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PicoDet NCNN Demo

该Demo提供的预测代码是根据Tencent's NCNN framework推理库预测的。

第一步：编译

Windows

Step1.

Download and Install Visual Studio from https://visualstudio.microsoft.com/vs/community/

Step2.

Download and install OpenCV from https://github.com/opencv/opencv/releases

为了方便，如果环境是gcc8.2 x86环境，可直接下载以下库：

wget https://paddledet.bj.bcebos.com/data/opencv-3.4.16_gcc8.2_ffmpeg.tar.gz
tar -xf opencv-3.4.16_gcc8.2_ffmpeg.tar.gz

Step3(可选).

Download and install Vulkan SDK from https://vulkan.lunarg.com/sdk/home

Step4：编译NCNN

git clone --recursive https://github.com/Tencent/ncnn.git

Build NCNN following this tutorial: Build for Windows x64 using VS2017

Step5.

增加 ncnn_DIR = YOUR_NCNN_PATH/build/install/lib/cmake/ncnn 到系统变量中

Build project: Open x64 Native Tools Command Prompt for VS 2019 or 2017

cd <this-folder>
mkdir -p build
cd build
cmake ..
msbuild picodet_demo.vcxproj /p:configuration=release /p:platform=x64

Linux

Step1.

Build and install OpenCV from https://github.com/opencv/opencv

Step2(可选).

Download Vulkan SDK from https://vulkan.lunarg.com/sdk/home

Step3：编译NCNN

Clone NCNN repository

git clone --recursive https://github.com/Tencent/ncnn.git

Build NCNN following this tutorial: Build for Linux / NVIDIA Jetson / Raspberry Pi

Step4：编译可执行文件

cd <this-folder>
mkdir build
cd build
cmake ..
make

Run demo

  • 准备模型 
    modelName=picodet_s_320_coco_lcnet
# 导出Inference model
python tools/export_model.py \
        -c configs/picodet/${modelName}.yml \
        -o weights=${modelName}.pdparams \
        --output_dir=inference_model
# 转换到ONNX
paddle2onnx --model_dir inference_model/${modelName} \
        --model_filename model.pdmodel  \
        --params_filename model.pdiparams \
        --opset_version 11 \
        --save_file ${modelName}.onnx
# 简化模型
python -m onnxsim ${modelName}.onnx ${modelName}_processed.onnx
# 将模型转换至NCNN格式
Run onnx2ncnn in ncnn tools to generate ncnn .param and .bin file.

转NCNN模型可以利用在线转换工具 https://convertmodel.com

为了快速测试，可直接下载：picodet_s_320_coco_lcnet-opt.bin/ picodet_s_320_coco_lcnet-opt.param（不带后处理）。

**注意：**由于带后处理后，NCNN预测会出NAN，暂时使用不带后处理Demo即可，带后处理的Demo正在升级中，很快发布。

开始运行

首先新建预测结果存放目录：

cp -r ../demo_onnxruntime/imgs .
cd build
mkdir ../results
  • 预测一张图片
./picodet_demo 0 ../picodet_s_320_coco_lcnet.bin ../picodet_s_320_coco_lcnet.param 320 320 ../imgs/dog.jpg 0

具体参数解析可参考main.cpp。

-测试速度Benchmark

./picodet_demo 1 ../picodet_s_320_lcnet.bin ../picodet_s_320_lcnet.param 320 320  0

FAQ

  • 预测结果精度不对： 请先确认模型输入shape是否对齐，并且模型输出name是否对齐，不带后处理的PicoDet增强版模型输出name如下：
# 分类分支  |  检测分支
{"transpose_0.tmp_0", "transpose_1.tmp_0"},
{"transpose_2.tmp_0", "transpose_3.tmp_0"},
{"transpose_4.tmp_0", "transpose_5.tmp_0"},
{"transpose_6.tmp_0", "transpose_7.tmp_0"},

可使用netron查看具体name，并修改picodet_mnn.hpp中相应non_postprocess_heads_info数组。