def get_student_cfg(cfg, path): if os.path.exists(path): with open(path) as file: student_file_cfg = config.load_cfg(file) else: print("File {} not exists".format(args.student_file)) print("Please input a specific student config file") print("Exiting...") exit(-1) student_cfg = copy.deepcopy(cfg) for key, value in student_file_cfg.items(): student_cfg[key] = value return student_cfg
def load_cfg_from_file(cfg_filename): """Load config from a file Args: cfg_filename (str): Returns: CfgNode: loaded configuration """ with open(cfg_filename, "r") as f: cfg = load_cfg(f) cfg_template = _C cfg_template.merge_from_other_cfg(cfg) return cfg_template
def make_config(config_file): with open(config_file, 'r') as f: cfg = config.load_cfg(f) cfg.set_new_allowed(True) return cfg.clone()
import numpy as np if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument("--mat", required=True) parser.add_argument("--weights", required=True) parser.add_argument("--var_name", required=True) parser.add_argument("--output_mat", required=True) parser.add_argument("--norm", required=True) args = parser.parse_args() var = sio.loadmat(args.mat) in_data = torch.tensor(var[args.var_name]) with open(args.norm, "r") as f: cfg = config.load_cfg(f) input_mean = torch.tensor(cfg.input.mean) input_std = torch.tensor(cfg.input.std) target_mean = torch.tensor(cfg.target.mean) target_std = torch.tensor(cfg.target.std) in_data = (in_data - input_mean) / input_std with open(args.weights, "rb") as f: net = torch.load(f) net.eval() output = net(in_data) * target_std + target_mean
def get_cfg_from_file(self, fbase): with open(fbase + "model_and_cfg/exp_cfg", "rt") as cfg_file: return load_cfg(cfg_file)
def make_config(config_file): with open(config_file, 'r') as f: cfg = config.load_cfg(f) return cfg.clone()
def read_yaml(filepath: str): with open(filepath, "r") as f: return load_cfg(f)