本文整理汇总了Python中maskrcnn_benchmark.utils.model_zoo.cache_url方法的典型用法代码示例。如果您正苦于以下问题：Python model_zoo.cache_url方法的具体用法？Python model_zoo.cache_url怎么用？Python model_zoo.cache_url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类maskrcnn_benchmark.utils.model_zoo的用法示例。
在下文中一共展示了model_zoo.cache_url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，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _load_file
# 需要导入模块: from maskrcnn_benchmark.utils import model_zoo [as 别名]
# 或者: from maskrcnn_benchmark.utils.model_zoo import cache_url [as 别名]
def _load_file(self, f):
# catalog lookup
if f.startswith("catalog://"):
paths_catalog = import_file(
"maskrcnn_benchmark.config.paths_catalog", self.cfg.PATHS_CATALOG, True
)
catalog_f = paths_catalog.ModelCatalog.get(f[len("catalog://") :])
self.logger.info("{} points to {}".format(f, catalog_f))
f = catalog_f
# download url files
if f.startswith("http"):
# if the file is a url path, download it and cache it
cached_f = cache_url(f)
self.logger.info("url {} cached in {}".format(f, cached_f))
f = cached_f
# convert Caffe2 checkpoint from pkl
if f.endswith(".pkl"):
return load_c2_format(self.cfg, f)
# load native detectron.pytorch checkpoint
loaded = super(DetectronCheckpointer, self)._load_file(f)
if "model" not in loaded:
loaded = dict(model=loaded)
return loaded 示例2: _load_file
# 需要导入模块: from maskrcnn_benchmark.utils import model_zoo [as 别名]
# 或者: from maskrcnn_benchmark.utils.model_zoo import cache_url [as 别名]
def _load_file(self, f):
# catalog lookup
if f.startswith("catalog://"):
paths_catalog = import_file(
"maskrcnn_benchmark.config.paths_catalog", self.cfg.PATHS_CATALOG, True
)
catalog_f = paths_catalog.ModelCatalog.get(f[len("catalog://") :])
self.logger.info("{} points to {}".format(f, catalog_f))
f = catalog_f
# download url files
if f.startswith("http"):
# if the file is a url path, download it and cache it
cached_f = cache_url(f, model_dir=self.cfg.MODEL.PRETRAINED_MODELS)
self.logger.info("url {} cached in {}".format(f, cached_f))
f = cached_f
# convert Caffe2 checkpoint from pkl
if f.endswith(".pkl"):
return load_c2_format(self.cfg, f)
# load native detectron.pytorch checkpoint
loaded = super(DetectronCheckpointer, self)._load_file(f)
if "model" not in loaded:
loaded = dict(model=loaded)
return loaded