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import hashlib | |
import os | |
import urllib | |
import warnings | |
from tqdm import tqdm | |
CACHE_DIR = os.getenv("AUDIOLDM_CACHE_DIR", "~/.cache") | |
_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(f"{CACHE_DIR}/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 | |