import json import logging import os from functools import partial from pathlib import Path from typing import Union import torch from torch.hub import HASH_REGEX, download_url_to_file, urlparse try: from torch.hub import get_dir except ImportError: from torch.hub import _get_torch_home as get_dir from timm import __version__ try: from huggingface_hub import HfApi, HfFolder, Repository, cached_download, hf_hub_url cached_download = partial(cached_download, library_name="timm", library_version=__version__) _has_hf_hub = True except ImportError: cached_download = None _has_hf_hub = False _logger = logging.getLogger(__name__) def get_cache_dir(child_dir=''): """ Returns the location of the directory where models are cached (and creates it if necessary). """ # Issue warning to move data if old env is set if os.getenv('TORCH_MODEL_ZOO'): _logger.warning('TORCH_MODEL_ZOO is deprecated, please use env TORCH_HOME instead') hub_dir = get_dir() child_dir = () if not child_dir else (child_dir,) model_dir = os.path.join(hub_dir, 'checkpoints', *child_dir) os.makedirs(model_dir, exist_ok=True) return model_dir def download_cached_file(url, check_hash=True, progress=False): parts = urlparse(url) filename = os.path.basename(parts.path) cached_file = os.path.join(get_cache_dir(), filename) if not os.path.exists(cached_file): _logger.info('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) return cached_file def has_hf_hub(necessary=False): if not _has_hf_hub and necessary: # if no HF Hub module installed and it is necessary to continue, raise error raise RuntimeError( 'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.') return _has_hf_hub def hf_split(hf_id): rev_split = hf_id.split('@') assert 0 < len(rev_split) <= 2, 'hf_hub id should only contain one @ character to identify revision.' hf_model_id = rev_split[0] hf_revision = rev_split[-1] if len(rev_split) > 1 else None return hf_model_id, hf_revision def load_cfg_from_json(json_file: Union[str, os.PathLike]): with open(json_file, "r", encoding="utf-8") as reader: text = reader.read() return json.loads(text) def _download_from_hf(model_id: str, filename: str): hf_model_id, hf_revision = hf_split(model_id) url = hf_hub_url(hf_model_id, filename, revision=hf_revision) return cached_download(url, cache_dir=get_cache_dir('hf')) def load_model_config_from_hf(model_id: str): assert has_hf_hub(True) cached_file = _download_from_hf(model_id, 'config.json') default_cfg = load_cfg_from_json(cached_file) default_cfg['hf_hub'] = model_id # insert hf_hub id for pretrained weight load during model creation model_name = default_cfg.get('architecture') return default_cfg, model_name def load_state_dict_from_hf(model_id: str): assert has_hf_hub(True) cached_file = _download_from_hf(model_id, 'pytorch_model.bin') state_dict = torch.load(cached_file, map_location='cpu') return state_dict def save_for_hf(model, save_directory, model_config=None): assert has_hf_hub(True) model_config = model_config or {} save_directory = Path(save_directory) save_directory.mkdir(exist_ok=True, parents=True) weights_path = save_directory / 'pytorch_model.bin' torch.save(model.state_dict(), weights_path) config_path = save_directory / 'config.json' hf_config = model.default_cfg hf_config['num_classes'] = model_config.pop('num_classes', model.num_classes) hf_config['num_features'] = model_config.pop('num_features', model.num_features) hf_config['labels'] = model_config.pop('labels', [f"LABEL_{i}" for i in range(hf_config['num_classes'])]) hf_config.update(model_config) with config_path.open('w') as f: json.dump(hf_config, f, indent=2) def push_to_hf_hub( model, local_dir, repo_namespace_or_url=None, commit_message='Add model', use_auth_token=True, git_email=None, git_user=None, revision=None, model_config=None, ): if repo_namespace_or_url: repo_owner, repo_name = repo_namespace_or_url.rstrip('/').split('/')[-2:] else: if isinstance(use_auth_token, str): token = use_auth_token else: token = HfFolder.get_token() if token is None: raise ValueError( "You must login to the Hugging Face hub on this computer by typing `transformers-cli login` and " "entering your credentials to use `use_auth_token=True`. Alternatively, you can pass your own " "token as the `use_auth_token` argument." ) repo_owner = HfApi().whoami(token)['name'] repo_name = Path(local_dir).name repo_url = f'https://huggingface.co/{repo_owner}/{repo_name}' repo = Repository( local_dir, clone_from=repo_url, use_auth_token=use_auth_token, git_user=git_user, git_email=git_email, revision=revision, ) # Prepare a default model card that includes the necessary tags to enable inference. readme_text = f'---\ntags:\n- image-classification\n- timm\nlibrary_tag: timm\n---\n# Model card for {repo_name}' with repo.commit(commit_message): # Save model weights and config. save_for_hf(model, repo.local_dir, model_config=model_config) # Save a model card if it doesn't exist. readme_path = Path(repo.local_dir) / 'README.md' if not readme_path.exists(): readme_path.write_text(readme_text) return repo.git_remote_url()