add c++ serving doc
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@ -198,6 +198,26 @@ The recognition model is the same.
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2021-05-13 03:42:36,979 chl1(In: ['det'], Out: ['rec']) size[6/0]
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2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
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```
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## C++ Serving
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1. Compile Serving
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To improve predictive performance, C++ services also provide multiple model concatenation services. Unlike Python Pipeline services, multiple model concatenation requires the pre - and post-model processing code to be written on the server side, so local recompilation is required to generate serving. Specific may refer to the official document: [how to compile Serving](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Compile_EN.md)
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2. Run the following command to start the service.
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```
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# Start the service and save the running log in log.txt
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python3 -m paddle_serving_server.serve --model ppocrv2_det_serving ppocrv2_rec_serving --op GeneralDetectionOp GeneralRecOp --port 9293 &>log.txt &
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```
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After the service is successfully started, a log similar to the following will be printed in log.txt
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3. Send service request
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```
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python3 ocr_cpp_client.py ppocrv2_det_client ppocrv2_rec_client
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```
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After successfully running, the predicted result of the model will be printed in the cmd window. An example of the result is:
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## WINDOWS Users
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@ -22,6 +22,7 @@ PaddleOCR提供2种服务部署方式：
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- [环境准备](#环境准备)
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- [模型转换](#模型转换)
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- [Paddle Serving pipeline部署](#部署)
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- [Paddle Serving C++ 部署](#C++)
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- [Windows用户](#Windows用户)
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- [FAQ](#FAQ)
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@ -31,28 +32,29 @@ PaddleOCR提供2种服务部署方式：
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需要准备PaddleOCR的运行环境和Paddle Serving的运行环境。
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- 准备PaddleOCR的运行环境[链接](../../doc/doc_ch/installation.md)
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根据环境下载对应的paddle whl包，推荐安装2.0.1版本
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根据环境下载对应的paddle whl包，推荐安装2.2.1版本
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- 准备PaddleServing的运行环境，步骤如下
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```bash
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# 安装serving，用于启动服务
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wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
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pip3 install paddle_serving_server_gpu-0.7.0.post102-py3-none-any.whl
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wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.8.3.post102-py3-none-any.whl
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pip3 install paddle_serving_server_gpu-0.8.3.post102-py3-none-any.whl
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# 如果是cuda10.1环境，可以使用下面的命令安装paddle-serving-server
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# wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
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# pip3 install paddle_serving_server_gpu-0.7.0.post101-py3-none-any.whl
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wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server_gpu-0.8.3.post101-py3-none-any.whl
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# pip3 install paddle_serving_server_gpu-0.8.3.post101-py3-none-any.whl
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# 安装client，用于向服务发送请求
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wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.7.0-cp37-none-any.whl
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pip3 install paddle_serving_client-0.7.0-cp37-none-any.whl
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wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_client-0.8.3-cp37-none-any.whl
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pip3 install paddle_serving_client-0.8.3-cp37-none-any.whl
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# 安装serving-app
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wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_app-0.7.0-py3-none-any.whl
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pip3 install paddle_serving_app-0.7.0-py3-none-any.whl
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wget https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_app-0.8.3-py3-none-any.whl
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pip3 install paddle_serving_app-0.8.3-py3-none-any.whl
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```
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**Note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/v0.7.0/doc/Latest_Packages_CN.md)。
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**Note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Latest_Packages_CN.md)。
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<a name="模型转换"></a>
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## 模型转换
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@ -188,6 +190,34 @@ python3 -m paddle_serving_client.convert --dirname ./ch_PP-OCRv2_rec_infer/ \
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2021-05-13 03:42:36,979 chl2(In: ['rec'], Out: ['@DAGExecutor']) size[0/0]
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```
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<a name="C++"></a>
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## Paddle Serving C++ 部署]
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1. 准备 Serving 环境
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为了提高预测性能，C++ 服务同样提供了多模型串联服务。与python pipeline服务不同，多模型串联的过程中需要将模型前后处理代码写在服务端，因此需要在本地重新编译生成serving。具体可参考官方文档：[如何编译Serving](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Compile_CN.md)
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完成编译后，注意要安装编译出的三个whl包，并设置SERVING_BIN环境变量。
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2. 启动服务可运行如下命令：
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一个服务启动两个模型串联，只需要在--model后依次按顺序传入模型文件夹的相对路径，且需要在--op后依次传入自定义C++OP类名称：
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```
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# 启动服务，运行日志保存在log.txt
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python3 -m paddle_serving_server.serve --model ppocrv2_det_serving ppocrv2_rec_serving --op GeneralDetectionOp GeneralRecOp --port 9293 &>log.txt &
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```
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成功启动服务后，log.txt中会打印类似如下日志
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3. 发送服务请求：
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```
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python3 ocr_cpp_client.py ppocrv2_det_client ppocrv2_rec_client
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```
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成功运行后，模型预测的结果会打印在cmd窗口中，结果示例为：
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<a name="Windows用户"></a>
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## Windows用户
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@ -45,7 +45,6 @@ for img_file in os.listdir(test_img_dir):
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image_data = file.read()
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image = cv2_to_base64(image_data)
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res_list = []
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#print(image)
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fetch_map = client.predict(
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feed={"x": image}, fetch=["save_infer_model/scale_0.tmp_1"], batch=True)
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print("fetrch map:", fetch_map)
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