add serving demo

demo/serving/Classification_vgg11_imagenet/README.md

+## 数据格式  
+input: {files: {"image": [file_1, file_2, ...]}}  
+output: {"result":[result_1, result_2, ...]}  
+
+## Serving快速启动命令  
+$ hub serving start -m vgg11_imagenet  
+
+## python脚本  
+python vgg11_imagenet_serving_demo.py  
+
+## 结果示例
+```python
+{  
+    "results": "[[{'Egyptian cat': 0.540287435054779}], [{'daisy': 0.9976677298545837}]]"  
+}  
+```  

demo/serving/Classification_vgg11_imagenet/vgg11_imagenet_serving_demo.py

+# coding: utf8
+import requests
+import json
+
+if __name__ == "__main__":
+    file_list = ["cat.jpg", "flower.jpg"]
+    files = [("image", (open(item, "rb"))) for item in file_list]
+    url = "http://127.0.0.1:8866/predict/image/vgg11_imagenet"
+    r = requests.post(url=url, files=files)
+
+    print(json.dumps(r.json(), indent=4, ensure_ascii=False))

demo/serving/GAN_stgan_celeba/README.md

+## 数据格式  
+#### 模型所需参数可通过嵌套字典形式传递
+input: {files: {"image": [file_1, file_2, ...]}, data: {...}}  
+output: {"result":[result_1, result_2, ...]}  
+
+## Serving快速启动命令  
+$ hub serving start -m stgan_celeba  
+
+## python脚本  
+python yolov3_coco2017_serving_demo.py
+
+
+## 结果示例  
+```python
+[  
+    {  
+        "path": "cat.jpg",  
+        "data": [  
+            {  
+                "left": 322.2323,  
+                "right": 1420.4119,  
+                "top": 208.81363,  
+                "bottom": 996.04395,  
+                "label": "cat",  
+                "confidence": 0.9289875  
+            }  
+        ]  
+    },  
+    {  
+        "path": "dog.jpg",  
+        "data": [  
+            {  
+                "left": 204.74722,  
+                "right": 746.02637,  
+                "top": 122.793274,  
+                "bottom": 566.6292,  
+                "label": "dog",  
+                "confidence": 0.86698055  
+            }  
+        ]  
+    }  
+]  
+```
+结果含有生成图片的base64编码，可提取生成图片，示例python脚本生成图片位置为当前目录下的output文件夹下。

demo/serving/GAN_stgan_celeba/stgan_celeba_serving_demo.py

+# coding: utf8
+import requests
+import json
+import base64
+import os
+
+if __name__ == "__main__":
+    file_list = ["../img/girl.jpg"]
+    files = [("image", (open(item, "rb"))) for item in file_list]
+    url = "http://127.0.0.1:8866/predict/image/stgan_celeba"
+
+    data = {"info": ["Female,Black_Hair"], "style": ["Eyeglasses"]}
+    r = requests.post(url=url, data=data, files=files)
+
+    results = eval(r.json()["results"])
+    if not os.path.exists("output"):
+        os.mkdir("output")
+    for item in results:
+        output_path = os.path.join("output", item["path"].split("/")[-1])
+        with open(output_path, "wb") as fp:
+            fp.write(base64.b64decode(item["base64"].split(',')[-1]))
+            item.pop("base64")
+    print(json.dumps(results, indent=4, ensure_ascii=False))

demo/serving/Language_Model_lm_lstm/README.md

+## 数据格式  
+input: {"text": [text_1, text_2, ...]}  
+output: {"result":[result_1, result_2, ...]}  
+
+## Serving快速启动命令  
+$ hub serving start -m lm_lstm  
+
+## python脚本  
+python lm_lstm_serving_demo.py
+
+## 结果示例
+```python
+{  
+    "results": [  
+        {  
+            "perplexity": 4.584166451916099,  
+            "text": "the plant which is owned by <unk> & <unk> co. was under            contract with <unk> to make the cigarette filter"  
+        },  
+        {  
+            "perplexity": 6.038358983397484,  
+            "text": "more common <unk> fibers are <unk> and are more easily            rejected by the body dr. <unk> explained"  
+        }  
+    ]  
+}  
+```  

demo/serving/Language_Model_lm_lstm/lm_lstm_serving_demo.py

+# coding: utf8
+import requests
+import json
+
+if __name__ == "__main__":
+    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}
+    url = "http://127.0.0.1:8866/predict/text/lm_lstm"
+    r = requests.post(url=url, data=text)
+
+    print(json.dumps(r.json(), indent=4, ensure_ascii=False))

demo/serving/Lexical_Analysis_lac/README.md

+## 数据格式  
+input: {"text": [text_1, text_2, ...]}  
+output: {"result":[result_1, result_2, ...]}  
+
+## Serving快速启动命令  
+$ hub serving start -m lac  
+
+## python脚本  
+#### 不携带用户自定义词典  
+python lac_no_dict_serving_demo.py  
+#### 携带用户自定义词典  
+python lac_with_dict_serving_demo.py  
+
+## 结果示例  
+```python
+{  
+    "results": [  
+        {  
+            "tag": [  
+                "TIME",  
+                "v",  
+                "q",  
+                "n"  
+            ],  
+            "word": [  
+                "今天",  
+                "是",  
+                "个",  
+                "好日子"  
+            ]  
+        },  
+        {  
+            "tag": [  
+                "n",  
+                "v",  
+                "TIME",  
+                "v",  
+                "v"  
+            ],  
+            "word": [  
+                "天气预报",  
+                "说",  
+                "今天",  
+                "要",  
+                "下雨"  
+            ]  
+        }  
+    ]  
+}  
+```

demo/serving/Lexical_Analysis_lac/dict.txt

+天气	n	400000
+经	v	1000
+常	d	1000

demo/serving/Lexical_Analysis_lac/lac_serving_demo.py

+# coding: utf8
+import requests
+import json
+
+if __name__ == "__main__":
+    text_list = ["今天是个好日子", "天气预报说今天要下雨"]
+    text = {"text": text_list}
+    url = "http://127.0.0.1:8866/predict/text/lac"
+    r = requests.post(url=url, data=text)
+
+    print(json.dumps(r.json(), indent=4, ensure_ascii=False))

demo/serving/Lexical_Analysis_lac/lac_with_dict_serving_demo.py

+# coding: utf8
+import requests
+import json
+
+if __name__ == "__main__":
+    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
+    with open("dict.txt", "rb") as fp:
+        file = {"user_dict": fp.read()}
+    url = "http://127.0.0.1:8866/predict/text/lac"
+    r = requests.post(url=url, files=file, data=text)
+
+    print(json.dumps(r.json(), indent=4, ensure_ascii=False))

demo/serving/Object_Detection_yolov3_coco2017/README.md

+## 数据格式  
+input: {files: {"image": [file_1, file_2, ...]}}  
+output: {"result":[result_1, result_2, ...]}  
+
+## Serving快速启动命令  
+$ hub serving start -m yolov3_coco2017  
+
+## python脚本  
+python yolov3_coco2017_serving_demo.py
+
+
+## 结果示例  
+```python
+[  
+    {  
+        "path": "cat.jpg",  
+        "data": [  
+            {  
+                "left": 322.2323,  
+                "right": 1420.4119,  
+                "top": 208.81363,  
+                "bottom": 996.04395,  
+                "label": "cat",  
+                "confidence": 0.9289875  
+            }  
+        ]  
+    },  
+    {  
+        "path": "dog.jpg",  
+        "data": [  
+            {  
+                "left": 204.74722,  
+                "right": 746.02637,  
+                "top": 122.793274,  
+                "bottom": 566.6292,  
+                "label": "dog",  
+                "confidence": 0.86698055  
+            }  
+        ]  
+    }  
+]  
+```
+结果含有生成图片的base64编码，可提取生成图片，示例python脚本生成图片位置为当前目录下的output文件夹下。

demo/serving/Object_Detection_yolov3_coco2017/yolov3_coco2017_serving_demo.py

+# coding: utf8
+import requests
+import json
+import base64
+import os
+
+if __name__ == "__main__":
+    file_list = ["../img/cat.jpg", "../img/dog.jpg"]
+    files = [("image", (open(item, "rb"))) for item in file_list]
+    url = "http://127.0.0.1:8866/predict/image/yolov3_coco2017"
+    r = requests.post(url=url, files=files)
+
+    results = eval(r.json()["results"])
+
+    if not os.path.exists("output"):
+        os.mkdir("output")
+    for item in results:
+        with open(os.path.join("output", item["path"]), "wb") as fp:
+            fp.write(base64.b64decode(item["base64"].split(',')[-1]))
+            item.pop("base64")
+    print(json.dumps(results, indent=4, ensure_ascii=False))

demo/serving/Semantic_Model_simnet_bow/README.md

+## 数据格式  
+input: {"text1": [text_a1, text_a2, ...], "text2": [text_b1, text_b2, ...]}  
+output: {"result":[result_1, result_2, ...]}  
+
+## Serving快速启动命令  
+$ hub serving start -m simnet_bow  
+
+## python脚本  
+python simnet_bow_serving_demo.py  
+
+
+## 结果示例  
+```python
+{  
+    "results": [  
+        {  
+            "similarity": 0.8445,  
+            "text_1": "这道题太难了",  
+            "text_2": "这道题是上一年的考题"  
+        },  
+        {  
+            "similarity": 0.9275,  
+            "text_1": "这道题太难了",  
+            "text_2": "这道题不简单"  
+        },  
+        {  
+            "similarity": 0.9083,  
+            "text_1": "这道题太难了",  
+            "text_2": "这道题很有意思"  
+        }  
+    ]  
+}  
+```

demo/serving/Semantic_Model_simnet_bow/simnet_bow_serving_demo.py

+# coding: utf8
+import requests
+import json
+
+if __name__ == "__main__":
+    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"]
+    }
+    url = "http://127.0.0.1:8866/predict/text/simnet_bow"
+    r = requests.post(url=url, data=text)
+
+    print(json.dumps(r.json(), indent=4, ensure_ascii=False))

demo/serving/Semantic_Segmentation_deeplabv3p_xception65_humanseg/README.md

+## 数据格式  
+input: {files: {"image": [file_1, file_2, ...]}}  
+output: {"result":[result_1, result_2, ...]}  
+
+## Serving快速启动命令  
+$ hub serving start -m deeplabv3p_xception65_humanseg  
+
+## python脚本  
+python deeplabv3p_xception65_humanseg_serving_demo.py
+
+
+## 结果示例  
+```python
+[  
+    {  
+        "origin": "girl_2.jpg",  
+        "processed": "humanseg_output/girl_2.png"  
+    }  
+]  
+```
+结果含有生成图片的base64编码，可提取生成图片，示例python脚本生成图片位置为当前目录下的output文件夹中。

demo/serving/Semantic_Segmentation_deeplabv3p_xception65_humanseg/deeplabv3p_xception65_humanseg_serving_demo.py

+# coding: utf8
+import requests
+import json
+import base64
+import os
+
+if __name__ == "__main__":
+    file_list = ["../img/girl_2.jpg"]
+    files = [("image", (open(item, "rb"))) for item in file_list]
+    url = "http://127.0.0.1:8866/predict/image/deeplabv3p_xception65_humanseg"
+    r = requests.post(url=url, files=files)
+
+    results = eval(r.json()["results"])
+
+    if not os.path.exists("output"):
+        os.mkdir("output")
+    for item in results:
+        with open(
+                os.path.join("output", item["processed"].split("/")[-1]),
+                "wb") as fp:
+            fp.write(base64.b64decode(item["base64"].split(',')[-1]))
+            item.pop("base64")
+    print(json.dumps(results, indent=4, ensure_ascii=False))

demo/serving/Sentiment_Analysis_senta_lstm/README.md

+## 数据格式  
+input: {"text1": [text_a1, text_a2, ...], "text2": [text_b1, text_b2, ...]}  
+output: {"result":[result_1, result_2, ...]}  
+
+## Serving快速启动命令  
+$ hub serving start -m senta_lstm  
+
+## python脚本  
+python senta_lstm_serving_demo.py  
+
+## 结果示例  
+```python
+{  
+    "results": [  
+        {  
+            "negative_probs": 0.7079,  
+            "positive_probs": 0.2921,  
+            "sentiment_key": "negative",  
+            "sentiment_label": 0,  
+            "text": "我不爱吃甜食"  
+        },  
+        {  
+            "negative_probs": 0.0149,  
+            "positive_probs": 0.9851,  
+            "sentiment_key": "positive",  
+            "sentiment_label": 1,  
+            "text": "我喜欢躺在床上看电影"  
+        }  
+    ]  
+}  
+```

demo/serving/Sentiment_Analysis_senta_lstm/senta_lstm_serving_demo.py

+# coding: utf8
+import requests
+import json
+
+if __name__ == "__main__":
+    text_list = ["我不爱吃甜食", "我喜欢躺在床上看电影"]
+    text = {"text": text_list}
+    url = "http://127.0.0.1:8866/predict/text/senta_lstm"
+    r = requests.post(url=url, data=text)
+
+    print(json.dumps(r.json(), indent=4, ensure_ascii=False))