def load()

in src/util.py [0:0]

• 21 lines of code
• 3 McCabe index (conditional complexity)

def load(model_class, dir_path, opt, reset_params=False):
    epoch_path = os.path.realpath(dir_path)
    optimizer_path = os.path.join(epoch_path, "optimizer.pth.tar")
    logger.info("Loading %s" % epoch_path)
    model = model_class.from_pretrained(epoch_path)
    model = model.to(opt.device)
    logger.info("loading checkpoint %s" %optimizer_path)
    checkpoint = torch.load(optimizer_path, map_location=opt.device)
    opt_checkpoint = checkpoint["opt"]
    step = checkpoint["step"]
    if "best_eval_metric" in checkpoint:
        best_eval_metric = checkpoint["best_eval_metric"]
    else:
        best_eval_metric = checkpoint["best_dev_em"]
    if not reset_params:
        optimizer, scheduler = set_optim(opt_checkpoint, model)
        scheduler.load_state_dict(checkpoint["scheduler"])
        optimizer.load_state_dict(checkpoint["optimizer"])
    else:
        optimizer, scheduler = set_optim(opt, model)

    return model, optimizer, scheduler, opt_checkpoint, step, best_eval_metric