Function _get_logger

tensorlayer/logging/tl_logging.py:44–87

()

def _get_logger():
    global _logger

    # Use double-checked locking to avoid taking lock unnecessarily.
    if _logger is not None:
        return _logger

    _logger_lock.acquire()

    try:
        if _logger:
            return _logger

        # Scope the TensorFlow logger to not conflict with users' loggers.
        logger = _logging.getLogger('tensorlayer')

        # Don't further configure the TensorFlow logger if the root logger is
        # already configured. This prevents double logging in those cases.
        if not _logging.getLogger().handlers:
            # Determine whether we are in an interactive environment
            # This is only defined in interactive shells.
            if hasattr(_sys, "ps1"):
                _interactive = True
            else:
                _interactive = _sys.flags.interactive

            # If we are in an interactive environment (like Jupyter), set loglevel
            # to INFO and pipe the output to stdout.
            if _interactive:
                logger.setLevel(INFO)
                _logging_target = _sys.stdout
            else:
                _logging_target = _sys.stderr

            # Add the output handler.
            _handler = _logging.StreamHandler(_logging_target)
            _handler.setFormatter(_logging.Formatter('[TL] %(message)s'))
            logger.addHandler(_handler)

        _logger = logger
        return _logger

    finally:
        _logger_lock.release()


def log(level, msg, *args, **kwargs):