maskrcnn_benchmark/utils/model_zoo.py

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import os
import sys

try:
    from torch.hub import _download_url_to_file
    from torch.hub import urlparse
    from torch.hub import HASH_REGEX
except ImportError:
    try:
        from torch.utils.model_zoo import _download_url_to_file
        from torch.utils.model_zoo import urlparse
        from torch.utils.model_zoo import HASH_REGEX
    except ImportError:
        from torch.hub import download_url_to_file as _download_url_to_file
        from torch.hub import urlparse
        from torch.hub import HASH_REGEX

from maskrcnn_benchmark.utils.comm import is_main_process
from maskrcnn_benchmark.utils.comm import synchronize


# very similar to https://github.com/pytorch/pytorch/blob/master/torch/utils/model_zoo.py
# but with a few improvements and modifications
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