/usr/include/sopt/wavelets/sara.h is in libsopt-dev 2.0.0-2.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | #ifndef SOPT_WAVELETS_SARA_H
#define SOPT_WAVELETS_SARA_H
#include "sopt/config.h"
#include <cmath>
#include <initializer_list>
#include <tuple>
#include <vector>
#include "sopt/logging.h"
#include "sopt/wavelets/wavelets.h"
namespace sopt {
namespace wavelets {
//! Sparsity Averaging Reweighted Analysis
class SARA : public std::vector<Wavelet> {
public:
#ifndef SOPT_HAS_NOT_USING
// Constructors
using std::vector<Wavelet>::vector;
#else
//! Default constructor
SARA() : std::vector<Wavelet>(){};
#endif
//! Easy constructor
SARA(std::initializer_list<std::tuple<std::string, t_uint>> const &init)
: SARA(init.begin(), init.end()) {}
//! Construct from any iterator over a (std:string, t_uint) tuple
template <class ITERATOR,
class T = typename std::
enable_if<std::is_convertible<decltype(std::get<0>(*std::declval<ITERATOR>())),
std::string>::value
and std::is_convertible<decltype(std::get<1>(*std::declval<ITERATOR>())),
t_uint>::value>::type>
SARA(ITERATOR first, ITERATOR last) {
for(; first != last; ++first)
emplace_back(std::get<0>(*first), std::get<1>(*first));
}
//! Destructor
virtual ~SARA() {}
//! \brief Direct transform
//! \param[in] signal: computes wavelet coefficients for this signal. Its size must be a
//! multiple of $2^l$ where $l$ is the maximum number of levels. Can be a matrix (2d-transform)
//! or a column vector (1-d transform).
//! \return wavelets coefficients arranged by columns: if the input is n by m, then the output
//! is n by m * d, with d the number of wavelets.
//! \details Supports 1 and 2 dimensional tranforms for real and complex data.
template <class T0> typename T0::PlainObject direct(Eigen::ArrayBase<T0> const &signal) const;
//! \brief Direct transform
//! \param[inout] coefficients: Output wavelet coefficients. Must be of the type as the input.
//! If the input is n by m, and d is the number of wavelets, then the output should be n by (m *
//! d).
//! \param[in] signal: computes wavelet coefficients for this signal. Its size must be a
//! multiple of $2^l$ where $l$ is the maximum number of levels. Can be a matrix (2d-transform)
//! or a column vector (1-d transform).
//! \details Supports 1 and 2 dimensional tranforms for real and complex data.
template <class T0, class T1>
void direct(Eigen::ArrayBase<T1> &coefficients, Eigen::ArrayBase<T0> const &signal) const;
//! \brief Direct transform
//! \param[inout] coefficients: Output wavelet coefficients. Must be of the type as the input.
//! If the input is n by m, and l is the number of wavelets, then the output should be n by (m *
//! l).
//! \param[in] signal: computes wavelet coefficients for this signal. Its size must be a
//! multiple of $2^l$ where $l$ is the number of levels. Can be a matrix (2d-transform) or a
//! column vector (1-d transform).
//! \details Supports 1 and 2 dimensional tranforms for real and complex data. This version
//! allows non-constant Eigen expressions to be passe on without the ugly `const_cast` of the
//! cannonical approach.
template <class T0, class T1>
void direct(Eigen::ArrayBase<T1> &&coefficients, Eigen::ArrayBase<T0> const &signal) const {
direct(coefficients, signal);
}
//! \brief Indirect transform
//! \param[in] coefficients: Input wavelet coefficients. Its size must be a multiple of $2^l$
//! where $l$ is the number of levels. Can be a matrix (2d-transform) or a column vector (1-d
//! transform).
//! \details Supports 1 and 2 dimensional tranforms for real and complex data.
template <class T0> typename T0::PlainObject indirect(Eigen::ArrayBase<T0> const &coeffs) const;
//! \brief Indirect transform
//! \param[in] coefficients: Input wavelet coefficients. Its size must be a multiple of $2^l$
//! where $l$ is the number of levels. Can be a matrix (2d-transform) or a column vector (1-d
//! \param[inout] signal: Reconstructed signal. Must be of the same size and type as the input.
//! \details Supports 1 and 2 dimensional tranforms for real and complex data.
template <class T0, class T1>
void indirect(Eigen::ArrayBase<T1> const &coefficients, Eigen::ArrayBase<T0> &signal) const;
//! \brief Indirect transform
//! \param[in] coefficients: Input wavelet coefficients. Its size must be a multiple of $2^l$
//! where $l$ is the number of levels. Can be a matrix (2d-transform) or a column vector (1-d
//! \param[inout] signal: Reconstructed signal. Must be of the same size and type as the input.
//! \details Supports 1 and 2 dimensional tranforms for real and complex data. This version
//! allows non-constant Eigen expressions to be passe on without the ugly `const_cast` of the
//! cannonical approach.
template <class T0, class T1>
void indirect(Eigen::ArrayBase<T1> const &coeffs, Eigen::ArrayBase<T0> &&signal) const {
indirect(coeffs, signal);
}
//! Number of levels over which to do transform
t_uint max_levels() const {
auto cmp = [](Wavelet const &a, Wavelet const &b) { return a.levels() < b.levels(); };
return std::max_element(begin(), end(), cmp)->levels();
}
//! Adds a wavelet of specific type
void emplace_back(std::string const &name, t_uint nlevels) {
std::vector<Wavelet>::emplace_back(factory(name, nlevels));
}
};
#define SOPT_WAVELET_ERROR_MACRO(INPUT) \
if(INPUT.rows() % (1u << max_levels()) != 0) \
throw std::length_error("Inconsistent number of columns and wavelet levels"); \
else if(INPUT.cols() != 1 and INPUT.cols() % (1u << max_levels())) \
throw std::length_error("Inconsistent number of rows and wavelet levels");
template <class T0, class T1>
void SARA::direct(Eigen::ArrayBase<T1> &coeffs, Eigen::ArrayBase<T0> const &signal) const {
SOPT_WAVELET_ERROR_MACRO(signal);
if(coeffs.rows() != signal.rows() or coeffs.cols() != signal.cols() * static_cast<t_int>(size()))
coeffs.derived().resize(signal.rows(), signal.cols() * size());
if(coeffs.rows() != signal.rows() or coeffs.cols() != signal.cols() * static_cast<t_int>(size()))
throw std::length_error("Incorrect size for output matrix(or could not resize)");
auto const Ncols = signal.cols();
#ifndef SOPT_OPENMP
SOPT_TRACE("Calling direct sara without threads");
for(size_type i(0); i < size(); ++i)
at(i).direct(coeffs.leftCols((i + 1) * Ncols).rightCols(Ncols), signal);
#else
#pragma omp parallel
{
if(omp_get_thread_num() == 0) {
SOPT_TRACE("Calling direct sara with {} threads of {}", omp_get_num_threads(),
omp_get_max_threads());
}
#pragma omp for
for(size_type i = 0; i < size(); ++i)
at(i).direct(coeffs.leftCols((i + 1) * Ncols).rightCols(Ncols), signal);
}
#endif
coeffs /= std::sqrt(size());
}
template <class T0, class T1>
void SARA::indirect(Eigen::ArrayBase<T1> const &coeffs, Eigen::ArrayBase<T0> &signal) const {
SOPT_WAVELET_ERROR_MACRO(coeffs);
if(coeffs.cols() % size() != 0)
throw std::length_error(
"Columns of coefficient matrix and number of wavelets are inconsistent");
if(coeffs.rows() != signal.rows() or coeffs.cols() != signal.cols() * static_cast<t_int>(size()))
signal.derived().resize(coeffs.rows(), coeffs.cols() / size());
if(coeffs.rows() != signal.rows() or coeffs.cols() != signal.cols() * static_cast<t_int>(size()))
throw std::length_error("Incorrect size for output matrix(or could not resize)");
auto const Ncols = signal.cols();
#ifndef SOPT_OPENMP
SOPT_TRACE("Calling indirect sara without threads");
signal = front().indirect(coeffs.leftCols(Ncols).rightCols(Ncols));
for(size_type i(1); i < size(); ++i)
signal += at(i).indirect(coeffs.leftCols((i + 1) * Ncols).rightCols(Ncols));
#else
signal.fill(0);
#pragma omp parallel
{
if(omp_get_thread_num() == 0) {
SOPT_TRACE("Calling indirect sara with {} threads of {}", omp_get_num_threads(),
omp_get_max_threads());
}
Image<typename T0::Scalar> reductor = Image<typename T0::Scalar>::Zero(signal.rows(), Ncols);
#pragma omp for
for(size_type i = 0; i < size(); ++i)
reductor += at(i).indirect(coeffs.leftCols((i + 1) * Ncols).rightCols(Ncols));
#pragma omp critical
signal += reductor;
}
#endif
signal /= std::sqrt(size());
}
#undef SOPT_WAVELET_ERROR_MACRO
template <class T0>
typename T0::PlainObject SARA::indirect(Eigen::ArrayBase<T0> const &coeffs) const {
typedef decltype(this->front().indirect(coeffs)) t_Output;
t_Output signal = t_Output::Zero(coeffs.rows(), coeffs.cols() / size());
(*this).indirect(coeffs, signal);
return signal;
}
template <class T0>
typename T0::PlainObject SARA::direct(Eigen::ArrayBase<T0> const &signal) const {
typedef decltype(this->front().direct(signal)) t_Output;
t_Output result = t_Output::Zero(signal.rows(), signal.cols() * size());
(*this).direct(result, signal);
return result;
}
}
}
#endif
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