Pytorch初始化模型权重最快捷的方法_class doubleconv(nn.module): def __init__(self, in

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=2
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
        )

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

Net = Normal_Down_Sampling(3,64)

遍历网络模块：

for i,m in enumerate(Net.modules()):
    print(i,m)

结果如下： 

第一层为网络结构

0 Normal_Down_Sampling(
  (conv): Sequential(
    (0): DoubleConv(
      (conv): Sequential(
        (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (2): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (3): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (4): ReLU(inplace=True)
      )
    )
    (1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2))
    (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (3): ReLU(inplace=True)
  )
)

第二层为网络中的Sequential块

1 Sequential(
  (0): DoubleConv(
    (conv): Sequential(
      (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (3): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (4): ReLU(inplace=True)
    )
  )
  (1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2))
  (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (3): ReLU(inplace=True)
) 

第三层为网络中Sequential块中的DoubleConv模块

2 DoubleConv(
  (conv): Sequential(
    (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (3): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (4): ReLU(inplace=True)
  )
)

第四层为DoubleConv中的Sequential块

3 Sequential(
  (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
  (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (2): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
  (3): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (4): ReLU(inplace=True)
)

剩下几层就是torch中的基本模块了 

4 Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
5 BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
6 Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
7 BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
8 ReLU(inplace=True)
9 Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2))
10 BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
11 ReLU(inplace=True)

网络参数初始化：（可以放在网络__init__函数的最后）

for m in self.modules():
    if isinstance(m, nn.Conv2d):
        n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
        m.weight.data.normal_(0, math.sqrt(2. / n))
    elif isinstance(m, nn.BatchNorm2d):
        m.weight.data.fill_(1)
        m.bias.data.zero_()
    elif isinstance(m,nn.ConvTranspose2d):
        n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
        m.weight.data.normal_(0, math.sqrt(2. / n))

总结：用self.modules()可以遍历到网络的基本模块（torch中的基本模块）从而进行初始化