def _load_file(self, f):
# pdb.set_trace()
# 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)
# pdb.set_trace()
# load native detectron.pytorch checkpoint
loaded = super(DetectronCheckpointer, self)._load_file(f)
# pdb.set_trace()
if "model" not in loaded:
loaded = dict(model=loaded)
# pdb.set_trace()
return loaded
def _load_file(self, f):
# catalog lookup
if f.startswith("catalog://"):
from maskrcnn_benchmark.config import paths_catalog
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)
if self.cfg is not None:
from .c2_model_loading import _rename_conv_weights_for_deformable_conv_layers
_rename_conv_weights_for_deformable_conv_layers(
loaded['model'], self.cfg)
return loaded
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"):
print
return load_c2_format(self.cfg, f)
# load native detectron.pytorch checkpoint
loaded = super(DetectronCheckpointer, self)._load_file(f)
# unload_keyword = list(self.cfg.FEW_SHOT.UNLOAD_KEYWORD)
# if self.cfg.FEW_SHOT.LOAD_PRETRIANED_RPN_ONLY:
# unload_keyword += ['roi_heads']
# new_dict = {}
# for key,value in loaded.items():
# if any([ keyword in key for keyword in unload_keyword]):
# continue
# new_dict[key] = value
# loaded = new_dict
if "model" not in loaded:
loaded = dict(model=loaded)
return loaded
def _load_file(self, f):
# f: catalog://ImageNetPretrained/MSRA/R-50
if f.startswith("catalog://"):
# 把paths_catalog.py作为一个module导入, 这个文件的路径是配置文件中设置的
# 有可能存在于文件系统的任何位置, 因此不能直接import
paths_catalog = import_file(
"maskrcnn_benchmark.config.paths_catalog",
self.cfg.PATHS_CATALOG, # paths_catalog.py的绝对路径
True)
# 'https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl'
# 获取pretrained model的url
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
# 把pkl文件转换成Caffe2模型文件
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
def resnet152(pretrained=False, **kwargs):
"""Constructs the ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet.
Returns:
`nn.Module' instance.
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
if pretrained:
model.load_state_dict(cache_url(model_urls['resnet152']))
return model
def resnet34(pretrained=False, **kwargs):
"""Constructs the ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet.
Returns:
`nn.Module' instance.
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(cache_url(model_urls['resnet34']))
return model
def _load_file(cfg):
f = cfg.MODEL.WEIGHT
# catalog lookup
if f.startswith("catalog://"):
paths_catalog = import_file("maskrcnn_benchmark.config.paths_catalog",
cfg.PATHS_CATALOG, True)
catalog_f = paths_catalog.ModelCatalog.get(f[len("catalog://"):])
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)
f = cached_f
# convert Caffe2 checkpoint from pkl
if f.endswith(".pkl"):
return load_c2_format(cfg, f)
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