Function train

NLP/Text2SQL-BASELINE/text2sql/launch/trainer.py:143–182

Args: model (TYPE): NULL optimizer (TYPE): NULL epochs (TYPE): NULL train_data (TYPE): NULL dev_data (TYPE): NULL test_data (TYPE): Default is None Returns: TODO Raises: NULL

(config, model, optimizer, epochs, train_data, dev_data, test_data=None)

def train(config, model, optimizer, epochs, train_data, dev_data, test_data=None):
    """

    Args:
        model (TYPE): NULL
        optimizer (TYPE): NULL
        epochs (TYPE): NULL
        train_data (TYPE): NULL
        dev_data (TYPE): NULL
        test_data (TYPE): Default is None

    Returns: TODO

    Raises: NULL
    """
    best_acc = -1e10
    best_epoch = 0
    timer = utils.Timer()
    for epoch in range(1, epochs + 1):
        loss = epoch_train(config, model, optimizer, epoch, train_data, config.general.is_debug)
        cost_time = timer.interval()
        logging.info(f'[train] epoch {epoch}/{epochs} loss is {loss:.6f}, cost {cost_time:.2f}s.')

        dev_loss, dev_acc = _eval_during_train(model, dev_data, epoch, config.data.output)
        log_str = f'[eval] dev loss {dev_loss:.6f}, acc {dev_acc:.4f}.'
        if test_data is not None:
            test_loss, test_acc = _eval_during_train(model, test_data, epoch, config.data.output)
            log_str += f' test loss {test_loss:.6f}, acc {test_acc:.4f}.'

        if dev_acc > best_acc:
            best_acc, best_epoch = dev_acc, epoch
            save_path = os.path.join(config.data.output, f'epoch{epoch:03d}_acc{best_acc:.4f}', 'model')
            io.save(model, optimizer, save_path)
            log_str += ' got best and saved.'
        else:
            log_str += f' best acc is {best_acc} on epoch {best_epoch}.'

        cost_time = timer.interval()
        log_str += f' cost [{cost_time:.2f}s]'
        logging.info(log_str)


if __name__ == "__main__":