本文整理匯總了Python中utils.logging.setup_logging方法的典型用法代碼示例。如果您正苦於以下問題：Python logging.setup_logging方法的具體用法？Python logging.setup_logging怎麽用？Python logging.setup_logging使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類utils.logging的用法示例。
在下文中一共展示了logging.setup_logging方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: main
# 需要導入模塊: from utils import logging [as 別名]
# 或者: from utils.logging import setup_logging [as 別名]
def main():
# Initialize C2
workspace.GlobalInit(
['caffe2', '--caffe2_log_level=0', '--caffe2_gpu_memory_tracking=1']
)
# Set up logging and load config options
logger = setup_logging(__name__)
logging.getLogger('roi_data.loader').setLevel(logging.INFO)
args = parse_args()
logger.info('Called with args:')
logger.info(args)
if args.cfg_file is not None:
merge_cfg_from_file(args.cfg_file)
if args.opts is not None:
merge_cfg_from_list(args.opts)
assert_and_infer_cfg()
logger.info('Training with config:')
logger.info(pprint.pformat(cfg))
# Note that while we set the numpy random seed network training will not be
# deterministic in general. There are sources of non-determinism that cannot
# be removed with a reasonble execution-speed tradeoff (such as certain
# non-deterministic cudnn functions).
np.random.seed(cfg.RNG_SEED)
# Execute the training run
checkpoints = utils.train.train_model()
# Test the trained model
if not args.skip_test:
test_model(checkpoints['final'], args.multi_gpu_testing, args.opts) 示例2: main
# 需要導入模塊: from utils import logging [as 別名]
# 或者: from utils.logging import setup_logging [as 別名]
def main():
# Initialize C2
workspace.GlobalInit(
['caffe2', '--caffe2_log_level=0', '--caffe2_gpu_memory_tracking=1']
)
# Set up logging and load config options
logger = setup_logging(__name__)
logging.getLogger('roi_data.loader').setLevel(logging.INFO)
args = parse_args()
logger.info('Called with args:')
logger.info(args)
if args.cfg_file is not None:
merge_cfg_from_file(args.cfg_file)
if args.opts is not None:
merge_cfg_from_list(args.opts)
assert_and_infer_cfg()
logger.info('Training with config:')
logger.info(pprint.pformat(cfg))
# Note that while we set the numpy random seed network training will not be
# deterministic in general. There are sources of non-determinism that cannot
# be removed with a reasonble execution-speed tradeoff (such as certain
# non-deterministic cudnn functions).
np.random.seed(cfg.RNG_SEED)
# Execute the training run
checkpoints = train_model()
# Test the trained model
if not args.skip_test:
test_model(checkpoints['final'], args.multi_gpu_testing, args.opts)