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torch.nn中Sequential的使用

news 2025/8/15 13:34:33

1、torch.nn中的Sequential介绍

结构：
torch.nn–>Containers–>Sequential

class torch.nn.Sequential(*args: Module)
class torch.nn.Sequential(arg: OrderedDict[str, Module])

一种顺序容器。模块将按照它们在构造函数中传递的顺序添加到其中。或者，可以传入模块的 OrderedDict。Sequential 的 forward（）方法接受任何输入并将其转发到它包含的第一个模块。然后，它将输出按顺序“链接”到每个后续模块的输入，最后返回最后一个模块的输出。
Sequential 提供的值相对于手动调用序列的模块是它允许将整个容器视为单个模块，以便在 Sequential 适用于它存储的每个模块（每个模块都是 Sequential 的注册子模块）。
Sequential 和 torch.nn.ModuleList的区别？ModuleList 顾名思义——一个用于存储模块的列表。另一方面， Sequential 中的层以级联方式连接。

# Using Sequential to create a small model. When `model` is run,
# input will first be passed to `Conv2d(1,20,5)`. The output of
# `Conv2d(1,20,5)` will be used as the input to the first
# `ReLU`; the output of the first `ReLU` will become the input
# for `Conv2d(20,64,5)`. Finally, the output of
# `Conv2d(20,64,5)` will be used as input to the second `ReLU`
model = nn.Sequential(nn.Conv2d(1, 20, 5), nn.ReLU(), nn.Conv2d(20, 64, 5), nn.ReLU()
)# Using Sequential with OrderedDict. This is functionally the
# same as the above code
model = nn.Sequential(OrderedDict([("conv1", nn.Conv2d(1, 20, 5)),("relu1", nn.ReLU()),("conv2", nn.Conv2d(20, 64, 5)),("relu2", nn.ReLU()),])
)

1.1 Sequential的方法

append(module)——将给定模块附加到末尾。
参数：module (nn.Module) – 要附加的模块
返回值：Self

import torch.nn as nn
n = nn.Sequential(nn.Linear(1, 2), nn.Linear(2, 3))
n.append(nn.Linear(3, 4))
Sequential((0): Linear(in_features=1, out_features=2, bias=True)(1): Linear(in_features=2, out_features=3, bias=True)(2): Linear(in_features=3, out_features=4, bias=True)
)

extend(sequential)——使用另一个顺序容器中的层扩展当前顺序容器。
参数：sequential (Sequential) – 一个顺序容器，其层将添加到当前容器中。
返回值：Self

import torch.nn as nn
n = nn.Sequential(nn.Linear(1, 2), nn.Linear(2, 3))
other = nn.Sequential(nn.Linear(3, 4), nn.Linear(4, 5))
n.extend(other) # or `n + other`
Sequential((0): Linear(in_features=1, out_features=2, bias=True)(1): Linear(in_features=2, out_features=3, bias=True)(2): Linear(in_features=3, out_features=4, bias=True)(3): Linear(in_features=4, out_features=5, bias=True)
)

insert(index, module)——将模块插入指定索引处的顺序容器中。
参数： - index (int) – 要插入模块的索引。 - module (Module) – 要插入的模块。
返回值：Self

import torch.nn as nn
n = nn.Sequential(nn.Linear(1, 2), nn.Linear(2, 3))
n.insert(0, nn.Linear(3, 4))
Sequential((0): Linear(in_features=3, out_features=4, bias=True)(1): Linear(in_features=1, out_features=2, bias=True)(2): Linear(in_features=2, out_features=3, bias=True)
)

2、Pytorch实战

2.1 参数设置

这里以CIFAR10数据集为例，使用如下网络模型：
在这里插入图片描述
卷积层的参数可以由torch.nn.Conv2d的介绍计算得到：

最大池化层的参数可以由torch.nn.MaxPool2d的介绍计算得到：
在这里插入图片描述

2.2 建立网络模型并验证结构

# pytorch实战——sequential practicefrom torch import nn
from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linearclass Seq(nn.Module):def __init__(self):super(Seq,self).__init__()self.conv1=Conv2d(3,32,5,1,2)self.maxpool1=MaxPool2d(2)self.conv2=Conv2d(32,32,5,1,2)self.maxpool2=MaxPool2d(2)self.conv3=Conv2d(32,64,5,1,2)self.maxpool3=MaxPool2d(2)self.flatten=Flattenself.linear1=Linear(1024,64)self.linear2=Linear(64,10)def forward(self,x):x=self.conv1(x)x=self.maxpool1(x)x=self.conv2(x)x=self.maxpool2(x)x=self.conv3(x)x=self.maxpool3(x)x=self.flatten(x)x=self.linear1(x)x=self.linear2(x)return xseq=Seq()
print(seq)
input=torch.ones((64,3,32,32))
output=seq(input)
print(output.shape)

结果：

Seq((conv1): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))(maxpool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(conv2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))(maxpool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(conv3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))(maxpool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(linear1): Linear(in_features=1024, out_features=64, bias=True)(linear2): Linear(in_features=64, out_features=10, bias=True)
)
torch.Size([64, 10])

2.3 使用Sequential重新建立网络模型

# pytorch实战——sequential practice
import torch
from torch import nn
from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linearclass Seq(nn.Module):def __init__(self):super(Seq,self).__init__()self.module=Sequential(Conv2d(3, 32, 5, 1, 2),MaxPool2d(2),Conv2d(32, 32, 5, 1, 2),MaxPool2d(2),Conv2d(32, 64, 5, 1, 2),MaxPool2d(2),Flatten(),Linear(1024, 64),Linear(64, 10))def forward(self,x):x=self.module(x)return xseq=Seq()

2.4 使用Tensorboard可视化网络结构

Tensorboard学习笔记：Pytorch中Tensorboard的学习

# pytorch实战——sequential practice
import torch
from torch import nn
from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.tensorboard import SummaryWriterclass Seq(nn.Module):def __init__(self):super(Seq,self).__init__()self.module=Sequential(Conv2d(3, 32, 5, 1, 2),MaxPool2d(2),Conv2d(32, 32, 5, 1, 2),MaxPool2d(2),Conv2d(32, 64, 5, 1, 2),MaxPool2d(2),Flatten(),Linear(1024, 64),Linear(64, 10))def forward(self,x):x=self.module(x)return xseq=Seq()
print(seq)
input=torch.ones((64,3,32,32))
output=seq(input)
print(output.shape)writer=SummaryWriter(".\logs_seq")
writer.add_graph(seq,input)
writer.close()

在终端进入当前环境，输入命令：

(mypytorch) PS E:\my_pycharm_projects\project1> tensorboard --logdir=logs_seq
#结果：
TensorFlow installation not found - running with reduced feature set.
W0811 23:16:22.249774 31868 plugin_event_accumulator.py:369] Found more than one graph event p
er run, or there was a metagraph containing a graph_def, as well as one or more graph events.  Overwriting the graph with the newest event.
Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all
TensorBoard 2.19.0 at http://localhost:6006/ (Press CTRL+C to quit)