fix ernie_gen bug. and plato and ddparser config (PaddlePaddle#817)

hub_module/modules/text/syntactic_analysis/DDParser/README.md

@@ -6,17 +6,18 @@ $ hub run ddparser --input_text="百度是一家高科技公司"
 
 # API
 
-## parse(texts=[])
+## parse(texts=[], return\_visual=False)
 
 依存分析接口，输入文本，输出依存关系。
 
 **参数**
 
-* texts(list[list[str] or list[str]]): 待预测数据。各元素可以是未分词的字符串，也可以是已分词的token列表。
+* texts(list\[list\[str\] or list\[str\]]): 待预测数据。各元素可以是未分词的字符串，也可以是已分词的token列表。
+* return\_visual(bool): 是否返回依存分析可视化结果。如果为True，返回结果中将包含'visual'字段。
 
 **返回**
 
-* results(list[dict]): 依存分析结果。每个元素都是dict类型，包含以下信息：  
+* results(list\[dict\]): 依存分析结果。每个元素都是dict类型，包含以下信息：  
 ```python
   {
       'word': list[str], 分词结果。
@@ -34,9 +35,9 @@ $ hub run ddparser --input_text="百度是一家高科技公司"
 
 **参数**
 
-* word(list[list[str]): 分词信息。
-* head(list[int]): 当前成分其支配者的id。
-* deprel(list[str]): 当前成分与支配者的依存关系。
+* word(list\[list\[str\]\): 分词信息。
+* head(list\[int\]): 当前成分其支配者的id。
+* deprel(list\[str\]): 当前成分与支配者的依存关系。
 
 **返回**
 
@@ -55,11 +56,12 @@ results = module.parse(texts=test_text)
 print(results)
 
 test_tokens = [['百度', '是', '一家', '高科技', '公司']]
-results = module.parse(texts=test_text)
+results = module.parse(texts=test_text, return_visual = True)
 print(results)
 
 result = results[0]
 data = module.visualize(result['word'],result['head'],result['deprel'])
+# or data = result['visual']
 cv2.imwrite('test.jpg',data)
 ```
 
@@ -81,7 +83,7 @@ Loading ddparser successful.
 
 这样就完成了服务化API的部署，默认端口号为8866。
 
-**NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA_VISIBLE_DEVICES环境变量，否则不用设置。
+**NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。
 
 ## 第二步：发送预测请求
 
@@ -105,12 +107,12 @@ data = {"texts": text, "return_visual": return_visual}
 url = "http://0.0.0.0:8866/predict/ddparser"
 headers = {"Content-Type": "application/json"}
 r = requests.post(url=url, headers=headers, data=json.dumps(data))
-results, visuals = r.json()['results']
+results = r.json()['results']
 
 for i in range(len(results)):
-  print(results[i])
+  print(results[i]['word'])
   # 不同于本地调用parse接口，serving返回的图像是list类型的，需要先用numpy加载再显示或保存。
-  cv2.imwrite('%s.jpg'%i, np.array(visuals[i]))
+  cv2.imwrite('%s.jpg'%i, np.array(results[i]['visual']))
 ```
 
 关于PaddleHub Serving更多信息参考[服务部署](https://github.com/PaddlePaddle/PaddleHub/blob/release/v1.6/docs/tutorial/serving.md)

hub_module/modules/text/syntactic_analysis/DDParser/module.py

@@ -32,15 +32,16 @@ def _initialize(self):
         """
         self.ddp = DDParserModel(prob=True, use_pos=True)
         self.font = font_manager.FontProperties(
-            fname=os.path.join(self.directory, "SimHei.ttf"))
+            fname=os.path.join(self.directory, "SourceHanSans-Regular.ttf"))
 
     @serving
     def serving_parse(self, texts=[], return_visual=False):
-        results, visuals = self.parse(texts, return_visual)
-        for i, visual in enumerate(visuals):
-            visuals[i] = visual.tolist()
+        results = self.parse(texts, return_visual)
+        if return_visual:
+            for i, result in enumerate(results):
+                result['visual'] = result['visual'].tolist()
 
-        return results, visuals
+        return results
 
     def parse(self, texts=[], return_visual=False):
         """
@@ -57,11 +58,9 @@ def parse(self, texts=[], return_visual=False):
                     'head': list[int], the head ids.
                     'deprel': list[str], the dependency relation.
                     'prob': list[float], the prediction probility of the dependency relation.
-                    'postag': list[str], the POS tag. If the element of the texts is list, the key 'postag' will not be returned.
+                    'postag': list[str], the POS tag. If the element of the texts is list, the key 'postag' will not return.
+                    'visual' : list[numpy.array]: the dependency visualization. Use cv2.imshow to show or cv2.imwrite to save it. If return_visual=False, it will not return.
                 }
-
-            visuals : list[numpy.array]: the dependency visualization. Use cv2.imshow to show or cv2.imwrite to save it. If return_visual=False, it will not be empty.
-
        """
 
         if not texts:
@@ -73,13 +72,11 @@ def parse(self, texts=[], return_visual=False):
         else:
             raise ValueError("All of the elements should be string or list")
         results = do_parse(texts)
-        visuals = []
         if return_visual:
             for result in results:
-                visuals.append(
-                    self.visualize(result['word'], result['head'],
-                                   result['deprel']))
-        return results, visuals
+                result['visual'] = self.visualize(
+                    result['word'], result['head'], result['deprel'])
+        return results
 
     @runnable
     def run_cmd(self, argvs):
@@ -194,10 +191,11 @@ def visualize(self, word, head, deprel):
     results = module.parse(texts=test_text)
     print(results)
     test_tokens = [['百度', '是', '一家', '高科技', '公司']]
-    results = module.parse(texts=test_text)
+    results = module.parse(texts=test_text, return_visual=True)
     print(results)
     result = results[0]
     data = module.visualize(result['word'], result['head'], result['deprel'])
     import cv2
     import numpy as np
-    cv2.imwrite('test.jpg', np.array(data))
+    cv2.imwrite('test1.jpg', data)
+    cv2.imwrite('test2.jpg', result['visual'])

hub_module/modules/text/text_generation/ernie_gen_couplet/README.md

@@ -97,3 +97,7 @@ paddlehub >= 1.7.0
 * 1.0.0
 
   初始发布
+
+* 1.0.1
+
+  修复windows中的编码问题

hub_module/modules/text/text_generation/ernie_gen_couplet/module.py

@@ -35,7 +35,7 @@
 
 @moduleinfo(
     name="ernie_gen_couplet",
-    version="1.0.0",
+    version="1.0.1",
     summary=
     "ERNIE-GEN is a multi-flow language generation framework for both pre-training and fine-tuning. This module has fine-tuned for couplet generation task.",
     author="baidu-nlp",
@@ -50,10 +50,10 @@ def _initialize(self):
         assets_path = os.path.join(self.directory, "assets")
         gen_checkpoint_path = os.path.join(assets_path, "ernie_gen_couplet")
         ernie_cfg_path = os.path.join(assets_path, 'ernie_config.json')
-        with open(ernie_cfg_path) as ernie_cfg_file:
+        with open(ernie_cfg_path, encoding='utf8') as ernie_cfg_file:
             ernie_cfg = dict(json.loads(ernie_cfg_file.read()))
         ernie_vocab_path = os.path.join(assets_path, 'vocab.txt')
-        with open(ernie_vocab_path) as ernie_vocab_file:
+        with open(ernie_vocab_path, encoding='utf8') as ernie_vocab_file:
             ernie_vocab = {
                 j.strip().split('\t')[0]: i
                 for i, j in enumerate(ernie_vocab_file.readlines())

hub_module/modules/text/text_generation/ernie_gen_poetry/README.md

@@ -97,3 +97,7 @@ paddlehub >= 1.7.0
 * 1.0.0
 
   初始发布
+
+* 1.0.1
+
+  修复windows中的编码问题

hub_module/modules/text/text_generation/ernie_gen_poetry/module.py

@@ -35,7 +35,7 @@
 
 @moduleinfo(
     name="ernie_gen_poetry",
-    version="1.0.0",
+    version="1.0.1",
     summary=
     "ERNIE-GEN is a multi-flow language generation framework for both pre-training and fine-tuning. This module has fine-tuned for poetry generation task.",
     author="baidu-nlp",
@@ -50,10 +50,10 @@ def _initialize(self):
         assets_path = os.path.join(self.directory, "assets")
         gen_checkpoint_path = os.path.join(assets_path, "ernie_gen_poetry")
         ernie_cfg_path = os.path.join(assets_path, 'ernie_config.json')
-        with open(ernie_cfg_path) as ernie_cfg_file:
+        with open(ernie_cfg_path, encoding='utf8') as ernie_cfg_file:
             ernie_cfg = dict(json.loads(ernie_cfg_file.read()))
         ernie_vocab_path = os.path.join(assets_path, 'vocab.txt')
-        with open(ernie_vocab_path) as ernie_vocab_file:
+        with open(ernie_vocab_path, encoding='utf8') as ernie_vocab_file:
             ernie_vocab = {
                 j.strip().split('\t')[0]: i
                 for i, j in enumerate(ernie_vocab_file.readlines())

hub_module/modules/text/text_generation/plato2_en_base/README.md

@@ -10,7 +10,7 @@ PLATO2是一个超大规模生成式对话系统模型。它承袭了PLATO隐变
 ## 命令行预测
 
 ```shell
-$ hub run plato2_en_base --input_text="Hello, how are you" --use_gpu
+$ hub run plato2_en_base --input_text="Hello, how are you"
 ```
 
 ## API

hub_module/modules/text/text_generation/plato2_en_large/README.md

@@ -7,10 +7,12 @@ PLATO2是一个超大规模生成式对话系统模型。它承袭了PLATO隐变
 
 更多详情参考论文[PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning](https://arxiv.org/abs/2006.16779)
 
+**注：plato2\_en\_large 模型大小12GB,下载时间较长，请耐心等候。运行此模型要求显存至少16GB。**
+
 ## 命令行预测
 
 ```shell
-$ hub run plato2_en_large --input_text="Hello, how are you" --use_gpu
+$ hub run plato2_en_large --input_text="Hello, how are you"
 ```
 
 ## API