def finetune(config_file: Path = None, gpu: int = None):
"""Finetune a pre-trained model.
Parameters
----------
config_file : Path, optional
Path to an additional config file. Each config argument in this file will overwrite the original run config.
The config file for finetuning must contain the argument `base_run_dir`, pointing to the folder of the
pre-trained model, as well as 'finetune_modules' to indicate which model parts will be trained during
fine-tuning.
gpu : int, optional
GPU id to use. Will override config argument 'device'. A value smaller than zero indicates CPU.
"""
# load finetune config and check for a non-empty list of finetune_modules
temp_config = Config(config_file)
if not temp_config.finetune_modules:
raise ValueError("For finetuning, at least one model part has to be specified by 'finetune_modules'.")
# extract base run dir, load base run config and combine with the finetune arguments
config = Config(temp_config.base_run_dir / "config.yml")
config.update_config({'run_dir': None, 'experiment_name': None})
config.update_config(config_file)
config.is_finetuning = True
# if the base run was a continue_training run, we need to override the continue_training flag from its config.
config.is_continue_training = False
# check if a GPU has been specified as command line argument. If yes, overwrite config
if gpu is not None and gpu >= 0:
config.device = f"cuda:{gpu}"
if gpu is not None and gpu < 0:
config.device = "cpu"
start_training(config)
def finetune(config_file: Path = None, gpu: int = None):
"""Finetune a pre-trained model.
Parameters
----------
config_file : Path, optional
Path to an additional config file. Each config argument in this file will overwrite the original run config.
The config file for finetuning must contain the argument `base_run_dir`, pointing to the folder of the
pre-trained model.
gpu : int, optional
GPU id to use. Will override config argument 'device'.
"""
# load finetune config, extract base run dir, load base run config and combine with the finetune arguments
temp_config = Config(config_file)
config = Config(temp_config.base_run_dir / "config.yml")
config.force_update({'run_dir': None, 'experiment_name': None})
config.update_config(config_file)
config.is_finetuning = True
# check if a GPU has been specified as command line argument. If yes, overwrite config
if gpu is not None:
config.device = f"cuda:{gpu}"
start_training(config)