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，以及使用tqdm库实现训练进度的代码。

首先需要导入需要的库，包括PyTorch、torchvision和tqdm：

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

接着需要定义数据集的路径和一些超参数：

# 数据集路径
data_path = "./data/"

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

然后需要定义数据转换，这里使用了常见的数据增强方式：

# 数据转换
transform = transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

接着加载数据集：

# 加载数据集
dataset = torchvision.datasets.ImageFolder(root=data_path, transform=transform)

# 划分训练集、验证集和测试集
train_size = int(0.8 * len(dataset))
val_size = int(0.1 * len(dataset))
test_size = len(dataset) - train_size - val_size
train_dataset, val_dataset, test_dataset = random_split(dataset, [train_size, val_size, test_size])

# 创建数据加载器
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)

然后定义ResNet34网络模型：

# 定义模型
class ResNet34(nn.Module):
    def __init__(self, num_classes=2):
        super(ResNet34, self).__init__()
        self.resnet = torchvision.models.resnet34(pretrained=True)
        self.fc = nn.Linear(512, num_classes)

    def forward(self, x):
        x = self.resnet(x)
        x = self.fc(x)
        return x

model = ResNet34()

接着定义损失函数和优化器：

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

然后定义训练函数和验证函数：

# 训练函数
def train():
    model.train()
    train_loss = 0
    train_acc = 0
    for inputs, labels in tqdm(train_loader):
        inputs = inputs.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()

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

        loss.backward()
        optimizer.step()

        train_loss += loss.item() * inputs.size(0)
        _, preds = torch.max(outputs, 1)
        train_acc += torch.sum(preds == labels.data)

    epoch_loss = train_loss / len(train_dataset)
    epoch_acc = train_acc.double() / len(train_dataset)

    return epoch_loss, epoch_acc

# 验证函数
def validate():
    model.eval()
    val_loss = 0
    val_acc = 0
    for inputs, labels in tqdm(val_loader):
        inputs = inputs.to(device)
        labels = labels.to(device)

        with torch.no_grad():
            outputs = model(inputs)
            loss = criterion(outputs, labels)

            val_loss += loss.item() * inputs.size(0)
            _, preds = torch.max(outputs, 1)
            val_acc += torch.sum(preds == labels.data)

    epoch_loss = val_loss / len(val_dataset)
    epoch_acc = val_acc.double() / len(val_dataset)

    return epoch_loss, epoch_acc

最后开始训练和测试：

# 判断是否有GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)

# 将模型移动到GPU上
model.to(device)

# 训练和测试
for epoch in range(num_epochs):
    print('Epoch {}/{}'.format(epoch + 1, num_epochs))
    print('-' * 10)

    train_loss, train_acc = train()
    print('Train Loss: {:.4f} Train Acc: {:.4f}'.format(train_loss, train_acc))

    val_loss, val_acc = validate()
    print('Val Loss: {:.4f} Val Acc: {:.4f}'.format(val_loss, val_acc))

# 测试模型
model.eval()
test_acc = 0
for inputs, labels in tqdm(test_loader):
    inputs = inputs.to(device)
    labels = labels.to(device)

    with torch.no_grad():
        outputs = model(inputs)
        _, preds = torch.max(outputs, 1)
        test_acc += torch.sum(preds == labels.data)

test_acc = test_acc.double() / len(test_dataset)
print('Test Acc: {:.4f}'.format(test_acc))

以上就是基于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，以及使用tqdm库实现训练进度的代码。