delete attention

PaddlePaddle · Dec 15, 2020 · 97b0103 · 97b0103
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diff --git a/doc/doc_ch/inference.md b/doc/doc_ch/inference.md
@@ -22,9 +22,8 @@ inference 模型（`paddle.jit.save`保存的模型）
 - [三、文本识别模型推理](#文本识别模型推理)
  - [1. 超轻量中文识别模型推理](#超轻量中文识别模型推理)
  - [2. 基于CTC损失的识别模型推理](#基于CTC损失的识别模型推理)
- - [3. 基于Attention损失的识别模型推理](#基于Attention损失的识别模型推理)
- - [4. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
- - [5. 多语言模型的推理](#多语言模型的推理)
+ - [3. 自定义文本识别字典的推理](#自定义文本识别字典的推理)
+ - [4. 多语言模型的推理](#多语言模型的推理)
 
 - [四、方向分类模型推理](#方向识别模型推理)
  - [1. 方向分类模型推理](#方向分类模型推理)
@@ -268,16 +267,6 @@ CRNN 文本识别模型推理，可以执行如下命令：
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_crnn/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
 ```
 
-<a name="基于Attention损失的识别模型推理"></a>
-### 3. 基于Attention损失的识别模型推理
-
-基于Attention损失的识别模型与ctc不同，需要额外设置识别算法参数 --rec_algorithm="RARE"
-RARE 文本识别模型推理，可以执行如下命令：
-```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rare/" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_algorithm="RARE"
-
-```
-
 ![](../imgs_words_en/word_336.png)
 
 执行命令后，上面图像的识别结果如下：
@@ -297,15 +286,15 @@ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
 dict_character = list(self.character_str)
 ```
 
-### 4. 自定义文本识别字典的推理
+### 3. 自定义文本识别字典的推理
 如果训练时修改了文本的字典，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径，并且设置 `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"
 ```
 
 <a name="多语言模型的推理"></a>
-### 5. 多语言模型的推理
+### 4. 多语言模型的推理
 如果您需要预测的是其他语言模型，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径, 同时为了得到正确的可视化结果，
 需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/` 路径下有默认提供的小语种字体，例如韩文识别：
 

diff --git a/doc/doc_en/inference_en.md b/doc/doc_en/inference_en.md
@@ -25,9 +25,8 @@ Next, we first introduce how to convert a trained model into an inference model,
 - [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
  - [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
  - [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION)
- - [3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE](#ATTENTION-BASED_RECOGNITION)
- - [4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
- - [5. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE)
+ - [3. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
+ - [4. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE)
 
 - [ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
  - [1. ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
@@ -275,15 +274,6 @@ 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"
 ```
 
-<a name="ATTENTION-BASED_RECOGNITION"></a>
-### 3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE
-
-The recognition model based on Attention loss is different from ctc, and additional recognition algorithm parameters need to be set --rec_algorithm="RARE"
-After executing the command, the recognition result of the above image is as follows:
-```bash
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rare/" --rec_image_shape="3, 32, 100" --rec_char_type="en" --rec_algorithm="RARE"
-```
-
 ![](../imgs_words_en/word_336.png)
 
 After executing the command, the recognition result of the above image is as follows:
@@ -303,15 +293,15 @@ dict_character = list(self.character_str)
 ```
 
 <a name="USING_CUSTOM_CHARACTERS"></a>
-### 4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
+### 3. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
 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"
 ```
 
 <a name="MULTILINGUAL_MODEL_INFERENCE"></a>
-### 5. MULTILINGAUL MODEL INFERENCE
+### 4. MULTILINGAUL MODEL INFERENCE
 If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
 You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/` path, such as Korean recognition: