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import io |
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import base64 |
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import shutil |
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import torch |
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from transformers import CLIPProcessor, CLIPModel, CLIPTokenizer |
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class PreTrainedPipeline(): |
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def __init__(self, path=""): |
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""" |
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Initialize model |
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""" |
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self.model = CLIPModel.from_pretrained(path) |
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self.tokenizer = CLIPTokenizer.from_pretrained(path) |
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def __call__(self, inputs: str): |
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""" |
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Args: |
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inputs (:obj:`str`): |
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a string containing some text |
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Return: |
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A :obj:`list`list of floats: The features computed by the model. |
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""" |
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inputs = self.tokenizer(inputs, padding=True, return_tensors="pt") |
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with torch.no_grad(): |
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text_features = self.model.get_text_features(**inputs) |
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return text_features[0].tolist() |
