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Running
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Zero
File size: 6,212 Bytes
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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
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