pytorch学习笔记二：pytorch构建模型的3种方式

前言

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。

import torch 
from torch import nn

一，继承nn.Module基类构建自定义模型

Module类是nn模块里提供的一个模型构造类，是所有神经网络模块的基类，我们可以继承它来定义我们想要的模型。

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 y
        
net = Net()
print(net)

Net(
  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))
  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (dropout): Dropout2d(p=0.1, inplace=False)
  (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))
  (flatten): Flatten()
  (linear1): Linear(in_features=64, out_features=32, bias=True)
  (relu): ReLU()
  (linear2): Linear(in_features=32, out_features=1, bias=True)
  (sigmoid): Sigmoid()
)

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)

Sequential(
  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))
  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (dropout): Dropout2d(p=0.1, inplace=False)
  (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))
  (flatten): Flatten()
  (linear1): Linear(in_features=64, out_features=32, bias=True)
  (relu): ReLU()
  (linear2): Linear(in_features=32, out_features=1, bias=True)
  (sigmoid): Sigmoid()
)

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)

Sequential(
  (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))
  (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
  (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (4): Dropout2d(p=0.1, inplace=False)
  (5): AdaptiveMaxPool2d(output_size=(1, 1))
  (6): Flatten()
  (7): Linear(in_features=64, out_features=32, bias=True)
  (8): ReLU()
  (9): Linear(in_features=32, out_features=1, bias=True)
  (10): Sigmoid()
)

3，利用OrderedDict

from collections import OrderedDict

net = 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)

Sequential(
  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))
  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (dropout): Dropout2d(p=0.1, inplace=False)
  (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))
  (flatten): Flatten()
  (linear1): Linear(in_features=64, out_features=32, bias=True)
  (relu): ReLU()
  (linear2): Linear(in_features=32, out_features=1, bias=True)
  (sigmoid): Sigmoid()
)

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)

Net(
  (conv): Sequential(
    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))
    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (4): Dropout2d(p=0.1, inplace=False)
    (5): AdaptiveMaxPool2d(output_size=(1, 1))
  )
  (dense): Sequential(
    (0): Flatten()
    (1): Linear(in_features=64, out_features=32, bias=True)
    (2): ReLU()
    (3): Linear(in_features=32, out_features=1, bias=True)
    (4): Sigmoid()
  )
)

2，nn.ModuleList作为模型容器，注意下面中的ModuleList不能用Python中的列表代替。

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)

Net(
  (layers): ModuleList(
    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))
    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (4): Dropout2d(p=0.1, inplace=False)
    (5): AdaptiveMaxPool2d(output_size=(1, 1))
    (6): Flatten()
    (7): Linear(in_features=64, out_features=32, bias=True)
    (8): ReLU()
    (9): Linear(in_features=32, out_features=1, bias=True)
    (10): Sigmoid()
  )
)

此外，ModuleList有和普通列表相似的属性： append(module)， extend(module)，insert(index, module)等。

但需要注意的是：ModuleList仅仅是一个储存各种模块的列表，这些模块之间没有联系也没有顺序（所以不用保证相邻层的输入输出维度匹配），而且没有实现forward功能需要自己实现，所以上面执行net(input)会报NotImplementedError；而Sequential内的模块需要按照顺序排列，要保证相邻层的输入输出大小相匹配，内部forward功能已经实现。

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)

Net(
  (layers_dict): ModuleDict(
    (adaptive): AdaptiveMaxPool2d(output_size=(1, 1))
    (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))
    (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
    (dropout): Dropout2d(p=0.1, inplace=False)
    (flatten): Flatten()
    (linear1): Linear(in_features=64, out_features=32, bias=True)
    (linear2): Linear(in_features=32, out_features=1, bias=True)
    (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (relu): ReLU()
    (sigmoid): Sigmoid()
  )
)

此外，ModuleDict有和普通字典相似的属性：clear()，items()，keys()，pop(key)，update(modules)等。和ModuleList一样，ModuleDict实例仅仅是存放了一些模块的字典，并没有定义forward函数需要自己定义。

参考链接：https://github.com/lyhue1991/eat_pytorch_in_20_days