From 7904c816c921f7ac09ff1e6bd9729c201a6af5b4 Mon Sep 17 00:00:00 2001 From: barrierye Date: Thu, 30 Apr 2020 00:40:45 +0800 Subject: [PATCH] update doc for fix #508 --- doc/NEW_WEB_SERVICE.md | 28 ++++++++++------------------ doc/NEW_WEB_SERVICE_CN.md | 28 ++++++++++------------------ 2 files changed, 20 insertions(+), 36 deletions(-) diff --git a/doc/NEW_WEB_SERVICE.md b/doc/NEW_WEB_SERVICE.md index 63f62a77..a36c1b76 100644 --- a/doc/NEW_WEB_SERVICE.md +++ b/doc/NEW_WEB_SERVICE.md @@ -21,7 +21,7 @@ class WebService(object): 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 `feed` is the feed part `request.json["feed"]` in the request data - 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. @@ -30,7 +30,7 @@ The return values are the feed and fetch values used in the prediction. The postprocess method has three input parameters, `feed`, `fetch` and `fetch_map`: -- The value of `feed` is request data `request.json` +- The value of `feed` is the feed part `request.json["feed"]` in the request data - 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. @@ -40,25 +40,17 @@ The return value will be processed as `{"reslut": fetch_map}` as the return of t ```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"]) + feed_batch = [] + for ins in feed: + if "image" not in ins: + raise ("feed data error!") + sample = base64.b64decode(ins["image"]) img = reader.process_image(sample) - res_feed = {} - res_feed["image"] = img.reshape(-1) - return res_feed, fetch + feed_batch.append({"image": img}) + return feed_batch, 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 index e1a21d8a..c9555f88 100644 --- a/doc/NEW_WEB_SERVICE_CN.md +++ b/doc/NEW_WEB_SERVICE_CN.md @@ -21,7 +21,7 @@ class WebService(object): 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` +- `feed`的值为请求数据中的feed部分`request.json["feed"]` - `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。 @@ -30,7 +30,7 @@ preprocess方法有两个输入参数，`feed`和`fetch`。对于一个HTTP请 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` +- `feed`的值为请求数据中的feed部分`request.json["feed"]` - `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 @@ -40,25 +40,17 @@ postprocess方法有三个输入参数，`feed`、`fetch`和`fetch_map`： ```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"]) + feed_batch = [] + for ins in feed: + if "image" not in ins: + raise ("feed data error!") + sample = base64.b64decode(ins["image"]) img = reader.process_image(sample) - res_feed = {} - res_feed["image"] = img.reshape(-1) - return res_feed, fetch + feed_batch.append({"image": img}) + return feed_batch, 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