1 一个简单的网络

1. 一个Pytorch模型应该以类的形式出现
2. Pytorch训练模型应该是nn.Module的子类
3. 一个训练模型最少包含init和forward（初始化和前向传播）两个过程。

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


class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

2 nn.Module.init_weight()

• 这个代码是SeNet的代码，放在这里学习init_weight

import numpy as np
import torch
from torch import nn
from torch.nn import init


class SEAttention(nn.Module):

    def __init__(self, channel=512, reduction=16):
        super().__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)  # 全局均值池化 输出的是c×1×1
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction, bias=False),  # channel // reduction代表通道压缩
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel, bias=False),  # 还原
            nn.Sigmoid()
        )

    def init_weights(self):
        for m in self.modules():
            print(m)  # 没运行到这儿
            if isinstance(m, nn.Conv2d):  # 判断类型函数——：m是nn.Conv2d类吗？
                init.kaiming_normal_(m.weight, mode='fan_out')
                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)
            elif isinstance(m, nn.Linear):
                init.normal_(m.weight, std=0.001)
                if m.bias is not None:
                    init.constant_(m.bias, 0)

    def forward(self, x):
        b, c, _, _ = x.size()  # 50×512×7×7
        y = self.avg_pool(x).view(b, c)  # ① maxpool之后得：50×512×1×1 ② view形状得到50×512
        y = self.fc(y).view(b, c, 1, 1)  # 50×512×1×1
        return x * y.expand_as(x)  # 根据x.size来扩展y


if __name__ == '__main__':
    input = torch.randn(50, 512, 7, 7)
    se = SEAttention(channel=512, reduction=8)  # 实例化模型se
    output = se(input)
    print(output.shape)

2.1 kaiming 高斯初始化

• 使得每一个卷积层的输出方差都为1，权值的初始化方法如下：
  
  
  

torch.nn.init.kaiming_normal_(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu')