From 1869fdd3ef653ae4e63275780049fbaa88166197 Mon Sep 17 00:00:00 2001
From: zhanghuan <1262329256@qq.com>
Date: Tue, 2 Sep 2025 17:11:30 +0800
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---
 food_classifier.py | 282 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 282 insertions(+)
 create mode 100644 food_classifier.py

diff --git a/food_classifier.py b/food_classifier.py
new file mode 100644
index 0000000..fc62041
--- /dev/null
+++ b/food_classifier.py
@@ -0,0 +1,282 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+from torchvision import datasets, transforms
+import matplotlib.pyplot as plt
+import numpy as np
+from tqdm import tqdm
+import os
+
+# 设置设备
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"使用设备: {device}")
+
+# 定义CNN模型（基于CIFAR10结构，输出层改为2分类）
+class FoodCNN(nn.Module):
+    def __init__(self):
+        super(FoodCNN, self).__init__()
+        # 第一个卷积块
+        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
+        self.conv2 = nn.Conv2d(32, 32, 3, padding=1)
+        self.pool1 = nn.MaxPool2d(2, 2)
+        self.dropout1 = nn.Dropout2d(0.25)
+        
+        # 第二个卷积块
+        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
+        self.conv4 = nn.Conv2d(64, 64, 3, padding=1)
+        self.pool2 = nn.MaxPool2d(2, 2)
+        self.dropout2 = nn.Dropout2d(0.25)
+        
+        # 第三个卷积块
+        self.conv5 = nn.Conv2d(64, 128, 3, padding=1)
+        self.conv6 = nn.Conv2d(128, 128, 3, padding=1)
+        self.pool3 = nn.MaxPool2d(2, 2)
+        self.dropout3 = nn.Dropout2d(0.25)
+        
+        # 全连接层
+        self.fc1 = nn.Linear(128 * 4 * 4, 512)
+        self.dropout4 = nn.Dropout(0.5)
+        self.fc2 = nn.Linear(512, 2)  # 改为2分类
+        
+    def forward(self, x):
+        # 第一个卷积块
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = self.pool1(x)
+        x = self.dropout1(x)
+        
+        # 第二个卷积块
+        x = F.relu(self.conv3(x))
+        x = F.relu(self.conv4(x))
+        x = self.pool2(x)
+        x = self.dropout2(x)
+        
+        # 第三个卷积块
+        x = F.relu(self.conv5(x))
+        x = F.relu(self.conv6(x))
+        x = self.pool3(x)
+        x = self.dropout3(x)
+        
+        # 展平
+        x = x.view(-1, 128 * 4 * 4)
+        
+        # 全连接层
+        x = F.relu(self.fc1(x))
+        x = self.dropout4(x)
+        x = self.fc2(x)
+        
+        return x
+
+# 数据预处理
+transform_train = transforms.Compose([
+    transforms.Resize((32, 32)),  # 调整为32x32以匹配CIFAR10结构
+    transforms.RandomHorizontalFlip(p=0.5),
+    transforms.RandomRotation(10),
+    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
+    transforms.ToTensor(),
+    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+])
+
+transform_test = transforms.Compose([
+    transforms.Resize((32, 32)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+])
+
+# 训练函数
+def train_epoch(model, train_loader, criterion, optimizer, device):
+    model.train()
+    running_loss = 0.0
+    correct = 0
+    total = 0
+    
+    train_bar = tqdm(train_loader, desc='训练中')
+    for batch_idx, (data, target) in enumerate(train_bar):
+        data, target = data.to(device), target.to(device)
+        
+        optimizer.zero_grad()
+        output = model(data)
+        loss = criterion(output, target)
+        loss.backward()
+        optimizer.step()
+        
+        running_loss += loss.item()
+        _, predicted = output.max(1)
+        total += target.size(0)
+        correct += predicted.eq(target).sum().item()
+        
+        # 更新进度条
+        train_bar.set_postfix({
+            'Loss': f'{running_loss/(batch_idx+1):.4f}',
+            'Acc': f'{100.*correct/total:.2f}%'
+        })
+    
+    return running_loss/len(train_loader), 100.*correct/total
+
+# 验证函数
+def validate(model, val_loader, criterion, device):
+    model.eval()
+    val_loss = 0.0
+    correct = 0
+    total = 0
+    
+    with torch.no_grad():
+        val_bar = tqdm(val_loader, desc='验证中')
+        for data, target in val_bar:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            val_loss += criterion(output, target).item()
+            
+            _, predicted = output.max(1)
+            total += target.size(0)
+            correct += predicted.eq(target).sum().item()
+            
+            val_bar.set_postfix({
+                'Loss': f'{val_loss/len(val_loader):.4f}',
+                'Acc': f'{100.*correct/total:.2f}%'
+            })
+    
+    return val_loss/len(val_loader), 100.*correct/total
+
+# 测试函数
+def test(model, test_loader, device, class_names):
+    model.eval()
+    correct = 0
+    total = 0
+    class_correct = list(0. for i in range(2))
+    class_total = list(0. for i in range(2))
+    
+    with torch.no_grad():
+        test_bar = tqdm(test_loader, desc='测试中')
+        for data, target in test_bar:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            _, predicted = output.max(1)
+            total += target.size(0)
+            correct += predicted.eq(target).sum().item()
+            
+            # 计算每个类别的准确率
+            c = (predicted == target).squeeze()
+            for i in range(target.size(0)):
+                label = target[i]
+                class_correct[label] += c[i].item()
+                class_total[label] += 1
+            
+            test_bar.set_postfix({
+                'Acc': f'{100.*correct/total:.2f}%'
+            })
+    
+    print(f'\n测试集总体准确率: {100.*correct/total:.2f}%')
+    for i in range(2):
+        if class_total[i] > 0:
+            print(f'{class_names[i]} 准确率: {100.*class_correct[i]/class_total[i]:.2f}%')
+    
+    return 100.*correct/total
+
+if __name__ == '__main__':
+    # 加载数据集
+    train_dataset = datasets.ImageFolder('dataset/train', transform=transform_train)
+    val_dataset = datasets.ImageFolder('dataset/val', transform=transform_test)
+    test_dataset = datasets.ImageFolder('dataset/test', transform=transform_test)
+
+    # 创建数据加载器
+    batch_size = 32
+    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=0)
+    val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=0)
+    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=0)
+
+    # 类别名称
+    class_names = train_dataset.classes
+    print(f"类别: {class_names}")
+    print(f"训练集大小: {len(train_dataset)}")
+    print(f"验证集大小: {len(val_dataset)}")
+    print(f"测试集大小: {len(test_dataset)}")
+
+    # 创建模型
+    model = FoodCNN().to(device)
+    print(f"模型参数数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
+
+    # 定义损失函数和优化器（使用与CIFAR10相同的超参数）
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-4)
+    scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
+
+    # 训练模型
+    num_epochs = 100
+    train_losses = []
+    train_accuracies = []
+    val_losses = []
+    val_accuracies = []
+
+    best_val_acc = 0.0
+    best_model_path = 'best_food_model.pth'
+
+    print("开始训练...")
+    for epoch in range(num_epochs):
+        print(f'\nEpoch {epoch+1}/{num_epochs}')
+        print('-' * 50)
+        
+        # 训练
+        train_loss, train_acc = train_epoch(model, train_loader, criterion, optimizer, device)
+        
+        # 验证
+        val_loss, val_acc = validate(model, val_loader, criterion, device)
+        
+        # 更新学习率
+        scheduler.step()
+        
+        # 记录结果
+        train_losses.append(train_loss)
+        train_accuracies.append(train_acc)
+        val_losses.append(val_loss)
+        val_accuracies.append(val_acc)
+        
+        print(f'训练损失: {train_loss:.4f}, 训练准确率: {train_acc:.2f}%')
+        print(f'验证损失: {val_loss:.4f}, 验证准确率: {val_acc:.2f}%')
+        print(f'当前学习率: {optimizer.param_groups[0]["lr"]:.6f}')
+        
+        # 保存最佳模型
+        if val_acc > best_val_acc:
+            best_val_acc = val_acc
+            torch.save(model.state_dict(), best_model_path)
+            print(f'保存最佳模型，验证准确率: {best_val_acc:.2f}%')
+
+    print(f'\n训练完成！最佳验证准确率: {best_val_acc:.2f}%')
+
+    # 加载最佳模型进行测试
+    print('\n加载最佳模型进行测试...')
+    model.load_state_dict(torch.load(best_model_path))
+    test_acc = test(model, test_loader, device, class_names)
+
+    # 绘制训练曲线
+    plt.figure(figsize=(12, 4))
+
+    plt.subplot(1, 2, 1)
+    plt.plot(train_losses, label='训练损失')
+    plt.plot(val_losses, label='验证损失')
+    plt.title('损失曲线')
+    plt.xlabel('Epoch')
+    plt.ylabel('Loss')
+    plt.legend()
+    plt.grid(True)
+
+    plt.subplot(1, 2, 2)
+    plt.plot(train_accuracies, label='训练准确率')
+    plt.plot(val_accuracies, label='验证准确率')
+    plt.title('准确率曲线')
+    plt.xlabel('Epoch')
+    plt.ylabel('Accuracy (%)')
+    plt.legend()
+    plt.grid(True)
+
+    plt.tight_layout()
+    plt.savefig('training_curves.png', dpi=300, bbox_inches='tight')
+    plt.show()
+
+    print(f'\n最终结果:')
+    print(f'最佳验证准确率: {best_val_acc:.2f}%')
+    print(f'测试准确率: {test_acc:.2f}%')
+    print(f'模型已保存为: {best_model_path}')
+    print(f'训练曲线已保存为: training_curves.png')
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