| from typing import Dict, List, Any |
| from transformers import CLIPTokenizer, CLIPModel |
| import numpy as np |
| import os |
| import torch |
|
|
|
|
| class EndpointHandler: |
| def __init__(self, path="."): |
| |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| self.model = CLIPModel.from_pretrained(path).to(self.device).eval() |
| self.tokenizer = CLIPTokenizer.from_pretrained(path) |
|
|
| def __call__(self, data: Dict[str, Any]) -> List[float]: |
| """ |
| data args: |
| inputs (:obj: `str` | `PIL.Image` | `np.array`) |
| kwargs |
| Return: |
| A :obj:`list` | `dict`: will be serialized and returned |
| """ |
| |
| query = data["inputs"] |
| inputs = self.tokenizer(query, padding=True, return_tensors="pt").to( |
| self.device |
| ) |
| with torch.no_grad(): |
| text_features = self.model.get_text_features(**inputs) |
|
|
| text_features = text_features.cpu().detach().numpy() |
| input_embedding = text_features[0] |
|
|
| |
| input_embedding /= np.linalg.norm(input_embedding) |
|
|
| return input_embedding.tolist() |
|
|
