def test_get_deprecated_models():
os.environ.pop(ENV_MMCV_HOME, None)
mmcv_home = osp.join(osp.dirname(__file__), 'data/model_zoo/mmcv_home/')
os.environ[ENV_MMCV_HOME] = mmcv_home
dep_urls = get_deprecated_model_names()
assert dep_urls == {
'train_old': 'train',
'test_old': 'test',
}
def _load_checkpoint(filename, map_location=None):
"""Load checkpoint from somewhere (modelzoo, file, url).
Args:
filename (str): Accept local filepath, URL, ``torchvision://xxx``,
``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
details.
map_location (str | None): Same as :func:`torch.load`. Default: None.
Returns:
dict | OrderedDict: The loaded checkpoint. It can be either an
OrderedDict storing model weights or a dict containing other
information, which depends on the checkpoint.
"""
if filename.startswith('modelzoo://'):
warnings.warn('The URL scheme of "modelzoo://" is deprecated, please '
'use "torchvision://" instead')
model_urls = get_torchvision_models()
model_name = filename[11:]
checkpoint = load_url_dist(model_urls[model_name])
elif filename.startswith('torchvision://'):
model_urls = get_torchvision_models()
model_name = filename[14:]
checkpoint = load_url_dist(model_urls[model_name])
elif filename.startswith('open-mmlab://'):
model_urls = get_external_models()
model_name = filename[13:]
deprecated_urls = get_deprecated_model_names()
if model_name in deprecated_urls:
warnings.warn(f'open-mmlab://{model_name} is deprecated in favor '
f'of open-mmlab://{deprecated_urls[model_name]}')
model_name = deprecated_urls[model_name]
model_url = model_urls[model_name]
# check if is url
if model_url.startswith(('http://', 'https://')):
checkpoint = load_url_dist(model_url, map_location=map_location)
else:
filename = osp.join(_get_mmcv_home(), model_url)
if not osp.isfile(filename):
raise IOError(f'{filename} is not a checkpoint file')
checkpoint = torch.load(filename, map_location=map_location)
elif filename.startswith(('http://', 'https://')):
checkpoint = load_url_dist(filename, map_location=map_location)
else:
if not osp.isfile(filename):
raise IOError(f'{filename} is not a checkpoint file')
checkpoint = torch.load(filename, map_location=map_location)
return checkpoint