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pytorch中常见的模型3种组织方式 nn.Sequential(OrderedDict)

news 2025/6/27 7:58:58

在nn.Sequential中嵌套OrderedDict组织网络,以对层进行命名

import torch
import torch.nn as nn
from collections import OrderedDictclass OrderedDictCNN(nn.Module):def __init__(self):super(OrderedDictCNN, self).__init__()# 使用 OrderedDict 定义网络层self.model = nn.Sequential(OrderedDict([('conv1', nn.Conv2d(in_channels=3, out_channels=64, kernel_size=7, stride=2, padding=3)),  # 初始卷积层('bn1', nn.BatchNorm2d(64)),('relu1', nn.ReLU(inplace=True)),('maxpool1', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),('conv2', nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)),  # 特征提取层('bn2', nn.BatchNorm2d(128)),('relu2', nn.ReLU(inplace=True)),('maxpool2', nn.MaxPool2d(kernel_size=2, stride=2, padding=0)),('flatten', nn.Flatten()),  # 展平层('fc1', nn.Linear(128 * 112 * 112, 1000)),  # 全连接层('relu3', nn.ReLU(inplace=True)),('fc2', nn.Linear(1000, 10))  # 输出层]))def forward(self, x):return self.model(x)

使用多个nn.Sequential组织网络

import torch.nn as nnclass SimpleCNN(nn.Module):def __init__(self):super(SimpleCNN, self).__init__()# 初始卷积层self.stem = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=64, kernel_size=7, stride=2, padding=3),nn.BatchNorm2d(64),nn.ReLU(inplace=True))# 特征提取层self.feature_extraction = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),nn.BatchNorm2d(128),nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=2, stride=2, padding=0))# 全连接层self.fc = nn.Sequential(nn.Flatten(),nn.Linear(128 * 112 * 112, 1000),nn.ReLU(inplace=True),nn.Linear(1000, 10))def forward(self, x):x = self.stem(x)x = self.feature_extraction(x)x = self.fc(x)return x

使用单个nn.Sequential组织网络

import torch
import torch.nn as nnclass SequentialCNN(nn.Module):def __init__(self):super(SequentialCNN, self).__init__()# 使用 nn.Sequential 定义网络层self.model = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=64, kernel_size=7, stride=2, padding=3),  # 初始卷积层nn.BatchNorm2d(64),nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=3, stride=2, padding=1),nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),  # 特征提取层nn.BatchNorm2d(128),nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=2, stride=2, padding=0),nn.Flatten(),  # 展平层nn.Linear(128 * 112 * 112, 1000),  # 全连接层nn.ReLU(inplace=True),nn.Linear(1000, 10)  # 输出层)def forward(self, x):return self.model(x)