models/CLAP/open_clip/pretrained.py

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