请用Anaconda3 写python代码： 设计自定义的ResNet 数据：MINST <导入必要的PyTorch包 1.使用Dataset与Dataloader加载数据 - 首次加载数据使用直接下载的方式，数据存放至./data文件夹 - 本次实验的提交文件中不必包含MINST数据文件 2.自定义ResidualBlock类 - 使用两层卷积层 - 每一个卷积层保持输入和输出的通道数、宽高一致（输入通道作为ResidualBlock初始化的一个参数由外部传入） - 使用3x3的卷积核 3.定义前面给出的模型结构并实例化网络模型、交叉熵损失、SGD优化器 4.定义单次训练/测试的函数 参考课程示例代码，训练阶段每300个batch打印一下损失 5.设置训练周期为10次，打印每个周期中训练过程中的模型损失及测试过程中的准确率 6.定义一个函数get_n_params，来计算上述模型中的参数个数 ```def get_n_params(model)``` Hint: 调研model.parameters()的使用 7.保存训练好的模型到文件resnet.pt

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor

# 自定义ResidualBlock类
class ResidualBlock(nn.Module):
    def __init__(self, in_channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(in_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(in_channels)
        
    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out += identity
        out = self.relu(out)
        return out

# 定义ResNet模型
class ResNet(nn.Module):
    def __init__(self):
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=5, padding=2, bias=False)
        self.bn1 = nn.BatchNorm2d(16)
        self.layer1 = nn.Sequential(
            ResidualBlock(16),
            ResidualBlock(16),
            ResidualBlock(16)
        )
        self.layer2 = nn.Sequential(
            ResidualBlock(16),
            ResidualBlock(16),
            ResidualBlock(16)
        )
        self.layer3 = nn.Sequential(
            ResidualBlock(16),
            ResidualBlock(16),
            ResidualBlock(16)
        )
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(16, 10)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = nn.functional.relu(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.avg_pool(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

# 加载数据
train_dataset = MNIST('./data', train=True, download=True, transform=ToTensor())
test_dataset = MNIST('./data', train=False, download=True, transform=ToTensor())
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False)

# 实例化模型、损失函数、优化器
model = ResNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9)

# 定义训练函数
def train(model, dataloader, criterion, optimizer):
    model.train()
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(dataloader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if (i+1) % 300 == 0:
            print('Batch [%d/%d], Loss: %.4f' % (i+1, len(dataloader), running_loss/300))
            running_loss = 0.0

# 定义测试函数
def test(model, dataloader):
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for inputs, labels in dataloader:
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    accuracy = 100 * correct / total
    return accuracy

# 定义获取模型参数个数的函数
def get_n_params(model):
    return sum([p.numel() for p in model.parameters() if p.requires_grad])

# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
    train(model, train_loader, criterion, optimizer)
    test_acc = test(model, test_loader)
    print('Epoch [%d/%d], Loss: %.4f, Test Accuracy: %.2f%%' % (epoch+1, num_epochs, running_loss/len(train_loader), test_acc))

# 保存模型
torch.save(model.state_dict(), 'resnet.pt')