[Doc]Add English version of documents in examples/ (#1042)

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examples/runtime/cpp/README.md

@@ -1,22 +1,23 @@
+English | [简体中文](README_CN.md)
 # C++推理
 
-在运行demo前，需确认以下两个步骤
+Before running demo, the following two steps need to be confirmed:
 
-- 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)
+- 1. Hardware and software environment meets the requirements. Please refer to [Environment requirements for FastDeploy](../../../docs/en/build_and_install/download_prebuilt_libraries.md).  
+- 2. Download pre-compiled libraries and samples according to the development environment. Please refer to [FastDeploy pre-compiled libraries](../../../docs/cn/build_and_install/download_prebuilt_libraries.md).
 
-本文档以 PaddleClas 分类模型 MobileNetV2 为例展示CPU上的推理示例
+This document shows an inference example on the CPU using the PaddleClas classification model MobileNetV2 as an example.
 
-## 1. 获取模型
+## 1. Obtaining the Model
 
 ```bash
 wget https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz
 tar xvf mobilenetv2.tgz
 ```
 
-## 2. 配置后端
+## 2. Backend Configuration
 
-如下C++代码保存为`infer_paddle_onnxruntime.cc`
+The following C++ code is saved as `infer_paddle_onnxruntime.cc`.
 
 ``` c++
 #include "fastdeploy/runtime.h"
@@ -66,35 +67,35 @@ int main(int argc, char* argv[]) {
   return 0;
 }
 ```
-加载完成，会输出提示如下，说明初始化的后端，以及运行的硬件设备
+When loading is complete, the following prompt will be output, indicating the initialized backend, and the running hardware devices.
 ```
 [INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init	Runtime initialized with Backend::OrtBackend in device Device::CPU.
 ```
 
-## 3. 准备CMakeLists.txt
+## 3. Prepare for CMakeLists.txt
 
-FastDeploy中包含多个依赖库，直接采用`g++`或编译器编译较为繁杂，推荐使用cmake进行编译配置。示例配置如下，
+FastDeploy contains several dependencies, it is complicated to compile directly with `g++` or compiler, so we recommend using cmake for compiling configuration. The sample configuration is as follows:
 
 ```cmake
 PROJECT(runtime_demo C CXX)
 CMAKE_MINIMUM_REQUIRED (VERSION 3.12)
 
-# 指定下载解压后的fastdeploy库路径
+# Specify the path to the fastdeploy library after downloading and unpacking.
 option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
 
 include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
 
-# 添加FastDeploy依赖头文件
+# Add FastDeploy dependency headers.
 include_directories(${FASTDEPLOY_INCS})
 
 add_executable(runtime_demo ${PROJECT_SOURCE_DIR}/infer_onnx_openvino.cc)
-# 添加FastDeploy库依赖
+# Adding FastDeploy library dependencies.
 target_link_libraries(runtime_demo ${FASTDEPLOY_LIBS})
 ```
 
-## 4. 编译可执行程序
+## 4. Compile executable program
 
-打开命令行终端，进入`infer_paddle_onnxruntime.cc`和`CMakeLists.txt`所在的目录，执行如下命令
+Open the terminal, go to the directory where `infer_paddle_onnxruntime.cc` and `CMakeLists.txt` are located, and run the following command:
 
 ```bash
 mkdir build & cd build
@@ -102,20 +103,20 @@ cmake .. -DFASTDEPLOY_INSTALL_DIR=$fastdeploy_cpp_sdk
 make -j
 ```
 
-```fastdeploy_cpp_sdk``` 为FastDeploy C++部署库路径
+```fastdeploy_cpp_sdk``` is path to FastDeploy C++ deployment libraries.
 
-编译完成后，使用如下命令执行可得到预测结果
+After compiling, run the following command and get the results.
 ```bash
 ./runtime_demo
 ```
-执行时如提示`error while loading shared libraries: libxxx.so: cannot open shared object file: No such file...`，说明程序执行时没有找到FastDeploy的库路径，可通过执行如下命令，将FastDeploy的库路径添加到环境变量之后，重新执行二进制程序。
+If you are prompted with `error while loading shared libraries: libxxx.so: cannot open shared object file: No such file... `, it means that the path to FastDeploy libraries is not found, you can run the program again after adding the path to the environment variable by executing the following command.
 ```bash
 source /Path/to/fastdeploy_cpp_sdk/fastdeploy_init.sh
 ```
 
-本示例代码在各平台(Windows/Linux/Mac)上通用，但编译过程仅支持(Linux/Mac)，Windows上使用msbuild进行编译，具体使用方式参考[Windows平台使用FastDeploy C++ SDK](../../../docs/cn/faq/use_sdk_on_windows.md)
+This sample code is common on all platforms (Windows/Linux/Mac), but the compilation process is only supported on (Linux/Mac),while using msbuild to compile on Windows. Please refer to [FastDeploy C++ SDK on Windows](../../../docs/en/faq/use_sdk_on_windows.md).
 
-## 其它文档
+## Other Documents
 
-- [Runtime Python 示例](../python)
-- [切换模型推理的硬件和后端](../../../docs/cn/faq/how_to_change_backend.md)
+- [A Python example for Runtime](../python)
+- [Switching hardware and backend for model inference](../../../docs/en/faq/how_to_change_backend.md)

examples/runtime/cpp/README_CN.md

@@ -0,0 +1,122 @@
+简体中文 ｜ [English](README.md)
+# C++推理
+
+在运行demo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)
+
+本文档以 PaddleClas 分类模型 MobileNetV2 为例展示CPU上的推理示例
+
+## 1. 获取模型
+
+```bash
+wget https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz
+tar xvf mobilenetv2.tgz
+```
+
+## 2. 配置后端
+
+如下C++代码保存为`infer_paddle_onnxruntime.cc`
+
+``` c++
+#include "fastdeploy/runtime.h"
+
+namespace fd = fastdeploy;
+
+int main(int argc, char* argv[]) {
+  std::string model_file = "mobilenetv2/inference.pdmodel";
+  std::string params_file = "mobilenetv2/inference.pdiparams";
+
+  // setup option
+  fd::RuntimeOption runtime_option;
+  runtime_option.SetModelPath(model_file, params_file, fd::ModelFormat::PADDLE);
+  runtime_option.UseOrtBackend();
+  runtime_option.SetCpuThreadNum(12);
+  // init runtime
+  std::unique_ptr<fd::Runtime> runtime =
+      std::unique_ptr<fd::Runtime>(new fd::Runtime());
+  if (!runtime->Init(runtime_option)) {
+    std::cerr << "--- Init FastDeploy Runitme Failed! "
+              << "\n--- Model:  " << model_file << std::endl;
+    return -1;
+  } else {
+    std::cout << "--- Init FastDeploy Runitme Done! "
+              << "\n--- Model:  " << model_file << std::endl;
+  }
+  // init input tensor shape
+  fd::TensorInfo info = runtime->GetInputInfo(0);
+  info.shape = {1, 3, 224, 224};
+
+  std::vector<fd::FDTensor> input_tensors(1);
+  std::vector<fd::FDTensor> output_tensors(1);
+
+  std::vector<float> inputs_data;
+  inputs_data.resize(1 * 3 * 224 * 224);
+  for (size_t i = 0; i < inputs_data.size(); ++i) {
+    inputs_data[i] = std::rand() % 1000 / 1000.0f;
+  }
+  input_tensors[0].SetExternalData({1, 3, 224, 224}, fd::FDDataType::FP32, inputs_data.data());
+
+  //get input name
+  input_tensors[0].name = info.name;
+
+  runtime->Infer(input_tensors, &output_tensors);
+
+  output_tensors[0].PrintInfo();
+  return 0;
+}
+```
+加载完成，会输出提示如下，说明初始化的后端，以及运行的硬件设备
+```
+[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init	Runtime initialized with Backend::OrtBackend in device Device::CPU.
+```
+
+## 3. 准备CMakeLists.txt
+
+FastDeploy中包含多个依赖库，直接采用`g++`或编译器编译较为繁杂，推荐使用cmake进行编译配置。示例配置如下，
+
+```cmake
+PROJECT(runtime_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.12)
+
+# 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(runtime_demo ${PROJECT_SOURCE_DIR}/infer_onnx_openvino.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(runtime_demo ${FASTDEPLOY_LIBS})
+```
+
+## 4. 编译可执行程序
+
+打开命令行终端，进入`infer_paddle_onnxruntime.cc`和`CMakeLists.txt`所在的目录，执行如下命令
+
+```bash
+mkdir build & cd build
+cmake .. -DFASTDEPLOY_INSTALL_DIR=$fastdeploy_cpp_sdk
+make -j
+```
+
+```fastdeploy_cpp_sdk``` 为FastDeploy C++部署库路径
+
+编译完成后，使用如下命令执行可得到预测结果
+```bash
+./runtime_demo
+```
+执行时如提示`error while loading shared libraries: libxxx.so: cannot open shared object file: No such file...`，说明程序执行时没有找到FastDeploy的库路径，可通过执行如下命令，将FastDeploy的库路径添加到环境变量之后，重新执行二进制程序。
+```bash
+source /Path/to/fastdeploy_cpp_sdk/fastdeploy_init.sh
+```
+
+本示例代码在各平台(Windows/Linux/Mac)上通用，但编译过程仅支持(Linux/Mac)，Windows上使用msbuild进行编译，具体使用方式参考[Windows平台使用FastDeploy C++ SDK](../../../docs/cn/faq/use_sdk_on_windows.md)
+
+## 其它文档
+
+- [Runtime Python 示例](../python)
+- [切换模型推理的硬件和后端](../../../docs/cn/faq/how_to_change_backend.md)