from typing import Dict, List, Any from PIL import Image import requests import torch import base64 from io import BytesIO from blip import blip_decoder 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_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_large_caption.pth' self.model = blip_decoder(pretrained=self.model_url, image_size=384, vit='large') self.model.eval() self.model = 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) -> List[List[Dict[str, float]]]: """ Args: data (:obj:): includes the input data and the parameters for the inference. Return: A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing : - "label": A string representing what the label/class is. There can be multiple labels. - "score": A score between 0 and 1 describing how confident the model is for this label/class. """ inputs = data.pop("inputs", data) parameters = data.pop("parameters", None) # decode base64 image to PIL image = Image.open(BytesIO(base64.b64decode(inputs['image']))) image = transform(image).unsqueeze(0).to(device) with torch.no_grad(): caption = self.model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5) # postprocess the prediction return caption