add rec model infer for pp_shitu cpp infer

deploy/cpp_shitu/CMakeLists.txt

+project(pp_shitu CXX C)
+cmake_minimum_required(VERSION 3.14)
+
+option(WITH_MKL        "Compile demo with MKL/OpenBlas support, default use MKL."       ON)
+option(WITH_GPU        "Compile demo with GPU/CPU, default use CPU."                    OFF)
+option(WITH_STATIC_LIB "Compile demo with static/shared library, default use static."   ON)
+option(WITH_TENSORRT "Compile demo with TensorRT."   OFF)
+
+SET(PADDLE_LIB "" CACHE PATH "Location of libraries")
+SET(OPENCV_DIR "" CACHE PATH "Location of libraries")
+SET(CUDA_LIB "" CACHE PATH "Location of libraries")
+SET(CUDNN_LIB "" CACHE PATH "Location of libraries")
+SET(TENSORRT_DIR "" CACHE PATH "Compile demo with TensorRT")
+
+set(DEMO_NAME "pp_shitu")
+
+include(external-cmake/yaml-cpp.cmake)
+include_directories("${CMAKE_SOURCE_DIR}/")
+include_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/src/ext-yaml-cpp/include")
+link_directories("${CMAKE_CURRENT_BINARY_DIR}/ext/yaml-cpp/lib")
+
+macro(safe_set_static_flag)
+    foreach(flag_var
+        CMAKE_CXX_FLAGS CMAKE_CXX_FLAGS_DEBUG CMAKE_CXX_FLAGS_RELEASE
+        CMAKE_CXX_FLAGS_MINSIZEREL CMAKE_CXX_FLAGS_RELWITHDEBINFO)
+      if(${flag_var} MATCHES "/MD")
+        string(REGEX REPLACE "/MD" "/MT" ${flag_var} "${${flag_var}}")
+      endif(${flag_var} MATCHES "/MD")
+    endforeach(flag_var)
+endmacro()
+
+
+if (WITH_MKL)
+    ADD_DEFINITIONS(-DUSE_MKL)
+endif()
+
+if(NOT DEFINED PADDLE_LIB)
+  message(FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/path/paddle/lib")
+endif()
+
+if(NOT DEFINED OPENCV_DIR)
+    message(FATAL_ERROR "please set OPENCV_DIR with -DOPENCV_DIR=/path/opencv")
+endif()
+
+
+if (WIN32)
+  include_directories("${PADDLE_LIB}/paddle/fluid/inference")
+  include_directories("${PADDLE_LIB}/paddle/include")
+  link_directories("${PADDLE_LIB}/paddle/fluid/inference")
+  find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/build/ NO_DEFAULT_PATH)
+
+else ()
+  find_package(OpenCV REQUIRED PATHS ${OPENCV_DIR}/share/OpenCV NO_DEFAULT_PATH)
+  include_directories("${PADDLE_LIB}/paddle/include")
+  link_directories("${PADDLE_LIB}/paddle/lib")
+endif ()
+include_directories(${OpenCV_INCLUDE_DIRS})
+
+if (WIN32)
+    add_definitions("/DGOOGLE_GLOG_DLL_DECL=")
+    set(CMAKE_C_FLAGS_DEBUG   "${CMAKE_C_FLAGS_DEBUG} /bigobj /MTd")
+    set(CMAKE_C_FLAGS_RELEASE  "${CMAKE_C_FLAGS_RELEASE} /bigobj /MT")
+    set(CMAKE_CXX_FLAGS_DEBUG  "${CMAKE_CXX_FLAGS_DEBUG} /bigobj /MTd")
+    set(CMAKE_CXX_FLAGS_RELEASE   "${CMAKE_CXX_FLAGS_RELEASE} /bigobj /MT")
+    if (WITH_STATIC_LIB)
+        safe_set_static_flag()
+        add_definitions(-DSTATIC_LIB)
+    endif()
+else()
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -o3 -std=c++11")
+    set(CMAKE_STATIC_LIBRARY_PREFIX "")
+endif()
+message("flags" ${CMAKE_CXX_FLAGS})
+
+
+if (WITH_GPU)
+    if (NOT DEFINED CUDA_LIB OR ${CUDA_LIB} STREQUAL "")
+        message(FATAL_ERROR "please set CUDA_LIB with -DCUDA_LIB=/path/cuda-8.0/lib64")
+    endif()
+    if (NOT WIN32)
+        if (NOT DEFINED CUDNN_LIB)
+            message(FATAL_ERROR "please set CUDNN_LIB with -DCUDNN_LIB=/path/cudnn_v7.4/cuda/lib64")
+        endif()
+    endif(NOT WIN32)
+endif()
+
+include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
+include_directories("${PADDLE_LIB}/third_party/install/glog/include")
+include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
+include_directories("${PADDLE_LIB}/third_party/install/xxhash/include")
+include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
+include_directories("${PADDLE_LIB}/third_party/boost")
+include_directories("${PADDLE_LIB}/third_party/eigen3")
+
+include_directories("${CMAKE_SOURCE_DIR}/")
+
+if (NOT WIN32)
+  if (WITH_TENSORRT AND WITH_GPU)
+     include_directories("${TENSORRT_DIR}/include")
+     link_directories("${TENSORRT_DIR}/lib")
+  endif()
+endif(NOT WIN32)
+
+link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
+
+link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
+link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
+link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
+link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
+link_directories("${PADDLE_LIB}/paddle/lib")
+
+
+if(WITH_MKL)
+  include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
+  if (WIN32)
+    set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.lib
+            ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.lib)
+  else ()
+    set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX}
+            ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5${CMAKE_SHARED_LIBRARY_SUFFIX})
+    execute_process(COMMAND cp -r ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel${CMAKE_SHARED_LIBRARY_SUFFIX} /usr/lib)
+  endif ()
+  set(MKLDNN_PATH "${PADDLE_LIB}/third_party/install/mkldnn")
+  if(EXISTS ${MKLDNN_PATH})
+    include_directories("${MKLDNN_PATH}/include")
+    if (WIN32)
+      set(MKLDNN_LIB ${MKLDNN_PATH}/lib/mkldnn.lib)
+    else ()
+      set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0)
+    endif ()
+  endif()
+else()
+  if (WIN32)
+    set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/openblas${CMAKE_STATIC_LIBRARY_SUFFIX})
+  else ()
+    set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/libopenblas${CMAKE_STATIC_LIBRARY_SUFFIX})
+  endif ()
+endif()
+
+# Note: libpaddle_inference_api.so/a must put before libpaddle_fluid.so/a
+if(WITH_STATIC_LIB)
+  if(WIN32)
+    set(DEPS
+        ${PADDLE_LIB}/paddle/lib/paddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX})
+  else()
+    set(DEPS
+        ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_STATIC_LIBRARY_SUFFIX})
+  endif()
+else()
+  if(WIN32)
+    set(DEPS
+        ${PADDLE_LIB}/paddle/lib/paddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX})
+  else()
+    set(DEPS
+        ${PADDLE_LIB}/paddle/lib/libpaddle_inference${CMAKE_SHARED_LIBRARY_SUFFIX})
+  endif()
+endif(WITH_STATIC_LIB)
+
+if (NOT WIN32)
+    set(DEPS ${DEPS}
+        ${MATH_LIB} ${MKLDNN_LIB}
+        glog gflags protobuf z xxhash yaml-cpp
+        )
+    if(EXISTS "${PADDLE_LIB}/third_party/install/snappystream/lib")
+        set(DEPS ${DEPS} snappystream)
+    endif()
+    if (EXISTS "${PADDLE_LIB}/third_party/install/snappy/lib")
+        set(DEPS ${DEPS} snappy)
+    endif()
+else()
+    set(DEPS ${DEPS}
+        ${MATH_LIB} ${MKLDNN_LIB}
+        glog gflags_static libprotobuf xxhash libyaml-cppmt)
+    set(DEPS ${DEPS} libcmt shlwapi)
+    if (EXISTS "${PADDLE_LIB}/third_party/install/snappy/lib")
+        set(DEPS ${DEPS} snappy)
+    endif()
+    if(EXISTS "${PADDLE_LIB}/third_party/install/snappystream/lib")
+        set(DEPS ${DEPS} snappystream)
+    endif()
+endif(NOT WIN32)
+
+
+if(WITH_GPU)
+  if(NOT WIN32)
+    if (WITH_TENSORRT)
+      set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer${CMAKE_SHARED_LIBRARY_SUFFIX})
+      set(DEPS ${DEPS} ${TENSORRT_DIR}/lib/libnvinfer_plugin${CMAKE_SHARED_LIBRARY_SUFFIX})
+    endif()
+    set(DEPS ${DEPS} ${CUDA_LIB}/libcudart${CMAKE_SHARED_LIBRARY_SUFFIX})
+    set(DEPS ${DEPS} ${CUDNN_LIB}/libcudnn${CMAKE_SHARED_LIBRARY_SUFFIX})
+  else()
+    set(DEPS ${DEPS} ${CUDA_LIB}/cudart${CMAKE_STATIC_LIBRARY_SUFFIX} )
+    set(DEPS ${DEPS} ${CUDA_LIB}/cublas${CMAKE_STATIC_LIBRARY_SUFFIX} )
+    set(DEPS ${DEPS} ${CUDNN_LIB}/cudnn${CMAKE_STATIC_LIBRARY_SUFFIX})
+  endif()
+endif()
+
+
+if (NOT WIN32)
+    set(EXTERNAL_LIB "-ldl -lrt -lgomp -lz -lm -lpthread")
+    set(DEPS ${DEPS} ${EXTERNAL_LIB})
+endif()
+
+set(DEPS ${DEPS} ${OpenCV_LIBS})
+
+include(FetchContent)
+include(external-cmake/auto-log.cmake)
+include_directories(${FETCHCONTENT_BASE_DIR}/extern_autolog-src)
+
+
+AUX_SOURCE_DIRECTORY(./src SRCS)
+add_executable(${DEMO_NAME} ${SRCS})
+ADD_DEPENDENCIES(${DEMO_NAME} ext-yaml-cpp)
+target_link_libraries(${DEMO_NAME} ${DEPS})
+
+if (WIN32 AND WITH_MKL)
+    add_custom_command(TARGET ${DEMO_NAME} POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.dll ./mklml.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.dll ./libiomp5md.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mkldnn/lib/mkldnn.dll ./mkldnn.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.dll ./release/mklml.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5md.dll ./release/libiomp5md.dll
+        COMMAND ${CMAKE_COMMAND} -E copy_if_different ${PADDLE_LIB}/third_party/install/mkldnn/lib/mkldnn.dll ./release/mkldnn.dll
+    )
+endif()

deploy/cpp_shitu/docs/windows_vs2019_build.md

+# Visual Studio 2019 Community CMake 编译指南
+
+PaddleClas在Windows 平台下基于`Visual Studio 2019 Community` 进行了测试。微软从`Visual Studio 2017`开始即支持直接管理`CMake`跨平台编译项目，但是直到`2019`才提供了稳定和完全的支持，所以如果你想使用CMake管理项目编译构建，我们推荐使用`Visual Studio 2019`。如果您希望通过生成`sln解决方案`的方式进行编译，可以参考该文档：[https://zhuanlan.zhihu.com/p/145446681](https://zhuanlan.zhihu.com/p/145446681)。
+
+
+## 前置条件
+* Visual Studio 2019
+* CUDA 9.0 / CUDA 10.0，cudnn 7.6+ （仅在使用GPU版本的预测库时需要）
+* CMake 3.0+
+
+请确保系统已经正确安装并配置好上述基本软件，其中：
+  * 在安装`Visual Studio 2019`时，`工作负载`需要勾选`使用C++的桌面开发`；
+  * CUDA需要正确安装并设置系统环境变量；
+  * CMake需要正确安装并将路径添加到系统环境变量中。
+
+以下测试基于`Visual Studio 2019 Community`版本。
+
+**下面所有示例以工作目录为 `D:\projects`演示**。
+
+### Step1: 下载PaddlePaddle C++ 预测库 paddle_inference_install_dir
+
+PaddlePaddle C++ 预测库针对不同的`CPU`和`CUDA`版本提供了不同的预编译版本，请根据实际情况下载:  [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/windows_cpp_inference.html)。
+
+解压后`D:\projects\paddle_inference_install_dir`目录包含内容为：
+
+```
+paddle_inference_install_dir
+├── paddle # paddle核心库和头文件
+|
+├── third_party # 第三方依赖库和头文件
+|
+└── version.txt # 版本和编译信息
+```
+
+然后需要将`Paddle预测库`的路径`D:\projects\paddle_inference_install_dir\paddle\lib`添加到系统环境变量`Path`中。
+
+### Step2: 安装配置OpenCV
+
+1. 在OpenCV官网下载适用于Windows平台的3.4.6版本， [下载地址](https://sourceforge.net/projects/opencvlibrary/files/3.4.6/opencv-3.4.6-vc14_vc15.exe/download)  
+2. 运行下载的可执行文件，将OpenCV解压至指定目录，如`D:\projects\opencv`
+3. 配置环境变量，如下流程所示  
+    - 此电脑（我的电脑）-> 属性 -> 高级系统设置 -> 环境变量
+    - 在系统变量中找到Path（如没有，自行创建），并双击编辑
+    - 新建，将OpenCV路径填入并保存，如 `D:\projects\opencv\build\x64\vc14\bin`
+
+### Step3: 使用Visual Studio 2019直接编译CMake
+
+1. 打开Visual Studio 2019 Community，点击 `继续但无需代码`
+
+![step2](./imgs/vs2019_step1.png)
+
+2. 点击： `文件`->`打开`->`CMake`
+
+![step2.1](./imgs/vs2019_step2.png)
+
+选择项目代码所在路径，并打开`CMakeList.txt`：
+
+![step2.2](./imgs/vs2019_step3.png)
+
+3. 点击：`项目`->`cpp_inference_demo的CMake设置`
+
+![step3](./imgs/vs2019_step4.png)
+
+4. 请设置以下参数的值
+
+
+| 名称                          | 值                 | 保存到 JSON |
+| ----------------------------- | ------------------ | ----------- |
+| CMAKE_BACKWARDS_COMPATIBILITY | 3.17               | [√]         |
+| CMAKE_BUILD_TYPE              | RelWithDebInfo     | [√]         |
+| CUDA_LIB                      | CUDA的库路径       | [√]         |
+| CUDNN_LIB                     | CUDNN的库路径      | [√]         |
+| OpenCV_DIR                    | OpenCV的安装路径   | [√]         |
+| PADDLE_LIB                    | Paddle预测库的路径 | [√]         |
+| WITH_GPU                      | [√]                | [√]         |
+| WITH_MKL                      | [√]                | [√]         |
+| WITH_STATIC_LIB               | [√]                | [√]         |
+
+**注意**：
+
+1. `CMAKE_BACKWARDS_COMPATIBILITY` 的值请根据自己 `cmake` 版本设置，`cmake` 版本可以通过命令：`cmake --version` 查询；
+2. `CUDA_LIB` 、 `CUDNN_LIB` 的值仅需在使用**GPU版本**预测库时指定，其中CUDA库版本尽量对齐，**使用9.0、10.0版本，不使用9.2、10.1等版本CUDA库**；
+3. 在设置 `CUDA_LIB`、`CUDNN_LIB`、`OPENCV_DIR`、`PADDLE_LIB` 时，点击 `浏览`，分别设置相应的路径；
+   * `CUDA_LIB`和`CUDNN_LIB`：该路径取决于CUDA与CUDNN的安装位置。
+   * `OpenCV_DIR`：该路径下需要有`.cmake`文件，一般为`opencv/build/`；
+   * `PADDLE_LIB`：该路径下需要有`CMakeCache.txt`文件，一般为`paddle_inference_install_dir/`。
+4. 在使用 `CPU` 版预测库时，请不要勾选 `WITH_GPU` - `保存到 JSON`。
+
+![step4](./imgs/vs2019_step5.png)
+
+**设置完成后**, 点击上图中 `保存并生成CMake缓存以加载变量` 。
+
+5. 点击`生成`->`全部生成`
+
+![step6](./imgs/vs2019_step6.png)
+
+
+### Step4: 预测及可视化
+
+在完成上述操作后，`Visual Studio 2019` 编译产出的可执行文件 `clas_system.exe` 在 `out\build\x64-Release`目录下，打开`cmd`，并切换到该目录：
+
+```
+cd D:\projects\PaddleClas\deploy\cpp_infer\out\build\x64-Release
+```
+可执行文件`clas_system.exe`即为编译产出的的预测程序，其使用方法如下：
+
+```shell
+.\clas_system.exe D:\projects\PaddleClas\deploy\cpp_infer\tools\config.txt .\docs\ILSVRC2012_val_00008306.JPEG
+```
+
+上述命令中，第一个参数（`D:\projects\PaddleClas\deploy\cpp_infer\tools\config.txt`）为配置文件路径，第二个参数（`.\docs\ILSVRC2012_val_00008306.JPEG`）为需要预测的图片路径。
+
+注意，需要在配置文件中正确设置预测参数，包括所用模型文件的路径（`cls_model_path`和`cls_params_path`）。
+
+
+### 注意
+* 在Windows下的终端中执行文件exe时，可能会发生乱码的现象，此时需要在终端中输入`CHCP 65001`，将终端的编码方式由GBK编码(默认)改为UTF-8编码，更加具体的解释可以参考这篇博客：[https://blog.csdn.net/qq_35038153/article/details/78430359](https://blog.csdn.net/qq_35038153/article/details/78430359)。
+* 如果需要使用CPU预测，PaddlePaddle在Windows上仅支持avx的CPU预测，目前不支持noavx的CPU预测。
+* 在使用生成的`clas_system.exe`进行预测时，如提示`由于找不到paddle_fluid.dll，无法继续执行代码。重新安装程序可能会解决此问题`，请检查是否将Paddle预测库路径添加到系统环境变量，详见[Step1: 下载PaddlePaddle C++ 预测库 paddle_inference_install_dir](#step1-下载paddlepaddle-c-预测库-paddle_inference_install_dir)

deploy/cpp_shitu/external-cmake/auto-log.cmake

+find_package(Git REQUIRED)
+include(FetchContent)
+
+set(FETCHCONTENT_BASE_DIR "${CMAKE_CURRENT_BINARY_DIR}/third-party")
+
+FetchContent_Declare(
+  extern_Autolog
+  PREFIX autolog
+  GIT_REPOSITORY https://github.com/LDOUBLEV/AutoLog.git
+  GIT_TAG        main
+)
+FetchContent_MakeAvailable(extern_Autolog)

deploy/cpp_shitu/external-cmake/yaml-cpp.cmake

+
+find_package(Git REQUIRED)
+
+include(ExternalProject)
+
+message("${CMAKE_BUILD_TYPE}")
+
+ExternalProject_Add(
+        ext-yaml-cpp
+        URL https://bj.bcebos.com/paddlex/deploy/deps/yaml-cpp.zip
+        URL_MD5 9542d6de397d1fbd649ed468cb5850e6
+        CMAKE_ARGS
+        -DYAML_CPP_BUILD_TESTS=OFF
+		-DYAML_CPP_BUILD_TOOLS=OFF
+        -DYAML_CPP_INSTALL=OFF
+        -DYAML_CPP_BUILD_CONTRIB=OFF
+		-DMSVC_SHARED_RT=OFF
+		-DBUILD_SHARED_LIBS=OFF
+        -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
+        -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
+        -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
+        -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
+        -DCMAKE_LIBRARY_OUTPUT_DIRECTORY=${CMAKE_BINARY_DIR}/ext/yaml-cpp/lib
+        -DCMAKE_ARCHIVE_OUTPUT_DIRECTORY=${CMAKE_BINARY_DIR}/ext/yaml-cpp/lib
+        PREFIX "${CMAKE_BINARY_DIR}/ext/yaml-cpp"
+        # Disable install step
+        INSTALL_COMMAND ""
+	    LOG_DOWNLOAD ON
+        LOG_BUILD 1
+)

deploy/cpp_shitu/include/cls.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include "paddle_inference_api.h"
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <vector>
+
+#include <cstring>
+#include <fstream>
+#include <numeric>
+
+#include <include/preprocess_op.h>
+#include <include/yaml_config.h>
+
+using namespace paddle_infer;
+
+namespace PaddleClas {
+
+class Classifier {
+public:
+  explicit Classifier(const YAML::Node &config_file) {
+    this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
+    if (config_file["Global"]["gpu_id"].IsDefined())
+      this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>();
+    else
+      this->gpu_id_ = 0;
+    this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>();
+    this->cpu_math_library_num_threads_ =
+        config_file["Global"]["cpu_num_threads"].as<int>();
+    this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>();
+    this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>();
+    this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>();
+
+    this->cls_model_path_ =
+        config_file["Global"]["rec_inference_model_dir"].as<std::string>() +
+        OS_PATH_SEP + "inference.pdmodel";
+    this->cls_params_path_ =
+        config_file["Global"]["rec_inference_model_dir"].as<std::string>() +
+        OS_PATH_SEP + "inference.pdiparams";
+    this->resize_size_ =
+        config_file["RecPreProcess"]["transform_ops"][0]["ResizeImage"]["size"]
+            .as<int>();
+    this->scale_ = config_file["RecPreProcess"]["transform_ops"][1]
+                              ["NormalizeImage"]["scale"]
+                                  .as<float>();
+    this->mean_ = config_file["RecPreProcess"]["transform_ops"][1]
+                             ["NormalizeImage"]["mean"]
+                                 .as<std::vector<float>>();
+    this->std_ = config_file["RecPreProcess"]["transform_ops"][1]
+                            ["NormalizeImage"]["std"]
+                                .as<std::vector<float>>();
+
+    LoadModel(cls_model_path_, cls_params_path_);
+  }
+
+  // Load Paddle inference model
+  void LoadModel(const std::string &model_path, const std::string &params_path);
+
+  // Run predictor
+  double Run(cv::Mat &img, std::vector<double> *times);
+
+private:
+  std::shared_ptr<Predictor> predictor_;
+
+  bool use_gpu_ = false;
+  int gpu_id_ = 0;
+  int gpu_mem_ = 4000;
+  int cpu_math_library_num_threads_ = 4;
+  bool use_mkldnn_ = false;
+  bool use_tensorrt_ = false;
+  bool use_fp16_ = false;
+  std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
+  std::vector<float> std_ = {0.229f, 0.224f, 0.225f};
+  float scale_ = 0.00392157;
+  int resize_size_ = 224;
+  int resize_short_ = 224;
+  std::string cls_model_path_;
+  std::string cls_params_path_;
+
+  // pre-process
+  ResizeImg resize_op_;
+  Normalize normalize_op_;
+  Permute permute_op_;
+};
+
+} // namespace PaddleClas

deploy/cpp_shitu/include/preprocess_op.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <vector>
+
+#include <cstring>
+#include <fstream>
+#include <numeric>
+
+using namespace std;
+
+namespace PaddleClas {
+
+class Normalize {
+public:
+  virtual void Run(cv::Mat *im, const std::vector<float> &mean,
+                   const std::vector<float> &std, float scale);
+};
+
+// RGB -> CHW
+class Permute {
+public:
+  virtual void Run(const cv::Mat *im, float *data);
+};
+
+class CenterCropImg {
+public:
+  virtual void Run(cv::Mat &im, const int crop_size = 224);
+};
+
+class ResizeImg {
+public:
+  virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len,
+                   int size = 0);
+};
+
+} // namespace PaddleClas

deploy/cpp_shitu/include/yaml_config.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#ifdef WIN32
+#define OS_PATH_SEP "\\"
+#else
+#define OS_PATH_SEP "/"
+#endif
+
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <stdlib.h>
+#include <vector>
+
+#include <algorithm>
+#include <cstring>
+#include <fstream>
+#include <map>
+#include <numeric>
+#include <regex>
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include "yaml-cpp/yaml.h"
+
+namespace PaddleClas {
+
+class YamlConfig {
+public:
+  explicit YamlConfig(const std::string &path) {
+    config_file = ReadYamlConfig(path);
+  }
+  static std::vector<std::string> ReadDict(const std::string &path);
+  static std::map<int, std::string> ReadIndexId(const std::string &path);
+  static YAML::Node ReadYamlConfig(const std::string &path);
+  void PrintConfigInfo();
+  YAML::Node config_file;
+};
+
+} // namespace PaddleClas

deploy/cpp_shitu/readme.md

+# 服务器端C++预测
+
+本教程将介绍在服务器端部署PaddleClas模型的详细步骤。
+
+
+## 1. 准备环境
+
+### 运行准备
+- Linux环境，推荐使用docker。
+- Windows环境，目前支持基于`Visual Studio 2019 Community`进行编译；此外，如果您希望通过生成`sln解决方案`的方式进行编译，可以参考该文档：[https://zhuanlan.zhihu.com/p/145446681](https://zhuanlan.zhihu.com/p/145446681)
+
+* 该文档主要介绍基于Linux环境下的PaddleClas C++预测流程，如果需要在Windows环境下使用预测库进行C++预测，具体编译方法请参考[Windows下编译教程](./docs/windows_vs2019_build.md)。
+
+### 1.1 编译opencv库
+
+* 首先需要从opencv官网上下载在Linux环境下源码编译的包，以3.4.7版本为例，下载及解压缩命令如下：
+
+```
+wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
+tar -xvf 3.4.7.tar.gz
+```
+
+最终可以在当前目录下看到`opencv-3.4.7/`的文件夹。
+
+* 编译opencv，首先设置opencv源码路径(`root_path`)以及安装路径(`install_path`)，`root_path`为下载的opencv源码路径，`install_path`为opencv的安装路径。在本例中，源码路径即为当前目录下的`opencv-3.4.7/`。
+
+```shell
+cd ./opencv-3.4.7
+export root_path=$PWD
+export install_path=${root_path}/opencv3
+```
+
+* 然后在opencv源码路径下，按照下面的方式进行编译。
+
+```shell
+rm -rf build
+mkdir build
+cd build
+
+cmake .. \
+    -DCMAKE_INSTALL_PREFIX=${install_path} \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DBUILD_SHARED_LIBS=OFF \
+    -DWITH_IPP=OFF \
+    -DBUILD_IPP_IW=OFF \
+    -DWITH_LAPACK=OFF \
+    -DWITH_EIGEN=OFF \
+    -DCMAKE_INSTALL_LIBDIR=lib64 \
+    -DWITH_ZLIB=ON \
+    -DBUILD_ZLIB=ON \
+    -DWITH_JPEG=ON \
+    -DBUILD_JPEG=ON \
+    -DWITH_PNG=ON \
+    -DBUILD_PNG=ON \
+    -DWITH_TIFF=ON \
+    -DBUILD_TIFF=ON
+
+make -j
+make install
+```
+
+* `make install`完成之后，会在该文件夹下生成opencv头文件和库文件，用于后面的PaddleClas代码编译。
+
+以opencv3.4.7版本为例，最终在安装路径下的文件结构如下所示。**注意**：不同的opencv版本，下述的文件结构可能不同。
+
+```
+opencv3/
+|-- bin
+|-- include
+|-- lib64
+|-- share
+```
+
+### 1.2 下载或者编译Paddle预测库
+
+* 有2种方式获取Paddle预测库，下面进行详细介绍。
+
+#### 1.2.1 预测库源码编译
+* 如果希望获取最新预测库特性，可以从Paddle github上克隆最新代码，源码编译预测库。
+* 可以参考[Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16)的说明，从github上获取Paddle代码，然后进行编译，生成最新的预测库。使用git获取代码方法如下。
+
+```shell
+git clone https://github.com/PaddlePaddle/Paddle.git
+```
+
+* 进入Paddle目录后，使用如下方法编译。
+
+```shell
+rm -rf build
+mkdir build
+cd build
+
+cmake  .. \
+    -DWITH_CONTRIB=OFF \
+    -DWITH_MKL=ON \
+    -DWITH_MKLDNN=ON  \
+    -DWITH_TESTING=OFF \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DWITH_INFERENCE_API_TEST=OFF \
+    -DON_INFER=ON \
+    -DWITH_PYTHON=ON
+make -j
+make inference_lib_dist
+```
+
+更多编译参数选项可以参考Paddle C++预测库官网：[https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16)。
+
+
+* 编译完成之后，可以在`build/paddle_inference_install_dir/`文件下看到生成了以下文件及文件夹。
+
+```
+build/paddle_inference_install_dir/
+|-- CMakeCache.txt
+|-- paddle
+|-- third_party
+|-- version.txt
+```
+
+其中`paddle`就是之后进行C++预测时所需的Paddle库，`version.txt`中包含当前预测库的版本信息。
+
+#### 1.2.2 直接下载安装
+
+* [Paddle预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id1)上提供了不同cuda版本的Linux预测库，可以在官网查看并选择合适的预测库版本，注意必须选择`develop`版本。
+
+  以`ubuntu14.04_cuda9.0_cudnn7_avx_mkl`的`develop`版本为例，使用下述命令下载并解压：
+
+
+```shell
+wget https://paddle-inference-lib.bj.bcebos.com/latest-gpu-cuda9-cudnn7-avx-mkl/paddle_inference.tgz
+
+tar -xvf paddle_inference.tgz
+```
+
+
+最终会在当前的文件夹中生成`paddle_inference/`的子文件夹。
+
+
+## 2 开始运行
+
+### 2.1 将模型导出为inference model
+
+* 可以参考[模型导出](../../tools/export_model.py)，导出`inference model`，用于模型预测。得到预测模型后，假设模型文件放在`inference`目录下，则目录结构如下。
+
+```
+inference/
+|--cls_infer.pdmodel
+|--cls_infer.pdiparams
+```
+**注意**：上述文件中，`cls_infer.pdmodel`文件存储了模型结构信息，`cls_infer.pdiparams`文件存储了模型参数信息。注意两个文件的路径需要与配置文件`tools/config.txt`中的`cls_model_path`和`cls_params_path`参数对应一致。
+
+### 2.2 编译PaddleClas C++预测demo
+
+* 编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。
+
+
+```shell
+sh tools/build.sh
+```
+
+具体地，`tools/build.sh`中内容如下。
+
+```shell
+OPENCV_DIR=your_opencv_dir
+LIB_DIR=your_paddle_inference_dir
+CUDA_LIB_DIR=your_cuda_lib_dir
+CUDNN_LIB_DIR=your_cudnn_lib_dir
+TENSORRT_DIR=your_tensorrt_lib_dir
+
+BUILD_DIR=build
+rm -rf ${BUILD_DIR}
+mkdir ${BUILD_DIR}
+cd ${BUILD_DIR}
+cmake .. \
+    -DPADDLE_LIB=${LIB_DIR} \
+    -DWITH_MKL=ON \
+    -DDEMO_NAME=clas_system \
+    -DWITH_GPU=OFF \
+    -DWITH_STATIC_LIB=OFF \
+    -DWITH_TENSORRT=OFF \
+    -DTENSORRT_DIR=${TENSORRT_DIR} \
+    -DOPENCV_DIR=${OPENCV_DIR} \
+    -DCUDNN_LIB=${CUDNN_LIB_DIR} \
+    -DCUDA_LIB=${CUDA_LIB_DIR} \
+
+make -j
+```
+
+上述命令中，
+
+* `OPENCV_DIR`为opencv编译安装的地址（本例中为`opencv-3.4.7/opencv3`文件夹的路径）；
+
+* `LIB_DIR`为下载的Paddle预测库（`paddle_inference`文件夹），或编译生成的Paddle预测库（`build/paddle_inference_install_dir`文件夹）的路径；
+
+* `CUDA_LIB_DIR`为cuda库文件地址，在docker中为`/usr/local/cuda/lib64`；
+
+* `CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。
+
+* `TENSORRT_DIR`是tensorrt库文件地址，在dokcer中为`/usr/local/TensorRT6-cuda10.0-cudnn7/`，TensorRT需要结合GPU使用。
+
+在执行上述命令，编译完成之后，会在当前路径下生成`build`文件夹，其中生成一个名为`clas_system`的可执行文件。
+
+
+### 运行demo
+* 首先修改`tools/config.txt`中对应字段：
+  * use_gpu：是否使用GPU；
+  * gpu_id：使用的GPU卡号；
+  * gpu_mem：显存；
+  * cpu_math_library_num_threads：底层科学计算库所用线程的数量；
+  * use_mkldnn：是否使用MKLDNN加速；
+  * use_tensorrt: 是否使用tensorRT进行加速；
+  * use_fp16：是否使用半精度浮点数进行计算，该选项仅在use_tensorrt为true时有效；
+  * cls_model_path：预测模型结构文件路径；
+  * cls_params_path：预测模型参数文件路径；
+  * resize_short_size：预处理时图像缩放大小；
+  * crop_size：预处理时图像裁剪后的大小。
+
+* 然后修改`tools/run.sh`：
+  * `./build/clas_system ./tools/config.txt ./docs/imgs/ILSVRC2012_val_00000666.JPEG`
+  * 上述命令中分别为：编译得到的可执行文件`clas_system`；运行时的配置文件`config.txt`；待预测的图像。
+
+* 最后执行以下命令，完成对一幅图像的分类。
+
+```shell
+sh tools/run.sh
+```
+
+* 最终屏幕上会输出结果，如下图所示。
+
+<div align="center">
+    <img src="./docs/imgs/cpp_infer_result.png" width="600">
+</div>
+
+
+其中`class id`表示置信度最高的类别对应的id，score表示图片属于该类别的概率。

deploy/cpp_shitu/readme_en.md

+# Server-side C++ inference
+
+
+In this tutorial, we will introduce the detailed steps of deploying PaddleClas models on the server side.
+
+
+## 1. Prepare the environment
+
+### Environment
+
+- Linux, docker is recommended.
+- Windows, compilation based on `Visual Studio 2019 Community` is supported. In addition, you can refer to [How to use PaddleDetection to make a complete project](https://zhuanlan.zhihu.com/p/145446681) to compile by generating the `sln solution`.
+- This document mainly introduces the compilation and inference of PaddleClas C++ in Linux environment.
+- If you need to use the Inference Library in Windows environment, please refer to [The compilation tutorial in Windows](./docs/windows_vs2019_build.md) for detailed information.
+
+
+### 1.1 Compile opencv
+
+* First of all, you need to download the source code compiled package in the Linux environment from the opencv official website. Taking opencv3.4.7 as an example, the download and uncompress command are as follows.
+
+```
+wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
+tar -xf 3.4.7.tar.gz
+```
+
+Finally, you can see the folder of `opencv-3.4.7/` in the current directory.
+
+* Compile opencv, the opencv source path (`root_path`) and installation path (`install_path`) should be set by yourself. Among them, `root_path` is the downloaded opencv source code path, and `install_path` is the installation path of opencv. In this case, the opencv source is `./opencv-3.4.7`.
+
+```shell
+cd ./opencv-3.4.7
+export root_path=$PWD
+export install_path=${root_path}/opencv3
+```
+
+* After entering the opencv source code path, you can compile it in the following way.
+
+
+```shell
+rm -rf build
+mkdir build
+cd build
+
+cmake .. \
+    -DCMAKE_INSTALL_PREFIX=${install_path} \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DBUILD_SHARED_LIBS=OFF \
+    -DWITH_IPP=OFF \
+    -DBUILD_IPP_IW=OFF \
+    -DWITH_LAPACK=OFF \
+    -DWITH_EIGEN=OFF \
+    -DCMAKE_INSTALL_LIBDIR=lib64 \
+    -DWITH_ZLIB=ON \
+    -DBUILD_ZLIB=ON \
+    -DWITH_JPEG=ON \
+    -DBUILD_JPEG=ON \
+    -DWITH_PNG=ON \
+    -DBUILD_PNG=ON \
+    -DWITH_TIFF=ON \
+    -DBUILD_TIFF=ON
+
+make -j
+make install
+```
+
+* After `make install` is completed, the opencv header file and library file will be generated in this folder for later PaddleClas source code compilation.
+
+Take opencv3.4.7 for example, the final file structure under the opencv installation path is as follows. **NOTICE**:The following file structure may be different for different Versions of Opencv.
+
+```
+opencv3/
+|-- bin
+|-- include
+|-- lib64
+|-- share
+```
+
+### 1.2 Compile or download the Paddle Inference Library
+
+* There are 2 ways to obtain the Paddle Inference Library, described in detail below.
+
+
+#### 1.2.1 Compile from the source code
+* If you want to get the latest Paddle Inference Library features, you can download the latest code from Paddle GitHub repository and compile the inference library from the source code.
+* You can refer to [Paddle Inference Library](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/05_inference_deployment/inference/build_and_install_lib_en.html#build-from-source-code) to get the Paddle source code from github, and then compile To generate the latest inference library. The method of using git to access the code is as follows.
+
+
+```shell
+git clone https://github.com/PaddlePaddle/Paddle.git
+```
+
+* After entering the Paddle directory, the compilation method is as follows.
+
+```shell
+rm -rf build
+mkdir build
+cd build
+
+cmake  .. \
+    -DWITH_CONTRIB=OFF \
+    -DWITH_MKL=ON \
+    -DWITH_MKLDNN=ON  \
+    -DWITH_TESTING=OFF \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DWITH_INFERENCE_API_TEST=OFF \
+    -DON_INFER=ON \
+    -DWITH_PYTHON=ON
+make -j
+make inference_lib_dist
+```
+
+For more compilation parameter options, please refer to the official website of the Paddle C++ inference library:[https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/05_inference_deployment/inference/build_and_install_lib_en.html#build-from-source-code](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/05_inference_deployment/inference/build_and_install_lib_en.html#build-from-source-code).
+
+
+* After the compilation process, you can see the following files in the folder of `build/paddle_inference_install_dir/`.
+
+```
+build/paddle_inference_install_dir/
+|-- CMakeCache.txt
+|-- paddle
+|-- third_party
+|-- version.txt
+```
+
+Among them, `paddle` is the Paddle library required for C++ prediction later, and `version.txt` contains the version information of the current inference library.
+
+
+
+#### 1.2.2 Direct download and installation
+
+* Different cuda versions of the Linux inference library (based on GCC 4.8.2) are provided on the
+[Paddle Inference Library official website](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/guides/05_inference_deployment/inference/build_and_install_lib_en.html). You can view and select the appropriate version of the inference library on the official website.
+
+* Please select the `develop` version.
+
+* After downloading, use the following method to uncompress.
+
+```
+tar -xf paddle_inference.tgz
+```
+
+Finally you can see the following files in the folder of `paddle_inference/`.
+
+
+## 2. Compile and run the demo
+
+### 2.1 Export the inference model
+
+* You can refer to [Model inference](../../tools/export_model.py)，export the inference model. After the model is exported, assuming it is placed in the `inference` directory, the directory structure is as follows.
+
+```
+inference/
+|--cls_infer.pdmodel
+|--cls_infer.pdiparams
+```
+
+**NOTICE**: Among them, `cls_infer.pdmodel` file stores the model structure information and the `cls_infer.pdiparams` file stores the model parameter information.The paths of the two files need to correspond to the parameters of `cls_model_path` and `cls_params_path` in the configuration file `tools/config.txt`.
+
+### 2.2 Compile PaddleClas C++ inference demo
+
+
+* The compilation commands are as follows. The addresses of Paddle C++ inference library, opencv and other Dependencies need to be replaced with the actual addresses on your own machines.
+
+```shell
+sh tools/build.sh
+```
+
+Specifically, the content in `tools/build.sh` is as follows.
+
+```shell
+OPENCV_DIR=your_opencv_dir
+LIB_DIR=your_paddle_inference_dir
+CUDA_LIB_DIR=your_cuda_lib_dir
+CUDNN_LIB_DIR=your_cudnn_lib_dir
+TENSORRT_DIR=your_tensorrt_lib_dir
+
+BUILD_DIR=build
+rm -rf ${BUILD_DIR}
+mkdir ${BUILD_DIR}
+cd ${BUILD_DIR}
+cmake .. \
+    -DPADDLE_LIB=${LIB_DIR} \
+    -DWITH_MKL=ON \
+    -DDEMO_NAME=clas_system \
+    -DWITH_GPU=OFF \
+    -DWITH_STATIC_LIB=OFF \
+    -DWITH_TENSORRT=OFF \
+    -DTENSORRT_DIR=${TENSORRT_DIR} \
+    -DOPENCV_DIR=${OPENCV_DIR} \
+    -DCUDNN_LIB=${CUDNN_LIB_DIR} \
+    -DCUDA_LIB=${CUDA_LIB_DIR} \
+
+make -j
+```
+
+In the above parameters of command:
+
+* `OPENCV_DIR` is the opencv installation path;
+
+* `LIB_DIR` is the download (`paddle_inference` folder) or the generated Paddle Inference Library path (`build/paddle_inference_install_dir` folder);
+
+* `CUDA_LIB_DIR` is the cuda library file path, in docker; it is `/usr/local/cuda/lib64`;
+
+* `CUDNN_LIB_DIR` is the cudnn library file path, in docker it is `/usr/lib/x86_64-linux-gnu/`.
+
+* `TENSORRT_DIR` is the tensorrt library file path，in dokcer it is `/usr/local/TensorRT6-cuda10.0-cudnn7/`，TensorRT is just enabled for GPU.
+
+After the compilation is completed, an executable file named `clas_system` will be generated in the `build` folder.
+
+
+### Run the demo
+* First, please modify the `tools/config.txt` and `tools/run.sh`.
+
+* Some key words in `tools/config.txt` is as follows.
+  * use_gpu: Whether to use GPU.
+  * gpu_id: GPU id.
+  * gpu_mem：GPU memory.
+  * cpu_math_library_num_threads：Number of thread for math library acceleration.
+  * use_mkldnn：Whether to use mkldnn.
+  * use_tensorrt: Whether to use tensorRT.
+  * use_fp16：Whether to use Float16 (half precision), it is just enabled when use_tensorrt is set as 1.
+  * cls_model_path: Model path of inference model.
+  * cls_params_path: Params path of inference model.
+  * resize_short_size：Short side length of the image after resize.
+  * crop_size：Image size after center crop.
+
+
+* Then execute the following command to complete the classification of an image.
+
+```shell
+sh tools/run.sh
+```
+
+* The detection results will be shown on the screen, which is as follows.
+
+<div align="center">
+    <img src="./docs/imgs/cpp_infer_result.png" width="600">
+</div>
+
+* In the above results,`class id` represents the id corresponding to the category with the highest confidence, and `score` represents the probability that the image belongs to that category.

deploy/cpp_shitu/src/cls.cpp

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <include/cls.h>
+
+namespace PaddleClas {
+
+void Classifier::LoadModel(const std::string &model_path,
+                           const std::string &params_path) {
+  paddle_infer::Config config;
+  config.SetModel(model_path, params_path);
+
+  if (this->use_gpu_) {
+    config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
+    if (this->use_tensorrt_) {
+      config.EnableTensorRtEngine(
+          1 << 20, 1, 3,
+          this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
+                          : paddle_infer::Config::Precision::kFloat32,
+          false, false);
+    }
+  } else {
+    config.DisableGpu();
+    if (this->use_mkldnn_) {
+      config.EnableMKLDNN();
+      // cache 10 different shapes for mkldnn to avoid memory leak
+      config.SetMkldnnCacheCapacity(10);
+    }
+    config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
+  }
+
+  config.SwitchUseFeedFetchOps(false);
+  // true for multiple input
+  config.SwitchSpecifyInputNames(true);
+
+  config.SwitchIrOptim(true);
+
+  config.EnableMemoryOptim();
+  config.DisableGlogInfo();
+
+  this->predictor_ = CreatePredictor(config);
+}
+
+double Classifier::Run(cv::Mat &img, std::vector<double> *times) {
+  cv::Mat srcimg;
+  cv::Mat resize_img;
+  img.copyTo(srcimg);
+
+  auto preprocess_start = std::chrono::system_clock::now();
+  this->resize_op_.Run(img, resize_img, this->resize_short_,
+                       this->resize_size_);
+
+  this->normalize_op_.Run(&resize_img, this->mean_, this->std_, this->scale_);
+  std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
+  this->permute_op_.Run(&resize_img, input.data());
+
+  auto input_names = this->predictor_->GetInputNames();
+  auto input_t = this->predictor_->GetInputHandle(input_names[0]);
+  input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
+  auto preprocess_end = std::chrono::system_clock::now();
+
+  auto infer_start = std::chrono::system_clock::now();
+  input_t->CopyFromCpu(input.data());
+  this->predictor_->Run();
+
+  std::vector<float> out_data;
+  auto output_names = this->predictor_->GetOutputNames();
+  auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
+  std::vector<int> output_shape = output_t->shape();
+  int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
+                                std::multiplies<int>());
+
+  out_data.resize(out_num);
+  output_t->CopyToCpu(out_data.data());
+  auto infer_end = std::chrono::system_clock::now();
+
+  auto postprocess_start = std::chrono::system_clock::now();
+  int maxPosition =
+      max_element(out_data.begin(), out_data.end()) - out_data.begin();
+  auto postprocess_end = std::chrono::system_clock::now();
+
+  std::chrono::duration<float> preprocess_diff =
+      preprocess_end - preprocess_start;
+  times->push_back(double(preprocess_diff.count() * 1000));
+  std::chrono::duration<float> inference_diff = infer_end - infer_start;
+  double inference_cost_time = double(inference_diff.count() * 1000);
+  times->push_back(inference_cost_time);
+  std::chrono::duration<float> postprocess_diff =
+      postprocess_end - postprocess_start;
+  times->push_back(double(postprocess_diff.count() * 1000));
+
+  std::cout << "result: " << std::endl;
+  std::cout << "\tclass id: " << maxPosition << std::endl;
+  std::cout << std::fixed << std::setprecision(10)
+            << "\tscore: " << double(out_data[maxPosition]) << std::endl;
+
+  return inference_cost_time;
+}
+
+} // namespace PaddleClas

deploy/cpp_shitu/src/main.cpp

+// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <opencv2/core/utils/filesystem.hpp>
+#include <ostream>
+#include <vector>
+
+#include <cstring>
+#include <fstream>
+#include <numeric>
+
+#include <auto_log/autolog.h>
+#include <include/cls.h>
+#include <include/yaml_config.h>
+
+using namespace std;
+using namespace cv;
+using namespace PaddleClas;
+
+int main(int argc, char **argv) {
+  if (argc != 2) {
+    std::cerr << "[ERROR] usage: " << argv[0] << " yaml_path\n";
+    exit(1);
+  }
+
+  YamlConfig config(argv[1]);
+  config.PrintConfigInfo();
+
+  std::string path =
+      config.config_file["Global"]["infer_imgs"].as<std::string>();
+  std::vector<std::string> img_files_list;
+  if (cv::utils::fs::isDirectory(path)) {
+    std::vector<cv::String> filenames;
+    cv::glob(path, filenames);
+    for (auto f : filenames) {
+      img_files_list.push_back(f);
+    }
+  } else {
+    img_files_list.push_back(path);
+  }
+
+  std::cout << "img_file_list length: " << img_files_list.size() << std::endl;
+
+  Classifier classifier(config.config_file);
+
+  double elapsed_time = 0.0;
+  std::vector<double> cls_times;
+  int warmup_iter = img_files_list.size() > 5 ? 5 : 0;
+  for (int idx = 0; idx < img_files_list.size(); ++idx) {
+    std::string img_path = img_files_list[idx];
+    cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
+    if (!srcimg.data) {
+      std::cerr << "[ERROR] image read failed! image path: " << img_path
+                << "\n";
+      exit(-1);
+    }
+
+    cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
+
+    double run_time = classifier.Run(srcimg, &cls_times);
+    if (idx >= warmup_iter) {
+      elapsed_time += run_time;
+      std::cout << "Current image path: " << img_path << std::endl;
+      std::cout << "Current time cost: " << run_time << " s, "
+                << "average time cost in all: "
+                << elapsed_time / (idx + 1 - warmup_iter) << " s." << std::endl;
+    } else {
+      std::cout << "Current time cost: " << run_time << " s." << std::endl;
+    }
+  }
+
+  std::string presion = "fp32";
+
+  // if (config.use_fp16)
+  //   presion = "fp16";
+  // if (config.benchmark) {
+  //   AutoLogger autolog("Classification", config.use_gpu, config.use_tensorrt,
+  //                      config.use_mkldnn, config.cpu_threads, 1,
+  //                      "1, 3, 224, 224", presion, cls_times,
+  //                      img_files_list.size());
+  //   autolog.report();
+  // }
+  return 0;
+}

deploy/cpp_shitu/src/preprocess_op.cpp

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
+#include "paddle_api.h"
+#include "paddle_inference_api.h"
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <ostream>
+#include <vector>
+
+#include <cstring>
+#include <fstream>
+#include <math.h>
+#include <numeric>
+
+#include <include/preprocess_op.h>
+
+namespace PaddleClas {
+
+void Permute::Run(const cv::Mat *im, float *data) {
+  int rh = im->rows;
+  int rw = im->cols;
+  int rc = im->channels();
+  for (int i = 0; i < rc; ++i) {
+    cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
+  }
+}
+
+void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
+                    const std::vector<float> &std, float scale) {
+  (*im).convertTo(*im, CV_32FC3, scale);
+  for (int h = 0; h < im->rows; h++) {
+    for (int w = 0; w < im->cols; w++) {
+      im->at<cv::Vec3f>(h, w)[0] =
+          (im->at<cv::Vec3f>(h, w)[0] - mean[0]) / std[0];
+      im->at<cv::Vec3f>(h, w)[1] =
+          (im->at<cv::Vec3f>(h, w)[1] - mean[1]) / std[1];
+      im->at<cv::Vec3f>(h, w)[2] =
+          (im->at<cv::Vec3f>(h, w)[2] - mean[2]) / std[2];
+    }
+  }
+}
+
+void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
+  int resize_w = img.cols;
+  int resize_h = img.rows;
+  int w_start = int((resize_w - crop_size) / 2);
+  int h_start = int((resize_h - crop_size) / 2);
+  cv::Rect rect(w_start, h_start, crop_size, crop_size);
+  img = img(rect);
+}
+
+void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
+                    int resize_short_size, int size) {
+  int resize_h = 0;
+  int resize_w = 0;
+  if (size > 0) {
+    resize_h = size;
+    resize_w = size;
+  } else {
+    int w = img.cols;
+    int h = img.rows;
+
+    float ratio = 1.f;
+    if (h < w) {
+      ratio = float(resize_short_size) / float(h);
+    } else {
+      ratio = float(resize_short_size) / float(w);
+    }
+    resize_h = round(float(h) * ratio);
+    resize_w = round(float(w) * ratio);
+  }
+  cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
+}
+
+} // namespace PaddleClas

deploy/cpp_shitu/src/yaml_config.cpp

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <iostream>
+#include <ostream>
+#include <vector>
+
+#include <include/yaml_config.h>
+
+namespace PaddleClas {
+
+std::vector<std::string> YamlConfig::ReadDict(const std::string &path) {
+  std::ifstream in(path);
+  std::string line;
+  std::vector<std::string> m_vec;
+  if (in) {
+    while (getline(in, line)) {
+      m_vec.push_back(line);
+    }
+  } else {
+    std::cout << "no such label file: " << path << ", exit the program..."
+              << std::endl;
+    exit(1);
+  }
+  return m_vec;
+}
+
+std::map<int, std::string> YamlConfig::ReadIndexId(const std::string &path) {
+  std::ifstream in(path);
+  std::string line;
+  std::map<int, std::string> m_vec;
+  if (in) {
+    while (getline(in, line)) {
+      std::regex ws_re("\\s+");
+      std::vector<std::string> v(
+          std::sregex_token_iterator(line.begin(), line.end(), ws_re, -1),
+          std::sregex_token_iterator());
+      if (v.size() != 3) {
+        std::cout << "The number of element for each line in : " << path
+                  << "must be 3, exit the program..." << std::endl;
+        exit(1);
+      } else
+        m_vec.insert(std::pair<int, std::string>(stoi(v[0]), v[2]));
+    }
+  }
+  return m_vec;
+}
+
+YAML::Node YamlConfig::ReadYamlConfig(const std::string &path) {
+  YAML::Node config;
+  try {
+    config = YAML::LoadFile(path);
+  } catch (YAML::BadFile &e) {
+    std::cout << "Something wrong in yaml file, please check yaml file"
+              << std::endl;
+    exit(1);
+  }
+  return config;
+}
+
+void YamlConfig::PrintConfigInfo() {
+  std::cout << this->config_file << std::endl;
+  //   for (YAML::const_iterator
+  //   it=config_file.begin();it!=config_file.end();++it)
+  // {
+  //   std::cout << it->as<std::string>() << "\n";
+  //   }
+}
+
+} // namespace PaddleClas

deploy/cpp_shitu/tools/build.sh

+OPENCV_DIR=/work/project/project/cpp_infer/opencv-3.4.7/opencv3
+LIB_DIR=/work/project/project/cpp_infer/paddle_inference/
+CUDA_LIB_DIR=/usr/local/cuda/lib64
+CUDNN_LIB_DIR=/usr/lib/x86_64-linux-gnu/
+
+BUILD_DIR=build
+rm -rf ${BUILD_DIR}
+mkdir ${BUILD_DIR}
+cd ${BUILD_DIR}
+cmake .. \
+    -DPADDLE_LIB=${LIB_DIR} \
+    -DWITH_MKL=ON \
+    -DWITH_GPU=OFF \
+    -DWITH_STATIC_LIB=OFF \
+    -DUSE_TENSORRT=OFF \
+    -DOPENCV_DIR=${OPENCV_DIR} \
+    -DCUDNN_LIB=${CUDNN_LIB_DIR} \
+    -DCUDA_LIB=${CUDA_LIB_DIR} \
+
+make -j

deploy/cpp_shitu/tools/config.txt

+# model load config
+use_gpu  0
+gpu_id  0
+gpu_mem  4000
+cpu_threads  10
+use_mkldnn 1
+use_tensorrt 0
+use_fp16 0
+
+# cls config
+cls_model_path  /PaddleClas/inference/cls_infer.pdmodel
+cls_params_path /PaddleClas/inference/cls_infer.pdiparams
+resize_short_size 256
+crop_size 224
+
+# for log env info
+benchmark 0

deploy/cpp_shitu/tools/run.sh

+./build/clas_system ../configs/inference_rec.yaml