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from typing import List, Dict, Any
import numpy as np
from transformers import BertTokenizer, BertModel
import torch
import pickle
def unpickle_obj(filepath):
with open(filepath, 'rb') as f_in:
data = pickle.load(f_in)
print(f"unpickled {filepath}")
return data
class EndpointHandler():
def __init__(self, path=""):
self.model = unpickle_obj(path)
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.bert = BertModel.from_pretrained('bert-base-uncased').to(self.device)
def get_embeddings(self, texts: List[str]):
inputs = self.tokenizer(texts, return_tensors='pt', truncation=True,
padding=True, max_length=512).to(self.device)
with torch.no_grad():
outputs = self.bert(**inputs)
return outputs.last_hidden_state.mean(dim=1).cpu().numpy()
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
queries = data['queries']
texts = data['texts']
queries_vec = self.get_embeddings(queries)
texts_vec = self.get_embeddings(texts)
diff = (np.array(texts_vec)[:, np.newaxis] - np.array(queries_vec))\
.reshape(-1, len(queries_vec[0]))
return [{
"outputs": self.model.predict_proba(diff)
}] |