import hashlib
import os
import re
import shutil
import sys
import tempfile
import torch
try:
from requests.utils import urlparse
from requests import get as urlopen
requests_available = True
except ImportError:
requests_available = False
if sys.version_info[0] == 2:
from urlparse import urlparse # noqa f811
from urllib2 import urlopen # noqa f811
else:
from urllib.request import urlopen
from urllib.parse import urlparse
try:
from tqdm import tqdm
except ImportError:
tqdm = None # defined below
# matches bfd8deac from resnet18-bfd8deac.pth
HASH_REGEX = re.compile(r'-([a-f0-9]*)\.')
[docs]def load_url(url, model_dir=None, map_location=None, progress=True):
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 ``$TORCH_HOME/models`` where
``$TORCH_HOME`` defaults to ``~/.torch``. The default directory can be
overridden with the ``$TORCH_MODEL_ZOO`` environment variable.
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
Example:
# >>> state_dict = model_zoo.load_url('https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth')
"""
if model_dir is None:
torch_home = os.path.expanduser(os.getenv('fastNLP_HOME', '~/.fastNLP'))
model_dir = os.getenv('fastNLP_MODEL_ZOO', os.path.join(torch_home, 'models'))
if not os.path.exists(model_dir):
os.makedirs(model_dir)
parts = urlparse(url)
filename = os.path.basename(parts.path)
cached_file = os.path.join(model_dir, filename)
if not os.path.exists(cached_file):
sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
# hash_prefix = HASH_REGEX.search(filename).group(1)
_download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
return torch.load(cached_file, map_location=map_location)
def _download_url_to_file(url, dst, hash_prefix, progress):
if requests_available:
u = urlopen(url, stream=True)
file_size = int(u.headers["Content-Length"])
u = u.raw
else:
u = urlopen(url)
meta = u.info()
if hasattr(meta, 'getheaders'):
file_size = int(meta.getheaders("Content-Length")[0])
else:
file_size = int(meta.get_all("Content-Length")[0])
f = tempfile.NamedTemporaryFile(delete=False)
try:
if hash_prefix is not None:
sha256 = hashlib.sha256()
with tqdm(total=file_size, disable=not progress) as pbar:
while True:
buffer = u.read(8192)
if len(buffer) == 0:
break
f.write(buffer)
if hash_prefix is not None:
sha256.update(buffer)
pbar.update(len(buffer))
f.close()
if hash_prefix is not None:
digest = sha256.hexdigest()
if digest[:len(hash_prefix)] != hash_prefix:
raise RuntimeError('invalid hash value (expected "{}", got "{}")'
.format(hash_prefix, digest))
shutil.move(f.name, dst)
finally:
f.close()
if os.path.exists(f.name):
os.remove(f.name)
if tqdm is None:
# fake tqdm if it's not installed
class tqdm(object):
def __init__(self, total, disable=False):
self.total = total
self.disable = disable
self.n = 0
def update(self, n):
if self.disable:
return
self.n += n
sys.stderr.write("\r{0:.1f}%".format(100 * self.n / float(self.total)))
sys.stderr.flush()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
if self.disable:
return
sys.stderr.write('\n')