Merge pull request #1603 from Intsigstephon/develop

add cpp serving for clas and pp-shitu

deploy/paddleserving/readme.md

-# 模型服务化部署
-- [1. 简介](#1)
-- [2. Serving 安装](#2)
-- [3. 图像分类服务部署](#3)
-    - [3.1 模型转换](#3.1)
-    - [3.2 服务部署和请求](#3.2)
-- [4. 图像识别服务部署](#4)
-  - [4.1 模型转换](#4.1)
-  - [4.2 服务部署和请求](#4.2)
-- [5. FAQ](#5)
-
-<a name="1"></a>
-## 1. 简介
-[Paddle Serving](https://github.com/PaddlePaddle/Serving) 旨在帮助深度学习开发者轻松部署在线预测服务，支持一键部署工业级的服务能力、客户端和服务端之间高并发和高效通信、并支持多种编程语言开发客户端。
-
-该部分以 HTTP 预测服务部署为例，介绍怎样在 PaddleClas 中使用 PaddleServing 部署模型服务。目前只支持 Linux 平台部署，暂不支持 Windows 平台。
-
-<a name="2"></a>
-## 2. Serving 安装
-
-Serving 官网推荐使用 docker 安装并部署 Serving 环境。首先需要拉取 docker 环境并创建基于 Serving 的 docker。
-
-```shell
-docker pull paddlepaddle/serving:0.7.0-cuda10.2-cudnn7-devel
-nvidia-docker run -p 9292:9292 --name test -dit paddlepaddle/serving:0.7.0-cuda10.2-cudnn7-devel bash
-nvidia-docker exec -it test bash
-```
-
-进入 docker 后，需要安装 Serving 相关的 python 包。
-
-```shell
-pip3 install paddle-serving-client==0.7.0
-pip3 install paddle-serving-server==0.7.0 # CPU
-pip3 install paddle-serving-app==0.7.0
-pip3 install paddle-serving-server-gpu==0.7.0.post102 #GPU with CUDA10.2 + TensorRT6
-# 其他GPU环境需要确认环境再选择执行哪一条
-pip3 install paddle-serving-server-gpu==0.7.0.post101 # GPU with CUDA10.1 + TensorRT6
-pip3 install paddle-serving-server-gpu==0.7.0.post112 # GPU with CUDA11.2 + TensorRT8
-```
-
-* 如果安装速度太慢，可以通过 `-i https://pypi.tuna.tsinghua.edu.cn/simple` 更换源，加速安装过程。
-* 其他环境配置安装请参考: [使用Docker安装Paddle Serving](https://github.com/PaddlePaddle/Serving/blob/v0.7.0/doc/Install_CN.md)
-
-* 如果希望部署 CPU 服务，可以安装 serving-server 的 cpu 版本，安装命令如下。
-
-```shell
-pip install paddle-serving-server
-```
-<a name="3"></a>
-
-## 3. 图像分类服务部署
-<a name="3.1"></a>
-### 3.1 模型转换
-使用 PaddleServing 做服务化部署时，需要将保存的 inference 模型转换为 Serving 模型。下面以经典的 ResNet50_vd 模型为例，介绍如何部署图像分类服务。
-- 进入工作目录：
-```shell
-cd deploy/paddleserving
-```
-- 下载 ResNet50_vd 的 inference 模型：
-```shell
-# 下载并解压 ResNet50_vd 模型
-wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar && tar xf ResNet50_vd_infer.tar
-```
-- 用 paddle_serving_client 把下载的 inference 模型转换成易于 Server 部署的模型格式：
-```
-# 转换 ResNet50_vd 模型
-python3 -m paddle_serving_client.convert --dirname ./ResNet50_vd_infer/ \
-                                         --model_filename inference.pdmodel  \
-                                         --params_filename inference.pdiparams \
-                                         --serving_server ./ResNet50_vd_serving/ \
-                                         --serving_client ./ResNet50_vd_client/
-```
-ResNet50_vd 推理模型转换完成后，会在当前文件夹多出 `ResNet50_vd_serving` 和 `ResNet50_vd_client` 的文件夹，具备如下格式：
-```
-|- ResNet50_vd_server/
-  |- inference.pdiparams
-  |- inference.pdmodel
-  |- serving_server_conf.prototxt  
-  |- serving_server_conf.stream.prototxt
-|- ResNet50_vd_client
-  |- serving_client_conf.prototxt  
-  |- serving_client_conf.stream.prototxt
-```
-得到模型文件之后，需要修改 `ResNet50_vd_server` 下文件 `serving_server_conf.prototxt` 中的 alias 名字：将 `fetch_var` 中的 `alias_name` 改为 `prediction`
-
-**备注**:  Serving 为了兼容不同模型的部署，提供了输入输出重命名的功能。这样，不同的模型在推理部署时，只需要修改配置文件的 alias_name 即可，无需修改代码即可完成推理部署。
-修改后的 serving_server_conf.prototxt 如下所示:
-```
-feed_var {
-  name: "inputs"
-  alias_name: "inputs"
-  is_lod_tensor: false
-  feed_type: 1
-  shape: 3
-  shape: 224
-  shape: 224
-}
-fetch_var {
-  name: "save_infer_model/scale_0.tmp_1"
-  alias_name: "prediction"
-  is_lod_tensor: false
-  fetch_type: 1
-  shape: 1000
-}
-```
-<a name="3.2"></a>
-### 3.2 服务部署和请求
-paddleserving 目录包含了启动 pipeline 服务和发送预测请求的代码，包括：
-```shell
-__init__.py
-config.yml                 # 启动服务的配置文件
-pipeline_http_client.py    # http方式发送pipeline预测请求的脚本
-pipeline_rpc_client.py     # rpc方式发送pipeline预测请求的脚本
-classification_web_service.py    # 启动pipeline服务端的脚本
-```
-
-- 启动服务：
-```shell
-# 启动服务，运行日志保存在 log.txt
-python3 classification_web_service.py &>log.txt &
-```
-成功启动服务后，log.txt 中会打印类似如下日志
-![](./imgs/start_server.png)
-
-- 发送请求：
-```shell
-# 发送服务请求
-python3 pipeline_http_client.py
-```
-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：
-![](./imgs/results.png)
-
-<a name="4"></a>
-## 4.图像识别服务部署
-使用 PaddleServing 做服务化部署时，需要将保存的 inference 模型转换为 Serving 模型。 下面以 PP-ShiTu 中的超轻量图像识别模型为例，介绍图像识别服务的部署。
-<a name="4.1"></a>
-## 4.1 模型转换
-- 下载通用检测 inference 模型和通用识别 inference 模型
-```
-cd deploy
-# 下载并解压通用识别模型
-wget -P models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
-cd models
-tar -xf general_PPLCNet_x2_5_lite_v1.0_infer.tar
-# 下载并解压通用检测模型
-wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
-tar -xf picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
-```
-- 转换识别 inference 模型为 Serving 模型：
-```
-# 转换识别模型
-python3 -m paddle_serving_client.convert --dirname ./general_PPLCNet_x2_5_lite_v1.0_infer/ \
-                                         --model_filename inference.pdmodel  \
-                                         --params_filename inference.pdiparams \
-                                         --serving_server ./general_PPLCNet_x2_5_lite_v1.0_serving/ \
-                                         --serving_client ./general_PPLCNet_x2_5_lite_v1.0_client/
-```
-识别推理模型转换完成后，会在当前文件夹多出 `general_PPLCNet_x2_5_lite_v1.0_serving/` 和 `general_PPLCNet_x2_5_lite_v1.0_client/` 的文件夹。修改 `general_PPLCNet_x2_5_lite_v1.0_serving/` 目录下的 serving_server_conf.prototxt 中的 alias 名字： 将 `fetch_var` 中的 `alias_name` 改为 `features`。
-修改后的 serving_server_conf.prototxt 内容如下：
-```
-feed_var {
-  name: "x"
-  alias_name: "x"
-  is_lod_tensor: false
-  feed_type: 1
-  shape: 3
-  shape: 224
-  shape: 224
-}
-fetch_var {
-  name: "save_infer_model/scale_0.tmp_1"
-  alias_name: "features"
-  is_lod_tensor: false
-  fetch_type: 1
-  shape: 512
-}
-```
-- 转换通用检测 inference 模型为 Serving 模型：
-```
-# 转换通用检测模型
-python3 -m paddle_serving_client.convert --dirname ./picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer/ \
-                                         --model_filename inference.pdmodel  \
-                                         --params_filename inference.pdiparams \
-                                         --serving_server ./picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/ \
-                                         --serving_client ./picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/
-```
-检测 inference 模型转换完成后，会在当前文件夹多出 `picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/` 和 `picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/` 的文件夹。
-
-**注意:** 此处不需要修改 `picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/` 目录下的 serving_server_conf.prototxt 中的 alias 名字。
-
-- 下载并解压已经构建后的检索库 index
-```
-cd ../
-wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar && tar -xf drink_dataset_v1.0.tar
-```
-<a name="4.2"></a>
-## 4.2 服务部署和请求
-**注意:** 识别服务涉及到多个模型，出于性能考虑采用 PipeLine 部署方式。Pipeline 部署方式当前不支持 windows 平台。
-- 进入到工作目录
-```shell
-cd ./deploy/paddleserving/recognition
-```
-paddleserving 目录包含启动 pipeline 服务和发送预测请求的代码，包括：
-```
-__init__.py
-config.yml                    # 启动服务的配置文件
-pipeline_http_client.py       # http方式发送pipeline预测请求的脚本
-pipeline_rpc_client.py        # rpc方式发送pipeline预测请求的脚本
-recognition_web_service.py    # 启动pipeline服务端的脚本
-```
-- 启动服务：
-```
-# 启动服务，运行日志保存在 log.txt
-python3 recognition_web_service.py &>log.txt &
-```
-成功启动服务后，log.txt 中会打印类似如下日志
-![](./imgs/start_server_shitu.png)
-
-- 发送请求：
-```
-python3 pipeline_http_client.py
-```
-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：
-![](./imgs/results_shitu.png)
-
-<a name="5"></a>
-## 5.FAQ
-**Q1**： 发送请求后没有结果返回或者提示输出解码报错
-
-**A1**： 启动服务和发送请求时不要设置代理，可以在启动服务前和发送请求前关闭代理，关闭代理的命令是：
-```
-unset https_proxy
-unset http_proxy
-```
-
-更多的服务部署类型，如 `RPC 预测服务` 等，可以参考 Serving 的[github 官网](https://github.com/PaddlePaddle/Serving/tree/v0.7.0/examples)

deploy/paddleserving/readme.md

+../../docs/zh_CN/inference_deployment/paddle_serving_deploy.md
\ No newline at end of file

deploy/paddleserving/recognition/run_cpp_serving.sh

+nohup python3 -m paddle_serving_server.serve \
+--model ../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving \
+ --port 9293 >>log_mainbody_detection.txt 1&>2 &
+
+nohup python3 -m paddle_serving_server.serve \
+--model ../../models/general_PPLCNet_x2_5_lite_v1.0_serving \
+--port 9294 >>log_feature_extraction.txt 1&>2 &

deploy/paddleserving/recognition/test_cpp_serving_client.py

+# 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.
+
+import sys
+import numpy as np
+
+from paddle_serving_client import Client
+from paddle_serving_app.reader import *
+import cv2
+import faiss
+import os
+import pickle
+
+
+class MainbodyDetect():
+    """
+    pp-shitu mainbody detect.
+    include preprocess, process, postprocess
+    return detect results
+    Attention: Postprocess include num limit and box filter; no nms 
+    """
+
+    def __init__(self):
+        self.preprocess = DetectionSequential([
+            DetectionFile2Image(), DetectionNormalize(
+                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True),
+            DetectionResize(
+                (640, 640), False, interpolation=2), DetectionTranspose(
+                    (2, 0, 1))
+        ])
+
+        self.client = Client()
+        self.client.load_client_config(
+            "../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/serving_client_conf.prototxt"
+        )
+        self.client.connect(['127.0.0.1:9293'])
+
+        self.max_det_result = 5
+        self.conf_threshold = 0.2
+
+    def predict(self, imgpath):
+        im, im_info = self.preprocess(imgpath)
+        im_shape = np.array(im.shape[1:]).reshape(-1)
+        scale_factor = np.array(list(im_info['scale_factor'])).reshape(-1)
+
+        fetch_map = self.client.predict(
+            feed={
+                "image": im,
+                "im_shape": im_shape,
+                "scale_factor": scale_factor,
+            },
+            fetch=["save_infer_model/scale_0.tmp_1"],
+            batch=False)
+        return self.postprocess(fetch_map, imgpath)
+
+    def postprocess(self, fetch_map, imgpath):
+        #1. get top max_det_result
+        det_results = fetch_map["save_infer_model/scale_0.tmp_1"]
+        if len(det_results) > self.max_det_result:
+            boxes_reserved = fetch_map[
+                "save_infer_model/scale_0.tmp_1"][:self.max_det_result]
+        else:
+            boxes_reserved = det_results
+
+        #2. do conf threshold
+        boxes_list = []
+        for i in range(boxes_reserved.shape[0]):
+            if (boxes_reserved[i, 1]) > self.conf_threshold:
+                boxes_list.append(boxes_reserved[i, :])
+
+        #3. add origin image box
+        origin_img = cv2.imread(imgpath)
+        boxes_list.append(
+            np.array([0, 1.0, 0, 0, origin_img.shape[1], origin_img.shape[0]]))
+        return np.array(boxes_list)
+
+
+class ObjectRecognition():
+    """
+    pp-shitu object recognion for all objects detected by MainbodyDetect.
+    include preprocess, process, postprocess
+    preprocess include preprocess for each image and batching.
+    Batch process
+    postprocess include retrieval and nms
+    """
+
+    def __init__(self):
+        self.client = Client()
+        self.client.load_client_config(
+            "../../models/general_PPLCNet_x2_5_lite_v1.0_client/serving_client_conf.prototxt"
+        )
+        self.client.connect(["127.0.0.1:9294"])
+
+        self.seq = Sequential([
+            BGR2RGB(), Resize((224, 224)), Div(255),
+            Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225],
+                      False), Transpose((2, 0, 1))
+        ])
+
+        self.searcher, self.id_map = self.init_index()
+
+        self.rec_nms_thresold = 0.05
+        self.rec_score_thres = 0.5
+        self.feature_normalize = True
+        self.return_k = 1
+
+    def init_index(self):
+        index_dir = "../../drink_dataset_v1.0/index"
+        assert os.path.exists(os.path.join(
+            index_dir, "vector.index")), "vector.index not found ..."
+        assert os.path.exists(os.path.join(
+            index_dir, "id_map.pkl")), "id_map.pkl not found ... "
+
+        searcher = faiss.read_index(os.path.join(index_dir, "vector.index"))
+
+        with open(os.path.join(index_dir, "id_map.pkl"), "rb") as fd:
+            id_map = pickle.load(fd)
+        return searcher, id_map
+
+    def predict(self, det_boxes, imgpath):
+        #1. preprocess
+        batch_imgs = []
+        origin_img = cv2.imread(imgpath)
+        for i in range(det_boxes.shape[0]):
+            box = det_boxes[i]
+            x1, y1, x2, y2 = [int(x) for x in box[2:]]
+            cropped_img = origin_img[y1:y2, x1:x2, :].copy()
+            tmp = self.seq(cropped_img)
+            batch_imgs.append(tmp)
+        batch_imgs = np.array(batch_imgs)
+
+        #2. process
+        fetch_map = self.client.predict(
+            feed={"x": batch_imgs}, fetch=["features"], batch=True)
+        batch_features = fetch_map["features"]
+
+        #3. postprocess
+        if self.feature_normalize:
+            feas_norm = np.sqrt(
+                np.sum(np.square(batch_features), axis=1, keepdims=True))
+            batch_features = np.divide(batch_features, feas_norm)
+        scores, docs = self.searcher.search(batch_features, self.return_k)
+
+        results = []
+        for i in range(scores.shape[0]):
+            pred = {}
+            if scores[i][0] >= self.rec_score_thres:
+                pred["bbox"] = [int(x) for x in det_boxes[i, 2:]]
+                pred["rec_docs"] = self.id_map[docs[i][0]].split()[1]
+                pred["rec_scores"] = scores[i][0]
+                results.append(pred)
+        return self.nms_to_rec_results(results)
+
+    def nms_to_rec_results(self, results):
+        filtered_results = []
+        x1 = np.array([r["bbox"][0] for r in results]).astype("float32")
+        y1 = np.array([r["bbox"][1] for r in results]).astype("float32")
+        x2 = np.array([r["bbox"][2] for r in results]).astype("float32")
+        y2 = np.array([r["bbox"][3] for r in results]).astype("float32")
+        scores = np.array([r["rec_scores"] for r in results])
+
+        areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+        order = scores.argsort()[::-1]
+        while order.size > 0:
+            i = order[0]
+            xx1 = np.maximum(x1[i], x1[order[1:]])
+            yy1 = np.maximum(y1[i], y1[order[1:]])
+            xx2 = np.minimum(x2[i], x2[order[1:]])
+            yy2 = np.minimum(y2[i], y2[order[1:]])
+
+            w = np.maximum(0.0, xx2 - xx1 + 1)
+            h = np.maximum(0.0, yy2 - yy1 + 1)
+            inter = w * h
+            ovr = inter / (areas[i] + areas[order[1:]] - inter)
+            inds = np.where(ovr <= self.rec_nms_thresold)[0]
+            order = order[inds + 1]
+            filtered_results.append(results[i])
+        return filtered_results
+
+
+if __name__ == "__main__":
+    det = MainbodyDetect()
+    rec = ObjectRecognition()
+
+    #1. get det_results    
+    imgpath = "../../drink_dataset_v1.0/test_images/001.jpeg"
+    det_results = det.predict(imgpath)
+
+    #2. get rec_results
+    rec_results = rec.predict(det_results, imgpath)
+    print(rec_results)

deploy/paddleserving/run_cpp_serving.sh

+#run cls server:
+nohup python3 -m paddle_serving_server.serve --model ResNet50_vd_serving --port 9292 &

deploy/paddleserving/test_cpp_serving_cient.py

+# 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.
+
+import sys
+from paddle_serving_client import Client
+
+#app
+from paddle_serving_app.reader import Sequential, URL2Image, Resize
+from paddle_serving_app.reader import CenterCrop, RGB2BGR, Transpose, Div, Normalize
+import time
+
+client = Client()
+client.load_client_config("./ResNet50_vd_serving/serving_server_conf.prototxt")
+client.connect(["127.0.0.1:9292"])
+
+label_dict = {}
+label_idx = 0
+with open("imagenet.label") as fin:
+    for line in fin:
+        label_dict[label_idx] = line.strip()
+        label_idx += 1
+
+#preprocess
+seq = Sequential([
+    URL2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)),
+    Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True)
+])
+
+start = time.time()
+image_file = "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg"
+for i in range(1):
+    img = seq(image_file)
+    fetch_map = client.predict(
+        feed={"inputs": img}, fetch=["prediction"], batch=False)
+
+    prob = max(fetch_map["prediction"][0])
+    label = label_dict[fetch_map["prediction"][0].tolist().index(prob)].strip(
+    ).replace(",", "")
+    print("prediction: {}, probability: {}".format(label, prob))
+end = time.time()
+print(end - start)

docs/zh_CN/inference_deployment/paddle_serving_deploy.md

@@ -6,9 +6,13 @@
 - [3. 图像分类服务部署](#3)
     - [3.1 模型转换](#3.1)
     - [3.2 服务部署和请求](#3.2)
+        - [3.2.1 Python Serving](#3.2.1)
+        - [3.2.2 C++ Serving](#3.2.2)
 - [4. 图像识别服务部署](#4)
   - [4.1 模型转换](#4.1)
   - [4.2 服务部署和请求](#4.2)
+       - [4.2.1 Python Serving](#4.2.1)
+       - [4.2.2 C++ Serving](#4.2.2)
 - [5. FAQ](#5)
 
 <a name="1"></a>
@@ -90,7 +94,7 @@ ResNet50_vd 推理模型转换完成后，会在当前文件夹多出 `ResNet50_
   |- serving_client_conf.prototxt  
   |- serving_client_conf.stream.prototxt
 ```
-得到模型文件之后，需要修改 `ResNet50_vd_server` 下文件 `serving_server_conf.prototxt` 中的 alias 名字：将 `fetch_var` 中的 `alias_name` 改为 `prediction`
+得到模型文件之后，需要分别修改 `ResNet50_vd_server` 和 `ResNet50_vd_client` 下文件 `serving_server_conf.prototxt` 中的 alias 名字：将 `fetch_var` 中的 `alias_name` 改为 `prediction`
 
 **备注**:  Serving 为了兼容不同模型的部署，提供了输入输出重命名的功能。这样，不同的模型在推理部署时，只需要修改配置文件的 alias_name 即可，无需修改代码即可完成推理部署。
 修改后的 serving_server_conf.prototxt 如下所示:
@@ -114,30 +118,51 @@ fetch_var {
 ```
 <a name="3.2"></a>
 ### 3.2 服务部署和请求
-paddleserving 目录包含了启动 pipeline 服务和发送预测请求的代码，包括：
+paddleserving 目录包含了启动 pipeline 服务、C++ serving服务和发送预测请求的代码，包括：
 ```shell
 __init__.py
-config.yml                 # 启动服务的配置文件
+config.yml                 # 启动pipeline服务的配置文件
 pipeline_http_client.py    # http方式发送pipeline预测请求的脚本
 pipeline_rpc_client.py     # rpc方式发送pipeline预测请求的脚本
 classification_web_service.py    # 启动pipeline服务端的脚本
+run_cpp_serving.sh         # 启动C++ Serving部署的脚本
+test_cpp_serving_client.py # rpc方式发送C++ serving预测请求的脚本
 ```
-
+<a name="3.2.1"></a>
+#### 3.2.1 Python Serving
 - 启动服务：
 ```shell
 # 启动服务，运行日志保存在 log.txt
 python3 classification_web_service.py &>log.txt &
 ```
-成功启动服务后，log.txt 中会打印类似如下日志
-![](../../../deploy/paddleserving/imgs/start_server.png)
 
 - 发送请求：
 ```shell
 # 发送服务请求
 python3 pipeline_http_client.py
 ```
-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：
-![](../../../deploy/paddleserving/imgs/results.png)
+成功运行后，模型预测的结果会打印在 cmd 窗口中，结果如下：
+```
+{'err_no': 0, 'err_msg': '', 'key': ['label', 'prob'], 'value': ["['daisy']", '[0.9341402053833008]'], 'tensors': []}
+```
+
+<a name="3.2.2"></a>
+#### 3.2.2 C++ Serving
+- 启动服务：
+```shell
+# 启动服务， 服务在后台运行，运行日志保存在 nohup.txt
+sh run_cpp_serving.sh
+```
+
+- 发送请求：
+```shell
+# 发送服务请求
+python3 test_cpp_serving_client.py
+```
+成功运行后，模型预测的结果会打印在 cmd 窗口中，结果如下：
+```
+prediction: daisy, probability: 0.9341399073600769
+```
 
 <a name="4"></a>
 ## 4.图像识别服务部署
@@ -164,7 +189,7 @@ python3 -m paddle_serving_client.convert --dirname ./general_PPLCNet_x2_5_lite_v
                                          --serving_server ./general_PPLCNet_x2_5_lite_v1.0_serving/ \
                                          --serving_client ./general_PPLCNet_x2_5_lite_v1.0_client/
 ```
-识别推理模型转换完成后，会在当前文件夹多出 `general_PPLCNet_x2_5_lite_v1.0_serving/` 和 `general_PPLCNet_x2_5_lite_v1.0_client/` 的文件夹。修改 `general_PPLCNet_x2_5_lite_v1.0_serving/` 目录下的 serving_server_conf.prototxt 中的 alias 名字： 将 `fetch_var` 中的 `alias_name` 改为 `features`。
+识别推理模型转换完成后，会在当前文件夹多出 `general_PPLCNet_x2_5_lite_v1.0_serving/` 和 `general_PPLCNet_x2_5_lite_v1.0_serving/` 的文件夹。分别修改 `general_PPLCNet_x2_5_lite_v1.0_serving/` 和 `general_PPLCNet_x2_5_lite_v1.0_client/` 目录下的 serving_server_conf.prototxt 中的 alias 名字： 将 `fetch_var` 中的 `alias_name` 改为 `features`。
 修改后的 serving_server_conf.prototxt 内容如下：
 ```
 feed_var {
@@ -209,28 +234,52 @@ wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_da
 ```shell
 cd ./deploy/paddleserving/recognition
 ```
-paddleserving 目录包含启动 pipeline 服务和发送预测请求的代码，包括：
+paddleserving 目录包含启动 Python Pipeline 服务、C++ Serving 服务和发送预测请求的代码，包括：
 ```
 __init__.py
-config.yml                    # 启动服务的配置文件
+config.yml                    # 启动python pipeline服务的配置文件
 pipeline_http_client.py       # http方式发送pipeline预测请求的脚本
 pipeline_rpc_client.py        # rpc方式发送pipeline预测请求的脚本
 recognition_web_service.py    # 启动pipeline服务端的脚本
+run_cpp_serving.sh            # 启动C++ Pipeline Serving部署的脚本
+test_cpp_serving_client.py    # rpc方式发送C++ Pipeline serving预测请求的脚本
 ```
+
+<a name="4.2.1"></a>
+#### 4.2.1 Python Serving
 - 启动服务：
 ```
 # 启动服务，运行日志保存在 log.txt
 python3 recognition_web_service.py &>log.txt &
 ```
-成功启动服务后，log.txt 中会打印类似如下日志
-![](../../../deploy/paddleserving/imgs/start_server_shitu.png)
 
 - 发送请求：
 ```
 python3 pipeline_http_client.py
 ```
-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：
-![](../../../deploy/paddleserving/imgs/results_shitu.png)
+成功运行后，模型预测的结果会打印在 cmd 窗口中，结果如下：
+```
+{'err_no': 0, 'err_msg': '', 'key': ['result'], 'value': ["[{'bbox': [345, 95, 524, 576], 'rec_docs': '红牛-强化型', 'rec_scores': 0.79903316}]"], 'tensors': []}
+```
+
+<a name="4.2.2"></a>
+#### 4.2.2 C++ Serving
+- 启动服务：
+```shell
+# 启动服务： 此处会在后台同时启动主体检测和特征提取服务，端口号分别为9293和9294；
+# 运行日志分别保存在 log_mainbody_detection.txt 和 log_feature_extraction.txt中
+sh run_cpp_serving.sh
+```
+
+- 发送请求：
+```shell
+# 发送服务请求
+python3 test_cpp_serving_client.py
+```
+成功运行后，模型预测的结果会打印在 cmd 窗口中，结果如下所示：
+```
+[{'bbox': [345, 95, 524, 586], 'rec_docs': '红牛-强化型', 'rec_scores': 0.8016462}]
+```
 
 <a name="5"></a>
 ## 5.FAQ