529 lines
20 KiB
Python
529 lines
20 KiB
Python
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import errno
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import hashlib
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import os
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import re
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import shutil
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import sys
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import tempfile
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import torch
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import warnings
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import zipfile
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from urllib.request import urlopen, Request
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from urllib.parse import urlparse # noqa: F401
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try:
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from tqdm.auto import tqdm # automatically select proper tqdm submodule if available
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except ImportError:
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try:
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from tqdm import tqdm
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except ImportError:
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# fake tqdm if it's not installed
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class tqdm(object): # type: ignore
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def __init__(self, total=None, disable=False,
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unit=None, unit_scale=None, unit_divisor=None):
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self.total = total
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self.disable = disable
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self.n = 0
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# ignore unit, unit_scale, unit_divisor; they're just for real tqdm
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def update(self, n):
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if self.disable:
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return
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self.n += n
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if self.total is None:
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sys.stderr.write("\r{0:.1f} bytes".format(self.n))
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else:
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sys.stderr.write("\r{0:.1f}%".format(100 * self.n / float(self.total)))
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sys.stderr.flush()
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def close(self):
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self.disable = True
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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if self.disable:
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return
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sys.stderr.write('\n')
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# matches bfd8deac from resnet18-bfd8deac.pth
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HASH_REGEX = re.compile(r'-([a-f0-9]*)\.')
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MASTER_BRANCH = 'master'
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ENV_TORCH_HOME = 'TORCH_HOME'
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ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
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DEFAULT_CACHE_DIR = '~/.cache'
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VAR_DEPENDENCY = 'dependencies'
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MODULE_HUBCONF = 'hubconf.py'
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READ_DATA_CHUNK = 8192
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_hub_dir = None
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# Copied from tools/shared/module_loader to be included in torch package
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def import_module(name, path):
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import importlib.util
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from importlib.abc import Loader
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spec = importlib.util.spec_from_file_location(name, path)
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module = importlib.util.module_from_spec(spec)
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assert isinstance(spec.loader, Loader)
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spec.loader.exec_module(module)
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return module
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def _remove_if_exists(path):
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if os.path.exists(path):
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if os.path.isfile(path):
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os.remove(path)
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else:
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shutil.rmtree(path)
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def _git_archive_link(repo_owner, repo_name, branch):
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return 'https://github.com/{}/{}/archive/{}.zip'.format(repo_owner, repo_name, branch)
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def _load_attr_from_module(module, func_name):
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# Check if callable is defined in the module
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if func_name not in dir(module):
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return None
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return getattr(module, func_name)
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def _get_torch_home():
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torch_home = os.path.expanduser(
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os.getenv(ENV_TORCH_HOME,
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os.path.join(os.getenv(ENV_XDG_CACHE_HOME,
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DEFAULT_CACHE_DIR), 'torch')))
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return torch_home
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def _parse_repo_info(github):
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branch = MASTER_BRANCH
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if ':' in github:
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repo_info, branch = github.split(':')
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else:
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repo_info = github
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repo_owner, repo_name = repo_info.split('/')
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return repo_owner, repo_name, branch
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def _get_cache_or_reload(github, force_reload, verbose=True):
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# Setup hub_dir to save downloaded files
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hub_dir = get_dir()
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if not os.path.exists(hub_dir):
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os.makedirs(hub_dir)
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# Parse github repo information
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repo_owner, repo_name, branch = _parse_repo_info(github)
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# Github allows branch name with slash '/',
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# this causes confusion with path on both Linux and Windows.
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# Backslash is not allowed in Github branch name so no need to
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# to worry about it.
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normalized_br = branch.replace('/', '_')
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# Github renames folder repo-v1.x.x to repo-1.x.x
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# We don't know the repo name before downloading the zip file
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# and inspect name from it.
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# To check if cached repo exists, we need to normalize folder names.
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repo_dir = os.path.join(hub_dir, '_'.join([repo_owner, repo_name, normalized_br]))
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use_cache = (not force_reload) and os.path.exists(repo_dir)
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if use_cache:
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if verbose:
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sys.stderr.write('Using cache found in {}\n'.format(repo_dir))
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else:
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cached_file = os.path.join(hub_dir, normalized_br + '.zip')
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_remove_if_exists(cached_file)
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url = _git_archive_link(repo_owner, repo_name, branch)
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sys.stderr.write('Downloading: \"{}\" to {}\n'.format(url, cached_file))
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download_url_to_file(url, cached_file, progress=False)
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with zipfile.ZipFile(cached_file) as cached_zipfile:
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extraced_repo_name = cached_zipfile.infolist()[0].filename
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extracted_repo = os.path.join(hub_dir, extraced_repo_name)
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_remove_if_exists(extracted_repo)
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# Unzip the code and rename the base folder
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cached_zipfile.extractall(hub_dir)
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_remove_if_exists(cached_file)
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_remove_if_exists(repo_dir)
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shutil.move(extracted_repo, repo_dir) # rename the repo
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return repo_dir
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def _check_module_exists(name):
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import importlib.util
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return importlib.util.find_spec(name) is not None
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def _check_dependencies(m):
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dependencies = _load_attr_from_module(m, VAR_DEPENDENCY)
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if dependencies is not None:
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missing_deps = [pkg for pkg in dependencies if not _check_module_exists(pkg)]
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if len(missing_deps):
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raise RuntimeError('Missing dependencies: {}'.format(', '.join(missing_deps)))
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def _load_entry_from_hubconf(m, model):
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if not isinstance(model, str):
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raise ValueError('Invalid input: model should be a string of function name')
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# Note that if a missing dependency is imported at top level of hubconf, it will
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# throw before this function. It's a chicken and egg situation where we have to
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# load hubconf to know what're the dependencies, but to import hubconf it requires
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# a missing package. This is fine, Python will throw proper error message for users.
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_check_dependencies(m)
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func = _load_attr_from_module(m, model)
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if func is None or not callable(func):
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raise RuntimeError('Cannot find callable {} in hubconf'.format(model))
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return func
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def get_dir():
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r"""
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Get the Torch Hub cache directory used for storing downloaded models & weights.
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If :func:`~torch.hub.set_dir` is not called, default path is ``$TORCH_HOME/hub`` where
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environment variable ``$TORCH_HOME`` defaults to ``$XDG_CACHE_HOME/torch``.
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``$XDG_CACHE_HOME`` follows the X Design Group specification of the Linux
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filesystem layout, with a default value ``~/.cache`` if the environment
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variable is not set.
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"""
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# Issue warning to move data if old env is set
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if os.getenv('TORCH_HUB'):
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warnings.warn('TORCH_HUB is deprecated, please use env TORCH_HOME instead')
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if _hub_dir is not None:
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return _hub_dir
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return os.path.join(_get_torch_home(), 'hub')
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def set_dir(d):
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r"""
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Optionally set the Torch Hub directory used to save downloaded models & weights.
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Args:
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d (string): path to a local folder to save downloaded models & weights.
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"""
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global _hub_dir
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_hub_dir = d
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def list(github, force_reload=False):
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r"""
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List all entrypoints available in `github` hubconf.
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Args:
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github (string): a string with format "repo_owner/repo_name[:tag_name]" with an optional
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tag/branch. The default branch is `master` if not specified.
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Example: 'pytorch/vision[:hub]'
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force_reload (bool, optional): whether to discard the existing cache and force a fresh download.
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Default is `False`.
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Returns:
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entrypoints: a list of available entrypoint names
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Example:
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>>> entrypoints = torch.hub.list('pytorch/vision', force_reload=True)
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"""
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repo_dir = _get_cache_or_reload(github, force_reload, True)
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sys.path.insert(0, repo_dir)
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hub_module = import_module(MODULE_HUBCONF, repo_dir + '/' + MODULE_HUBCONF)
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sys.path.remove(repo_dir)
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# We take functions starts with '_' as internal helper functions
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entrypoints = [f for f in dir(hub_module) if callable(getattr(hub_module, f)) and not f.startswith('_')]
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return entrypoints
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def help(github, model, force_reload=False):
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r"""
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Show the docstring of entrypoint `model`.
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Args:
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github (string): a string with format <repo_owner/repo_name[:tag_name]> with an optional
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tag/branch. The default branch is `master` if not specified.
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Example: 'pytorch/vision[:hub]'
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model (string): a string of entrypoint name defined in repo's hubconf.py
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force_reload (bool, optional): whether to discard the existing cache and force a fresh download.
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Default is `False`.
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Example:
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>>> print(torch.hub.help('pytorch/vision', 'resnet18', force_reload=True))
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"""
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repo_dir = _get_cache_or_reload(github, force_reload, True)
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sys.path.insert(0, repo_dir)
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hub_module = import_module(MODULE_HUBCONF, repo_dir + '/' + MODULE_HUBCONF)
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sys.path.remove(repo_dir)
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entry = _load_entry_from_hubconf(hub_module, model)
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return entry.__doc__
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# Ideally this should be `def load(github, model, *args, forece_reload=False, **kwargs):`,
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# but Python2 complains syntax error for it. We have to skip force_reload in function
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# signature here but detect it in kwargs instead.
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# TODO: fix it after Python2 EOL
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def load(repo_or_dir, model, *args, **kwargs):
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r"""
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Load a model from a github repo or a local directory.
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Note: Loading a model is the typical use case, but this can also be used to
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for loading other objects such as tokenizers, loss functions, etc.
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If :attr:`source` is ``'github'``, :attr:`repo_or_dir` is expected to be
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of the form ``repo_owner/repo_name[:tag_name]`` with an optional
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tag/branch.
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If :attr:`source` is ``'local'``, :attr:`repo_or_dir` is expected to be a
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path to a local directory.
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Args:
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repo_or_dir (string): repo name (``repo_owner/repo_name[:tag_name]``),
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if ``source = 'github'``; or a path to a local directory, if
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``source = 'local'``.
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model (string): the name of a callable (entrypoint) defined in the
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repo/dir's ``hubconf.py``.
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*args (optional): the corresponding args for callable :attr:`model`.
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source (string, optional): ``'github'`` | ``'local'``. Specifies how
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``repo_or_dir`` is to be interpreted. Default is ``'github'``.
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force_reload (bool, optional): whether to force a fresh download of
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the github repo unconditionally. Does not have any effect if
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``source = 'local'``. Default is ``False``.
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verbose (bool, optional): If ``False``, mute messages about hitting
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local caches. Note that the message about first download cannot be
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muted. Does not have any effect if ``source = 'local'``.
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Default is ``True``.
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**kwargs (optional): the corresponding kwargs for callable
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:attr:`model`.
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Returns:
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The output of the :attr:`model` callable when called with the given
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``*args`` and ``**kwargs``.
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Example:
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>>> # from a github repo
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>>> repo = 'pytorch/vision'
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>>> model = torch.hub.load(repo, 'resnet50', pretrained=True)
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>>> # from a local directory
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>>> path = '/some/local/path/pytorch/vision'
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>>> model = torch.hub.load(path, 'resnet50', pretrained=True)
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"""
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source = kwargs.pop('source', 'github').lower()
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force_reload = kwargs.pop('force_reload', False)
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verbose = kwargs.pop('verbose', True)
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if source not in ('github', 'local'):
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raise ValueError(
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f'Unknown source: "{source}". Allowed values: "github" | "local".')
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if source == 'github':
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repo_or_dir = _get_cache_or_reload(repo_or_dir, force_reload, verbose)
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model = _load_local(repo_or_dir, model, *args, **kwargs)
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return model
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def _load_local(hubconf_dir, model, *args, **kwargs):
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r"""
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Load a model from a local directory with a ``hubconf.py``.
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Args:
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hubconf_dir (string): path to a local directory that contains a
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``hubconf.py``.
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model (string): name of an entrypoint defined in the directory's
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`hubconf.py`.
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*args (optional): the corresponding args for callable ``model``.
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**kwargs (optional): the corresponding kwargs for callable ``model``.
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Returns:
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a single model with corresponding pretrained weights.
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Example:
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>>> path = '/some/local/path/pytorch/vision'
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>>> model = _load_local(path, 'resnet50', pretrained=True)
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"""
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sys.path.insert(0, hubconf_dir)
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hubconf_path = os.path.join(hubconf_dir, MODULE_HUBCONF)
|
||
|
|
hub_module = import_module(MODULE_HUBCONF, hubconf_path)
|
||
|
|
|
||
|
|
entry = _load_entry_from_hubconf(hub_module, model)
|
||
|
|
model = entry(*args, **kwargs)
|
||
|
|
|
||
|
|
sys.path.remove(hubconf_dir)
|
||
|
|
|
||
|
|
return model
|
||
|
|
|
||
|
|
|
||
|
|
def download_url_to_file(url, dst, hash_prefix=None, progress=True):
|
||
|
|
r"""Download object at the given URL to a local path.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
url (string): URL of the object to download
|
||
|
|
dst (string): Full path where object will be saved, e.g. `/tmp/temporary_file`
|
||
|
|
hash_prefix (string, optional): If not None, the SHA256 downloaded file should start with `hash_prefix`.
|
||
|
|
Default: None
|
||
|
|
progress (bool, optional): whether or not to display a progress bar to stderr
|
||
|
|
Default: True
|
||
|
|
|
||
|
|
Example:
|
||
|
|
>>> torch.hub.download_url_to_file('https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth', '/tmp/temporary_file')
|
||
|
|
|
||
|
|
"""
|
||
|
|
file_size = None
|
||
|
|
# We use a different API for python2 since urllib(2) doesn't recognize the CA
|
||
|
|
# certificates in older Python
|
||
|
|
req = Request(url, headers={"User-Agent": "torch.hub"})
|
||
|
|
u = urlopen(req)
|
||
|
|
meta = u.info()
|
||
|
|
if hasattr(meta, 'getheaders'):
|
||
|
|
content_length = meta.getheaders("Content-Length")
|
||
|
|
else:
|
||
|
|
content_length = meta.get_all("Content-Length")
|
||
|
|
if content_length is not None and len(content_length) > 0:
|
||
|
|
file_size = int(content_length[0])
|
||
|
|
|
||
|
|
# We deliberately save it in a temp file and move it after
|
||
|
|
# download is complete. This prevents a local working checkpoint
|
||
|
|
# being overridden by a broken download.
|
||
|
|
dst = os.path.expanduser(dst)
|
||
|
|
dst_dir = os.path.dirname(dst)
|
||
|
|
f = tempfile.NamedTemporaryFile(delete=False, dir=dst_dir)
|
||
|
|
|
||
|
|
try:
|
||
|
|
if hash_prefix is not None:
|
||
|
|
sha256 = hashlib.sha256()
|
||
|
|
with tqdm(total=file_size, disable=not progress,
|
||
|
|
unit='B', unit_scale=True, unit_divisor=1024) 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)
|
||
|
|
|
||
|
|
def _download_url_to_file(url, dst, hash_prefix=None, progress=True):
|
||
|
|
warnings.warn('torch.hub._download_url_to_file has been renamed to\
|
||
|
|
torch.hub.download_url_to_file to be a public API,\
|
||
|
|
_download_url_to_file will be removed in after 1.3 release')
|
||
|
|
download_url_to_file(url, dst, hash_prefix, progress)
|
||
|
|
|
||
|
|
# Hub used to support automatically extracts from zipfile manually compressed by users.
|
||
|
|
# The legacy zip format expects only one file from torch.save() < 1.6 in the zip.
|
||
|
|
# We should remove this support since zipfile is now default zipfile format for torch.save().
|
||
|
|
def _is_legacy_zip_format(filename):
|
||
|
|
if zipfile.is_zipfile(filename):
|
||
|
|
infolist = zipfile.ZipFile(filename).infolist()
|
||
|
|
return len(infolist) == 1 and not infolist[0].is_dir()
|
||
|
|
return False
|
||
|
|
|
||
|
|
def _legacy_zip_load(filename, model_dir, map_location):
|
||
|
|
warnings.warn('Falling back to the old format < 1.6. This support will be '
|
||
|
|
'deprecated in favor of default zipfile format introduced in 1.6. '
|
||
|
|
'Please redo torch.save() to save it in the new zipfile format.')
|
||
|
|
# Note: extractall() defaults to overwrite file if exists. No need to clean up beforehand.
|
||
|
|
# We deliberately don't handle tarfile here since our legacy serialization format was in tar.
|
||
|
|
# E.g. resnet18-5c106cde.pth which is widely used.
|
||
|
|
with zipfile.ZipFile(filename) as f:
|
||
|
|
members = f.infolist()
|
||
|
|
if len(members) != 1:
|
||
|
|
raise RuntimeError('Only one file(not dir) is allowed in the zipfile')
|
||
|
|
f.extractall(model_dir)
|
||
|
|
extraced_name = members[0].filename
|
||
|
|
extracted_file = os.path.join(model_dir, extraced_name)
|
||
|
|
return torch.load(extracted_file, map_location=map_location)
|
||
|
|
|
||
|
|
def load_state_dict_from_url(url, model_dir=None, map_location=None, progress=True, check_hash=False, file_name=None):
|
||
|
|
r"""Loads the Torch serialized object at the given URL.
|
||
|
|
|
||
|
|
If downloaded file is a zip file, it will be automatically
|
||
|
|
decompressed.
|
||
|
|
|
||
|
|
If the object is already present in `model_dir`, it's deserialized and
|
||
|
|
returned.
|
||
|
|
The default value of `model_dir` is ``<hub_dir>/checkpoints`` where
|
||
|
|
`hub_dir` is the directory returned by :func:`~torch.hub.get_dir`.
|
||
|
|
|
||
|
|
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.
|
||
|
|
Default: True
|
||
|
|
check_hash(bool, optional): If True, 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.
|
||
|
|
Default: False
|
||
|
|
file_name (string, optional): name for the downloaded file. Filename from `url` will be used if not set.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
>>> state_dict = torch.hub.load_state_dict_from_url('https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth')
|
||
|
|
|
||
|
|
"""
|
||
|
|
# Issue warning to move data if old env is set
|
||
|
|
if os.getenv('TORCH_MODEL_ZOO'):
|
||
|
|
warnings.warn('TORCH_MODEL_ZOO is deprecated, please use env TORCH_HOME instead')
|
||
|
|
|
||
|
|
if model_dir is None:
|
||
|
|
hub_dir = get_dir()
|
||
|
|
model_dir = os.path.join(hub_dir, 'checkpoints')
|
||
|
|
|
||
|
|
try:
|
||
|
|
os.makedirs(model_dir)
|
||
|
|
except OSError as e:
|
||
|
|
if e.errno == errno.EEXIST:
|
||
|
|
# Directory already exists, ignore.
|
||
|
|
pass
|
||
|
|
else:
|
||
|
|
# Unexpected OSError, re-raise.
|
||
|
|
raise
|
||
|
|
|
||
|
|
parts = urlparse(url)
|
||
|
|
filename = os.path.basename(parts.path)
|
||
|
|
if file_name is not None:
|
||
|
|
filename = file_name
|
||
|
|
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 = None
|
||
|
|
if check_hash:
|
||
|
|
r = HASH_REGEX.search(filename) # r is Optional[Match[str]]
|
||
|
|
hash_prefix = r.group(1) if r else None
|
||
|
|
download_url_to_file(url, cached_file, hash_prefix, progress=progress)
|
||
|
|
|
||
|
|
if _is_legacy_zip_format(cached_file):
|
||
|
|
return _legacy_zip_load(cached_file, model_dir, map_location)
|
||
|
|
return torch.load(cached_file, map_location=map_location)
|