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from typing import Dict, Any |
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from PIL import Image |
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import torch |
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import requests |
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from io import BytesIO |
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from transformers import BlipForConditionalGeneration, BlipProcessor |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") |
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self.model = BlipForConditionalGeneration.from_pretrained( |
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"Salesforce/blip-image-captioning-base" |
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).to(device) |
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self.model.eval() |
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self.model = self.model.to(device) |
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def __call__(self, data: Any) -> Dict[str, Any]: |
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""" |
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Args: |
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data (:obj:): |
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includes the input data and the parameters for the inference. |
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Return: |
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A :obj:`dict`:. The object returned should be a dict of one list like {"captions": ["A hugging face at the office"]} containing : |
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- "caption": A string corresponding to the generated caption. |
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""" |
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inputs = data.pop("inputs", data) |
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parameters = data.pop("parameters", {}) |
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raw_images = [Image.open(BytesIO(requests.get(_img).content)) for _img in inputs] |
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processed_image = self.processor(images=raw_images, return_tensors="pt") |
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processed_image["pixel_values"] = processed_image["pixel_values"].to(device) |
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processed_image = {**processed_image, **parameters} |
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with torch.no_grad(): |
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out = self.model.generate( |
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**processed_image |
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) |
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captions = self.processor.batch_decode(out, skip_special_tokens=True) |
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return {"captions": captions} |
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