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# PP-YOLOE 量化模型 C++ 部署示例
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English | [简体中文](README_CN.md)
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# PP-YOLOE Quantitative Model C++ Deployment Example
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本目录下提供的 `infer.cc`，可以帮助用户快速完成 PP-YOLOE 量化模型在 RV1126 上的部署推理加速。
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`infer.cc` in this directory can help you quickly complete the inference acceleration of PP-YOLOE quantization model deployment on RV1126.
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## 部署准备
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### FastDeploy 交叉编译环境准备
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1. 软硬件环境满足要求，以及交叉编译环境的准备，请参考：[FastDeploy 交叉编译环境准备](../../../../../../docs/cn/build_and_install/rv1126.md#交叉编译环境搭建)
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## Deployment Preparations
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### FastDeploy Cross-compile Environment Preparations
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1. For the software and hardware environment, and the cross-compile environment, please refer to [Preparations for FastDeploy Cross-compile environment](../../../../../../docs/en/build_and_install/rv1126.md#Cross-compilation-environment-construction).
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### 模型准备
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1. 用户可以直接使用由 FastDeploy 提供的量化模型进行部署。
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2. 用户可以先使用 PaddleDetection 自行导出 Float32 模型，注意导出模型模型时设置参数：use_shared_conv=False，更多细节请参考：[PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe)
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3. 用户可以使用 FastDeploy 提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/)，自行进行模型量化, 并使用产出的量化模型进行部署。（注意: 推理量化后的检测模型仍然需要FP32模型文件夹下的 infer_cfg.yml 文件，自行量化的模型文件夹内不包含此 yaml 文件，用户从 FP32 模型文件夹下复制此yaml文件到量化后的模型文件夹内即可。）
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4. 模型需要异构计算，异构计算文件可以参考：[异构计算](./../../../../../../docs/cn/faq/heterogeneous_computing_on_timvx_npu.md)，由于 FastDeploy 已经提供了模型，可以先测试我们提供的异构文件，验证精度是否符合要求。
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### Model Preparations
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1. You can directly use the quantized model provided by FastDeploy for deployment.
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2. You can use PaddleDetection to export Float32 models, note that you need to set the parameter when exporting model: use_shared_conv=False. For more information: [PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe).
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3. You can use [one-click automatical compression tool](../../../../../../tools/common_tools/auto_compression/) provided by FastDeploy to quantize model by yourself, and use the generated quantized model for deployment.(Note: The quantized classification model still needs the infer_cfg.yml file in the FP32 model folder. Self-quantized model folder does not contain this yaml file, you can copy it from the FP32 model folder to the quantized model folder.)
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4. The model requires heterogeneous computation. Please refer to: [Heterogeneous Computation](./../../../../../../docs/en/faq/heterogeneous_computing_on_timvx_npu.md). Since the model is already provided, you can test the heterogeneous file we provide first to verify whether the accuracy meets the requirements.
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更多量化相关相关信息可查阅[模型量化](../../quantize/README.md)
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For more information, please refer to [Model Quantization](../../quantize/README.md)
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## 在 RV1126 上部署量化后的 PP-YOLOE 检测模型
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请按照以下步骤完成在 RV1126 上部署 PP-YOLOE 量化模型：
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1. 交叉编译编译 FastDeploy 库，具体请参考：[交叉编译 FastDeploy](../../../../../../docs/cn/build_and_install/rv1126.md#基于-paddlelite-的-fastdeploy-交叉编译库编译)
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## Deploying the Quantized PP-YOLOE Detection model on RV1126
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Please follow these steps to complete the deployment of the PP-YOLOE quantization model on RV1126.
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1. Cross-compile the FastDeploy library as described in [Cross-compile FastDeploy](../../../../../../docs/en/build_and_install/rv1126.md#FastDeploy-cross-compilation-library-compilation-based-on-Paddle-Lite)
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2. 将编译后的库拷贝到当前目录，可使用如下命令：
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2. Copy the compiled library to the current directory. You can run this line:
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```bash
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cp -r FastDeploy/build/fastdeploy-timvx/ FastDeploy/examples/vision/detection/paddledetection/rv1126/cpp
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```
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3. 在当前路径下载部署所需的模型和示例图片：
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3. Download the model and example images required for deployment in current path.
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```bash
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cd FastDeploy/examples/vision/detection/paddledetection/rv1126/cpp
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mkdir models && mkdir images
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@@ -34,26 +35,26 @@ wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/0000000
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cp -r 000000014439.jpg images
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```
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4. 编译部署示例，可使入如下命令：
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4. Compile the deployment example. You can run the following lines:
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```bash
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cd FastDeploy/examples/vision/detection/paddledetection/rv1126/cpp
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mkdir build && cd build
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cmake -DCMAKE_TOOLCHAIN_FILE=${PWD}/../fastdeploy-timvx/toolchain.cmake -DFASTDEPLOY_INSTALL_DIR=${PWD}/../fastdeploy-timvx -DTARGET_ABI=armhf ..
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make -j8
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make install
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# 成功编译之后，会生成 install 文件夹，里面有一个运行 demo 和部署所需的库
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# After success, an install folder will be created with a running demo and libraries required for deployment.
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```
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5. 基于 adb 工具部署 PP-YOLOE 检测模型到 Rockchip RV1126，可使用如下命令：
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5. Deploy the PP-YOLOE detection model to Rockchip RV1126 based on adb. You can run the following lines:
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```bash
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# 进入 install 目录
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# Go to the install directory.
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cd FastDeploy/examples/vision/detection/paddledetection/rv1126/cpp/build/install/
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# 如下命令表示：bash run_with_adb.sh 需要运行的demo 模型路径 图片路径 设备的DEVICE_ID
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# The following line represents: bash run_with_adb.sh, demo needed to run, model path, image path, DEVICE ID.
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bash run_with_adb.sh infer_demo ppyoloe_noshare_qat 000000014439.jpg $DEVICE_ID
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```
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部署成功后运行结果如下：
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The output is:
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<img width="640" src="https://user-images.githubusercontent.com/30516196/203708564-43c49485-9b48-4eb2-8fe7-0fa517979fff.png">
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需要特别注意的是，在 RV1126 上部署的模型需要是量化后的模型，模型的量化请参考：[模型量化](../../../../../../docs/cn/quantize.md)
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Please note that the model deployed on RV1126 needs to be quantized. You can refer to [Model Quantization](../../../../../../docs/en/quantize.md)
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