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PyTorch nn.Module

January 19, 2020 • Read: 1357 • Deep Learning阅读设置

本节将介绍在pytorch中非常重要的类:nn.Module。在实现自己设计的网络时,必须要继承这个类,示例写法如下

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

# 先定义自己的类
class MyNN(nn.Module):
    def __init__(self, inp, outp):
        # 初始化自己定义的类
        super(MyNN, self).__init__()

        self.w = nn.Parameter(torch.randn(outp, inp))
        self.b = nn.Parameter(torch.randn(outp))
        
    # 定义前向传播
    def forward(self, x):
        x = x @ self.w.t() + self.b
        return x

那么nn.Module这个类有哪些功能?

  • nn.Module提供了很多已经编写好的功能,如LinearReLUConv2dDropout
  • 书写代码方便。例如我们要定义一个基本的CNN结构,代码如下
self.net = nn.Sequential(
    # .Sequential()相当于设置了一个容器(Container)
    # 将需要进行forward的函数写在其中
    
    nn.Conv2d(1, 32, 5, 1, 1),
    nn.MaxPool2d(2, 2),
    nn.ReLU(True),
    nn.BatchNorm2d(32),
    
    nn.Conv2d(32, 64, 3, 1, 1),
    nn.ReLU(True),
    nn.BatchNorm2d(64),
    
    nn.Conv2d(64, 64, 3, 1, 1),
    nn.MaxPool2d(2, 2),
    nn.ReLU(True),
    nn.BatchNorm2d(64),
    
    nn.Conv2d(64, 128, 3, 1, 1),
    nn.ReLU(True),
    nn.BatchNorm2d(128)
)

或者需要将自己设计的层连接在一起的情况

class Faltten(nn.Module):
    def __init__(self):
        super(Faltten, self).__init__()
    
    def forward(self, input):
        return input.view(inputt.size(0), -1)

class TestNet(nn.Module):
    def __init__(self):
        super(TestNet, self).__init__()
        self.net = nn.Sequential(
            nn.Conv2d(1, 16, stride=1, padding=1),
            nn.MaxPool2d(2, 2),
            Flatten(),
            nn.Linear(1*14*14, 10)
        )
    
    def forward(self, x):
        return self.net(x)
  • 使用nn.Module可以对网络中的参数进行有效的管理
net = nn.Sequential(
    nn.Linear(in_features=4, out_features=2),
    nn.Linear(in_features=2, out_features=2)
)

# 隐藏层的编号是从0开始的
list(net.parameters())[0] # [0]是layer0的w
list(net.parameters())[3].shape # [3]是layer1的b
dict(net.named_parameters()).items() # 返回所有层的参数
 
optimizer = optim.SGD(net.parameters(), lr=1e-3)

输出

torch.Size([2, 4])
torch.Size([2])
dict_items([('0.weight', Parameter containing:
tensor([[ 0.0195,  0.4698, -0.4913, -0.3336],
        [ 0.1422,  0.2908, -0.2469,  0.0583]], requires_grad=True)), ('0.bias', Parameter containing:
tensor([-0.4704, -0.1133], requires_grad=True)), ('1.weight', Parameter containing:
tensor([[-0.6511,  0.2442],
        [ 0.5658,  0.4419]], requires_grad=True)), ('1.bias', Parameter containing:
tensor([ 0.0114, -0.5664], requires_grad=True))])
  • 可以很方便的将所有运算都转入到GPU上去,使用.device()函数
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
net = Net()
net.to(device)
  • 可以很方便的进行save和load,以防止突然发生的断点和系统崩溃现象
torch.save(net.state_dict(), 'ckpt.mdl')
net.load_state_dict(torch.load('ckpt.mdl'))
  • 还可以很方便的切换train和test的状态
# train
net.train()

# test
net.eval()
Last Modified: August 2, 2021
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