Pytorch构建模型的3种方法

这个地方一直是我思考的地方！因为学的代码太多了，构建的模型各有不同，这里记录一下！
可以使用以下3种方式构建模型：

1，继承nn.Module基类构建自定义模型。

2，使用nn.Sequential按层顺序构建模型。

3，继承nn.Module基类构建模型并辅助应用模型容器进行封装(nn.Sequential,nn.ModuleList,nn.ModuleDict)。

其中第1种方式最为常见，第2种方式最简单，第3种方式最为灵活也较为复杂。

推荐使用第1种方式构建模型。

头文件：

import torch 
from torch import nn

一，继承nn.Module基类构建自定义模型

以下是继承nn.Module基类构建自定义模型的一个范例。模型中的用到的层一般在__init__函数中定义，然后在forward方法中定义模型的正向传播逻辑。

class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.conv1 = nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3)self.pool1 = nn.MaxPool2d(kernel_size = 2,stride = 2)self.conv2 = nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5)self.pool2 = nn.MaxPool2d(kernel_size = 2,stride = 2)self.dropout = nn.Dropout2d(p = 0.1)self.adaptive_pool = nn.AdaptiveMaxPool2d((1,1))self.flatten = nn.Flatten()self.linear1 = nn.Linear(64,32)self.relu = nn.ReLU()self.linear2 = nn.Linear(32,1)self.sigmoid = nn.Sigmoid()def forward(self,x):x = self.conv1(x)x = self.pool1(x)x = self.conv2(x)x = self.pool2(x)x = self.dropout(x)x = self.adaptive_pool(x)x = self.flatten(x)x = self.linear1(x)x = self.relu(x)x = self.linear2(x)y = self.sigmoid(x)return ynet = Net()
print(net)

二，使用nn.Sequential按层顺序构建模型

使用nn.Sequential按层顺序构建模型无需定义forward方法。仅仅适合于简单的模型。

以下是使用nn.Sequential搭建模型的一些等价方法。

1，利用add_module方法

net = nn.Sequential()
net.add_module("conv1",nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3))
net.add_module("pool1",nn.MaxPool2d(kernel_size = 2,stride = 2))
net.add_module("conv2",nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5))
net.add_module("pool2",nn.MaxPool2d(kernel_size = 2,stride = 2))
net.add_module("dropout",nn.Dropout2d(p = 0.1))
net.add_module("adaptive_pool",nn.AdaptiveMaxPool2d((1,1)))
net.add_module("flatten",nn.Flatten())
net.add_module("linear1",nn.Linear(64,32))
net.add_module("relu",nn.ReLU())
net.add_module("linear2",nn.Linear(32,1))
net.add_module("sigmoid",nn.Sigmoid())print(net)

2，利用变长参数

这种方式构建时不能给每个层指定名称。

net = nn.Sequential(nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Dropout2d(p = 0.1),nn.AdaptiveMaxPool2d((1,1)),nn.Flatten(),nn.Linear(64,32),nn.ReLU(),nn.Linear(32,1),nn.Sigmoid()
)print(net)

3，利用OrderedDict

from collections import OrderedDictnet = nn.Sequential(OrderedDict([("conv1",nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3)),("pool1",nn.MaxPool2d(kernel_size = 2,stride = 2)),("conv2",nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5)),("pool2",nn.MaxPool2d(kernel_size = 2,stride = 2)),("dropout",nn.Dropout2d(p = 0.1)),("adaptive_pool",nn.AdaptiveMaxPool2d((1,1))),("flatten",nn.Flatten()),("linear1",nn.Linear(64,32)),("relu",nn.ReLU()),("linear2",nn.Linear(32,1)),("sigmoid",nn.Sigmoid())]))
print(net)

三，继承nn.Module基类构建模型并辅助应用模型容器进行封装

当模型的结构比较复杂时，我们可以应用模型容器(nn.Sequential,nn.ModuleList,nn.ModuleDict)对模型的部分结构进行封装。

这样做会让模型整体更加有层次感，有时候也能减少代码量。

注意，在下面的范例中我们每次仅仅使用一种模型容器，但实际上这些模型容器的使用是非常灵活的，可以在一个模型中任意组合任意嵌套使用。

1，nn.Sequential作为模型容器

class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.conv = nn.Sequential(nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Dropout2d(p = 0.1),nn.AdaptiveMaxPool2d((1,1)))self.dense = nn.Sequential(nn.Flatten(),nn.Linear(64,32),nn.ReLU(),nn.Linear(32,1),nn.Sigmoid())def forward(self,x):x = self.conv(x)y = self.dense(x)return y net = Net()
print(net)

2，nn.ModuleList作为模型容器

class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.layers = nn.ModuleList([nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),nn.MaxPool2d(kernel_size = 2,stride = 2),nn.Dropout2d(p = 0.1),nn.AdaptiveMaxPool2d((1,1)),nn.Flatten(),nn.Linear(64,32),nn.ReLU(),nn.Linear(32,1),nn.Sigmoid()])def forward(self,x):for layer in self.layers:x = layer(x)return x
net = Net()
print(net)

3，nn.ModuleDict作为模型容器

注意下面中的ModuleDict不能用Python中的字典代替。

class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.layers_dict = nn.ModuleDict({"conv1":nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3),"pool": nn.MaxPool2d(kernel_size = 2,stride = 2),"conv2":nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),"dropout": nn.Dropout2d(p = 0.1),"adaptive":nn.AdaptiveMaxPool2d((1,1)),"flatten": nn.Flatten(),"linear1": nn.Linear(64,32),"relu":nn.ReLU(),"linear2": nn.Linear(32,1),"sigmoid": nn.Sigmoid()})def forward(self,x):layers = ["conv1","pool","conv2","pool","dropout","adaptive","flatten","linear1","relu","linear2","sigmoid"]for layer in layers:x = self.layers_dict[layer](x)return x
net = Net()
print(net)