假设你是一个pytorch，请写出一个pretrain=true、class=10的resnet50的代码，训练cifar10数据集

好的，以下是一个PyTorch中pretrain=true、class=10的ResNet50的代码，用于训练CIFAR10数据集。

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# 定义transform
transform_train = transforms.Compose(
    [transforms.RandomCrop(32, padding=4),
     transforms.RandomHorizontalFlip(),
     transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

transform_test = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

# 加载数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=128,
                                         shuffle=False, num_workers=2)

# 定义ResNet50
class ResNet50(nn.Module):
    def __init__(self, num_classes=10):
        super(ResNet50, self).__init__()
        self.resnet50 = torchvision.models.resnet50(pretrained=True)
        num_ftrs = self.resnet50.fc.in_features
        self.resnet50.fc = nn.Linear(num_ftrs, num_classes)
        
    def forward(self, x):
        x = self.resnet50(x)
        return x

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = ResNet50().to(device)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)

# 训练模型
for epoch in range(100):  # 训练100个epoch
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 100 == 99:    # 每100个batch输出一次损失值
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

print('Finished Training')

# 保存模型
PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH)

需要说明的是，这段代码中使用了预训练的ResNet50模型，该模型在ImageNet数据集上进行了预训练，因此具有较好的特征提取能力。在训练CIFAR10数据集时，只需对ResNet50的最后一层进行修改，使其输出10个类别的概率即可。