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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["inputs"]
parameters = {"mode": "image"}
# decode base64 image to PIL
image = Image.open(BytesIO(base64.b64decode(inputs)))
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
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