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PyTorch中nn.Module使用示例指南

作者：点云SLAM

本文详解PyTorch中nn.Module的核心作用,涵盖其作为神经网络基类的功能,帮助掌握模型构建与训练流程,感兴趣的朋友跟随小编一起看看吧

在 PyTorch 中，nn.Module 是神经网络中最核心的基类，用于构建所有模型。理解并熟练使用 nn.Module 是掌握 PyTorch 的关键。

一、什么是nn.Module

nn.Module 是 PyTorch 中所有神经网络模块的基类。可以把它看作是“神经网络的容器”，它封装了以下几件事：

  1. 网络层（如 Linear、Conv2d 等）
  2. 前向传播逻辑（forward 函数）
  3. 模型参数（自动注册并可训练）
  4. 可嵌套（可以包含多个子模块）
  5. 便捷的模型保存 / 加载等工具函数

二、基础用法

2.1 自定义模型类

import torch
import torch.nn as nn
class MyNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 128)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(128, 10)
    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

2.2 实例化与调用

model = MyNet()
x = torch.randn(32, 784)     # batch_size = 32
output = model(x)            # 自动调用 forward

三、构造方法详解

3.1__init__()

3.2forward()

四、常见模块层

模块名作用示例
nn.Linear全连接层nn.Linear(128, 64)
nn.Conv2d卷积层nn.Conv2d(3, 16, 3)
nn.ReLU激活函数nn.ReLU()
nn.Sigmoid激活函数nn.Sigmoid()
nn.BatchNorm2d批归一化nn.BatchNorm2d(16)
nn.DropoutDropout 层nn.Dropout(0.5)
nn.LSTMLSTM 层nn.LSTM(10, 20)
nn.Sequential层的顺序容器见下文说明

五、模型嵌套结构（子模块）

你可以将一个 nn.Module 作为另一个模块的子模块嵌套：

class Block(nn.Module):
    def __init__(self):
        super().__init__()
        self.layer = nn.Sequential(
            nn.Linear(64, 64),
            nn.ReLU()
        )
    def forward(self, x):
        return self.layer(x)
class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.block1 = Block()
        self.block2 = Block()
        self.output = nn.Linear(64, 10)
    def forward(self, x):
        x = self.block1(x)
        x = self.block2(x)
        return self.output(x)

六、内置方法和属性

方法 / 属性说明
model.parameters()返回所有可训练参数（用于优化器）
model.named_parameters()返回带名字的参数迭代器
model.children()返回子模块迭代器
model.eval()设置为评估模式（Dropout、BN失效）
model.train()设置为训练模式
model.to(device)将模型转移到 GPU/CPU
model.state_dict()获取模型参数字典（保存）
model.load_state_dict()加载模型参数字典

七、使用nn.Sequential

nn.Sequential 是一个顺序容器，可以用来简化网络结构定义：

model = nn.Sequential(
    nn.Linear(784, 128),
    nn.ReLU(),
    nn.Linear(128, 10)
)

等价于手写的自定义 nn.Module。适合前向传播是线性“流动”的结构。

八、实战完整示例：MNIST 分类网络

class MNISTNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.net = nn.Sequential(
            nn.Flatten(),
            nn.Linear(28*28, 256),
            nn.ReLU(),
            nn.Linear(256, 10)
        )
    def forward(self, x):
        return self.net(x)
# 实例化模型
model = MNISTNet()
print(model)
# 配置训练
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
# 示例训练循环
for epoch in range(10):
    for images, labels in train_loader:
        output = model(images)
        loss = criterion(output, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

九、常见陷阱和建议

问题说明
forward() 不起作用应该使用 model(x)，而不是手动调用 model.forward(x)
忘记 super().__init__()子模块将不会被注册
参数未注册层/模块必须赋值为 self.xxx = ...
训练/测试模式混淆注意 model.eval() 和 model.train()

十、总结

项目说明
__init__()定义模型结构（子模块、层）
forward()定义前向传播
自动注册参数所有 self.xxx = nn.XXX(...) 都会被追踪
嵌套模块支持递归子模块调用
便捷方法.parameters()、.to()、.eval() 等

十一、综合示例

以下是基于 PyTorch nn.Module 封装的三种经典深度学习架构（ResNet18、UNet、Transformer）的简洁而完整的实现，适合初学者快速上手。

1、ResNet18 简洁实现（适合图像分类）

import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
    expansion = 1
    def __init__(self, in_planes, planes, stride=1, downsample=None):
        super().__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1   = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2   = nn.BatchNorm2d(planes)
        self.downsample = downsample
    def forward(self, x):
        identity = x
        if self.downsample:
            identity = self.downsample(x)
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += identity
        return F.relu(out)
class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        super().__init__()
        self.in_planes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1   = nn.BatchNorm2d(64)
        self.pool  = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64,  layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc      = nn.Linear(512 * block.expansion, num_classes)
    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.in_planes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.in_planes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion)
            )
        layers = [block(self.in_planes, planes, stride, downsample)]
        self.in_planes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.in_planes, planes))
        return nn.Sequential(*layers)
    def forward(self, x):
        x = self.pool(F.relu(self.bn1(self.conv1(x))))
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x).flatten(1)
        return self.fc(x)
def ResNet18(num_classes=1000):
    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes)

2、UNet（适合图像分割）

class UNetBlock(nn.Module):
    def __init__(self, in_ch, out_ch):
        super().__init__()
        self.block = nn.Sequential(
            nn.Conv2d(in_ch, out_ch, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_ch, out_ch, 3, padding=1),
            nn.ReLU(inplace=True)
        )
    def forward(self, x):
        return self.block(x)
class UNet(nn.Module):
    def __init__(self, in_channels=1, out_channels=1):
        super().__init__()
        self.enc1 = UNetBlock(in_channels, 64)
        self.enc2 = UNetBlock(64, 128)
        self.enc3 = UNetBlock(128, 256)
        self.enc4 = UNetBlock(256, 512)
        self.pool = nn.MaxPool2d(2)
        self.bottleneck = UNetBlock(512, 1024)
        self.upconv4 = nn.ConvTranspose2d(1024, 512, 2, stride=2)
        self.dec4 = UNetBlock(1024, 512)
        self.upconv3 = nn.ConvTranspose2d(512, 256, 2, stride=2)
        self.dec3 = UNetBlock(512, 256)
        self.upconv2 = nn.ConvTranspose2d(256, 128, 2, stride=2)
        self.dec2 = UNetBlock(256, 128)
        self.upconv1 = nn.ConvTranspose2d(128, 64, 2, stride=2)
        self.dec1 = UNetBlock(128, 64)
        self.final = nn.Conv2d(64, out_channels, kernel_size=1)
    def forward(self, x):
        e1 = self.enc1(x)
        e2 = self.enc2(self.pool(e1))
        e3 = self.enc3(self.pool(e2))
        e4 = self.enc4(self.pool(e3))
        b  = self.bottleneck(self.pool(e4))
        d4 = self.upconv4(b)
        d4 = self.dec4(torch.cat([d4, e4], dim=1))
        d3 = self.upconv3(d4)
        d3 = self.dec3(torch.cat([d3, e3], dim=1))
        d2 = self.upconv2(d3)
        d2 = self.dec2(torch.cat([d2, e2], dim=1))
        d1 = self.upconv1(d2)
        d1 = self.dec1(torch.cat([d1, e1], dim=1))
        return self.final(d1)

3、简化版 Transformer 编码器（适合序列建模）

class TransformerBlock(nn.Module):
    def __init__(self, embed_dim, heads, ff_hidden_dim, dropout=0.1):
        super().__init__()
        self.attn = nn.MultiheadAttention(embed_dim, heads, dropout=dropout, batch_first=True)
        self.ff = nn.Sequential(
            nn.Linear(embed_dim, ff_hidden_dim),
            nn.ReLU(),
            nn.Linear(ff_hidden_dim, embed_dim)
        )
        self.norm1 = nn.LayerNorm(embed_dim)
        self.norm2 = nn.LayerNorm(embed_dim)
        self.dropout = nn.Dropout(dropout)
    def forward(self, x, mask=None):
        attn_out, _ = self.attn(x, x, x, attn_mask=mask)
        x = self.norm1(x + self.dropout(attn_out))
        ff_out = self.ff(x)
        x = self.norm2(x + self.dropout(ff_out))
        return x
class TransformerEncoder(nn.Module):
    def __init__(self, vocab_size, embed_dim=512, n_heads=8, ff_dim=2048, num_layers=6, max_len=512):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim)
        self.pos_encoding = self._generate_positional_encoding(max_len, embed_dim)
        self.layers = nn.ModuleList([
            TransformerBlock(embed_dim, n_heads, ff_dim)
            for _ in range(num_layers)
        ])
        self.dropout = nn.Dropout(0.1)
    def _generate_positional_encoding(self, max_len, d_model):
        pos = torch.arange(0, max_len).unsqueeze(1)
        i = torch.arange(0, d_model, 2)
        angle_rates = 1 / torch.pow(10000, (i / d_model))
        pos_enc = torch.zeros(max_len, d_model)
        pos_enc[:, 0::2] = torch.sin(pos * angle_rates)
        pos_enc[:, 1::2] = torch.cos(pos * angle_rates)
        return pos_enc.unsqueeze(0)
    def forward(self, x):
        B, T = x.shape
        x = self.embedding(x) + self.pos_encoding[:, :T].to(x.device)
        x = self.dropout(x)
        for layer in self.layers:
            x = layer(x)
        return x

4、 总结对比

模型类型场景特点
ResNet18图像分类深残差网络结构，适合迁移学习
UNet图像分割对称结构，编码 + 解码 + skip
TransformerNLP / 序列建模全注意力机制，无卷积无循环

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