Merge pull request #224 from ShenYuhan/update_file

update serving demo, add notes

demo/serving/Classification_vgg11_imagenet/vgg11_imagenet_serving_demo.py

@@ -3,9 +3,12 @@ import requests
 import json
 
 if __name__ == "__main__":
+    # 指定要预测的图片并生成列表[("image", img_1), ("image", img_2), ... ]
     file_list = ["../img/cat.jpg", "../img/flower.jpg"]
     files = [("image", (open(item, "rb"))) for item in file_list]
+    # 指定预测方法为vgg11_imagenet并发送post请求
     url = "http://127.0.0.1:8866/predict/image/vgg11_imagenet"
     r = requests.post(url=url, files=files)
 
+    # 打印预测结果
     print(json.dumps(r.json(), indent=4, ensure_ascii=False))

demo/serving/GAN_stgan_celeba/stgan_celeba_serving_demo.py

@@ -5,14 +5,17 @@ import base64
 import os
 
 if __name__ == "__main__":
+    # 指定要使用的图片文件并生成列表[("image", img_1), ("image", img_2), ... ]
     file_list = ["../img/woman.png"]
     files = [("image", (open(item, "rb"))) for item in file_list]
-    url = "http://127.0.0.1:8866/predict/image/stgan_celeba"
-
+    # 为每张图片对应指定info和style
     data = {"info": ["Female,Brown_Hair"], "style": ["Aged"]}
+    # 指定图片生成方法为stgan_celeba并发送post请求
+    url = "http://127.0.0.1:8866/predict/image/stgan_celeba"
     r = requests.post(url=url, data=data, files=files)
 
     results = eval(r.json()["results"])
+    # 保存生成的图片到output文件夹，打印模型输出结果
     if not os.path.exists("output"):
         os.mkdir("output")
     for item in results:

demo/serving/Language_Model_lm_lstm/lm_lstm_serving_demo.py

@@ -3,12 +3,15 @@ import requests
 import json
 
 if __name__ == "__main__":
+    # 指定用于用于预测的文本并生成字典{"text": [text_1, text_2, ... ]}
     text_list = [
         "the plant which is owned by <unk> & <unk> co. was under contract with <unk> to make the cigarette filter",
         "more common <unk> fibers are <unk> and are more easily rejected by the body dr. <unk> explained"
     ]
     text = {"text": text_list}
+    # 指定预测方法为lm_lstm并发送post请求
     url = "http://127.0.0.1:8866/predict/text/lm_lstm"
     r = requests.post(url=url, data=text)
 
+    # 打印预测结果
     print(json.dumps(r.json(), indent=4, ensure_ascii=False))

demo/serving/Lexical_Analysis_lac/lac_serving_demo.py

@@ -3,9 +3,12 @@ import requests
 import json
 
 if __name__ == "__main__":
+    # 指定用于用于预测的文本并生成字典{"text": [text_1, text_2, ... ]}
     text_list = ["今天是个好日子", "天气预报说今天要下雨"]
     text = {"text": text_list}
+    # 指定预测方法为lac并发送post请求
     url = "http://127.0.0.1:8866/predict/text/lac"
     r = requests.post(url=url, data=text)
 
+    # 打印预测结果
     print(json.dumps(r.json(), indent=4, ensure_ascii=False))

demo/serving/Lexical_Analysis_lac/lac_with_dict_serving_demo.py

@@ -3,11 +3,14 @@ import requests
 import json
 
 if __name__ == "__main__":
+    # 指定用于用于预测的文本并生成字典{"text": [text_1, text_2, ... ]}
     text_list = ["今天是个好日子", "天气预报说今天要下雨"]
     text = {"text": text_list}
-    # 将用户自定义词典文件发送到预测接口即可
+    # 指定自定义词典{"user_dict": dict.txt}
     file = {"user_dict": open("dict.txt", "rb")}
+    # 指定预测方法为lac并发送post请求
     url = "http://127.0.0.1:8866/predict/text/lac"
     r = requests.post(url=url, files=file, data=text)
 
+    # 打印预测结果
     print(json.dumps(r.json(), indent=4, ensure_ascii=False))

demo/serving/Object_Detection_yolov3_coco2017/yolov3_coco2017_serving_demo.py

@@ -5,13 +5,16 @@ import base64
 import os
 
 if __name__ == "__main__":
+    # 指定要检测的图片并生成列表[("image", img_1), ("image", img_2), ... ]
     file_list = ["../img/cat.jpg", "../img/dog.jpg"]
     files = [("image", (open(item, "rb"))) for item in file_list]
+    # 指定检测方法为yolov3_coco2017并发送post请求
     url = "http://127.0.0.1:8866/predict/image/yolov3_coco2017"
     r = requests.post(url=url, files=files)
 
     results = eval(r.json()["results"])
 
+    # 保存检测生成的图片到output文件夹，打印模型输出结果
     if not os.path.exists("output"):
         os.mkdir("output")
     for item in results:

demo/serving/Semantic_Model_simnet_bow/simnet_bow_serving_demo.py

@@ -3,11 +3,15 @@ import requests
 import json
 
 if __name__ == "__main__":
+    # 指定用于用于匹配的文本并生成字典{"text_1": [text_a1, text_a2, ... ]
+    #                              "text_2": [text_b1, text_b2, ... ]}
     text = {
         "text_1": ["这道题太难了", "这道题太难了", "这道题太难了"],
         "text_2": ["这道题是上一年的考题", "这道题不简单", "这道题很有意思"]
     }
+    # 指定匹配方法为simnet_bow并发送post请求
     url = "http://127.0.0.1:8866/predict/text/simnet_bow"
     r = requests.post(url=url, data=text)
 
+    # 打印匹配结果
     print(json.dumps(r.json(), indent=4, ensure_ascii=False))

demo/serving/Semantic_Segmentation_deeplabv3p_xception65_humanseg/deeplabv3p_xception65_humanseg_serving_demo.py

@@ -5,13 +5,16 @@ import base64
 import os
 
 if __name__ == "__main__":
+    # 指定要使用的图片文件并生成列表[("image", img_1), ("image", img_2), ... ]
     file_list = ["../img/girl.jpg"]
     files = [("image", (open(item, "rb"))) for item in file_list]
+    # 指定图片分割方法为deeplabv3p_xception65_humanseg并发送post请求
     url = "http://127.0.0.1:8866/predict/image/deeplabv3p_xception65_humanseg"
     r = requests.post(url=url, files=files)
 
     results = eval(r.json()["results"])
 
+    # 保存分割后的图片到output文件夹，打印模型输出结果
     if not os.path.exists("output"):
         os.mkdir("output")
     for item in results:

demo/serving/Sentiment_Analysis_senta_lstm/senta_lstm_serving_demo.py

@@ -3,9 +3,12 @@ import requests
 import json
 
 if __name__ == "__main__":
+    # 指定用于用于预测的文本并生成字典{"text": [text_1, text_2, ... ]}
     text_list = ["我不爱吃甜食", "我喜欢躺在床上看电影"]
     text = {"text": text_list}
+    # 指定预测方法为senta_lstm并发送post请求
     url = "http://127.0.0.1:8866/predict/text/senta_lstm"
     r = requests.post(url=url, data=text)
 
+    # 打印预测结果
     print(json.dumps(r.json(), indent=4, ensure_ascii=False))

paddlehub/common/hub_server.py

@@ -287,7 +287,10 @@ class CacheUpdater(threading.Thread):
             "command": "update_cache",
             "mtime": os.stat(cache_path).st_mtime
         }
-        r = srv_utils.hub_request(api_url, payload, extra)
+        try:
+            r = srv_utils.hub_request(api_url, payload, extra)
+        except Exception as err:
+            pass
         if r.get("update_cache", 0) == 1:
             with open(cache_path, 'w+') as fp:
                 yaml.safe_dump({'resource_list': r['data']}, fp)

paddlehub/module/module.py

@@ -128,6 +128,8 @@ class Module(object):
         elif module_dir:
             self._init_with_module_file(module_dir=module_dir[0])
             lock.flock(fp_lock, lock.LOCK_UN)
+            name = module_dir[0].split("/")[-1]
+            version = module_dir[1]
         elif signatures:
             if processor:
                 if not issubclass(processor, BaseProcessor):