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。

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")