import torch
import torch.nn as nn
import torch.nn.functional as F

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()
# 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.eval()  # 设置为推理模式

# 3. 创建示例输入 (假设输入是 3x224x224 的图片)
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")