麻烦讲解一下C语言单元测试中的“测试覆盖率”

以下是一个基于PyTorch的具有三个输入特征和三个输出的一维卷积神经网络的ResNet代码示例，包括训练和测试部分。

import torch
import torch.nn as nn
import torch.optim as optim

class ResBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResBlock, self).__init__()
        self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm1d(out_channels)
        self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm1d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = nn.Sequential(
            nn.Conv1d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
            nn.BatchNorm1d(out_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)
        if self.downsample is not None:
            identity = self.downsample(x)
        out += identity
        out = self.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ResNet, self).__init__()
        self.conv = nn.Conv1d(in_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn = nn.BatchNorm1d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(64, 64, 3)
        self.layer2 = self._make_layer(64, 128, 4, stride=2)
        self.layer3 = self._make_layer(128, 256, 6, stride=2)
        self.layer4 = self._make_layer(256, 512, 3, stride=2)
        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.fc = nn.Linear(512, out_channels)
        
    def _make_layer(self, in_channels, out_channels, blocks, stride=1):
        layers = []
        layers.append(ResBlock(in_channels, out_channels, stride))
        for i in range(1, blocks):
            layers.append(ResBlock(out_channels, out_channels))
        return nn.Sequential(*layers)
    
    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

# 训练模型
def train(model, train_loader, optimizer, criterion, device):
    model.train()
    train_loss = 0
    for data, target in train_loader:
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * data.size(0)
    train_loss /= len(train_loader.dataset)
    return train_loss

# 测试模型
def test(model, test_loader, criterion, device):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += criterion(output, target).item() * data.size(0)
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()
    test_loss /= len(test_loader.dataset)
    accuracy = 100. * correct / len(test_loader.dataset)
    return test_loss, accuracy

# 加载数据
train_data = torch.randn(100, 3, 100)
train_target = torch.randint(0, 3, (100,))
test_data = torch.randn(20, 3, 100)
test_target = torch.randint(0, 3, (20,))

train_dataset = torch.utils.data.TensorDataset(train_data, train_target)
test_dataset = torch.utils.data.TensorDataset(test_data, test_target)

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=False)

# 训练模型
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = ResNet(3, 3).to(device)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
criterion = nn.CrossEntropyLoss()

for epoch in range(10):
    train_loss = train(model, train_loader, optimizer, criterion, device)
    test_loss, accuracy = test(model, test_loader, criterion, device)
    print(f"Epoch {epoch+1}: train loss = {train_loss:.4f}, test loss = {test_loss:.4f}, accuracy = {accuracy:.2f}%")

在这个示例中，我们定义了一个名为`ResBlock`的模块，用于实现ResNet中的残差块。然后我们定义了一个名为`ResNet`的模型，该模型包含了多个ResBlock模块，用于构建一个深度卷积神经网络。最后，我们定义了一个训练和测试函数，用于训练和测试模型。我们使用PyTorch自带的优化器和损失函数进行训练。在训练过程中，我们使用GPU进行计算（如果可用）。最后，我们使用示例数据集对模型进行了10个周期的训练和测试。