首先、在pytorch中本身带有参数初始化。使用初始化主要是因为在模型构建中的需求

1.使用apply()

from torch import nn

def weights_init(mod):
    """对于卷积层和batchnorm进行初始化"""
    classname=mod.__class__.__name__
    # 返回传入的module类型
    if classname.find('Conv')!= -1:    
        mod.weight.data.normal_(0.0,0.02)
    elif classname.find('BatchNorm')!= -1:
        mod.weight.data.normal_(1.0,0.02) #bn层里初始化γ，服从（1，0.02）的正态分布
        mod.bias.data.fill_(0)  #bn层里初始化β，默认为0

class Net(nn.Module):
　　def __init__(self):
　　　　...
　　def forward(self,x):
　　　　...
net = Net()
net.apply(weights_init) #初始化

2、在模型初始化中进行初始化

class DnCNN(nn.Module):
    def __init__(self, depth=17, n_channels=64, image_channels=3, use_bnorm=True, kernel_size=3):
        super(DnCNN, self).__init__()
        kernel_size = 3
        padding = 1
        layers = []

        layers.append(nn.Conv2d(in_channels=image_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=True))
        layers.append(nn.ReLU(inplace=True))
        for _ in range(depth-2):
            layers.append(nn.Conv2d(in_channels=n_channels, out_channels=n_channels, kernel_size=kernel_size, padding=padding, bias=False))
            layers.append(nn.BatchNorm2d(n_channels, eps=0.0001, momentum = 0.95))
            layers.append(nn.ReLU(inplace=True))
        layers.append(nn.Conv2d(in_channels=n_channels, out_channels=image_channels, kernel_size=kernel_size, padding=padding, bias=False))
        self.dncnn = nn.Sequential(*layers)
        self._initialize_weights()

    def forward(self, x):
        y = x
        out = self.dncnn(x)
        return y-out
    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.orthogonal_(m.weight)
                if m.bias is not None:
                    init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias, 0)