Merge pull request #1682 from felixhjh/official_develop

Official develop

examples/Pipeline/PaddleVideo/PPTSN_K400/README_CN.md

+# PP-TSN 视频分类模型
+
+## 模型简介
+PP-TSN模型是对TSN模型进行改进，在不增加参数量和计算量的情况下，得到了的更高精度的2D实用视频分类模型。
+
+## 获取模型
+```
+sh get_model.sh
+```
+
+## 部署模型并测试
+
+### 启动 server
+```
+python3 web_service.py &>log.txt &
+```
+
+## 启动 client
+
+**一. http client**
+```
+python3 pipeline_http_client.py
+```
+**二. rpc client**
+
+```
+python3 pipeline_rpc_client.py
+```

examples/Pipeline/PaddleVideo/PPTSN_K400/pipeline_http_client.py

@@ -16,7 +16,7 @@ import requests
 import json
 
 url = "http://127.0.0.1:9999/ppTSN/prediction"
-video_url = "https://paddle-serving.bj.bcebos.com/huangjianhui04/example.avi"
+video_url = "https://paddle-serving.bj.bcebos.com/model/PaddleVideo/example.avi"
 for i in range(4):
     data = {"key": ["filename"], "value": [video_url]}
     r = requests.post(url=url, data=json.dumps(data))

examples/Pipeline/PaddleVideo/PPTSN_K400/pipeline_rpc_client.py

+# 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.
+try:
+    from paddle_serving_server.pipeline import PipelineClient
+except ImportError:
+    from paddle_serving_server.pipeline import PipelineClient
+import numpy as np
+import requests
+import json
+import cv2
+import base64
+import os
+
+client = PipelineClient()
+client.connect(['127.0.0.1:18090'])
+
+video_url = "https://paddle-serving.bj.bcebos.com/model/PaddleVideo/example.avi"
+for i in range(1):
+    ret = client.predict(feed_dict={"video_url": video_url}, fetch=["res"])
+    print(ret)