43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
|
from clip.clip import tokenize as _tokenize, load as _load, available_models as _available_models
|
||
|
|
import re
|
||
|
|
import string
|
||
|
|
|
||
|
|
#dependencies = ["torch", "torchvision", "ftfy", "regex", "tqdm"]
|
||
|
|
|
||
|
|
# For compatibility (cannot include special characters in function name)
|
||
|
|
model_functions = { model: re.sub(f'[{string.punctuation}]', '_', model) for model in _available_models()}
|
||
|
|
|
||
|
|
def _create_hub_entrypoint(model):
|
||
|
|
def entrypoint(**kwargs):
|
||
|
|
return _load(model, **kwargs)
|
||
|
|
|
||
|
|
entrypoint.__doc__ = f"""Loads the {model} CLIP model
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
device : Union[str, torch.device]
|
||
|
|
The device to put the loaded model
|
||
|
|
|
||
|
|
jit : bool
|
||
|
|
Whether to load the optimized JIT model or more hackable non-JIT model (default).
|
||
|
|
|
||
|
|
download_root: str
|
||
|
|
path to download the model files; by default, it uses "~/.cache/clip"
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
model : torch.nn.Module
|
||
|
|
The {model} CLIP model
|
||
|
|
|
||
|
|
preprocess : Callable[[PIL.Image], torch.Tensor]
|
||
|
|
A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
|
||
|
|
"""
|
||
|
|
return entrypoint
|
||
|
|
|
||
|
|
def tokenize():
|
||
|
|
return _tokenize
|
||
|
|
|
||
|
|
_entrypoints = {model_functions[model]: _create_hub_entrypoint(model) for model in _available_models()}
|
||
|
|
|
||
|
|
globals().update(_entrypoints)
|