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import logging
class Logger(object):
def __init__(self, txt_path):
root_logger = logging.getLogger()
for handler in root_logger.handlers[:]:
root_logger.removeHandler(handler)
root_logger.setLevel(logging.WARNING)
self.txt_path = txt_path
self.logger = logging.getLogger('train')
self.formatter = logging.Formatter('%(asctime)s.%(msecs)03d - %(levelname)s: %(message)s', datefmt='%y-%m-%d %H:%M:%S')
self.logger.setLevel(logging.INFO)
def __console(self, level, message):
root_logger = logging.getLogger()
for handler in root_logger.handlers[:]:
root_logger.removeHandler(handler)
file_handler = logging.FileHandler(self.txt_path, mode='a')
console_handler = logging.StreamHandler()
file_handler.setFormatter(self.formatter)
console_handler.setFormatter(self.formatter)
self.logger.addHandler(file_handler)
self.logger.addHandler(console_handler)
if level == 'info':
self.logger.info(message)
elif level == 'debug':
self.logger.debug(message)
elif level == 'warning':
self.logger.warning(message)
elif level == 'error':
self.logger.error(message)
self.logger.removeHandler(file_handler)
self.logger.removeHandler(console_handler)
file_handler.close()
def debug(self, message):
self.__console('debug', message)
def info(self, message):
self.__console('info', message)
def warning(self, message):
self.__console('warning', message)
def error(self, message):
self.__console('error', message)
def log_metrics(metrics, logger, tensorboard_logger, epoch):
def log_single_class(data, tag):
logger.info(
'{:>15} \t\tI-Auroc:{:.2f} \tI-F1:{:.2f} \tI-AP:{:.2f} \tP-Auroc:{:.2f} \tP-F1:{:.2f} \tP-AP:{:.2f}'.
format(tag,
data['auroc_im'],
data['f1_im'],
data['ap_im'],
data['auroc_px'],
data['f1_px'],
data['ap_px'])
)
# Adding scalar metrics to TensorBoard
for metric_name in ['auroc_im', 'f1_im', 'ap_im', 'auroc_px', 'f1_px', 'ap_px']:
tensorboard_logger.add_scalar(f'{tag}-{metric_name}', data[metric_name], epoch)
for tag, data in metrics.items():
log_single_class(data, tag)
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