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update picodet ncnn and mnn demo (PaddlePaddle#5721)
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| Original file line number | Diff line number | Diff line change |
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| # PicoDet MNN Demo | ||
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| This fold provides PicoDet inference code using | ||
| [Alibaba's MNN framework](https://github.com/alibaba/MNN). Most of the implements in | ||
| this fold are same as *demo_ncnn*. | ||
| 本Demo提供的预测代码是根据[Alibaba's MNN framework](https://github.com/alibaba/MNN) 推理库预测的。 | ||
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| ## Install MNN | ||
| ## C++ Demo | ||
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| ### Python library | ||
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| Just run: | ||
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| ``` shell | ||
| pip install MNN | ||
| - 第一步：根据[MNN官方编译文档](https://www.yuque.com/mnn/en/build_linux) 编译生成预测库. | ||
| - 第二步：编译或下载得到OpenCV库，可参考OpenCV官网，为了方便如果环境是gcc8.2 x86环境，可直接下载以下库： | ||
| ```shell | ||
| 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 | ||
| ``` | ||
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| ### C++ library | ||
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| Please follow the [official document](https://www.yuque.com/mnn/en/build_linux) to build MNN engine. | ||
| - Create picodet_m_416_coco.onnx | ||
| - 第三步：准备模型 | ||
| ```shell | ||
| modelName=picodet_m_416_coco | ||
| # export model | ||
| 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 | ||
| # convert to onnx | ||
| # 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 | ||
| # onnxsim | ||
| # 简化模型 | ||
| python -m onnxsim ${modelName}.onnx ${modelName}_processed.onnx | ||
| # 将模型转换至MNN格式 | ||
| python -m MNN.tools.mnnconvert -f ONNX --modelFile picodet_s_320_lcnet_processed.onnx --MNNModel picodet_s_320_lcnet.mnn | ||
| ``` | ||
| 为了快速测试，可直接下载：[picodet_s_320_lcnet.mnn](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_s_320_lcnet.mnn)（不带后处理）。 | ||
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| - Convert model | ||
| ``` shell | ||
| python -m MNN.tools.mnnconvert -f ONNX --modelFile picodet-416.onnx --MNNModel picodet-416.mnn | ||
| ``` | ||
| Here are converted model [download link](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_416.mnn). | ||
| **注意：**由于MNN里，Matmul算子的输入shape如果不一致计算有问题，带后处理的Demo正在升级中，很快发布。 | ||
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| ## Build | ||
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| 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. | ||
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| 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 | ||
| ## 编译可执行程序 | ||
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| - 第一步：导入lib包 | ||
| ``` | ||
| mkdir mnn && cd mnn && mkdir lib | ||
| cp /path/to/MNN/build/libMNN.so . | ||
| cd .. | ||
| cp -r /path/to/MNN/include . | ||
| ``` | ||
| - 第二步：修改CMakeLists.txt中OpenCV和MNN的路径 | ||
| - 第三步：开始编译 | ||
| ``` shell | ||
| mkdir build && cd build | ||
| cmake .. | ||
| make | ||
| ``` | ||
| 如果在build目录下生成`picodet-mnn`可执行文件，就证明成功了。 | ||
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| Note that a flag at `main.cpp` is used to control whether to show the detection result or save it into a fold. | ||
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| ``` c++ | ||
| #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: | ||
| ## 开始运行 | ||
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| 首先新建预测结果存放目录： | ||
| ```shell | ||
| 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}] | ||
| cp -r ../demo_onnxruntime/imgs . | ||
| cd build | ||
| mkdir ../results | ||
| ``` | ||
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| For example: | ||
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| - 预测一张图片 | ||
| ``` shell | ||
| # 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 | ||
| ./picodet-mnn 0 ../picodet_s_320_lcnet_3.mnn 320 320 ../imgs/dog.jpg | ||
| ``` | ||
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| ### C++ | ||
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| C++ inference interface is same with NCNN code, to detect images in a fold, run: | ||
| -测试速度Benchmark | ||
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| ``` shell | ||
| ./picodet-mnn "1" "../imgs/test.jpg" | ||
| ./picodet-mnn 1 ../picodet_s_320_lcnet.mnn 320 320 | ||
| ``` | ||
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| For speed benchmark | ||
| ## FAQ | ||
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| ``` shell | ||
| ./picodet-mnn "3" "0" | ||
| - 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： | ||
| ```shell | ||
| # 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](https://netron.app)查看具体name，并修改`picodet_mnn.hpp`中相应`non_postprocess_heads_info`数组。 | ||
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| ## Reference | ||
| [MNN](https://github.com/alibaba/MNN) |
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