add NEW_WEB_SERVICE doc

README.md

@@ -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)
 

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)
 

doc/NEW_WEB_SERVICE.md

+# 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.

doc/NEW_WEB_SERVICE_CN.md

+# 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。