# 示意代码,不可直接运行 —— 演示 PyTorch PTQ/QAT 流程骨架,数据加载与部署环节未实现;完整方案见 ONNX Runtime/SNPE 官方文档
import torch
import torch.nn as nn
import numpy as np

class DMSModel(nn.Module):
    """
    轻量DMS模型示例
    
    功能: 疲劳+分心+手机使用三分类
    输入: 224x224 RGB面部图像
    输出: 3类 + PERCLOS回归
    """
    
    def __init__(self, n_classes: int = 3):
        super().__init__()
        
        # 轻量骨干
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, 3, stride=2, padding=1), nn.BatchNorm2d(32), nn.ReLU(),
            nn.Conv2d(32, 64, 3, stride=2, padding=1), nn.BatchNorm2d(64), nn.ReLU(),
            nn.Conv2d(64, 128, 3, stride=2, padding=1), nn.BatchNorm2d(128), nn.ReLU(),
            nn.Conv2d(128, 256, 3, stride=2, padding=1), nn.BatchNorm2d(256), nn.ReLU(),
            nn.AdaptiveAvgPool2d((1, 1))
        )
        
        # 分类头
        self.cls_head = nn.Sequential(
            nn.Flatten(),
            nn.Linear(256, 64), nn.ReLU(), nn.Dropout(0.3),
            nn.Linear(64, n_classes)
        )
        
        # PERCLOS回归头
        self.perclos_head = nn.Sequential(
            nn.Linear(256, 32), nn.ReLU(),
            nn.Linear(32, 1)
        )
    
    def forward(self, x):
        feat = self.features(x)
        cls = self.cls_head(feat)
        perclos = self.perclos_head(feat)
        return cls, perclos


def quantize_ptq(model: nn.Module, calibration_data: torch.utils.data.DataLoader) -> nn.Module:
    """
    Post-Training Quantization (PTQ)
    
    步骤:
    1. 设置量化配置（per-tensor/per-channel）
    2. 校准（用代表性数据确定激活范围）
    3. 转换为量化模型
    
    优点: 无需重训，10分钟完成
    缺点: 精度略低于QAT
    """
    model.eval()
    
    # 量化配置
    qconfig = torch.ao.quantization.get_default_qconfig('x86')  # x86/QNNPACK
    model.qconfig = qconfig
    
    # 插入观察者
    model_prepared = torch.ao.quantization.prepare(model)
    
    # 校准（前向传播校准数据）
    with torch.no_grad():
        for i, (images, _) in enumerate(calibration_data):
            model_prepared(images)
            if i >= 100:  # 100个batch够校准
                break
    
    # 转换为量化模型
    model_quantized = torch.ao.quantization.convert(model_prepared)
    
    return model_quantized


def quantize_qat(model: nn.Module, train_data, epochs: int = 10) -> nn.Module:
    """
    Quantization-Aware Training (QAT)
    
    步骤:
    1. 插入伪量化节点
    2. 用原始训练数据微调
    3. 转换为量化模型
    
    优点: 精度最佳（接近FP32）
    缺点: 需要训练数据和时间
    """
    model.train()
    
    # QAT配置
    qconfig = torch.ao.quantization.get_default_qat_qconfig('x86')
    model.qconfig = qconfig
    
    # 插入伪量化
    model_prepared = torch.ao.quantization.prepare_qat(model)
    
    # 微调训练
    optimizer = torch.optim.Adam(model_prepared.parameters(), lr=1e-4)
    criterion = nn.CrossEntropyLoss()
    
    for epoch in range(epochs):
        for images, labels in train_data:
            optimizer.zero_grad()
            cls, _ = model_prepared(images)
            loss = criterion(cls, labels)
            loss.backward()
            optimizer.step()
        print(f"QAT Epoch {epoch+1}: loss={loss.item():.4f}")
    
    # 转换
    model_prepared.eval()
    model_quantized = torch.ao.quantization.convert(model_prepared)
    
    return model_quantized


# 性能对比
def benchmark_model(model, input_shape=(1, 3, 224, 224), n_runs=100):
    """基准测试"""
    import time
    
    model.eval()
    x = torch.randn(*input_shape)
    
    # 预热
    with torch.no_grad():
        for _ in range(10):
            model(x)
    
    # 计时
    start = time.time()
    with torch.no_grad():
        for _ in range(n_runs):
            model(x)
    elapsed = time.time() - start
    
    ms_per_run = elapsed / n_runs * 1000
    n_params = sum(p.numel() for p in model.parameters())
    
    return {
        'latency_ms': ms_per_run,
        'fps': 1000 / ms_per_run,
        'params_M': n_params / 1e6,
    }


if __name__ == "__main__":
    # 创建模型
    model_fp32 = DMSModel(n_classes=3)
    
    # 基准测试
    print("=== FP32 (基线) ===")
    stats_fp32 = benchmark_model(model_fp32)
    for k, v in stats_fp32.items():
        print(f"  {k}: {v:.2f}")
    
    # 模型大小
    torch.save(model_fp32.state_dict(), '/tmp/model_fp32.pth')
    import os
    size_fp32 = os.path.getsize('/tmp/model_fp32.pth') / 1e6
    
    # PTQ量化
    print("\n=== INT8 PTQ ===")
    # 模拟校准数据
    cal_data = [(torch.randn(8, 3, 224, 224), torch.randint(0, 3, (8,))) for _ in range(200)]
    
    try:
        model_int8 = quantize_ptq(model_fp32, cal_data)
        stats_int8 = benchmark_model(model_int8)
        torch.save(model_int8.state_dict(), '/tmp/model_int8.pth')
        size_int8 = os.path.getsize('/tmp/model_int8.pth') / 1e6
        
        for k, v in stats_int8.items():
            print(f"  {k}: {v:.2f}")
        
        print(f"\n=== 对比 ===")
        print(f"模型大小: {size_fp32:.1f}MB → {size_int8:.1f}MB (压缩{size_fp32/size_int8:.1f}x)")
        print(f"推理速度: {stats_fp32['latency_ms']:.1f}ms → {stats_int8['latency_ms']:.1f}ms (加速{stats_fp32['latency_ms']/stats_int8['latency_ms']:.1f}x)")
    except Exception as e:
        print(f"  PTQ需校准数据支持，错误: {e}")
        print(f"  实际部署使用ONNX Runtime/TFLite量化管道")

# 示意代码,不可直接运行 —— 流程骨架,load_calibration_data/load_test_data 等函数未实现
def onnx_quantize_pipeline():
    """
    ONNX Runtime量化管道（推荐量产）
    
    优势:
    - 支持QCS8255 Hexagon NPU
    - INT8推理加速
    - 跨平台部署
    """
    
    quantize_code = """
    import onnx
    from onnxruntime.quantization import quantize_dynamic, QuantType
    
    # 1. 导出ONNX
    # torch.onnx.export(model, dummy_input, 'dms_fp32.onnx', opset_version=17)
    
    # 2. 动态量化（最简单）
    quantize_dynamic('dms_fp32.onnx', 'dms_int8.onnx',
                     weight_type=QuantType.QUInt8)
    
    # 3. 静态量化（精度更好）
    from onnxruntime.quantization import quantize_static, CalibrationDataReader
    
    class CalDataReader(CalibrationDataReader):
        def __init__(self, data_dir):
            self.data = load_calibration_data(data_dir)
            self.idx = 0
        
        def get_next(self):
            if self.idx >= len(self.data):
                return None
            item = {'input': self.data[self.idx]}
            self.idx += 1
            return item
    
    quantize_static('dms_fp32.onnx', 'dms_int8_static.onnx',
                    calibration_data_reader=CalDataReader('cal_data/'))
    
    # 4. 验证
    import onnxruntime as ort
    
    sess_fp32 = ort.InferenceSession('dms_fp32.onnx')
    sess_int8 = ort.InferenceSession('dms_int8_static.onnx')
    
    # 精度对比
    test_data = load_test_data()
    for x, y in test_data:
        out_fp32 = sess_fp32.run(None, {'input': x})
        out_int8 = sess_int8.run(None, {'input': x})
        # 计算精度差异
    """
    print(quantize_code)

# 示意代码,不可直接运行 —— 技巧思路片段
# 技巧1: 逐层量化敏感度分析
def layer_sensitivity_analysis(model, test_data):
    """逐层量化，评估精度影响"""
    results = {}
    for name, module in model.named_modules():
        if isinstance(module, nn.Conv2d):
            # 量化单层，其余保持FP32
            # ...
            results[name] = accuracy_drop
    return results

# 技巧2: 混合精度（敏感层FP16，其余INT8）
# 技巧3: QAT + 知识蒸馏
# 技巧4: per-channel量化（比per-tensor好）