp, typename Dims, typename XprType>
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struct eval<TensorTupleReducerOp<ReduceOp, Dims, XprType>, Eigen::Dense>
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{
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typedef const TensorTupleReducerOp<ReduceOp, Dims, XprType>& type;
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};
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template<typename ReduceOp, typename Dims, typename XprType>
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struct nested<TensorTupleReducerOp<ReduceOp, Dims, XprType>, 1,
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typename eval<TensorTupleReducerOp<ReduceOp, Dims, XprType> >::type>
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{
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typedef TensorTupleReducerOp<ReduceOp, Dims, XprType> type;
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};
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} // end namespace internal
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template<typename ReduceOp, typename Dims, typename XprType>
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class TensorTupleReducerOp : public TensorBase<TensorTupleReducerOp<ReduceOp, Dims, XprType>, ReadOnlyAccessors>
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{
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public:
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typedef typename Eigen::internal::traits<TensorTupleReducerOp>::Scalar Scalar;
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typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
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typedef typename Eigen::internal::nested<TensorTupleReducerOp>::type Nested;
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typedef typename Eigen::internal::traits<TensorTupleReducerOp>::StorageKind StorageKind;
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typedef typename Eigen::internal::traits<TensorTupleReducerOp>::Index Index;
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typedef Index CoeffReturnType;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorTupleReducerOp(const XprType& expr,
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const ReduceOp& reduce_op,
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const int return_dim,
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const Dims& reduce_dims)
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: m_xpr(expr), m_reduce_op(reduce_op), m_return_dim(return_dim), m_reduce_dims(reduce_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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EIGEN_DEVICE_FUNC
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const ReduceOp& reduce_op() const { return m_reduce_op; }
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EIGEN_DEVICE_FUNC
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const Dims& reduce_dims() const { return m_reduce_dims; }
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EIGEN_DEVICE_FUNC
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int return_dim() const { return m_return_dim; }
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protected:
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typename XprType::Nested m_xpr;
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const ReduceOp m_reduce_op;
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const int m_return_dim;
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const Dims m_reduce_dims;
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};
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// Eval as rvalue
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template<typename ReduceOp, typename Dims, typename ArgType, typename Device>
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struct TensorEvaluator<const TensorTupleReducerOp<ReduceOp, Dims, ArgType>, Device>
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{
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typedef TensorTupleReducerOp<ReduceOp, Dims, ArgType> XprType;
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typedef typename XprType::Index Index;
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typedef typename XprType::Scalar Scalar;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename TensorIndexTupleOp<ArgType>::CoeffReturnType TupleType;
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typedef typename TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device>::Dimensions Dimensions;
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typedef typename TensorEvaluator<const TensorIndexTupleOp<ArgType> , Device>::Dimensions InputDimensions;
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static const int NumDims = internal::array_size<InputDimensions>::value;
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typedef array<Index, NumDims> StrideDims;
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enum {
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IsAligned = /*TensorEvaluator<ArgType, Device>::IsAligned*/ false,
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PacketAccess = /*TensorEvaluator<ArgType, Device>::PacketAccess*/ false,
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BlockAccess = false,
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Layout = TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<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, const Device& device)
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: m_orig_impl(op.expression(), device),
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m_impl(op.expression().index_tuples().reduce(op.reduce_dims(), op.reduce_op()), device),
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m_return_dim(op.return_dim()),
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m_strides(gen_strides(m_orig_impl.dimensions())),
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m_stride_mod(gen_stride_mod(m_orig_impl.dimensions())),
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m_stride_div(gen_stride_div()) { }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const {
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return m_impl.dimensions();
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
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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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const TupleType v = m_impl.coeff(index);
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return (m_return_dim < 0) ? v.first : (v.first % m_stride_mod) / m_stride_div;
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}
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EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
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private:
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EIGEN_DEVICE_FUNC StrideDims gen_strides(const InputDimensions& dims) {
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StrideDims strides;
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if (m_return_dim < 0) return strides; // Won't be using these.
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eigen_assert(m_return_dim < NumDims &&
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"Asking to convert index to a dimension outside of the rank");
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// Calculate m_stride_div and m_stride_mod, which are used to
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// calculate the value of an index w.r.t. the m_return_dim.
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if (Layout == static_cast<int>(ColMajor)) {
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strides[0] = 1;
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for (int i = 1; i < NumDims; ++i) {
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strides[i] = strides[i-1] * dims[i-1];
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}
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} else {
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strides[NumDims-1] = 1;
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for (int i = NumDims - 2; i >= 0; --i) {
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strides[i] = strides[i+1] * dims[i+1];
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}
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}
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return strides;
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}
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EIGEN_DEVICE_FUNC Index gen_stride_mod(const InputDimensions& dims) {
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if (Layout == static_cast<int>(ColMajor)) {
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return (m_return_dim < NumDims - 1) ? m_strides[m_return_dim + 1] : dims.TotalSize();
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} else {
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return (m_return_dim > 0) ? m_strides[m_return_dim - 1] : dims.TotalSize();
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}
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}
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EIGEN_DEVICE_FUNC Index gen_stride_div() {
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return m_strides[m_return_dim];
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}
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protected:
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TensorEvaluator<const TensorIndexTupleOp<ArgType>, Device> m_orig_impl;
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TensorEvaluator<const TensorReductionOp<ReduceOp, Dims, const TensorIndexTupleOp<ArgType> >, Device> m_impl;
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const int m_return_dim;
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const StrideDims m_strides;
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const Index m_stride_mod;
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const Index m_stride_div;
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};
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} // end namespace Eigen
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#endif // EIGEN_CXX11_TENSOR_TENSOR_ARG_MAX_H
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