import hashlib import os import urllib import warnings from tqdm import tqdm _RN50 = dict( openai="https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt", cc12m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt" ) _RN50_quickgelu = dict( openai="https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt", cc12m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt" ) _RN101 = dict( openai="https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt", yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt" ) _RN101_quickgelu = dict( openai="https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt", yfcc15m="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt" ) _RN50x4 = dict( openai="https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt", ) _RN50x16 = dict( openai="https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt", ) _RN50x64 = dict( openai="https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt", ) _VITB32 = dict( openai="https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", laion400m_e31="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt", laion400m_e32="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt", laion400m_avg="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_avg-8a00ab3c.pt", ) _VITB32_quickgelu = dict( openai="https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", laion400m_e31="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt", laion400m_e32="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt", laion400m_avg="https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_avg-8a00ab3c.pt", ) _VITB16 = dict( openai="https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt", ) _VITL14 = dict( openai="https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt", ) _PRETRAINED = { "RN50": _RN50, "RN50-quickgelu": _RN50_quickgelu, "RN101": _RN101, "RN101-quickgelu": _RN101_quickgelu, "RN50x4": _RN50x4, "RN50x16": _RN50x16, "ViT-B-32": _VITB32, "ViT-B-32-quickgelu": _VITB32_quickgelu, "ViT-B-16": _VITB16, "ViT-L-14": _VITL14, } 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_tag_models(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_model_tags(model: str): """ return all pretrain tags for the specified model architecture """ tags = [] if model in _PRETRAINED: tags.extend(_PRETRAINED[model].keys()) return tags def get_pretrained_url(model: str, tag: str): if model not in _PRETRAINED: return '' model_pretrained = _PRETRAINED[model] if tag not in model_pretrained: return '' return model_pretrained[tag] def download_pretrained(url: str, root: str = os.path.expanduser("~/.cache/clip")): os.makedirs(root, exist_ok=True) filename = os.path.basename(url) if 'openaipublic' in url: expected_sha256 = url.split("/")[-2] else: expected_sha256 = '' download_target = os.path.join(root, 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() == 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.info().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 hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256: raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") return download_target