Merge remote-tracking branch 'origin/dygraph' into dygraph

404a3b31 · Leif · a5530565 · d128c1df · 404a3b31 · 404a3b31
Commit 404a3b31 authored Oct 20, 2021 by Leif
19 changed files
--- a/doc/doc_ch/training.md
+++ b/doc/doc_ch/training.md
@@ -129,3 +129,9 @@ PaddleOCR主要聚焦通用OCR，如果有垂类需求，您可以用PaddleOCR+
    A：识别模型训练初期acc为0是正常的，多训一段时间指标就上来了。
+***
+具体的训练教程可点击下方链接跳转：  
+- [文本检测模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/detection.md)  
+- [文本识别模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/recognition.md)  
+- [文本方向分类器训练](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/angle_class.md)  
\ No newline at end of file
--- a/doc/doc_en/config_en.md
+++ b/doc/doc_en/config_en.md
-# Configuration 
+# Configuration
 - [1. Optional Parameter List](#1-optional-parameter-list)
 - [2. Intorduction to Global Parameters of Configuration File](#2-intorduction-to-global-parameters-of-configuration-file)
@@ -37,9 +37,8 @@ Take rec_chinese_lite_train_v2.0.yml as an example
 |      checkpoints         |    set model parameter path            |       None        |   Used to load parameters after interruption to continue training|
 |      use_visualdl  |    Set whether to enable visualdl for visual log display |          False        |    [Tutorial](https://www.paddlepaddle.org.cn/paddle/visualdl) |
 |      infer_img            |    Set inference image path or folder path     |       ./infer_img | \|
-|      character_dict_path |    Set dictionary path            |  ./ppocr/utils/ppocr_keys_v1.txt  |    \                 |
+|      character_dict_path |    Set dictionary path            |  ./ppocr/utils/ppocr_keys_v1.txt  |    If the character_dict_path is None, model can only recognize number and lower letters                 |
 |      max_text_length     |    Set the maximum length of text        |       25          |                \                 |
-|      character_type      |    Set character type            |       ch          |    en/ch, the default dict will be used for en, and the custom dict will be used for ch |
 |      use_space_char     |    Set whether to recognize spaces             |        True      |          Only support in character_type=ch mode                 |
 |      label_list          |    Set the angle supported by the direction classifier       |    ['0','180']    |     Only valid in angle classifier model |
 |      save_res_path          |    Set the save address of the test model results       |    ./output/det_db/predicts_db.txt    |     Only valid in the text detection model |
@@ -196,40 +195,39 @@ Italian is made up of Latin letters, so after executing the command, you will ge
      use_gpu: True
      epoch_num: 500
      ...
-      character_type: it  # language
      character_dict_path:  {path/of/dict} # path of dict
   Train:
      dataset:
        name: SimpleDataSet
        data_dir: train_data/ # root directory of training data
        label_file_list: ["./train_data/train_list.txt"] # train label path
      ...
   Eval:
      dataset:
        name: SimpleDataSet
        data_dir: train_data/ # root directory of val data
        label_file_list: ["./train_data/val_list.txt"] # val label path
      ...
   ```
 Currently, the multi-language algorithms supported by PaddleOCR are:
-| Configuration file |  Algorithm name |   backbone |   trans   |   seq      |     pred     |  language | character_type |
+| Configuration file |  Algorithm name |   backbone |   trans   |   seq      |     pred     |  language |
-| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  | :-----:  |
+| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  |
-| rec_chinese_cht_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | chinese traditional  | chinese_cht|
+| rec_chinese_cht_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | chinese traditional  |
-| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | English(Case sensitive)   | EN |
+| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | English(Case sensitive)   |
-| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | French |  french |
+| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | French |  
-| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | German   | german |
+| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | German   |
-| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Japanese | japan |
+| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Japanese |
-| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Korean  | korean |
+| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Korean  |
-| rec_latin_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Latin  | latin |
+| rec_latin_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Latin  |
-| rec_arabic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | arabic |  ar |
+| rec_arabic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | arabic |  
-| rec_cyrillic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | cyrillic   | cyrillic |
+| rec_cyrillic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | cyrillic   |
-| rec_devanagari_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | devanagari  | devanagari |
+| rec_devanagari_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | devanagari  |
 For more supported languages, please refer to : [Multi-language model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md#4-support-languages-and-abbreviations)

--- a/doc/doc_en/environment_en.md
+++ b/doc/doc_en/environment_en.md
 # Environment Preparation
+Recommended working environment:
+- PaddlePaddle >= 2.0.0 (2.1.2)
+- python3.7
+- CUDA10.1 / CUDA10.2
+- CUDNN 7.6
 * [1. Python Environment Setup](#1)
  + [1.1 Windows](#1.1)
  + [1.2 Mac](#1.2)
  + [1.3 Linux](#1.3)
 * [2. Install PaddlePaddle 2.0](#2)
 <a name="1"></a>
 ## 1. Python Environment Setup
@@ -38,7 +45,7 @@
  - Check conda to add environment variables and ignore the warning that
    <img src="../install/windows/anaconda_install_env.png" alt="add conda to path" width="500" align="center"/>
 #### 1.1.2 Opening the terminal and creating the conda environment
@@ -69,7 +76,7 @@
  # View the current location of python
  where python
  ```
  <img src="../install/windows/conda_list_env.png" alt="create environment" width="600" align="center"/>
 The above anaconda environment and python environment are installed
@@ -133,13 +140,13 @@ The above anaconda environment and python environment are installed
    # !!! Contents within this block are managed by 'conda init' !!!
    __conda_setup="$('/Users/xxx/opt/anaconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
    if [ $? -eq 0 ]; then
-    		eval "$__conda_setup"
+            eval "$__conda_setup"
    else
-    		if [ -f "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh" ]; then
+            if [ -f "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh" ]; then
-    				. "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh"
+                    . "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh"
-    		else
+            else
-    				export PATH="/Users/xxx/opt/anaconda3/bin:$PATH"
+                    export PATH="/Users/xxx/opt/anaconda3/bin:$PATH"
-    		fi
+            fi
    fi
    unset __conda_setup
    # <<< conda initialize <<<
@@ -197,11 +204,10 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
 - **Download Anaconda**.
  - Download at: https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/?C=M&O=D
  <img src="../install/linux/anaconda_download.png" akt="anaconda download" width="800" align="center"/>
  - Select the appropriate version for your operating system
      - Type `uname -m` in the terminal to check the command set used by your system
@@ -216,12 +222,12 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
    sudo yum install wget # CentOS
    ```
    ```bash
-    # Then use wget to download from Tsinghua source 
+    # Then use wget to download from Tsinghua source
    # If you want to download Anaconda3-2021.05-Linux-x86_64.sh, the download command is as follows
    wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2021.05-Linux-x86_64.sh
    # If you want to download another version, you need to change the file name after the last 1 / to the version you want to download
    ```
 - To install Anaconda.
  - Type `sh Anaconda3-2021.05-Linux-x86_64.sh` at the command line
@@ -309,7 +315,18 @@ cd /home/Projects
 # Create a docker container named ppocr and map the current directory to the /paddle directory of the container
 # If using CPU, use docker instead of nvidia-docker to create docker
-sudo docker run --name ppocr -v $PWD:/paddle --network=host -it  paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82  /bin/bash
+sudo docker run --name ppocr -v $PWD:/paddle --network=host -it  registry.baidubce.com/paddlepaddle/paddle:2.1.3-gpu-cuda10.2-cudnn7  /bin/bash
+# If using GPU, use nvidia-docker to create docker
+# docker image registry.baidubce.com/paddlepaddle/paddle:2.1.3-gpu-cuda11.2-cudnn8 is recommended for CUDA11.2 + CUDNN8.
+sudo nvidia-docker run --name ppocr -v $PWD:/paddle --shm-size=64G --network=host -it registry.baidubce.com/paddlepaddle/paddle:2.1.3-gpu-cuda10.2-cudnn7 /bin/bash
+```
+You can also visit [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to get the image that fits your machine.
+```
+# ctrl+P+Q to exit docker, to re-enter docker using the following command:
+sudo docker container exec -it ppocr /bin/bash
 ```
 <a name="2"></a>
@@ -329,4 +346,3 @@ python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
 ```
 For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
--- a/doc/doc_en/inference_en.md
+++ b/doc/doc_en/inference_en.md
@@ -21,7 +21,7 @@ Next, we first introduce how to convert a trained model into an inference model,
    - [2.2 DB Text Detection Model Inference](#DB_DETECTION)
    - [2.3 East Text Detection Model Inference](#EAST_DETECTION)
    - [2.4 Sast Text Detection Model Inference](#SAST_DETECTION)
 - [3. Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE)
    - [3.1 Lightweight Chinese Text Recognition Model Reference](#LIGHTWEIGHT_RECOGNITION)
    - [3.2 CTC-Based Text Recognition Model Inference](#CTC-BASED_RECOGNITION)
@@ -281,7 +281,7 @@ python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o G
 For CRNN text recognition model inference, execute the following commands:
 ```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
 ```
 ![](../imgs_words_en/word_336.png)
@@ -314,7 +314,7 @@ with the training, such as: --rec_image_shape="1, 64, 256"
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
                                    --rec_model_dir="./inference/srn/" \
                                    --rec_image_shape="1, 64, 256" \
-                                    --rec_char_type="en" \
+                                    --rec_char_dict_path="./ppocr/utils/ic15_dict.txt" \
                                    --rec_algorithm="SRN"
 ```
@@ -323,7 +323,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
 If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path`, and set `rec_char_type=ch`
 ```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="ch" --rec_char_dict_path="your text dict path"
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100"  --rec_char_dict_path="your text dict path"
 ```
 <a name="MULTILINGUAL_MODEL_INFERENCE"></a>
@@ -333,7 +333,7 @@ If you need to predict other language models, when using inference model predict
 You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition:
 ```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model"  --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
 ```
 ![](../imgs_words/korean/1.jpg)
@@ -399,7 +399,7 @@ If you want to try other detection algorithms or recognition algorithms, please
 The following command uses the combination of the EAST text detection and STAR-Net text recognition:
 ```
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
 ```
 After executing the command, the recognition result image is as follows:

--- a/doc/doc_en/recognition_en.md
+++ b/doc/doc_en/recognition_en.md
@@ -161,7 +161,7 @@ The current multi-language model is still in the demo stage and will continue to
 If you like, you can submit the dictionary file to [dict](../../ppocr/utils/dict) and we will thank you in the Repo.
-To customize the dict file, please modify the `character_dict_path` field in `configs/rec/rec_icdar15_train.yml` and set `character_type` to `ch`.
+To customize the dict file, please modify the `character_dict_path` field in `configs/rec/rec_icdar15_train.yml` .
 - Custom dictionary
@@ -172,8 +172,6 @@ If you need to customize dic file, please add character_dict_path field in confi
 If you want to support the recognition of the `space` category, please set the `use_space_char` field in the yml file to `True`.
-**Note: use_space_char only takes effect when character_type=ch**
 <a name="TRAINING"></a>
 ## 2.Training
@@ -250,7 +248,6 @@ Global:
  # Add a custom dictionary, such as modify the dictionary, please point the path to the new dictionary
  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
  # Modify character type
-  character_type: ch
  ...
  # Whether to recognize spaces
  use_space_char: True
@@ -312,18 +309,18 @@ Eval:
 Currently, the multi-language algorithms supported by PaddleOCR are:
-| Configuration file |  Algorithm name |   backbone |   trans   |   seq      |     pred     |  language | character_type |
+| Configuration file |  Algorithm name |   backbone |   trans   |   seq      |     pred     |  language |
-| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  | :-----:  |
+| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  |
-| rec_chinese_cht_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | chinese traditional  | chinese_cht|
+| rec_chinese_cht_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | chinese traditional  |
-| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | English(Case sensitive)   | EN |
+| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | English(Case sensitive)   |
-| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | French |  french |
+| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | French |  
-| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | German   | german |
+| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | German   |
-| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Japanese | japan |
+| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Japanese |
-| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Korean  | korean |
+| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Korean  |
-| rec_latin_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Latin  | latin |
+| rec_latin_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Latin  |
-| rec_arabic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | arabic |  ar |
+| rec_arabic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | arabic |
-| rec_cyrillic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | cyrillic   | cyrillic |
+| rec_cyrillic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | cyrillic   |
-| rec_devanagari_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | devanagari  | devanagari |
+| rec_devanagari_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | devanagari  |
 For more supported languages, please refer to : [Multi-language model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md#4-support-languages-and-abbreviations)
@@ -471,6 +468,3 @@ inference/det_db/
  ```
  python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="ch" --rec_char_dict_path="your text dict path"
  ```
--- a/doc/doc_en/training_en.md
+++ b/doc/doc_en/training_en.md
@@ -147,3 +147,9 @@ There are several experiences for reference when constructing the data set:
    A: It is normal for the acc to be 0 at the beginning of the recognition model training, and the indicator will come up after a longer training period.
+***
+Click the following links for detailed training tutorial:  
+- [text detection model training](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/detection.md)  
+- [text recognition model training](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/recognition.md)  
+- [text direction classification model training](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/angle_class.md)  
--- a/ppocr/data/imaug/label_ops.py
+++ b/ppocr/data/imaug/label_ops.py
@@ -21,6 +21,8 @@ import numpy as np
 import string
 import json
+from ppocr.utils.logging import get_logger
 class ClsLabelEncode(object):
    def __init__(self, label_list, **kwargs):
@@ -92,31 +94,23 @@ class BaseRecLabelEncode(object):
    def __init__(self,
                 max_text_length,
                 character_dict_path=None,
-                 character_type='ch',
                 use_space_char=False):
-        support_character_type = [
-            'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
-            'EN', 'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs',
-            'oc', 'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi',
-            'mr', 'ne', 'latin', 'arabic', 'cyrillic', 'devanagari'
-        ]
-        assert character_type in support_character_type, "Only {} are supported now but get {}".format(
-            support_character_type, character_type)
        self.max_text_len = max_text_length
        self.beg_str = "sos"
        self.end_str = "eos"
-        if character_type == "en":
+        self.lower = False
+        if character_dict_path is None:
+            logger = get_logger()
+            logger.warning(
+                "The character_dict_path is None, model can only recognize number and lower letters"
+            )
            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
            dict_character = list(self.character_str)
-        elif character_type == "EN_symbol":
+            self.lower = True
-            # same with ASTER setting (use 94 char).
+        else:
-            self.character_str = string.printable[:-6]
-            dict_character = list(self.character_str)
-        elif character_type in support_character_type:
            self.character_str = ""
-            assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
-                character_type)
            with open(character_dict_path, "rb") as fin:
                lines = fin.readlines()
                for line in lines:
@@ -125,7 +119,6 @@ class BaseRecLabelEncode(object):
            if use_space_char:
                self.character_str += " "
            dict_character = list(self.character_str)
-        self.character_type = character_type
        dict_character = self.add_special_char(dict_character)
        self.dict = {}
        for i, char in enumerate(dict_character):
@@ -147,7 +140,7 @@ class BaseRecLabelEncode(object):
        """
        if len(text) == 0 or len(text) > self.max_text_len:
            return None
-        if self.character_type == "en":
+        if self.lower:
            text = text.lower()
        text_list = []
        for char in text:
@@ -167,13 +160,11 @@ class NRTRLabelEncode(BaseRecLabelEncode):
    def __init__(self,
                 max_text_length,
                 character_dict_path=None,
-                 character_type='EN_symbol',
                 use_space_char=False,
                 **kwargs):
-        super(NRTRLabelEncode,
+        super(NRTRLabelEncode, self).__init__(
-              self).__init__(max_text_length, character_dict_path,
+            max_text_length, character_dict_path, use_space_char)
-                             character_type, use_space_char)
    def __call__(self, data):
        text = data['label']
@@ -200,12 +191,10 @@ class CTCLabelEncode(BaseRecLabelEncode):
    def __init__(self,
                 max_text_length,
                 character_dict_path=None,
-                 character_type='ch',
                 use_space_char=False,
                 **kwargs):
-        super(CTCLabelEncode,
+        super(CTCLabelEncode, self).__init__(
-              self).__init__(max_text_length, character_dict_path,
+            max_text_length, character_dict_path, use_space_char)
-                             character_type, use_space_char)
    def __call__(self, data):
        text = data['label']
@@ -231,12 +220,10 @@ class E2ELabelEncodeTest(BaseRecLabelEncode):
    def __init__(self,
                 max_text_length,
                 character_dict_path=None,
-                 character_type='EN',
                 use_space_char=False,
                 **kwargs):
-        super(E2ELabelEncodeTest,
+        super(E2ELabelEncodeTest, self).__init__(
-              self).__init__(max_text_length, character_dict_path,
+            max_text_length, character_dict_path, use_space_char)
-                             character_type, use_space_char)
    def __call__(self, data):
        import json
@@ -305,12 +292,10 @@ class AttnLabelEncode(BaseRecLabelEncode):
    def __init__(self,
                 max_text_length,
                 character_dict_path=None,
-                 character_type='ch',
                 use_space_char=False,
                 **kwargs):
-        super(AttnLabelEncode,
+        super(AttnLabelEncode, self).__init__(
-              self).__init__(max_text_length, character_dict_path,
+            max_text_length, character_dict_path, use_space_char)
-                             character_type, use_space_char)
    def add_special_char(self, dict_character):
        self.beg_str = "sos"
@@ -353,12 +338,10 @@ class SEEDLabelEncode(BaseRecLabelEncode):
    def __init__(self,
                 max_text_length,
                 character_dict_path=None,
-                 character_type='ch',
                 use_space_char=False,
                 **kwargs):
-        super(SEEDLabelEncode,
+        super(SEEDLabelEncode, self).__init__(
-              self).__init__(max_text_length, character_dict_path,
+            max_text_length, character_dict_path, use_space_char)
-                             character_type, use_space_char)
    def add_special_char(self, dict_character):
        self.end_str = "eos"
@@ -385,12 +368,10 @@ class SRNLabelEncode(BaseRecLabelEncode):
    def __init__(self,
                 max_text_length=25,
                 character_dict_path=None,
-                 character_type='en',
                 use_space_char=False,
                 **kwargs):
-        super(SRNLabelEncode,
+        super(SRNLabelEncode, self).__init__(
-              self).__init__(max_text_length, character_dict_path,
+            max_text_length, character_dict_path, use_space_char)
-                             character_type, use_space_char)
    def add_special_char(self, dict_character):
        dict_character = dict_character + [self.beg_str, self.end_str]
@@ -598,12 +579,10 @@ class SARLabelEncode(BaseRecLabelEncode):
    def __init__(self,
                 max_text_length,
                 character_dict_path=None,
-                 character_type='ch',
                 use_space_char=False,
                 **kwargs):
-        super(SARLabelEncode,
+        super(SARLabelEncode, self).__init__(
-              self).__init__(max_text_length, character_dict_path,
+            max_text_length, character_dict_path, use_space_char)
-                             character_type, use_space_char)
    def add_special_char(self, dict_character):
        beg_end_str = "<BOS/EOS>"

--- a/ppocr/data/imaug/rec_img_aug.py
+++ b/ppocr/data/imaug/rec_img_aug.py
@@ -87,17 +87,17 @@ class RecResizeImg(object):
    def __init__(self,
                 image_shape,
                 infer_mode=False,
-                 character_type='ch',
+                 character_dict_path='./ppocr/utils/ppocr_keys_v1.txt',
                 padding=True,
                 **kwargs):
        self.image_shape = image_shape
        self.infer_mode = infer_mode
-        self.character_type = character_type
+        self.character_dict_path = character_dict_path
        self.padding = padding
    def __call__(self, data):
        img = data['image']
-        if self.infer_mode and self.character_type == "ch":
+        if self.infer_mode and self.character_dict_path is not None:
            norm_img = resize_norm_img_chinese(img, self.image_shape)
        else:
            norm_img = resize_norm_img(img, self.image_shape, self.padding)

--- a/ppocr/losses/ace_loss.py
+++ b/ppocr/losses/ace_loss.py
@@ -32,6 +32,7 @@ class ACELoss(nn.Layer):
    def __call__(self, predicts, batch):
        if isinstance(predicts, (list, tuple)):
            predicts = predicts[-1]
        B, N = predicts.shape[:2]
        div = paddle.to_tensor([N]).astype('float32')
@@ -42,9 +43,7 @@ class ACELoss(nn.Layer):
        length = batch[2].astype("float32")
        batch = batch[3].astype("float32")
        batch[:, 0] = paddle.subtract(div, length)
        batch = paddle.divide(batch, div)
        loss = self.loss_func(aggregation_preds, batch)
        return {"loss_ace": loss}
--- a/ppocr/losses/center_loss.py
+++ b/ppocr/losses/center_loss.py
@@ -27,7 +27,6 @@ class CenterLoss(nn.Layer):
    """
    Reference: Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.
    """
    def __init__(self,
                 num_classes=6625,
                 feat_dim=96,
@@ -37,8 +36,7 @@ class CenterLoss(nn.Layer):
        self.num_classes = num_classes
        self.feat_dim = feat_dim
        self.centers = paddle.randn(
-            shape=[self.num_classes, self.feat_dim]).astype(
+            shape=[self.num_classes, self.feat_dim]).astype("float64")
-                "float64")  #random center
        if init_center:
            assert os.path.exists(
@@ -60,22 +58,23 @@ class CenterLoss(nn.Layer):
        batch_size = feats_reshape.shape[0]
-        #calc feat * feat   
+        #calc l2 distance between feats and centers  
-        dist1 = paddle.sum(paddle.square(feats_reshape), axis=1, keepdim=True)
+        square_feat = paddle.sum(paddle.square(feats_reshape),
-        dist1 = paddle.expand(dist1, [batch_size, self.num_classes])
+                                 axis=1,
+                                 keepdim=True)
+        square_feat = paddle.expand(square_feat, [batch_size, self.num_classes])
-        #dist2 of centers
+        square_center = paddle.sum(paddle.square(self.centers),
-        dist2 = paddle.sum(paddle.square(self.centers), axis=1,
+                                   axis=1,
-                           keepdim=True)  #num_classes
+                                   keepdim=True)
-        dist2 = paddle.expand(dist2,
+        square_center = paddle.expand(
-                              [self.num_classes, batch_size]).astype("float64")
+            square_center, [self.num_classes, batch_size]).astype("float64")
-        dist2 = paddle.transpose(dist2, [1, 0])
+        square_center = paddle.transpose(square_center, [1, 0])
-        #first x * x + y * y
+        distmat = paddle.add(square_feat, square_center)
-        distmat = paddle.add(dist1, dist2)
+        feat_dot_center = paddle.matmul(feats_reshape,
-        tmp = paddle.matmul(feats_reshape,
+                                        paddle.transpose(self.centers, [1, 0]))
-                            paddle.transpose(self.centers, [1, 0]))
+        distmat = distmat - 2.0 * feat_dot_center
-        distmat = distmat - 2.0 * tmp
        #generate the mask
        classes = paddle.arange(self.num_classes).astype("int64")
@@ -83,7 +82,8 @@ class CenterLoss(nn.Layer):
            paddle.unsqueeze(label, 1), (batch_size, self.num_classes))
        mask = paddle.equal(
            paddle.expand(classes, [batch_size, self.num_classes]),
-            label).astype("float64")  #get mask
+            label).astype("float64")
        dist = paddle.multiply(distmat, mask)
        loss = paddle.sum(paddle.clip(dist, min=1e-12, max=1e+12)) / batch_size
        return {'loss_center': loss}
--- a/ppocr/modeling/backbones/rec_mv1_enhance.py
+++ b/ppocr/modeling/backbones/rec_mv1_enhance.py
-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -16,26 +16,17 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
-import numpy as np
-import paddle
-from paddle import ParamAttr
-import paddle.nn as nn
-import paddle.nn.functional as F
-from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
-from paddle.nn.initializer import KaimingNormal
 import math
 import numpy as np
 import paddle
-from paddle import ParamAttr, reshape, transpose, concat, split
+from paddle import ParamAttr, reshape, transpose
 import paddle.nn as nn
 import paddle.nn.functional as F
 from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
 from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 from paddle.nn.initializer import KaimingNormal
-import math
-from paddle.nn.functional import hardswish, hardsigmoid
 from paddle.regularizer import L2Decay
+from paddle.nn.functional import hardswish, hardsigmoid
 class ConvBNLayer(nn.Layer):

--- a/ppocr/postprocess/rec_postprocess.py
+++ b/ppocr/postprocess/rec_postprocess.py
@@ -21,33 +21,15 @@ import re
 class BaseRecLabelDecode(object):
    """ Convert between text-label and text-index """
-    def __init__(self,
+    def __init__(self, character_dict_path=None, use_space_char=False):
-                 character_dict_path=None,
-                 character_type='ch',
-                 use_space_char=False):
-        support_character_type = [
-            'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
-            'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc',
-            'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr',
-            'ne', 'EN', 'latin', 'arabic', 'cyrillic', 'devanagari'
-        ]
-        assert character_type in support_character_type, "Only {} are supported now but get {}".format(
-            support_character_type, character_type)
        self.beg_str = "sos"
        self.end_str = "eos"
-        if character_type == "en":
+        self.character_str = []
+        if character_dict_path is None:
            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
            dict_character = list(self.character_str)
-        elif character_type == "EN_symbol":
+        else:
-            # same with ASTER setting (use 94 char).
-            self.character_str = string.printable[:-6]
-            dict_character = list(self.character_str)
-        elif character_type in support_character_type:
-            self.character_str = []
-            assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
-                character_type)
            with open(character_dict_path, "rb") as fin:
                lines = fin.readlines()
                for line in lines:
@@ -57,9 +39,6 @@ class BaseRecLabelDecode(object):
                self.character_str.append(" ")
            dict_character = list(self.character_str)
-        else:
-            raise NotImplementedError
-        self.character_type = character_type
        dict_character = self.add_special_char(dict_character)
        self.dict = {}
        for i, char in enumerate(dict_character):
@@ -102,13 +81,10 @@ class BaseRecLabelDecode(object):
 class CTCLabelDecode(BaseRecLabelDecode):
    """ Convert between text-label and text-index """
-    def __init__(self,
+    def __init__(self, character_dict_path=None, use_space_char=False,
-                 character_dict_path=None,
-                 character_type='ch',
-                 use_space_char=False,
                 **kwargs):
        super(CTCLabelDecode, self).__init__(character_dict_path,
-                                             character_type, use_space_char)
+                                             use_space_char)
    def __call__(self, preds, label=None, *args, **kwargs):
        if isinstance(preds, tuple):
@@ -136,13 +112,12 @@ class DistillationCTCLabelDecode(CTCLabelDecode):
    def __init__(self,
                 character_dict_path=None,
-                 character_type='ch',
                 use_space_char=False,
                 model_name=["student"],
                 key=None,
                 **kwargs):
-        super(DistillationCTCLabelDecode, self).__init__(
+        super(DistillationCTCLabelDecode, self).__init__(character_dict_path,
-            character_dict_path, character_type, use_space_char)
+                                                         use_space_char)
        if not isinstance(model_name, list):
            model_name = [model_name]
        self.model_name = model_name
@@ -162,13 +137,9 @@ class DistillationCTCLabelDecode(CTCLabelDecode):
 class NRTRLabelDecode(BaseRecLabelDecode):
    """ Convert between text-label and text-index """
-    def __init__(self,
+    def __init__(self, character_dict_path=None, use_space_char=True, **kwargs):
-                 character_dict_path=None,
-                 character_type='EN_symbol',
-                 use_space_char=True,
-                 **kwargs):
        super(NRTRLabelDecode, self).__init__(character_dict_path,
-                                              character_type, use_space_char)
+                                              use_space_char)
    def __call__(self, preds, label=None, *args, **kwargs):
@@ -230,13 +201,10 @@ class NRTRLabelDecode(BaseRecLabelDecode):
 class AttnLabelDecode(BaseRecLabelDecode):
    """ Convert between text-label and text-index """
-    def __init__(self,
+    def __init__(self, character_dict_path=None, use_space_char=False,
-                 character_dict_path=None,
-                 character_type='ch',
-                 use_space_char=False,
                 **kwargs):
        super(AttnLabelDecode, self).__init__(character_dict_path,
-                                              character_type, use_space_char)
+                                              use_space_char)
    def add_special_char(self, dict_character):
        self.beg_str = "sos"
@@ -313,13 +281,10 @@ class AttnLabelDecode(BaseRecLabelDecode):
 class SEEDLabelDecode(BaseRecLabelDecode):
    """ Convert between text-label and text-index """
-    def __init__(self,
+    def __init__(self, character_dict_path=None, use_space_char=False,
-                 character_dict_path=None,
-                 character_type='ch',
-                 use_space_char=False,
                 **kwargs):
        super(SEEDLabelDecode, self).__init__(character_dict_path,
-                                              character_type, use_space_char)
+                                              use_space_char)
    def add_special_char(self, dict_character):
        self.beg_str = "sos"
@@ -394,13 +359,10 @@ class SEEDLabelDecode(BaseRecLabelDecode):
 class SRNLabelDecode(BaseRecLabelDecode):
    """ Convert between text-label and text-index """
-    def __init__(self,
+    def __init__(self, character_dict_path=None, use_space_char=False,
-                 character_dict_path=None,
-                 character_type='en',
-                 use_space_char=False,
                 **kwargs):
        super(SRNLabelDecode, self).__init__(character_dict_path,
-                                             character_type, use_space_char)
+                                             use_space_char)
        self.max_text_length = kwargs.get('max_text_length', 25)
    def __call__(self, preds, label=None, *args, **kwargs):
@@ -616,13 +578,10 @@ class TableLabelDecode(object):
 class SARLabelDecode(BaseRecLabelDecode):
    """ Convert between text-label and text-index """
-    def __init__(self,
+    def __init__(self, character_dict_path=None, use_space_char=False,
-                 character_dict_path=None,
-                 character_type='ch',
-                 use_space_char=False,
                 **kwargs):
        super(SARLabelDecode, self).__init__(character_dict_path,
-                                             character_type, use_space_char)
+                                             use_space_char)
        self.rm_symbol = kwargs.get('rm_symbol', False)

--- a/ppocr/utils/EN_symbol_dict.txt
+++ b/ppocr/utils/EN_symbol_dict.txt
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+!
+"
+#
+$
+%
+&
+'
+(
+)
+*
+
+,
+-
+.
+/
+:
+;
+<
+=
+>
+?
+@
+[
+\
+]
+^
+_
+`
+{
+|
+}
+~
\ No newline at end of file
--- a/requirements.txt
+++ b/requirements.txt
 shapely
-scikit-image==0.17.2
+scikit-image==0.18.3
 imgaug==0.4.0
 pyclipper
 lmdb

--- a/tests/readme.md
+++ b/tests/readme.md
-# 从训练到推理部署工具链测试方法介绍
-test.sh和params.txt文件配合使用，完成OCR轻量检测和识别模型从训练到预测的流程测试。
-# 安装依赖
- 安装PaddlePaddle >= 2.0
- 安装PaddleOCR依赖
-    ```
-    pip3 install  -r ../requirements.txt
-    ```
- 安装autolog
-    ```
-    git clone https://github.com/LDOUBLEV/AutoLog
-    cd AutoLog
-    pip3 install -r requirements.txt
-    python3 setup.py bdist_wheel
-    pip3 install ./dist/auto_log-1.0.0-py3-none-any.whl
-    cd ../
-    ```
-# 目录介绍
-```bash
-tests/
-├── ocr_det_params.txt            # 测试OCR检测模型的参数配置文件
-├── ocr_rec_params.txt            # 测试OCR识别模型的参数配置文件
-├── ocr_ppocr_mobile_params.txt   # 测试OCR检测+识别模型串联的参数配置文件
-└── prepare.sh                    # 完成test.sh运行所需要的数据和模型下载
-└── test.sh                       # 测试主程序
-```
-# 使用方法
-test.sh包含四种运行模式，每种模式的运行数据不同，分别用于测试速度和精度，分别是：
- 模式1：lite_train_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
-```shell
-bash tests/prepare.sh ./tests/ocr_det_params.txt 'lite_train_infer'
-bash tests/test.sh ./tests/ocr_det_params.txt 'lite_train_infer'
-```  
- 模式2：whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理；
-```shell
-bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_infer'
-bash tests/test.sh ./tests/ocr_det_params.txt 'whole_infer'
-```  
- 模式3：infer 不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度;
-```shell
-bash tests/prepare.sh ./tests/ocr_det_params.txt 'infer'
-# 用法1:
-bash tests/test.sh ./tests/ocr_det_params.txt 'infer'
-# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
-bash tests/test.sh ./tests/ocr_det_params.txt 'infer' '1'
-```  
- 模式4：whole_train_infer , CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度；
-```shell
-bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_train_infer'
-bash tests/test.sh ./tests/ocr_det_params.txt 'whole_train_infer'
-```  
- 模式5：cpp_infer , CE： 验证inference model的c++预测是否走通；
-```shell
-bash tests/prepare.sh ./tests/ocr_det_params.txt 'cpp_infer'
-bash tests/test.sh ./tests/ocr_det_params.txt 'cpp_infer'
-```  
-# 日志输出
-最终在```tests/output```目录下生成.log后缀的日志文件
--- a/tests/test_python.sh
+++ b/tests/test_python.sh
-#!/bin/bash
-source tests/common_func.sh
-FILENAME=$1
-dataline=$(awk 'NR==1, NR==51{print}'  $FILENAME)
-# parser params
-IFS=$'\n'
-lines=(${dataline})
-# The training params
-model_name=$(func_parser_value "${lines[1]}")
-python=$(func_parser_value "${lines[2]}")
-gpu_list=$(func_parser_value "${lines[3]}")
-train_use_gpu_key=$(func_parser_key "${lines[4]}")
-train_use_gpu_value=$(func_parser_value "${lines[4]}")
-autocast_list=$(func_parser_value "${lines[5]}")
-autocast_key=$(func_parser_key "${lines[5]}")
-epoch_key=$(func_parser_key "${lines[6]}")
-epoch_num=$(func_parser_params "${lines[6]}")
-save_model_key=$(func_parser_key "${lines[7]}")
-train_batch_key=$(func_parser_key "${lines[8]}")
-train_batch_value=$(func_parser_params "${lines[8]}")
-pretrain_model_key=$(func_parser_key "${lines[9]}")
-pretrain_model_value=$(func_parser_value "${lines[9]}")
-train_model_name=$(func_parser_value "${lines[10]}")
-train_infer_img_dir=$(func_parser_value "${lines[11]}")
-train_param_key1=$(func_parser_key "${lines[12]}")
-train_param_value1=$(func_parser_value "${lines[12]}")
-trainer_list=$(func_parser_value "${lines[14]}")
-trainer_norm=$(func_parser_key "${lines[15]}")
-norm_trainer=$(func_parser_value "${lines[15]}")
-pact_key=$(func_parser_key "${lines[16]}")
-pact_trainer=$(func_parser_value "${lines[16]}")
-fpgm_key=$(func_parser_key "${lines[17]}")
-fpgm_trainer=$(func_parser_value "${lines[17]}")
-distill_key=$(func_parser_key "${lines[18]}")
-distill_trainer=$(func_parser_value "${lines[18]}")
-trainer_key1=$(func_parser_key "${lines[19]}")
-trainer_value1=$(func_parser_value "${lines[19]}")
-trainer_key2=$(func_parser_key "${lines[20]}")
-trainer_value2=$(func_parser_value "${lines[20]}")
-eval_py=$(func_parser_value "${lines[23]}")
-eval_key1=$(func_parser_key "${lines[24]}")
-eval_value1=$(func_parser_value "${lines[24]}")
-save_infer_key=$(func_parser_key "${lines[27]}")
-export_weight=$(func_parser_key "${lines[28]}")
-norm_export=$(func_parser_value "${lines[29]}")
-pact_export=$(func_parser_value "${lines[30]}")
-fpgm_export=$(func_parser_value "${lines[31]}")
-distill_export=$(func_parser_value "${lines[32]}")
-export_key1=$(func_parser_key "${lines[33]}")
-export_value1=$(func_parser_value "${lines[33]}")
-export_key2=$(func_parser_key "${lines[34]}")
-export_value2=$(func_parser_value "${lines[34]}")
-# parser inference model 
-infer_model_dir_list=$(func_parser_value "${lines[36]}")
-infer_export_list=$(func_parser_value "${lines[37]}")
-infer_is_quant=$(func_parser_value "${lines[38]}")
-# parser inference 
-inference_py=$(func_parser_value "${lines[39]}")
-use_gpu_key=$(func_parser_key "${lines[40]}")
-use_gpu_list=$(func_parser_value "${lines[40]}")
-use_mkldnn_key=$(func_parser_key "${lines[41]}")
-use_mkldnn_list=$(func_parser_value "${lines[41]}")
-cpu_threads_key=$(func_parser_key "${lines[42]}")
-cpu_threads_list=$(func_parser_value "${lines[42]}")
-batch_size_key=$(func_parser_key "${lines[43]}")
-batch_size_list=$(func_parser_value "${lines[43]}")
-use_trt_key=$(func_parser_key "${lines[44]}")
-use_trt_list=$(func_parser_value "${lines[44]}")
-precision_key=$(func_parser_key "${lines[45]}")
-precision_list=$(func_parser_value "${lines[45]}")
-infer_model_key=$(func_parser_key "${lines[46]}")
-image_dir_key=$(func_parser_key "${lines[47]}")
-infer_img_dir=$(func_parser_value "${lines[47]}")
-save_log_key=$(func_parser_key "${lines[48]}")
-benchmark_key=$(func_parser_key "${lines[49]}")
-benchmark_value=$(func_parser_value "${lines[49]}")
-infer_key1=$(func_parser_key "${lines[50]}")
-infer_value1=$(func_parser_value "${lines[50]}")
-LOG_PATH="./tests/output"
-mkdir -p ${LOG_PATH}
-status_log="${LOG_PATH}/results_python.log"
-function func_inference(){
-    IFS='|'
-    _python=$1
-    _script=$2
-    _model_dir=$3
-    _log_path=$4
-    _img_dir=$5
-    _flag_quant=$6
-    # inference 
-    for use_gpu in ${use_gpu_list[*]}; do
-        if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
-            for use_mkldnn in ${use_mkldnn_list[*]}; do
-                if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
-                    continue
-                fi
-                for threads in ${cpu_threads_list[*]}; do
-                    for batch_size in ${batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
-                        set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
-                        set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
-                        set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
-                        set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
-                        set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
-                        set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
-                        eval $command
-                        last_status=${PIPESTATUS[0]}
-                        eval "cat ${_save_log_path}"
-                        status_check $last_status "${command}" "${status_log}"
-                    done
-                done
-            done
-        elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
-            for use_trt in ${use_trt_list[*]}; do
-                for precision in ${precision_list[*]}; do
-                    if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
-                        continue
-                    fi 
-                    if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
-                        continue
-                    fi
-                    if [[ ${use_trt} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then
-                        continue
-                    fi
-                    for batch_size in ${batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
-                        set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
-                        set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
-                        set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
-                        set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
-                        set_precision=$(func_set_params "${precision_key}" "${precision}")
-                        set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
-                        set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
-                        eval $command
-                        last_status=${PIPESTATUS[0]}
-                        eval "cat ${_save_log_path}"
-                        status_check $last_status "${command}" "${status_log}"
-                    done
-                done
-            done
-        else
-            echo "Does not support hardware other than CPU and GPU Currently!"
-        fi
-    done
-}
-# set cuda device
-GPUID=$2
-if [ ${#GPUID} -le 0 ];then
-    env=" "
-else
-    env="export CUDA_VISIBLE_DEVICES=${GPUID}"
-fi
-set CUDA_VISIBLE_DEVICES
-eval $env
-echo "################### run test ###################"
--- a/tools/infer/predict_cls.py
+++ b/tools/infer/predict_cls.py
@@ -131,14 +131,9 @@ def main(args):
        img_list.append(img)
    try:
        img_list, cls_res, predict_time = text_classifier(img_list)
-    except:
+    except Exception as E:
        logger.info(traceback.format_exc())
-        logger.info(
+        logger.info(E)
-            "ERROR!!!! \n"
-            "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
-            "If your model has tps module:  "
-            "TPS does not support variable shape.\n"
-            "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
        exit()
    for ino in range(len(img_list)):
        logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],

--- a/tools/infer/predict_rec.py
+++ b/tools/infer/predict_rec.py
@@ -38,40 +38,34 @@ logger = get_logger()
 class TextRecognizer(object):
    def __init__(self, args):
        self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
-        self.character_type = args.rec_char_type
        self.rec_batch_num = args.rec_batch_num
        self.rec_algorithm = args.rec_algorithm
        postprocess_params = {
            'name': 'CTCLabelDecode',
-            "character_type": args.rec_char_type,
            "character_dict_path": args.rec_char_dict_path,
            "use_space_char": args.use_space_char
        }
        if self.rec_algorithm == "SRN":
            postprocess_params = {
                'name': 'SRNLabelDecode',
-                "character_type": args.rec_char_type,
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char
            }
        elif self.rec_algorithm == "RARE":
            postprocess_params = {
                'name': 'AttnLabelDecode',
-                "character_type": args.rec_char_type,
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char
            }
        elif self.rec_algorithm == 'NRTR':
            postprocess_params = {
                'name': 'NRTRLabelDecode',
-                "character_type": args.rec_char_type,
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char
            }
        elif self.rec_algorithm == "SAR":
            postprocess_params = {
                'name': 'SARLabelDecode',
-                "character_type": args.rec_char_type,
                "character_dict_path": args.rec_char_dict_path,
                "use_space_char": args.use_space_char
            }

--- a/tools/infer/utility.py
+++ b/tools/infer/utility.py
@@ -74,7 +74,6 @@ def init_args():
    parser.add_argument("--rec_algorithm", type=str, default='CRNN')
    parser.add_argument("--rec_model_dir", type=str)
    parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
-    parser.add_argument("--rec_char_type", type=str, default='ch')
    parser.add_argument("--rec_batch_num", type=int, default=6)
    parser.add_argument("--max_text_length", type=int, default=25)
    parser.add_argument(
@@ -268,10 +267,11 @@ def create_predictor(args, mode, logger):
            # cache 10 different shapes for mkldnn to avoid memory leak
            config.set_mkldnn_cache_capacity(10)
            config.enable_mkldnn()
+            if args.precision == "fp16":
+                config.enable_mkldnn_bfloat16()
    # enable memory optim
    config.enable_memory_optim()
-    #config.disable_glog_info()
+    config.disable_glog_info()
    config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
    if mode == 'table':