python - 运行时错误 : DataLoader worker exited unexpectedly

Question

我是 PyTorch 和机器学习的新手，所以我尝试从这里开始学习教程: https://medium.com/@nutanbhogendrasharma/pytorch-convolutional-neural-network-with-mnist-dataset-4e8a4265e118

我一步步复制代码，莫名出现如下错误。我在另一台电脑上试过这个程序，它给出了语法错误。但是，我的 IDE 没有对语法发出任何警告。我真的很困惑如何解决这个问题。感谢您的帮助。

RuntimeError: DataLoader worker exited unexpectedly

这是代码。

import torch
from torchvision import datasets
from torchvision.transforms import ToTensor
import torch.nn as nn
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from torch import optim
from torch.autograd import Variable

train_data = datasets.MNIST(
    root='data',
    train=True,
    transform=ToTensor(),
    download=True,
)
test_data = datasets.MNIST(
    root='data',
    train=False,
    transform=ToTensor()
)
print(train_data)
print(test_data)

print(train_data.data.size())
print(train_data.targets.size())

plt.imshow(train_data.data[0], cmap='gray')
plt.title('%i' % train_data.targets[0])
plt.show()

figure = plt.figure(figsize=(10, 8))
cols, rows = 5, 5
for i in range(1, cols * rows + 1):
    sample_idx = torch.randint(len(train_data), size=(1,)).item()
    img, label = train_data[sample_idx]
    figure.add_subplot(rows, cols, i)
    plt.title(label)
    plt.axis("off")
    plt.imshow(img.squeeze(), cmap="gray")
plt.show()

loaders = {
    'train': DataLoader(train_data,
                        batch_size=100,
                        shuffle=True,
                        num_workers=1),

    'test': DataLoader(test_data,
                       batch_size=100,
                       shuffle=True,
                       num_workers=1),
}


class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(
                in_channels=1,
                out_channels=16,
                kernel_size=5,
                stride=1,
                padding=2,
            ),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2),
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(16, 32, 5, 1, 2),
            nn.ReLU(),
            nn.MaxPool2d(2),
        )
        # fully connected layer, output 10 classes
        self.out = nn.Linear(32 * 7 * 7, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        # flatten the output of conv2 to (batch_size, 32 * 7 * 7)
        x = x.view(x.size(0), -1)
        output = self.out(x)
        return output, x  # return x for visualization


cnn = CNN()
print(cnn)

loss_func = nn.CrossEntropyLoss()
print(loss_func)
optimizer = optim.Adam(cnn.parameters(), lr=0.01)
print(optimizer)
num_epochs = 10


def train(num_epochs, cnn, loaders):
    cnn.train()

    # Train the model
    total_step = len(loaders['train'])

    for epoch in range(num_epochs):
        for i, (images, labels) in enumerate(loaders['train']):
            # gives batch data, normalize x when iterate train_loader
            b_x = Variable(images)  # batch x
            b_y = Variable(labels)  # batch y

            output = cnn(b_x)[0]
            loss = loss_func(output, b_y)

            # clear gradients for this training step
            optimizer.zero_grad()

            # backpropagation, compute gradients
            loss.backward()
            # apply gradients
            optimizer.step()

            if (i + 1) % 100 == 0:
                print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                      .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))
                pass

        pass

    pass


train(num_epochs, cnn, loaders)


def evalFunc():
    # Test the model
    cnn.eval()
    with torch.no_grad():
        correct = 0
        total = 0
        for images, labels in loaders['test']:
            test_output, last_layer = cnn(images)
            pred_y = torch.max(test_output, 1)[1].data.squeeze()
            accuracy = (pred_y == labels).sum().item() / float(labels.size(0))
            pass

        print('Test Accuracy of the model on the 10000 test images: %.2f' % accuracy)

    pass


evalFunc()

sample = next(iter(loaders['test']))
imgs, lbls = sample

actual_number = lbls[:10].numpy()

test_output, last_layer = cnn(imgs[:10])
pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
print(f'Prediction number: {pred_y}')
print(f'Actual number: {actual_number}')

Answer 1

如果您正在使用 jupyter notebook。问题更有可能是 num_worker。您应该设置 num_worker=0。你可以找到here一些解决方案。因为不幸的是，jupyter notebook 在运行多处理方面存在一些问题。