import os import importlib from typing import Type, TypeVar from argparse import ArgumentParser from omegaconf import OmegaConf, DictConfig def get_module_config(cfg_model: DictConfig, paths: list[str], cfg_root: str) -> DictConfig: files = [os.path.join(cfg_root, 'modules', p+'.yaml') for p in paths] for file in files: assert os.path.exists(file), f'{file} is not exists.' with open(file, 'r') as f: cfg_model.merge_with(OmegaConf.load(f)) return cfg_model def get_obj_from_str(string: str, reload: bool = False) -> Type: module, cls = string.rsplit(".", 1) if reload: module_imp = importlib.import_module(module) importlib.reload(module_imp) return getattr(importlib.import_module(module, package=None), cls) def instantiate_from_config(config: DictConfig) -> TypeVar: return get_obj_from_str(config["target"])(**config.get("params", dict())) def parse_args() -> DictConfig: parser = ArgumentParser() parser.add_argument("--cfg", type=str, required=True, help="The main config file") parser.add_argument('--example', type=str, required=False, help="The input texts and lengths with txt format") parser.add_argument('--example_hint', type=str, required=False, help="The input hint ids and lengths with txt format") parser.add_argument('--no-plot', action="store_true", required=False, help="Whether to plot the skeleton-based motion") parser.add_argument('--replication', type=int, default=1, help="The number of replications of sampling") parser.add_argument('--vis', type=str, default="tb", choices=['tb', 'swanlab'], help="The visualization backends: tensorboard or swanlab") parser.add_argument('--optimize', action='store_true', help="Enable optimization for motion control") args = parser.parse_args() cfg = OmegaConf.load(args.cfg) cfg_root = os.path.dirname(args.cfg) cfg_model = get_module_config(cfg.model, cfg.model.target, cfg_root) cfg = OmegaConf.merge(cfg, cfg_model) cfg.example = args.example cfg.example_hint = args.example_hint cfg.no_plot = args.no_plot cfg.replication = args.replication cfg.vis = args.vis cfg.optimize = args.optimize return cfg