def main(argv):
parser = get_argument_parser()
args = parser.parse_args(args=argv)
config = create_sample_config(args, parser)
if config.dist_url == "env://":
config.update_from_env()
configure_paths(config)
copyfile(args.config, osp.join(config.log_dir, 'config.json'))
source_root = Path(__file__).absolute().parents[2] # nncf root
create_code_snapshot(source_root,
osp.join(config.log_dir, "snapshot.tar.gz"))
if config.seed is not None:
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
config.execution_mode = get_execution_mode(config)
if config.metrics_dump is not None:
write_metrics(0, config.metrics_dump)
if not is_staged_quantization(config):
start_worker(main_worker, config)
else:
from examples.classification.staged_quantization_worker import staged_quantization_main_worker
start_worker(staged_quantization_main_worker, config)
def main(argv):
parser = get_argument_parser()
args = parser.parse_args(args=argv)
config = create_sample_config(args, parser)
configure_paths(config)
source_root = Path(__file__).absolute().parents[2] # nncf root
create_code_snapshot(source_root, osp.join(config.log_dir, "snapshot.tar.gz"))
config.execution_mode = get_execution_mode(config)
if config.dataset_dir is not None:
config.train_imgs = config.train_anno = config.test_imgs = config.test_anno = config.dataset_dir
start_worker(main_worker, config)
def main(argv):
parser = get_arguments_parser()
arguments = parser.parse_args(args=argv)
config = create_sample_config(arguments, parser)
if arguments.dist_url == "env://":
config.update_from_env()
if not osp.exists(config.log_dir):
os.makedirs(config.log_dir)
config.log_dir = str(config.log_dir)
configure_paths(config)
logger.info("Save directory: {}".format(config.log_dir))
config.execution_mode = get_execution_mode(config)
start_worker(main_worker, config)