电脑基础 · 2023年3月31日

RuntimeError: DataLoader worker (pid(s) 8548, 6916) exited unexpectedly

经过了两天的摸索,对于这个问题,终于圆满的解决了,对于一个深度学习的小白来说,面对这样的问题,实在太难受了。

在这几天里,不断去找一些博客的经验,很多都说把num_workers设置为0,但是却没有具体的关于如何设置的教程,使我在这个问题上特别难受。现在终于找到了解决办法了,希望能帮助到有同样问题的朋友。

由于在windows中是不能使用多个子进程加载数据的,在linux系统中可以。所以在windows中要将num_workers设置为0的。

具体怎么做呢,请往下看:

本文的例子问李沐老师的深度学习的示例(Lenet):

代码如下:

import torch
from torch import nn
from d2l import torch as d2l
from torch.utils import data
class Reshape(torch.nn.Module):
    def forward(self,x):
        return x.view(-1,1,28,28)
net = torch.nn.Sequential(
    Reshape(),nn.Conv2d(1,6,kernel_size=5,padding=2),nn.Sigmoid(),
    nn.AvgPool2d(2,stride=2),
    nn.Conv2d(6,16,kernel_size=5),nn.Sigmoid(),
    nn.AvgPool2d(kernel_size=2,stride=2),nn.Flatten(),#把低维保持,高维的拉成一维,最终为一维向量
    nn.Linear(16*5*5,120),nn.Sigmoid(),
    nn.Linear(120,84),nn.Sigmoid(),
    nn.Linear(84,10)
)
X = torch.rand(size=(1,1,28,28),dtype=torch.float32)
for layer in net:
    X = layer(X)
    #print(layer.__class__.__name__,'output shape:\t',X.shape)
# 模型训练
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=batch_size)
#使⽤GPU计算数据集
def evaluate_accuracy_gpu(net, data_iter, device=None): #@save
    """使⽤GPU计算模型在数据集上的精度"""
    if isinstance(net, nn.Module):
        net.eval() # 设置为评估模式
        if not device:
            device = next(iter(net.parameters())).device
    # 正确预测的数量,总预测的数量
    metric = d2l.Accumulator(2)
    with torch.no_grad():
        for X, y in data_iter:
            if isinstance(X, list):
                # BERT微调所需的(之后将介绍)
                X = [x.to(device) for x in X]
            else:
                X = X.to(device)
            y = y.to(device)
            metric.add(d2l.accuracy(net(X), y), y.numel())
    return metric[0] / metric[1]
def train_ch6(net, train_iter, test_iter, num_epochs, lr, device):
    """⽤GPU训练模型(在第六章定义)"""
    def init_weights(m):
        if type(m) == nn.Linear or type(m) == nn.Conv2d:
            nn.init.xavier_uniform_(m.weight)
    net.apply(init_weights)
    print('training on', device)
    net.to(device)
    optimizer = torch.optim.SGD(net.parameters(), lr=lr)
    loss = nn.CrossEntropyLoss()
    animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs],
                            legend=['train loss', 'train acc', 'test acc'])
    timer, num_batches = d2l.Timer(), len(train_iter)
    for epoch in range(num_epochs):
        # 训练损失之和,训练准确率之和,样本数
        metric = d2l.Accumulator(3)
        net.train()
        for i, (X, y) in enumerate(train_iter):
            timer.start()
            optimizer.zero_grad()
            X, y = X.to(device), y.to(device)
            y_hat = net(X)
            l = loss(y_hat, y)
            l.backward()
            optimizer.step()
            with torch.no_grad():
                metric.add(l * X.shape[0], d2l.accuracy(y_hat, y), X.shape[0])
            timer.stop()
            train_l = metric[0] / metric[2]
            train_acc = metric[1] / metric[2]
            if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
                animator.add(epoch + (i + 1) / num_batches,
                             (train_l, train_acc, None))
        test_acc = evaluate_accuracy_gpu(net, test_iter)
        animator.add(epoch + 1, (None, None, test_acc))
    print(f'loss {train_l:.3f}, train acc {train_acc:.3f}, '
          f'test acc {test_acc:.3f}')
    print(f'{metric[2] * num_epochs / timer.sum():.1f} examples/sec '
          f'on {str(device)}')
    d2l.plt.show()
lr, num_epochs = 0.9, 10
train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())
d2l.plt.show()

window中,要是直接运行将会出现以下错误:

RuntimeError: DataLoader worker (pid(s) 8548, 6916) exited unexpectedly.

解决方法如下(把num_workers设置为0):

RuntimeError: DataLoader worker (pid(s) 8548, 6916) exited unexpectedly

 按住Ctrl键,然后点击进入torch.py文件,搜索“get_dataloader_workers”,到达以下函数的位置:

RuntimeError: DataLoader worker (pid(s) 8548, 6916) exited unexpectedly

 到达红线处的位置,然后把里面的函数体修改为黄线的语句:

def get_dataloader_workers():
    """Use 4 processes to read the data.
    Defined in :numref:`sec_fashion_mnist`"""
    return 0 if sys.platform.startswith('win') else 4

把torch函数做了以上修改之后,就可以解决这个问题了。

下面是本人运行的结果:

RuntimeError: DataLoader worker (pid(s) 8548, 6916) exited unexpectedly

 RuntimeError: DataLoader worker (pid(s) 8548, 6916) exited unexpectedly

 看到结果的我超级无敌激动。

  以上就是我解决问题“RuntimeError: DataLoader worker (pid(s) 8548, 6916) exited unexpectedly”的方法,希望能帮助到有需要的人,希望能够帮助到更多人避坑。