After interpretor.invoke() using multiprocessing, it eternally waits

import numpy as np
import time
import multiprocessing as mp
import tflite_runtime.interpreter as tflite

armnn_delegate = tflite.load_delegate( library="./libarmnnDelegate.so",
                                       options={"backends": "CpuAcc,CpuRef", "logging-severity":"DEBUG"})

def load_model(model_path, armnn_delegate):
    interpreter = tflite.Interpreter(model_path=model_path, experimental_delegates=[armnn_delegate])
    interpreter.allocate_tensors()
    return interpreter

model_path = "./ssdlite_mobilenet_v2_coco.tflite"
interpreter = load_model(model_path, armnn_delegate)
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
input_shape = input_details[0]['shape']
input_index = input_details[0]['index']
input_data = np.array(np.ones(input_shape), dtype=np.float32)

def execute():
    print("CPU")
    interpreter.set_tensor(input_index, input_data)
    start_time=time.time()
    print(start_time)
    interpreter.invoke()
    print("CPU --- %s seconds ---" % ((time.time()-start_time)/100))
    output_data = interpreter.get_tensor(output_details[0]['index'])
    print(output_data)

p1 = mp.Process(target=execute)
#p2 = mp.Process(target=execute)
p1.start()
#p2.start()

Hi, I used this code of tflite delegate with python binding. The result is this.

Info: ArmNN v25.0.0 Info: Initialization time: 6.17 ms INFO: TfLiteArmnnDelegate: Created TfLite ArmNN delegate. CPU 1624342939.6050785

When using multiprocessing, it waits after print(start_time) eternally. Maybe interpreter.invoke() does not reply.

If i use the code normally or with threading, it works well. In addition, the same code on desktop works well, too

import numpy as np
import time
import multiprocessing as mp
import tensorflow.lite as tflite
def load_model(model_path):
    interpreter = tflite.Interpreter(model_path=model_path)
    interpreter.allocate_tensors()
    return interpreter
model_path = 'ssd_mobilenet_v2_coco.tflite'
interpreter = load_model(model_path)
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
input_shape = input_details[0]['shape']
input_index = input_details[0]['index']
input_data = np.array(np.ones(input_shape), dtype=np.float32)
def execute():
    print("CPU")
    interpreter.set_tensor(input_index, input_data)
    start_time=time.time()
    print(start_time)
    interpreter.invoke()
    print("CPU --- %s seconds ---" % ((time.time()-start_time)/100))
    output_data = interpreter.get_tensor(output_details[0]['index'])
    print(output_data)
p1 = mp.Process(target=execute)
p2 = mp.Process(target=execute)
#p3 = mp.Process(target=execute)
#p4 = mp.Process(target=execute)
p1.start()
p2.start()
#p3.start()
#p4.start()

CPU 1624342916.1923797CPU 1624342916.1952572 CPU --- 0.0010329008102416993 seconds --- CPU --- 0.0010196781158447266 seconds --- [[[-8.99108 -8.407066 -7.5616293 -9.077816 ] [-9.104538 -9.439804 -8.893764 -8.005322 ] [-8.853685 -8.000054 -9.184245 -8.621555 ] [-7.63076 -7.564349 -8.118874 -6.897669 ] [-8.256776 -7.657664 -7.289326 -9.266909 ] [-6.9567194 -9.266215 -9.2662115 -8.564189 ] [-9.266026 -6.9817686 -6.2814064 -8.547486 ] [-7.692247 -7.11787 -7.3582535 -8.933831 ] [-6.8463497 -8.767875 -9.676924 -6.9147243] [-9.266149 -6.541151 -7.3526998 -8.238716 ]]] [[[-8.99108 -8.407066 -7.5616293 -9.077816 ] [-9.104538 -9.439804 -8.893764 -8.005322 ] [-8.853685 -8.000054 -9.184245 -8.621555 ] [-7.63076 -7.564349 -8.118874 -6.897669 ] [-8.256776 -7.657664 -7.289326 -9.266909 ] [-6.9567194 -9.266215 -9.2662115 -8.564189 ] [-9.266026 -6.9817686 -6.2814064 -8.547486 ] [-7.692247 -7.11787 -7.3582535 -8.933831 ] [-6.8463497 -8.767875 -9.676924 -6.9147243] [-9.266149 -6.541151 -7.3526998 -8.238716 ]]]

Is there any way to using multiprocessing with tflite interpreter?

ARM-software / armnn

After interpretor.invoke() using multiprocessing, it eternally waits #558