pytorch训练时使用tqdm，给出完整实例代码

好的，以下是一个使用tqdm的PyTorch训练示例代码。在这个例子中，我们使用CIFAR-10数据集进行图像分类任务。

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

# 定义超参数
num_epochs = 10
batch_size = 128
learning_rate = 0.001

# 加载数据集并进行预处理
transform_train = 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))
])
transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
test_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
        self.fc1 = nn.Linear(4*4*128, 512)
        self.fc2 = nn.Linear(512, 10)
        self.relu = nn.ReLU()
        self.maxpool = nn.MaxPool2d(2, 2)

    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.maxpool(x)
        x = self.relu(self.conv2(x))
        x = self.maxpool(x)
        x = self.relu(self.conv3(x))
        x = self.maxpool(x)
        x = x.view(-1, 4*4*128)
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 实例化模型和损失函数
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = CNN().to(device)
criterion = nn.CrossEntropyLoss()

# 定义优化器
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    # 训练阶段
    model.train()
    train_loss = 0.0
    train_correct = 0
    for data, target in tqdm(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)
        pred = output.argmax(dim=1, keepdim=True)
        train_correct += pred.eq(target.view_as(pred)).sum().item()
    train_loss /= len(train_loader.dataset)
    train_acc = train_correct / len(train_loader.dataset)

    # 测试阶段
    model.eval()
    test_loss = 0.0
    test_correct = 0
    with torch.no_grad():
        for data, target in tqdm(test_loader):
            data, target = data.to(device), target.to(device)
            output = model(data)
            loss = criterion(output, target)
            test_loss += loss.item() * data.size(0)
            pred = output.argmax(dim=1, keepdim=True)
            test_correct += pred.eq(target.view_as(pred)).sum().item()
    test_loss /= len(test_loader.dataset)
    test_acc = test_correct / len(test_loader.dataset)

    # 输出训练和测试结果
    print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'
          .format(epoch+1, num_epochs, train_loss, train_acc, test_loss, test_acc))

在上述代码中，我们使用了tqdm包装了训练和测试的数据加载器，从而实现了进度条的显示。