update porn_detection_gru (#2103)

modules/text/text_review/porn_detection_gru/README.md

@@ -22,7 +22,7 @@
 - ### 1、环境依赖
 
   - paddlepaddle >= 1.6.2
-  
+
   - paddlehub >= 1.6.0    | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst)
 
 - ### 2、安装
@@ -42,45 +42,45 @@
   - ```shell
     $ hub run porn_detection_gru --input_text "黄片下载"
     ```
-    
+
   - 或者
 
   - ```shell
     $ hub run porn_detection_gru --input_file test.txt
     ```
-    
+
     - 其中test.txt存放待审查文本，每行仅放置一段待审核文本
-    
+
   - 通过命令行方式实现hub模型的调用，更多请见 [PaddleHub命令行指令](../../../../docs/docs_ch/tutorial/cmd_usage.rst)
 
 - ### 2、预测代码示例
 
   - ```python
     import paddlehub as hub
-    
+
     porn_detection_gru = hub.Module(name="porn_detection_gru")
-    
+
     test_text = ["黄片下载", "打击黄牛党"]
-    
+
     results = porn_detection_gru.detection(texts=test_text, use_gpu=True, batch_size=1)   # 如不使用GPU，请修改为use_gpu=False
-    
+
     for index, text in enumerate(test_text):
         results[index]["text"] = text
     for index, result in enumerate(results):
         print(results[index])
-    
+
     # 输出结果如下：
     # {'text': '黄片下载', 'porn_detection_label': 1, 'porn_detection_key': 'porn', 'porn_probs': 0.9324, 'not_porn_probs': 0.0676}
     # {'text': '打击黄牛党', 'porn_detection_label': 0, 'porn_detection_key': 'not_porn', 'porn_probs': 0.0004, 'not_porn_probs': 0.9996}
     ```
 
-  
+
 - ### 3、API
 
   - ```python
     def detection(texts=[], data={}, use_gpu=False, batch_size=1)
     ```
-  
+
     - porn_detection_gru预测接口，鉴定输入句子是否包含色情文案
 
     - **参数**
@@ -146,7 +146,7 @@
   - ```python
     import requests
     import json
-    
+
     # 待预测数据
     text = ["黄片下载", "打击黄牛党"]
 
@@ -179,7 +179,11 @@
 
   大幅提升预测性能，同时简化接口使用
 
+* 1.2.0
+
+  移除 Fluid API
+
   - ```shell
-    $ hub install porn_detection_gru==1.1.0
+    $ hub install porn_detection_gru==1.2.0
     ```
-    
+

modules/text/text_review/porn_detection_gru/README_en.md

@@ -22,7 +22,7 @@
 - ### 1、Environmental dependence
 
   - paddlepaddle >= 1.6.2
-  
+
   - paddlehub >= 1.6.0    | [How to install PaddleHub](../../../../docs/docs_ch/get_start/installation.rst)
 
 - ### 2、Installation
@@ -42,45 +42,45 @@
   - ```shell
     $ hub run porn_detection_gru --input_text "黄片下载"
     ```
-    
+
   - or
 
   - ```shell
     $ hub run porn_detection_gru --input_file test.txt
     ```
-    
+
     - test.txt stores the text to be reviewed. Each line contains only one text
-    
+
   - If you want to call the Hub module through the command line, please refer to: [PaddleHub Command line instruction](../../../../docs/docs_ch/tutorial/cmd_usage.rst)
 
 - ### 2、Prediction Code Example
 
   - ```python
     import paddlehub as hub
-    
+
     porn_detection_gru = hub.Module(name="porn_detection_gru")
-    
+
     test_text = ["黄片下载", "打击黄牛党"]
-    
+
     results = porn_detection_gru.detection(texts=test_text, use_gpu=True, batch_size=1)   # If you do not use GPU, please set use_gpu=False
-    
+
     for index, text in enumerate(test_text):
         results[index]["text"] = text
     for index, result in enumerate(results):
         print(results[index])
-    
+
     # The output：
     # {'text': '黄片下载', 'porn_detection_label': 1, 'porn_detection_key': 'porn', 'porn_probs': 0.9324, 'not_porn_probs': 0.0676}
     # {'text': '打击黄牛党', 'porn_detection_label': 0, 'porn_detection_key': 'not_porn', 'porn_probs': 0.0004, 'not_porn_probs': 0.9996}
     ```
 
-  
+
 - ### 3、API
 
   - ```python
     def detection(texts=[], data={}, use_gpu=False, batch_size=1)
     ```
-  
+
     - prediction api of porn_detection_gru，to identify whether input sentences contain pornography
 
     - **Parameter**
@@ -144,7 +144,7 @@
   - ```python
     import requests
     import json
-    
+
     # data to be predicted
     text = ["黄片下载", "打击黄牛党"]
 
@@ -177,7 +177,11 @@
 
   Improves prediction performance and simplifies interface usage
 
+* 1.2.0
+
+  移除 Fluid API
+
   - ```shell
-    $ hub install porn_detection_gru==1.1.0
+    $ hub install porn_detection_gru==1.2.0
     ```
-    
+

modules/text/text_review/porn_detection_gru/module.py

-# -*- coding:utf-8 -*-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import json
 import math
 import os
-import six
-
-import paddle.fluid as fluid
-import paddlehub as hub
-from paddlehub.common.paddle_helper import get_variable_info
-from paddlehub.module.module import moduleinfo, serving
-from paddlehub.reader import tokenization
-
-from porn_detection_gru.processor import load_vocab, preprocess, postprocess
 
 
-@moduleinfo(
-    name="porn_detection_gru",
-    version="1.1.0",
-    summary="Baidu's open-source Porn Detection Model.",
-    author="baidu-nlp",
-    author_email="",
-    type="nlp/sentiment_analysis")
+import paddlehub as hub
+from .processor import load_vocab
+from .processor import postprocess
+from .processor import preprocess
+from paddlehub.compat.task import tokenization
+from paddlehub.module.module import moduleinfo
+from paddlehub.module.module import serving
+
+
+@moduleinfo(name="porn_detection_gru",
+            version="1.2.0",
+            summary="Baidu's open-source Porn Detection Model.",
+            author="baidu-nlp",
+            author_email="",
+            type="nlp/sentiment_analysis")
 class PornDetectionGRU(hub.NLPPredictionModule):
-    def _initialize(self):
+
+    def __init__(self):
         """
         initialize with the necessary elements
         """
@@ -42,41 +36,6 @@ class PornDetectionGRU(hub.NLPPredictionModule):
 
         self._set_config()
 
-    def context(self, trainable=False):
-        """
-        Get the input ,output and program of the pretrained porn_detection_gru
-        Args:
-             trainable(bool): whether fine-tune the pretrained parameters of porn_detection_gru or not
-        Returns:
-             inputs(dict): the input variables of porn_detection_gru (words)
-             outputs(dict): the output variables of porn_detection_gru (the sentiment prediction results)
-             main_program(Program): the main_program of lac with pretrained prameters
-        """
-        place = fluid.CPUPlace()
-        exe = fluid.Executor(place)
-        program, feed_target_names, fetch_targets = fluid.io.load_inference_model(
-            dirname=self.pretrained_model_path, executor=exe)
-
-        with open(self.param_file, 'r') as file:
-            params_list = file.readlines()
-        for param in params_list:
-            param = param.strip()
-            var = program.global_block().var(param)
-            var_info = get_variable_info(var)
-            program.global_block().create_parameter(
-                shape=var_info['shape'], dtype=var_info['dtype'], name=var_info['name'])
-
-        for param in program.global_block().iter_parameters():
-            param.trainable = trainable
-
-        for name, var in program.global_block().vars.items():
-            if name == feed_target_names[0]:
-                inputs = {"words": var}
-            # output of sencond layer from the end prediction layer (fc-softmax)
-            if name == "@HUB_porn_detection_gru@layer_norm_0.tmp_2":
-                outputs = {"class_probs": fetch_targets[0], "sentence_feature": var}
-        return inputs, outputs, program
-
     @serving
     def detection(self, texts=[], data={}, use_gpu=False, batch_size=1):
         """
@@ -134,28 +93,3 @@ class PornDetectionGRU(hub.NLPPredictionModule):
         """
         self.labels = {"porn": 1, "not_porn": 0}
         return self.labels
-
-
-if __name__ == "__main__":
-    porn_detection_gru = PornDetectionGRU()
-    porn_detection_gru.context()
-    # porn_detection_gru = hub.Module(name='porn_detection_gru')
-    test_text = ["黄片下载", "打击黄牛党"]
-
-    results = porn_detection_gru.detection(texts=test_text)
-    for index, text in enumerate(test_text):
-        results[index]["text"] = text
-    for index, result in enumerate(results):
-        if six.PY2:
-            print(json.dumps(results[index], encoding="utf8", ensure_ascii=False))
-        else:
-            print(results[index])
-    input_dict = {"text": test_text}
-    results = porn_detection_gru.detection(data=input_dict)
-    for index, text in enumerate(test_text):
-        results[index]["text"] = text
-    for index, result in enumerate(results):
-        if six.PY2:
-            print(json.dumps(results[index], encoding="utf8", ensure_ascii=False))
-        else:
-            print(results[index])

modules/text/text_review/porn_detection_gru/processor.py

-# -*- coding: utf-8 -*-
 import io
+
 import numpy as np