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import hashlib |
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import os |
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import urllib |
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import warnings |
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from functools import partial |
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from typing import Dict, Union |
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from tqdm import tqdm |
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try: |
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from huggingface_hub import hf_hub_download |
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_has_hf_hub = True |
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except ImportError: |
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hf_hub_download = None |
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_has_hf_hub = False |
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|
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def _pcfg(url='', hf_hub='', filename='', mean=None, std=None): |
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return dict( |
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url=url, |
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hf_hub=hf_hub, |
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mean=mean, |
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std=std, |
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) |
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|
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_VITB32 = dict( |
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openai=_pcfg( |
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"https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), |
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laion400m_e31=_pcfg( |
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"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), |
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laion400m_e32=_pcfg( |
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"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), |
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laion2b_e16=_pcfg( |
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"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth"), |
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laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/') |
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) |
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_VITB32_quickgelu = dict( |
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openai=_pcfg( |
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"https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), |
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laion400m_e31=_pcfg( |
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"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), |
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laion400m_e32=_pcfg( |
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"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), |
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) |
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_VITB16 = dict( |
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openai=_pcfg( |
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"https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt"), |
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laion400m_e31=_pcfg( |
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"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt"), |
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laion400m_e32=_pcfg( |
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"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt"), |
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laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-B-16-laion2B-s34B-b88K/'), |
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) |
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_EVAB16 = dict( |
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eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'), |
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eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_B_psz14to16.pt'), |
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eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'), |
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eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_B_psz16_s8B.pt'), |
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) |
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_VITB16_PLUS_240 = dict( |
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laion400m_e31=_pcfg( |
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"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt"), |
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laion400m_e32=_pcfg( |
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"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt"), |
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) |
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_VITL14 = dict( |
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openai=_pcfg( |
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"https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"), |
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laion400m_e31=_pcfg( |
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"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt"), |
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laion400m_e32=_pcfg( |
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"https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt"), |
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laion2b_s32b_b82k=_pcfg( |
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hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/', |
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), |
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) |
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_EVAL14 = dict( |
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eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'), |
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eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_L_psz14.pt'), |
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eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'), |
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eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_s4B.pt'), |
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) |
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_VITL14_336 = dict( |
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openai=_pcfg( |
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"https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"), |
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) |
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_EVAL14_336 = dict( |
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eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'), |
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eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_336_psz14_s6B.pt'), |
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eva_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.pt'), |
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eva02_clip_224to336=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_L_psz14_224to336.pt'), |
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) |
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_VITH14 = dict( |
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laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'), |
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) |
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_VITg14 = dict( |
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laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'), |
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laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s34B-b88K/'), |
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) |
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_EVAg14 = dict( |
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eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'), |
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eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'), |
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eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'), |
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eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_psz14_s11B.pt'), |
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) |
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_EVAg14_PLUS = dict( |
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eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/'), |
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eva01=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_g_psz14.pt'), |
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eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'), |
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eva01_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA01_CLIP_g_14_plus_psz14_s11B.pt'), |
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) |
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_VITbigG14 = dict( |
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laion2b_s39b_b160k=_pcfg(hf_hub='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/'), |
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) |
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_EVAbigE14 = dict( |
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eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), |
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eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), |
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eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'), |
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eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_s4B.pt'), |
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) |
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_EVAbigE14_PLUS = dict( |
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eva=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), |
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eva02=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_E_psz14.pt'), |
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eva_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'), |
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eva02_clip=_pcfg(hf_hub='QuanSun/EVA-CLIP/EVA02_CLIP_E_psz14_plus_s9B.pt'), |
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) |
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_PRETRAINED = { |
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"OpenaiCLIP-B-32": _VITB32, |
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"OpenCLIP-B-32": _VITB32, |
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"OpenaiCLIP-B-32-quickgelu": _VITB32_quickgelu, |
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"OpenCLIP-B-32-quickgelu": _VITB32_quickgelu, |
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"OpenaiCLIP-B-16": _VITB16, |
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"OpenCLIP-B-16": _VITB16, |
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"EVA02-B-16": _EVAB16, |
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"EVA02-CLIP-B-16": _EVAB16, |
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"OpenCLIP-B-16-plus-240": _VITB16_PLUS_240, |
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"OpenaiCLIP-L-14": _VITL14, |
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"OpenCLIP-L-14": _VITL14, |
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"EVA02-L-14": _EVAL14, |
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"EVA02-CLIP-L-14": _EVAL14, |
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"OpenaiCLIP-L-14-336": _VITL14_336, |
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"EVA02-CLIP-L-14-336": _EVAL14_336, |
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"OpenCLIP-H-14": _VITH14, |
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"OpenCLIP-g-14": _VITg14, |
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"EVA01-CLIP-g-14": _EVAg14, |
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"EVA01-CLIP-g-14-plus": _EVAg14_PLUS, |
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"OpenCLIP-bigG-14": _VITbigG14, |
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"EVA02-CLIP-bigE-14": _EVAbigE14, |
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"EVA02-CLIP-bigE-14-plus": _EVAbigE14_PLUS, |
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} |
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def _clean_tag(tag: str): |
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return tag.lower().replace('-', '_') |
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def list_pretrained(as_str: bool = False): |
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""" returns list of pretrained models |
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Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True |
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""" |
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return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()] |
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def list_pretrained_models_by_tag(tag: str): |
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""" return all models having the specified pretrain tag """ |
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models = [] |
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tag = _clean_tag(tag) |
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for k in _PRETRAINED.keys(): |
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if tag in _PRETRAINED[k]: |
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models.append(k) |
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return models |
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def list_pretrained_tags_by_model(model: str): |
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""" return all pretrain tags for the specified model architecture """ |
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tags = [] |
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if model in _PRETRAINED: |
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tags.extend(_PRETRAINED[model].keys()) |
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return tags |
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def is_pretrained_cfg(model: str, tag: str): |
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if model not in _PRETRAINED: |
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return False |
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return _clean_tag(tag) in _PRETRAINED[model] |
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def get_pretrained_cfg(model: str, tag: str): |
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if model not in _PRETRAINED: |
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return {} |
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model_pretrained = _PRETRAINED[model] |
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return model_pretrained.get(_clean_tag(tag), {}) |
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def get_pretrained_url(model: str, tag: str): |
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cfg = get_pretrained_cfg(model, _clean_tag(tag)) |
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return cfg.get('url', '') |
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def download_pretrained_from_url( |
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url: str, |
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cache_dir: Union[str, None] = None, |
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): |
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if not cache_dir: |
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cache_dir = os.path.expanduser("~/.cache/clip") |
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os.makedirs(cache_dir, exist_ok=True) |
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filename = os.path.basename(url) |
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|
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if 'openaipublic' in url: |
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expected_sha256 = url.split("/")[-2] |
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elif 'mlfoundations' in url: |
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expected_sha256 = os.path.splitext(filename)[0].split("-")[-1] |
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else: |
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expected_sha256 = '' |
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download_target = os.path.join(cache_dir, filename) |
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if os.path.exists(download_target) and not os.path.isfile(download_target): |
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raise RuntimeError(f"{download_target} exists and is not a regular file") |
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|
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if os.path.isfile(download_target): |
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if expected_sha256: |
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if hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): |
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return download_target |
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else: |
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warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") |
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else: |
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return download_target |
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with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: |
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with tqdm(total=int(source.headers.get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: |
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while True: |
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buffer = source.read(8192) |
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if not buffer: |
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break |
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output.write(buffer) |
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loop.update(len(buffer)) |
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if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): |
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raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") |
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return download_target |
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|
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def has_hf_hub(necessary=False): |
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if not _has_hf_hub and necessary: |
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|
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raise RuntimeError( |
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'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.') |
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return _has_hf_hub |
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|
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def download_pretrained_from_hf( |
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model_id: str, |
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filename: str = 'open_clip_pytorch_model.bin', |
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revision=None, |
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cache_dir: Union[str, None] = None, |
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): |
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has_hf_hub(True) |
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cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir) |
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return cached_file |
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|
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def download_pretrained( |
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cfg: Dict, |
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force_hf_hub: bool = False, |
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cache_dir: Union[str, None] = None, |
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): |
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target = '' |
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if not cfg: |
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return target |
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|
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download_url = cfg.get('url', '') |
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download_hf_hub = cfg.get('hf_hub', '') |
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if download_hf_hub and force_hf_hub: |
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download_url = '' |
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if download_url: |
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target = download_pretrained_from_url(download_url, cache_dir=cache_dir) |
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elif download_hf_hub: |
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has_hf_hub(True) |
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model_id, filename = os.path.split(download_hf_hub) |
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if filename: |
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target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir) |
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else: |
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target = download_pretrained_from_hf(model_id, cache_dir=cache_dir) |
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return target |
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|
