请帮我写出基于pytorch的ResNet34网络实现图像二分类的代码并且包含训练集、验证集和测试集代码，且用tqdm代码实现训练进度

好的，以下是基于pytorch的ResNet34网络实现图像二分类的代码，包括训练集、验证集和测试集代码，并使用tqdm实现训练进度。注意，代码中的路径需要根据实际数据集路径进行修改。

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

# 定义训练、验证、测试数据集路径
train_data_path = "path/to/train/dataset"
val_data_path = "path/to/val/dataset"
test_data_path = "path/to/test/dataset"

# 定义训练、验证、测试数据集的transform
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))
])

# 加载数据集
train_dataset = torchvision.datasets.CIFAR10(root=train_data_path, train=True, download=True, transform=transform_train)
val_dataset = torchvision.datasets.CIFAR10(root=val_data_path, train=False, download=True, transform=transform_test)
test_dataset = torchvision.datasets.CIFAR10(root=test_data_path, train=False, download=True, transform=transform_test)

# 定义超参数
batch_size = 64
learning_rate = 0.001
num_epochs = 10

# 定义数据加载器
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 定义ResNet34模型
class ResNet34(nn.Module):
    def __init__(self, num_classes):
        super(ResNet34, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(64, 3)
        self.layer2 = self._make_layer(128, 4, stride=2)
        self.layer3 = self._make_layer(256, 6, stride=2)
        self.layer4 = self._make_layer(512, 3, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or planes != 64:
            downsample = nn.Sequential(
                nn.Conv2d(64, planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes)
            )
        layers = []
        layers.append(BasicBlock(64, planes, stride, downsample))
        for i in range(1, blocks):
            layers.append(BasicBlock(planes, planes))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(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

class BasicBlock(nn.Module):
    def __init__(self, in_planes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample

    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

# 定义模型、损失函数和优化器
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = ResNet34(num_classes=2).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    train_loss = 0.0
    train_correct = 0
    train_total = 0

    model.train()
    for images, labels in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{num_epochs}"):
        images = images.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()

        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        train_total += labels.size(0)
        train_correct += (predicted == labels).sum().item()

    train_accuracy = 100 * train_correct / train_total
    train_loss /= len(train_loader)

    val_loss = 0.0
    val_correct = 0
    val_total = 0

    model.eval()
    with torch.no_grad():
        for images, labels in val_loader:
            images = images.to(device)
            labels = labels.to(device)

            outputs = model(images)
            loss = criterion(outputs, labels)

            val_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            val_total += labels.size(0)
            val_correct += (predicted == labels).sum().item()

    val_accuracy = 100 * val_correct / val_total
    val_loss /= len(val_loader)

    print(f"Epoch {epoch + 1}/{num_epochs}, Train Loss: {train_loss:.4f}, Train Accuracy: {train_accuracy:.2f}%, Val Loss: {val_loss:.4f}, Val Accuracy: {val_accuracy:.2f}%")

# 测试模型
test_loss = 0.0
test_correct = 0
test_total = 0

model.eval()
with torch.no_grad():
    for images, labels in tqdm(test_loader, desc="Testing"):
        images = images.to(device)
        labels = labels.to(device)

        outputs = model(images)
        loss = criterion(outputs, labels)

        test_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        test_total += labels.size(0)
        test_correct += (predicted == labels).sum().item()

test_accuracy = 100 * test_correct / test_total
test_loss /= len(test_loader)

print(f"Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.2f}%")