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from typing import Dict, List, Any |
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from PIL import Image |
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import requests |
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import torch |
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import base64 |
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import os |
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from io import BytesIO |
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from models.blip_feature_extractor import blip_feature_extractor |
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from torchvision import transforms |
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from torchvision.transforms.functional import InterpolationMode |
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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_path = os.path.join(path,'model_large_retrieval_coco.pth') |
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self.model = blip_feature_extractor( |
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pretrained=self.model_path, |
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image_size=384, |
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vit='large', |
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med_config=os.path.join(path, 'configs/med_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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image_size = 384 |
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self.transform = transforms.Compose([ |
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transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC), |
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transforms.ToTensor(), |
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transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) |
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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 (: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 like {"feature_vector": [0.6331314444541931,0.8802216053009033,...,-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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parameters = data.pop("parameters", {"mode": "image"}) |
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image = Image.open(BytesIO(base64.b64decode(inputs['image']))) |
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image = self.transform(image).unsqueeze(0).to(device) |
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text="" |
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with torch.no_grad(): |
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feature_vector = self.model(image, text, mode=parameters["mode"])[0,0].tolist() |
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return {"feature_vector": feature_vector} |
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