Python 如何在类上下文中以方法作为目标启动流程？

我试图在类上下文中启动几个进程，这些进程应该共享一个队列：

import multiprocessing
import queue

class MyMulti:
    def __init__(self):
        self.myq = queue.Queue()

    def printhello(self):
        print("hello")
        self.myq.put("hello")

    def run(self):
        for _ in range(5):
            p = multiprocessing.Process(target=self.printhello)
            p.start()

if __name__ == "__main__":
    multiprocessing.freeze_support()
    m = MyMulti()
    m.run()
    # at that point the queue is being filled in with five elements

这与

C:\Python34\python.exe C:/Users/yop/dev/GetNessusScans/tests/testm.py
Traceback (most recent call last):
  File "C:/Users/yop/dev/GetNessusScans/tests/testm.py", line 20, in <module>
    m.run()
  File "C:/Users/yop/dev/GetNessusScans/tests/testm.py", line 15, in run
    p.start()
  File "C:\Python34\lib\multiprocessing\process.py", line 105, in start
    self._popen = self._Popen(self)
  File "C:\Python34\lib\multiprocessing\context.py", line 212, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "C:\Python34\lib\multiprocessing\context.py", line 313, in _Popen
    return Popen(process_obj)
  File "C:\Python34\lib\multiprocessing\popen_spawn_win32.py", line 66, in __init__
    reduction.dump(process_obj, to_child)
  File "C:\Python34\lib\multiprocessing\reduction.py", line 59, in dump
    ForkingPickler(file, protocol).dump(obj)
_pickle.PicklingError: Can't pickle <class '_thread.lock'>: attribute lookup lock on _thread failed

不过，这也会以同样的方式崩溃

---

有没有办法以方法作为目标启动进程？

我应该使用

self.myq=multiprocessing.Queue（）

而不是

Queue.Queue（）

除了

queue.queue（）

，是进程安全的

---

我暂时没有回答这个问题，如果整个方法是错误的，可能会有人评论。

如果我记得，你需要调用m.start（）而不是m.run（）。

。run（）

是我的类

MyMulti

的一个方法。我的盲症占了上风。

import multiprocessing
import queue

def work(foo):
    foo.printhello()

class MyMulti:
    def __init__(self):
        self.myq = queue.Queue()

    def printhello(self):
        print("hello")
        self.myq.put("hello")

    def run(self):
        for _ in range(5):
            p = multiprocessing.Process(target=work, args=(self,))
            p.start()

if __name__ == "__main__":
    multiprocessing.freeze_support()
    m = MyMulti()
    m.run()
    # at that point the queue is being filled in with five elements