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# PP-YOLOE-l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-l Quantitative Model C++ Deployment Example
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本目录下提供的`infer_ppyoloe.cc`,可以帮助用户快速完成PP-YOLOE-l量化模型在CPU/GPU上的部署推理加速.
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`infer_ppyoloe.cc` in this directory can help you quickly complete the inference acceleration of PP-YOLOE-l quantization model deployment on CPU/GPU.
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## 部署准备
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### FastDeploy环境准备
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- 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)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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## Deployment Preparations
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### FastDeploy Environment Preparations
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- 1. For the software and hardware requirements, please refer to [FastDeploy Environment Requirements](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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- 2. For the installation of FastDeploy Python whl package, please refer to [FastDeploy Python Installation](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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### 量化模型准备
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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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### Quantized 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 [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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## 以量化后的PP-YOLOE-l模型为例, 进行部署。支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
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在本目录执行如下命令即可完成编译,以及量化模型部署.
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## Take the Quantized PP-YOLOE-l Model as an example for Deployment, FastDeploy version 0.7.0 or higher is required (x.x.x>=0.7.0)
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Run the following commands in this directory to compile and deploy the quantized model.
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```bash
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mkdir build
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cd build
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# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
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# Download pre-compiled FastDeploy libraries. You can choose the appropriate version from `pre-compiled FastDeploy libraries` mentioned above.
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
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tar xvf fastdeploy-linux-x64-x.x.x.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
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make -j
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#下载FastDeloy提供的ppyoloe_crn_l_300e_coco量化模型文件和测试图片
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# Download the ppyoloe_crn_l_300e_coco quantized model and test images provided by FastDeloy.
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco_qat.tar
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tar -xvf ppyoloe_crn_l_300e_coco_qat.tar
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# 在CPU上使用ONNX Runtime推理量化模型
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# Use ONNX Runtime inference quantization model on CPU.
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./infer_ppyoloe_demo ppyoloe_crn_l_300e_coco_qat 000000014439.jpg 0
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# 在GPU上使用TensorRT推理量化模型
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# Use TensorRT inference quantization model on GPU.
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./infer_ppyoloe_demo ppyoloe_crn_l_300e_coco_qat 000000014439.jpg 1
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# 在GPU上使用Paddle-TensorRT推理量化模型
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# Use Paddle-TensorRT inference quantization model on GPU.
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./infer_ppyoloe_demo ppyoloe_crn_l_300e_coco_qat 000000014439.jpg 2
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```
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@@ -0,0 +1,37 @@
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[English](README.md) | 简体中文
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# PP-YOLOE-l量化模型 C++部署示例
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本目录下提供的`infer_ppyoloe.cc`,可以帮助用户快速完成PP-YOLOE-l量化模型在CPU/GPU上的部署推理加速.
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## 部署准备
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### FastDeploy环境准备
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- 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)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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### 量化模型准备
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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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## 以量化后的PP-YOLOE-l模型为例, 进行部署。支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
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在本目录执行如下命令即可完成编译,以及量化模型部署.
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```bash
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mkdir build
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cd build
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# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
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tar xvf fastdeploy-linux-x64-x.x.x.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
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make -j
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#下载FastDeloy提供的ppyoloe_crn_l_300e_coco量化模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco_qat.tar
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tar -xvf ppyoloe_crn_l_300e_coco_qat.tar
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# 在CPU上使用ONNX Runtime推理量化模型
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./infer_ppyoloe_demo ppyoloe_crn_l_300e_coco_qat 000000014439.jpg 0
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# 在GPU上使用TensorRT推理量化模型
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./infer_ppyoloe_demo ppyoloe_crn_l_300e_coco_qat 000000014439.jpg 1
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# 在GPU上使用Paddle-TensorRT推理量化模型
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./infer_ppyoloe_demo ppyoloe_crn_l_300e_coco_qat 000000014439.jpg 2
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```
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