将函数或类实例传递给multiprocessing.Process的“target”参数是否安全,而不存在密集复制的风险?

我想知道是否有可能将另一个类的函数传递给一个 multiprocessing.Process 作为论据?请注意,我将在基于Unix的机器上运行此代码,因此 Process fork 而不是 spawn . 举个例子:

#class1.py
from class3 import Class3
class Class1(object):
    def __init__(self):
        self.class3Instance = Class3()

    def func1(self):
        self.class3Instance.func3()

#class3.py
import numpy as np
import pandas
import cv2 # OpenCV library
# there are many other things that I am importing here

class Class3(object):
    def __init__(self):
        pass

    def func3(self):
        np.random.seed(1)
        print ('func3 changed the random seed')

#class2.py
import numpy as np
class Class2(object):
    def __init__(self):
        pass

    def func2(self, funcInput):
        funcInput()

#main.py
from class1 import Class1
from class2 import Class2

class1Instance = Class1()
class2Instance = Class2()
from multiprocessing import Process

class2Process = Process(target=class2Instance.func2, kwargs={'funcInput': class1Instance.func1})
class2Process.start()
class2Process.join()

这个例子在这么小的范围内似乎很管用,但恐怕 将无法 叉