Source code for tensorpack.utils.logger

# -*- coding: utf-8 -*-
# File: logger.py


import logging
import os
import shutil
import os.path
from termcolor import colored
from datetime import datetime
from six.moves import input
import sys

__all__ = ['set_logger_dir', 'auto_set_dir', 'get_logger_dir']


class _MyFormatter(logging.Formatter):
    def format(self, record):
        date = colored('[%(asctime)s @%(filename)s:%(lineno)d]', 'green')
        msg = '%(message)s'
        if record.levelno == logging.WARNING:
            fmt = date + ' ' + colored('WRN', 'red', attrs=['blink']) + ' ' + msg
        elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:
            fmt = date + ' ' + colored('ERR', 'red', attrs=['blink', 'underline']) + ' ' + msg
        else:
            fmt = date + ' ' + msg
        if hasattr(self, '_style'):
            # Python3 compatibility
            self._style._fmt = fmt
        self._fmt = fmt
        return super(_MyFormatter, self).format(record)


def _getlogger():
    logger = logging.getLogger('tensorpack')
    logger.propagate = False
    logger.setLevel(logging.INFO)
    handler = logging.StreamHandler(sys.stdout)
    handler.setFormatter(_MyFormatter(datefmt='%m%d %H:%M:%S'))
    logger.addHandler(handler)
    return logger


_logger = _getlogger()
_LOGGING_METHOD = ['info', 'warning', 'error', 'critical', 'warn', 'exception', 'debug', 'setLevel']
# export logger functions
for func in _LOGGING_METHOD:
    locals()[func] = getattr(_logger, func)
    __all__.append(func)


def _get_time_str():
    return datetime.now().strftime('%m%d-%H%M%S')


# globals: logger file and directory:
LOG_DIR = None
_FILE_HANDLER = None


def _set_file(path):
    global _FILE_HANDLER
    if os.path.isfile(path):
        backup_name = path + '.' + _get_time_str()
        shutil.move(path, backup_name)
        _logger.info("Existing log file '{}' backuped to '{}'".format(path, backup_name))  # noqa: F821
    hdl = logging.FileHandler(
        filename=path, encoding='utf-8', mode='w')
    hdl.setFormatter(_MyFormatter(datefmt='%m%d %H:%M:%S'))

    _FILE_HANDLER = hdl
    _logger.addHandler(hdl)
    _logger.info("Argv: " + ' '.join(sys.argv))


[docs]def set_logger_dir(dirname, action=None): """ Set the directory for global logging. Args: dirname(str): log directory action(str): an action of ["k","d","q"] to be performed when the directory exists. Will ask user by default. "d": delete the directory. Note that the deletion may fail when the directory is used by tensorboard. "k": keep the directory. This is useful when you resume from a previous training and want the directory to look as if the training was not interrupted. Note that this option does not load old models or any other old states for you. It simply does nothing. """ global LOG_DIR, _FILE_HANDLER if _FILE_HANDLER: # unload and close the old file handler, so that we may safely delete the logger directory _logger.removeHandler(_FILE_HANDLER) del _FILE_HANDLER def dir_nonempty(dirname): # If directory exists and nonempty (ignore hidden files), prompt for action return os.path.isdir(dirname) and len([x for x in os.listdir(dirname) if x[0] != '.']) if dir_nonempty(dirname): if not action: _logger.warn("""\ Log directory {} exists! Use 'd' to delete it. """.format(dirname)) _logger.warn("""\ If you're resuming from a previous run, you can choose to keep it. Press any other key to exit. """) while not action: action = input("Select Action: k (keep) / d (delete) / q (quit):").lower().strip() act = action if act == 'b': backup_name = dirname + _get_time_str() shutil.move(dirname, backup_name) info("Directory '{}' backuped to '{}'".format(dirname, backup_name)) # noqa: F821 elif act == 'd': shutil.rmtree(dirname, ignore_errors=True) if dir_nonempty(dirname): shutil.rmtree(dirname, ignore_errors=False) elif act == 'n': dirname = dirname + _get_time_str() info("Use a new log directory {}".format(dirname)) # noqa: F821 elif act == 'k': pass else: raise OSError("Directory {} exits!".format(dirname)) LOG_DIR = dirname from .fs import mkdir_p mkdir_p(dirname) _set_file(os.path.join(dirname, 'log.log'))
[docs]def auto_set_dir(action=None, name=None): """ Use :func:`logger.set_logger_dir` to set log directory to "./train_log/{scriptname}:{name}". "scriptname" is the name of the main python file currently running""" mod = sys.modules['__main__'] basename = os.path.basename(mod.__file__) auto_dirname = os.path.join('train_log', basename[:basename.rfind('.')]) if name: auto_dirname += ':%s' % name set_logger_dir(auto_dirname, action=action)
[docs]def get_logger_dir(): """ Returns: The logger directory, or None if not set. The directory is used for general logging, tensorboard events, checkpoints, etc. """ return LOG_DIR