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