"""
DMS/OMS 模型边缘部署压缩工具链

目标: 将学术论文级模型压缩到车规芯片可部署尺寸
流程: PyTorch → ONNX → INT8量化 → 芯片SDK
"""

import torch
import onnx
import onnxruntime as ort
import numpy as np

class DMSModelCompression:
    """
    DMS模型压缩工具链
    
    示例: EyeCue 模型 (110M参数) → 量化后 <30MB
    """
    
    def __init__(self, model, input_shapes):
        self.model = model
        self.input_shapes = input_shapes
    
    def export_onnx(self, output_path):
        """PyTorch → ONNX"""
        self.model.eval()
        dummy_inputs = {
            name: torch.randn(*shape) 
            for name, shape in self.input_shapes.items()
        }
        
        torch.onnx.export(
            self.model,
            tuple(dummy_inputs.values()),
            output_path,
            input_names=list(self.input_shapes.keys()),
            output_names=['logits'],
            dynamic_axes={
                name: {0: 'batch'} 
                for name in self.input_shapes
            },
            opset_version=17
        )
        print(f"✅ ONNX 导出: {output_path}")
        
    def quantize_int8(self, onnx_path, output_path, calibration_data):
        """ONNX → INT8 量化"""
        from onnxruntime.quantization import (
            quantize_dynamic, QuantType, QuantFormat
        )
        
        # 动态量化 (不需要校准数据)
        quantize_dynamic(
            onnx_path,
            output_path,
            weight_type=QuantType.QInt8,
            op_types_to_quantize=['MatMul', 'Gemm', 'Conv']
        )
        
        # 检查压缩比
        import os
        orig_size = os.path.getsize(onnx_path) / 1024 / 1024
        quant_size = os.path.getsize(output_path) / 1024 / 1024
        ratio = (1 - quant_size / orig_size) * 100
        
        print(f"✅ INT8 量化: {output_path}")
        print(f"   原始: {orig_size:.1f} MB")
        print(f"   量化: {quant_size:.1f} MB")
        print(f"   压缩: {ratio:.1f}%")
        
    def benchmark(self, model_path, test_inputs):
        """基准测试"""
        sess = ort.InferenceSession(model_path)
        
        # 延迟测试
        import time
        times = []
        for _ in range(100):
            start = time.perf_counter()
            for name, data in test_inputs.items():
                inputs = {name: data.numpy().astype(np.float32)}
                sess.run(None, inputs)
            times.append((time.perf_counter() - start) * 1000)
        
        times = np.array(times)
        print(f"延迟: {np.median(times):.1f}ms (中位)")
        print(f"延迟: {np.percentile(times, 99):.1f}ms (P99)")
        print(f"FPS:  {1000 / np.median(times):.1f}")


# 使用示例
if __name__ == "__main__":
    # 模拟 EyeCue 模型
    # model = EyeCue(clip_len=8)
    # compression = DMSModelCompression(model, {'video': (1, 3, 8, 224, 224), 'gaze': (1, 8, 2)})
    # compression.export_onnx('eyecue.onnx')
    # compression.quantize_int8('eyecue.onnx', 'eyecue_int8.onnx', None)
    print("DMS 模型压缩工具链示例")