问题描述
我有两个 ONNX 深度学习模型。 我想并行运行这两个模型。 我正在使用 python 中的线程。但令人惊讶的是,与顺序运行这两个模型相比,它需要更多时间。
要完成的任务。
- 制作一类模型
- 在该类的 init 中加载这两个模型。
- 并行运行两个模型以对给定输入进行推理。
class ModelImp:
def __init__(self):
print('loading model...')
# Load your model here
curr_dir = os.getcwd()
model_path = os.path.join(curr_dir,"model","hatev5.onnx")
self.hate_sess = onnxruntime.InferenceSession(model_path)
self.hate_input_name = self.hate_sess.get_inputs()[0].name
self.hate_seq_len=15
self.corona_seq_len=16
print('********************************Hate model loaded.**********************************************************')
model_path = os.path.join(curr_dir,"corona.onnx")
self.corona_sess = onnxruntime.InferenceSession(model_path)
self.corona_input_name = self.corona_sess.get_inputs()[0].name
# self.model = keras.models.load_model(model_path,custom_objects={"gelu": gelu})
# print(self.model.summary())
print('********************************Corona model loaded.**********************************************************')
print("_________________________************MODEL.py : loading tokenizer ************___________________________")
curr_dir = os.getcwd()
vocab_path = os.path.join(curr_dir,"vocab.txt")
self.wordpiece_tokenizer = tokenization.FullTokenizer(vocab_path,do_lower_case=True)
tokenizer_path = os.path.join(curr_dir,"hate_tokenizer.json")
with open(tokenizer_path) as f:
data = json.load(f)
self.hate_tokenizer = tokenizer_from_json(data)
print("_________________________************ HATE MODEL.py : tokenizer loaded************___________________________")
tokenizer_path = os.path.join(curr_dir,"corona_tokenizer.json")
with open(tokenizer_path) as f:
data = json.load(f)
self.corona_tokenizer = tokenizer_from_json(data)
print("_________________________************ CORONA MODEL.py : tokenizer loaded************___________________________")
curr_dir = os.getcwd()
# string version of Eval
# data is a string
def thread_eval(self,data,q):
# print("--------------------------------------corona started----------------------------------------------------------")
corona_lines = []
corona_line = ' '.join(trim(self.wordpiece_tokenizer.tokenize(data.strip()),self.corona_seq_len))
corona_lines.append(corona_line)
# print(texts)
corona_line_1 = self.corona_tokenizer.texts_to_sequences(corona_lines)
corona_line_2 = sequence.pad_sequences(corona_line_1,padding='post',maxlen=self.corona_seq_len)
corona_pred = self.corona_sess.run(None,{self.corona_input_name: corona_line_2})
corona_prob = corona_pred[0][0][1]
q.put(corona_prob)
# print("---------------------------------------corona ended------------------------------------------------------------")
def Eval(self,data):
try:
# pre_start = time.time()
# mp = ModelImp()
# with tf.Graph().as_default() as graph: #tf.device(config['gpu_device'] )
# print(data)
d = json.loads(data)
out_json = {}
if (not (("query" in d) or ("Query" in d))):
# print("Query: ",data)
score = -2 * 10000 # new_change
output = {"Output": [[score]]} # {"score" :score,"Succ" : False }
output_str = json.dumps(output)
return output_str
if ("query" in d):
query = d["query"][0] # new_change
# print("Query 1: ",query)
elif ("Query" in d):
query = d["Query"][0] # new_change
# print("Query 2: ",query)
if (len(query.strip()) == 0):
query = "good"
# print("Query 3: ",query)
## HATE MODEL input preprocess
que = queue.Queue()
x = threading.Thread(target=self.thread_eval,args=(query,que),daemon=True)
x.start()
hate_lines = []
hate_line = ' '.join(trim(self.wordpiece_tokenizer.tokenize(query.strip()),self.hate_seq_len))
hate_lines.append(hate_line)
# print(texts)
hate_line_1 = self.hate_tokenizer.texts_to_sequences(hate_lines)
hate_line_2 = sequence.pad_sequences(hate_line_1,maxlen=self.hate_seq_len)
## CORONA MODEL input preprocess
# print(line_2)
# print("----------------------------------------hate started----------------------------------------")
hate_pred = self.hate_sess.run(None,{self.hate_input_name: hate_line_2})
# print("----------------------------------------hate ended----------------------------------------")
# print("pred: ",pred[0])
# prob = math.exp(pred[0][0][1])/(math.exp(pred[0][0][0]) + math.exp(pred[0][0][1]))
hate_prob = hate_pred[0][0][1]
# print("hate_prob: ",hate_prob)
# hate_score = int(hate_prob * 10000) # new_change
x.join()
corona_prob=que.get()
# print("pred: ",pred[0])
# prob = math.exp(pred[0][0][1])/(math.exp(pred[0][0][0]) + math.exp(pred[0][0][1]))
# print("corona_prob: ",corona_prob)
output_prob = max(corona_prob,hate_prob)
# corona_score = int(corona_prob * 10000) # new_change
output_score = int(output_prob * 10000)
output = {"Output": [[output_score]]} # {"score" :score,"Succ" : True }
output_str = json.dumps(output)
return output_str
except Exception as e:
print("Exception: ",data)
score = -3 * 10000 # new_change
output = {"Output": [[score]]} # {"score" :score,"Succ" : False }
output_str = json.dumps(output)
print(e)
return output_str
解决方法
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