def main():
args = get_argument_parser().parse_args()
file_dir = args.file_dir
config_file = args.config_file
file_ext = args.file_ext
verbose = args.verbose
# Load settings file.
settings = load_yaml_file(Path(file_dir, f'{config_file}.{file_ext}'))
init_loggers(verbose=verbose, settings=settings['dirs_and_files'])
logger_main = logger.bind(is_caption=False, indent=0)
logger_sec = logger.bind(is_caption=False, indent=1)
logger_main.info(datetime.now().strftime('%Y-%m-%d %H:%M'))
logger_main.info('Doing only dataset creation')
# Create the dataset.
logger_main.info('Starting Clotho dataset creation')
logger_sec.info('Creating examples')
create_dataset(settings_dataset=settings['dataset_creation_settings'],
settings_dirs_and_files=settings['dirs_and_files'])
logger_sec.info('Examples created')
logger_sec.info('Extracting features')
extract_features(root_dir=settings['dirs_and_files']['root_dirs']['data'],
settings_data=settings['dirs_and_files']['dataset'],
settings_features=settings['feature_extraction_settings'])
logger_sec.info('Features extracted')
logger_main.info('Dataset created')
def main():
args = get_argument_parser().parse_args()
file_dir = args.file_dir
config_file = args.config_file
file_ext = args.file_ext
verbose = args.verbose
settings = load_yaml_file(Path(file_dir, f'{config_file}.{file_ext}'))
init_loggers(verbose=verbose, settings=settings['dirs_and_files'])
logger_main = logger.bind(is_caption=False, indent=0)
logger_inner = logger.bind(is_caption=False, indent=1)
if settings['workflow']['dataset_creation']:
logger_main.info('Starting creation of dataset')
logger_inner.info('Creating examples')
dataset_multiprocess.create_dataset(
settings_dataset=settings['dataset_creation_settings'],
settings_dirs_and_files=settings['dirs_and_files'])
logger_inner.info('Examples created')
logger_inner.info('Extracting features')
dataset_multiprocess.extract_features(
root_dir=settings['dirs_and_files']['root_dirs']['data'],
settings_data=settings['dirs_and_files']['dataset'],
settings_features=settings['feature_extraction_settings'])
logger_inner.info('Features extracted')
logger_main.info('Creation of dataset ended')
def main():
args = get_argument_parser().parse_args()
file_dir = args.file_dir
config_file = args.config_file
file_ext = args.file_ext
verbose = args.verbose
settings = load_yaml_file(Path(
file_dir, f'{config_file}.{file_ext}'))
init_loggers(verbose=verbose,
settings=settings['dirs_and_files'])
logger_main = logger.bind(is_caption=False, indent=0)
logger_inner = logger.bind(is_caption=False, indent=1)
if settings['workflow']['dataset_creation']:
logger_main.info('Starting creation of dataset')
logger_inner.info('Creating examples')
dataset.create_dataset(
settings_dataset=settings['dataset_creation_settings'],
settings_dirs_and_files=settings['dirs_and_files'])
logger_inner.info('Examples created')
logger_inner.info('Extracting features')
dataset.extract_features(
root_dir=settings['dirs_and_files']['root_dirs']['data'],
settings_data=settings['dirs_and_files']['dataset'],
settings_features=settings['feature_extraction_settings'])
logger_inner.info('Features extracted')
logger_main.info('Creation of dataset ended')
if settings['workflow']['dnn_testing']:
# Create test dataset if not yet created
test_split_feat_dir = Path(settings['dirs_and_files']['root_dirs']['data']) \
.joinpath(settings['dirs_and_files']['dataset']['features_dirs']['output'],
settings['dirs_and_files']['dataset']['features_dirs']['test'])
if not test_split_feat_dir.exists():
logger_main.info('Starting creation of test dataset')
logger_inner.info('Extracting features')
dataset.extract_features_test(
root_dir=settings['dirs_and_files']['root_dirs']['data'],
settings_data=settings['dirs_and_files']['dataset'],
settings_features=settings['feature_extraction_settings'],
settings_audio=settings['dataset_creation_settings']['audio'])
logger_inner.info('Features extracted')
logger_main.info('Creation of test dataset ended')
else:
logger_inner.info('Found existing test data')
if settings['workflow']['dnn_training'] or \
settings['workflow']['dnn_evaluation'] or \
settings['workflow']['dnn_testing']:
method.method(settings)
def main():
args = get_argument_parser().parse_args()
file_dir = args.file_dir
config_file = args.config_file
file_ext = args.file_ext
verbose = args.verbose
settings = load_yaml_file(Path(file_dir, f'{config_file}.{file_ext}'))
init_loggers(verbose=verbose, settings=settings['logging'])
method.method(settings)
def main():
args = get_argument_parser().parse_args()
file_dir = args.file_dir
config_file = args.config_file
file_ext = args.file_ext
verbose = args.verbose
job_id = args.job_id
settings = load_yaml_file(Path(file_dir, f'{config_file}.{file_ext}'))
init_loggers(verbose=verbose,
settings=settings['dirs_and_files'],
job_id=job_id)
method.method(settings, job_id)
def main():
args = get_argument_parser().parse_args()
file_dir = args.file_dir
config_file = args.config_file
file_ext = args.file_ext
verbose = args.verbose
settings = file_io.load_yaml_file(
Path(file_dir, f'{config_file}.{file_ext}'))
printing.init_loggers(verbose=verbose, settings=settings['dirs_and_files'])
logger_main = logger.bind(is_caption=False, indent=0)
logger_main.info('Starting method only')
method(settings)
logger_main.info('Method\'s done')
training_epochs = 50
log_interval = 100
checkpoint_save_interval = 5
device = torch.device('cuda:0')
args = get_argument_parser().parse_args()
file_dir = args.file_dir
config_file = args.config_file
file_ext = args.file_ext
verbose = args.verbose
print("load settings start")
settings = load_yaml_file(Path(file_dir, f'{config_file}.{file_ext}'))
settings_training = settings['dnn_training_settings']['training'],
settings_data = settings['dnn_training_settings']['data'],
settings_io = settings['dirs_and_files']
indices_list = _load_indices_file(
settings['dirs_and_files'], settings['dnn_training_settings']['data'])
data_path_evaluation = Path(
settings_io['root_dirs']['data'],
settings_io['dataset']['features_dirs']['output'],
settings_io['dataset']['features_dirs']['evaluation'])
validation_data = get_clotho_loader(
settings_io['dataset']['features_dirs']['evaluation'],
