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
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：
- 高性能: 支持GPU加速，优化推理速度
- 模块化: 清晰的模块划分，便于维护和扩展
- 可配置: 灵活的配置系统，支持超参数调优
- 易用性: 完整的CLI工具和Python API
- 可扩展: 支持新的模型架构和损失函数
- 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
- 逐层压缩: 从128 → 64 → 32，逐层减少神经元数量
- 激活函数: 使用ReLU激活函数，避免梯度消失
- 正则化: 在前两层使用Dropout防止过拟合
- 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
# 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
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
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
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
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
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
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
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
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
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
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/)
# 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/)
# 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/)
# 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)
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)
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)
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)
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
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
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
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
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
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
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。