端侧AutoML：硬件感知NAS 2.0

# 创建虚拟环境
conda create -n endsideaml python=3.9
conda activate endsideaml

# 安装依赖
pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113
pip install naslib==0.4.0 opencv-python==4.5.5.64 android-ndk==23.1.7779620

# 导入必要的库
import torch
import torch.nn as nn
from naslib.predictors import MLP, GCN, GIN
from naslib.search_spaces import NasBench201SearchSpace
from naslib.optimizers import DARTSOptimizer, RegularizedEvolution, RandomSearch

# 定义硬件感知搜索空间
class HardwareAwareSearchSpace(NasBench201SearchSpace):
    def __init__(self, hardware_metrics=None):
        super().__init__()
        self.hardware_metrics = hardware_metrics  # 包含目标硬件的性能指标
        
    def get_hardware_metrics(self):
        return self.hardware_metrics

# 定义硬件性能预测器
class HardwarePerformancePredictor:
    def __init__(self, predictor_type='mlp', metric='latency'):
        self.predictor_type = predictor_type
        self.metric = metric
        
        # 初始化不同的预测器
        if predictor_type == 'mlp':
            self.predictor = MLP()
        elif predictor_type == 'gcn':
            self.predictor = GCN()
        elif predictor_type == 'gin':
            self.predictor = GIN()
        else:
            raise ValueError(f"不支持的预测器类型: {predictor_type}")
        
    def train(self, training_data):
        # 训练性能预测器
        self.predictor.fit(training_data)
        
    def predict(self, architecture):
        # 预测架构在目标硬件上的性能
        return self.predictor.predict(architecture)

# 定义硬件感知搜索策略
class HardwareAwareSearch:
    def __init__(self, 
                 search_space, 
                 search_optimizer='darts', 
                 hardware_predictor=None,
                 num_samples=100,
                 max_time_budget=3600):
        self.search_space = search_space
        self.search_optimizer = search_optimizer
        self.hardware_predictor = hardware_predictor
        self.num_samples = num_samples
        self.max_time_budget = max_time_budget
        
        # 初始化搜索优化器
        if search_optimizer == 'darts':
            self.optimizer = DARTSOptimizer()
        elif search_optimizer == 're':
            self.optimizer = RegularizedEvolution()
        elif search_optimizer == 'rs':
            self.optimizer = RandomSearch()
        else:
            raise ValueError(f"不支持的搜索优化器: {search_optimizer}")
        
    def search(self, train_loader, val_loader):
        # 在训练数据上进行搜索
        best_architecture = None
        best_performance = float('inf')  # 初始化为无穷大
        
        for sample_idx in range(self.num_samples):
            # 检查时间预算
            if time.time() > self.start_time + self.max_time_budget:
                print(f"达到最大时间预算，提前终止搜索")
                break
                
            # 使用优化器获取候选架构
            candidate = self.optimizer.suggest(self.search_space)
            
            # 训练候选架构
            self.optimizer.fit(candidate, train_loader, val_loader)
            
            # 评估精度性能
            accuracy = self.optimizer.evaluate(candidate, val_loader)
            
            # 预测硬件性能
            if self.hardware_predictor:
                hardware_metric = self.hardware_predictor.predict(candidate)
            else:
                # 如果没有硬件预测器，则使用实际测量（这在实际部署中可能不可行）
                hardware_metric = self._measure_hardware_metric(candidate)
            
            # 计算综合性能（示例：简单加权和）
            combined_performance = (1 - self.hardware_weight) * (1 - accuracy) + self.hardware_weight * hardware_metric
            
            # 更新最佳架构
            if combined_performance < best_performance:
                best_performance = combined_performance
                best_architecture = candidate
                
            print(f"样本 {sample_idx+1}/{self.num_samples}, 当前最佳性能: {best_performance:.4f}")
            
        return best_architecture
    
    def _measure_hardware_metric(self, architecture):
        # 实际测量硬件性能指标（如推理延迟）
        # 注意：这通常需要在目标硬件上执行，可能涉及设备间通信
        # 这里仅提供模拟实现
        return 0.5 + 0.1 * torch.randn(1).item()  # 模拟延迟，单位：秒

# 使用示例
if __name__ == "__main__":
    # 准备数据
    from torchvision import datasets, transforms
    
    transform = transforms.Compose([transforms.ToTensor()])
    train_data = datasets.CIFAR10(root='./data', train=True, transform=transform, download=True)
    val_data = datasets.CIFAR10(root='./data', train=False, transform=transform, download=True)
    
    train_loader = torch.utils.data.DataLoader(train_data, batch_size=128, shuffle=True)
    val_loader = torch.utils.data.DataLoader(val_data, batch_size=128, shuffle=False)
    
    # 初始化硬件感知搜索空间
    search_space = HardwareAwareSearchSpace(
        hardware_metrics={'latency': 0.1, 'energy': 0.05}  # 示例硬件指标
    )
    
    # 初始化硬件性能预测器
    predictor = HardwarePerformancePredictor(
        predictor_type='gcn', 
        metric='latency'
    )
    
    # 初始化硬件感知搜索策略
    search = HardwareAwareSearch(
        search_space=search_space,
        search_optimizer='re',  # 使用正则化演化搜索
        hardware_predictor=predictor,
        num_samples=50,
        max_time_budget=7200  # 2小时搜索时间预算
    )
    
    # 开始搜索
    best_architecture = search.search(train_loader, val_loader)
    
    print(f"找到的最佳架构: {best_architecture}")

# 模型转换为TFLite示例代码
import tensorflow as tf

def convert_to_tflite(pytorch_model, input_shape=(1, 3, 224, 224)):
    # 将PyTorch模型转换为ONNX格式
    dummy_input = torch.randn(input_shape)
    torch.onnx.export(pytorch_model, dummy_input, "model.onnx")
    
    # 使用ONNX转换为TFLite
    onnx_model = onnx.load("model.onnx")
    tf_model = onnx_tf.backend.prepare(onnx_model)
    tf_model.export_graph("model.pb")
    
    # 转换为TFLite格式
    converter = tf.lite.TFLiteConverter.from_saved_model("model.pb")
    tflite_model = converter.convert()
    
    # 保存TFLite模型
    with open("model.tflite", "wb") as f:
        f.write(tflite_model)
    
    print("模型成功转换为TFLite格式")