如何使用 PyTorch 多处理?

How to use PyTorch multiprocessing?(如何使用 PyTorch 多处理?)

问题描述

我正在尝试在 pytorch 中使用 python 的多处理 Pool 方法来处理图像.代码如下:

I'm trying to use python's multiprocessing Pool method in pytorch to process a image. Here's the code:

from multiprocessing import Process, Pool
from torch.autograd import Variable
import numpy as np
from scipy.ndimage import zoom

def get_pred(args):

  img = args[0]
  scale = args[1]
  scales = args[2]
  img_scale = zoom(img.numpy(),
                     (1., 1., scale, scale),
                     order=1,
                     prefilter=False,
                     mode='nearest')

  # feed input data
  input_img = Variable(torch.from_numpy(img_scale),
                     volatile=True).cuda()
  return input_img

scales = [1,2,3,4,5]
scale_list = []
for scale in scales: 
    scale_list.append([img,scale,scales])
multi_pool = Pool(processes=5)
predictions = multi_pool.map(get_pred,scale_list)
multi_pool.close() 
multi_pool.join()

我收到此错误:

`RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method

`在这一行:

predictions = multi_pool.map(get_pred,scale_list)

谁能告诉我我做错了什么?

Can anyone tell me what I'm doing wrong ?

推荐答案

如 pytorch 中所述文档 处理多处理的最佳实践是使用 torch.multiprocessing 而不是 multiprocessing.

As stated in pytorch documentation the best practice to handle multiprocessing is to use torch.multiprocessing instead of multiprocessing.

请注意，仅 Python 3 支持在进程之间共享 CUDA 张量，使用 spawn 或 forkserver 作为启动方法.

Be aware that sharing CUDA tensors between processes is supported only in Python 3, either with spawn or forkserver as start method.

在不触及您的代码的情况下，您遇到的错误的解决方法是替换

Without touching your code, a workaround for the error you got is replacing

from multiprocessing import Process, Pool

与:

from torch.multiprocessing import Pool, Process, set_start_method
try:
     set_start_method('spawn')
except RuntimeError:
    pass

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