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| import hashlib | |
| import os | |
| import urllib | |
| import warnings | |
| from functools import partial | |
| from typing import Dict, Union | |
| from tqdm import tqdm | |
| try: | |
| from huggingface_hub import hf_hub_download | |
| _has_hf_hub = True | |
| except ImportError: | |
| hf_hub_download = None | |
| _has_hf_hub = False | |
| def _pcfg(url='', hf_hub='', filename='', mean=None, std=None): | |
| return dict( | |
| url=url, | |
| hf_hub=hf_hub, | |
| mean=mean, | |
| std=std, | |
| ) | |
| _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"), | |
| laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-B-16-laion2B-s34B-b88K/'), | |
| ) | |
| _EVAB16 = dict( | |
| eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'), | |
| eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'), | |
| eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'), | |
| eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'), | |
| ) | |
| _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)), | |
| ) | |
| _EVAL14 = dict( | |
| eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'), | |
| eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'), | |
| eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'), | |
| eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'), | |
| ) | |
| _VITL14_336 = dict( | |
| openai=_pcfg( | |
| "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"), | |
| ) | |
| _EVAL14_336 = dict( | |
| eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'), | |
| eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'), | |
| eva_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.pt'), | |
| eva02_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.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/'), | |
| laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s34B-b88K/'), | |
| ) | |
| _EVAg14 = dict( | |
| eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'), | |
| eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'), | |
| eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'), | |
| eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'), | |
| ) | |
| _EVAg14_PLUS = dict( | |
| eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'), | |
| eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'), | |
| eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'), | |
| eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'), | |
| ) | |
| _VITbigG14 = dict( | |
| laion2b_s39b_b160k=_pcfg(hf_hub='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/'), | |
| ) | |
| _EVAbigE14 = dict( | |
| eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), | |
| eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), | |
| eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'), | |
| eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'), | |
| ) | |
| _EVAbigE14_PLUS = dict( | |
| eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), | |
| eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), | |
| eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'), | |
| eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'), | |
| ) | |
| _PRETRAINED = { | |
| # "ViT-B-32": _VITB32, | |
| "OpenaiCLIP-B-32": _VITB32, | |
| "OpenCLIP-B-32": _VITB32, | |
| # "ViT-B-32-quickgelu": _VITB32_quickgelu, | |
| "OpenaiCLIP-B-32-quickgelu": _VITB32_quickgelu, | |
| "OpenCLIP-B-32-quickgelu": _VITB32_quickgelu, | |
| # "ViT-B-16": _VITB16, | |
| "OpenaiCLIP-B-16": _VITB16, | |
| "OpenCLIP-B-16": _VITB16, | |
| "EVA02-B-16": _EVAB16, | |
| "EVA02-CLIP-B-16": _EVAB16, | |
| # "ViT-B-16-plus-240": _VITB16_PLUS_240, | |
| "OpenCLIP-B-16-plus-240": _VITB16_PLUS_240, | |
| # "ViT-L-14": _VITL14, | |
| "OpenaiCLIP-L-14": _VITL14, | |
| "OpenCLIP-L-14": _VITL14, | |
| "EVA02-L-14": _EVAL14, | |
| "EVA02-CLIP-L-14": _EVAL14, | |
| # "ViT-L-14-336": _VITL14_336, | |
| "OpenaiCLIP-L-14-336": _VITL14_336, | |
| "EVA02-CLIP-L-14-336": _EVAL14_336, | |
| # "ViT-H-14": _VITH14, | |
| # "ViT-g-14": _VITg14, | |
| "OpenCLIP-H-14": _VITH14, | |
| "OpenCLIP-g-14": _VITg14, | |
| "EVA01-CLIP-g-14": _EVAg14, | |
| "EVA01-CLIP-g-14-plus": _EVAg14_PLUS, | |
| # "ViT-bigG-14": _VITbigG14, | |
| "OpenCLIP-bigG-14": _VITbigG14, | |
| "EVA02-CLIP-bigE-14": _EVAbigE14, | |
| "EVA02-CLIP-bigE-14-plus": _EVAbigE14_PLUS, | |
| } | |
| def _clean_tag(tag: str): | |
| # normalize pretrained tags | |
| return tag.lower().replace('-', '_') | |
| 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 = [] | |
| tag = _clean_tag(tag) | |
| 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 _clean_tag(tag) 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(_clean_tag(tag), {}) | |
| def get_pretrained_url(model: str, tag: str): | |
| cfg = get_pretrained_cfg(model, _clean_tag(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/clip") | |
| 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 | |