给出一个cnn进行信道估计训练和测试的代码，用pytorch写，程序内包括snr_num,per_snr_num

好的，下面是一个简单的CNN进行信道估计的训练和测试代码，使用PyTorch实现：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import numpy as np
from sklearn.metrics import confusion_matrix, accuracy_score

# 定义CNN模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv1d(in_channels=2, out_channels=64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv1d(in_channels=64, out_channels=128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv1d(in_channels=128, out_channels=256, kernel_size=3, padding=1)
        self.fc1 = nn.Linear(in_features=256*8, out_features=512)
        self.fc2 = nn.Linear(in_features=512, out_features=32)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool1d(x, kernel_size=2)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool1d(x, kernel_size=2)
        x = self.conv3(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool1d(x, kernel_size=2)
        x = x.view(-1, 256*8)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        return x

# 定义数据集类
class ChannelDataset(data.Dataset):
    def __init__(self, snr_num, per_snr_num):
        self.snr_num = snr_num
        self.per_snr_num = per_snr_num
        self.data = []
        self.labels = []
        for i in range(snr_num):
            for j in range(per_snr_num):
                h_real = np.random.normal(0, 1, [2, 1024])
                h_imag = np.random.normal(0, 1, [2, 1024])
                y_real = np.random.normal(0, 1, [2, 1024])
                y_imag = np.random.normal(0, 1, [2, 1024])
                y = np.concatenate((y_real, y_imag), axis=0)
                h = np.concatenate((h_real, h_imag), axis=0)
                input_data = np.concatenate((y, h), axis=1)
                label = i
                self.data.append(input_data)
                self.labels.append(label)

    def __getitem__(self, index):
        input_data = self.data[index]
        label = self.labels[index]
        return torch.from_numpy(input_data).float(), torch.tensor(label).long()

    def __len__(self):
        return len(self.data)

# 定义训练函数
def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = nn.functional.cross_entropy(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

# 定义测试函数
def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    y_true = []
    y_pred = []
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += nn.functional.cross_entropy(output, target, reduction='sum').item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()
            y_true += target.tolist()
            y_pred += pred.tolist()
    test_loss /= len(test_loader.dataset)
    accuracy = 100. * correct / len(test_loader.dataset)
    print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)'.format(
        test_loss, correct, len(test_loader.dataset), accuracy))
    print('Confusion Matrix:\n', confusion_matrix(y_true, y_pred))
    print('Accuracy Score:', accuracy_score(y_true, y_pred))

# 设置超参数
batch_size = 64
epochs = 10
lr = 0.001
momentum = 0.9
no_cuda = False

# 设置随机种子
torch.manual_seed(1)
np.random.seed(1)

# 判断是否使用GPU
use_cuda = not no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")

# 加载数据集
train_dataset = ChannelDataset(snr_num=10, per_snr_num=500)
test_dataset = ChannelDataset(snr_num=10, per_snr_num=100)
train_loader = data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True)

# 初始化模型和优化器
model = CNN().to(device)
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)

# 训练和测试模型
for epoch in range(1, epochs + 1):
    train(model, device, train_loader, optimizer, epoch)
    test(model, device, test_loader)

这个代码中，我们定义了一个简单的CNN模型来进行信道估计的训练和测试，数据集类ChannelDataset用于生成随机的信道和接收信号，train函数用于训练模型，test函数用于测试模型的准确率和混淆矩阵。在这个代码中，我们使用了PyTorch的自动求导和优化器来进行训练，同时使用了sklearn.metrics包中的函数来计算混淆矩阵和准确率。