编写pytorch代码，对训练集进行归一化, 然后转换为张量，对训练集进行fit操作, 使用相同的均值和标准差对测试集进行transform操作,将归一化的训练集和测试集转换为张量

好的，以下是对应的代码：

import torch
import torchvision.transforms as transforms

# 加载训练集和测试集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transforms.ToTensor())
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transforms.ToTensor())

# 对训练集进行归一化操作
train_mean = trainset.data.mean(axis=(0, 1, 2)) / 255
train_std = trainset.data.std(axis=(0, 1, 2)) / 255
train_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=train_mean.tolist(), std=train_std.tolist())
])
trainset.transform = train_transform

# 将归一化的训练集和测试集转换为张量
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)
train_data = []
train_labels = []
for data in trainloader:
    inputs, labels = data
    train_data.append(inputs)
    train_labels.append(labels)
train_data = torch.cat(train_data, dim=0)
train_labels = torch.cat(train_labels, dim=0)

testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)
test_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=train_mean.tolist(), std=train_std.tolist())
])
testset.transform = test_transform

test_data = []
test_labels = []
for data in testloader:
    inputs, labels = data
    test_data.append(inputs)
    test_labels.append(labels)
test_data = torch.cat(test_data, dim=0)
test_labels = torch.cat(test_labels, dim=0)

# 对训练集进行fit操作
train_mean = train_data.mean(dim=(0, 2, 3))
train_std = train_data.std(dim=(0, 2, 3))
print('train_mean:', train_mean)
print('train_std:', train_std)

希望这个回答可以帮到你！