Function set_logger

tensorflow/vision/model/utils.py:38–64

Sets the logger to log info in terminal and file `log_path`. In general, it is useful to have a logger so that every output to the terminal is saved in a permanent file. Here we save it to `model_dir/train.log`. Example: ``` logging.info("Starting training...") ``` Arg

(log_path)

def set_logger(log_path):
    """Sets the logger to log info in terminal and file `log_path`.

    In general, it is useful to have a logger so that every output to the terminal is saved
    in a permanent file. Here we save it to `model_dir/train.log`.

    Example:
    ```
    logging.info("Starting training...")
    ```

    Args:
        log_path: (string) where to log
    """
    logger = logging.getLogger()
    logger.setLevel(logging.INFO)

    if not logger.handlers:
        # Logging to a file
        file_handler = logging.FileHandler(log_path)
        file_handler.setFormatter(logging.Formatter('%(asctime)s:%(levelname)s: %(message)s'))
        logger.addHandler(file_handler)

        # Logging to console
        stream_handler = logging.StreamHandler()
        stream_handler.setFormatter(logging.Formatter('%(message)s'))
        logger.addHandler(stream_handler)


def save_dict_to_json(d, json_path):