fastNLP/legacy/api/utils.py

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.auto import tqdm
except:
    from fastNLP.core.utils import _pseudo_tqdm as tqdm
    
# matches bfd8deac from resnet18-bfd8deac.pth
HASH_REGEX = re.compile(r'-([a-f0-9]*)\.')


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')