Adapt to PaddleHub2.0 (#512)

deploy/hubserving/clas/module.py

@@ -62,6 +62,7 @@ class ClasSystem(hub.Module):
         else:
             print("Use CPU")
             print("Enable MKL-DNN") if enable_mkldnn else None
+        self.predictor = create_paddle_predictor(self.args)
 
     def read_images(self, paths=[]):
         images = []
@@ -96,7 +97,6 @@ class ClasSystem(hub.Module):
 
         assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
 
-        predictor = create_paddle_predictor(self.args)
         all_results = []
         for img in predicted_data:
             if img is None:
@@ -107,7 +107,7 @@ class ClasSystem(hub.Module):
 
             self.args.image_file = img
             self.args.top_k = top_k
-            classes, scores = paddle_predict.predict(self.args, predictor)
+            classes, scores = paddle_predict.predict(self.args, self.predictor)
 
             elapse = time.time() - starttime
             logger.info("Predict time: {}".format(elapse))

deploy/hubserving/readme.md

@@ -14,8 +14,8 @@ deploy/hubserving/clas/
 ## 快速启动服务
 ### 1. 准备环境
 ```shell
-# 安装paddlehub  
-pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
+# 安装paddlehub,请安装2.0版本
+pip3 install paddlehub==2.0.0b1 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
 ```
 
 ### 2. 下载推理模型

deploy/hubserving/readme_en.md

@@ -14,8 +14,8 @@ deploy/hubserving/clas/
 ## Quick start service
 ### 1. Prepare the environment
 ```shell
-# Install paddlehub  
-pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
+# Install version 2.0 of PaddleHub  
+pip3 install paddlehub==2.0.0b1 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple
 ```
 
 ### 2. Download inference model

tools/test_hubserving.py

@@ -73,7 +73,7 @@ def main(url, image_path, top_k=1):
             continue
         elapse = time.time() - starttime
         total_time += elapse
-        if r.json()['status'] != '0':
+        if r.json()['status'] != '000':
             logger.error(
                 "File:{}, The parameters returned by the server are: {}".
                 format(file_str, r.json()['msg']))