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SMART/LogitsGap/UnderfittingExperiments/train_underfitted_complete.py

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+#!/usr/bin/env python3
+"""
+完整的欠拟合模型训练脚本
+支持CIFAR-10, CIFAR-100, ImageNet
+包含单次训练、批量训练和快速测试功能
+训练轮数：5轮和10轮用于生成欠拟合模型
+"""
+
+import os
+import sys
+import time
+import random
+import argparse
+import subprocess
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from datetime import datetime
+
+# Add the current directory to Python path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(current_dir)
+
+# Import utilities and models
+from utils import models_dict, dataset_loader, dataset_num_classes
+
+def set_seed(seed):
+    """设置随机种子以确保可重现性"""
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+def get_optimizer(model, optimizer_name, lr, momentum=0.9, weight_decay=1e-4):
+    """获取优化器"""
+    if optimizer_name.lower() == 'sgd':
+        return optim.SGD(model.parameters(), lr=lr, momentum=momentum, weight_decay=weight_decay)
+    elif optimizer_name.lower() == 'adam':
+        return optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)
+    else:
+        raise ValueError(f"Unsupported optimizer: {optimizer_name}")
+
+def get_loss_function(loss_name):
+    """获取损失函数"""
+    if loss_name.lower() == 'cross_entropy':
+        return nn.CrossEntropyLoss()
+    elif loss_name.lower() == 'focal_loss':
+        # 简单的focal loss实现
+        class FocalLoss(nn.Module):
+            def __init__(self, alpha=1, gamma=2):
+                super(FocalLoss, self).__init__()
+                self.alpha = alpha
+                self.gamma = gamma
+                self.ce = nn.CrossEntropyLoss()
+                
+            def forward(self, inputs, targets):
+                ce_loss = self.ce(inputs, targets)
+                pt = torch.exp(-ce_loss)
+                focal_loss = self.alpha * (1-pt)**self.gamma * ce_loss
+                return focal_loss
+                
+        return FocalLoss()
+    elif loss_name.lower() in ['brier_score', 'brier']:
+        # Brier Score Loss
+        class BrierLoss(nn.Module):
+            def __init__(self):
+                super(BrierLoss, self).__init__()
+                
+            def forward(self, inputs, targets):
+                probs = torch.softmax(inputs, dim=1)
+                targets_one_hot = torch.zeros_like(probs)
+                targets_one_hot.scatter_(1, targets.unsqueeze(1), 1)
+                return torch.mean(torch.sum((probs - targets_one_hot)**2, dim=1))
+                
+        return BrierLoss()
+    else:
+        raise ValueError(f"Unsupported loss function: {loss_name}")
+
+def train_epoch(model, train_loader, criterion, optimizer, device, max_batches=None):
+    """训练一个epoch"""
+    model.train()
+    running_loss = 0.0
+    correct = 0
+    total = 0
+    
+    pbar = tqdm(train_loader, desc='Training')
+    batch_count = 0
+    
+    for batch_idx, (inputs, targets) in enumerate(pbar):
+        if max_batches and batch_count >= max_batches:
+            break
+            
+        inputs, targets = inputs.to(device), targets.to(device)
+        
+        optimizer.zero_grad()
+        outputs = model(inputs)
+        loss = criterion(outputs, targets)
+        loss.backward()
+        optimizer.step()
+        
+        running_loss += loss.item()
+        _, predicted = outputs.max(1)
+        total += targets.size(0)
+        correct += predicted.eq(targets).sum().item()
+        
+        batch_count += 1
+        
+        # 更新进度条
+        pbar.set_postfix({
+            'Loss': f'{running_loss/batch_count:.3f}',
+            'Acc': f'{100.*correct/total:.2f}%'
+        })
+    
+    epoch_loss = running_loss / batch_count if batch_count > 0 else 0
+    epoch_acc = 100. * correct / total if total > 0 else 0
+    return epoch_loss, epoch_acc
+
+def validate(model, val_loader, criterion, device, max_batches=None):
+    """验证模型"""
+    model.eval()
+    val_loss = 0.0
+    correct = 0
+    total = 0
+    batch_count = 0
+    
+    with torch.no_grad():
+        for inputs, targets in tqdm(val_loader, desc='Validation'):
+            if max_batches and batch_count >= max_batches:
+                break
+                
+            inputs, targets = inputs.to(device), targets.to(device)
+            outputs = model(inputs)
+            loss = criterion(outputs, targets)
+            
+            val_loss += loss.item()
+            _, predicted = outputs.max(1)
+            total += targets.size(0)
+            correct += predicted.eq(targets).sum().item()
+            batch_count += 1
+    
+    val_loss = val_loss / batch_count if batch_count > 0 else 0
+    val_acc = 100. * correct / total if total > 0 else 0
+    return val_loss, val_acc
+
+def save_model(model, save_path, epoch, train_acc, val_acc, train_loss, val_loss, args):
+    """保存模型"""
+    os.makedirs(os.path.dirname(save_path), exist_ok=True)
+    
+    # 如果是DataParallel包装的模型，保存原始模型
+    if isinstance(model, nn.DataParallel):
+        model_state_dict = model.module.state_dict()
+    else:
+        model_state_dict = model.state_dict()
+    
+    torch.save({
+        'model_state_dict': model_state_dict,
+        'epoch': epoch,
+        'train_acc': train_acc,
+        'val_acc': val_acc,
+        'train_loss': train_loss,
+        'val_loss': val_loss,
+        'args': vars(args) if hasattr(args, '__dict__') else args,
+    }, save_path)
+
+def get_data_loaders(dataset, batch_size, num_workers, data_root, use_cuda):
+    """获取数据加载器"""
+    if dataset in ['cifar10', 'cifar100']:
+        train_loader, val_loader = dataset_loader[dataset].get_train_valid_loader(
+            batch_size=batch_size,
+            augment=True,
+            random_seed=1,
+            valid_size=0.1,
+            shuffle=True,
+            num_workers=num_workers,
+            pin_memory=use_cuda,
+            root=data_root
+        )
+        
+        test_loader = dataset_loader[dataset].get_test_loader(
+            batch_size=batch_size,
+            shuffle=False,
+            num_workers=num_workers,
+            pin_memory=use_cuda,
+            root=data_root
+        )
+        
+        return train_loader, val_loader, test_loader
+        
+    elif dataset == 'imagenet':
+        # 对于ImageNet，我们从validation集中分出一部分来做训练（用于训练欠拟合模型）
+        # 使用原始的ImageNet val数据，通过valid_size参数进行分割
+        # valid_size=0.6意味着前60%作为val，后40%作为test
+        # 我们将"val"作为训练集，"test"作为验证集和测试集
+        
+        train_loader = dataset_loader[dataset].get_data_loader(
+            root=data_root,
+            split='val',  # 取前60%作为训练集
+            batch_size=batch_size,
+            shuffle=True,
+            valid_size=0.6,
+            num_workers=num_workers,
+            pin_memory=use_cuda,
+            random_seed=1
+        )
+        
+        val_test_loader = dataset_loader[dataset].get_data_loader(
+            root=data_root,
+            split='test',  # 取后40%作为验证+测试集
+            batch_size=batch_size,
+            shuffle=False,
+            valid_size=0.6,  # 保持一致的分割比例
+            num_workers=num_workers,
+            pin_memory=use_cuda,
+            random_seed=1
+        )
+        
+        # 验证集和测试集使用相同的数据（简化处理）
+        return train_loader, val_test_loader, val_test_loader
+    else:
+        raise ValueError(f"Unsupported dataset: {dataset}")
+
+def create_model(dataset, arch, device):
+    """创建模型"""
+    model_fn = models_dict[dataset][arch]
+    if dataset.startswith('cifar') or dataset == 'tiny_imagenet':
+        # CIFAR和Tiny ImageNet需要指定类别数
+        num_classes = dataset_num_classes[dataset]
+        model = model_fn(num_classes=num_classes)
+    else:
+        # ImageNet使用预训练模型
+        model = model_fn(pretrained=False)  # 不使用预训练权重，从头开始训练
+    
+    model = model.to(device)
+    
+    # 如果有多个GPU，使用DataParallel
+    if torch.cuda.device_count() > 1 and device.type == 'cuda':
+        print(f"Using {torch.cuda.device_count()} GPUs")
+        model = nn.DataParallel(model)
+    
+    return model
+
+def train_model(args, quick_test=False):
+    """训练模型主函数"""
+    # 设置随机种子
+    set_seed(args.seed)
+    
+    # 设置设备
+    device = torch.device('cuda' if torch.cuda.is_available() and args.use_cuda else 'cpu')
+    print(f"Using device: {device}")
+    
+    # 创建模型
+    model = create_model(args.dataset, args.arch, device)
+    
+    # 显示模型信息
+    if quick_test:
+        total_params = sum(p.numel() for p in model.parameters())
+        trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+        print(f"模型参数: 总数 {total_params:,}, 可训练 {trainable_params:,}")
+    
+    # 获取数据加载器
+    train_loader, val_loader, test_loader = get_data_loaders(
+        args.dataset, args.batch_size, args.num_workers, args.data_root, args.use_cuda
+    )
+    
+    # 获取优化器和损失函数
+    optimizer = get_optimizer(model, args.optimizer, args.lr, args.momentum, args.weight_decay)
+    criterion = get_loss_function(args.loss_fn)
+    criterion = criterion.to(device)
+    
+    # 学习率调度器
+    if args.lr_scheduler == 'step':
+        scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_step_size, gamma=args.lr_gamma)
+    elif args.lr_scheduler == 'cosine':
+        scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
+    else:
+        scheduler = None
+    
+    # 训练日志
+    train_history = {'loss': [], 'acc': []}
+    val_history = {'loss': [], 'acc': []}
+    
+    if not quick_test:
+        print(f"Starting training for {args.epochs} epochs...")
+        print(f"Dataset: {args.dataset}, Architecture: {args.arch}")
+        print(f"Loss function: {args.loss_fn}, Optimizer: {args.optimizer}")
+    
+    start_time = time.time()
+    
+    # 确定最大批次数（用于快速测试）
+    max_batches = getattr(args, 'max_batches', None) if quick_test else None
+    
+    for epoch in range(args.epochs):
+        if not quick_test:
+            print(f'\nEpoch {epoch+1}/{args.epochs}:')
+            print('-' * 50)
+        else:
+            print(f'\n快速测试 Epoch {epoch+1}/{args.epochs}:')
+        
+        # 训练
+        train_loss, train_acc = train_epoch(model, train_loader, criterion, optimizer, device, max_batches)
+        
+        # 验证
+        val_max_batches = max_batches // 2 if max_batches else None
+        val_loss, val_acc = validate(model, val_loader, criterion, device, val_max_batches)
+        
+        # 更新学习率
+        if scheduler:
+            scheduler.step()
+            current_lr = scheduler.get_last_lr()[0]
+        else:
+            current_lr = args.lr
+        
+        # 记录历史
+        train_history['loss'].append(train_loss)
+        train_history['acc'].append(train_acc)
+        val_history['loss'].append(val_loss)
+        val_history['acc'].append(val_acc)
+        
+        print(f'Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.2f}%')
+        print(f'Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.2f}%')
+        if not quick_test:
+            print(f'Learning Rate: {current_lr:.6f}')
+    
+    training_time = time.time() - start_time
+    print(f'\n训练完成，用时 {training_time/60:.2f} 分钟')
+    
+    # 保存模型（非快速测试模式）
+    model_path = None
+    if not quick_test:
+        save_path = f"/home/haolan/pretrained_weights/{args.dataset}_{args.arch}_{args.loss_fn}_epochs{args.epochs}_seed{args.seed}.model"
+        save_model(model, save_path, args.epochs, train_acc, val_acc, train_loss, val_loss, args)
+        print(f"Model saved to: {save_path}")
+        model_path = save_path
+    
+    # 最终测试
+    if not quick_test:
+        print("\nFinal test evaluation:")
+        test_loss, test_acc = validate(model, test_loader, criterion, device)
+        print(f'Test Loss: {test_loss:.4f}, Test Acc: {test_acc:.2f}%')
+    else:
+        test_acc = val_acc
+        test_loss = val_loss
+    
+    return {
+        'train_history': train_history,
+        'val_history': val_history,
+        'final_test_acc': test_acc,
+        'final_test_loss': test_loss,
+        'training_time': training_time,
+        'model_path': model_path
+    }
+
+def quick_test_training(dataset='cifar10', arch='resnet50', epochs=1, max_batches=10):
+    """快速测试训练过程"""
+    print(f"快速测试: {dataset} {arch} {epochs}轮 (最多{max_batches}个批次)")
+    
+    # 创建args对象
+    class Args:
+        def __init__(self):
+            self.dataset = dataset
+            self.arch = arch
+            self.epochs = epochs
+            self.max_batches = max_batches
+            self.batch_size = 64 if dataset != 'imagenet' else 32
+            self.lr = 0.01
+            self.momentum = 0.9
+            self.weight_decay = 1e-4
+            self.optimizer = 'sgd'
+            self.loss_fn = 'cross_entropy'
+            self.lr_scheduler = None
+            self.lr_step_size = 30
+            self.lr_gamma = 0.1
+            self.seed = 1
+            self.num_workers = 2
+            self.use_cuda = torch.cuda.is_available()
+            self.data_root = '/share/datasets'
+    
+    args = Args()
+    
+    # 验证参数
+    if args.dataset not in models_dict:
+        print(f"✗ 不支持的数据集: {args.dataset}")
+        return False
+    
+    if args.arch not in models_dict[args.dataset]:
+        print(f"✗ 数据集 {args.dataset} 不支持模型 {args.arch}")
+        return False
+    
+    try:
+        results = train_model(args, quick_test=True)
+        print(f"\n✓ 快速测试完成!")
+        print(f"  最终精度: {results['final_test_acc']:.2f}%") 
+        return True
+    except Exception as e:
+        print(f"✗ 快速测试失败: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+def batch_training():
+    """批量训练欠拟合模型"""
+    print("批量训练欠拟合模型")
+    print(f"开始时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    
+    # 训练配置
+    datasets = ['cifar10', 'cifar100', 'imagenet']
+    epochs_list = [5, 10]
+    loss_functions = ['cross_entropy']
+    seeds = [1]
+    
+    # 为每个数据集选择合适的模型
+    dataset_models = {
+        'cifar10': ['resnet50', 'densenet121'],
+        'cifar100': ['resnet50', 'densenet121'], 
+        'imagenet': ['resnet50']  # ImageNet训练时间较长，先只用ResNet50
+    }
+    
+    total_experiments = 0
+    successful_experiments = 0
+    failed_experiments = []
+    
+    # 计算总实验数量
+    for dataset in datasets:
+        for arch in dataset_models[dataset]:
+            for epochs in epochs_list:
+                for loss_fn in loss_functions:
+                    for seed in seeds:
+                        total_experiments += 1
+    
+    print(f"总共需要进行 {total_experiments} 个训练实验")
+    
+    experiment_count = 0
+    
+    # 创建args类
+    class Args:
+        def __init__(self, dataset, arch, epochs, loss_fn, seed):
+            self.dataset = dataset
+            self.arch = arch
+            self.epochs = epochs
+            self.loss_fn = loss_fn
+            self.seed = seed
+            self.batch_size = 128 if dataset == 'imagenet' else 256
+            self.lr = 0.1
+            self.momentum = 0.9
+            self.weight_decay = 1e-4
+            self.optimizer = 'sgd'
+            self.lr_scheduler = 'step'
+            self.lr_step_size = 30
+            self.lr_gamma = 0.1
+            self.num_workers = 16 if dataset == 'imagenet' else 8
+            self.use_cuda = True
+            self.data_root = '/share/datasets'
+    
+    # 开始批量训练
+    for dataset in datasets:
+        for arch in dataset_models[dataset]:
+            for epochs in epochs_list:
+                for loss_fn in loss_functions:
+                    for seed in seeds:
+                        experiment_count += 1
+                        print(f"\n进度: {experiment_count}/{total_experiments}")
+                        
+                        # 检查是否已存在训练好的模型
+                        model_path = f"/home/haolan/pretrained_weights/{dataset}_{arch}_{loss_fn}_epochs{epochs}_seed{seed}.model"
+                        if os.path.exists(model_path):
+                            print(f"模型已存在，跳过: {model_path}")
+                            successful_experiments += 1
+                            continue
+                        
+                        # 创建参数对象
+                        args = Args(dataset, arch, epochs, loss_fn, seed)
+                        
+                        print(f"训练: {dataset} {arch} {epochs}轮 {loss_fn} seed{seed}")
+                        
+                        try:
+                            # 执行训练
+                            results = train_model(args)
+                            print(f"✓ 训练成功! 最终精度: {results['final_test_acc']:.2f}%")
+                            successful_experiments += 1
+                            
+                        except Exception as e:
+                            print(f"✗ 训练失败: {e}")
+                            failed_experiments.append({
+                                'dataset': dataset,
+                                'arch': arch,
+                                'epochs': epochs,
+                                'loss_fn': loss_fn,
+                                'seed': seed,
+                                'error': str(e)
+                            })
+                        
+                        # 简短休息以避免系统过载
+                        time.sleep(5)
+    
+    # 训练完成报告
+    print(f"\n{'='*60}")
+    print("批量训练完成!")
+    print(f"结束时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+    print(f"成功训练: {successful_experiments}/{total_experiments}")
+    print(f"失败训练: {len(failed_experiments)}")
+    
+    if failed_experiments:
+        print("\n失败的训练:")
+        for exp in failed_experiments:
+            print(f"  - {exp['dataset']} {exp['arch']} {exp['epochs']}epochs {exp['loss_fn']} seed{exp['seed']}: {exp['error']}")
+    
+    # 检查生成的模型文件
+    print("\n检查生成的模型文件:")
+    weights_dir = "/home/haolan/pretrained_weights/"
+    if os.path.exists(weights_dir):
+        model_files = [f for f in os.listdir(weights_dir) if f.endswith('.model')]
+        underfitted_models = [f for f in model_files if ('epochs5' in f or 'epochs10' in f)]
+        
+        print(f"总模型文件数: {len(model_files)}")
+        print(f"欠拟合模型文件数: {len(underfitted_models)}")
+        
+        print("\n欠拟合模型列表:")
+        for model in sorted(underfitted_models):
+            model_path = os.path.join(weights_dir, model)
+            if os.path.exists(model_path):
+                file_size = os.path.getsize(model_path) / (1024*1024)  # MB
+                print(f"  {model} ({file_size:.1f}MB)")
+    else:
+        print(f"权重目录不存在: {weights_dir}")
+
+def main():
+    parser = argparse.ArgumentParser(description='欠拟合模型训练工具')
+    
+    # 运行模式
+    parser.add_argument('--mode', type=str, default='single', 
+                       choices=['single', 'batch', 'test'],
+                       help='运行模式: single(单次训练), batch(批量训练), test(快速测试)')
+    
+    # 数据集和模型参数
+    parser.add_argument('--dataset', type=str, default='cifar10', 
+                       choices=['cifar10', 'cifar100', 'imagenet'],
+                       help='数据集名称')
+    parser.add_argument('--arch', type=str, default='resnet50',
+                       help='模型架构')
+    parser.add_argument('--data_root', type=str, default='/share/datasets',
+                       help='数据集根目录')
+    
+    # 训练参数
+    parser.add_argument('--epochs', type=int, default=5,
+                       help='训练轮数 (5 或 10 用于欠拟合)')
+    parser.add_argument('--batch_size', type=int, default=256,
+                       help='批大小')
+    parser.add_argument('--lr', type=float, default=0.1,
+                       help='学习率')
+    parser.add_argument('--momentum', type=float, default=0.9,
+                       help='SGD动量')
+    parser.add_argument('--weight_decay', type=float, default=1e-4,
+                       help='权重衰减')
+    parser.add_argument('--optimizer', type=str, default='sgd',
+                       choices=['sgd', 'adam'],
+                       help='优化器')
+    parser.add_argument('--loss_fn', type=str, default='cross_entropy',
+                       choices=['cross_entropy', 'focal_loss', 'brier_score'],
+                       help='损失函数')
+    
+    # 学习率调度
+    parser.add_argument('--lr_scheduler', type=str, default='step',
+                       choices=['step', 'cosine', 'none'],
+                       help='学习率调度器')
+    parser.add_argument('--lr_step_size', type=int, default=30,
+                       help='StepLR的步长')
+    parser.add_argument('--lr_gamma', type=float, default=0.1,
+                       help='StepLR的衰减因子')
+    
+    # 快速测试参数
+    parser.add_argument('--max_batches', type=int, default=10,
+                       help='快速测试时每轮最大批次数')
+    
+    # 其他参数
+    parser.add_argument('--seed', type=int, default=1,
+                       help='随机种子')
+    parser.add_argument('--num_workers', type=int, default=4,
+                       help='数据加载的工作进程数')
+    parser.add_argument('--use_cuda', action='store_true', default=True,
+                       help='使用CUDA')
+    
+    args = parser.parse_args()
+    
+    if args.lr_scheduler == 'none':
+        args.lr_scheduler = None
+    
+    print("="*60)
+    print("欠拟合模型训练工具")
+    print("="*60)
+    
+    if args.mode == 'test':
+        print("运行模式: 快速测试")
+        success = quick_test_training(args.dataset, args.arch, args.epochs, args.max_batches)
+        if success:
+            print("\n🎉 所有测试通过! 训练脚本工作正常")
+        else:
+            print("\n❌ 测试失败，请检查配置")
+            
+    elif args.mode == 'batch':
+        print("运行模式: 批量训练")
+        batch_training()
+        
+    elif args.mode == 'single':
+        print("运行模式: 单次训练")
+        
+        # 验证参数
+        if args.dataset not in models_dict:
+            raise ValueError(f"Dataset {args.dataset} not supported")
+        
+        if args.arch not in models_dict[args.dataset]:
+            available_models = list(models_dict[args.dataset].keys())
+            raise ValueError(f"Model {args.arch} not available for {args.dataset}. Available: {available_models}")
+        
+        print("训练配置:")
+        for arg, value in vars(args).items():
+            if arg != 'mode':
+                print(f"  {arg}: {value}")
+        
+        # 开始训练
+        results = train_model(args)
+        
+        print("\n训练完成!")
+        print(f"最终测试精度: {results['final_test_acc']:.2f}%")
+        if results['model_path']:
+            print(f"模型已保存: {results['model_path']}")
+
+if __name__ == '__main__':
+    main()

SMART/LogitsGap/cache/cifar100_densenet121_brier_score_seed2/logitsgap_values.json

@@ -0,0 +1 @@
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SMART/LogitsGap/cache/cifar100_resnet50_mmce_seed3/logitsgap_values.json

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