本文整理汇总了Python中detectron.core.config.cfg.RNG_SEED属性的典型用法代码示例。如果您正苦于以下问题：Python cfg.RNG_SEED属性的具体用法？Python cfg.RNG_SEED怎么用？Python cfg.RNG_SEED使用的例子？那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类detectron.core.config.cfg的用法示例。
在下文中一共展示了cfg.RNG_SEED属性的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，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from detectron.core.config import cfg [as 别名]
# 或者: from detectron.core.config.cfg import RNG_SEED [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('detectron.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()
smi_output, cuda_ver, cudnn_ver = c2_utils.get_nvidia_info()
logger.info("cuda version : {}".format(cuda_ver))
logger.info("cudnn version: {}".format(cudnn_ver))
logger.info("nvidia-smi output:\n{}".format(smi_output))
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 = detectron.utils.train.train_model()
# Test the trained model
if not args.skip_test:
test_model(checkpoints['final'], args.single_gpu_testing, args.opts) 示例2: main
# 需要导入模块: from detectron.core.config import cfg [as 别名]
# 或者: from detectron.core.config.cfg import RNG_SEED [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('detectron.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()
smi_output, cuda_ver, cudnn_ver = c2_utils.get_nvidia_info()
logger.info("cuda version : {}".format(cuda_ver))
logger.info("cudnn version: {}".format(cudnn_ver))
logger.info("nvidia-smi output:\n{}".format(smi_output))
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 = detectron.utils.train.train_model()
# Test the trained model
if not args.skip_test:
test_model(checkpoints['final'], args.multi_gpu_testing, args.opts)