把网络模型单独拎出来了，方便解耦。下一步准备把重要的配置全部拎出来。

classifier/food_classifier_app.py

@@ -13,66 +13,14 @@ from typing import List, Optional, Tuple
 from tkinterdnd2 import DND_FILES, TkinterDnD
 import threading
 import time
+from net import create_food_cnn
 
 # 设置customtkinter的外观
 ctk.set_appearance_mode("System")
 ctk.set_default_color_theme("blue")
 
-# 定义CNN模型（与训练代码中的结构相同）
+
-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)  # 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
 
 class FoodClassifierApp:
     def __init__(self, root):
@@ -81,7 +29,7 @@ class FoodClassifierApp:
         self.root.geometry("1400x800")
         
         # 食物类别（根据您的数据集）
-        self.food_classes = ["回锅肉", "西红柿鸡蛋"]
+        self.food_classes = ["回锅肉", "西红柿鸡蛋","麻辣小面"]
         
         # 设备设置
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -106,10 +54,10 @@ class FoodClassifierApp:
     def load_model(self):
         """加载训练好的PyTorch模型"""
         try:
-            model_path = "../model/01/best_food_model.pth"
+            model_path = "../model/02/best_food_model.pth"
             if os.path.exists(model_path):
                 # 创建模型实例
-                self.model = FoodCNN()
+                self.model = create_food_cnn()
                 # 加载模型权重
                 self.model.load_state_dict(torch.load(model_path, map_location=self.device))
                 self.model.to(self.device)

toAndroid/toAndroid.py

@@ -1,66 +1,13 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from net import create_food_cnn
 
-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)  # 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
 
 # 1. 初始化模型
-model = FoodCNN()
+model = create_food_cnn()
 # 2. 加载训练好的权重
-model.load_state_dict(torch.load("../model/01/best_food_model.pth", map_location='cpu'))
+model.load_state_dict(torch.load("../model/02/best_food_model.pth", map_location='cpu'))
 model.eval()  # 设置为推理模式
 
 # 3. 创建示例输入 (假设输入是 3x224x224 的图片)
@@ -68,4 +15,4 @@ example_input = torch.randn(1, 3, 224, 224)
 
 # 4. 转换为 TorchScript
 traced_script_module = torch.jit.trace(model, example_input)
-traced_script_module.save("../model/01/best_food_model_mobile.pt")
+traced_script_module.save("../model/02/best_food_model_mobile.pt")

train/food_classifier.py

@@ -9,8 +9,14 @@ import matplotlib
 import numpy as np
 from tqdm import tqdm
 import os
+import sys
 import time
 
+# 添加net目录到路径
+# sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'net'))
+# from food_net import create_food_cnn
+from net import create_food_cnn
+
 # 设置matplotlib支持中文显示
 plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']  # 指定默认字体
 plt.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显示为方块的问题
@@ -19,61 +25,7 @@ plt.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显
 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3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, 3)  # 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([
@@ -201,7 +153,7 @@ if __name__ == '__main__':
     print(f"测试集大小: {len(test_dataset)}")
 
     # 创建模型
-    model = FoodCNN().to(device)
+    model = create_food_cnn().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）