Merge pull request #204 from tink2123/1.8

Adapted to 1.8

README.md

@@ -176,19 +176,16 @@ PaddleOCR文本识别算法的训练和使用请参考文档教程中[文本识
 ![](doc/imgs_results/chinese_db_crnn_server/8.jpg)
 
 ## FAQ
-1. **预测报错：got an unexpected keyword argument 'gradient_clip'**  
-安装的paddle版本不对，目前本项目仅支持paddle1.7，近期会适配到1.8。  
-
-2. **转换attention识别模型时报错：KeyError: 'predict'**  
+1. **转换attention识别模型时报错：KeyError: 'predict'**  
 问题已解，请更新到最新代码。  
 
-3. **关于推理速度**  
+2. **关于推理速度**  
 图片中的文字较多时，预测时间会增，可以使用--rec_batch_num设置更小预测batch num，默认值为30，可以改为10或其他数值。  
 
-4. **服务部署与移动端部署**  
+3. **服务部署与移动端部署**  
 预计6月中下旬会先后发布基于Serving的服务部署方案和基于Paddle Lite的移动端部署方案，欢迎持续关注。  
 
-5. **自研算法发布时间**  
+4. **自研算法发布时间**  
 自研算法SAST、SRN、End2End-PSL都将在6-7月陆续发布，敬请期待。  
 
 [more](./doc/doc_ch/FAQ.md)

README_en.md

@@ -176,23 +176,19 @@ Please refer to the document for training guide and use of PaddleOCR text recogn
 ![](doc/imgs_results/chinese_db_crnn_server/8.jpg)
 
 ## FAQ
-1. Prediction error：got an unexpected keyword argument 'gradient_clip'
-
-    The installed paddle version is not correct. At present, this project only supports paddle1.7, which will be adapted to 1.8 in the near future.
-
-2. Error when using attention-based recognition model: KeyError: 'predict'
+1. Error when using attention-based recognition model: KeyError: 'predict'
 
     The inference of recognition model based on attention loss is still being debugged. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss first. In practice, it is also found that the recognition model based on attention loss is not as effective as the one based on CTC loss.
 
-3. About inference speed
+2. About inference speed
 
     When there are a lot of texts in the picture, the prediction time will increase. You can use `--rec_batch_num` to set a smaller prediction batch size. The default value is 30, which can be changed to 10 or other values.
 
-4. Service deployment and mobile deployment
+3. Service deployment and mobile deployment
 
     It is expected that the service deployment based on Serving and the mobile deployment based on Paddle Lite will be released successively in mid-to-late June. Stay tuned for more updates.
 
-5. Release time of self-developed algorithm
+4. Release time of self-developed algorithm
 
     Baidu Self-developed algorithms such as SAST, SRN and end2end PSL will be released in June or July. Please be patient.
 

ppocr/modeling/heads/det_db_head.py

@@ -115,7 +115,6 @@ class DBHead(object):
         initializer = fluid.initializer.Uniform(-stdv, stdv)
         bias_attr = fluid.ParamAttr(
             regularizer=regularizer,
-            gradient_clip=gradient_clip,
             initializer=initializer,
             name=name + "_b_attr")
         return bias_attr