1 一个简单的网络
- 一个Pytorch模型应该以类的形式出现
- Pytorch训练模型应该是nn.Module的子类
- 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)