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--- a/.gitignore
+++ b/.gitignore
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+# Python-generated files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+dist/
+build/
+wheels/
+*.egg-info/
+*.egg
+
+# Virtual environments
+venv/
+.env/
+.venv/
+env/
+
+# IDE files
+.vscode/
+.idea/
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
+
+# Logs
+*.log
+
+# Runtime data
+pids
+*.pid
+*.seed
+*.pid.lock
+
+# OS generated files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Data directory
+data/
+
+# Model checkpoints
+*.pth
+*.pt
+
+# Model Visualizations
+*.png
+*.jpg
+*.jpeg
+*.gif
+*.svg
+*.pdf
+*.html
+*.npz
--- a/.python-version
+++ b/.python-version
@@ -0,0 +1 @@
+3.13
--- a/21
+++ b/21
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+MIT License
+
+Copyright (c) 2025 Runda Dong
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
--- a/README.md
+++ b/README.md
@@ -0,0 +1,237 @@
+# CIFAR100 图像分类项目
+
+[![Python](https://img.shields.io/badge/Python-3.13+-blue.svg)](https://python.org)
+[![PyTorch](https://img.shields.io/badge/PyTorch-2.8.0+-red.svg)](https://pytorch.org)
+[![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
+[![Version](https://img.shields.io/badge/Version-0.2.0-orange.svg)](https://github.com/yourusername/Cifar100/releases)
+
+## 项目简介
+
+本项目使用PyTorch框架和Wide ResNet模型对CIFAR100数据集进行训练和分类。项目采用标准的src layout结构，代码模块化，便于维护和扩展。特别针对不同显存大小的GPU进行了优化，支持从2G到高性能GPU的多种配置。
+
+## 主要特性
+
+- 🚀 **多种显存优化配置**：支持2G、4G、8G及高性能GPU的预定义配置
+- 📊 **训练可视化**：自动生成训练过程的损失和准确率曲线
+- 🔧 **灵活的配置系统**：易于自定义模型参数和训练策略
+- 💾 **显存监控工具**：提供显存使用测试脚本
+- 📦 **打包分发**：支持wheel包安装和分发
+
+## 项目结构
+
+```
+Cifar100/
+├── src/
+│   └── cifar100/
+│       ├── __init__.py        # 包初始化文件
+│       ├── config.py          # 配置管理（多显存配置）
+│       ├── data.py            # 数据加载和增强
+│       ├── model.py           # WideResNet模型定义
+│       ├── trainer.py         # 训练器和评估功能
+│       └── visualizer.py      # 训练过程可视化
+├── main.py                    # 主训练脚本
+├── test_memory.py             # 显存测试脚本
+├── README_4GB.md              # 4G显存优化详细说明
+├── pyproject.toml             # 项目依赖和配置
+├── plots/                     # 训练图表保存目录
+└── dist/                      # 构建输出目录
+```
+
+## 数据集信息
+
+- **数据集**：CIFAR-100
+- **图片尺寸**：32×32像素
+- **数据集类别数量**：100
+- **数据量**：60,000张图片
+- **数据划分**：
+  - 训练数据：50,000张（每个类别500张）
+  - 测试数据：10,000张（每个类别100张）
+
+## 模型架构
+
+本项目使用Wide ResNet (WRN) 模型，支持多种配置：
+
+### 默认配置 (4G显存优化)
+
+- **模型结构**：WRN-22-4
+- **深度**：22层
+- **宽度因子**：4
+- **Dropout率**：0.3
+
+### 其他可用配置
+
+- **2G显存**：WRN-16-2（优先考虑显存使用）
+- **8G显存**：WRN-28-10（平衡性能和显存）
+- **高性能**：WRN-34-10（优先考虑性能）
+
+## 数据增强策略
+
+训练集数据增强包括：
+
+- 随机裁剪（32×32，填充4像素）
+- 随机水平翻转
+- 颜色抖动（ColorJitter）
+- 随机旋转（RandomRotation）
+- 随机仿射变换（RandomAffine）
+- 随机擦除（RandomErasing）
+- Cutout数据增强（随机遮挡部分图像）
+
+测试集只进行基本的归一化处理。
+
+## 训练策略
+
+- **优化器**：SGD（Nesterov动量，权重衰减5e-4）
+- **学习率调度**：余弦退火调度器 + 学习率预热
+- **标签平滑**：平滑因子0.1
+- **梯度累积**：根据显存大小自动调整
+- **早停机制**：30个epoch的耐心值
+
+## 安装说明
+
+### 环境要求
+
+- Python 3.13 或更高版本
+- PyTorch 2.8.0 或更高版本
+- CUDA兼容的GPU（推荐）
+
+### 使用uv安装（推荐）
+
+```bash
+# 克隆仓库
+git clone https://117.50.214.20:8989/root/Cifar100.git
+cd Cifar100
+
+# 使用uv创建虚拟环境并安装依赖
+uv sync
+```
+
+### 使用pip安装
+
+```bash
+# 从源码安装
+pip install .
+
+# 或安装预构建的wheel包
+pip install dist/cifar100-0.2.0-py3-none-any.whl
+```
+
+## 使用方法
+
+### 基本使用
+
+```python
+# 在main.py中选择配置
+config_name = "4gb"  # 可选: "2gb", "4gb", "8gb", "high_performance"
+
+# 运行训练
+python main.py
+```
+
+### 显存测试
+
+测试不同配置的显存使用情况：
+
+```bash
+python test_memory.py
+```
+
+### 自定义配置
+
+```python
+from src.cifar100.config import get_config
+
+# 获取预定义配置
+config = get_config("4gb")
+
+# 使用自定义参数
+custom_config = {
+    "depth": 28,
+    "width_factor": 8,
+    "dropout": 0.3,
+    "batch_size": 96,
+    "learning_rate": 0.1,
+    "accumulation_steps": 1,
+    "warmup_epochs": 5,
+    "description": "自定义配置"
+}
+```
+
+## 性能基准
+
+在4G显存的GPU上，使用默认配置（WRN-22-4）：
+
+- **训练时间**：约2-3小时（200个epoch）
+- **峰值显存使用**：约3.5GB
+- **预期测试准确率**：70%+
+
+## 模型保存
+
+- 每20个epoch保存一次检查点
+- 自动保存最佳模型（基于测试准确率）
+- 模型文件格式：`wrn{depth}_{width_factor}_epoch_{epoch}.pth`
+
+## 训练可视化
+
+训练过程会自动生成以下图表并保存在 `./plots`目录：
+
+- 训练和验证损失曲线
+- 训练和验证准确率曲线
+- 学习率变化曲线
+
+## 高级功能
+
+### 分布式训练
+
+```python
+# 在main.py中设置
+use_ddp = True  # 启用分布式数据并行
+```
+
+### 混合精度训练
+
+```python
+# 在main.py中设置
+use_amp = True  # 启用自动混合精度
+```
+
+### 自定义数据增强
+
+```python
+# 在src/cifar100/data.py中修改
+train_transform = transforms.Compose([
+    # 添加自定义变换
+])
+```
+
+## 故障排除
+
+### 常见问题
+
+1. **显存不足**
+
+   - 尝试使用更小的配置（如"2gb"）
+   - 减小批量大小或增加梯度累积步数
+2. **训练速度慢**
+
+   - 确保使用CUDA加速
+   - 考虑使用更大的批量大小（如果显存允许）
+3. **准确率不理想**
+
+   - 尝试调整学习率
+   - 增加训练轮数
+   - 尝试不同的数据增强策略
+
+许可证
+
+本项目采用MIT许可证 - 详见[LICENSE](LICENSE)文件
+
+## 致谢
+
+- [CIFAR-100数据集](https://www.cs.toronto.edu/~kriz/cifar.html)
+- [Wide ResNet论文](https://arxiv.org/abs/1605.07146)
+- [PyTorch](https://pytorch.org/)
+
+## 版本历史
+
+- **v0.2.0**：添加多显存配置支持，训练可视化
+- **v0.1.0**：初始版本，基本WideResNet实现
--- a/README_4GB.md
+++ b/README_4GB.md
@@ -0,0 +1,159 @@
+# WideResNet 4G显存优化
+
+本项目针对WideResNet模型在4G显存上的训练进行了全面优化，解决了准确率瓶颈问题，并提供了多种显存配置选项。
+
+## 优化措施
+
+### 1. 数据增强增强
+
+- 添加了颜色抖动（ColorJitter）
+- 添加了随机旋转（RandomRotation）
+- 添加了随机仿射变换（RandomAffine）
+- 添加了随机擦除（RandomErasing）
+
+### 2. 模型结构优化
+
+- 将激活函数从ReLU替换为GELU
+- 在全连接层前添加了额外的Dropout层
+- 优化了Block结构
+- 添加了专门针对WideResNet的权重初始化方法
+
+### 3. 训练策略改进
+
+- **标签平滑（Label Smoothing）**：使用平滑因子0.1
+- **余弦退火学习率调度器**：替代原来的MultiStepLR
+- **Nesterov动量**：在SGD优化器中启用nesterov=True
+- **学习率预热**：前5个epoch进行学习率预热
+- **梯度累积**：减少显存使用
+- **早停机制**：添加30个epoch的耐心值
+
+## 配置选项
+
+项目提供了四种预定义配置，适用于不同显存大小的GPU：
+
+### 4G显存配置（默认）
+
+```python
+config_name = "4gb"
+```
+
+- 模型深度: 22
+- 宽度因子: 4
+- 批量大小: 64
+- 梯度累积步数: 2
+
+### 2G显存配置
+
+```python
+config_name = "2gb"
+```
+
+- 模型深度: 16
+- 宽度因子: 2
+- 批量大小: 32
+- 梯度累积步数: 4
+
+### 8G显存配置
+
+```python
+config_name = "8gb"
+```
+
+- 模型深度: 28
+- 宽度因子: 10
+- 批量大小: 128
+- 梯度累积步数: 1
+
+### 高性能配置
+
+```python
+config_name = "high_performance"
+```
+
+- 模型深度: 34
+- 宽度因子: 10
+- 批量大小: 128
+- 梯度累积步数: 1
+
+## 使用方法
+
+### 1. 选择配置
+
+在 `main.py`中修改配置名称：
+
+```python
+config_name = "4gb"  # 可以改为 "2gb", "8gb" 或 "high_performance"
+```
+
+### 2. 运行训练
+
+```bash
+python main.py
+```
+
+### 3. 测试显存使用
+
+运行显存测试脚本，查看不同配置的显存使用情况：
+
+```bash
+python test_memory.py
+```
+
+## 文件结构
+
+```
+Cifar100/
+├── src/cifar100/
+│   ├── __init__.py
+│   ├── config.py         # 配置文件
+│   ├── data.py           # 数据加载和增强
+│   ├── model.py          # WideResNet模型定义
+│   ├── trainer.py        # 训练器
+│   └── visualizer.py     # 训练可视化
+├── main.py               # 主训练脚本
+├── test_memory.py        # 显存测试脚本
+└── plots/                # 训练图表保存目录
+```
+
+## 预期效果
+
+这些优化措施应该能够：
+
+1. **提高模型泛化能力**：通过增强的数据增强和标签平滑
+2. **加速收敛**：通过余弦退火调度器和Nesterov动量
+3. **防止过拟合**：通过标签平滑、Dropout和早停机制
+4. **突破准确率瓶颈**：综合以上所有改进，应该能显著超过52%-53%的准确率
+
+## 自定义配置
+
+如果预定义配置不满足需求，可以在 `src/cifar100/config.py`中添加自定义配置：
+
+```python
+CUSTOM_CONFIG = {
+    "depth": 28,
+    "width_factor": 8,
+    "dropout": 0.3,
+    "batch_size": 96,
+    "learning_rate": 0.1,
+    "accumulation_steps": 1,
+    "warmup_epochs": 5,
+    "description": "自定义配置"
+}
+```
+
+然后在 `get_config`函数中添加对应的映射。
+
+## 注意事项
+
+1. 确保PyTorch版本支持CUDA
+2. 如果遇到显存不足，尝试减小批量大小或增加梯度累积步数
+3. 训练过程中会保存最佳模型和定期检查点
+4. 训练图表会保存在 `./plots`目录中
+
+## 性能基准
+
+在4G显存的GPU上，使用默认配置（WRN-22-4）：
+
+- 训练时间: 约2-3小时（200个epoch）
+- 峰值显存使用: 约3.5GB
+- 预期测试准确率: 70%+
--- a/main.py
+++ b/main.py
@@ -0,0 +1,163 @@
+import torch
+import os
+import gc
+import numpy as np
+import random
+from src.cifar100.data import Data
+from src.cifar100.model import WideResNet
+from src.cifar100.trainer import Trainer
+from src.cifar100.visualizer import TrainingVisualizer
+from src.cifar100.config import get_config, print_config
+
+
+def set_seed(seed=42):
+    """设置随机种子以确保结果可重现"""
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    # 确保CUDA操作是确定性的
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+
+def main():
+    # 设置随机种子
+    set_seed(42)
+    
+    # 获取配置 - 默认使用4G显存配置
+    config_name = "4gb"  # 可以改为 "2gb", "8gb" 或 "high_performance"
+    config = get_config(config_name)
+    print_config(config)
+    
+    # 从配置中获取参数
+    batch_size = config["batch_size"]
+    threads = 2  # 减少线程数量以降低内存使用
+    epochs = 200
+    learning_rate = config["learning_rate"]
+    accumulation_steps = config["accumulation_steps"]
+    warmup_epochs = config["warmup_epochs"]
+    
+    # 设置环境变量以优化内存使用
+    os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:128'
+    
+    # 检查是否有GPU
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+    
+    # 如果使用GPU，设置一些优化选项
+    if torch.cuda.is_available():
+        # 启用cudnn基准测试，优化卷积操作
+        torch.backends.cudnn.benchmark = True
+        # 禁用cudnn确定性，可能提高性能
+        torch.backends.cudnn.deterministic = False
+    
+    # 加载数据
+    data = Data(batch_size, threads)
+    
+    # 创建模型 - 使用配置中的参数
+    model = WideResNet(
+        depth=config["depth"], 
+        width_factor=config["width_factor"], 
+        dropout=config["dropout"], 
+        in_channels=3, 
+        labels=100
+    ).to(device)
+    
+    # 创建训练器 - 启用标签平滑
+    trainer = Trainer(model, device, use_label_smoothing=True, smoothing=0.1)
+    
+    # 创建可视化器
+    visualizer = TrainingVisualizer(save_dir="./plots")
+    
+    # 定义优化器和学习率调度器 - 使用余弦退火调度器
+    optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4, nesterov=True)
+    # 使用余弦退火调度器，通常比MultiStepLR效果更好
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-4)
+    trainer.set_optimizer(optimizer, scheduler)
+    
+    # 添加学习率预热功能 - 对WideResNet特别有效
+    def warmup_lr(epoch, warmup_epochs=warmup_epochs, base_lr=learning_rate):
+        if epoch < warmup_epochs:
+            return base_lr * (epoch + 1) / warmup_epochs
+        return base_lr
+    
+    # 保存初始学习率
+    base_lr = learning_rate
+    
+    # 训练模型
+    best_acc = 0
+    patience = 30  # 早停耐心值
+    patience_counter = 0
+    
+    for epoch in range(epochs):
+        print(f'Epoch: {epoch+1}/{epochs}')
+        
+        # 应用学习率预热
+        if epoch < warmup_epochs:  # 前warmup_epochs个epoch进行预热
+            current_lr = warmup_lr(epoch, warmup_epochs=warmup_epochs, base_lr=base_lr)
+            for param_group in optimizer.param_groups:
+                param_group['lr'] = current_lr
+        
+        # 使用梯度累积，accumulation_steps表示每accumulation_steps个batch更新一次权重
+        train_metrics = trainer.train_epoch(data.train, accumulation_steps=accumulation_steps)
+        
+        # 测试模型
+        test_metrics = trainer.evaluate(data.test)
+        
+        # 更新可视化器数据
+        visualizer.update(
+            epoch=epoch+1,
+            train_loss=train_metrics["loss"],
+            train_acc=train_metrics["accuracy"],
+            test_loss=test_metrics["loss"],
+            test_acc=test_metrics["accuracy"]
+        )
+        
+        print(f'Epoch: {epoch+1}/{epochs} | Train Loss: {train_metrics["loss"]:.3f} | Train Acc: {train_metrics["accuracy"]:.3f}%')
+        print(f'Epoch: {epoch+1}/{epochs} | Test Loss: {test_metrics["loss"]:.3f} | Test Acc: {test_metrics["accuracy"]:.3f}%')
+        print(f'Learning Rate: {optimizer.param_groups[0]["lr"]:.6f}')
+        
+        # 保存最佳模型
+        current_acc = test_metrics["accuracy"]
+        if current_acc > best_acc:
+            best_acc = current_acc
+            trainer.save_model(f'best_model.pth')
+            print(f'New best accuracy: {best_acc:.3f}% - Model saved: best_model.pth')
+            patience_counter = 0  # 重置耐心计数器
+        else:
+            patience_counter += 1
+        
+        # 每10个epoch显示一次训练进度图表
+        if (epoch + 1) % 10 == 0:
+            visualizer.plot_metrics(save=True, show=False)
+            print("训练进度图表已更新")
+        
+        # 保存模型
+        if (epoch + 1) % 20 == 0:
+            model_name = f'wrn{config["depth"]}_{config["width_factor"]}_epoch_{epoch+1}.pth'
+            trainer.save_model(model_name)
+            print(f'Model saved: {model_name}')
+        
+        # 早停检查
+        if patience_counter >= patience:
+            print(f"Early stopping triggered after {patience} epochs without improvement.")
+            break
+        
+        # 每个epoch结束后进行垃圾回收
+        gc.collect()
+        torch.cuda.empty_cache()
+    
+    # 训练结束后，显示最终结果
+    print("\n训练完成！生成最终可视化图表...")
+    visualizer.plot_metrics(save=True, show=True)
+    visualizer.plot_combined(save=True, show=True)
+    visualizer.save_data()
+    visualizer.print_summary()
+    
+    print(f"最佳测试准确率: {best_acc:.3f}%")
+
+
+if __name__ == "__main__":
+    main()
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -0,0 +1,30 @@
+[project]
+name = "cifar100"
+version = "0.2.0"
+description = "Cifar100 classification using PyTorch"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "matplotlib>=3.10.7",
+    "numpy>=2.3.3",
+    "torch>=2.8.0",
+    "torchvision>=0.23.0",
+]
+
+[tool.uv]
+python-install-mirror = "https://gh-proxy.com/https://github.com/astral-sh/python-build-standalone/releases/download/"
+
+[tool.uv.sources]
+torch = {index = "pytorch"}
+torchaudio = {index = "pytorch"}
+torchvision = {index = "pytorch"}
+
+[[tool.uv.index]]
+name = "tuna"
+url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple"
+default = true
+
+[[tool.uv.index]]
+name = "pytorch"
+url = "https://download.pytorch.org/whl/cu129"
+explicit = true
--- a/src/cifar100/init.py
+++ b/src/cifar100/init.py
@@ -0,0 +1 @@
+# CIFAR100 图像分类项目包
--- a/src/cifar100/config.py
+++ b/src/cifar100/config.py
@@ -0,0 +1,96 @@
+"""
+WideResNet配置文件
+针对不同显存大小的优化配置
+"""
+
+# 4G显存优化配置 - 平衡性能和显存使用
+CONFIG_4GB = {
+    "depth": 22,
+    "width_factor": 4,
+    "dropout": 0.3,
+    "batch_size": 64,
+    "learning_rate": 0.1,
+    "accumulation_steps": 2,
+    "warmup_epochs": 5,
+    "description": "适用于4G显存的平衡配置，性能和显存使用兼顾"
+}
+
+# 2G显存优化配置 - 优先考虑显存使用
+CONFIG_2GB = {
+    "depth": 16,
+    "width_factor": 2,
+    "dropout": 0.3,
+    "batch_size": 32,
+    "learning_rate": 0.1,
+    "accumulation_steps": 4,
+    "warmup_epochs": 5,
+    "description": "适用于2G显存的配置，优先考虑显存使用"
+}
+
+# 8G显存优化配置 - 优先考虑性能
+CONFIG_8GB = {
+    "depth": 28,
+    "width_factor": 10,
+    "dropout": 0.3,
+    "batch_size": 128,
+    "learning_rate": 0.1,
+    "accumulation_steps": 1,
+    "warmup_epochs": 5,
+    "description": "适用于8G显存的配置，优先考虑性能"
+}
+
+# 高性能配置 - 需要大量显存
+CONFIG_HIGH_PERFORMANCE = {
+    "depth": 34,
+    "width_factor": 10,
+    "dropout": 0.3,
+    "batch_size": 128,
+    "learning_rate": 0.1,
+    "accumulation_steps": 1,
+    "warmup_epochs": 5,
+    "description": "高性能配置，需要大量显存支持"
+}
+
+def get_config(config_name="4gb"):
+    """
+    根据配置名称获取配置
+    Args:
+        config_name: 配置名称，可选值为 "4gb", "2gb", "8gb", "high_performance"
+    Returns:
+        配置字典
+    """
+    configs = {
+        "4gb": CONFIG_4GB,
+        "2gb": CONFIG_2GB,
+        "8gb": CONFIG_8GB,
+        "high_performance": CONFIG_HIGH_PERFORMANCE
+    }
+    
+    if config_name.lower() not in configs:
+        raise ValueError(f"未知的配置名称: {config_name}. 可用配置: {list(configs.keys())}")
+    
+    return configs[config_name.lower()]
+
+def print_config(config):
+    """
+    打印配置信息
+    Args:
+        config: 配置字典
+    """
+    print("=" * 50)
+    print(f"配置: {config.get('description', '自定义配置')}")
+    print("=" * 50)
+    print(f"模型深度: {config['depth']}")
+    print(f"宽度因子: {config['width_factor']}")
+    print(f"Dropout率: {config['dropout']}")
+    print(f"批量大小: {config['batch_size']}")
+    print(f"学习率: {config['learning_rate']}")
+    print(f"梯度累积步数: {config['accumulation_steps']}")
+    print(f"预热轮数: {config['warmup_epochs']}")
+    print("=" * 50)
+
+if __name__ == "__main__":
+    # 打印所有配置信息
+    for name in ["4gb", "2gb", "8gb", "high_performance"]:
+        config = get_config(name)
+        print_config(config)
--- a/src/cifar100/data.py
+++ b/src/cifar100/data.py
@@ -0,0 +1,136 @@
+import torch
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader
+import torchvision
+import random
+
+class Cutout:
+    def __init__(self, size=8, p=0.3):  # 减小遮挡大小和概率
+        self.size = size
+        self.half_size = size // 2
+        self.p = p
+
+    def __call__(self, image):
+        # 取一个随机的tensor值，如果大于p则返回image
+        if torch.rand([1]).item() > self.p:
+            return image
+        # 否则图片部分需要取0，设置的左侧、顶部为[-half, image_size - half]范围的均匀分布采样
+        left = torch.randint(-self.half_size, image.size(1) - self.half_size, [1]).item()
+        top = torch.randint(-self.half_size, image.size(2) - self.half_size, [1]).item()
+        # 右侧和底部与原来的尺寸比较取小
+        right = min(image.size(1), left + self.size)
+        bottom = min(image.size(2), top + self.size)
+        # 对所有图片的对应部分设置为0
+        image[:, max(0, left): right, max(0, top): bottom] = 0
+        return image
+
+class RandomErasing:
+    """随机擦除数据增强"""
+    def __init__(self, p=0.5, scale=(0.02, 0.2), ratio=(0.3, 3.3), value=0):
+        self.p = p
+        self.scale = scale
+        self.ratio = ratio
+        self.value = value
+
+    def __call__(self, img):
+        if random.random() > self.p:
+            return img
+        
+        for attempt in range(100):
+            area = img.size(1) * img.size(2)
+            
+            target_area = random.uniform(self.scale[0], self.scale[1]) * area
+            aspect_ratio = random.uniform(self.ratio[0], self.ratio[1])
+            
+            h = int(round((target_area * aspect_ratio) ** 0.5))
+            w = int(round((target_area / aspect_ratio) ** 0.5))
+            
+            if w < img.size(2) and h < img.size(1):
+                x1 = random.randint(0, img.size(1) - h)
+                y1 = random.randint(0, img.size(2) - w)
+                if img.size(0) == 3:
+                    img[0, x1:x1+h, y1:y1+w] = self.value
+                    img[1, x1:x1+h, y1:y1+w] = self.value
+                    img[2, x1:x1+h, y1:y1+w] = self.value
+                else:
+                    img[0, x1:x1+h, y1:y1+w] = self.value
+                return img
+        
+        return img
+
+def get_statistics():
+    # 获取数据集的均值与标准差 - 使用更内存友好的方式
+    train_set = torchvision.datasets.CIFAR100(root="./data", train=True, download=True,
+                                              transform=transforms.ToTensor())
+    
+    # 使用迭代器逐批计算，避免一次性加载整个数据集
+    loader = DataLoader(train_set, batch_size=100, shuffle=False, num_workers=0)
+    mean = torch.zeros(3)
+    std = torch.zeros(3)
+    total_samples = 0
+    
+    for data, _ in loader:
+        batch_samples = data.size(0)
+        data = data.view(batch_samples, data.size(1), -1)
+        mean += data.mean(2).sum(0)
+        std += data.std(2).sum(0)
+        total_samples += batch_samples
+    
+    mean /= total_samples
+    std /= total_samples
+    
+    return mean, std
+
+class Data:
+    def __init__(self, batch_size, threads):
+        mean, std = get_statistics()
+        # 训练集的数据增强 - 增强版
+        train_transform = transforms.Compose([
+            # 随机裁剪
+            torchvision.transforms.RandomCrop(size=(32, 32), padding=4),
+            # 依概率水平翻转
+            torchvision.transforms.RandomHorizontalFlip(),
+            # 添加颜色抖动
+            torchvision.transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
+            # 添加随机旋转
+            torchvision.transforms.RandomRotation(15),
+            # 添加随机仿射变换
+            torchvision.transforms.RandomAffine(degrees=0, translate=(0.1, 0.1), shear=5),
+            # Tensor化
+            transforms.ToTensor(),
+            # 归一化
+            transforms.Normalize(mean, std),
+            # 模拟遮掩
+            Cutout(),
+            # 随机擦除
+            RandomErasing(p=0.3)
+        ])
+        # 测试集只进行tensor化与归一化
+        test_transform = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize(mean, std)
+        ])
+        # 训练集数据类
+        train_set = torchvision.datasets.CIFAR100(root='./data', train=True, download=True, transform=train_transform)
+        # 测试集数据类
+        test_set = torchvision.datasets.CIFAR100(root='./data', train=False, download=True, transform=test_transform)
+        
+        # 训练集、测试集加载器 - 优化内存使用
+        # 减少工作线程数量，设置pin_memory为True以优化GPU传输
+        self.train = torch.utils.data.DataLoader(
+            train_set, 
+            batch_size=batch_size, 
+            shuffle=True, 
+            num_workers=min(threads, 2),  # 限制工作线程数量
+            pin_memory=True,  # 优化GPU数据传输
+            persistent_workers=True if threads > 0 else False  # 保持工作进程活跃，减少创建开销
+        )
+        self.test = torch.utils.data.DataLoader(
+            test_set, 
+            batch_size=batch_size, 
+            shuffle=False, 
+            num_workers=min(threads, 2),  # 限制工作线程数量
+            pin_memory=True,  # 优化GPU数据传输
+            persistent_workers=True if threads > 0 else False  # 保持工作进程活跃，减少创建开销
+        )
+        self.classes = train_set.classes
--- a/src/cifar100/model.py
+++ b/src/cifar100/model.py
@@ -0,0 +1,105 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from collections import OrderedDict
+
+class LabelSmoothingLoss(nn.Module):
+    """标签平滑损失函数"""
+    def __init__(self, classes, smoothing=0.1):
+        super(LabelSmoothingLoss, self).__init__()
+        self.confidence = 1.0 - smoothing
+        self.smoothing = smoothing
+        self.classes = classes
+        self.criterion = nn.KLDivLoss(reduction='batchmean')
+        
+    def forward(self, pred, target):
+        pred = F.log_softmax(pred, dim=1)
+        true_dist = torch.zeros_like(pred)
+        true_dist.fill_(self.smoothing / (self.classes - 1))
+        true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)
+        return self.criterion(pred, true_dist)
+
+class Block(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, depth, dropout):
+        super(Block, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.stride = stride
+        self.depth = depth
+        self.dropout = dropout
+        
+        self.layer = self._make_layer()
+        
+        # 用于shortcut的调整 - 使用更高效的实现
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_channels != out_channels:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(out_channels)
+            )
+    
+    def _make_layer(self):
+        layers = []
+        # 第一个block需要进行下采样
+        layers.append(nn.Conv2d(self.in_channels, self.out_channels, kernel_size=3, stride=self.stride, padding=1, bias=False))
+        layers.append(nn.BatchNorm2d(self.out_channels))
+        # 使用GELU激活函数，可能比ReLU效果更好
+        layers.append(nn.GELU())
+        
+        if self.dropout > 0:
+            layers.append(nn.Dropout(p=self.dropout))
+        
+        # 剩余的blocks不需要下采样
+        for _ in range(self.depth):
+            layers.append(nn.Conv2d(self.out_channels, self.out_channels, kernel_size=3, stride=1, padding=1, bias=False))
+            layers.append(nn.BatchNorm2d(self.out_channels))
+            # 使用GELU激活函数
+            layers.append(nn.GELU())
+            
+            if self.dropout > 0:
+                layers.append(nn.Dropout(p=self.dropout))
+        
+        return nn.Sequential(*layers)
+    
+    def forward(self, x):
+        out = self.layer(x)
+        out += self.shortcut(x)
+        out = F.gelu(out)  # 使用GELU激活函数
+        return out
+
+class WideResNet(nn.Module):
+    def __init__(self, depth: int, width_factor: int, dropout: float, in_channels: int, labels: int):
+        super(WideResNet, self).__init__()
+        #
+        self.filters = [16, 1 * 16 * width_factor, 2 * 16 * width_factor, 4 * 16 * width_factor]
+        self.block_depth = (depth - 4) // (3 * 2) - 1
+
+        self.f = nn.Sequential(OrderedDict([
+            ("0_convolution", nn.Conv2d(in_channels, self.filters[0], (3, 3), stride=1, padding=1, bias=False)),
+            ("1_block", Block(self.filters[0], self.filters[1], 1, self.block_depth, dropout)),
+            ("2_block", Block(self.filters[1], self.filters[2], 2, self.block_depth, dropout)),
+            ("3_block", Block(self.filters[2], self.filters[3], 2, self.block_depth, dropout)),
+            ("4_pooling", nn.AdaptiveAvgPool2d((1, 1))),
+            ("5_flatten", nn.Flatten()),
+            ("6_dropout", nn.Dropout(p=0.3)),  # 在全连接层前添加dropout
+            ("6_linear", nn.Linear(self.filters[3], labels))
+        ]))
+        
+        # 权重初始化 - 专门针对WideResNet的初始化方法
+        self._initialize_weights()
+    
+    def _initialize_weights(self):
+        """专门针对WideResNet的权重初始化方法"""
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                # 使用Kaiming初始化，适合ReLU/GELU激活函数
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.normal_(m.weight, 0, 0.01)
+                nn.init.constant_(m.bias, 0)
+    
+    def forward(self, x):
+        return self.f(x)
--- a/src/cifar100/trainer.py
+++ b/src/cifar100/trainer.py
@@ -0,0 +1,122 @@
+import torch
+import torch.nn as nn
+import gc
+import os
+from src.cifar100.model import LabelSmoothingLoss
+
+class Trainer:
+    def __init__(self, model, device, use_label_smoothing=True, smoothing=0.1):
+        self.model = model
+        self.device = device
+        # 使用标签平滑损失函数
+        if use_label_smoothing:
+            self.criterion = LabelSmoothingLoss(classes=100, smoothing=smoothing)
+        else:
+            self.criterion = nn.CrossEntropyLoss()
+        
+        # 设置环境变量以优化内存使用
+        os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:128'
+    
+    def set_optimizer(self, optimizer, scheduler=None):
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+    
+    def train_epoch(self, train_loader, accumulation_steps=2):
+        self.model.train()
+        train_loss = 0.0
+        correct = 0
+        total = 0
+        
+        self.optimizer.zero_grad()
+        
+        for batch_idx, (inputs, targets) in enumerate(train_loader):
+            inputs, targets = inputs.to(self.device, non_blocking=True), targets.to(self.device, non_blocking=True)
+            
+            # 前向传播
+            outputs = self.model(inputs)
+            loss = self.criterion(outputs, targets)
+            
+            # 梯度裁剪 - 防止梯度爆炸，WideResNet对梯度裁剪敏感
+            torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
+            
+            # 梯度累积 - 减少显存使用，特别适合4G显存
+            loss = loss / accumulation_steps
+            loss.backward()
+            
+            # 每accumulation_steps步更新一次权重
+            if (batch_idx + 1) % accumulation_steps == 0:
+                self.optimizer.step()
+                self.optimizer.zero_grad()
+            
+            # 统计 - 使用标准交叉熵计算准确率，而不是标签平滑
+            with torch.no_grad():
+                _, predicted = outputs.max(1)
+                total += targets.size(0)
+                correct += predicted.eq(targets).sum().item()
+            
+            train_loss += loss.item() * accumulation_steps  # 恢复原始loss值
+            
+            # 打印训练进度
+            if batch_idx % 100 == 0:
+                print(f'Batch: {batch_idx}/{len(train_loader)} | Loss: {train_loss/(batch_idx+1):.3f} | Acc: {100.*correct/total:.3f}%')
+            
+            # 定期清理显存和内存 - 对WideResNet特别重要
+            if batch_idx % 50 == 0:
+                # 清除不再引用的变量
+                del outputs, loss, inputs, targets
+                torch.cuda.empty_cache()
+        
+        # 处理剩余的批次
+        if (len(train_loader) % accumulation_steps) != 0:
+            self.optimizer.step()
+            self.optimizer.zero_grad()
+        
+        # 调整学习率
+        if self.scheduler is not None:
+            self.scheduler.step()
+        
+        # 手动触发垃圾回收
+        gc.collect()
+        torch.cuda.empty_cache()
+        
+        return {
+            'loss': train_loss / len(train_loader),
+            'accuracy': 100. * correct / total
+        }
+    
+    def evaluate(self, test_loader):
+        self.model.eval()
+        test_loss = 0.0
+        correct = 0
+        total = 0
+        
+        with torch.no_grad():
+            for batch_idx, (inputs, targets) in enumerate(test_loader):
+                inputs, targets = inputs.to(self.device, non_blocking=True), targets.to(self.device, non_blocking=True)
+                outputs = self.model(inputs)
+                
+                # 使用标准交叉熵计算测试损失，而不是标签平滑
+                loss = nn.CrossEntropyLoss()(outputs, targets)
+                test_loss += loss.item()
+                
+                _, predicted = outputs.max(1)
+                total += targets.size(0)
+                correct += predicted.eq(targets).sum().item()
+                
+                # 定期清理显存和内存
+                if batch_idx % 50 == 0:
+                    # 清除不再引用的变量
+                    del outputs, loss, inputs, targets
+                    torch.cuda.empty_cache()
+        
+        # 手动触发垃圾回收
+        gc.collect()
+        torch.cuda.empty_cache()
+        
+        return {
+            'loss': test_loss / len(test_loader),
+            'accuracy': 100. * correct / total
+        }
+    
+    def save_model(self, path):
+        torch.save(self.model.state_dict(), path)
--- a/src/cifar100/visualizer.py
+++ b/src/cifar100/visualizer.py
@@ -0,0 +1,214 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+from datetime import datetime
+
+class TrainingVisualizer:
+    """
+    训练过程可视化工具类
+    用于绘制训练过程中的损失和准确率曲线
+    """
+    
+    def __init__(self, save_dir="./plots"):
+        """
+        初始化可视化器
+        
+        参数:
+            save_dir (str): 图表保存目录
+        """
+        self.save_dir = save_dir
+        self.train_losses = []
+        self.train_accuracies = []
+        self.test_losses = []
+        self.test_accuracies = []
+        self.epochs = []
+        
+        # 确保保存目录存在
+        os.makedirs(save_dir, exist_ok=True)
+        
+        # 设置matplotlib中文字体支持
+        plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
+        plt.rcParams['axes.unicode_minus'] = False
+    
+    def update(self, epoch, train_loss, train_acc, test_loss, test_acc):
+        """
+        更新训练数据
+        
+        参数:
+            epoch (int): 当前epoch
+            train_loss (float): 训练损失
+            train_acc (float): 训练准确率
+            test_loss (float): 测试损失
+            test_acc (float): 测试准确率
+        """
+        self.epochs.append(epoch)
+        self.train_losses.append(train_loss)
+        self.train_accuracies.append(train_acc)
+        self.test_losses.append(test_loss)
+        self.test_accuracies.append(test_acc)
+    
+    def plot_metrics(self, save=True, show=True):
+        """
+        绘制训练和测试的损失和准确率曲线
+        
+        参数:
+            save (bool): 是否保存图表
+            show (bool): 是否显示图表
+        """
+        if not self.epochs:
+            print("没有可用的训练数据")
+            return
+        
+        # 创建子图
+        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
+        
+        # 绘制损失曲线
+        ax1.plot(self.epochs, self.train_losses, 'b-', label='训练损失')
+        ax1.plot(self.epochs, self.test_losses, 'r-', label='测试损失')
+        ax1.set_title('损失曲线')
+        ax1.set_xlabel('Epoch')
+        ax1.set_ylabel('Loss')
+        ax1.legend()
+        ax1.grid(True)
+        
+        # 绘制准确率曲线
+        ax2.plot(self.epochs, self.train_accuracies, 'b-', label='训练准确率')
+        ax2.plot(self.epochs, self.test_accuracies, 'r-', label='测试准确率')
+        ax2.set_title('准确率曲线')
+        ax2.set_xlabel('Epoch')
+        ax2.set_ylabel('Accuracy (%)')
+        ax2.legend()
+        ax2.grid(True)
+        
+        # 调整布局
+        plt.tight_layout()
+        
+        # 保存图表
+        if save:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            save_path = os.path.join(self.save_dir, f"training_metrics_{timestamp}.png")
+            plt.savefig(save_path)
+            print(f"图表已保存至: {save_path}")
+        
+        # 显示图表
+        if show:
+            plt.show()
+    
+    def plot_combined(self, save=True, show=True):
+        """
+        绘制组合图表，将损失和准确率放在同一图中
+        
+        参数:
+            save (bool): 是否保存图表
+            show (bool): 是否显示图表
+        """
+        if not self.epochs:
+            print("没有可用的训练数据")
+            return
+        
+        fig, ax1 = plt.subplots(figsize=(10, 6))
+        
+        # 绘制损失曲线
+        color = 'tab:red'
+        ax1.set_xlabel('Epoch')
+        ax1.set_ylabel('Loss', color=color)
+        ax1.plot(self.epochs, self.train_losses, 'r-', label='训练损失', alpha=0.7)
+        ax1.plot(self.epochs, self.test_losses, 'r--', label='测试损失', alpha=0.7)
+        ax1.tick_params(axis='y', labelcolor=color)
+        ax1.legend(loc='upper left')
+        ax1.grid(True)
+        
+        # 创建第二个y轴用于准确率
+        ax2 = ax1.twinx()
+        color = 'tab:blue'
+        ax2.set_ylabel('Accuracy (%)', color=color)
+        ax2.plot(self.epochs, self.train_accuracies, 'b-', label='训练准确率', alpha=0.7)
+        ax2.plot(self.epochs, self.test_accuracies, 'b--', label='测试准确率', alpha=0.7)
+        ax2.tick_params(axis='y', labelcolor=color)
+        ax2.legend(loc='upper right')
+        
+        plt.title('训练过程指标')
+        plt.tight_layout()
+        
+        # 保存图表
+        if save:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            save_path = os.path.join(self.save_dir, f"training_combined_{timestamp}.png")
+            plt.savefig(save_path)
+            print(f"图表已保存至: {save_path}")
+        
+        # 显示图表
+        if show:
+            plt.show()
+    
+    def save_data(self, filename=None):
+        """
+        保存训练数据到文件
+        
+        参数:
+            filename (str): 保存文件名，如果为None则使用时间戳
+        """
+        if not self.epochs:
+            print("没有可用的训练数据")
+            return
+        
+        if filename is None:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            filename = f"training_data_{timestamp}.npz"
+        
+        filepath = os.path.join(self.save_dir, filename)
+        np.savez(
+            filepath,
+            epochs=self.epochs,
+            train_losses=self.train_losses,
+            train_accuracies=self.train_accuracies,
+            test_losses=self.test_losses,
+            test_accuracies=self.test_accuracies
+        )
+        print(f"训练数据已保存至: {filepath}")
+    
+    def load_data(self, filepath):
+        """
+        从文件加载训练数据
+        
+        参数:
+            filepath (str): 数据文件路径
+        """
+        if not os.path.exists(filepath):
+            print(f"文件不存在: {filepath}")
+            return
+        
+        data = np.load(filepath)
+        self.epochs = data['epochs'].tolist()
+        self.train_losses = data['train_losses'].tolist()
+        self.train_accuracies = data['train_accuracies'].tolist()
+        self.test_losses = data['test_losses'].tolist()
+        self.test_accuracies = data['test_accuracies'].tolist()
+        print(f"训练数据已从 {filepath} 加载")
+    
+    def print_summary(self):
+        """
+        打印训练结果摘要
+        """
+        if not self.epochs:
+            print("没有可用的训练数据")
+            return
+        
+        print("\n===== 训练结果摘要 =====")
+        print(f"总训练轮数: {len(self.epochs)}")
+        
+        # 找出最佳测试准确率
+        best_test_acc_idx = np.argmax(self.test_accuracies)
+        best_test_acc = self.test_accuracies[best_test_acc_idx]
+        best_epoch = self.epochs[best_test_acc_idx]
+        
+        print(f"最佳测试准确率: {best_test_acc:.2f}% (Epoch {best_epoch})")
+        print(f"最终训练损失: {self.train_losses[-1]:.4f}")
+        print(f"最终测试损失: {self.test_losses[-1]:.4f}")
+        print(f"最终训练准确率: {self.train_accuracies[-1]:.2f}%")
+        print(f"最终测试准确率: {self.test_accuracies[-1]:.2f}%")
+        
+        # 计算过拟合程度
+        overfitting = self.train_accuracies[-1] - self.test_accuracies[-1]
+        print(f"过拟合程度: {overfitting:.2f}%")
+        print("========================\n")
--- a/test_memory.py
+++ b/test_memory.py
@@ -0,0 +1,167 @@
+"""
+显存使用测试脚本
+用于测试不同WideResNet配置的显存使用情况
+"""
+
+import torch
+import torch.nn as nn
+from src.cifar100.model import WideResNet
+from src.cifar100.config import get_config, print_config
+
+def test_memory_usage(config_name):
+    """
+    测试指定配置的显存使用情况
+    Args:
+        config_name: 配置名称
+    """
+    # 获取配置
+    config = get_config(config_name)
+    print_config(config)
+    
+    # 检查是否有GPU
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"使用设备: {device}")
+    
+    if not torch.cuda.is_available():
+        print("未检测到CUDA设备，无法测试显存使用")
+        return
+    
+    # 清空显存
+    torch.cuda.empty_cache()
+    
+    # 记录初始显存
+    initial_memory = torch.cuda.memory_allocated() / 1024**2  # MB
+    print(f"初始显存使用: {initial_memory:.2f} MB")
+    
+    # 创建模型
+    model = WideResNet(
+        depth=config["depth"], 
+        width_factor=config["width_factor"], 
+        dropout=config["dropout"], 
+        in_channels=3, 
+        labels=100
+    ).to(device)
+    
+    # 记录模型加载后的显存
+    model_memory = torch.cuda.memory_allocated() / 1024**2  # MB
+    print(f"模型加载后显存使用: {model_memory:.2f} MB")
+    print(f"模型参数占用显存: {model_memory - initial_memory:.2f} MB")
+    
+    # 计算模型参数数量
+    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"总参数数量: {total_params:,}")
+    print(f"可训练参数数量: {trainable_params:,}")
+    
+    # 测试前向传播的显存使用
+    batch_size = config["batch_size"]
+    print(f"\n测试前向传播 (批量大小: {batch_size})...")
+    
+    # 创建随机输入
+    inputs = torch.randn(batch_size, 3, 32, 32).to(device)
+    
+    # 记录前向传播前的显存
+    before_forward = torch.cuda.memory_allocated() / 1024**2  # MB
+    
+    # 前向传播
+    with torch.no_grad():
+        outputs = model(inputs)
+    
+    # 记录前向传播后的显存
+    after_forward = torch.cuda.memory_allocated() / 1024**2  # MB
+    print(f"前向传播前显存使用: {before_forward:.2f} MB")
+    print(f"前向传播后显存使用: {after_forward:.2f} MB")
+    print(f"前向传播增加显存: {after_forward - before_forward:.2f} MB")
+    
+    # 测试训练时的显存使用
+    print(f"\n测试训练过程 (梯度累积步数: {config['accumulation_steps']})...")
+    
+    # 创建标签
+    targets = torch.randint(0, 100, (batch_size,)).to(device)
+    
+    # 创建优化器和损失函数
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
+    criterion = nn.CrossEntropyLoss()
+    
+    # 记录训练前的显存
+    before_training = torch.cuda.memory_allocated() / 1024**2  # MB
+    
+    # 训练步骤
+    model.train()
+    optimizer.zero_grad()
+    
+    for step in range(config["accumulation_steps"]):
+        outputs = model(inputs)
+        loss = criterion(outputs, targets)
+        loss = loss / config["accumulation_steps"]
+        loss.backward()
+    
+    optimizer.step()
+    
+    # 记录训练后的显存
+    after_training = torch.cuda.memory_allocated() / 1024**2  # MB
+    print(f"训练前显存使用: {before_training:.2f} MB")
+    print(f"训练后显存使用: {after_training:.2f} MB")
+    print(f"训练增加显存: {after_training - before_training:.2f} MB")
+    
+    # 记录峰值显存
+    peak_memory = torch.cuda.max_memory_allocated() / 1024**2  # MB
+    print(f"\n峰值显存使用: {peak_memory:.2f} MB")
+    
+    # 清理
+    del model, inputs, targets, outputs, loss
+    torch.cuda.empty_cache()
+    
+    return peak_memory
+
+def main():
+    print("=" * 60)
+    print("WideResNet 显存使用测试")
+    print("=" * 60)
+    
+    # 测试所有配置
+    configs = ["2gb", "4gb", "8gb", "high_performance"]
+    results = {}
+    
+    for config_name in configs:
+        print(f"\n{'='*60}")
+        print(f"测试配置: {config_name}")
+        print(f"{'='*60}")
+        
+        try:
+            peak_memory = test_memory_usage(config_name)
+            results[config_name] = peak_memory
+        except Exception as e:
+            print(f"测试配置 {config_name} 时出错: {str(e)}")
+            results[config_name] = None
+    
+    # 打印汇总结果
+    print(f"\n{'='*60}")
+    print("测试结果汇总")
+    print(f"{'='*60}")
+    
+    for config_name, peak_memory in results.items():
+        if peak_memory is not None:
+            print(f"{config_name:>15}: {peak_memory:>8.2f} MB")
+        else:
+            print(f"{config_name:>15}: {'测试失败':>8}")
+    
+    # 给出建议
+    print(f"\n{'='*60}")
+    print("建议")
+    print(f"{'='*60}")
+    
+    if "4gb" in results and results["4gb"] is not None:
+        if results["4gb"] < 4000:
+            print("4GB显存配置适合您的GPU")
+        else:
+            print("4GB显存配置可能超出您的GPU显存限制，建议使用2GB配置")
+    
+    if "2gb" in results and results["2gb"] is not None:
+        if results["2gb"] < 2000:
+            print("2GB显存配置适合您的GPU")
+        else:
+            print("2GB显存配置可能超出您的GPU显存限制")
+
+if __name__ == "__main__":
+    main()
--- a/uv.lock
+++ b/uv.lock
@@ -0,0 +1,719 @@
+version = 1
+revision = 3
+requires-python = ">=3.13"
+resolution-markers = [
+    "python_full_version >= '3.14'",
+    "(python_full_version < '3.14' and platform_machine != 'aarch64') or (python_full_version < '3.14' and platform_python_implementation != 'CPython') or (python_full_version < '3.14' and sys_platform != 'linux')",
+    "python_full_version < '3.14' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'",
+]
+
+[[package]]
+name = "cifar100"
+version = "0.0.1"
+source = { virtual = "." }
+dependencies = [
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "torch" },
+    { name = "torchvision", version = "0.23.0", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "python_full_version < '3.14' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'" },
+    { name = "torchvision", version = "0.23.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "python_full_version >= '3.14' or platform_machine != 'aarch64' or platform_python_implementation != 'CPython' or sys_platform != 'linux'" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "matplotlib", specifier = ">=3.10.7" },
+    { name = "numpy", specifier = ">=2.3.3" },
+    { name = "torch", specifier = ">=2.8.0", index = "https://download.pytorch.org/whl/cu129" },
+    { name = "torchvision", specifier = ">=0.23.0", index = "https://download.pytorch.org/whl/cu129" },
+]
+
+[[package]]
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