PyTorch 教程错误训练分类器

Question

我刚刚开始了 PyTorch 教程使用 PyTorch 进行深度学习：60 分钟闪电战，我应该补充一点，我之前没有编写过任何 Python（但其他语言，如 Java）。 p>

现在，我的代码看起来像

import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np


print("\n-------------------Backpropagation-------------------\n")
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,download=True, transform=transform)

trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)

testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

dataiter = iter(trainloader)
images, labels = dataiter.next()


def imshow(img):
    img = img / 2 + 0.5
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))


imshow(torchvision.utils.make_grid(images))

print(' '.join('%5s' % classes[labels[j]] for j in range(4)))

应该与教程一致。 如果我执行这个，我会得到以下错误：

"C:\Program Files\Anaconda3\python.exe" C:/MA/pytorch/deepLearningWithPytorchTutorial/trainingClassifier.py

-------------------Backpropagation-------------------

Files already downloaded and verified
Files already downloaded and verified

-------------------Backpropagation-------------------

Files already downloaded and verified
Files already downloaded and verified
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "C:\Program Files\Anaconda3\lib\multiprocessing\spawn.py", line 105, in spawn_main
    exitcode = _main(fd)
  File "C:\Program Files\Anaconda3\lib\multiprocessing\spawn.py", line 114, in _main
    prepare(preparation_data)
  File "C:\Program Files\Anaconda3\lib\multiprocessing\spawn.py", line 225, in prepare
    _fixup_main_from_path(data['init_main_from_path'])
  File "C:\Program Files\Anaconda3\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path
    run_name="__mp_main__")
  File "C:\Program Files\Anaconda3\lib\runpy.py", line 263, in run_path
    pkg_name=pkg_name, script_name=fname)
  File "C:\Program Files\Anaconda3\lib\runpy.py", line 96, in _run_module_code
    mod_name, mod_spec, pkg_name, script_name)
  File "C:\Program Files\Anaconda3\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "C:\MA\pytorch\deepLearningWithPytorchTutorial\trainingClassifier.py", line 23, in <module>
    dataiter = iter(trainloader)
  File "C:\Program Files\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 451, in __iter__
    return _DataLoaderIter(self)
  File "C:\Program Files\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 239, in __init__
    w.start()
  File "C:\Program Files\Anaconda3\lib\multiprocessing\process.py", line 105, in start
    self._popen = self._Popen(self)
  File "C:\Program Files\Anaconda3\lib\multiprocessing\context.py", line 223, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "C:\Program Files\Anaconda3\lib\multiprocessing\context.py", line 322, in _Popen
    return Popen(process_obj)
  File "C:\Program Files\Anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 33, in __init__
    prep_data = spawn.get_preparation_data(process_obj._name)
  File "C:\Program Files\Anaconda3\lib\multiprocessing\spawn.py", line 143, in get_preparation_data
    _check_not_importing_main()
  File "C:\Program Files\Anaconda3\lib\multiprocessing\spawn.py", line 136, in _check_not_importing_main
    is not going to be frozen to produce an executable.''')
RuntimeError: 
        An attempt has been made to start a new process before the
        current process has finished its bootstrapping phase.

        This probably means that you are not using fork to start your
        child processes and you have forgotten to use the proper idiom
        in the main module:

            if __name__ == '__main__':
                freeze_support()
                ...

        The "freeze_support()" line can be omitted if the program
        is not going to be frozen to produce an executable.
Traceback (most recent call last):
  File "C:/MA/pytorch/deepLearningWithPytorchTutorial/trainingClassifier.py", line 23, in <module>
    dataiter = iter(trainloader)
  File "C:\Program Files\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 451, in __iter__
    return _DataLoaderIter(self)
  File "C:\Program Files\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 239, in __init__
    w.start()
  File "C:\Program Files\Anaconda3\lib\multiprocessing\process.py", line 105, in start
    self._popen = self._Popen(self)
  File "C:\Program Files\Anaconda3\lib\multiprocessing\context.py", line 223, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "C:\Program Files\Anaconda3\lib\multiprocessing\context.py", line 322, in _Popen
    return Popen(process_obj)
  File "C:\Program Files\Anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__
    reduction.dump(process_obj, to_child)
  File "C:\Program Files\Anaconda3\lib\multiprocessing\reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
BrokenPipeError: [Errno 32] Broken pipe

Process finished with exit code 1

我已经下载了 *.py 和 *.ipynb。 使用 jupyter 运行 *.ipynb 效果很好（但我不想在瞻博网络界面中编程，我更喜欢 pyCharm），而 *.py 在控制台（Anaconda 提示符和 cmd）失败并出现同样的错误。

有谁知道如何解决这个问题？ （我正在使用 Python 3.6.5（来自 Anaconda）和 pyCharm，操作系统：Win10 64 位）

谢谢！ 福利

更新： 如果它是相关的，我只需将num_workers=2 设置为num_workers=0（两者），然后它就会工作.. .

Answer 1

查看适用于 Windows 的 multiprocessing: programming guidelines 的文档。您应该将所有操作包装在函数中，然后在 if __name__ == '__main__' 子句中调用它们：

# required imports

def load_datasets(...):
    # Code to load the datasets with multiple workers

def train(...):
    # Code to train the model

if __name__ == '__main__':
    load_datasets()
    train()

简而言之，这里的想法是将示例代码包装在 if __name__ == '__main__' 语句中。

Answer 2

由于 multiprocessing 在 Windows 中的实现不同，您需要使用此块包装您的主代码：

if __name__ == '__main__':

更多信息，您可以查看the official PyTorch Windows notes。