Question

The paper code encountered a problem similar to the following. After some efforts, it can be regarded as solving this problem.

# import torch
# # print(torch.__version__)
# # print(torch.cuda.is_available())
import multiprocessing
cores = multiprocessing.cpu_count() // 2
def b(xs):

    t = temp_num[xs[0]] * 2
    return t
def a():
    global temp_num
    temp_num = [4, 5, 6]
    pool = multiprocessing.Pool(cores)
    xs = range(3)
    batch_result = pool.map(b,xs)
    print(batch_result)
def main():
    a()
if __name__ == '__main__':
    main()

Error:

solve

import multiprocessing
cores = multiprocessing.cpu_count() // 2
def b(x):
    xs = x[0]
    temp_num = x[1]
    t = temp_num[xs] * 2
    return t
def a():
    global temp_num
    temp_num = [4, 5, 6]
    pool = multiprocessing.Pool(cores)
    xs = range(3)

    z =zip(xs,[temp_num] * 3)
    result = pool.map(b,z)
    print(result)
def main():
    a()

if __name__ == '__main__':

    main()