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Python logging.setup_logging方法代码示例

本文整理汇总了Python中utils.logging.setup_logging方法的典型用法代码示例。如果您正苦于以下问题:Python logging.setup_logging方法的具体用法?Python logging.setup_logging怎么用?Python logging.setup_logging使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块utils.logging的用法示例。

在下文中一共展示了logging.setup_logging方法的2个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: main

# 需要导入模块: from utils import logging [as 别名]
# 或者: from utils.logging import setup_logging [as 别名]
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) 
开发者ID:ronghanghu,项目名称:seg_every_thing,代码行数:30,代码来源:train_net.py


示例2: main

# 需要导入模块: from utils import logging [as 别名]
# 或者: from utils.logging import setup_logging [as 别名]
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) 
开发者ID:lvpengyuan,项目名称:masktextspotter.caffe2,代码行数:30,代码来源:train_net.py



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