from typing import Dict, List, Any from PIL import Image import requests import torch import base64 import os from io import BytesIO from models.blip_feature_extractor import blip_feature_extractor from torchvision import transforms from torchvision.transforms.functional import InterpolationMode device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') class PreTrainedPipeline(): def __init__(self, path=""): # load the optimized model self.model_path = os.path.join(path,'model_large_retrieval_coco.pth') self.model = blip_feature_extractor( pretrained=self.model_path, image_size=384, vit='large', med_config=os.path.join(path, 'configs/med_config.json') ) self.model.eval() self.model = self.model.to(device) image_size = 384 self.transform = transforms.Compose([ transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC), transforms.ToTensor(), transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) ]) def __call__(self, data: Any) -> Dict[str, List[float]]: """ Args: data (:obj:): includes the input data and the parameters for the inference. Return: A :obj:`dict`:. The object returned should be a dict like {"feature_vector": [0.6331314444541931,0.8802216053009033,...,-0.7866355180740356,]} containing : - "feature_vector": A list of floats corresponding to the image embedding. """ inputs = data.pop("inputs", data) parameters = data.pop("parameters", {"mode": "image"}) # decode base64 image to PIL image = Image.open(BytesIO(base64.b64decode(inputs['image']))) image = self.transform(image).unsqueeze(0).to(device) text="" with torch.no_grad(): feature_vector = self.model(image, text, mode=parameters["mode"])[0,0].tolist() # postprocess the prediction return {"feature_vector": feature_vector}