def download(self):
import tarfile
if self.voc_root.is_dir():
print("VOC found. Skip download or extract")
else:
download_url(self.url, self.root, self.filename, self.md5)
with tarfile.open(self.root / self.filename, "r") as tar:
tar.extractall(path=self.root)
if self.augmented:
mask_dir = self.voc_root / 'SegmentationClassAug'
if mask_dir.is_dir():
print("SBT found. Skip download or extract")
else:
file_id = re.match(r"https://drive.google.com/open\?id=(.*)",
TRAINAUG_FILE['url']).group(1)
filename = TRAINAUG_FILE['name']
download_google_drive(file_id, self.voc_root, filename,
TRAINAUG_FILE['md5'])
file_path = self.voc_root / filename
with tarfile.open(file_path, "r") as tar:
tar.extractall(path=self.voc_root)
split_f = self.voc_root / 'trainaug.txt'
splits_dir = self.voc_root / 'ImageSets' / 'Segmentation'
split_f.rename(splits_dir / split_f.name)
def download(self):
import tarfile
if self._check_integrity():
print('Files already downloaded and verified')
return
info = self.split_info[self.split]
download_google_drive(info['url'], self.root, info['tgz_filename'],
info['tgz_md5'])
# extract file
with tarfile.open(os.path.join(self.root, info['tgz_filename']),
"r:gz") as tar:
tar.extractall(path=self.root)
def download(self):
import tarfile
if self.voc_root.is_dir() and self.ann_file.exists():
print("Dataset found. Skip download or extract.")
return
if not self.voc_root.is_dir():
download_url(self.url, self.root, self.filename, self.md5)
with tarfile.open(self.root / self.filename, "r") as tar:
tar.extractall(path=self.root)
if not self.ann_file.exists():
google_drive_match = re.match(
r"https://drive.google.com/open\?id=(.*)", self.ann_file_url)
file_id = google_drive_match.group(1)
download_google_drive(file_id, self.voc_root, self.ann_file.name)
def download(self):
if self.img_dir.is_dir() and self.ann_file.exists():
print("Dataset found. Skip download or extract")
return
google_drive_match = re.match(
r"https://drive.google.com/open\?id=(.*)", self.url)
if google_drive_match:
file_id = google_drive_match.group(1)
download_google_drive(file_id, self.root, self.filename, self.md5)
else:
download_url(self.url, self.root, self.filename, self.md5)
file_path = self.root / self.filename
with tarfile.open(file_path, "r") as tar:
tar.extractall(path=self.root)
ann_file = self.root / self.ann_filename
ann_file.rename(self.ann_file)
def load_state_dict_from_google_drive(file_id,
filename,
md5,
model_dir=None,
map_location=None):
r"""Loads the Torch serialized object at the given URL.
If the object is already present in `model_dir`, it's deserialized and
returned. The filename part of the URL should follow the naming convention
``filename-<sha256>.ext`` where ``<sha256>`` is the first eight or more
digits of the SHA256 hash of the contents of the file. The hash is used to
ensure unique names and to verify the contents of the file.
The default value of `model_dir` is ``$TORCH_HOME/checkpoints`` where
environment variable ``$TORCH_HOME`` defaults to ``$XDG_CACHE_HOME/torch``.
``$XDG_CACHE_HOME`` follows the X Design Group specification of the Linux
filesytem layout, with a default value ``~/.cache`` if not set.
Args:
url (string): URL of the object to download
model_dir (string, optional): directory in which to save the object
map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load)
progress (bool, optional): whether or not to display a progress bar to stderr
"""
if model_dir is None:
from torch.hub import _get_torch_home
torch_home = _get_torch_home()
torch_home = Path(torch_home)
model_dir = torch_home / 'checkpoints'
else:
model_dir = Path(model_dir)
model_dir.mkdir(parents=True, exist_ok=True)
download_google_drive(file_id, model_dir, filename, md5)
cached_file = model_dir / filename
return torch.load(cached_file, map_location=map_location)