【PyTorch】模型构造：Module 和其子类Sequential、ModuleList、ModuleDict_综合

0. 导入用到的库

import torch
import torch.nn as nn
from collections import OrderedDict

1. 继承Module构造和访问模型

Module 类是 torch.nn 模块里提供的?个模型构造类，是所有神经?络模块的基类，我们可以继承它来定义我们想要的模型。

只需重写 forward方法

class MLP(nn.Module):def __init__(self, **kwargs):super(MLP,self).__init__(**kwargs)self.hidden = nn.Linear(784, 256)self.act = nn.ReLU()self.output = nn.Linear(256, 10)def forward(self, x):a = self.act(self.hidden(x))return self.output

查看模型

X = torch.rand(2, 784)
net = MLP()
print(net)
net(X)

输出

MLP(
(hidden): Linear(in_features=784, out_features=256, bias=True)
(act): ReLU()
(output): Linear(in_features=256, out_features=10, bias=True)
)
tensor([[-0.4208, -0.1205, -0.1616, -0.0421, -0.0989, -0.1204, -0.0447, 0.0534,
0.3461, 0.2020],
[-0.1718, -0.0578, -0.0979, 0.1144, -0.1164, -0.1234, -0.0635, 0.0248,
0.1519, 0.1203]], grad_fn=<AddmmBackward>)

2. 使用Sequential构造和访问模型

net = nn.Sequential(nn.Linear(784, 256),nn.ReLU(),nn.Linear(256,10)
)
print(net)
print(net[-1])

Sequential((0): Linear(in_features=784, out_features=256, bias=True)(1): ReLU()(2): Linear(in_features=256, out_features=10, bias=True)
)
Linear(in_features=256, out_features=10, bias=True)

创建指定索引的模型

net = nn.Sequential(OrderedDict({"linear1":nn.Linear(784,256),"ReLU1": nn.ReLU(),"linear2": nn.Linear(256,10)}))
print(net)
print(net.linear2)

Sequential((linear1): Linear(in_features=784, out_features=256, bias=True)(ReLU1): ReLU()(linear2): Linear(in_features=256, out_features=10, bias=True)
)
Linear(in_features=256, out_features=10, bias=True)

还可以嵌套构建：

net = nn.Sequential(nn.Sequential(OrderedDict({"linear1":nn.Linear(784,256),"ReLU1": nn.ReLU(),"linear2": nn.Linear(256,10)})),nn.Sequential(OrderedDict({"linear1":nn.Linear(10,10),"ReLU1": nn.ReLU(),"linear2": nn.Linear(10,10)})))
print(net)
print(net[0].linear2)

Sequential((0): Sequential((linear1): Linear(in_features=784, out_features=256, bias=True)(ReLU1): ReLU()(linear2): Linear(in_features=256, out_features=10, bias=True))(1): Sequential((linear1): Linear(in_features=10, out_features=10, bias=True)(ReLU1): ReLU()(linear2): Linear(in_features=10, out_features=10, bias=True))
)
Linear(in_features=256, out_features=10, bias=True)

3. 使用ModuleList构造和访问模型

net = nn.ModuleList([nn.Linear(784, 256), nn.ReLU()])
net2 = nn.ModuleList([nn.Linear(256, 10)])
net.extend(net2)
net.append(nn.Linear(10, 10))
net.insert(3,nn.ReLU())print(net)

ModuleList((0): Linear(in_features=784, out_features=256, bias=True)(1): ReLU()(2): Linear(in_features=256, out_features=10, bias=True)(3): ReLU()(4): Linear(in_features=10, out_features=10, bias=True)
)

ModuleList主要方法：

append():在ModuleList后面添加网络层
extend()：拼接两个ModuleList
insert()：指定在ModuleList中位置插入网络层

4. 使用ModuleDict构造和访问模型

class ModuleDict(nn.Module):def __init__(self):super(ModuleDict, self).__init__()self.choices = nn.ModuleDict({'conv': nn.Conv2d(10, 10, 3),'pool': nn.MaxPool2d(2,2)})self.activations = nn.ModuleDict({'relu': nn.ReLU(),'prelu': nn.PReLU()})def forward(self, x, choice, act):x = self.choices[choice](x)x = self.activations[act](x)return xnet = ModuleDict()
print(net)
fake_img = torch.randn((4, 10, 32, 32))
output = net(fake_img, 'conv', 'relu')
print(output.shape)output = net(fake_img, 'pool', 'relu')
print(output.shape)

ModuleDict((choices): ModuleDict((conv): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1))(pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False))(activations): ModuleDict((prelu): PReLU(num_parameters=1)(relu): ReLU())
)
torch.Size([4, 10, 30, 30])
torch.Size([4, 10, 16, 16])