[Example] Move SOLOv2 jetson example -> cpp (#1600)

* move solov2

* move solov2

---------

Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>

examples/vision/detection/paddledetection/cpp/CMakeLists.txt

@@ -71,3 +71,6 @@ target_link_libraries(infer_tood_demo ${FASTDEPLOY_LIBS})
 
 add_executable(infer_gfl_demo ${PROJECT_SOURCE_DIR}/infer_gfl.cc)
 target_link_libraries(infer_gfl_demo ${FASTDEPLOY_LIBS})
+
+add_executable(infer_solov2_demo ${PROJECT_SOURCE_DIR}/infer_solov2.cc)
+target_link_libraries(infer_solov2_demo ${FASTDEPLOY_LIBS})

examples/vision/detection/paddledetection/jetson/README.md

@@ -1,21 +0,0 @@
-English | [简体中文](README_CN.md)
-
-# PaddleDetection Model Deployment
-
-FastDeploy supports the SOLOV2 model of [PaddleDetection version 2.6](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6).
-
-You can enter the following command to get the static diagram model of SOLOV2.
-
-```bash
-# install PaddleDetection
-git clone https://github.com/PaddlePaddle/PaddleDetection.git
-cd PaddleDetection
-
-python tools/export_model.py -c configs/solov2/solov2_r50_fpn_1x_coco.yml --output_dir=./inference_model \
- -o weights=https://paddledet.bj.bcebos.com/models/solov2_r50_fpn_1x_coco.pdparams
-```
-
-## Detailed Deployment Documents
-
-- [Python Deployment](python)
-- [C++ Deployment](cpp)

examples/vision/detection/paddledetection/jetson/README_CN.md

@@ -1,20 +0,0 @@
-[English](README.md) | 简体中文
-# PaddleDetection模型部署
-
-FastDeploy支持[PaddleDetection 2.6](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6)版本的SOLOv2模型，
-
-你可以输入以下命令得到SOLOv2的静态图模型。
-
-```bash
-# install PaddleDetection
-git clone https://github.com/PaddlePaddle/PaddleDetection.git
-cd PaddleDetection
-
-python tools/export_model.py -c configs/solov2/solov2_r50_fpn_1x_coco.yml --output_dir=./inference_model \
- -o weights=https://paddledet.bj.bcebos.com/models/solov2_r50_fpn_1x_coco.pdparams
-```
-
-## 详细部署文档
-
-- [Python部署](python)
-- [C++部署](cpp)

examples/vision/detection/paddledetection/jetson/cpp/CMakeLists.txt

@@ -1,11 +0,0 @@
-PROJECT(infer_demo C CXX)
-CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
-
-option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
-
-include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
-
-include_directories(${FASTDEPLOY_INCS})
-
-add_executable(infer_solov2_demo ${PROJECT_SOURCE_DIR}/infer_solov2.cc)
-target_link_libraries(infer_solov2_demo ${FASTDEPLOY_LIBS})

examples/vision/detection/paddledetection/jetson/cpp/README.md

@@ -1,28 +0,0 @@
-English | [简体中文](README_CN.md)
-# PaddleDetection C++ Deployment Example
-
-This directory provides examples that `infer_xxx.cc` fast finishes the deployment of PaddleDetection models, including SOLOv2 on CPU/GPU and GPU accelerated by TensorRT.
-
-Before deployment, two steps require confirmation
-
-- 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 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.
-
-```bash
-mkdir build
-cd build
-
-# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
-tar xvf fastdeploy-linux-x64-x.x.x.tgz
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
-make -j
-
-wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
-# CPU inference
-./infer_solov2_demo ./solov2_r50_fpn_1x_coco 000000014439.jpg 0
-# GPU inference
-./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 1
-```

examples/vision/detection/paddledetection/jetson/cpp/README_CN.md

@@ -1,29 +0,0 @@
-[English](README.md) | 简体中文
-# PaddleDetection C++部署示例
-
-本目录下提供`infer_xxx.cc`快速完成PaddleDetection模型包括SOLOv2在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
-
-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. 根据开发环境，下载预编译部署库和examples代码，参考[FastDeploy预编译库](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
-
-以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
-
-
-```bash
-mkdir build
-cd build
-
-# 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
-wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
-tar xvf fastdeploy-linux-x64-x.x.x.tgz
-cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
-make -j
-
-wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
-# CPU推理
-./infer_solov2_demo ./solov2_r50_fpn_1x_coco 000000014439.jpg 0
-# GPU推理
-./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 1
-```

examples/vision/detection/paddledetection/jetson/python/README.md

@@ -1,96 +0,0 @@
-English | [简体中文](README_CN.md)
-# PaddleDetection Python Deployment Example
-
-Before deployment, two steps require confirmation.
-
-- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
-- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
-
-This directory provides examples that `infer_xxx.py` fast finishes the deployment of PPYOLOE/PicoDet models on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
-
-```bash
-# Download deployment example code
-git clone https://github.com/PaddlePaddle/FastDeploy.git
-cd FastDeploy/examples/vision/detection/paddledetection/python/
-
-# Download the PPYOLOE model file and test images
-wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
-wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
-tar xvf ppyoloe_crn_l_300e_coco.tgz
-
-# CPU inference
-python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device cpu
-# GPU inference
-python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device gpu
-# TensorRT inference on GPU  （Attention: It is somewhat time-consuming for the operation of model serialization when running TensorRT inference for the first time. Please be patient.）
-python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device gpu --use_trt True
-# Kunlunxin XPU Inference
-python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device kunlunxin
-# Huawei Ascend Inference
-python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device ascend
-```
-
-The visualized result after running is as follows
-<div  align="center">  
-<img src="https://user-images.githubusercontent.com/19339784/184326520-7075e907-10ed-4fad-93f8-52d0e35d4964.jpg", width=480px, height=320px />
-</div>
-
-## PaddleDetection Python Interface
-
-```python
-fastdeploy.vision.detection.PPYOLOE(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PicoDet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PaddleYOLOX(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.YOLOv3(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PPYOLO(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.FasterRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.MaskRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.SSD(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PaddleYOLOv5(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PaddleYOLOv6(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PaddleYOLOv7(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.RTMDet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.CascadeRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PSSDet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.RetinaNet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PPYOLOESOD(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.FCOS(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.TTFNet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.TOOD(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.GFL(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.SOLOv2(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-```
-
-PaddleDetection model loading and initialization, among which model_file and params_file are the exported Paddle model format. config_file is the configuration yaml file exported by PaddleDetection simultaneously
-
-**Parameter**
-
-> * **model_file**(str): Model file path
-> * **params_file**(str): Parameter file path
-> * **config_file**(str): Inference configuration yaml file path
-> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default. (use the default configuration)
-> * **model_format**(ModelFormat): Model format. Paddle format by default
-
-### predict Function
-
-PaddleDetection models, including PPYOLOE/PicoDet/PaddleYOLOX/YOLOv3/PPYOLO/FasterRCNN, all provide the following member functions for image detection
-> ```python
-> PPYOLOE.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
-> ```
->
-> Model prediction interface. Input images and output results directly.
->
-> **Parameter**
->
-> > * **image_data**(np.ndarray): Input data in HWC or BGR format
-
-> **Return**
->
-> > Return `fastdeploy.vision.DetectionResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure.
-
-## Other Documents
-
-- [PaddleDetection Model Description](..)
-- [PaddleDetection C++ Deployment](../cpp)
-- [Model Prediction Results](../../../../../docs/api/vision_results/)
-- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)

examples/vision/detection/paddledetection/jetson/python/README_CN.md

@@ -1,96 +0,0 @@
-[English](README.md) | 简体中文
-# PaddleDetection Python部署示例
-
-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)
-
-本目录下提供`infer_xxx.py`快速完成PPYOLOE/PicoDet等模型在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
-
-```bash
-#下载部署示例代码
-git clone https://github.com/PaddlePaddle/FastDeploy.git
-cd FastDeploy/examples/vision/detection/paddledetection/python/
-
-#下载PPYOLOE模型文件和测试图片
-wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
-wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
-tar xvf ppyoloe_crn_l_300e_coco.tgz
-
-# CPU推理
-python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device cpu
-# GPU推理
-python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device gpu
-# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
-python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device gpu --use_trt True
-# 昆仑芯XPU推理
-python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device kunlunxin
-# 华为昇腾推理
-python infer_ppyoloe.py --model_dir ppyoloe_crn_l_300e_coco --image 000000014439.jpg --device ascend
-```
-
-运行完成可视化结果如下图所示
-<div  align="center">  
-<img src="https://user-images.githubusercontent.com/19339784/184326520-7075e907-10ed-4fad-93f8-52d0e35d4964.jpg", width=480px, height=320px />
-</div>
-
-## PaddleDetection Python接口
-
-```python
-fastdeploy.vision.detection.PPYOLOE(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PicoDet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PaddleYOLOX(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.YOLOv3(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PPYOLO(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.FasterRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.MaskRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.SSD(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PaddleYOLOv5(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PaddleYOLOv6(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PaddleYOLOv7(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.RTMDet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.CascadeRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PSSDet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.RetinaNet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.PPYOLOESOD(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.FCOS(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.TTFNet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.TOOD(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.GFL(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-fastdeploy.vision.detection.SOLOv2(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
-```
-
-PaddleDetection模型加载和初始化，其中model_file， params_file为导出的Paddle部署模型格式, config_file为PaddleDetection同时导出的部署配置yaml文件
-
-**参数**
-
-> * **model_file**(str): 模型文件路径
-> * **params_file**(str): 参数文件路径
-> * **config_file**(str): 推理配置yaml文件路径
-> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
-> * **model_format**(ModelFormat): 模型格式，默认为Paddle
-
-### predict函数
-
-PaddleDetection中各个模型，包括PPYOLOE/PicoDet/PaddleYOLOX/YOLOv3/PPYOLO/FasterRCNN，均提供如下同样的成员函数用于进行图像的检测
-> ```python
-> PPYOLOE.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
-> ```
->
-> 模型预测结口，输入图像直接输出检测结果。
->
-> **参数**
->
-> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
-
-> **返回**
->
-> > 返回`fastdeploy.vision.DetectionResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
-
-## 其它文档
-
-- [PaddleDetection 模型介绍](..)
-- [PaddleDetection C++部署](../cpp)
-- [模型预测结果说明](../../../../../docs/api/vision_results/)
-- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)