PyTorch的模型通过继承nn.Moudle类，在类的内部定义子模块的实例化，通过前向计算调用子模块，最后实现

深度学习模型的搭建。

 

基础结构：

import torch.nn as nn

class MyNet(nn.Module):
    def __init__(self, ...):  # 定义类的初始化函数，...是用户的传入参数
        super(MyNet, self).__init__()  # 调用父类的初始化方法
        ...  # 根据传入的参数来定义子模块

    def forward(self, ...):  # 前向计算
        ret = ...  # 根据传入的张量和子模块计算返回张量
        return ret

 

整个模块的函数主要由两部分组成：通过_init_方法初始化整个模型，forward方法对该模型进行前向计算。

 

示例：

import torch
import torch.nn as nn
from torchvision.models import alexnet

class AlexNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),

            nn.Conv2d(64, 192, kernel_size=5, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2),

            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),

            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),

            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2)
        )
        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 6 * 6, 4096),
            nn.ReLU(inplace=True),

            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),

            nn.Linear(4096, num_classes)
        )

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x