多重处理:在某些情况下会在赋值之前引用变量，但在其他情况下不会 [英] multiprocessing: variable being referenced before assignment in some cases but not others

问题描述

我在此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

上面的代码可以正常工作，但是如果我想向共享数组中添加一个数组，就像shared_array + = some_other_array(而不是上面的shared_array [i ,;] = i)，我得到

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

分配前引用的本地变量'shared_array'

local variable 'shared_array' referenced before assignment

我为什么不能做到这一点的任何想法?

Any ideas why I cannot do that?

推荐答案

如果将变量分配给函数中的任何位置，则将其视为局部变量. shared_array += some_other_array等效于shared_array = shared_array + some_other_array.因此，shared_array被视为局部变量，当您尝试在赋值的右侧使用它时，该变量不存在.

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.

如果要使用全局shared_array变量，则需要通过在函数中放置global shared_array来将其明确标记为全局变量.

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，它将使该对象变异，并分配给它的一部分.在Python中，分配裸名(例如shared_array = ...)与任何其他类型的分配(例如shared_array[...] = ...)都大不相同，即使它们看起来很相似.

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，该错误与多处理无关.

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

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