From 8f5aadc13b5679fff7ac710df9ff3b521553fafe Mon Sep 17 00:00:00 2001 From: barrierye Date: Fri, 24 Apr 2020 15:07:07 +0800 Subject: [PATCH] add NEW_WEB_SERVICE doc --- README.md | 3 +- README_CN.md | 1 + doc/NEW_WEB_SERVICE.md | 64 +++++++++++++++++++++++++++++++++++++++ doc/NEW_WEB_SERVICE_CN.md | 64 +++++++++++++++++++++++++++++++++++++++ 4 files changed, 131 insertions(+), 1 deletion(-) create mode 100644 doc/NEW_WEB_SERVICE.md create mode 100644 doc/NEW_WEB_SERVICE_CN.md diff --git a/README.md b/README.md index 46b97be4..7c6df8d5 100644 --- a/README.md +++ b/README.md @@ -55,7 +55,7 @@ pip install paddle-serving-server-gpu # GPU ``` You may need to use a domestic mirror source (in China, you can use the Tsinghua mirror source, add `-i https://pypi.tuna.tsinghua.edu.cn/simple` to pip command) to speed up the download. - + Client package support Centos 7 and Ubuntu 18, or you can use HTTP service without install client.

Quick Start Example

@@ -256,6 +256,7 @@ curl -H "Content-Type:application/json" -X POST -d '{"url": "https://paddle-serv ### Developers - [How to config Serving native operators on server side?](doc/SERVER_DAG.md) - [How to develop a new Serving operator?](doc/NEW_OPERATOR.md) +- [How to develop a new Web Service?](doc/NEW_WEB_SERVICE.md) - [Golang client](doc/IMDB_GO_CLIENT.md) - [Compile from source code](doc/COMPILE.md) diff --git a/README_CN.md b/README_CN.md index 4cafb499..e7f97609 100644 --- a/README_CN.md +++ b/README_CN.md @@ -262,6 +262,7 @@ curl -H "Content-Type:application/json" -X POST -d '{"url": "https://paddle-serv ### 开发者教程 - [如何配置Server端的计算图?](doc/SERVER_DAG_CN.md) - [如何开发一个新的General Op?](doc/NEW_OPERATOR_CN.md) +- [如何开发一个新的Web Service?](doc/NEW_WEB_SERVICE_CN.md) - [如何在Paddle Serving使用Go Client?](doc/IMDB_GO_CLIENT_CN.md) - [如何编译PaddleServing?](doc/COMPILE_CN.md) diff --git a/doc/NEW_WEB_SERVICE.md b/doc/NEW_WEB_SERVICE.md new file mode 100644 index 00000000..b76c6d42 --- /dev/null +++ b/doc/NEW_WEB_SERVICE.md @@ -0,0 +1,64 @@ +# How to develop a new Web service? + +([简体中文](NEW_WEB_SERVICE_CN.md)|English) + +This document will take the image classification service based on the Imagenet data set as an example to introduce how to develop a new web service. The complete code can be visited at [here](https://github.com/PaddlePaddle/Serving/blob/develop/python/examples/imagenet/image_classification_service.py). + +## WebService base class + +Paddle Serving implements the [WebService](https://github.com/PaddlePaddle/Serving/blob/develop/python/paddle_serving_server/web_service.py#L23) base class. You need to override its `preprocess` and `postprocess` method. The default implementation is as follows: + +```python +class WebService(object): + + def preprocess(self, feed={}, fetch=[]): + return feed, fetch + def postprocess(self, feed={}, fetch=[], fetch_map=None): + return fetch_map +``` + +### preprocess + +The preprocess method has two input parameters, `feed` and `fetch`. For an HTTP request `request`: + +- The value of `feed` is request data `request.json` +- The value of `fetch` is the fetch part `request.json["fetch"]` in the request data + +The return values are the feed and fetch values used in the prediction. + +### postprocess + +The postprocess method has three input parameters, `feed`, `fetch` and `fetch_map`: + +- The value of `feed` is request data `request.json` +- The value of `fetch` is the fetch part `request.json["fetch"]` in the request data +- The value of `fetch_map` is the model output value. + +The return value will be processed as `{"reslut": fetch_map}` as the return of the HTTP request. + +## Develop ImageService class + +```python +class ImageService(WebService): + def preprocess(self, feed={}, fetch=[]): + reader = ImageReader() + if "image" not in feed: + raise ("feed data error!") + if isinstance(feed["image"], list): + feed_batch = [] + for image in feed["image"]: + sample = base64.b64decode(image) + img = reader.process_image(sample) + res_feed = {} + res_feed["image"] = img.reshape(-1) + feed_batch.append(res_feed) + return feed_batch, fetch + else: + sample = base64.b64decode(feed["image"]) + img = reader.process_image(sample) + res_feed = {} + res_feed["image"] = img.reshape(-1) + return res_feed, fetch +``` + +For the above `ImageService`, only the `preprocess` method is rewritten to process the image data in Base64 format into the data format required by prediction. diff --git a/doc/NEW_WEB_SERVICE_CN.md b/doc/NEW_WEB_SERVICE_CN.md new file mode 100644 index 00000000..067780e3 --- /dev/null +++ b/doc/NEW_WEB_SERVICE_CN.md @@ -0,0 +1,64 @@ +# 如何开发一个新的Web Service？ + +(简体中文|[English](NEW_WEB_SERVICE.md)) + +本文档将以Imagenet图像分类服务为例，来介绍如何开发一个新的Web Service。您可以在[这里](https://github.com/PaddlePaddle/Serving/blob/develop/python/examples/imagenet/image_classification_service.py)查阅完整的代码。 + +## WebService基类 + +Paddle Serving实现了[WebService](https://github.com/PaddlePaddle/Serving/blob/develop/python/paddle_serving_server/web_service.py#L23)基类，您需要重写它的`preprocess`方法和`postprocess`方法，默认实现如下： + +```python +class WebService(object): + + def preprocess(self, feed={}, fetch=[]): + return feed, fetch + def postprocess(self, feed={}, fetch=[], fetch_map=None): + return fetch_map +``` + +###preprocess方法 + +preprocess方法有两个输入参数，`feed`和`fetch`。对于一个HTTP请求`request`： + +- `feed`的值为请求数据`request.json` +- `fetch`的值为请求数据中的fetch部分`request.json["fetch"]` + +返回值分别是预测过程中用到的feed和fetch值。 + +###postprocess方法 + +postprocess方法有三个输入参数，`feed`、`fetch`和`fetch_map`： + +- `feed`的值为请求数据`request.json` +- `fetch`的值为请求数据中的fetch部分`request.json["fetch"]` +- `fetch_map`的值为fetch到的模型输出值 + +返回值将会被处理成`{"reslut": fetch_map}`作为HTTP请求的返回。 + +## 开发ImageService类 + +```python +class ImageService(WebService): + def preprocess(self, feed={}, fetch=[]): + reader = ImageReader() + if "image" not in feed: + raise ("feed data error!") + if isinstance(feed["image"], list): + feed_batch = [] + for image in feed["image"]: + sample = base64.b64decode(image) + img = reader.process_image(sample) + res_feed = {} + res_feed["image"] = img.reshape(-1) + feed_batch.append(res_feed) + return feed_batch, fetch + else: + sample = base64.b64decode(feed["image"]) + img = reader.process_image(sample) + res_feed = {} + res_feed["image"] = img.reshape(-1) + return res_feed, fetch +``` + +对于上述的`ImageService`，只重写了前处理方法，将base64格式的图片数据处理成模型预测需要的数据格式。 -- GitLab