[CP]update visionlan (#7991)

* update visionlan

* update visionlan

doc/doc_ch/algorithm_overview.md

@@ -99,9 +99,9 @@ PaddleOCR将**持续新增**支持OCR领域前沿算法与模型，**欢迎广
 |SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) |
 |ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce | [训练模型](https://paddleocr.bj.bcebos.com/rec_vitstr_none_ce_train.tar) |
 |ABINet|Resnet45| 90.75% | rec_r45_abinet | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) |
-|VisionLAN|Resnet45| 90.30% | rec_r45_visionlan | [训练模型](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar) |
-|SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | coming soon |
-|RobustScanner|ResNet31| 87.77% | rec_r31_robustscanner | coming soon |
+|VisionLAN|Resnet45| 90.30% | rec_r45_visionlan | [训练模型](https://paddleocr.bj.bcebos.com/VisionLAN/rec_r45_visionlan_train.tar) |
+|SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | [训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_r32_gaspin_bilstm_att.tar) |
+|RobustScanner|ResNet31| 87.77% | rec_r31_robustscanner | [训练模型](https://paddleocr.bj.bcebos.com/contribution/rec_r31_robustscanner.tar)|
 
 
 <a name="2"></a>

doc/doc_ch/algorithm_rec_visionlan.md

@@ -27,7 +27,7 @@
 
 |模型|骨干网络|配置文件|Acc|下载链接|
 | --- | --- | --- | --- | --- |
-|VisionLAN|ResNet45|[rec_r45_visionlan.yml](../../configs/rec/rec_r45_visionlan.yml)|90.3%|[预训练、训练模型](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar)|
+|VisionLAN|ResNet45|[rec_r45_visionlan.yml](../../configs/rec/rec_r45_visionlan.yml)|90.3%|[预训练、训练模型](https://paddleocr.bj.bcebos.com/VisionLAN/rec_r45_visionlan_train.tar)|
 
 <a name="2"></a>
 ## 2. 环境配置
@@ -80,7 +80,7 @@ python3 tools/infer_rec.py -c configs/rec/rec_r45_visionlan.yml -o Global.infer_
 
 <a name="4-1"></a>
 ### 4.1 Python推理
-首先将训练得到best模型，转换成inference model。这里以训练完成的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar))，可以使用如下命令进行转换：
+首先将训练得到best模型，转换成inference model。这里以训练完成的模型为例（[模型下载地址](https://paddleocr.bj.bcebos.com/VisionLAN/rec_r45_visionlan_train.tar))，可以使用如下命令进行转换：
 
 ```shell
 # 注意将pretrained_model的路径设置为本地路径。

doc/doc_en/algorithm_overview_en.md

@@ -96,9 +96,9 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
 |SVTR|SVTR-Tiny| 89.25% | rec_svtr_tiny_none_ctc_en | [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/rec_svtr_tiny_none_ctc_en_train.tar) |
 |ViTSTR|ViTSTR| 79.82% | rec_vitstr_none_ce | [trained model](https://paddleocr.bj.bcebos.com/rec_vitstr_none_none_train.tar) |
 |ABINet|Resnet45| 90.75% | rec_r45_abinet | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_abinet_train.tar) |
-|VisionLAN|Resnet45| 90.30% | rec_r45_visionlan | [trained model](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar) |
-|SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | coming soon |
-|RobustScanner|ResNet31| 87.77% | rec_r31_robustscanner | coming soon |
+|VisionLAN|Resnet45| 90.30% | rec_r45_visionlan | [trained model](https://paddleocr.bj.bcebos.com/VisionLAN/rec_r45_visionlan_train.tar) |
+|SPIN|ResNet32| 90.00% | rec_r32_gaspin_bilstm_att | [trained model](https://paddleocr.bj.bcebos.com/contribution/rec_r32_gaspin_bilstm_att.tar) |
+|RobustScanner|ResNet31| 87.77% | rec_r31_robustscanner | [trained model](https://paddleocr.bj.bcebos.com/contribution/rec_r31_robustscanner.tar)|
 
 
 <a name="2"></a>

doc/doc_en/algorithm_rec_visionlan_en.md

@@ -25,7 +25,7 @@ Using MJSynth and SynthText two text recognition datasets for training, and eval
 
 |Model|Backbone|config|Acc|Download link|
 | --- | --- | --- | --- | --- |
-|VisionLAN|ResNet45|[rec_r45_visionlan.yml](../../configs/rec/rec_r45_visionlan.yml)|90.3%|[预训练、训练模型](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar)|
+|VisionLAN|ResNet45|[rec_r45_visionlan.yml](../../configs/rec/rec_r45_visionlan.yml)|90.3%|[预训练、训练模型](https://paddleocr.bj.bcebos.com/VisionLAN/rec_r45_visionlan_train.tar)|
 
 <a name="2"></a>
 ## 2. Environment
@@ -68,7 +68,7 @@ python3 tools/infer_rec.py -c configs/rec/rec_r45_visionlan.yml -o Global.infer_
 
 <a name="4-1"></a>
 ### 4.1 Python Inference
-First, the model saved during the VisionLAN text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/rec_r45_visionlan_train.tar)) ), you can use the following command to convert:
+First, the model saved during the VisionLAN text recognition training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/VisionLAN/rec_r45_visionlan_train.tar)) ), you can use the following command to convert:
 
 ```
 python3 tools/export_model.py -c configs/rec/rec_r45_visionlan.yml -o Global.pretrained_model=./rec_r45_visionlan_train/best_accuracy Global.save_inference_dir=./inference/rec_r45_visionlan/

ppocr/data/imaug/label_ops.py

@@ -107,7 +107,6 @@ class BaseRecLabelEncode(object):
         self.beg_str = "sos"
         self.end_str = "eos"
         self.lower = lower
-        self.use_default_dict = False
 
         if character_dict_path is None:
             logger = get_logger()
@@ -117,11 +116,8 @@ class BaseRecLabelEncode(object):
             self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
             dict_character = list(self.character_str)
             self.lower = True
-            self.use_default_dict = True
         else:
             self.character_str = []
-            if 'ppocr/utils/ic15_dict.txt' in character_dict_path:
-                self.use_default_dict = True
             with open(character_dict_path, "rb") as fin:
                 lines = fin.readlines()
                 for line in lines:
@@ -1348,9 +1344,6 @@ class VLLabelEncode(BaseRecLabelEncode):
                  **kwargs):
         super(VLLabelEncode, self).__init__(
             max_text_length, character_dict_path, use_space_char, lower)
-        if self.use_default_dict:
-            self.character = self.character[10:] + self.character[
-                1:10] + [self.character[0]]
         self.dict = {}
         for i, char in enumerate(self.character):
             self.dict[char] = i

ppocr/postprocess/rec_postprocess.py

@@ -26,15 +26,11 @@ class BaseRecLabelDecode(object):
         self.end_str = "eos"
         self.reverse = False
         self.character_str = []
-        self.use_default_dict = False
 
         if character_dict_path is None:
             self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
             dict_character = list(self.character_str)
-            self.use_default_dict = True
         else:
-            if 'ppocr/utils/ic15_dict.txt' in character_dict_path:
-                self.use_default_dict = True
             with open(character_dict_path, "rb") as fin:
                 lines = fin.readlines()
                 for line in lines:
@@ -725,9 +721,6 @@ class VLLabelDecode(BaseRecLabelDecode):
         super(VLLabelDecode, self).__init__(character_dict_path, use_space_char)
         self.max_text_length = kwargs.get('max_text_length', 25)
         self.nclass = len(self.character) + 1
-        if self.use_default_dict:
-            self.character = self.character[10:] + self.character[
-                1:10] + [self.character[0]]
 
     def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
         """ convert text-index into text-label. """