Merge pull request #1603 from Intsigstephon/develop
add cpp serving for clas and pp-shitupull/1621/head
Bin Lu
GitHub
commit
0aa85d4f7f
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@ -1,236 +0,0 @@
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# 模型服务化部署
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- [1. 简介](#1)
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- [2. Serving 安装](#2)
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- [3. 图像分类服务部署](#3)
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- [3.1 模型转换](#3.1)
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- [3.2 服务部署和请求](#3.2)
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- [4. 图像识别服务部署](#4)
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- [4.1 模型转换](#4.1)
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- [4.2 服务部署和请求](#4.2)
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- [5. FAQ](#5)
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<a name="1"></a>
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## 1. 简介
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[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。
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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。
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<a name="2"></a>
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## 2. Serving 安装
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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。
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```shell
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docker pull paddlepaddle/serving:0.7.0-cuda10.2-cudnn7-devel
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nvidia-docker run -p 9292:9292 --name test -dit paddlepaddle/serving:0.7.0-cuda10.2-cudnn7-devel bash
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nvidia-docker exec -it test bash
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```
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进入 docker 后，需要安装 Serving 相关的 python 包。
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```shell
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pip3 install paddle-serving-client==0.7.0
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pip3 install paddle-serving-server==0.7.0 # CPU
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pip3 install paddle-serving-app==0.7.0
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pip3 install paddle-serving-server-gpu==0.7.0.post102 #GPU with CUDA10.2 + TensorRT6
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# 其他GPU环境需要确认环境再选择执行哪一条
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pip3 install paddle-serving-server-gpu==0.7.0.post101 # GPU with CUDA10.1 + TensorRT6
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pip3 install paddle-serving-server-gpu==0.7.0.post112 # GPU with CUDA11.2 + TensorRT8
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```
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* 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。
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* 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)
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* 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。
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```shell
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pip install paddle-serving-server
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```
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<a name="3"></a>
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## 3. 图像分类服务部署
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<a name="3.1"></a>
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### 3.1 模型转换
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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。
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- 进入工作目录：
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```shell
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cd deploy/paddleserving
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```
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- 下载 ResNet50_vd 的 inference 模型：
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```shell
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# 下载并解压 ResNet50_vd 模型
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wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar && tar xf ResNet50_vd_infer.tar
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```
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- 用 paddle_serving_client 把下载的 inference 模型转换成易于 Server 部署的模型格式：
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```
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# 转换 ResNet50_vd 模型
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python3 -m paddle_serving_client.convert --dirname ./ResNet50_vd_infer/ \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--serving_server ./ResNet50_vd_serving/ \
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--serving_client ./ResNet50_vd_client/
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```
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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：
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```
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|- ResNet50_vd_server/
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|- inference.pdiparams
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|- inference.pdmodel
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|- serving_server_conf.prototxt
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|- serving_server_conf.stream.prototxt
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|- ResNet50_vd_client
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|- serving_client_conf.prototxt
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|- serving_client_conf.stream.prototxt
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```
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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`
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**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。
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修改后的 serving_server_conf.prototxt 如下所示:
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```
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feed_var {
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name: "inputs"
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alias_name: "inputs"
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is_lod_tensor: false
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feed_type: 1
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shape: 3
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shape: 224
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shape: 224
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}
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fetch_var {
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name: "save_infer_model/scale_0.tmp_1"
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alias_name: "prediction"
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is_lod_tensor: false
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fetch_type: 1
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shape: 1000
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}
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```
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<a name="3.2"></a>
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### 3.2 服务部署和请求
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paddleserving 目录包含了启动 pipeline 服务和发送预测请求的代码，包括：
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```shell
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__init__.py
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config.yml # 启动服务的配置文件
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pipeline_http_client.py # http方式发送pipeline预测请求的脚本
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pipeline_rpc_client.py # rpc方式发送pipeline预测请求的脚本
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classification_web_service.py # 启动pipeline服务端的脚本
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```
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- 启动服务：
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```shell
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# 启动服务，运行日志保存在 log.txt
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python3 classification_web_service.py &>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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- 发送请求：
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```shell
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# 发送服务请求
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python3 pipeline_http_client.py
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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="4"></a>
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## 4.图像识别服务部署
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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。
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<a name="4.1"></a>
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## 4.1 模型转换
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- 下载通用检测 inference 模型和通用识别 inference 模型
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```
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cd deploy
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# 下载并解压通用识别模型
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wget -P models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
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cd models
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tar -xf general_PPLCNet_x2_5_lite_v1.0_infer.tar
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# 下载并解压通用检测模型
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wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
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tar -xf picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
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```
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- 转换识别 inference 模型为 Serving 模型：
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```
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# 转换识别模型
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python3 -m paddle_serving_client.convert --dirname ./general_PPLCNet_x2_5_lite_v1.0_infer/ \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--serving_server ./general_PPLCNet_x2_5_lite_v1.0_serving/ \
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--serving_client ./general_PPLCNet_x2_5_lite_v1.0_client/
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```
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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`。
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修改后的 serving_server_conf.prototxt 内容如下：
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```
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feed_var {
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name: "x"
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alias_name: "x"
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is_lod_tensor: false
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feed_type: 1
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shape: 3
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shape: 224
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shape: 224
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}
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fetch_var {
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name: "save_infer_model/scale_0.tmp_1"
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alias_name: "features"
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is_lod_tensor: false
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fetch_type: 1
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shape: 512
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}
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```
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- 转换通用检测 inference 模型为 Serving 模型：
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```
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# 转换通用检测模型
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python3 -m paddle_serving_client.convert --dirname ./picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer/ \
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--model_filename inference.pdmodel \
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--params_filename inference.pdiparams \
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--serving_server ./picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving/ \
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--serving_client ./picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/
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```
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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。
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**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。
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- 下载并解压已经构建后的检索库 index
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```
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cd ../
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wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar && tar -xf drink_dataset_v1.0.tar
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```
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<a name="4.2"></a>
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## 4.2 服务部署和请求
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**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。
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- 进入到工作目录
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```shell
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cd ./deploy/paddleserving/recognition
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```
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paddleserving 目录包含启动 pipeline 服务和发送预测请求的代码，包括：
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```
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__init__.py
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config.yml # 启动服务的配置文件
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pipeline_http_client.py # http方式发送pipeline预测请求的脚本
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pipeline_rpc_client.py # rpc方式发送pipeline预测请求的脚本
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recognition_web_service.py # 启动pipeline服务端的脚本
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```
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- 启动服务：
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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 recognition_web_service.py &>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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- 发送请求：
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```
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python3 pipeline_http_client.py
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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="5"></a>
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## 5.FAQ
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**Q1**： 发送请求后没有结果返回或者提示输出解码报错
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**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：
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```
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unset https_proxy
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unset http_proxy
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```
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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)
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../../docs/zh_CN/inference_deployment/paddle_serving_deploy.md
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nohup python3 -m paddle_serving_server.serve \
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--model ../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_serving \
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--port 9293 >>log_mainbody_detection.txt 1&>2 &
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nohup python3 -m paddle_serving_server.serve \
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--model ../../models/general_PPLCNet_x2_5_lite_v1.0_serving \
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--port 9294 >>log_feature_extraction.txt 1&>2 &
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@ -0,0 +1,202 @@
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import numpy as np
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from paddle_serving_client import Client
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from paddle_serving_app.reader import *
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import cv2
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import faiss
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import os
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import pickle
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class MainbodyDetect():
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"""
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pp-shitu mainbody detect.
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include preprocess, process, postprocess
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return detect results
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Attention: Postprocess include num limit and box filter; no nms
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"""
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def __init__(self):
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self.preprocess = DetectionSequential([
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DetectionFile2Image(), DetectionNormalize(
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[0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True),
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DetectionResize(
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(640, 640), False, interpolation=2), DetectionTranspose(
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(2, 0, 1))
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])
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self.client = Client()
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self.client.load_client_config(
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"../../models/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_client/serving_client_conf.prototxt"
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)
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self.client.connect(['127.0.0.1:9293'])
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self.max_det_result = 5
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self.conf_threshold = 0.2
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def predict(self, imgpath):
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im, im_info = self.preprocess(imgpath)
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im_shape = np.array(im.shape[1:]).reshape(-1)
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scale_factor = np.array(list(im_info['scale_factor'])).reshape(-1)
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fetch_map = self.client.predict(
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feed={
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"image": im,
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"im_shape": im_shape,
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"scale_factor": scale_factor,
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},
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fetch=["save_infer_model/scale_0.tmp_1"],
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batch=False)
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return self.postprocess(fetch_map, imgpath)
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def postprocess(self, fetch_map, imgpath):
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#1. get top max_det_result
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det_results = fetch_map["save_infer_model/scale_0.tmp_1"]
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if len(det_results) > self.max_det_result:
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boxes_reserved = fetch_map[
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"save_infer_model/scale_0.tmp_1"][:self.max_det_result]
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else:
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boxes_reserved = det_results
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#2. do conf threshold
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boxes_list = []
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for i in range(boxes_reserved.shape[0]):
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if (boxes_reserved[i, 1]) > self.conf_threshold:
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boxes_list.append(boxes_reserved[i, :])
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#3. add origin image box
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origin_img = cv2.imread(imgpath)
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boxes_list.append(
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np.array([0, 1.0, 0, 0, origin_img.shape[1], origin_img.shape[0]]))
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return np.array(boxes_list)
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class ObjectRecognition():
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"""
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pp-shitu object recognion for all objects detected by MainbodyDetect.
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include preprocess, process, postprocess
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preprocess include preprocess for each image and batching.
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Batch process
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postprocess include retrieval and nms
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"""
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def __init__(self):
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self.client = Client()
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self.client.load_client_config(
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"../../models/general_PPLCNet_x2_5_lite_v1.0_client/serving_client_conf.prototxt"
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)
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self.client.connect(["127.0.0.1:9294"])
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self.seq = Sequential([
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BGR2RGB(), Resize((224, 224)), Div(255),
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Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225],
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False), Transpose((2, 0, 1))
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])
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self.searcher, self.id_map = self.init_index()
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self.rec_nms_thresold = 0.05
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self.rec_score_thres = 0.5
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self.feature_normalize = True
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self.return_k = 1
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def init_index(self):
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index_dir = "../../drink_dataset_v1.0/index"
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assert os.path.exists(os.path.join(
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index_dir, "vector.index")), "vector.index not found ..."
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assert os.path.exists(os.path.join(
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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)
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
#run cls server:
|
||||
nohup python3 -m paddle_serving_server.serve --model ResNet50_vd_serving --port 9292 &
|
||||
|
|
@ -0,0 +1,52 @@
|
|||
# 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)
|
||||
|
|
@ -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
|
||||
|
||||
|
||||
- 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：
|
||||
|
||||
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
|
||||
|
||||
|
||||
- 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：
|
||||
|
||||
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
|
||||
|
|
|
|||
Loading…
Reference in New Issue