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)
def __init__(self, cfg: Config): self._train = True self.log_interval = cfg.log_interval self.log_dir = cfg.run_dir self._img_log_dir = cfg.img_log_dir # get git commit hash if folder is a git repository current_dir = str(Path(__file__).absolute().parent) if subprocess.call(["git", "-C", current_dir, "branch"], stderr=subprocess.DEVNULL, stdout=subprocess.DEVNULL) == 0: git_output = subprocess.check_output( ["git", "-C", current_dir, "describe", "--always"]) cfg.force_update(key='commit_hash', value=git_output.strip().decode('ascii')) # Additionally, the package version is stored in the config cfg.force_update(key="package_version", value=__version__) # store a copy of the config into the run folder cfg.dump_config(folder=self.log_dir) self.epoch = 0 self.update = 0 self._metrics = defaultdict(list) self.writer = None
def create_config_files(base_config_path: Path, modify_dict: Dict[str, list], output_dir: Path): """Create configs, given a base config and a dictionary of parameters to modify. This function will create one config file for each combination of parameters defined in the modify_dict. Parameters ---------- base_config_path : Path Path to a base config file (.yml) modify_dict : dict Dictionary, mapping from parameter names to lists of possible parameter values. output_dir : Path Path to a folder where the generated configs will be stored """ if not output_dir.is_dir(): output_dir.mkdir(parents=True) # load base config as dictionary base_config = Config(base_config_path) experiment_name = base_config.experiment_name option_names = list(modify_dict.keys()) # iterate over each possible combination of hyper parameters for i, options in enumerate( itertools.product(*[val for val in modify_dict.values()])): for key, val in zip(option_names, options): base_config.force_update(key=key, value=val) # create a unique run name name = experiment_name for key, val in zip(option_names, options): name += f"_{key}{val}" base_config.force_update(key="experiment_name", value=name) base_config.dump_config(output_dir, f"config_{i+1}.yml") print(f"Finished. Configs are stored in {output_dir}")