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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 |