深度集成学习不均衡样本图像分类

Secede.2025-04-09 13:06

用五个不同的网络，然后对分类概率进行平均，得到分类结果。基本上分类精度可以提升10%

1.导入基本库

python 复制代码 
import torch
import copy
import torch.nn as nn
import torchvision.models as models
from torchvision import datasets
from torchvision import transforms
from tqdm import tqdm
from torch.utils.data import DataLoader
from torch.utils.data import random_split
from transformers import AutoModelForImageClassification,AutoConfig

2.数据集准备

python 复制代码 
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 数据预处理
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),  
])

train_dataset = datasets.ImageFolder(root='./aug_datasets1', transform=transform)
dataset_size  = len(train_dataset)

train_size = int(0.8 * dataset_size)
val_size = dataset_size - train_size

train_dataset, val_dataset = random_split(train_dataset, [train_size, val_size])


train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_dataloader = DataLoader(val_dataset, batch_size=32, shuffle=False)

3.定义不同模型与对应的训练策略

模型1 ResNet

python 复制代码 
class ResNet(nn.Module):
    def __init__(self, num_classes=21,train=True):
        super(ResNet, self).__init__()
        if(train):
            self.resnet = models.resnet50(weights=torchvision.models.ResNet50_Weights.IMAGENET1K_V1)
        else:
            self.resnet = models.resnet50(weights=None)
        in_features = self.resnet.fc.in_features
        self.resnet.fc = nn.Sequential(
            nn.Linear(in_features, 512),
            nn.ReLU(inplace=True),
            nn.Dropout(0.5),
            nn.Linear(512, num_classes)
        )
        self.resnet.to(device)
    def forward(self, x):
        return self.resnet(x)

    # 训练策略
    def startTrain(self, train_loader, val_loader):
        criterion = nn.CrossEntropyLoss()
        optimizer = torch.optim.AdamW(self.parameters(), lr=1e-4, weight_decay=1e-4)
        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50)
        Best_Acc = 0.0
        print("Training ResNet.....")
        for epoch in range(10):  # 训练 10 个 epoch
            self.train()
            train_loss = 0
            for batch in tqdm(train_loader):
                images, labels = batch
                images, labels = images.to(device), labels.to(device)
                
                optimizer.zero_grad()
                # 处理图像并将其传递给模型
                logits = self(images)

                # 计算损失并进行反向传播
                loss = criterion(logits, labels)
                loss.backward()
                optimizer.step()
                

                train_loss += loss.item()
            print(f"Epoch {epoch+1}/{10}, Train Loss: {train_loss/len(train_loader)}")
            scheduler.step()
            self.eval()
            val_loss = 0
            correct = 0
            total = 0

            with torch.no_grad():
                for batch in tqdm(val_loader):
                    images, labels = batch
                    images, labels = images.to(device), labels.to(device)

                    # 处理图像并传递给模型
                    logits = self(images)

                    # 计算损失
                    loss = criterion(logits, labels)
                    val_loss += loss.item()

                    # 计算准确率
                    _, predicted = torch.max(logits, 1)
                    total += labels.size(0)
                    correct += (predicted == labels).sum().item()

            print(f"Validation Loss: {val_loss/len(val_loader)}")
            print(f"Accuracy: {100 * correct / total}%")
            if(100 * correct / total > Best_Acc):
                Best_Acc = 100 * correct / total
                torch.save(self.state_dict(), './saved/resnet/model_weights_{}.pth'.format(Best_Acc))

模型2 EfficientNet

python 复制代码 
class EfficientNet(nn.Module):
    def __init__(self, num_classes=21,train=True):
        super(EfficientNet, self).__init__()
        if(train):
            self.effnet = models.efficientnet_b2(weights=torchvision.models.EfficientNet_B2_Weights.IMAGENET1K_V1)
        else:
            self.effnet = models.efficientnet_b2(weights=None)
        
        in_features = self.effnet.classifier[1].in_features
        self.effnet.classifier = nn.Sequential(
            nn.Linear(in_features, 512),
            nn.ReLU(inplace=True),
            nn.Dropout(0.5),
            nn.Linear(512, num_classes)
        )
        self.effnet.to(device)
    def forward(self, x):
        return self.effnet(x)

    # 训练策略
    def startTrain(self, train_loader, val_loader):
        # 焦点损失，gamma参数增强对少数类的关注
        criterion = nn.CrossEntropyLoss()
        optimizer = torch.optim.AdamW(self.parameters(), lr=1e-4, weight_decay=1e-4)
        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=5)
        Best_Acc = 0.0
        print("Training EfficientNet.....")
        for epoch in range(10):  # 训练 10 个 epoch
            self.train()
            train_loss = 0
            for batch in tqdm(train_loader):
                images, labels = batch
                images, labels = images.to(device), labels.to(device)

                optimizer.zero_grad()
                # 处理图像并将其传递给模型
                logits = self(images)
                
                # 计算损失并进行反向传播
                loss = criterion(logits, labels)
                loss.backward()
                optimizer.step()
                

                train_loss += loss.item()
            print(f"Epoch {epoch+1}/{10}, Train Loss: {train_loss/len(train_loader)}")
            scheduler.step(train_loss/len(train_loader))
            self.eval()
            val_loss = 0
            correct = 0
            total = 0

            with torch.no_grad():
                for batch in tqdm(val_loader):
                    images, labels = batch
                    images, labels = images.to(device), labels.to(device)

                    # 处理图像并传递给模型
                    logits = self(images)

                    # 计算损失
                    loss = criterion(logits, labels)
                    val_loss += loss.item()

                    # 计算准确率
                    _, predicted = torch.max(logits, 1)
                    total += labels.size(0)
                    correct += (predicted == labels).sum().item()

            print(f"Validation Loss: {val_loss/len(val_loader)}")
            print(f"Accuracy: {100 * correct / total}%")
            if(100 * correct / total > Best_Acc):
                Best_Acc = 100 * correct / total
                torch.save(self.state_dict(), './saved/efficientnet/model_weights_{}.pth'.format(Best_Acc))

模型3 DenseNet

python 复制代码 
class DenseNet(nn.Module):
    def __init__(self, num_classes=21, train=True):
        super(DenseNet, self).__init__()
        self.num_classes = num_classes
        if(train):
            self.densenet = models.densenet121(weights=torchvision.models.DenseNet121_Weights.IMAGENET1K_V1)
        else:
            self.densenet = models.densenet121(weights=None) 
        
        in_features = self.densenet.classifier.in_features
        self.densenet.classifier = nn.Sequential(
            nn.BatchNorm1d(in_features),
            nn.Linear(in_features, 512),
            nn.ReLU(inplace=True),
            nn.Dropout(0.5),
            nn.Linear(512, num_classes)
        )
        self.densenet.to(device)
    def forward(self, x):
        return self.densenet(x)

    # 训练策略
    def startTrain(self, train_loader, val_loader):
        
        criterion = nn.CrossEntropyLoss()
        optimizer = torch.optim.Adam(self.parameters(), lr=1e-4)
        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50)
        Best_Acc = 0.0
        print("Training DenseNet.....")
        for epoch in range(10):  # 训练 10 个 epoch
            self.train()
            train_loss = 0
            for batch in tqdm(train_loader):
                images, labels = batch
                images, labels = images.to(device), labels.to(device)

                optimizer.zero_grad()
                # 处理图像并将其传递给模型
                logits = self(images)

                # 计算损失并进行反向传播
                loss = criterion(logits, labels)
                loss.backward()
                optimizer.step()
                

                train_loss += loss.item()
            print(f"Epoch {epoch+1}/{10}, Train Loss: {train_loss/len(train_loader)}")
            scheduler.step()
            self.eval()
            val_loss = 0
            correct = 0
            total = 0

            with torch.no_grad():
                for batch in tqdm(val_loader):
                    images, labels = batch
                    images, labels = images.to(device), labels.to(device)

                    # 处理图像并传递给模型
                    logits = self(images)

                    # 计算损失
                    loss = criterion(logits, labels)
                    val_loss += loss.item()

                    # 计算准确率
                    _, predicted = torch.max(logits, 1)
                    total += labels.size(0)
                    correct += (predicted == labels).sum().item()

            print(f"Validation Loss: {val_loss/len(val_loader)}")
            print(f"Accuracy: {100 * correct / total}%")
            if(100 * correct / total > Best_Acc):
                Best_Acc = 100 * correct / total
                torch.save(self.state_dict(), './saved/densenet/model_weights_{}.pth'.format(Best_Acc))

模型4 ResNeXt

python 复制代码 
class ResNeXt(nn.Module):

    def __init__(self, num_classes=21,train=True):
        super(ResNeXt, self).__init__()
        if(train):
            self.resnext50 = models.resnext50_32x4d(weights=torchvision.models.ResNeXt50_32X4D_Weights.IMAGENET1K_V1)
        else:
            self.resnext50 = models.resnext50_32x4d(weights=None)
        
        in_features = self.resnext50.fc.in_features
        self.resnext50.fc = nn.Sequential(
            nn.BatchNorm1d(in_features),
            nn.Linear(in_features, 512),
            nn.ReLU(inplace=True),
            nn.Dropout(0.5),
            nn.Linear(512, num_classes)
        )
        self.resnext50.to(device)
              
        self.to(device)
    def forward(self, x):
        return self.resnext50(x)

    def startTrain(self, train_loader, val_loader):
        
        optimizer = torch.optim.AdamW(self.parameters(), lr=1e-4)
        scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=5e-4, epochs=30, steps_per_epoch=len(train_loader))        
        criterion = nn.CrossEntropyLoss()
        Best_Acc = 0.0
        print("Training ResNeXt.....")
        for epoch in range(10):  # 训练 10 个 epoch
            self.train()
            train_loss = 0
            for batch in tqdm(train_loader):
                images, labels = batch
                images, labels = images.to(device), labels.to(device)

                optimizer.zero_grad()
                # 处理图像并将其传递给模型
                logits = self(images)

                # 计算损失并进行反向传播
                loss = criterion(logits, labels)
                loss.backward()
                optimizer.step()

                train_loss += loss.item()
            print(f"Epoch {epoch+1}/{10}, Train Loss: {train_loss/len(train_loader)}")
            scheduler.step(train_loss/len(train_loader))
            self.eval()
            val_loss = 0
            correct = 0
            total = 0

            with torch.no_grad():
                for batch in tqdm(val_loader):
                    images, labels = batch
                    images, labels = images.to(device), labels.to(device)

                    # 处理图像并传递给模型
                    logits = self(images)

                    # 计算损失
                    loss = criterion(logits, labels)
                    val_loss += loss.item()

                    # 计算准确率
                    _, predicted = torch.max(logits, 1)
                    total += labels.size(0)
                    correct += (predicted == labels).sum().item()

            print(f"Validation Loss: {val_loss/len(val_loader)}")
            print(f"Accuracy: {100 * correct / total}%")
            if(100 * correct / total > Best_Acc):
                Best_Acc = 100 * correct / total
                torch.save(self.state_dict(), './saved/se-resnext/model_weights_{}.pth'.format(Best_Acc))

模型5 SwinTransformer

python 复制代码 
class SwinTransformer(nn.Module):
    def __init__(self, num_classes=21,train=True):
        super(SwinTransformer, self).__init__()
        if(train):
            self.vit = AutoModelForImageClassification.from_pretrained('./swinv2-tiny-patch4-window16-256/models--microsoft--swinv2-tiny-patch4-window16-256/snapshots/f4d3075206f2ad5eda586c30d6b4d0500f312421/')   
            #这个地方怎么写加载模型
            self.vit.classifier = nn.Sequential(
                nn.Dropout(0.5),
                nn.Linear(self.vit.classifier.in_features, num_classes)
            )
            # 冻结Swin Transformer模型中的所有层
            for param in self.vit.parameters():
                param.requires_grad = False        
            
            # 只解冻最后两个Transformer块和分类头
            for param in self.vit.swinv2.encoder.layers[-4:].parameters():  # 假设你想解冻最后两层
                param.requires_grad = True
            for param in self.vit.classifier.parameters():
                param.requires_grad = True
        else:
            # 先加载 config，然后手动修改 num_labels
            config = AutoConfig.from_pretrained('./saved/swin-transformer/')
            config.num_labels = 21
            self.vit = AutoModelForImageClassification.from_pretrained('./saved/swin-transformer/',config=config)   
        self.vit.to(device)
        
    def forward(self, x):
        return self.vit(x)

    # 训练策略
    def startTrain(self, train_loader, val_loader):
        # 使用标签平滑处理，考虑到类别是连续尺度
        criterion = nn.CrossEntropyLoss()
        # 两阶段训练策略
        # 阶段1: 只训练解冻的层
        num_epochs_stage1 = 10
        num_epochs_stage2 = 10
        optimizer_stage1 = torch.optim.AdamW([p for p in self.parameters() if p.requires_grad], lr=1e-3)

        scheduler_stage1 = torch.optim.lr_scheduler.OneCycleLR(
            optimizer_stage1, max_lr=1e-3, epochs=num_epochs_stage1, steps_per_epoch=len(train_loader)
        )
        best_model_wts = copy.deepcopy(self.state_dict())
        print("Training SwinTransformer.....") 
        print("===== Stage 1 Training =====")
        Best_Acc = 0.0
        for epoch in range(num_epochs_stage1):  # 训练 10 个 epoch
            self.train()
            train_loss = 0
            for batch in tqdm(train_loader):
                images, labels = batch
                images, labels = images.to(device), labels.to(device)

                optimizer_stage1.zero_grad()
                # 处理图像并将其传递给模型
                outputs = self(images)
                logits = outputs.logits

                # 计算损失并进行反向传播
                loss = criterion(logits, labels)
                loss.backward()
                optimizer_stage1.step()


                train_loss += loss.item()
            print(f"Epoch {epoch+1}/{10}, Train Loss: {train_loss/len(train_loader)}")
            scheduler_stage1.step()
            self.eval()
            val_loss = 0
            correct = 0
            total = 0

            with torch.no_grad():
                for batch in tqdm(val_loader):
                    images, labels = batch
                    images, labels = images.to(device), labels.to(device)

                    # 处理图像并传递给模型
                    outputs = self(images)
                    logits = outputs.logits

                    # 计算损失
                    loss = criterion(logits, labels)
                    val_loss += loss.item()

                    # 计算准确率
                    _, predicted = torch.max(logits, 1)
                    total += labels.size(0)
                    correct += (predicted == labels).sum().item()

            print(f"Validation Loss: {val_loss/len(val_loader)}")
            print(f"Accuracy: {100 * correct / total}%")
            if(100 * correct / total > Best_Acc):
                Best_Acc = 100 * correct / total
                best_model_wts = copy.deepcopy(self.state_dict())
                self.vit.save_pretrained('./saved/swin-transformer/', safe_serialization=False)       
        
        # 阶段1结束后加载最佳模型权重
        self.load_state_dict(best_model_wts)    
        Best_Acc = 0.0
        print("===== Stage 2 Training =====")
        # 阶段2: 微调整个网络
        for param in self.parameters():
            param.requires_grad = True
        optimizer_stage2 = torch.optim.Adam(self.parameters(), lr=1e-6)
        scheduler_stage2 = torch.optim.lr_scheduler.OneCycleLR(
            optimizer_stage2, max_lr=5e-6, epochs=num_epochs_stage2, steps_per_epoch=len(train_loader)
        )
        for epoch in range(num_epochs_stage2):  # 训练 10 个 epoch
            self.train()
            train_loss = 0
            for batch in tqdm(train_loader):
                images, labels = batch
                images, labels = images.to(device), labels.to(device)

                optimizer_stage2.zero_grad()
                # 处理图像并将其传递给模型
                outputs = self(images)
                logits = outputs.logits

                # 计算损失并进行反向传播
                loss = criterion(logits, labels)
                loss.backward()
                optimizer_stage2.step()
                

                train_loss += loss.item()
            print(f"Epoch {epoch+1}/{10}, Train Loss: {train_loss/len(train_loader)}")
            scheduler_stage2.step()
            self.eval()
            val_loss = 0
            correct = 0
            total = 0

            with torch.no_grad():
                for batch in tqdm(val_loader):
                    images, labels = batch
                    images, labels = images.to(device), labels.to(device)

                    # 处理图像并传递给模型
                    outputs = self(images)
                    logits = outputs.logits

                    # 计算损失
                    loss = criterion(logits, labels)
                    val_loss += loss.item()

                    # 计算准确率
                    _, predicted = torch.max(logits, 1)
                    total += labels.size(0)
                    correct += (predicted == labels).sum().item()

            print(f"Validation Loss: {val_loss/len(val_loader)}")
            print(f"Accuracy: {100 * correct / total}%")
            if(100 * correct / total > Best_Acc):
                Best_Acc = 100 * correct / total
                self.vit.save_pretrained('./saved/swin-transformer/', safe_serialization=False)

4.分别训练，然后得到权重

python 复制代码 
    swinTransformer= SwinTransformer()
    swinTransformer.startTrain(train_dataloader,val_dataloader)
      
    efficientNet= EfficientNet()
    efficientNet.startTrain(train_dataloader,val_dataloader)

    resNet= ResNet()
    resNet.startTrain(train_dataloader,val_dataloader)
    
    resNeXt= ResNeXt()
    resNeXt.startTrain(train_dataloader,val_dataloader)
    
    denseNet= DenseNet()
    denseNet.startTrain(train_dataloader,val_dataloader)

5.构建集成分类模型

python 复制代码 
import torch
import torchvision.transforms as transforms
import torch.nn as nn
from torchvision import datasets
from torchvision import transforms
from tqdm import tqdm
from torch.utils.data import DataLoader
from torch.utils.data import random_split
from tqdm import tqdm
from PIL import Image

def remove_prefix_from_state_dict(state_dict, prefix='resnext.'):
    return {"resnext50." + k[len(prefix):] if k.startswith(prefix) else k: v for k, v in state_dict.items()}


# 定义集成模型
class EnsembleModel():
    def __init__(self, efficientNet, resNet, resNeXt, denseNet,swinTransformer):
        super(EnsembleModel, self).__init__()

        self.efficientNet= efficientNet.eval()
        self.resNet= resNet.eval()
        self.resNeXt= resNeXt.eval()
        self.denseNet= denseNet.eval()
        self.swinTransformer= swinTransformer.eval()

    def predict(self, x):
        efficientNet_out = torch.softmax(self.efficientNet(x),dim=1)
        resNet_out = torch.softmax(self.resNet(x),dim=1)
        resNeXt_out = torch.softmax(self.resNeXt(x),dim=1)
        denseNet_out = torch.softmax(self.denseNet(x),dim=1)
        swinTransformer_out = torch.softmax(self.swinTransformer(x).logits,dim=1)
        avg_pred = (efficientNet_out + resNet_out + resNeXt_out + denseNet_out + swinTransformer_out ) / 5
        return avg_pred

这样就可以提升性能

相关推荐
轻闲一号机2 小时前
【机器学习】机器学习笔记
人工智能·笔记·机器学习
蹦蹦跳跳真可爱5893 小时前
Python----机器学习（基于PyTorch的乳腺癌逻辑回归）
人工智能·pytorch·python·分类·逻辑回归·学习方法
Hali_Botebie4 小时前
【端到端】端到端自动驾驶依赖Occupancy进行运动规划？还是可以具有生成局部地图来规划？
人工智能·机器学习·自动驾驶
workworkwork勤劳又勇敢4 小时前
Adversarial Attack对抗攻击--李宏毅机器学习笔记
人工智能·笔记·深度学习·机器学习
乌旭5 小时前
从Ampere到Hopper：GPU架构演进对AI模型训练的颠覆性影响
人工智能·pytorch·分布式·深度学习·机器学习·ai·gpu算力
zy_destiny8 小时前
【非机动车检测】用YOLOv8实现非机动车及驾驶人佩戴安全帽检测
人工智能·python·算法·yolo·机器学习·安全帽·非机动车
AI糊涂是福8 小时前
数字政府与智慧城市区别报告分析
大数据·人工智能·机器学习·架构·智慧城市
大神薯条老师10 小时前
Python高级爬虫之JS逆向+安卓逆向1.4节:数据运算
爬虫·python·机器学习·数据分析·网络爬虫
潇湘馆记11 小时前
集成学习介绍
机器学习·集成学习
蹦蹦跳跳真可爱58912 小时前
Python----机器学习（基于PyTorch的垃圾邮件逻辑回归）
人工智能·pytorch·python·机器学习·逻辑回归
热门推荐
01我决定放弃搞 Java 了02RAG 实践- Ollama+RagFlow 部署本地知识库03DeepSeek各版本说明与优缺点分析04Scipy||第三章 线性代数 (scipy.linalg)05DeepSeek RAGFlow构建本地知识库系统06如何在WPS和Word/Excel中直接使用DeepSeek功能07C#/.NET/.NET Core技术前沿周刊 | 第 34 期（2025年4.7-4.13）08汽车上的各种质量：整备质量、总质量、装载质量、簧上质量、簧下质量09本地化部署AI知识库：基于Ollama+DeepSeek+AnythingLLM保姆级教程10ZNS SSD垃圾回收优化方案解读-1