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RKNN optimization for exporting model

Source

Base on https://github.com/ultralytics/ultralytics with commit id as 0b0bc56675997fe66b13aa0d250b777c8a467e32

What different

With inference result values unchanged, the following optimizations were applied:

  • Change output node, remove post-process from the model. (post-process block in model is unfriendly for quantization)
  • Remove dfl structure at the end of the model. (which slowdown the inference speed on NPU device)
  • Add a score-sum output branch to speedup post-process.

All the removed operation will be done on CPU. (the CPU post-process could be found in RKNN_Model_Zoo)

Export ONNX model

After meeting the environment requirements specified in "./requirements.txt," execute the following command to export the model (support detect/segment/pose/obb model):

# Adjust the model file path in "./ultralytics/cfg/default.yaml" (default is yolov8n.pt). If you trained your own model, please provide the corresponding path. 
# For example, filled with yolov8n.pt for detection model.
# Filling with yolov8n-seg.pt for segmentation model.

export PYTHONPATH=./
python ./ultralytics/engine/exporter.py

# Upon completion, the ".onnx" model will be generated. If the original model is "yolov8n.pt," the generated model will be "yolov8n.onnx"

Convert to RKNN model, Python demo, C demo

Please refer to https://github.com/airockchip/rknn_model_zoo.