demo_mnn

PicoDet MNN Demo

This fold provides PicoDet inference code using Alibaba's MNN framework. Most of the implements in this fold are same as demo_ncnn.

Install MNN

Python library

Just run:

pip install MNN

C++ library

Please follow the official document to build MNN engine.

• Create picodet_m_416_coco.onnx

  modelName=picodet_m_416_coco
  # export model
  python tools/export_model.py \
          -c configs/picodet/${modelName}.yml \
          -o weights=${modelName}.pdparams \
          --output_dir=inference_model
  # convert to onnx
  paddle2onnx --model_dir inference_model/${modelName} \
          --model_filename model.pdmodel  \
          --params_filename model.pdiparams \
          --opset_version 11 \
          --save_file ${modelName}.onnx
  # onnxsim
  python -m onnxsim ${modelName}.onnx ${modelName}_processed.onnx

• Convert model

  python -m MNN.tools.mnnconvert -f ONNX --modelFile picodet-416.onnx --MNNModel picodet-416.mnn

Here are converted model download link.

Build

The python code demo_mnn.py can run directly and independently without main PicoDet repo. PicoDetONNX and PicoDetTorch are two classes used to check the similarity of MNN inference results with ONNX model and Pytorch model. They can be remove with no side effects.

For C++ code, replace libMNN.so under ./mnn/lib with the one you just compiled, modify OpenCV path and MNN path at CMake file, and run

mkdir build && cd build
cmake ..
make

Note that a flag at main.cpp is used to control whether to show the detection result or save it into a fold.

#define __SAVE_RESULT__ // if defined save drawed results to ../results, else show it in windows

Run

Python

demo_mnn.py provide an inference class PicoDetMNN that combines preprocess, post process, visualization. Besides it can be used in command line with the form:

demo_mnn.py [-h] [--model_path MODEL_PATH] [--cfg_path CFG_PATH]
    [--img_fold IMG_FOLD] [--result_fold RESULT_FOLD]
    [--input_shape INPUT_SHAPE INPUT_SHAPE]
    [--backend {MNN,ONNX,torch}]

For example:

# run MNN 416 model
python ./demo_mnn.py --model_path ../model/picodet-416.mnn --img_fold ../imgs --result_fold ../results
# run MNN 320 model
python ./demo_mnn.py --model_path ../model/picodet-320.mnn --input_shape 320 320 --backend MNN
# run onnx model
python ./demo_mnn.py --model_path ../model/sim.onnx --backend ONNX

C++

C++ inference interface is same with NCNN code, to detect images in a fold, run:

./picodet-mnn "1" "../imgs/test.jpg"

For speed benchmark

./picodet-mnn "3" "0"

Reference

MNN