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