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
昆曲之源_娄江河畔5 分钟前
婴儿版训练GPT
python·gpt·机器学习·大模型训练
zhengyquan24 分钟前
特斯拉无方向盘Cybercab落地，自动驾驶商业化再提速！
人工智能·机器学习·自动驾驶
lkforce1 小时前
MiniMind学习笔记（零）--基础概念
人工智能·算法·机器学习·token·分词器·minimind·词汇表
BFT白芙堂1 小时前
基于 AR 阻抗可视化的 Franka Research3 机械臂遥操作设计与应用
人工智能·深度学习·机器学习·机器人·ar·franka
360智汇云3 小时前
AI标注平台TLP：AI预标+人工精修，重塑数据标注效率
人工智能·深度学习·机器学习
落羽的落羽3 小时前
【算法札记】练习 | Week2
android·linux·服务器·c++·python·算法·机器学习
做cv的小昊3 小时前
【TJU】研究生应用统计学课程笔记（3）——第一章 数理统计的基本知识（1.4 正态总体的样本均值和样本方差的分布、1.5 充分统计量和完备统计量）
笔记·学习·线性代数·机器学习·数学建模·概率论
码农的神经元3 小时前
从零搭建一个带 GUI 的机器学习建模系统：多模型切换、遗传算法优化与可视化实战复盘
人工智能·机器学习
一楼的猫3 小时前
茄子小说AI辅助智能写作助手：10倍速创作神器
人工智能·学习·机器学习·学习方法·ai写作·迁移学习·集成学习
gjhave3 小时前
强化学习论文（Double-DQN）
人工智能·机器学习
热门推荐
012026年4月技术前沿：AI大模型爆发、智能体革命与量子安全新纪元02GitHub 镜像站点032026年4月AI大事件深度解读：大模型竞争进入“深水区“042026 年 AI 编程助手全面对比评测：Cursor vs Copilot vs Claude Code vs GitHub Copilot Free05近期有什么ai的新消息，新动态？ 2026.4月06AI Weekly | 2026年4月第二周 · GitHub热门项目与AI发展趋势深度解析07Claude Code Windows 兼容性问题：指定版本 2.1.112 可解决08从限购到畅通：GLM-5.1 Coding Plan接入攻略09UBUNTU Claude Code 报错 claude native binary not installed10免费！不限量！用opencode接入英伟达（NVIDIA）大模型，轻松打造你的 AI 编程助手