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lbann_aecycgan.cpp
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////////////////////////////////////////////////////////////////////////////////
// Copyright (c) 2014-2021, Lawrence Livermore National Security, LLC.
// Produced at the Lawrence Livermore National Laboratory.
// Written by the LBANN Research Team (B. Van Essen, et al.) listed in
// the CONTRIBUTORS file. <[email protected]>
//
// LLNL-CODE-697807.
// All rights reserved.
//
// This file is part of LBANN: Livermore Big Artificial Neural Network
// Toolkit. For details, see http://software.llnl.gov/LBANN or
// https://github.com/LLNL/LBANN.
//
// Licensed under the Apache License, Version 2.0 (the "Licensee"); you
// may not use this file except in compliance with the License. You may
// obtain a copy of the License at:
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
// implied. See the License for the specific language governing
// permissions and limitations under the license.
//
// lbann_proto.cpp - prototext application
////////////////////////////////////////////////////////////////////////////////
#include "lbann/lbann.hpp"
#include "lbann/proto/proto_common.hpp"
#include "lbann/utils/argument_parser.hpp"
#include "lbann/utils/protobuf_utils.hpp"
#include "lbann/proto/lbann.pb.h"
#include "lbann/proto/model.pb.h"
#include <cstdlib>
using namespace lbann;
namespace {
int guess_global_rank() noexcept
{
int have_mpi;
MPI_Initialized(&have_mpi);
if (have_mpi) {
int rank;
MPI_Comm_rank(MPI_COMM_WORLD, &rank);
return rank;
}
else {
if (char const* slurm_flag = std::getenv("SLURM_PROCID"))
return std::stoi(slurm_flag);
if (char const* open_mpi_flag = std::getenv("OMPI_WORLD_COMM_RANK"))
return std::stoi(open_mpi_flag);
else if (char const* mv2_flag = std::getenv("MV2_COMM_WORLD_LOCAL_RANK"))
return std::stoi(mv2_flag);
else
return -1;
}
}
} // namespace
int main(int argc, char* argv[])
{
auto& arg_parser = global_argument_parser();
construct_all_options();
try {
arg_parser.parse(argc, argv);
}
catch (std::exception const& e) {
auto guessed_rank = guess_global_rank();
if (guessed_rank <= 0)
// Cannot call `El::ReportException` because MPI hasn't been
// initialized yet.
std::cerr << "Error during argument parsing:\n\ne.what():\n\n "
<< e.what() << "\n\nProcess terminating." << std::endl;
std::terminate();
}
world_comm_ptr comm = initialize(argc, argv);
const bool master = comm->am_world_master();
try {
// Split MPI into trainers
allocate_trainer_resources(comm.get());
if (arg_parser.help_requested() or argc == 1) {
if (master)
std::cout << arg_parser << std::endl;
return EXIT_SUCCESS;
}
std::ostringstream err;
auto pbs = protobuf_utils::load_prototext(master);
// Optionally over-ride some values in the prototext for each model
for (size_t i = 0; i < pbs.size(); i++) {
get_cmdline_overrides(*comm, *(pbs[i]));
}
lbann_data::LbannPB& pb = *(pbs[0]);
lbann_data::Trainer* pb_trainer = pb.mutable_trainer();
// Construct the trainer
auto& trainer = construct_trainer(comm.get(), pb_trainer, *(pbs[0]));
thread_pool& io_thread_pool = trainer.get_io_thread_pool();
auto model_1 =
build_model_from_prototext(argc,
argv,
pb_trainer,
*(pbs[0]),
comm.get(),
io_thread_pool,
trainer.get_callbacks_with_ownership()); // ae
std::unique_ptr<model> model_2, // cycgan
model_3; // ae+cycgan
if (pbs.size() > 1) {
model_2 =
build_model_from_prototext(argc,
argv,
pb_trainer,
*(pbs[1]),
comm.get(),
io_thread_pool,
trainer.get_callbacks_with_ownership());
}
if (pbs.size() > 2) {
model_3 =
build_model_from_prototext(argc,
argv,
pb_trainer,
*(pbs[2]),
comm.get(),
io_thread_pool,
trainer.get_callbacks_with_ownership());
}
const lbann_data::Model pb_model_1 = pbs[0]->model();
const lbann_data::Model pb_model_2 = pbs[1]->model();
const lbann_data::Model pb_model_3 = pbs[2]->model();
if (master)
std::cout << " Pre-train autoencoder " << std::endl;
trainer.train(model_1.get(), pb_model_1.num_epochs());
trainer.evaluate(model_1.get(), execution_mode::testing);
auto ae_weights = model_1->get_weights();
model_2->copy_trained_weights_from(ae_weights);
model_3->copy_trained_weights_from(ae_weights);
// Train cycle GAN
if (master)
std::cerr << "\nSTARTING train - cycle GAN \n\n";
trainer.train(model_2.get(), pb_model_2.num_epochs());
trainer.evaluate(model_2.get(), execution_mode::testing);
auto model2_weights = model_2->get_weights();
// Evaluate on pretrained autoencoder
if (master)
std::cout << " Copy trained weights from cycle GAN" << std::endl;
model_3->copy_trained_weights_from(model2_weights);
if (master)
std::cout << " Save AE + cycleGAN" << std::endl;
model_3->save_model();
if (master)
std::cout << " Evaluate cycleGAN model on pretrained autoencoder"
<< std::endl;
trainer.evaluate(model_3.get(), execution_mode::testing);
}
catch (std::exception& e) {
El::ReportException(e);
// It's possible that a proper subset of ranks throw some
// exception. But we want to tear down the whole world.
El::mpi::Abort(El::mpi::COMM_WORLD, EXIT_FAILURE);
}
return EXIT_SUCCESS;
}