[Other] add code and docs for ppclas examples (PaddlePaddle#1312)

* add code and docs for ppclas examples * fix doc * add code for printing results * add ppcls demo and docs * modify example according to refined c api * modify example code and docs for ppcls and ppdet * modify example code and docs for ppcls and ppdet * update ppdet demo * fix demo codes * fix doc * release resource when failed * fix * fix name * fix name
heliqi · Feb 17, 2023 · 859896c · 859896c
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diff --git a/csharp/fastdeploy/vision/detection/ppdet/model.cs b/csharp/fastdeploy/vision/detection/ppdet/model.cs
diff --git a/examples/vision/classification/paddleclas/c/CMakeLists.txt b/examples/vision/classification/paddleclas/c/CMakeLists.txt
@@ -0,0 +1,13 @@
+PROJECT(infer_demo C)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.c)
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
diff --git a/examples/vision/classification/paddleclas/c/README.md b/examples/vision/classification/paddleclas/c/README.md
@@ -0,0 +1,183 @@
+English | [简体中文](README_CN.md)
+# PaddleClas C Deployment Example
+
+This directory provides examples that `infer.c` fast finishes the deployment of PaddleClas models on CPU/GPU.
+
+Before deployment, two steps require confirmation.
+
+- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).  
+- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).
+
+Taking ResNet50_vd inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 1.0.4 or above (x.x.x>=1.0.4)  is required to support this model.
+
+```bash
+mkdir build
+cd build
+# Download FastDeploy precompiled library. Users can choose your appropriate version in the`FastDeploy Precompiled Library` mentioned above
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
+make -j
+
+# Download ResNet50_vd model file and test images
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+
+# CPU inference
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0
+# GPU inference
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1
+```
+
+The above command works for Linux or MacOS. Refer to
+- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/cn/faq/use_sdk_on_windows.md) for SDK use-pattern in Windows
+
+## PaddleClas C Interface
+
+### RuntimeOption
+
+```c
+FD_C_RuntimeOptionWrapper* FD_C_CreateRuntimeOptionWrapper()
+```
+
+> Create a RuntimeOption object, and return a pointer to manipulate it.
+>
+> **Return**
+>
+> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): Pointer to manipulate RuntimeOption object.
+
+
+```c
+void FD_C_RuntimeOptionWrapperUseCpu(
+     FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper)
+```
+
+> Enable Cpu inference.
+>
+> **Params**
+>
+> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): Pointer to manipulate RuntimeOption object.
+
+```c
+void FD_C_RuntimeOptionWrapperUseGpu(
+    FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
+    int gpu_id)
+```
+> 开启GPU推理
+>
+> **参数**
+>
+> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): Pointer to manipulate RuntimeOption object.
+> * **gpu_id**(int): gpu id
+
+
+### Model
+
+```c
+
+FD_C_PaddleClasModelWrapper* FD_C_CreatePaddleClasModelWrapper(
+    const char* model_file, const char* params_file, const char* config_file,
+    FD_C_RuntimeOptionWrapper* runtime_option,
+    const FD_C_ModelFormat model_format)
+
+```
+
+> Create a PaddleClas model object, and return a pointer to manipulate it.
+>
+> **Params**
+>
+> * **model_file**(const char*): Model file path
+> * **params_file**(const char*): Parameter file path
+> * **config_file**(const char*): Configuration file path, which is the deployment yaml file exported by PaddleClas.
+> * **runtime_option**(FD_C_RuntimeOptionWrapper*): Backend inference configuration. None by default, which is the default configuration
+> * **model_format**(FD_C_ModelFormat): Model format. Paddle format by default
+>
+> **Return**
+> * **fd_c_ppclas_wrapper**(FD_C_PaddleClasModelWrapper*): Pointer to manipulate PaddleClas object.
+
+
+#### Read and write image
+
+```c
+FD_C_Mat FD_C_Imread(const char* imgpath)
+```
+
+> Read an image, and return a pointer to cv::Mat.
+>
+> **Params**
+>
+> * **imgpath**(const char*): image path
+>
+> **Return**
+>
+> * **imgmat**(FD_C_Mat): pointer to cv::Mat object which holds the image.
+
+
+```c
+FD_C_Bool FD_C_Imwrite(const char* savepath,  FD_C_Mat img);
+```
+
+> Write image to a file.
+>
+> **Params**
+>
+> * **savepath**(const char*): save path
+> * **img**(FD_C_Mat): pointer to cv::Mat object
+>
+> **Return**
+>
+> * **result**(FD_C_Bool): bool to indicate success or failure
+
+
+#### Prediction
+
+```c
+FD_C_Bool FD_C_PaddleClasModelWrapperPredict(
+    __fd_take FD_C_PaddleClasModelWrapper* fd_c_ppclas_wrapper, FD_C_Mat img,
+    FD_C_ClassifyResult* fd_c_ppclas_result)
+```
+>
+> Predict an image, and generate classification result.
+>
+> **Params**
+> * **fd_c_ppclas_wrapper**(FD_C_PaddleClasModelWrapper*): pointer to manipulate PaddleClas object
+> * **img**（FD_C_Mat）: pointer to cv::Mat object, which can be obained by FD_C_Imread interface
+> * **fd_c_ppclas_result** (FD_C_ClassifyResult*): The classification result, including label_id, and the corresponding confidence. Refer to [Visual Model Prediction Results](../../../../../docs/api/vision_results/) for the description of ClassifyResult
+
+
+#### Result
+
+```c
+FD_C_ClassifyResultWrapper* FD_C_CreateClassifyResultWrapperFromData(
+    FD_C_ClassifyResult* fd_c_classify_result)
+```
+>
+> Create a pointer to FD_C_ClassifyResultWrapper structure, which contains `fastdeploy::vision::ClassifyResult` object in C++. You can call methods in C++ ClassifyResult object by C API with this pointer.
+>
+> **Params**
+> * **fd_c_classify_result**(FD_C_ClassifyResult*): pointer to FD_C_ClassifyResult structure
+>
+> **Return**
+> * **fd_c_classify_result_wrapper**(FD_C_ClassifyResultWrapper*): pointer to FD_C_ClassifyResultWrapper structure
+
+
+```c
+char* FD_C_ClassifyResultWrapperStr(
+    FD_C_ClassifyResultWrapper* fd_c_classify_result_wrapper);
+```
+>
+> Call Str() methods in `fastdeploy::vision::ClassifyResult` object contained in FD_C_ClassifyResultWrapper structure，and return a string to describe information in result.
+>
+> **Params**
+> * **fd_c_classify_result_wrapper**(FD_C_ClassifyResultWrapper*): pointer to FD_C_ClassifyResultWrapper structure
+>
+> **Return**
+> * **str**(char*): a string to describe information in result
+
+
+- [Model Description](../../)
+- [Python Deployment](../python)
+- [Visual Model prediction results](../../../../../docs/api/vision_results/)
+- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)
diff --git a/examples/vision/classification/paddleclas/c/README_CN.md b/examples/vision/classification/paddleclas/c/README_CN.md
@@ -0,0 +1,189 @@
+[English](README.md) | 简体中文
+# PaddleClas C 部署示例
+
+本目录下提供`infer_xxx.c`来调用C API快速完成PaddleClas系列模型在CPU/GPU上部署的示例。
+
+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samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
+
+以Linux上ResNet50_vd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1.0.4以上(x.x.x>=1.0.4)
+
+```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
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+
+# CPU推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0
+# GPU推理
+./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1
+```
+
+以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:  
+- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
+
+如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
+- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
+
+## PaddleClas C API接口
+
+### 配置
+
+```c
+FD_C_RuntimeOptionWrapper* FD_C_CreateRuntimeOptionWrapper()
+```
+
+> 创建一个RuntimeOption的配置对象，并且返回操作它的指针。
+>
+> **返回**
+>
+> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption对象的指针
+
+
+```c
+void FD_C_RuntimeOptionWrapperUseCpu(
+     FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper)
+```
+
+> 开启CPU推理
+>
+> **参数**
+>
+> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption对象的指针
+
+```c
+void FD_C_RuntimeOptionWrapperUseGpu(
+    FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
+    int gpu_id)
+```
+> 开启GPU推理
+>
+> **参数**
+>
+> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption对象的指针
+> * **gpu_id**(int): 显卡号
+
+
+### 模型
+
+```c
+
+FD_C_PaddleClasModelWrapper* FD_C_CreatePaddleClasModelWrapper(
+    const char* model_file, const char* params_file, const char* config_file,
+    FD_C_RuntimeOptionWrapper* runtime_option,
+    const FD_C_ModelFormat model_format)
+
+```
+
+> 创建一个PaddleClas的模型，并且返回操作它的指针。
+>
+> **参数**
+>
+> * **model_file**(const char*): 模型文件路径
+> * **params_file**(const char*): 参数文件路径
+> * **config_file**(const char*): 配置文件路径，即PaddleClas导出的部署yaml文件
+> * **runtime_option**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption的指针，表示后端推理配置
+> * **model_format**(FD_C_ModelFormat): 模型格式
+>
+> **返回**
+> * **fd_c_ppclas_wrapper**(FD_C_PaddleClasModelWrapper*): 指向PaddleClas模型对象的指针
+
+
+#### 读写图像
+
+```c
+FD_C_Mat FD_C_Imread(const char* imgpath)
+```
+
+> 读取一个图像，并且返回cv::Mat的指针。
+>
+> **参数**
+>
+> * **imgpath**(const char*): 图像文件路径
+>
+> **返回**
+>
+> * **imgmat**(FD_C_Mat): 指向图像数据cv::Mat的指针。
+
+
+```c
+FD_C_Bool FD_C_Imwrite(const char* savepath,  FD_C_Mat img);
+```
+
+> 将图像写入文件中。
+>
+> **参数**
+>
+> * **savepath**(const char*): 保存图像的路径
+> * **img**(FD_C_Mat): 指向图像数据的指针
+>
+> **返回**
+>
+> * **result**(FD_C_Bool): 表示操作是否成功
+
+
+#### Predict函数
+
+```c
+FD_C_Bool FD_C_PaddleClasModelWrapperPredict(
+    __fd_take FD_C_PaddleClasModelWrapper* fd_c_ppclas_wrapper, FD_C_Mat img,
+    FD_C_ClassifyResult* fd_c_ppclas_result)
+```
+>
+> 模型预测接口，输入图像直接并生成分类结果。
+>
+> **参数**
+> * **fd_c_ppclas_wrapper**(FD_C_PaddleClasModelWrapper*): 指向PaddleClas模型的指针
+> * **img**（FD_C_Mat）: 输入图像的指针，指向cv::Mat对象，可以调用FD_C_Imread读取图像获取
+> * **fd_c_ppclas_result**（FD_C_ClassifyResult*): 分类结果，包括label_id，以及相应的置信度, ClassifyResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
+
+
+#### Predict结果
+
+```c
+FD_C_ClassifyResultWrapper* FD_C_CreateClassifyResultWrapperFromData(
+    FD_C_ClassifyResult* fd_c_classify_result)
+```
+>
+> 创建一个FD_C_ClassifyResultWrapper对象的指针，FD_C_ClassifyResultWrapper中包含了C++的`fastdeploy::vision::ClassifyResult`对象，通过该指针，使用C API可以访问调用对应C++中的函数。
+>
+>
+> **参数**
+> * **fd_c_classify_result**(FD_C_ClassifyResult*): 指向FD_C_ClassifyResult对象的指针
+>
+> **返回**
+> * **fd_c_classify_result_wrapper**(FD_C_ClassifyResultWrapper*): 指向FD_C_ClassifyResultWrapper的指针
+
+
+```c
+char* FD_C_ClassifyResultWrapperStr(
+    FD_C_ClassifyResultWrapper* fd_c_classify_result_wrapper);
+```
+>
+> 调用FD_C_ClassifyResultWrapper所包含的`fastdeploy::vision::ClassifyResult`对象的Str()方法，返回相关结果内数据信息的字符串。
+>
+> **参数**
+> * **fd_c_classify_result_wrapper**(FD_C_ClassifyResultWrapper*): 指向FD_C_ClassifyResultWrapper对象的指针
+>
+> **返回**
+> * **str**(char*): 表示结果数据信息的字符串
+
+
+
+
+- [模型介绍](../../)
+- [Python部署](../python)
+- [视觉模型预测结果](../../../../../docs/api/vision_results/)
+- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)