输出利用resnet18预训练cifar10并保存训练权重的python代码

以下是利用resnet18预训练cifar10并保存训练权重的Python代码：

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

# Define transforms for training data
train_transform = 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))
])

# Define transforms for test data
test_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# Load CIFAR10 dataset
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=train_transform)
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=test_transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=128,
                                         shuffle=False, num_workers=2)

# Define ResNet18 model
net = torchvision.models.resnet18(pretrained=False, num_classes=10)

# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# Train the model
for epoch in range(10):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 100 == 99:    # print every 100 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

print('Finished Training')

# Save the trained model weights
PATH = './resnet18_cifar10.pth'
torch.save(net.state_dict(), PATH)

在上面的代码中，我们首先定义了训练和测试数据的transforms，并利用`torchvision.datasets.CIFAR10`加载CIFAR10数据集。然后，我们定义了ResNet18模型，以及损失函数和优化器。我们使用SGD优化器，学习率设置为0.001，动量设置为0.9。接下来，我们训练了模型，共进行了10个epochs，每个epoch中使用128个mini-batch。在训练过程中，我们输出了每100个mini-batches的平均损失。最后，我们保存了训练好的模型权重到文件中。