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from typing import Dict, List, Any |
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import PIL |
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import torch |
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import base64 |
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import os |
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import io |
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from transformers import ViTImageProcessor, ViTModel |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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class PreTrainedPipeline(): |
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def __init__(self, path=""): |
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self.model = ViTModel.from_pretrained( |
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pretrained_model_name_or_path=path, |
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config=os.path.join(path, 'config.json') |
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) |
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self.model.eval() |
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self.model = self.model.to(device) |
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self.processor = ViTImageProcessor.from_pretrained( |
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pretrained_model_name_or_path=os.path.join( |
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path, 'preprocessor_config.json') |
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) |
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def __call__(self, data: Any) -> Dict[str, List[float]]: |
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""" |
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Args: |
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data (:dict | str:): |
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Includes the input data and the parameters for the inference. |
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Inputs should be an image encoded in base 64. |
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Return: |
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A :obj:`dict`:. The object returned should be a dict like |
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{"feature_vector": [0.6331314444541931,...,-0.7866355180740356,]} containing : |
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- "feature_vector": A list of floats corresponding to the image embedding. |
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""" |
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inputs = data.pop("inputs", data) |
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image = PIL.Image.open(io.BytesIO(base64.b64decode(inputs['image']))) |
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inputs = self.processor(images=image, return_tensors="pt") |
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outputs = self.model(**inputs) |
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feature_vector = outputs.last_hidden_state[0, 0].tolist() |
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return {"feature_vector": feature_vector} |
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