import torch
import torch.nn as nn
import torch.nn.utils.prune as prune

class EyeTrackingModel(nn.Module):
    """
    眼动追踪模型
    
    原始模型：100 MB, 精度 95%
    剪枝后：50 MB, 精度 94%
    """
    
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.fc = nn.Linear(256 * 28 * 28, 2)  # 眼动坐标 (x, y)
        
    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = torch.relu(self.conv2(x))
        x = torch.relu(self.conv3(x))
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x


def apply_weight_pruning(model, amount=0.5):
    """
    应用权重剪枝
    
    Args:
        model: 原始模型
        amount: 剪枝比例（0-1）
        
    Returns:
        pruned_model: 剪枝后模型
    """
    for name, module in model.named_modules():
        if isinstance(module, nn.Conv2d):
            prune.l1_unstructured(module, name='weight', amount=amount)
    
    return model


# 测试
if __name__ == "__main__":
    # 创建模型
    model = EyeTrackingModel()
    
    # 计算原始参数量
    original_params = sum(p.numel() for p in model.parameters())
    print(f"原始参数量：{original_params:,}")
    
    # 应用剪枝
    pruned_model = apply_weight_pruning(model, amount=0.5)
    
    # 计算剪枝后参数量
    # 注意：剪枝后仍需移除零权重才能真正减小模型
    # 这里仅展示剪枝过程
    print("剪枝完成")

import torch
import torch.quantization as quant

def quantize_model_ptq(model, calibration_loader):
    """
    训练后量化（PTQ）
    
    Args:
        model: 原始 FP32 模型
        calibration_loader: 校准数据集
        
    Returns:
        quantized_model: INT8 量化模型
    """
    # 设置量化配置
    model.qconfig = quant.get_default_qconfig('fbgemm')
    
    # 准备量化
    quant.prepare(model, inplace=True)
    
    # 校准（运行校准数据集）
    with torch.no_grad():
        for data, _ in calibration_loader:
            model(data)
    
    # 转换为量化模型
    quant.convert(model, inplace=True)
    
    return model


def quantize_model_qat(model, train_loader, epochs=10):
    """
    量化感知训练（QAT）
    
    Args:
        model: 原始 FP32 模型
        train_loader: 训练数据集
        epochs: 训练轮数
        
    Returns:
        quantized_model: INT8 量化模型
    """
    # 设置量化配置
    model.qconfig = quant.get_default_qconfig('fbgemm')
    
    # 准备量化
    quant.prepare_qat(model, inplace=True)
    
    # 训练
    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
    criterion = torch.nn.MSELoss()
    
    for epoch in range(epochs):
        for data, target in train_loader:
            optimizer.zero_grad()
            output = model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()
    
    # 转换为量化模型
    quant.convert(model, inplace=True)
    
    return model


# 测试
if __name__ == "__main__":
    # 创建模型
    model = EyeTrackingModel()
    
    # 模拟校准数据集
    calibration_loader = [(torch.randn(1, 3, 224, 224), torch.randn(1, 2)) for _ in range(100)]
    
    # 训练后量化
    quantized_model = quantize_model_ptq(model, calibration_loader)
    
    # 计算模型大小
    original_size = sum(p.numel() * 4 for p in model.parameters()) / 1024 / 1024  # MB
    quantized_size = sum(p.numel() * 1 for p in quantized_model.parameters()) / 1024 / 1024  # MB
    
    print(f"原始模型大小：{original_size:.2f} MB")
    print(f"量化后模型大小：{quantized_size:.2f} MB")
    print(f"压缩比：{original_size / quantized_size:.2f}x")

# 伪代码：Qualcomm SNPE 部署
import snpe

# 加载模型
model = snpe.Model("model.dlc")  # SNPE 格式

# 设置运行时
runtime = snpe.Runtime(
    device="DSP",  # 使用 Hexagon DSP
    precision="INT8"
)

# 运行推理
input_tensor = load_input_image()
output = model.execute(input_tensor, runtime)

# 后处理
gaze_vector = output['gaze']
print(f"眼动向量：{gaze_vector}")