def main(argv):
parser = get_argument_parser()
args = parser.parse_args(args=argv)
config = Config.from_json(args.config)
config.update_from_args(args, parser)
if config.dist_url == "env://":
config.update_from_env()
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"))
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 not is_binarization(config):
start_worker(main_worker, config)
else:
from examples.classification.binarization_worker import main_worker_binarization
start_worker(main_worker_binarization, config)
def main(argv):
parser = get_argument_parser()
args = parser.parse_args(args=argv)
config = Config.from_json(args.config)
config.update_from_args(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_argument_parser()
config = get_config_from_argv(argv, parser)
serialize_config(config, config.log_dir)
nncf_root = Path(__file__).absolute().parents[2]
create_code_snapshot(nncf_root,
os.path.join(config.log_dir, "snapshot.tar.gz"))
if 'train' in config.mode or 'test' in config.mode:
train_test_export(config)
elif 'export' in config.mode:
export(config)
def main(argv):
parser = get_argument_parser()
config = get_config_from_argv(argv, parser)
#config['eager_mode'] = True
serialize_config(config, config.log_dir)
print('*'*50)
print(f'Using model type: {config.model_type}')
print('*'*50)
nncf_root = Path(__file__).absolute().parents[2]
create_code_snapshot(nncf_root, osp.join(config.log_dir, "snapshot.tar.gz"))
if 'train' in config.mode or 'test' in config.mode:
train_test_export(config)
elif 'export' in config.mode:
export(config)