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# 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/readme_en.md 已删除 100644 → 0
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# 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.
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