File size: 1,820 Bytes
4016518 96355e1 a61e58e 59199c3 4016518 96355e1 22aa375 59199c3 4016518 89b609f 76f4690 89b609f 4016518 89b609f |
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 |
from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModel
from optimum.pipelines import pipeline
from optimum.onnxruntime import ORTModelForFeatureExtraction
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
class EndpointHandler():
def __init__(self, path=""):
# self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# self.tokenizer = AutoTokenizer.from_pretrained("optimum/sbert-all-MiniLM-L6-with-pooler")
model_regular = ORTModelForFeatureExtraction.from_pretrained("", file_name="model.onnx", from_transformers=False)
self.onnx_extractor = pipeline(task, model=model_regular, tokenizer=tokenizer)
# self.model.to(self.device)
# print("model will run on ", self.device)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
sentences = data.pop("inputs",data)
# inputs = tokenizer("I love burritos!", return_tensors="pt")
pred = self.onnx_extractor(sentences)
return pred
# Perform pooling. In this case, max pooling.
# sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# return sentence_embeddings.tolist() |