Fix ci issue

docs/tutorial/how_to_load_data.md

@@ -70,15 +70,14 @@ cat
 dog
 ```
 
-### 自定义数据加载	
+### 自定义数据加载
 
-加载图像类自定义数据集，用户仅需要继承基类BaseCVDatast，修改数据集存放地址即可。具体使用如下：	
-
-**NOTE:**	
-* 数据集文件编码格式建议为utf8格式。	
-* dataset_dir为数据集实际路径，需要填写全路径，以下示例以`/test/data`为例。	
-* 训练/验证/测试集的数据列表文件中的图片路径需要相对于dataset_dir的相对路径，例如图片的实际位置为`/test/data/dog/dog1.jpg`。base_path为`/test/data`，则文件中填写的路径应该为`dog/dog1.jpg`。	
-* 如果您还有预测数据（没有文本类别），可以将预测数据存放在predict_list.txt文件，文件格式和train_list.txt类似。去掉label一列即可	
-* 如果您的数据集类别较少，可以不用定义label_list.txt，可以选择定义label_list=["数据集所有类别"]。	
-* 分类任务中，数据集的label必须从0开始计数	
+加载图像类自定义数据集，用户仅需要继承基类BaseCVDatast，修改数据集存放地址即可。具体使用如下：
 
+**NOTE:**
+* 数据集文件编码格式建议为utf8格式。
+* dataset_dir为数据集实际路径，需要填写全路径，以下示例以`/test/data`为例。
+* 训练/验证/测试集的数据列表文件中的图片路径需要相对于dataset_dir的相对路径，例如图片的实际位置为`/test/data/dog/dog1.jpg`。base_path为`/test/data`，则文件中填写的路径应该为`dog/dog1.jpg`。
+* 如果您还有预测数据（没有文本类别），可以将预测数据存放在predict_list.txt文件，文件格式和train_list.txt类似。去掉label一列即可
+* 如果您的数据集类别较少，可以不用定义label_list.txt，可以选择定义label_list=["数据集所有类别"]。
+* 分类任务中，数据集的label必须从0开始计数

hub_module/modules/text/text_generation/reading_pictures_writing_poems/__init__.py

-

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

@@ -12,6 +12,7 @@ import paddle.fluid as fluid
 import paddlehub as hub
 from translate import Translator
 
+
 @moduleinfo(
     name="reading_pictures_writing_poems",
     version="1.0.0",
@@ -26,9 +27,9 @@ class ReadingPicturesWritingPoems(hub.Module):
         """
         self.pretrained_model_path = os.path.join(self.directory, "assets",
                                                   "infer_model")
-        self.module_image = hub.Module(name="xception71_imagenet") # 调用图像分类的模型
-        self.module_similar = hub.Module(name="ernie_gen_couplet") # 调用对联生成的模型
-        self.module_poem = hub.Module(name="ernie_gen_poetry")     # 调用古诗生成的模型
+        self.module_image = hub.Module(name="xception71_imagenet")  # 调用图像分类的模型
+        self.module_similar = hub.Module(name="ernie_gen_couplet")  # 调用对联生成的模型
+        self.module_poem = hub.Module(name="ernie_gen_poetry")  # 调用古诗生成的模型
 
     def is_chinese(self, string):
         """
@@ -38,10 +39,10 @@ class ReadingPicturesWritingPoems(hub.Module):
         Return
             bool
         """
-        if (len(string) <= 1): # 去除只有单个字或者为空的字符串
+        if (len(string) <= 1):  # 去除只有单个字或者为空的字符串
             return False
 
-        for chart in string: # 把除了中文的所有字母、数字、符号去除
+        for chart in string:  # 把除了中文的所有字母、数字、符号去除
             if (chart < u'\u4e00' or chart > u'\u9fff'):
                 return False
 
@@ -52,10 +53,12 @@ class ReadingPicturesWritingPoems(hub.Module):
         results_image = self.module_image.classification(data=input_dict)
         PictureClassification = list(results_image[0][0].keys())[0]
         translator = Translator(to_lang="chinese")
-        PictureClassification_ch = translator.translate("{}".format(PictureClassification))
+        PictureClassification_ch = translator.translate(
+            "{}".format(PictureClassification))
         texts = ["{}".format(PictureClassification_ch)]
-        results_keywords = self.module_similar.generate(texts=texts, use_gpu=use_gpu, beam_width=20)
-        Words = [] # 将符合标准的近义词保存在这里（标准：字符串为中文且长度大于1）
+        results_keywords = self.module_similar.generate(
+            texts=texts, use_gpu=use_gpu, beam_width=20)
+        Words = []  # 将符合标准的近义词保存在这里（标准：字符串为中文且长度大于1）
         for item in range(20):
             if (self.is_chinese(results_keywords[0][item])):
                 Words.append(results_keywords[0][item])
@@ -65,10 +68,11 @@ class ReadingPicturesWritingPoems(hub.Module):
         ThirdWord = Words[4] + Words[5]
         FourthWord = Words[6] + Words[7]
         # 出句和对句，也可以理解为上下句（专业讲法是出句和对句，古诗词是中国传统文化，出句和对句的英文翻译即拼音）
-        ChuJu = FirstWord + SecondWord # 出句
-        DuiJu = ThirdWord + FourthWord # 对句
-        FirstPoetry = ["{:.5}，{:.5}。".format(ChuJu, DuiJu)] # 古诗词的上阕
-        results = self.module_poem.generate(texts=FirstPoetry, use_gpu=use_gpu, beam_width=5)
+        ChuJu = FirstWord + SecondWord  # 出句
+        DuiJu = ThirdWord + FourthWord  # 对句
+        FirstPoetry = ["{:.5}，{:.5}。".format(ChuJu, DuiJu)]  # 古诗词的上阕
+        results = self.module_poem.generate(
+            texts=FirstPoetry, use_gpu=use_gpu, beam_width=5)
         SecondPoetry = ["{:.12}".format(results[0][0])]
         Poetrys = []
         Poetrys.append(FirstPoetry)
@@ -80,7 +84,7 @@ class ReadingPicturesWritingPoems(hub.Module):
             'image': image,
             'Poetrys': "{}".format(Poetrys[0][0] + Poetrys[1][0])
         }]
-        
+
         return results
 
     @runnable
@@ -135,7 +139,7 @@ class ReadingPicturesWritingPoems(hub.Module):
             type=str,
             default=None,
             help="Pictures to write poetry")
-        
+
     def check_input_data(self, args):
         input_data = []
         if args.input_image:
@@ -145,6 +149,7 @@ class ReadingPicturesWritingPoems(hub.Module):
                 input_data = args.input_image
 
         if input_data == []:
-            raise RuntimeError("The input data is inconsistent with expectations.")
+            raise RuntimeError(
+                "The input data is inconsistent with expectations.")
 
         return input_data

hub_module/modules/text/text_generation/reading_pictures_writing_poems_for_midautumn/MidAutumnDetection/__init__.py

-

hub_module/modules/text/text_generation/reading_pictures_writing_poems_for_midautumn/MidAutumnDetection/module.py

@@ -118,9 +118,7 @@ class MODULE(hub.Module):
         self.add_module_config_arg()
         self.add_module_input_arg()
         args = self.parser.parse_args(argvs)
-        results = self.predict(
-            paths=[args.input_path],
-            use_gpu=args.use_gpu)
+        results = self.predict(paths=[args.input_path], use_gpu=args.use_gpu)
         return results
 
     def add_module_config_arg(self):

hub_module/modules/text/text_generation/reading_pictures_writing_poems_for_midautumn/MidAutumnPoetry/__init__.py

-

hub_module/modules/text/text_generation/reading_pictures_writing_poems_for_midautumn/MidAutumnPoetry/module.py

@@ -174,4 +174,3 @@ class ErnieGen(hub.NLPPredictionModule):
             texts=input_data, use_gpu=args.use_gpu, beam_width=args.beam_width)
 
         return results
-        
\ No newline at end of file

hub_module/modules/text/text_generation/reading_pictures_writing_poems_for_midautumn/__init__.py

-

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

@@ -14,6 +14,7 @@ from translate import Translator
 import reading_pictures_writing_poems_for_midautumn.MidAutumnDetection.module as MidAutumnDetection
 import reading_pictures_writing_poems_for_midautumn.MidAutumnPoetry.module as MidAutumnPoetry
 
+
 @moduleinfo(
     name="reading_pictures_writing_poems_for_midautumn",
     version="1.0.0",
@@ -26,20 +27,27 @@ class ReadingPicturesWritingPoems(hub.Module):
         """
         Initialize with the necessary elements
         """
-        self.pretrained_model_path = os.path.join(self.directory, "assets", "infer_model")
-        self.module_image = MidAutumnDetection.MODULE(directory="reading_pictures_writing_poems_for_midautumn/MidAutumnDetection") # 调用目标检测的模型
-        self.module_similar = MidAutumnPoetry.ErnieGen(directory='reading_pictures_writing_poems_for_midautumn/MidAutumnPoetry') # 调用根据关键词生成古诗上阕的模型
-        self.module_poem = hub.Module(name="ernie_gen_poetry")     # 调用古诗生成的模型
-
+        self.pretrained_model_path = os.path.join(self.directory, "assets",
+                                                  "infer_model")
+        self.module_image = MidAutumnDetection.MODULE(
+            directory=
+            "reading_pictures_writing_poems_for_midautumn/MidAutumnDetection"
+        )  # 调用目标检测的模型
+        self.module_similar = MidAutumnPoetry.ErnieGen(
+            directory=
+            'reading_pictures_writing_poems_for_midautumn/MidAutumnPoetry'
+        )  # 调用根据关键词生成古诗上阕的模型
+        self.module_poem = hub.Module(name="ernie_gen_poetry")  # 调用古诗生成的模型
 
     def WritingPoem(self, images, use_gpu=False):
         # 目标检测，输入图片，输入得分最高的标签
-        results_image = self.module_image.predict(images = images)
-        best = {'score':0, 'category':'none'}
+        results_image = self.module_image.predict(images=images)
+        best = {'score': 0, 'category': 'none'}
         for item in results_image:
             for items in item:
                 if (items['score'] > best['score']):
-                    best['score'], best['category'] = items['score'], items['category']
+                    best['score'], best['category'] = items['score'], items[
+                        'category']
         if best['category'] == 'MoonCake':
             objects = ['月饼']
         elif best['category'] == 'moon':
@@ -51,10 +59,12 @@ class ReadingPicturesWritingPoems(hub.Module):
         else:
             objects = ['中秋节']
         # 根据关键词生成古诗上阕
-        FirstPoetrys = self.module_similar.generate(texts=objects, use_gpu=True, beam_width=5)
+        FirstPoetrys = self.module_similar.generate(
+            texts=objects, use_gpu=True, beam_width=5)
         FirstPoetry = [FirstPoetrys[0][0]]
         # 调用古诗生成模型，使用上阕生成下阕
-        SecondPoetry = self.module_poem.generate(texts=FirstPoetry, use_gpu=True, beam_width=5)
+        SecondPoetry = self.module_poem.generate(
+            texts=FirstPoetry, use_gpu=True, beam_width=5)
         Poetrys = []
         Poetrys.append(FirstPoetry[0])
         Poetrys.append(SecondPoetry[0][0])
@@ -62,7 +72,7 @@ class ReadingPicturesWritingPoems(hub.Module):
             'images': images,
             'Poetrys': "{}".format(Poetrys[0] + Poetrys[1])
         }]
-        
+
         return results
 
     @runnable
@@ -117,7 +127,7 @@ class ReadingPicturesWritingPoems(hub.Module):
             type=str,
             default=None,
             help="Pictures to write poetry")
-        
+
     def check_input_data(self, args):
         input_data = []
         if args.input_image:
@@ -127,6 +137,7 @@ class ReadingPicturesWritingPoems(hub.Module):
                 input_data = args.input_image
 
         if input_data == []:
-            raise RuntimeError("The input data is inconsistent with expectations.")
+            raise RuntimeError(
+                "The input data is inconsistent with expectations.")
 
-        return input_data
\ No newline at end of file
+        return input_data

hub_module/tests/unittests/test_reading_pictures_writing_poems.py

@@ -13,13 +13,12 @@ class ReadingPicturesWritingPoemsTestCase(TestCase):
             'image': 'castle.jpg',
             'Poetrys': '山川山陵山，沟渠村庄沟。我来春雨余，草木亦已柔。'
         }]
-        
 
     def test_writing_poems(self):
         # test gpu
-        results = self.module.WritingPoem(
-            image=self.test_image, use_gpu=True)
+        results = self.module.WritingPoem(image=self.test_image, use_gpu=True)
         self.assertEqual(results, self.results)
 
+
 if __name__ == '__main__':
-    main()
\ No newline at end of file
+    main()