File size: 2,250 Bytes
eba3dbd 273c236 eba3dbd 273c236 eba3dbd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
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": "multimodal"})
# decode base64 image to PIL
image = Image.open(BytesIO(base64.b64decode(inputs['image'])))
image = self.transform(image).unsqueeze(0).to(device)
text = inputs['text'] if 'text' in inputs else ''# already gets tokenised in the model
with torch.no_grad():
feature_vector = self.model(image, text, mode=parameters["mode"])[0,0].tolist()
# postprocess the prediction
return {"feature_vector": feature_vector}
|