本文整理匯總了Python中detectron.core.config.cfg.MEMONGER屬性的典型用法代碼示例。如果您正苦於以下問題：Python cfg.MEMONGER屬性的具體用法？Python cfg.MEMONGER怎麽用？Python cfg.MEMONGER使用的例子？那麽, 這裏精選的屬性代碼示例或許可以為您提供幫助。您也可以進一步了解該屬性所在類detectron.core.config.cfg的用法示例。
在下文中一共展示了cfg.MEMONGER屬性的1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: create_model
# 需要導入模塊: from detectron.core.config import cfg [as 別名]
# 或者: from detectron.core.config.cfg import MEMONGER [as 別名]
def create_model():
"""Build the model and look for saved model checkpoints in case we can
resume from one.
"""
logger = logging.getLogger(__name__)
start_iter = 0
checkpoints = {}
output_dir = get_output_dir(cfg.TRAIN.DATASETS, training=True)
weights_file = cfg.TRAIN.WEIGHTS
if cfg.TRAIN.AUTO_RESUME:
# Check for the final model (indicates training already finished)
final_path = os.path.join(output_dir, 'model_final.pkl')
if os.path.exists(final_path):
logger.info('model_final.pkl exists; no need to train!')
return None, None, None, {'final': final_path}, output_dir
if cfg.TRAIN.COPY_WEIGHTS:
copyfile(
weights_file,
os.path.join(output_dir, os.path.basename(weights_file)))
logger.info('Copy {} to {}'.format(weights_file, output_dir))
# Find the most recent checkpoint (highest iteration number)
files = os.listdir(output_dir)
for f in files:
iter_string = re.findall(r'(?<=model_iter)\d+(?=\.pkl)', f)
if len(iter_string) > 0:
checkpoint_iter = int(iter_string[0])
if checkpoint_iter > start_iter:
# Start one iteration immediately after the checkpoint iter
start_iter = checkpoint_iter + 1
resume_weights_file = f
if start_iter > 0:
# Override the initialization weights with the found checkpoint
weights_file = os.path.join(output_dir, resume_weights_file)
logger.info(
'========> Resuming from checkpoint {} at start iter {}'.
format(weights_file, start_iter)
)
logger.info('Building model: {}'.format(cfg.MODEL.TYPE))
model = model_builder.create(cfg.MODEL.TYPE, train=True)
if cfg.MEMONGER:
optimize_memory(model)
# Performs random weight initialization as defined by the model
workspace.RunNetOnce(model.param_init_net)
return model, weights_file, start_iter, checkpoints, output_dir