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#include "purify/types.h"
#include <array>
#include <memory>
#include <random>
#include <boost/filesystem.hpp>
#include <boost/math/special_functions/erf.hpp>
#include "purify/directories.h"
#include "purify/distribute.h"
#include "purify/logging.h"
#include "purify/mpi_utilities.h"
#include "purify/operators.h"
#include "purify/pfitsio.h"
#include "purify/read_measurements.h"
#include "purify/utilities.h"
#include "purify/uvfits.h"
#include <sopt/imaging_padmm.h>
#include <sopt/mpi/communicator.h>
#include <sopt/mpi/session.h>
#include <sopt/power_method.h>
#include <sopt/relative_variation.h>
#include <sopt/utilities.h>
#include <sopt/wavelets.h>
#include <sopt/wavelets/sara.h>
#ifdef PURIFY_GPU
#include "purify/operators_gpu.h"
#endif
#ifndef PURIFY_PADMM_ALGORITHM
#define PURIFY_PADMM_ALGORITHM 2
#endif
using namespace purify;
using namespace purify::notinstalled;
utilities::vis_params dirty_visibilities(const std::vector<std::string> &names) {
return utilities::read_visibility(names, true);
}
utilities::vis_params dirty_visibilities(const std::vector<std::string> &names,
sopt::mpi::Communicator const &comm) {
if (comm.size() == 1) return dirty_visibilities(names);
if (comm.is_root()) {
auto result = dirty_visibilities(names);
auto const order = distribute::distribute_measurements(result, comm, distribute::plan::w_term);
return utilities::regroup_and_scatter(result, order, comm);
}
auto result = utilities::scatter_visibilities(comm);
return result;
}
std::shared_ptr<sopt::algorithm::ImagingProximalADMM<t_complex>> padmm_factory(
std::shared_ptr<sopt::LinearTransform<Vector<t_complex>> const> const &measurements,
t_real const sigma, const sopt::wavelets::SARA &sara, const utilities::vis_params &uv_data,
const sopt::mpi::Communicator &comm, const t_uint &imsizex, const t_uint &imsizey) {
auto const Psi = sopt::linear_transform<t_complex>(sara, imsizey, imsizex, comm);
#if PURIFY_PADMM_ALGORITHM == 2
auto const epsilon = 3 * std::sqrt(comm.all_sum_all(std::pow(sigma, 2))) *
std::sqrt(2 * comm.all_sum_all(uv_data.size()));
#elif PURIFY_PADMM_ALGORITHM == 3 || PURIFY_PADMM_ALGORITHM == 1
auto const epsilon = 3 * std::sqrt(2 * uv_data.size()) * sigma;
#endif
const t_real gamma =
utilities::step_size(uv_data.vis, measurements,
std::make_shared<sopt::LinearTransform<Vector<t_complex>> const>(Psi),
sara.size()) *
1e-3;
PURIFY_MEDIUM_LOG("Epsilon {}", epsilon);
PURIFY_MEDIUM_LOG("Gamma {}", gamma);
// shared pointer because the convergence function need access to some data that we would rather
// not reproduce. E.g. padmm definition is self-referential.
auto padmm = std::make_shared<sopt::algorithm::ImagingProximalADMM<t_complex>>(uv_data.vis);
padmm->itermax(50)
.gamma(comm.all_reduce<t_real>(gamma, MPI_MAX))
.relative_variation(1e-3)
.l2ball_proximal_epsilon(epsilon)
#if PURIFY_PADMM_ALGORITHM == 2
// communicator ensuring l2 norm in l2ball proximal is global
.l2ball_proximal_communicator(comm)
#endif
// communicator ensuring l1 norm in l1 proximal is global
.l1_proximal_adjoint_space_comm(comm)
.tight_frame(false)
.l1_proximal_tolerance(1e-2)
.l1_proximal_nu(1)
.l1_proximal_itermax(50)
.l1_proximal_positivity_constraint(true)
.l1_proximal_real_constraint(true)
.residual_tolerance(epsilon)
.lagrange_update_scale(0.9)
.nu(1e0)
.Psi(Psi)
.Phi(*measurements);
sopt::ScalarRelativeVariation<t_complex> conv(padmm->relative_variation(),
padmm->relative_variation(), "Objective function");
std::weak_ptr<decltype(padmm)::element_type> const padmm_weak(padmm);
padmm->residual_convergence([padmm_weak, conv, comm](
Vector<t_complex> const &x,
Vector<t_complex> const &residual) mutable -> bool {
auto const padmm = padmm_weak.lock();
#if PURIFY_PADMM_ALGORITHM == 2
auto const residual_norm = sopt::mpi::l2_norm(residual, padmm->l2ball_proximal_weights(), comm);
auto const result = residual_norm < padmm->residual_tolerance();
#elif PURIFY_PADMM_ALGORITHM == 3 || PURIFY_PADMM_ALGORITHM == 1
auto const residual_norm = sopt::l2_norm(residual, padmm->l2ball_proximal_weights());
auto const result =
comm.all_reduce<int8_t>(residual_norm < padmm->residual_tolerance(), MPI_LAND);
#endif
SOPT_LOW_LOG(" - [PADMM] Residuals: {} <? {}", residual_norm, padmm->residual_tolerance());
return result;
});
padmm->objective_convergence([padmm_weak, conv, comm](Vector<t_complex> const &x,
Vector<t_complex> const &) mutable -> bool {
auto const padmm = padmm_weak.lock();
#if PURIFY_PADMM_ALGORITHM == 2
return conv(sopt::mpi::l1_norm(padmm->Psi().adjoint() * x, padmm->l1_proximal_weights(), comm));
#elif PURIFY_PADMM_ALGORITHM == 3 || PURIFY_PADMM_ALGORITHM == 1
return comm.all_reduce<uint8_t>(
conv(sopt::l1_norm(padmm->Psi().adjoint() * x, padmm->l1_proximal_weights())), MPI_LAND);
#endif
});
auto convergence_function = [](const Vector<t_complex> &x) { return true; };
const std::shared_ptr<t_uint> iter = std::make_shared<t_uint>(0);
const auto algo_update = [uv_data, imsizex, imsizey, padmm_weak, iter,
comm](const Vector<t_complex> &x) -> bool {
auto padmm = padmm_weak.lock();
if (comm.is_root()) PURIFY_MEDIUM_LOG("Step size γ {}", padmm->gamma());
*iter = *iter + 1;
Vector<t_complex> const alpha = padmm->Psi().adjoint() * x;
const t_real new_gamma = comm.all_reduce(alpha.real().cwiseAbs().maxCoeff(), MPI_MAX) * 1e-3;
if (comm.is_root()) PURIFY_MEDIUM_LOG("Step size γ update {}", new_gamma);
padmm->gamma(((std::abs(padmm->gamma() - new_gamma) > 0.2) and *iter < 200) ? new_gamma
: padmm->gamma());
// updating parameter
Vector<t_complex> const residual = padmm->Phi().adjoint() * (uv_data.vis - padmm->Phi() * x);
if (comm.is_root()) {
pfitsio::write2d(x, imsizey, imsizex, "mpi_solution_update.fits");
pfitsio::write2d(residual, imsizey, imsizex, "mpi_residual_update.fits");
}
return true;
};
auto lambda = [convergence_function, algo_update](Vector<t_complex> const &x) {
return convergence_function(x) and algo_update(x);
};
padmm->is_converged(lambda);
return padmm;
}
int main(int nargs, char const **args) {
sopt::logging::set_level("debug");
purify::logging::set_level("debug");
auto const session = sopt::mpi::init(nargs, args);
auto const world = sopt::mpi::Communicator::World();
const std::string name = "realdata";
const std::string filename_base = vla_filename("../mwa/uvdump_");
const std::vector<std::string> filenames = {filename_base +
"01.vis"}; //, filename_base + "02.vis"};
auto const kernel = kernels::kernel::kb;
std::string kernel_name = "kb";
const bool w_term = false;
const t_real cellsize = 20; // arcsec
const t_uint imsizex = 1024;
const t_uint imsizey = 1024;
// Generating random uv(w) coverage
utilities::vis_params data = dirty_visibilities(filenames, world);
t_real const sigma =
data.weights.norm() / std::sqrt(world.all_sum_all(data.weights.size())) * 0.5;
data.vis = (data.vis.array() * data.weights.array()) /
world.all_reduce(data.weights.array().cwiseAbs().maxCoeff(), MPI_MAX);
#if PURIFY_PADMM_ALGORITHM == 2 || PURIFY_PADMM_ALGORITHM == 3
#ifndef PURIFY_GPU
auto const measurements = std::get<2>(sopt::algorithm::normalise_operator<Vector<t_complex>>(
measurementoperator::init_degrid_operator_2d<Vector<t_complex>>(
world, data, imsizey, imsizex, cellsize, cellsize, 2, kernel, 4, 4, w_term),
100, 1e-4, world.broadcast(Vector<t_complex>::Random(imsizex * imsizey).eval())));
#else
af::setDevice(0);
auto const measurements = std::get<2>(sopt::algorithm::normalise_operator<Vector<t_complex>>(
gpu::measurementoperator::init_degrid_operator_2d(world, data, imsizey, imsizex, cellsize,
cellsize, 2, kernel, 4, 4, w_term),
100, 1e-4, world.broadcast(Vector<t_complex>::Random(imsizex * imsizey).eval())));
#endif
#elif PURIFY_PADMM_ALGORITHM == 1
#ifndef PURIFY_GPU
auto const measurements = std::get<2>(sopt::algorithm::normalise_operator<Vector<t_complex>>(
measurementoperator::init_degrid_operator_2d_mpi<Vector<t_complex>>(
world, data, imsizey, imsizex, cellsize, cellsize, 2, kernel, 4, 4, w_term),
100, 1e-4, world.broadcast(Vector<t_complex>::Random(imsizex * imsizey).eval())));
#else
af::setDevice(0);
auto const measurements = std::get<2>(sopt::algorithm::normalise_operator<Vector<t_complex>>(
gpu::measurementoperator::init_degrid_operator_2d_mpi(world, data, imsizey, imsizex, cellsize,
cellsize, 2, kernel, 4, 4, w_term),
100, 1e-4, world.broadcast(Vector<t_complex>::Random(imsizex * imsizey).eval())));
#endif
#endif
auto const sara = sopt::wavelets::distribute_sara(
sopt::wavelets::SARA{
std::make_tuple("Dirac", 3u), std::make_tuple("DB1", 3u), std::make_tuple("DB2", 3u),
std::make_tuple("DB3", 3u), std::make_tuple("DB4", 3u), std::make_tuple("DB5", 3u),
std::make_tuple("DB6", 3u), std::make_tuple("DB7", 3u), std::make_tuple("DB8", 3u)},
world);
Vector<t_real> const dirty_image = (measurements->adjoint() * (data.vis)).real();
if (world.is_root()) {
// then writes stuff to files
boost::filesystem::path const path(output_filename(name));
#if PURIFY_PADMM_ALGORITHM == 3
auto const pb_path = path / kernel_name / "local_epsilon_replicated_grids";
#elif PURIFY_PADMM_ALGORITHM == 2
auto const pb_path = path / kernel_name / "global_epsilon_replicated_grids";
#elif PURIFY_PADMM_ALGORITHM == 1
auto const pb_path = path / kernel_name / "local_epsilon_distributed_grids";
#else
#error Unknown or unimplemented algorithm
#endif
mkdir_recursive(pb_path);
pfitsio::write2d(dirty_image, imsizey, imsizex, (pb_path / "dirty.fits").native());
}
// Create the padmm solver
auto const padmm = padmm_factory(measurements, sigma, sara, data, world, imsizey, imsizex);
// calls padmm
auto const diagnostic = (*padmm)();
// makes sure we set things up correctly
assert(world.broadcast(diagnostic.x).isApprox(diagnostic.x));
Vector<t_real> const residual_image = (measurements->adjoint() * diagnostic.residual).real();
if (world.is_root()) {
// then writes stuff to files
boost::filesystem::path const path(output_filename(name));
#if PURIFY_PADMM_ALGORITHM == 3
auto const pb_path = path / kernel_name / "local_epsilon_replicated_grids";
#elif PURIFY_PADMM_ALGORITHM == 2
auto const pb_path = path / kernel_name / "global_epsilon_replicated_grids";
#elif PURIFY_PADMM_ALGORITHM == 1
auto const pb_path = path / kernel_name / "local_epsilon_distributed_grids";
#else
#error Unknown or unimplemented algorithm
#endif
mkdir_recursive(pb_path);
pfitsio::write2d(dirty_image, imsizey, imsizex, (pb_path / "dirty.fits").native());
pfitsio::write2d(diagnostic.x.real(), imsizey, imsizex, (pb_path / "solution.fits").native());
pfitsio::write2d(residual_image, imsizey, imsizex, (pb_path / "residual.fits").native());
}
return 0;
}
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