学习笔记18：Inception模型

网络结构

因为主要是学习pytorch，具体原理没有深究。如果将来搞CV的话，可能再回来搞懂吧。

网络结构大概就是，用多个卷积核提取特征，然后将提取到的特征拼接在一起

网络结构如下：

实现思路是，首先定义卷积模型（包括卷积层和BN层），然后再实现Inception的Block（图中所示结构）

卷积模型实现

class BasicConv(nn.Module):
    def __init__(self, in_channels, out_channels, **kwargs):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, bias = False, **kwargs)
        self.bn = nn.BatchNorm2d(out_channels)
    def forward(self, x):
        x = self.conv(x)
        x = F.relu(self.bn(x), inplace = True)
        return x

这里因为后面要传入卷积核的大小，padding的大小，因此要使用可变长参数

Inception结构实现

class InceptionBlock(nn.Module):
    def __init__(self, in_channels, pool_features):
        super().__init__()
        self.b1x1 = BasicConv(in_channels, 64, kernel_size = 1)
        
        self.b3x3_1 = BasicConv(in_channels, 64, kernel_size = 1)
        self.b3x3_2 = BasicConv(64, 96, kernel_size = 3, padding = 1)
        
        self.b5x5_1 = BasicConv(in_channels, 48, kernel_size = 1)
        self.b5x5_2 = BasicConv(48, 64, kernel_size = 5, padding = 2)
        
        self.bpool = BasicConv(in_channels, pool_features, kernel_size = 1)
    def forward(self, x):
        b1x1_out = self.b1x1(x)
        b3x3_out = self.b3x3_2(self.b3x3_1(x))
        b5x5_out = self.b5x5_2(self.b5x5_1(x))
        bpool_out = self.bpool(F.max_pool2d(x, kernel_size = 3, stride = 1, padding = 1))
        out= [b1x1_out, b3x3_out, b5x5_out, bpool_out]
        return torch.cat(out, dim = 1)