import hashlib import os import urllib import warnings from functools import partial from typing import Dict, Union from tqdm import tqdm from .version import __version__ try: from huggingface_hub import hf_hub_download hf_hub_download = partial(hf_hub_download, library_name="open_clip", library_version=__version__) _has_hf_hub = True except ImportError: hf_hub_download = None _has_hf_hub = False def _pcfg(url='', hf_hub='', mean=None, std=None): return dict( url=url, hf_hub=hf_hub, mean=mean, std=std, ) DEFAULT_CACHE_DIR = '~/.cache/clip' ENV_TORCH_HOME = 'TORCH_HOME' CACHE_DIR = os.getenv(ENV_TORCH_HOME, DEFAULT_CACHE_DIR) _RN50 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"), yfcc15m=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"), cc12m=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"), ) _RN50_quickgelu = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"), yfcc15m=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"), cc12m=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"), ) _RN101 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"), yfcc15m=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"), ) _RN101_quickgelu = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"), yfcc15m=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"), ) _RN50x4 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt"), ) _RN50x16 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt"), ) _RN50x64 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt"), ) _VITB32 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), laion400m_e31=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), laion400m_e32=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), laion2b_e16=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth"), laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/') ) _VITB32_quickgelu = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), laion400m_e31=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), laion400m_e32=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), ) _VITB16 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt"), laion400m_e31=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt"), laion400m_e32=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt"), # laion400m_32k=_pcfg( # url="", # mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), # laion400m_64k=_pcfg( # url="", # mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), ) _VITB16_PLUS_240 = dict( laion400m_e31=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt"), laion400m_e32=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt"), ) _VITL14 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"), laion400m_e31=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt"), laion400m_e32=_pcfg( "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt"), laion2b_s32b_b82k=_pcfg( hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), ) _VITL14_336 = dict( openai=_pcfg( "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"), ) _VITH14 = dict( laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'), ) _VITg14 = dict( laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'), ) _robertaViTB32 = dict( laion2b_s12b_b32k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-roberta-base-laion2B-s12B-b32k/'), ) _xlmRobertaBaseViTB32 = dict( laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k/'), ) _xlmRobertaLargeFrozenViTH14 = dict( frozen_laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-frozen-xlm-roberta-large-laion5B-s13B-b90k/'), ) _PRETRAINED = { "RN50": _RN50, "RN50-quickgelu": _RN50_quickgelu, "RN101": _RN101, "RN101-quickgelu": _RN101_quickgelu, "RN50x4": _RN50x4, "RN50x16": _RN50x16, "RN50x64": _RN50x64, "ViT-B-32": _VITB32, "ViT-B-32-quickgelu": _VITB32_quickgelu, "ViT-B-16": _VITB16, "ViT-B-16-plus-240": _VITB16_PLUS_240, "ViT-L-14": _VITL14, "ViT-L-14-336": _VITL14_336, "ViT-H-14": _VITH14, "ViT-g-14": _VITg14, "roberta-ViT-B-32": _robertaViTB32, "xlm-roberta-base-ViT-B-32": _xlmRobertaBaseViTB32, "xlm-roberta-large-ViT-H-14": _xlmRobertaLargeFrozenViTH14, } def list_pretrained(as_str: bool = False): """ returns list of pretrained models Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True """ return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()] def list_pretrained_models_by_tag(tag: str): """ return all models having the specified pretrain tag """ models = [] for k in _PRETRAINED.keys(): if tag in _PRETRAINED[k]: models.append(k) return models def list_pretrained_tags_by_model(model: str): """ return all pretrain tags for the specified model architecture """ tags = [] if model in _PRETRAINED: tags.extend(_PRETRAINED[model].keys()) return tags def is_pretrained_cfg(model: str, tag: str): if model not in _PRETRAINED: return False return tag.lower() in _PRETRAINED[model] def get_pretrained_cfg(model: str, tag: str): if model not in _PRETRAINED: return {} model_pretrained = _PRETRAINED[model] return model_pretrained.get(tag.lower(), {}) def get_pretrained_url(model: str, tag: str): cfg = get_pretrained_cfg(model, tag) return cfg.get('url', '') def download_pretrained_from_url( url: str, cache_dir: Union[str, None] = None, ): if not cache_dir: cache_dir = os.path.expanduser(CACHE_DIR) os.makedirs(cache_dir, exist_ok=True) filename = os.path.basename(url) if 'openaipublic' in url: expected_sha256 = url.split("/")[-2] elif 'mlfoundations' in url: expected_sha256 = os.path.splitext(filename)[0].split("-")[-1] else: expected_sha256 = '' download_target = os.path.join(cache_dir, filename) if os.path.exists(download_target) and not os.path.isfile(download_target): raise RuntimeError(f"{download_target} exists and is not a regular file") if os.path.isfile(download_target): if expected_sha256: if hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): return download_target else: warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") else: return download_target with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: with tqdm(total=int(source.headers.get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: while True: buffer = source.read(8192) if not buffer: break output.write(buffer) loop.update(len(buffer)) if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") return download_target 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 download_pretrained_from_hf( model_id: str, filename: str = 'open_clip_pytorch_model.bin', revision=None, cache_dir: Union[str, None] = None, ): has_hf_hub(True) cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir) return cached_file def download_pretrained( cfg: Dict, force_hf_hub: bool = False, cache_dir: Union[str, None] = None, ): target = '' if not cfg: return target download_url = cfg.get('url', '') download_hf_hub = cfg.get('hf_hub', '') if download_hf_hub and force_hf_hub: # use HF hub even if url exists download_url = '' if download_url: target = download_pretrained_from_url(download_url, cache_dir=cache_dir) elif download_hf_hub: has_hf_hub(True) # we assume the hf_hub entries in pretrained config combine model_id + filename in # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'. model_id, filename = os.path.split(download_hf_hub) if filename: target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir) else: target = download_pretrained_from_hf(model_id, cache_dir=cache_dir) return target