问题描述

我在这个网站的某个地方找到了以下示例:

I found the following example on this website somewhere:

import multiprocessing
import ctypes
import numpy as np

shared_array_base = multiprocessing.Array(ctypes.c_double, 10*10)
shared_array = np.ctypeslib.as_array(shared_array_base.get_obj())
shared_array = shared_array.reshape(10, 10)

# No copy was made
assert shared_array.base.base is shared_array_base.get_obj()

# Parallel processing
def my_func(i, def_param=shared_array):
    shared_array[i,:] = i

if __name__ == '__main__':
    pool = multiprocessing.Pool(processes=4)
    pool.map(my_func, range(10))

    print shared_array

上面的代码工作正常，但是如果我想向共享数组添加一个数组，比如 shared_array += some_other_array (而不是上面的 shared_array[i,;] = i)我得到了

The above code works fine, but if I want to add an array to the shared array, something like shared_array += some_other_array (instead of the above shared_array[i,;] = i) I get

赋值前引用的局部变量shared_array"

local variable 'shared_array' referenced before assignment

任何想法为什么我不能这样做?

Any ideas why I cannot do that?

推荐答案

如果一个变量被赋值给函数中的任何地方，它就会被视为一个局部变量.shared_array += some_other_array 等价于 shared_array = shared_array + some_other_array.因此 shared_array 被视为局部变量，当您尝试在赋值右侧使用它时，该变量并不存在.

If a variable is assigned to anywhere in a function, it is treated as a local variable. shared_array += some_other_array is equivalent to shared_array = shared_array + some_other_array. Thus shared_array is treated as a local variable, which does not exist at the time you try to use it on the right-hand side of the assignment.

如果你想使用全局 shared_array 变量，你需要通过在你的函数中放置一个 global shared_array 来显式地将它标记为全局变量.

If you want to use the global shared_array variable, you need to explicitly mark it as global by putting a global shared_array in your function.

您没有看到 shared_array[i,:] = i 错误的原因是它没有分配给变量 shared_array.相反，它改变了该对象，分配给它的一部分.在 Python 中，分配给一个裸名(例如，shared_array = ...)与任何其他类型的分配(例如，shared_array[...] = ...)，尽管它们看起来很相似.

The reason you don't see the error with shared_array[i,:] = i is that this does not assign to the variable shared_array. Rather, it mutates that object, assigning to a slice of it. In Python, assigning to a bare name (e.g., shared_array = ...) is very different from any other kind of assignment (e.g., shared_array[...] = ...), even though they look similar.

请注意，顺便说一下，该错误与多处理无关.

Note, incidentally, that the error has nothing to do with multiprocessing.