docs/ARCHITECTURE.md

# 系统架构文档

本文档详细描述了情绪与生理状态变化预测模型的系统架构、设计原则和实现细节。

## 目录

1. [系统概述](#系统概述)
2. [整体架构](#整体架构)
3. [模型架构](#模型架构)
4. [数据处理流程](#数据处理流程)
5. [训练流程](#训练流程)
6. [推理流程](#推理流程)
7. [模块设计](#模块设计)
8. [设计模式](#设计模式)
9. [性能优化](#性能优化)
10. [扩展性设计](#扩展性设计)

## 系统概述

### 设计目标

本系统旨在实现一个高效、可扩展、易维护的情绪与生理状态变化预测模型，主要设计目标包括：

1. **高性能**: 支持GPU加速，优化推理速度
2. **模块化**: 清晰的模块划分，便于维护和扩展
3. **可配置**: 灵活的配置系统，支持超参数调优
4. **易用性**: 完整的CLI工具和Python API
5. **可扩展**: 支持新的模型架构和损失函数
6. **可观测**: 完整的日志和监控系统

### 技术栈

- **深度学习框架**: PyTorch 1.12+
- **数据处理**: NumPy, Pandas, scikit-learn
- **配置管理**: PyYAML, OmegaConf
- **可视化**: Matplotlib, Seaborn, Plotly
- **命令行**: argparse, Click
- **日志系统**: Loguru
- **实验跟踪**: MLflow, Weights & Biases
- **性能分析**: py-spy, memory-profiler

## 整体架构

### 系统架构图

```
┌─────────────────────────────────────────────────────────────────┐
│                        用户接口层                                │
├─────────────────────────────────────────────────────────────────┤
│  CLI工具  │  Python API  │  Web API  │  Jupyter Notebook       │
├─────────────────────────────────────────────────────────────────┤
│                        业务逻辑层                                │
├─────────────────────────────────────────────────────────────────┤
│  训练管理器  │  推理引擎  │  评估器  │  配置管理器  │  日志管理器  │
├─────────────────────────────────────────────────────────────────┤
│                        核心模型层                                │
├─────────────────────────────────────────────────────────────────┤
│  PAD预测器  │  损失函数  │  评估指标  │  模型工厂  │  优化器      │
├─────────────────────────────────────────────────────────────────┤
│                        数据处理层                                │
├─────────────────────────────────────────────────────────────────┤
│  数据加载器  │  预处理器  │  数据增强器  │  合成数据生成器        │
├─────────────────────────────────────────────────────────────────┤
│                        基础设施层                                │
├─────────────────────────────────────────────────────────────────┤
│  文件系统  │  GPU计算  │  内存管理  │  异常处理  │  工具函数      │
└─────────────────────────────────────────────────────────────────┘
```

### 模块依赖关系

```
CLI模块 → 业务逻辑层 → 核心模型层 → 数据处理层 → 基础设施层
   ↓
配置管理器 → 所有模块
   ↓
日志管理器 → 所有模块
```

## 模型架构

### 网络结构

PAD预测器采用多层感知机(MLP)架构：

```
输入层 (7维)
    ↓
隐藏层1 (128神经元) + ReLU + Dropout(0.3)
    ↓
隐藏层2 (64神经元) + ReLU + Dropout(0.3)
    ↓
隐藏层3 (32神经元) + ReLU
    ↓
输出层 (5神经元) + Linear激活
```

### 网络组件详解

#### 输入层
- **维度**: 7维特征向量
- **特征组成**:
  - User PAD: 3维 (Pleasure, Arousal, Dominance)
  - Vitality: 1维 (生理活力值)
  - Current PAD: 3维 (当前情绪状态)

#### 隐藏层设计原则
1. **逐层压缩**: 从128 → 64 → 32，逐层减少神经元数量
2. **激活函数**: 使用ReLU激活函数，避免梯度消失
3. **正则化**: 在前两层使用Dropout防止过拟合
4. **权重初始化**: 使用Xavier均匀初始化，适合ReLU激活

#### 输出层设计
- **维度**: 3维输出向量
- **输出组成**:
  - ΔPAD: 3维 (情绪变化量：ΔPleasure, ΔArousal, ΔDominance)
  - ΔPressure: 通过 PAD 变化动态计算（公式：1.0×(-ΔP) + 0.8×(ΔA) + 0.6×(-ΔD)）
- **激活函数**: 线性激活，适用于回归任务

### 模型配置系统

```python
# 默认架构配置
DEFAULT_ARCHITECTURE = {
    'input_dim': 7,
    'output_dim': 3,
    'hidden_dims': [512, 256, 128],
    'dropout_rate': 0.3,
    'activation': 'relu',
    'weight_init': 'xavier_uniform',
    'bias_init': 'zeros'
}

# 可配置参数
CONFIGURABLE_PARAMS = {
    'hidden_dims': {
        'type': list,
        'default': [128, 64, 32],
        'constraints': [
            lambda x: len(x) >= 1,
            lambda x: all(isinstance(n, int) and n > 0 for n in x),
            lambda x: x == sorted(x, reverse=True)  # 递减序列
        ]
    },
    'dropout_rate': {
        'type': float,
        'default': 0.3,
        'range': [0.0, 0.9]
    },
    'activation': {
        'type': str,
        'default': 'relu',
        'choices': ['relu', 'tanh', 'sigmoid', 'leaky_relu']
    }
}
```

## 数据处理流程

### 数据流水线

```
原始数据 → 数据验证 → 特征提取 → 数据预处理 → 数据增强 → 批次生成
    ↓
模型训练/推理
```

### 数据预处理流程

#### 1. 数据验证
```python
class DataValidator:
    """数据验证器，确保数据质量"""
    
    def validate_input_shape(self, data: np.ndarray) -> bool:
        """验证输入数据形状"""
        return data.shape[1] == 7
    
    def validate_value_ranges(self, data: np.ndarray) -> Dict[str, bool]:
        """验证数值范围"""
        return {
            'pad_features_valid': np.all(data[:, :6] >= -1) and np.all(data[:, :6] <= 1),
            'vitality_valid': np.all(data[:, 3] >= 0) and np.all(data[:, 3] <= 100)
        }
    
    def check_missing_values(self, data: np.ndarray) -> Dict[str, Any]:
        """检查缺失值"""
        return {
            'has_missing': np.isnan(data).any(),
            'missing_count': np.isnan(data).sum(),
            'missing_ratio': np.isnan(data).mean()
        }
```

#### 2. 特征工程
```python
class FeatureEngineer:
    """特征工程器"""
    
    def extract_pad_features(self, data: np.ndarray) -> np.ndarray:
        """提取PAD特征"""
        user_pad = data[:, :3]
        current_pad = data[:, 4:7]
        return np.hstack([user_pad, current_pad])
    
    def compute_pad_differences(self, data: np.ndarray) -> np.ndarray:
        """计算PAD差异"""
        user_pad = data[:, :3]
        current_pad = data[:, 4:7]
        return user_pad - current_pad
    
    def create_interaction_features(self, data: np.ndarray) -> np.ndarray:
        """创建交互特征"""
        user_pad = data[:, :3]
        current_pad = data[:, 4:7]
        
        # PAD内积
        pad_interaction = np.sum(user_pad * current_pad, axis=1, keepdims=True)
        
        # PAD欧氏距离
        pad_distance = np.linalg.norm(user_pad - current_pad, axis=1, keepdims=True)
        
        return np.hstack([data, pad_interaction, pad_distance])
```

#### 3. 数据标准化
```python
class DataNormalizer:
    """数据标准化器"""
    
    def __init__(self, method: str = 'standard'):
        self.method = method
        self.scalers = {}
    
    def fit_pad_features(self, features: np.ndarray):
        """拟合PAD特征标准化器"""
        if self.method == 'standard':
            self.scalers['pad'] = StandardScaler()
        elif self.method == 'minmax':
            self.scalers['pad'] = MinMaxScaler(feature_range=(-1, 1))
        
        self.scalers['pad'].fit(features)
    
    def fit_vitality_feature(self, features: np.ndarray):
        """拟合活力值标准化器"""
        if self.method == 'standard':
            self.scalers['vitality'] = StandardScaler()
        elif self.method == 'minmax':
            self.scalers['vitality'] = MinMaxScaler(feature_range=(0, 1))
        
        self.scalers['vitality'].fit(features.reshape(-1, 1))
```

### 数据增强策略

```python
class DataAugmenter:
    """数据增强器"""
    
    def __init__(self, noise_std: float = 0.01, mixup_alpha: float = 0.2):
        self.noise_std = noise_std
        self.mixup_alpha = mixup_alpha
    
    def add_gaussian_noise(self, features: np.ndarray) -> np.ndarray:
        """添加高斯噪声"""
        noise = np.random.normal(0, self.noise_std, features.shape)
        return features + noise
    
    def mixup_augmentation(self, features: np.ndarray, labels: np.ndarray) -> tuple:
        """Mixup数据增强"""
        batch_size = features.shape[0]
        lam = np.random.beta(self.mixup_alpha, self.mixup_alpha)
        
        # 随机打乱索引
        index = np.random.permutation(batch_size)
        
        # 混合特征和标签
        mixed_features = lam * features + (1 - lam) * features[index]
        mixed_labels = lam * labels + (1 - lam) * labels[index]
        
        return mixed_features, mixed_labels
```

## 训练流程

### 训练架构

```
配置加载 → 数据准备 → 模型初始化 → 训练循环 → 模型保存 → 结果评估
```

### 训练管理器设计

```python
class ModelTrainer:
    """模型训练管理器"""
    
    def __init__(self, model, preprocessor=None, device='auto'):
        self.model = model
        self.preprocessor = preprocessor
        self.device = self._setup_device(device)
        self.logger = logging.getLogger(__name__)
        
        # 训练状态
        self.training_state = {
            'epoch': 0,
            'best_loss': float('inf'),
            'patience_counter': 0,
            'training_history': []
        }
    
    def setup_training(self, config: Dict[str, Any]):
        """设置训练环境"""
        # 优化器设置
        self.optimizer = self._create_optimizer(config['optimizer'])
        
        # 学习率调度器
        self.scheduler = self._create_scheduler(config['scheduler'])
        
        # 损失函数
        self.criterion = self._create_criterion(config['loss'])
        
        # 早停机制
        self.early_stopping = self._setup_early_stopping(config['early_stopping'])
        
        # 检查点管理
        self.checkpoint_manager = CheckpointManager(config['checkpointing'])
    
    def train_epoch(self, train_loader: DataLoader) -> Dict[str, float]:
        """训练一个epoch"""
        self.model.train()
        epoch_loss = 0.0
        num_batches = len(train_loader)
        
        for batch_idx, (features, labels) in enumerate(train_loader):
            features = features.to(self.device)
            labels = labels.to(self.device)
            
            # 前向传播
            self.optimizer.zero_grad()
            outputs = self.model(features)
            loss = self.criterion(outputs, labels)
            
            # 反向传播
            loss.backward()
            
            # 梯度裁剪
            torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
            
            # 参数更新
            self.optimizer.step()
            
            epoch_loss += loss.item()
            
            # 日志记录
            if batch_idx % 100 == 0:
                self.logger.debug(f'Batch {batch_idx}/{num_batches}, Loss: {loss.item():.6f}')
        
        return {'train_loss': epoch_loss / num_batches}
    
    def validate_epoch(self, val_loader: DataLoader) -> Dict[str, float]:
        """验证一个epoch"""
        self.model.eval()
        val_loss = 0.0
        num_batches = len(val_loader)
        
        with torch.no_grad():
            for features, labels in val_loader:
                features = features.to(self.device)
                labels = labels.to(self.device)
                
                outputs = self.model(features)
                loss = self.criterion(outputs, labels)
                
                val_loss += loss.item()
        
        return {'val_loss': val_loss / num_batches}
```

### 训练策略

#### 1. 学习率调度
```python
class LearningRateScheduler:
    """学习率调度策略"""
    
    @staticmethod
    def cosine_annealing_scheduler(optimizer, T_max, eta_min=1e-6):
        """余弦退火调度器"""
        return torch.optim.lr_scheduler.CosineAnnealingLR(
            optimizer, T_max=T_max, eta_min=eta_min
        )
    
    @staticmethod
    def reduce_on_plateau_scheduler(optimizer, patience=5, factor=0.5):
        """平台衰减调度器"""
        return torch.optim.lr_scheduler.ReduceLROnPlateau(
            optimizer, mode='min', patience=patience, factor=factor
        )
    
    @staticmethod
    def warmup_cosine_scheduler(optimizer, warmup_epochs, total_epochs):
        """预热余弦调度器"""
        def lr_lambda(epoch):
            if epoch < warmup_epochs:
                return epoch / warmup_epochs
            else:
                progress = (epoch - warmup_epochs) / (total_epochs - warmup_epochs)
                return 0.5 * (1 + math.cos(math.pi * progress))
        
        return torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
```

#### 2. 早停机制
```python
class EarlyStopping:
    """早停机制"""
    
    def __init__(self, patience=10, min_delta=1e-4, mode='min'):
        self.patience = patience
        self.min_delta = min_delta
        self.mode = mode
        self.counter = 0
        self.best_score = None
        
        if mode == 'min':
            self.is_better = lambda x, y: x < y - min_delta
        else:
            self.is_better = lambda x, y: x > y + min_delta
    
    def __call__(self, score):
        if self.best_score is None:
            self.best_score = score
            return False
        
        if self.is_better(score, self.best_score):
            self.best_score = score
            self.counter = 0
            return False
        else:
            self.counter += 1
            return self.counter >= self.patience
```

## 推理流程

### 推理架构

```
模型加载 → 输入验证 → 数据预处理 → 模型推理 → 结果后处理 → 输出格式化
```

### 推理引擎设计

```python
class InferenceEngine:
    """高性能推理引擎"""
    
    def __init__(self, model, preprocessor=None, device='auto'):
        self.model = model
        self.preprocessor = preprocessor
        self.device = self._setup_device(device)
        self.model.to(self.device)
        self.model.eval()
        
        # 性能优化
        self._optimize_model()
        
        # 预热
        self._warmup_model()
    
    def _optimize_model(self):
        """模型性能优化"""
        # TorchScript优化
        try:
            self.model = torch.jit.script(self.model)
            self.logger.info("模型已优化为TorchScript格式")
        except Exception as e:
            self.logger.warning(f"TorchScript优化失败: {e}")
        
        # 混合精度
        if self.device.type == 'cuda':
            self.scaler = torch.cuda.amp.GradScaler()
    
    def _warmup_model(self, num_warmup=5):
        """模型预热"""
        dummy_input = torch.randn(1, 7).to(self.device)
        
        with torch.no_grad():
            for _ in range(num_warmup):
                _ = self.model(dummy_input)
        
        self.logger.info(f"模型预热完成，预热次数: {num_warmup}")
    
    def predict_single(self, input_data: Union[List, np.ndarray]) -> Dict[str, Any]:
        """单样本推理"""
        # 输入验证
        validated_input = self._validate_input(input_data)
        
        # 数据预处理
        processed_input = self._preprocess_input(validated_input)
        
        # 模型推理
        with torch.no_grad():
            if self.device.type == 'cuda':
                with torch.cuda.amp.autocast():
                    output = self.model(processed_input)
            else:
                output = self.model(processed_input)
        
        # 结果后处理
        result = self._postprocess_output(output)
        
        return result
    
    def predict_batch(self, input_batch: Union[List, np.ndarray]) -> List[Dict[str, Any]]:
        """批量推理"""
        # 输入验证和预处理
        validated_batch = self._validate_batch(input_batch)
        processed_batch = self._preprocess_batch(validated_batch)
        
        # 分批推理
        batch_size = min(32, len(processed_batch))
        results = []
        
        for i in range(0, len(processed_batch), batch_size):
            batch_input = processed_batch[i:i+batch_size]
            
            with torch.no_grad():
                if self.device.type == 'cuda':
                    with torch.cuda.amp.autocast():
                        batch_output = self.model(batch_input)
                else:
                    batch_output = self.model(batch_input)
            
            # 后处理
            batch_results = self._postprocess_batch(batch_output)
            results.extend(batch_results)
        
        return results
```

### 性能优化策略

#### 1. 内存优化
```python
class MemoryOptimizer:
    """内存优化器"""
    
    @staticmethod
    def optimize_memory_usage():
        """优化内存使用"""
        # 清理GPU缓存
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        
        # 设置内存分配策略
        if torch.cuda.is_available():
            torch.cuda.set_per_process_memory_fraction(0.9)
    
    @staticmethod
    def monitor_memory_usage():
        """监控内存使用"""
        if torch.cuda.is_available():
            allocated = torch.cuda.memory_allocated() / 1024**3  # GB
            cached = torch.cuda.memory_reserved() / 1024**3  # GB
            return {'allocated': allocated, 'cached': cached}
        return {'allocated': 0, 'cached': 0}
```

#### 2. 计算优化
```python
class ComputeOptimizer:
    """计算优化器"""
    
    @staticmethod
    def enable_tf32():
        """启用TF32加速（Ampere架构GPU）"""
        if torch.cuda.is_available():
            torch.backends.cuda.matmul.allow_tf32 = True
            torch.backends.cudnn.allow_tf32 = True
    
    @staticmethod
    def optimize_dataloader(dataloader, num_workers=4, pin_memory=True):
        """优化数据加载器"""
        return DataLoader(
            dataloader.dataset,
            batch_size=dataloader.batch_size,
            shuffle=dataloader.shuffle,
            num_workers=num_workers,
            pin_memory=pin_memory and torch.cuda.is_available(),
            persistent_workers=True if num_workers > 0 else False
        )
```

## 模块设计

### 核心模块

#### 1. 模型模块 (`src.models/`)
```python
# 模型模块结构
src/models/
├── __init__.py
├── pad_predictor.py      # 核心预测器
├── loss_functions.py     # 损失函数
├── metrics.py           # 评估指标
├── model_factory.py     # 模型工厂
└── base_model.py        # 基础模型类
```

**设计原则**:
- 单一职责：每个类只负责一个特定功能
- 开闭原则：对扩展开放，对修改封闭
- 依赖倒置：依赖抽象而非具体实现

#### 2. 数据模块 (`src.data/`)
```python
# 数据模块结构
src/data/
├── __init__.py
├── dataset.py           # 数据集类
├── data_loader.py       # 数据加载器
├── preprocessor.py      # 数据预处理器
├── synthetic_generator.py # 合成数据生成器
└── data_validator.py    # 数据验证器
```

**设计模式**:
- 策略模式：不同的数据预处理策略
- 工厂模式：数据生成器工厂
- 观察者模式：数据质量监控

#### 3. 工具模块 (`src.utils/`)
```python
# 工具模块结构
src/utils/
├── __init__.py
├── inference_engine.py  # 推理引擎
├── trainer.py          # 训练器
├── logger.py           # 日志工具
├── config.py           # 配置管理
└── exceptions.py       # 自定义异常
```

**功能特性**:
- 高性能推理引擎
- 灵活的训练管理
- 结构化日志系统
- 统一的配置管理

## 设计模式

### 1. 工厂模式 (Factory Pattern)

```python
class ModelFactory:
    """模型工厂类"""
    
    _models = {
        'pad_predictor': PADPredictor,
        'advanced_predictor': AdvancedPADPredictor,
        'ensemble_predictor': EnsemblePredictor
    }
    
    @classmethod
    def create_model(cls, model_type: str, config: Dict[str, Any]):
        """创建模型实例"""
        if model_type not in cls._models:
            raise ValueError(f"不支持的模型类型: {model_type}")
        
        model_class = cls._models[model_type]
        return model_class(**config)
    
    @classmethod
    def register_model(cls, name: str, model_class):
        """注册新的模型类型"""
        cls._models[name] = model_class
```

### 2. 策略模式 (Strategy Pattern)

```python
class LossStrategy(ABC):
    """损失策略抽象基类"""
    
    @abstractmethod
    def compute_loss(self, predictions, targets):
        pass

class WeightedMSELoss(LossStrategy):
    """加权均方误差损失"""
    
    def compute_loss(self, predictions, targets):
        # 实现加权MSE
        pass

class HuberLoss(LossStrategy):
    """Huber损失"""
    
    def compute_loss(self, predictions, targets):
        # 实现Huber损失
        pass

class LossContext:
    """损失上下文"""
    
    def __init__(self, strategy: LossStrategy):
        self._strategy = strategy
    
    def set_strategy(self, strategy: LossStrategy):
        self._strategy = strategy
    
    def compute_loss(self, predictions, targets):
        return self._strategy.compute_loss(predictions, targets)
```

### 3. 观察者模式 (Observer Pattern)

```python
class TrainingObserver(ABC):
    """训练观察者抽象基类"""
    
    @abstractmethod
    def on_epoch_start(self, epoch, metrics):
        pass
    
    @abstractmethod
    def on_epoch_end(self, epoch, metrics):
        pass

class LoggingObserver(TrainingObserver):
    """日志观察者"""
    
    def on_epoch_end(self, epoch, metrics):
        self.logger.info(f"Epoch {epoch}: {metrics}")

class CheckpointObserver(TrainingObserver):
    """检查点观察者"""
    
    def on_epoch_end(self, epoch, metrics):
        if self.should_save_checkpoint(metrics):
            self.save_checkpoint(epoch, metrics)

class TrainingSubject:
    """训练主题"""
    
    def __init__(self):
        self._observers = []
    
    def attach(self, observer: TrainingObserver):
        self._observers.append(observer)
    
    def detach(self, observer: TrainingObserver):
        self._observers.remove(observer)
    
    def notify_epoch_end(self, epoch, metrics):
        for observer in self._observers:
            observer.on_epoch_end(epoch, metrics)
```

### 4. 建造者模式 (Builder Pattern)

```python
class ModelBuilder:
    """模型建造者"""
    
    def __init__(self):
        self.input_dim = 7
        self.output_dim = 3
        self.hidden_dims = [128, 64, 32]
        self.dropout_rate = 0.3
        self.activation = 'relu'
    
    def with_dimensions(self, input_dim, output_dim):
        self.input_dim = input_dim
        self.output_dim = output_dim
        return self
    
    def with_hidden_layers(self, hidden_dims):
        self.hidden_dims = hidden_dims
        return self
    
    def with_dropout(self, dropout_rate):
        self.dropout_rate = dropout_rate
        return self
    
    def with_activation(self, activation):
        self.activation = activation
        return self
    
    def build(self):
        return PADPredictor(
            input_dim=self.input_dim,
            output_dim=self.output_dim,
            hidden_dims=self.hidden_dims,
            dropout_rate=self.dropout_rate
        )

# 使用示例
model = (ModelBuilder()
         .with_dimensions(7, 5)
         .with_hidden_layers([256, 128, 64])
         .with_dropout(0.3)
         .build())
```

## 性能优化

### 1. 模型优化

#### 量化
```python
class ModelQuantizer:
    """模型量化器"""
    
    @staticmethod
    def quantize_model(model, calibration_data):
        """动态量化模型"""
        model.eval()
        
        # 动态量化
        quantized_model = torch.quantization.quantize_dynamic(
            model, {nn.Linear}, dtype=torch.qint8
        )
        
        return quantized_model
    
    @staticmethod
    def quantize_aware_training(model, train_loader):
        """量化感知训练"""
        model.eval()
        model.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm')
        torch.quantization.prepare_qat(model, inplace=True)
        
        # 量化感知训练
        for epoch in range(num_epochs):
            for batch in train_loader:
                # 训练步骤
                pass
        
        # 转换为量化模型
        quantized_model = torch.quantization.convert(model.eval(), inplace=False)
        return quantized_model
```

#### 模型剪枝
```python
class ModelPruner:
    """模型剪枝器"""
    
    @staticmethod
    def prune_model(model, pruning_ratio=0.2):
        """结构化剪枝"""
        import torch.nn.utils.prune as prune
        
        # 剪枝所有线性层
        for name, module in model.named_modules():
            if isinstance(module, nn.Linear):
                prune.l1_unstructured(module, name='weight', amount=pruning_ratio)
        
        return model
    
    @staticmethod
    def remove_pruning(model):
        """移除剪枝重参数化"""
        import torch.nn.utils.prune as prune
        
        for name, module in model.named_modules():
            if isinstance(module, nn.Linear):
                prune.remove(module, 'weight')
        
        return model
```

### 2. 推理优化

#### 批量推理优化
```python
class BatchInferenceOptimizer:
    """批量推理优化器"""
    
    def __init__(self, model, device):
        self.model = model
        self.device = device
        self.optimal_batch_size = self._find_optimal_batch_size()
    
    def _find_optimal_batch_size(self):
        """寻找最优批次大小"""
        batch_sizes = [1, 2, 4, 8, 16, 32, 64, 128]
        best_batch_size = 1
        best_throughput = 0
        
        dummy_input = torch.randn(1, 7).to(self.device)
        
        for batch_size in batch_sizes:
            try:
                # 测试批次大小
                batch_input = dummy_input.repeat(batch_size, 1)
                
                start_time = time.time()
                with torch.no_grad():
                    for _ in range(10):
                        _ = self.model(batch_input)
                end_time = time.time()
                
                throughput = (batch_size * 10) / (end_time - start_time)
                
                if throughput > best_throughput:
                    best_throughput = throughput
                    best_batch_size = batch_size
                    
            except RuntimeError:
                break  # 内存不足
        
        return best_batch_size
```

## 扩展性设计

### 1. 插件系统

```python
class PluginManager:
    """插件管理器"""
    
    def __init__(self):
        self.plugins = {}
        self.hooks = defaultdict(list)
    
    def register_plugin(self, name: str, plugin):
        """注册插件"""
        self.plugins[name] = plugin
        
        # 注册插件钩子
        if hasattr(plugin, 'get_hooks'):
            for hook_name, hook_func in plugin.get_hooks().items():
                self.hooks[hook_name].append(hook_func)
    
    def execute_hooks(self, hook_name: str, *args, **kwargs):
        """执行钩子"""
        for hook_func in self.hooks[hook_name]:
            hook_func(*args, **kwargs)

class PluginBase(ABC):
    """插件基类"""
    
    @abstractmethod
    def initialize(self, config):
        pass
    
    @abstractmethod
    def cleanup(self):
        pass
    
    def get_hooks(self):
        return {}
```

### 2. 配置扩展

```python
class ConfigManager:
    """配置管理器"""
    
    def __init__(self):
        self.config_schemas = {}
        self.config_validators = {}
    
    def register_config_schema(self, name: str, schema: Dict):
        """注册配置模式"""
        self.config_schemas[name] = schema
    
    def register_validator(self, name: str, validator: callable):
        """注册配置验证器"""
        self.config_validators[name] = validator
    
    def validate_config(self, config: Dict[str, Any]) -> bool:
        """验证配置"""
        for name, validator in self.config_validators.items():
            if name in config:
                if not validator(config[name]):
                    raise ValueError(f"配置验证失败: {name}")
        return True
```

### 3. 模型注册系统

```python
class ModelRegistry:
    """模型注册系统"""
    
    _models = {}
    _model_metadata = {}
    
    @classmethod
    def register(cls, name: str, metadata: Dict = None):
        """模型注册装饰器"""
        def decorator(model_class):
            cls._models[name] = model_class
            cls._model_metadata[name] = metadata or {}
            return model_class
        return decorator
    
    @classmethod
    def create_model(cls, name: str, **kwargs):
        """创建模型"""
        if name not in cls._models:
            raise ValueError(f"未注册的模型: {name}")
        
        model_class = cls._models[name]
        return model_class(**kwargs)
    
    @classmethod
    def list_models(cls):
        """列出所有注册的模型"""
        return list(cls._models.keys())

# 使用示例
@ModelRegistry.register("advanced_pad", 
                       {"description": "高级PAD预测器", "version": "2.0"})
class AdvancedPADPredictor(nn.Module):
    def __init__(self, **kwargs):
        super().__init__()
        # 模型实现
        pass
```

---

本架构文档描述了系统的整体设计和实现细节。随着项目的发展，架构会持续优化和扩展。如有建议或问题，请通过GitHub Issues反馈。