Image/LeNet5/code/model.py

'''
LeNet5 in PyTorch

LeNet5是由Yann LeCun等人在1998年提出的一个经典卷积神经网络模型。
主要用于手写数字识别,具有以下特点:
1. 使用卷积层提取特征
2. 使用平均池化层降低特征维度
3. 使用全连接层进行分类
4. 网络结构简单,参数量少

网络架构:
                                    5x5 conv, 6    2x2 pool     5x5 conv, 16    2x2 pool      FC 120    FC 84    FC 10
input(32x32x3) -> [conv1+relu+pool] --------> 28x28x6 -----> 14x14x6 -----> 10x10x16 -----> 5x5x16 -> 120 -> 84 -> 10
                                    stride 1      stride 2     stride 1       stride 2

参考论文: 
[1] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," 
    Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
'''

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


class ConvBlock(nn.Module):
    """卷积块模块
    
    包含: 卷积层 -> ReLU -> 最大池化层
    
    Args:
        in_channels (int): 输入通道数
        out_channels (int): 输出通道数
        kernel_size (int): 卷积核大小
        stride (int): 卷积步长
        padding (int): 填充大小
    """
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
        super(ConvBlock, self).__init__()
        self.conv = nn.Conv2d(
            in_channels=in_channels, 
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding
        )
        self.relu = nn.ReLU(inplace=True)  # inplace操作可以节省内存
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        
    def forward(self, x):
        """前向传播
        
        Args:
            x (torch.Tensor): 输入特征图
            
        Returns:
            torch.Tensor: 输出特征图
        """
        x = self.conv(x)
        x = self.relu(x)
        x = self.pool(x)
        return x


class LeNet5(nn.Module):
    '''LeNet5网络模型
    
    网络结构:
    1. 卷积层1: 3通道输入,6个5x5卷积核,步长1
    2. 最大池化层1: 2x2窗口,步长2
    3. 卷积层2: 6通道输入,16个5x5卷积核,步长1  
    4. 最大池化层2: 2x2窗口,步长2
    5. 全连接层1: 400->120
    6. 全连接层2: 120->84
    7. 全连接层3: 84->num_classes
    
    Args:
        num_classes (int): 分类数量,默认为10
        init_weights (bool): 是否初始化权重,默认为True
    '''
    def __init__(self, num_classes=10, init_weights=True):
        super(LeNet5, self).__init__()
        
        # 第一个卷积块: 32x32x3 -> 28x28x6 -> 14x14x6
        self.conv1 = ConvBlock(
            in_channels=3,
            out_channels=6,
            kernel_size=5,
            stride=1
        )
        
        # 第二个卷积块: 14x14x6 -> 10x10x16 -> 5x5x16
        self.conv2 = ConvBlock(
            in_channels=6,
            out_channels=16,
            kernel_size=5,
            stride=1
        )
        
        # 全连接层
        self.classifier = nn.Sequential(
            nn.Linear(5*5*16, 120),
            nn.ReLU(inplace=True),
            nn.Linear(120, 84),
            nn.ReLU(inplace=True),
            nn.Linear(84, num_classes)
        )
        
        # 初始化权重
        if init_weights:
            self._initialize_weights()
            
    def forward(self, x):
        '''前向传播
        
        Args:
            x (torch.Tensor): 输入图像张量,[N,3,32,32]
            
        Returns:
            torch.Tensor: 输出预测张量,[N,num_classes]
        '''
        # 特征提取
        x = self.conv1(x)  # -> [N,6,14,14]
        x = self.conv2(x)  # -> [N,16,5,5]
        
        # 分类
        x = torch.flatten(x, 1)  # -> [N,16*5*5]
        x = self.classifier(x)  # -> [N,num_classes]
        return x
    
    def _initialize_weights(self):
        '''初始化模型权重
        
        采用kaiming初始化方法:
        - 卷积层权重采用kaiming_normal_初始化
        - 线性层权重采用normal_初始化
        - 所有偏置项初始化为0
        '''
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                # 采用kaiming初始化,适合ReLU激活函数
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Linear):
                # 采用正态分布初始化
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.zeros_(m.bias)


def test():
    """测试函数
    
    创建模型并进行前向传播测试,打印模型结构和参数信息
    """
    # 创建模型
    net = LeNet5()
    print('Model Structure:')
    print(net)
    
    # 测试前向传播
    x = torch.randn(2,3,32,32)
    y = net(x)
    print('\nInput Shape:', x.shape)
    print('Output Shape:', y.shape)
    
    # 打印模型信息
    from torchinfo import summary
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    net = net.to(device)
    summary(net, (2,3,32,32))


if __name__ == '__main__':
    test()