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from typing import Dict, List, Any
from transformers import CLIPTokenizer, CLIPModel
import numpy as np
import torch
class PreTrainedPipeline():
def __init__(self, path="."):
# load the model
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
"""
# compute the embedding of the input
# query = data["inputs"]
inputs = self.tokenizer(data, 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]
# normalize the embedding
input_embedding /= np.linalg.norm(input_embedding)
return input_embedding.tolist()
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