请帮我写出基于pytorch实现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，且用tqdm实现训练进度可视化。

好的，以下是基于PyTorch实现ResNet34网络实现图像二分类的代码，包含训练集、验证集和测试集，同时用tqdm实现训练进度可视化：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import torchvision
from torchvision import datasets, models, transforms
import numpy as np
import matplotlib.pyplot as plt
import time
import os
import copy
from tqdm import tqdm

# 数据增强和归一化处理
data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

data_dir = 'path/to/data' # 数据存放路径
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])
                  for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4, shuffle=True, num_workers=4)
               for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# 定义ResNet34模型
model_ft = models.resnet34(pretrained=True)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, 2)
model_ft = model_ft.to(device)

criterion = nn.CrossEntropyLoss()

# 优化器
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)

# 学习率调整规则
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

# 训练模型
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    # 训练过程
    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # 每个epoch都有训练和验证阶段
        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # 训练模式
            else:
                model.eval()   # 验证模式

            running_loss = 0.0
            running_corrects = 0

            # 迭代数据
            for inputs, labels in tqdm(dataloaders[phase], desc='Epoch {} {}'.format(epoch, phase)):
                inputs = inputs.to(device)
                labels = labels.to(device)

                # 清零梯度
                optimizer.zero_grad()

                # 前向传递
                # 训练模式下跟踪历史记录，验证模式下不用跟踪历史记录
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # 反向传播和优化
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # 统计损失和正确预测的数量
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            # 学习率调整
            if phase == 'train':
                scheduler.step()

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))

            # 如果当前模型在验证集上表现更好，则保存当前模型
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))

    # 加载最佳模型权重
    model.load_state_dict(best_model_wts)
    return model

# 训练模型
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25)

# 测试模型
def test_model(model, dataloaders):
    model.eval()
    running_loss = 0.0
    running_corrects = 0

    for inputs, labels in tqdm(dataloaders['val'], desc='Test'):
        inputs = inputs.to(device)
        labels = labels.to(device)

        with torch.set_grad_enabled(False):
            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)
            loss = criterion(outputs, labels)

        running_loss += loss.item() * inputs.size(0)
        running_corrects += torch.sum(preds == labels.data)

    test_loss = running_loss / dataset_sizes['val']
    test_acc = running_corrects.double() / dataset_sizes['val']
    print('Test Loss: {:.4f} Acc: {:.4f}'.format(test_loss, test_acc))

test_model(model_ft, dataloaders)

希望能够对你有所帮助！