Pytorch初始化模型权重最快捷的方法

用self.modules()方法批量初始化模型权重

用self.modules()可以遍历组成网络的所有模块，以及这些模块的后代模块。

Example:

 创建一个网络，其中包括一个预先定义的DoubleConv类

class DoubleConv(nn.Module):
    def __init__(self,in_channels,out_channels):
        super(DoubleConv,self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels,out_channels,3,1,1,bias=False),
            nn.BatchNorm2d(out_channels),
            nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
        )

    def forward(self,x):
        return self.conv(x)

class Normal_Down_Sampling(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Normal_Down_Sampling, self).__init__()
        self.conv = nn.Sequential(
            DoubleConv(in_channels,in_channels),
            nn.Conv2d(in_channels, out_channels, 7, 2),  # 7*7 step