diff --git a/docs/cn/build_and_install/rknpu2.md b/docs/cn/build_and_install/rknpu2.md index f432c94ce8..9e91cd7c94 100644 --- a/docs/cn/build_and_install/rknpu2.md +++ b/docs/cn/build_and_install/rknpu2.md @@ -1,11 +1,153 @@ [English](../../en/build_and_install/rknpu2.md) | 简体中文 -# FastDeploy RKNPU2资源导航 -## 写在前面 +# FastDeploy RKNPU2 导航文档 + RKNPU2指的是Rockchip推出的RK356X以及RK3588系列芯片的NPU。 目前FastDeploy已经初步支持使用RKNPU2来部署模型。 如果您在使用的过程中出现问题，请附带上您的运行环境，在Issues中反馈。 +## FastDeploy RKNPU2 环境安装简介 + +如果您想在FastDeploy中使用RKNPU2推理引擎，你需要配置以下几个环境。 + +| 工具名 | 是否必须 | 安装设备 | 用途 | +|--------------|------|-------|---------------------------------| +| Paddle2ONNX | 必装 | PC | 用于转换PaddleInference模型到ONNX模型 | +| RKNNToolkit2 | 必装 | PC | 用于转换ONNX模型到rknn模型 | +| RKNPU2 | 选装 | Board | RKNPU2的基础驱动，FastDeploy已经集成，可以不装 | + +## 安装模型转换环境 + +模型转换环境需要在Ubuntu下完成，我们建议您使用conda作为python控制器，并使用python3.6作为您的模型转换环境。 +例如您可以输入以下命令行完成对python3.6环境的创建 + +```bash +conda create -n rknn2 python=3.6 +conda activate rknn2 +``` + +### 安装必备的依赖软件包 + +在安装RKNNtoolkit2之前我们需要安装一下必备的软件包 + +```bash +sudo apt-get install libxslt1-dev zlib1g zlib1g-dev libglib2.0-0 libsm6 libgl1-mesa-glx libprotobuf-dev gcc g++ +``` + + +### 安装RKNNtoolkit2 + +目前，FastDeploy使用的转化工具版本号为1.4.2b3。如果你有使用最新版本的转换工具的需求，你可以在Rockchip提供的[百度网盘(提取码为rknn)](https://eyun.baidu.com/s/3eTDMk6Y) +中找到最新版本的模型转换工具。 + +```bash +# rknn_toolkit2对numpy存在特定依赖,因此需要先安装numpy==1.16.6 +pip install numpy==1.16.6 + +# 安装rknn_toolkit2-1.3.0_11912b58-cp38-cp38-linux_x86_64.whl +wget https://bj.bcebos.com/fastdeploy/third_libs/rknn_toolkit2-1.4.2b3+0bdd72ff-cp36-cp36m-linux_x86_64.whl +pip install rknn_toolkit2-1.4.2b3+0bdd72ff-cp36-cp36m-linux_x86_64.whl +``` + +## 安装FastDeploy C++ SDK + +针对RK356X和RK3588的性能差异，我们提供了两种编译FastDeploy的方式。 + + +### 板端编译FastDeploy C++ SDK + +针对RK3588，其CPU性能较强，板端编译的速度还是可以接受的，我们推荐在板端上进行编译。以下教程在RK356X(debian10),RK3588(debian 11) 环境下完成。 + +```bash +git clone https://github.com/PaddlePaddle/FastDeploy.git +cd FastDeploy + +# 如果您使用的是develop分支输入以下命令 +git checkout develop + +mkdir build && cd build +cmake .. -DENABLE_ORT_BACKEND=ON \ + -DENABLE_RKNPU2_BACKEND=ON \ + -DENABLE_VISION=ON \ + -DRKNN2_TARGET_SOC=RK3588 \ + -DCMAKE_INSTALL_PREFIX=${PWD}/fastdeploy-0.0.0 +make -j8 +make install +``` + +### 交叉编译FastDeploy C++ SDK + +针对RK356X，其CPU性能较弱，我们推荐使用交叉编译进行编译。以下教程在Ubuntu 22.04环境下完成。 + +```bash +git clone https://github.com/PaddlePaddle/FastDeploy.git +cd FastDeploy + +# 如果您使用的是develop分支输入以下命令 +git checkout develop + +mkdir build && cd build +cmake .. -DCMAKE_C_COMPILER=/home/zbc/opt/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu-gcc \ + -DCMAKE_CXX_COMPILER=/home/zbc/opt/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu-g++ \ + -DCMAKE_TOOLCHAIN_FILE=./../cmake/toolchain.cmake \ + -DTARGET_ABI=arm64 \ + -DENABLE_ORT_BACKEND=OFF \ + -DENABLE_RKNPU2_BACKEND=ON \ + -DENABLE_VISION=ON \ + -DRKNN2_TARGET_SOC=RK356X \ + -DCMAKE_INSTALL_PREFIX=${PWD}/fastdeploy-0.0.0 +make -j8 +make install +``` + +如果你找不到编译工具，你可以复制[交叉编译工具](https://bj.bcebos.com/paddle2onnx/libs/gcc-linaro-6.3.1-2017.zip)进行下载。 + +### 配置环境变量 + +为了方便大家配置环境变量，FastDeploy提供了一键配置环境变量的脚本，在运行程序前，你需要执行以下命令 + +```bash +# 临时配置 +source PathToFastDeploySDK/fastdeploy_init.sh + +# 永久配置 +source PathToFastDeploySDK/fastdeploy_init.sh +sudo cp PathToFastDeploySDK/fastdeploy_libs.conf /etc/ld.so.conf.d/ +sudo ldconfig +``` + +## 编译FastDeploy Python SDK + +除了NPU，Rockchip的芯片还有其他的一些功能。 +这些功能大部分都是需要C/C++进行编程，因此如果您使用到了这些模块，我们不推荐您使用Python SDK. +Python SDK的编译暂时仅支持板端编译, 以下教程在RK3568(debian 10)、RK3588(debian 11) 环境下完成。Python打包依赖`wheel`，编译前请先执行`pip install wheel` + + +```bash +git clone https://github.com/PaddlePaddle/FastDeploy.git +cd FastDeploy + +# 如果您使用的是develop分支输入以下命令 +git checkout develop + +cd python +export ENABLE_ORT_BACKEND=ON +export ENABLE_RKNPU2_BACKEND=ON +export ENABLE_VISION=ON + +# 请根据你的开发版的不同，选择RK3588和RK356X +export RKNN2_TARGET_SOC=RK3588 + +# 如果你的核心板的运行内存大于等于8G，我们建议您执行以下命令进行编译。 +python3 setup.py build +# 值得注意的是，如果你的核心板的运行内存小于8G，我们建议您执行以下命令进行编译。 +python3 setup.py build -j1 + +python3 setup.py bdist_wheel +cd dist +pip3 install fastdeploy_python-0.0.0-cp39-cp39-linux_aarch64.whl +``` + ## 导航目录 * [RKNPU2开发环境搭建](../faq/rknpu2/environment.md) diff --git a/examples/vision/ocr/PP-OCRv3/rknpu2/cpp/README.md b/examples/vision/ocr/PP-OCRv3/rknpu2/cpp/README.md index af5be5360a..9864627811 100755 --- a/examples/vision/ocr/PP-OCRv3/rknpu2/cpp/README.md +++ b/examples/vision/ocr/PP-OCRv3/rknpu2/cpp/README.md @@ -5,8 +5,8 @@ This directory provides examples that `infer.cc` fast finishes the deployment of Two steps before deployment -- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md) -- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md) +- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md) +- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md) Taking the CPU inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model. @@ -49,7 +49,7 @@ The visualized result after running is as follows ## Other Documents - [C++ API Reference](https://baidu-paddle.github.io/fastdeploy-api/cpp/html/) -- [PPOCR Model Description](../../) +- [PPOCR Model Description](../README.md) - [PPOCRv3 Python Deployment](../python) - [Model Prediction Results](../../../../../../docs/en/faq/how_to_change_backend.md) - [How to switch the model inference backend engine](../../../../../../docs/en/faq/how_to_change_backend.md) diff --git a/examples/vision/ocr/PP-OCRv3/rknpu2/python/README.md b/examples/vision/ocr/PP-OCRv3/rknpu2/python/README.md index d281daf833..b12cef6610 100755 --- a/examples/vision/ocr/PP-OCRv3/rknpu2/python/README.md +++ b/examples/vision/ocr/PP-OCRv3/rknpu2/python/README.md @@ -3,8 +3,8 @@ English | [简体中文](README_CN.md) Two steps before deployment -- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md) -- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md) +- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md) +- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md) This directory provides examples that `infer.py` fast finishes the deployment of PPOCRv3 on CPU/GPU and GPU accelerated by TensorRT. The script is as follows @@ -43,7 +43,7 @@ The visualized result after running is as follows ## Other Documents - [Python API reference](https://baidu-paddle.github.io/fastdeploy-api/python/html/) -- [PPOCR Model Description](../../) +- [PPOCR Model Description](../README.md) - [PPOCRv3 C++ Deployment](../cpp) -- [Model Prediction Results](../../../../../../docs/api/vision_results/) +- [Model Prediction Results](../../../../../../docs/api/vision_results/README.md) - [How to switch the model inference backend engine](../../../../../../docs/en/faq/how_to_change_backend.md) diff --git a/examples/vision/ocr/PP-OCRv3/rknpu2/python/README_CN.md b/examples/vision/ocr/PP-OCRv3/rknpu2/python/README_CN.md index 663a6b62d0..e2f6e3d430 100644 --- a/examples/vision/ocr/PP-OCRv3/rknpu2/python/README_CN.md +++ b/examples/vision/ocr/PP-OCRv3/rknpu2/python/README_CN.md @@ -3,8 +3,8 @@ 在部署前，需确认以下两个步骤 -- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md) -- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md) +- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md) +- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md) 本目录下提供`infer.py`快速完成PPOCRv3在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成 @@ -56,7 +56,7 @@ python3 infer_static_shape.py \ ## 其它文档 - [Python API文档查阅](https://baidu-paddle.github.io/fastdeploy-api/python/html/) -- [PPOCR 系列模型介绍](../../) +- [PPOCR 系列模型介绍](../README.md) - [PPOCRv3 C++部署](../cpp) -- [模型预测结果说明](../../../../../../docs/api/vision_results/) +- [模型预测结果说明](../../../../../../docs/api/vision_results/README_CN.md) - [如何切换模型推理后端引擎](../../../../../../docs/cn/faq/how_to_change_backend.md)