int main( int argc, char* argv[] )
{
// Our progress counter.
auto counter = pc::ProgressCounter::Create();
// Our tasks queue.
TaskQueue tasks;
// Produce the tasks.
std::mt19937 rnd_device( (uint32_t)time( 0 ) );
for( int32_t task_id = 0; task_id < 680; ++task_id )
{
auto goal = counter->ProduceGoal();
std::chrono::milliseconds interval{ 600 + rnd_device() % 1400 };
tasks.PushTask( { [goal, interval](){ std::this_thread::sleep_for( interval ); } } );
};
// Produce the workers.
tasks.Lock();
size_t threads_count = std::thread::hardware_concurrency();
while( threads_count-- > 0 )
{
std::thread t( ThreadFunction, std::ref( tasks ) );
t.detach();
};
tasks.Unlock();
// Display the progress of tasks completion until the progress reaches 100%
do
{
printf( "\rCurrent progress : %3.3f%%; Tasks remained : %zd;", 100.0f * counter->GetFinisedPercent(), tasks.GetCount() );
std::this_thread::sleep_for( std::chrono::milliseconds( 500 ) );
}
while( counter->GetRemainsPercent() > 0.0f );
};
int main(int argc, char* argv[]) {
std::random_device rnd_device;
std::mt19937 rng(rnd_device());
std::unique_ptr<Forest> forest(new Forest(1, 300));
std::stringstream filename;
for(size_t L : {
50, 100, 200, 300
}) {
std::chrono::steady_clock::time_point start = std::chrono::steady_clock::now();
forest->Resize(L);
filename << "./data/clustersize/" << L << ".txt";
filename.flush();
std::ofstream file(filename.str());
for(double p=0.1; p<=0.9; p+=0.05) {
forest->Fill(p, rng);
file << p << "\t" << forest->biggestCluster() << std::endl;
}
file.flush();
file.close();
std::cout << filename.str() << " written." << std::endl;
filename.str(std::string());
std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now();
std::cout << "This one (L=" << L << ") took "
<< std::chrono::duration_cast<std::chrono::milliseconds>(end-start).count()
<< " ms." << std::endl;
}
return 0;
}
void QuickSortTest::SetUp(){
std::random_device rnd_device;
std::mt19937 mersenne_engine(rnd_device());
std::uniform_int_distribution<int> dist(1, 100);
auto gen = std::bind( dist, mersenne_engine );
std::generate( std::begin( d_array ), std::end( d_array ), gen );
}
int main()
{
disable_sigint();
std::random_device rnd_device;
std::mt19937 mersenne_engine(rnd_device());
std::uniform_int_distribution<size_t> dist(1, 20);
auto gen = std::bind(dist, mersenne_engine);
size_t i = 0;
for (;;)
{
if ((i % (16 * 1024)) == 0)
{
if (is_sigint_pending())
break;
std::cout << '.' << std::flush;
}
size_t size = gen();
std::vector<char> input = random_vector(size, mersenne_engine);
/*write_string_screened(std::cout, std::string(input.begin(), input.end()));
std::cout << std::endl;*/
input.push_back(guard_value);
lexer lex(input.data(), input.data() + input.size() - 1);
token tok = lex.fetch(true);
for (;;)
{
if (tok.tok_type == token_type::eof)
break;
tok = lex.fetch(false);
}
++i;
}
std::cout << "run " << i << " times\n";
}
int main(int argc, const char* argv[]) {
try {
// Parse command line arguments.
TCLAP::CmdLine cmd("Depth RF trainer", ' ', "0.3");
TCLAP::ValueArg<std::string> image_list_file_arg("f", "image-list-file", "File containing the names of image files", true, "", "string", cmd);
TCLAP::ValueArg<int> num_of_classes_arg("n", "num-of-classes", "Number of classes in the data", true, 1, "int", cmd);
TCLAP::SwitchArg print_confusion_matrix_switch("m", "conf-matrix", "Print confusion matrix", cmd, true);
TCLAP::ValueArg<int> background_label_arg("l", "background-label", "Lower bound of background labels to be ignored", false, -1, "int", cmd);
TCLAP::ValueArg<std::string> json_forest_file_arg("j", "json-forest-file", "JSON file where the trained forest should be saved", false, "forest.json", "string");
TCLAP::ValueArg<std::string> binary_forest_file_arg("b", "binary-forest-file", "Binary file where the trained forest should be saved", false, "forest.bin", "string");
TCLAP::ValueArg<std::string> config_file_arg("c", "config", "YAML file with training parameters", false, "", "string", cmd);
#if AIT_MULTI_THREADING
TCLAP::ValueArg<int> num_of_threads_arg("t", "threads", "Number of threads to use", false, -1, "int", cmd);
#endif
cmd.xorAdd(json_forest_file_arg, binary_forest_file_arg);
cmd.parse(argc, argv);
const int num_of_classes = num_of_classes_arg.getValue();
bool print_confusion_matrix = print_confusion_matrix_switch.getValue();
const std::string image_list_file = image_list_file_arg.getValue();
// Initialize training and weak-learner parameters to defaults or load from file
ForestTrainerT::ParametersT training_parameters;
WeakLearnerT::ParametersT weak_learner_parameters;
if (config_file_arg.isSet()) {
ait::log_info(false) << "Reading config file " << config_file_arg.getValue() << "... " << std::flush;
std::ifstream ifile_config(config_file_arg.getValue());
cereal::JSONInputArchive iarchive(ifile_config);
iarchive(cereal::make_nvp("training_parameters", training_parameters));
iarchive(cereal::make_nvp("weak_learner_parameters", weak_learner_parameters));
ait::log_info(false) << " Done." << std::endl;
}
#if AIT_MULTI_THREADING
if (num_of_threads_arg.isSet()) {
training_parameters.num_of_threads = num_of_threads_arg.getValue();
}
#endif
// Read image file list
ait::log_info(false) << "Reading image list ... " << std::flush;
std::vector<std::tuple<std::string, std::string>> image_list;
std::ifstream ifile(image_list_file);
if (!ifile.good()) {
throw std::runtime_error("Unable to open image list file");
}
ait::CSVReader<std::string> csv_reader(ifile);
for (auto it = csv_reader.begin(); it != csv_reader.end(); ++it) {
if (it->size() != 2) {
cmd.getOutput()->usage(cmd);
ait::log_error() << "Image list file should contain two columns with the data and label filenames.";
exit(-1);
}
const std::string& data_filename = (*it)[0];
const std::string& label_filename = (*it)[1];
boost::filesystem::path data_path = boost::filesystem::path(data_filename);
boost::filesystem::path label_path = boost::filesystem::path(label_filename);
if (!data_path.is_absolute()) {
data_path = boost::filesystem::path(image_list_file).parent_path();
data_path /= data_filename;
}
if (!label_path.is_absolute()) {
label_path = boost::filesystem::path(image_list_file).parent_path();
label_path /= label_filename;
}
image_list.push_back(std::make_tuple(data_path.string(), label_path.string()));
}
ait::log_info(false) << " Done." << std::endl;
// TODO: Ensure that label images do not contain values > num_of_classes except for background pixels. Other approach: Test samples directly below.
// Set lower bound for background pixel lables
ait::label_type background_label;
if (background_label_arg.isSet()) {
background_label = background_label_arg.getValue();
} else {
background_label = num_of_classes;
}
weak_learner_parameters.background_label = background_label;
// Create weak learner and trainer.
StatisticsT::Factory statistics_factory(num_of_classes);
WeakLearnerT iwl(weak_learner_parameters, statistics_factory);
ForestTrainerT trainer(iwl, training_parameters);
SampleProviderT sample_provider(image_list, weak_learner_parameters);
BaggingWrapperT bagging_wrapper(trainer, sample_provider);
#ifdef AIT_TESTING
RandomEngineT rnd_engine(11);
#else
std::random_device rnd_device;
ait::log_info() << "rnd(): " << rnd_device();
RandomEngineT rnd_engine(rnd_device());
#endif
// Train a forest and time it.
auto start_time = std::chrono::high_resolution_clock::now();
// TODO
// ForestTrainerT::ForestT forest = bagging_wrapper.train_forest(rnd_engine);
// TODO: Testing all samples for comparison with depth_trainer
sample_provider.clear_samples();
for (int i = 0; i < image_list.size(); ++i) {
sample_provider.load_samples_from_image(i, rnd_engine);
}
SampleIteratorT samples_start = sample_provider.get_samples_begin();
SampleIteratorT samples_end = sample_provider.get_samples_end();
ait::log_info() << "Starting training ...";
ForestTrainerT::ForestT forest = trainer.train_forest(samples_start, samples_end, rnd_engine);
auto stop_time = std::chrono::high_resolution_clock::now();
auto duration = stop_time - start_time;
auto period = std::chrono::high_resolution_clock::period();
double elapsed_seconds = duration.count() * period.num / static_cast<double>(period.den);
ait::log_info() << "Done.";
ait::log_info() << "Running time: " << elapsed_seconds;
// Optionally: Serialize forest to JSON file.
if (json_forest_file_arg.isSet()) {
{
ait::log_info(false) << "Writing json forest file " << json_forest_file_arg.getValue() << "... " << std::flush;
std::ofstream ofile(json_forest_file_arg.getValue());
cereal::JSONOutputArchive oarchive(ofile);
oarchive(cereal::make_nvp("forest", forest));
ait::log_info(false) << " Done." << std::endl;
}
// Optionally: Serialize forest to binary file.
} else if (binary_forest_file_arg.isSet()) {
{
ait::log_info(false) << "Writing binary forest file " << binary_forest_file_arg.getValue() << "... " << std::flush;
std::ofstream ofile(binary_forest_file_arg.getValue(), std::ios_base::binary);
cereal::BinaryOutputArchive oarchive(ofile);
oarchive(cereal::make_nvp("forest", forest));
ait::log_info(false) << " Done." << std::endl;
}
} else {
throw("This should never happen. Either a JSON or a binary forest file have to be specified!");
}
// Optionally: Compute some stats and print them.
if (print_confusion_matrix) {
ait::log_info(false) << "Creating samples for testing ... " << std::flush;
sample_provider.clear_samples();
for (int i = 0; i < image_list.size(); ++i) {
sample_provider.load_samples_from_image(i, rnd_engine);
}
SampleIteratorT samples_start = sample_provider.get_samples_begin();
SampleIteratorT samples_end = sample_provider.get_samples_end();
ait::log_info(false) << " Done." << std::endl;
std::vector<ait::size_type> sample_counts(num_of_classes, 0);
for (auto sample_it = samples_start; sample_it != samples_end; sample_it++) {
++sample_counts[sample_it->get_label()];
}
auto logger = ait::log_info(true);
logger << "Sample counts>> ";
for (int c = 0; c < num_of_classes; ++c) {
if (c > 0) {
logger << ", ";
}
logger << "class " << c << ": " << sample_counts[c];
}
logger.close();
// For each tree extract leaf node indices for each sample.
std::vector<std::vector<ait::size_type>> forest_leaf_indices = forest.evaluate(samples_start, samples_end);
// Compute number of prediction matches based on a majority vote among the forest.
int match = 0;
int no_match = 0;
for (auto tree_it = forest.cbegin(); tree_it != forest.cend(); ++tree_it) {
for (auto sample_it = samples_start; sample_it != samples_end; sample_it++) {
const auto &node_it = tree_it->cbegin() + (forest_leaf_indices[tree_it - forest.cbegin()][sample_it - samples_start]);
const auto &statistics = node_it->get_statistics();
auto max_it = std::max_element(statistics.get_histogram().cbegin(), statistics.get_histogram().cend());
auto label = max_it - statistics.get_histogram().cbegin();
if (label == sample_it->get_label()) {
match++;
} else {
no_match++;
}
}
}
ait::log_info() << "Match: " << match << ", no match: " << no_match;
// Compute confusion matrix.
auto forest_utils = ait::make_forest_utils(forest);
auto confusion_matrix = forest_utils.compute_confusion_matrix(samples_start, samples_end);
ait::log_info() << "Confusion matrix:" << std::endl << confusion_matrix;
auto norm_confusion_matrix = ait::EvaluationUtils::normalize_confusion_matrix(confusion_matrix);
ait::log_info() << "Normalized confusion matrix:" << std::endl << norm_confusion_matrix;
ait::log_info() << "Diagonal of normalized confusion matrix:" << std::endl << norm_confusion_matrix.diagonal();
// Computing per-frame confusion matrix
ait::log_info() << "Computing per-frame confusion matrix.";
using ConfusionMatrixType = typename decltype(forest_utils)::MatrixType;
ConfusionMatrixType per_frame_confusion_matrix(num_of_classes, num_of_classes);
per_frame_confusion_matrix.setZero();
WeakLearnerT::ParametersT full_parameters(weak_learner_parameters);
// Modify parameters to retrieve all pixels per sample
full_parameters.samples_per_image_fraction = 1.0;
SampleProviderT full_sample_provider(image_list, full_parameters);
for (int i = 0; i < image_list.size(); ++i) {
full_sample_provider.clear_samples();
full_sample_provider.load_samples_from_image(i, rnd_engine);
samples_start = full_sample_provider.get_samples_begin();
samples_end = full_sample_provider.get_samples_end();
forest_utils.update_confusion_matrix(per_frame_confusion_matrix, samples_start, samples_end);
}
ait::log_info() << "Per-frame confusion matrix:" << std::endl << per_frame_confusion_matrix;
ConfusionMatrixType per_frame_norm_confusion_matrix = ait::EvaluationUtils::normalize_confusion_matrix(per_frame_confusion_matrix);
ait::log_info() << "Normalized per-frame confusion matrix:" << std::endl << per_frame_norm_confusion_matrix;
ait::log_info() << "Diagonal of normalized per-frame confusion matrix:" << std::endl << per_frame_norm_confusion_matrix.diagonal();
ait::log_info() << "Mean of diagonal of normalized per-frame confusion matrix:" << std::endl << per_frame_norm_confusion_matrix.diagonal().mean();
}
} catch (const std::runtime_error& error) {
std::cerr << "Runtime exception occured" << std::endl;
std::cerr << error.what() << std::endl;
}
return 0;
}
