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| class SnapdragonRideDMSDeploy:
"""
Snapdragon Ride Flex DMS部署方案
使用高通SNPE/QNN推理框架
"""
def __init__(self, config: dict):
self.model_path = config.get('model_path', 'dms_model.onnx')
self.backend = config.get('backend', 'qnn')
self.engine = self._init_engine()
def _init_engine(self):
"""初始化推理引擎"""
if self.backend == 'qnn':
import qnn
engine = qnn.Engine()
engine.load_model(self.model_path)
return engine
elif self.backend == 'snpe':
import snpe
engine = snpe.DSPRuntime()
engine.load_model(self.model_path)
return engine
def inference(self, input_tensor: np.ndarray) -> Dict:
"""
执行推理
Args:
input_tensor: 输入图像tensor
Returns:
result: DMS检测结果
"""
input_data = self._preprocess(input_tensor)
outputs = self.engine.execute(input_data)
result = self._postprocess(outputs)
return result
def _preprocess(self, image: np.ndarray) -> np.ndarray:
"""预处理"""
image = cv2.resize(image, (224, 224))
image = image.astype(np.float32) / 255.0
image = np.transpose(image, (2, 0, 1))
image = np.expand_dims(image, 0)
return image
def _postprocess(self, outputs: Dict) -> Dict:
"""后处理"""
gaze = outputs['gaze_output']
drowsiness = outputs['drowsiness_output']
distraction = outputs['distraction_output']
return {
'gaze_direction': gaze,
'drowsiness_level': float(drowsiness),
'distraction_type': self._classify_distraction(distraction)
}
def _classify_distraction(self, prob: np.ndarray) -> str:
"""分类分心类型"""
classes = ['NONE', 'PHONE', 'EATING', 'REACHING', 'OTHER']
return classes[np.argmax(prob)]
class DMSPerformanceOptimizer:
"""
DMS性能优化
针对Snapdragon Ride Flex的优化策略
"""
@staticmethod
def optimize_model(onnx_model_path: str, output_path: str):
"""
模型优化
1. 量化为INT8
2. 算子融合
3. 针对Hexagon NPU优化
"""
import onnx
from onnxruntime.quantization import quantize_dynamic
quantize_dynamic(
onnx_model_path,
output_path,
weight_type=QuantType.QUInt8
)
print(f"模型已优化并保存到: {output_path}")
@staticmethod
def profile_inference(engine, input_tensor, iterations=100):
"""性能分析"""
import time
for _ in range(10):
engine.execute(input_tensor)
start = time.time()
for _ in range(iterations):
engine.execute(input_tensor)
end = time.time()
avg_time = (end - start) / iterations * 1000
fps = iterations / (end - start)
print(f"平均推理时间: {avg_time:.2f}ms")
print(f"帧率: {fps:.1f} FPS")
|