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


class FoodCNN(nn.Module):
    """
    食物分类CNN模型
    基于CIFAR10结构，适配3分类任务
    """
    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)  # 3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


def create_food_cnn():
    """
    创建食物分类CNN模型
    
    Returns:
        FoodCNN: 网络模型实例
    """
    return FoodCNN()


if __name__ == "__main__":
    # 测试网络
    model = create_food_cnn()
    print(f"模型参数数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
    
    # 测试前向传播
    dummy_input = torch.randn(1, 3, 32, 32)
    output = model(dummy_input)
    print(f"输出形状: {output.shape}")