安装文件内的具体要求，给出完整的代码，保证不会报错的代码

根据您提供的文档内容，下面是一个完整的代码示例，用于构建一个基于深度学习的桃子智能分拣系统。该代码使用了PyTorch框架，并假设您已经安装了必要的依赖项（如PyTorch、Pandas、Matplotlib等）。代码包括数据加载、模型定义、训练和评估等步骤。

### 安装必要的依赖项
首先，确保您已经安装了所需的Python包：

pip install torch torchvision pandas matplotlib

### 完整代码示例

import os
import json
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, models
from PIL import Image
import pandas as pd
from sklearn.metrics import accuracy_score, f1_score

# 数据集路径
data_dir = 'C:/Users/24067/Desktop/peach_split'
train_dir = os.path.join(data_dir, 'train')
val_dir = os.path.join(data_dir, 'val')
test_dir = os.path.join(data_dir, 'test')

# 标签文件路径
train_label_path = 'C:/Users/24067/Desktop/train_label.json'
val_label_path = 'C:/Users/24067/Desktop/val_label.json'

# 加载标签数据
with open(train_label_path, 'r') as f:
    train_labels = json.load(f)

with open(val_label_path, 'r') as f:
    val_labels = json.load(f)

# 定义数据集类
class PeachDataset(Dataset):
    def __init__(self, data_dir, label_dict, transform=None):
        self.data_dir = data_dir
        self.label_dict = label_dict
        self.transform = transform
        self.image_files = list(label_dict.keys())

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

    def __getitem__(self, idx):
        img_name = self.image_files[idx]
        img_path = os.path.join(self.data_dir, img_name)
        image = Image.open(img_path).convert('RGB')
        label = self.label_dict[img_name]

        if self.transform:
            image = self.transform(image)

        return image, label

# 数据预处理
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 创建数据集对象
train_dataset = PeachDataset(train_dir, train_labels, transform=transform)
val_dataset = PeachDataset(val_dir, val_labels, transform=transform)

# 创建数据加载器
batch_size = 32
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4)

# 定义模型
model = models.resnet18(pretrained=True)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 4)  # 4个类别：特级、一级、二级、三级
model = model.to('cuda' if torch.cuda.is_available() else 'cpu')

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
def train_model(model, criterion, optimizer, num_epochs=10):
    for epoch in range(num_epochs):
        model.train()
        running_loss = 0.0
        for inputs, labels in train_loader:
            inputs, labels = inputs.to('cuda' if torch.cuda.is_available() else 'cpu'), labels.to('cuda' if torch.cuda.is_available() else 'cpu')
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()

        print(f'Epoch {epoch + 1}/{num_epochs}, Loss: {running_loss / len(train_loader)}')

# 评估模型
def evaluate_model(model, dataloader):
    model.eval()
    all_preds = []
    all_labels = []

    with torch.no_grad():
        for inputs, labels in dataloader:
            inputs, labels = inputs.to('cuda' if torch.cuda.is_available() else 'cpu'), labels.to('cuda' if torch.cuda.is_available() else 'cpu')
            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)
            all_preds.extend(preds.cpu().numpy())
            all_labels.extend(labels.cpu().numpy())

    accuracy = accuracy_score(all_labels, all_preds)
    f1 = f1_score(all_labels, all_preds, average='weighted')
    return accuracy, f1

# 训练模型
train_model(model, criterion, optimizer, num_epochs=10)

# 评估模型
accuracy, f1 = evaluate_model(model, val_loader)
print(f'Validation Accuracy: {accuracy:.4f}')
print(f'Validation F1 Score: {f1:.4f}')

# 保存模型
torch.save(model.state_dict(), 'peach_grading_model.pth')

# 生成提交文件
def generate_submission(model, test_dir, sample_submission_path):
    model.eval()
    submission = pd.read_csv(sample_submission_path)
    test_transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])

    with torch.no_grad():
        for i, filename in enumerate(submission['filename']):
            img_path = os.path.join(test_dir, filename)
            image = Image.open(img_path).convert('RGB')
            image = test_transform(image).unsqueeze(0).to('cuda' if torch.cuda.is_available() else 'cpu')
            output = model(image)
            _, pred = torch.max(output, 1)
            submission.at[i, 'label'] = int(pred.item())

    submission.to_csv('submission.csv', index=False)

# 生成并保存提交文件
generate_submission(model, test_dir, 'C:/Users/24067/Desktop/sample_submission.csv')

### 代码说明
1. **数据加载**：定义了一个自定义的`PeachDataset`类来加载和预处理图像数据。
2. **模型定义**：使用预训练的ResNet18模型，并修改最后一层以适应4个类别。
3. **训练过程**：定义了训练循环，包括前向传播、反向传播和参数更新。
4. **评估过程**：定义了评估函数，计算验证集上的准确率和F1分数。
5. **模型保存**：将训练好的模型权重保存到文件。
6. **生成提交文件**：对测试集进行预测，并生成符合要求的提交文件。

希望这段代码能帮助您完成任务。如果有任何问题或需要进一步的帮助，请随时告诉我！