Python HASH_REGEX.search方法代码示例

# 需要导入模块: from torch.hub import HASH_REGEX [as 别名]
# 或者: from torch.hub.HASH_REGEX import search [as 别名]
def cache_url(url, model_dir=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
        progress (bool, optional): whether or not to display a progress bar to stderr
    Example:
        >>> cached_file = maskrcnn_benchmark.utils.model_zoo.cache_url('https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth')
    """
    if model_dir is None:
        torch_home = os.path.expanduser(os.getenv('TORCH_HOME', '~/.torch'))
        model_dir = os.getenv('TORCH_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)
    if filename == "model_final.pkl":
        # workaround as pre-trained Caffe2 models from Detectron have all the same filename
        # so make the full path the filename by replacing / with _
        filename = parts.path.replace("/", "_")
    cached_file = os.path.join(model_dir, filename)
    if not os.path.exists(cached_file) and is_main_process():
        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
        hash_prefix = HASH_REGEX.search(filename)
        if hash_prefix is not None:
            hash_prefix = hash_prefix.group(1)
            # workaround: Caffe2 models don't have a hash, but follow the R-50 convention,
            # which matches the hash PyTorch uses. So we skip the hash matching
            # if the hash_prefix is less than 6 characters
            if len(hash_prefix) < 6:
                hash_prefix = None
        _download_url_to_file(url, cached_file, hash_prefix, progress=progress)
    synchronize()
    return cached_file 

# 需要导入模块: from torch.hub import HASH_REGEX [as 别名]
# 或者: from torch.hub.HASH_REGEX import search [as 别名]
def cache_url(url, model_dir=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
        progress (bool, optional): whether or not to display a progress bar to stderr
    Example:
        >>> cached_file = maskrcnn_benchmark.utils.model_zoo.cache_url('https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth')
    """
    if model_dir is None:
        torch_home = os.path.expanduser(os.getenv("TORCH_HOME", "~/.torch"))
        model_dir = os.getenv("TORCH_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)
    if filename == "model_final.pkl":
        # workaround as pre-trained Caffe2 models from Detectron have all the same filename
        # so make the full path the filename by replacing / with _
        filename = parts.path.replace("/", "_")
    cached_file = os.path.join(model_dir, filename)
    if not os.path.exists(cached_file) and is_main_process():
        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
        hash_prefix = HASH_REGEX.search(filename)
        if hash_prefix is not None:
            hash_prefix = hash_prefix.group(1)
            # workaround: Caffe2 models don't have a hash, but follow the R-50 convention,
            # which matches the hash PyTorch uses. So we skip the hash matching
            # if the hash_prefix is less than 6 characters
            if len(hash_prefix) < 6:
                hash_prefix = None
        _download_url_to_file(url, cached_file, hash_prefix, progress=progress)
    synchronize()
    return cached_file 

# 需要导入模块: from torch.hub import HASH_REGEX [as 别名]
# 或者: from torch.hub.HASH_REGEX import search [as 别名]
def cache_url(url, model_dir=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
        progress (bool, optional): whether or not to display a progress bar to stderr
    Example:
        >>> cached_file = fcos_core.utils.model_zoo.cache_url('https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth')
    """
    if model_dir is None:
        torch_home = os.path.expanduser(os.getenv('TORCH_HOME', '~/.torch'))
        model_dir = os.getenv('TORCH_MODEL_ZOO', os.path.join(torch_home, 'models'))
    if not os.path.exists(model_dir):
        os.makedirs(model_dir)
    parts = urlparse(url)
    if parts.fragment != "":
        filename = parts.fragment
    else:
        filename = os.path.basename(parts.path)
    if filename == "model_final.pkl":
        # workaround as pre-trained Caffe2 models from Detectron have all the same filename
        # so make the full path the filename by replacing / with _
        filename = parts.path.replace("/", "_")
    cached_file = os.path.join(model_dir, filename)
    if not os.path.exists(cached_file) and is_main_process():
        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
        hash_prefix = HASH_REGEX.search(filename)
        if hash_prefix is not None:
            hash_prefix = hash_prefix.group(1)
            # workaround: Caffe2 models don't have a hash, but follow the R-50 convention,
            # which matches the hash PyTorch uses. So we skip the hash matching
            # if the hash_prefix is less than 6 characters
            if len(hash_prefix) < 6:
                hash_prefix = None
        _download_url_to_file(url, cached_file, hash_prefix, progress=progress)
    synchronize()
    return cached_file 

# 需要导入模块: from torch.hub import HASH_REGEX [as 别名]
# 或者: from torch.hub.HASH_REGEX import search [as 别名]
def cache_url(url, model_dir=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
        progress (bool, optional): whether or not to display a progress bar to stderr
    Example:
        >>> cached_file = maskrcnn_benchmark.utils.model_zoo.cache_url('https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth')
    """
    if model_dir is None:
        torch_home = os.path.expanduser(os.getenv("TORCH_HOME", "~/.torch"))
        model_dir = os.getenv("TORCH_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)
    if filename == "model_final.pkl":
        # workaround as pre-trained Caffe2 models from Detectron have all the same filename
        # so make the full path the filename by replacing / with _
        filename = parts.path.replace("/", "_")
    cached_file = os.path.join(model_dir, filename)
    if not os.path.exists(cached_file) and is_main_process():
        sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))
        hash_prefix = HASH_REGEX.search(filename)
        if hash_prefix is not None:
            hash_prefix = hash_prefix.group(1)
            # workaround: Caffe2 models don't have a hash, but follow the R-50 convention,
            # which matches the hash PyTorch uses. So we skip the hash matching
            # if the hash_prefix is less than 6 characters
            if len(hash_prefix) < 6:
                hash_prefix = None
        download_url_to_file(url, cached_file, hash_prefix, progress=progress)
    synchronize()
    return cached_file