docs: 添加 CIFAR-100 WideResNet 项目开发指南文档

创建详细的 AGENTS.md 文档，为项目贡献者提供完整的开发指导。文档涵盖了项目架构、模块组织、编码规范、测试方法以及内存配置管理。

主要内容包括：
- 项目目录结构及核心模块功能说明
- 推荐的安装方式和运行命令
- Python 编码风格和命名规范
- 手动测试验证清单
- 提交信息和 PR 指南
- 不同 GPU 内存限制的配置预设
- 内存优化技术说明

这份文档将帮助新贡献者快速理解项目结构，遵循统一的开发规范，有效管理 GPU 资源。

AGENTS.md

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+# Repository Guidelines
+
+This document provides coding conventions and workflow guidance for contributing to the CIFAR-100 WideResNet classification project.
+
+## Project Structure & Module Organization
+
+The project follows a standard `src` layout for clean packaging and distribution:
+
+```
+Cifar100/
+├── src/cifar100/          # Core package modules
+│   ├── config.py          # Model configuration presets (2GB/4GB/8GB)
+│   ├── data.py            # Dataset loading and augmentation
+│   ├── model.py           # WideResNet architecture implementation
+│   ├── trainer.py         # Training and evaluation loops
+│   └── visualizer.py      # Training metrics visualization
+├── main.py                # Main training script entry point
+├── test_memory.py         # GPU memory usage testing utility
+├── plots/                 # Generated training visualization outputs
+└── dist/                  # Build artifacts (wheel packages)
+```
+
+**Key modules:**
+- `config.py`: Memory-optimized configurations for different GPU sizes
+- `model.py`: WideResNet blocks with GELU activation, dropout, and label smoothing
+- `trainer.py`: Gradient accumulation, label smoothing, early stopping logic
+- `visualizer.py`: Matplotlib-based training curve plotting
+
+## Build, Test, and Development Commands
+
+**Installation:**
+```bash
+# Recommended: Install with uv package manager
+uv sync
+
+# Alternative: Install with pip
+pip install .
+```
+
+**Run training:**
+```bash
+python main.py
+```
+
+**Memory testing:**
+```bash
+python test_memory.py
+```
+
+**Build distribution:**
+```bash
+python -m build
+```
+
+## Coding Style & Naming Conventions
+
+- **Indentation**: 4 spaces (Python PEP 8 standard)
+- **Imports**: Group standard library, third-party, and local imports separately
+- **Docstrings**: Use triple quotes for module and function documentation
+- **Naming**:
+  - Classes: `PascalCase` (e.g., `WideResNet`, `TrainingVisualizer`)
+  - Functions/methods: `snake_case` (e.g., `train_epoch`, `get_config`)
+  - Constants: `UPPER_SNAKE_CASE` (e.g., `CONFIG_4GB`)
+  - Private methods: prefix with underscore (e.g., `_initialize_weights`)
+- **File naming**: Use `snake_case` for Python modules
+
+**Code patterns:**
+- Use `torch.device` for GPU/CPU device management
+- Apply gradient accumulation via `accumulation_steps` parameter
+- Include memory optimization: `torch.cuda.empty_cache()`, `gc.collect()`
+- Use `non_blocking=True` for async GPU transfers
+
+## Testing Guidelines
+
+This project does not currently have a formal test suite. For validation:
+
+- Run `test_memory.py` to verify GPU memory usage across configurations
+- Monitor training metrics (loss/accuracy) to validate model changes
+- Check visualizations in `plots/` directory after training runs
+
+**Manual validation checklist:**
+- Model loads without errors
+- Training completes at least 10 epochs
+- Memory usage stays within GPU limits
+- Plots are generated correctly
+
+## Commit & Pull Request Guidelines
+
+**Commit message format** (based on project history):
+```
+<type>: <brief description>
+
+Types:
+- docs: Documentation updates (README, comments)
+- build: Build system or dependencies
+- chore: Maintenance tasks (version bumps, cleanup)
+- feat: New features
+- fix: Bug fixes
+```
+
+**Examples:**
+- `docs: Update README.md with installation instructions`
+- `build: Add build artifacts to dist/`
+- `chore: Fix version badge repository link`
+
+**Before committing:**
+1. Verify code runs without errors
+2. Test memory usage if modifying model architecture
+3. Update relevant documentation (README, docstrings)
+4. Clean up any debug print statements
+
+## Configuration & Memory Management
+
+**Memory presets** in `config.py`:
+- `2gb`: WRN-16-2, batch_size=32, accumulation_steps=4
+- `4gb`: WRN-22-4, batch_size=64, accumulation_steps=2 (default)
+- `8gb`: WRN-28-10, batch_size=128, accumulation_steps=1
+
+**To modify configuration:**
+```python
+config = get_config("4gb")  # Choose preset
+# Or override specific parameters
+config["learning_rate"] = 0.05
+```
+
+**Memory optimization techniques used:**
+- Gradient accumulation to simulate larger batch sizes
+- Periodic `torch.cuda.empty_cache()` calls
+- `cudnn.benchmark = True` for performance
+- Gradient clipping at `max_norm=1.0`