update inference docs for sast
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# 基于Python预测引擎推理
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inference 模型（fluid.io.save_inference_model保存的模型）
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inference 模型（`fluid.io.save_inference_model`保存的模型）
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一般是模型训练完成后保存的固化模型，多用于预测部署。训练过程中保存的模型是checkpoints模型，保存的是模型的参数，多用于恢复训练等。
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与checkpoints模型相比，inference 模型会额外保存模型的结构信息，在预测部署、加速推理上性能优越，灵活方便，适合与实际系统集成。更详细的介绍请参考文档[分类预测框架](https://paddleclas.readthedocs.io/zh_CN/latest/extension/paddle_inference.html).
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@ -12,21 +12,18 @@ inference 模型（fluid.io.save_inference_model保存的模型）
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- [检测模型转inference模型](#检测模型转inference模型)
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- [识别模型转inference模型](#识别模型转inference模型)
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- [二、文本检测模型推理](#文本检测模型推理)
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- [1. 超轻量中文检测模型推理](#超轻量中文检测模型推理)
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- [2. DB文本检测模型推理](#DB文本检测模型推理)
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- [3. EAST文本检测模型推理](#EAST文本检测模型推理)
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- [4. SAST文本检测模型推理](#SAST文本检测模型推理)
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- [三、文本识别模型推理](#文本识别模型推理)
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- [1. 超轻量中文识别模型推理](#超轻量中文识别模型推理)
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- [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)
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- [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)
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- [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)
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- [四、文本检测、识别串联推理](#文本检测、识别串联推理)
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- [1. 超轻量中文OCR模型推理](#超轻量中文OCR模型推理)
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- [2. 其他模型推理](#其他模型推理)
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@ -154,7 +151,7 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_
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python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.checkpoints="./models/det_r50_vd_east/best_accuracy" Global.save_inference_dir="./inference/det_east"
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```
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**EAST文本检测模型推理，需要设置参数`det_algorithm`，指定检测算法类型为EAST**，可以执行如下命令：
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**EAST文本检测模型推理，需要设置参数`--det_algorithm="EAST"`**，可以执行如下命令：
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```
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python3 tools/infer/predict_det.py --det_algorithm="EAST" --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/"
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@ -173,7 +170,7 @@ python3 tools/infer/predict_det.py --det_algorithm="EAST" --image_dir="./doc/img
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```
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python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.checkpoints="./models/sast_r50_vd_icdar2015/best_accuracy" Global.save_inference_dir="./inference/det_sast_ic15"
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```
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**SAST文本检测模型推理，需要设置参数`det_algorithm`，指定检测算法类型为SAST**，可以执行如下命令：
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**SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`**，可以执行如下命令：
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```
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python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_sast_ic15/"
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```
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@ -188,7 +185,7 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
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python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.checkpoints="./models/sast_r50_vd_total_text/best_accuracy" Global.save_inference_dir="./inference/det_sast_tt"
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```
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**SAST文本检测模型推理，需要设置参数`det_algorithm`，指定检测算法类型为SAST**，可以执行如下命令：
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**SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`，同时，还需要增加参数`--det_sast_polygon=True`，**可以执行如下命令：
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```
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python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_sast_tt/" --det_sast_polygon=True
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```
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@ -298,7 +295,7 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model
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如果想尝试使用其他检测算法或者识别算法，请参考上述文本检测模型推理和文本识别模型推理，更新相应配置和模型。
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**注意：由于检测框矫正逻辑的局限性，SAST弯曲文本检测模型（即，使用参数`--det_sast_polygon=True`时）暂时无法用来模型串联。**
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**注意：由于检测框矫正逻辑的局限性，暂不支持使用SAST弯曲文本检测模型（即，使用参数`--det_sast_polygon=True`时）进行模型串联。**
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下面给出基于EAST文本检测和STAR-Net文本识别执行命令：
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# Reasoning based on Python prediction engine
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The inference model (the model saved by fluid.io.save_inference_model) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.
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The inference model (the model saved by `fluid.io.save_inference_model`) is generally a solidified model saved after the model training is completed, and is mostly used to give prediction in deployment.
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The model saved during the training process is the checkpoints model, which saves the parameters of the model and is mostly used to resume training.
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@ -9,7 +9,31 @@ Compared with the checkpoints model, the inference model will additionally save
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Next, we first introduce how to convert a trained model into an inference model, and then we will introduce text detection, text recognition, and the concatenation of them based on inference model.
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- [CONVERT TRAINING MODEL TO INFERENCE MODEL](#CONVERT)
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- [Convert detection model to inference model](#Convert_detection_model)
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- [Convert recognition model to inference model](#Convert_recognition_model)
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- [TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE)
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- [1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE](#LIGHTWEIGHT_DETECTION)
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- [2. DB TEXT DETECTION MODEL INFERENCE](#DB_DETECTION)
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- [3. EAST TEXT DETECTION MODEL INFERENCE](#EAST_DETECTION)
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- [4. SAST TEXT DETECTION MODEL INFERENCE](#SAST_DETECTION)
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- [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
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- [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
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- [2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE](#CTC-BASED_RECOGNITION)
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- [3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE](#ATTENTION-BASED_RECOGNITION)
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- [4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY](#USING_CUSTOM_CHARACTERS)
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- [TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION)
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- [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_CHINESE_MODEL)
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- [2. OTHER MODELS](#OTHER_MODELS)
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<a name="CONVERT"></a>
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## CONVERT TRAINING MODEL TO INFERENCE MODEL
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<a name="Convert_detection_model"></a>
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### Convert detection model to inference model
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Download the lightweight Chinese detection model:
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@ -35,6 +59,7 @@ inference/det_db/
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└─ params Check the parameter file of the inference model
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```
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<a name="Convert_recognition_model"></a>
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### Convert recognition model to inference model
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Download the lightweight Chinese recognition model:
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@ -62,11 +87,13 @@ After the conversion is successful, there are two files in the directory:
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└─ params Identify the parameter files of the inference model
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```
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<a name="DETECTION_MODEL_INFERENCE"></a>
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## TEXT DETECTION MODEL INFERENCE
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The following will introduce the lightweight Chinese detection model inference, DB text detection model inference and EAST text detection model inference. The default configuration is based on the inference setting of the DB text detection model.
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Because EAST and DB algorithms are very different, when inference, it is necessary to **adapt the EAST text detection algorithm by passing in corresponding parameters**.
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<a name="LIGHTWEIGHT_DETECTION"></a>
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### 1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE
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For lightweight Chinese detection model inference, you can execute the following commands:
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@ -90,6 +117,7 @@ If you want to use the CPU for prediction, execute the command as follows
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python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
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```
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<a name="DB_DETECTION"></a>
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### 2. DB TEXT DETECTION MODEL INFERENCE
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First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/det_r50_vd_db.tar)), you can use the following command to convert:
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@ -114,6 +142,7 @@ The visualized text detection results are saved to the `./inference_results` fol
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**Note**: Since the ICDAR2015 dataset has only 1,000 training images, mainly for English scenes, the above model has very poor detection result on Chinese text images.
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<a name="EAST_DETECTION"></a>
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### 3. EAST TEXT DETECTION MODEL INFERENCE
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First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/det_r50_vd_east.tar)), you can use the following command to convert:
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@ -126,23 +155,64 @@ First, convert the model saved in the EAST text detection training process into
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python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.checkpoints="./models/det_r50_vd_east/best_accuracy" Global.save_inference_dir="./inference/det_east"
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```
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For EAST text detection model inference, you need to set the parameter det_algorithm, specify the detection algorithm type to EAST, run the following command:
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**For EAST text detection model inference, you need to set the parameter ``--det_algorithm="EAST"``**, run the following command:
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```
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python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST"
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```
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The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
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**Note**: The Python version of NMS in EAST post-processing used in this codebase so the prediction speed is quite slow. If you use the C++ version, there will be a significant speedup.
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**Note**: EAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases.
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<a name="SAST_DETECTION"></a>
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### 4. SAST TEXT DETECTION MODEL INFERENCE
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#### (1). Quadrangle text detection model (ICDAR2015)
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First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_icdar2015.tar)), you can use the following command to convert:
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```
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python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.checkpoints="./models/sast_r50_vd_icdar2015/best_accuracy" Global.save_inference_dir="./inference/det_sast_ic15"
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```
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**For SAST quadrangle text detection model inference, you need to set the parameter `--det_algorithm="SAST"`**, run the following command:
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```
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python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_sast_ic15/"
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```
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The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
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#### (2). Curved text detection model (Total-Text)
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First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)), you can use the following command to convert:
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```
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python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.checkpoints="./models/sast_r50_vd_total_text/best_accuracy" Global.save_inference_dir="./inference/det_sast_tt"
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```
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**For SAST curved text detection model inference, you need to set the parameter `--det_algorithm="SAST"` and `--det_sast_polygon=True`**, run the following command:
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```
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python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/imgs_en/img623.jpg" --det_model_dir="./inference/det_sast_tt/" --det_sast_polygon=True
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```
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The visualized text detection results are saved to the `./inference_results` folder by default, and the name of the result file is prefixed with 'det_res'. Examples of results are as follows:
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**Note**: SAST post-processing locality aware NMS has two versions: Python and C++. The speed of C++ version is obviously faster than that of Python version. Due to the compilation version problem of NMS of C++ version, C++ version NMS will be called only in Python 3.5 environment, and python version NMS will be called in other cases.
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<a name="RECOGNITION_MODEL_INFERENCE"></a>
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## TEXT RECOGNITION MODEL INFERENCE
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The following will introduce the lightweight Chinese recognition model inference, other CTC-based and Attention-based text recognition models inference. For Chinese text recognition, it is recommended to choose the recognition model based on CTC loss. In practice, it is also found that the result of the model based on Attention loss is not as good as the one based on CTC loss. In addition, if the characters dictionary is modified during training, make sure that you use the same characters set during inferencing. Please check below for details.
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<a name="LIGHTWEIGHT_RECOGNITION"></a>
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### 1. LIGHTWEIGHT CHINESE TEXT RECOGNITION MODEL REFERENCE
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For lightweight Chinese recognition model inference, you can execute the following commands:
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@ -158,6 +228,7 @@ After executing the command, the prediction results (recognized text and score)
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Predicts of ./doc/imgs_words/ch/word_4.jpg:['实力活力', 0.89552695]
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<a name="CTC-BASED_RECOGNITION"></a>
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### 2. CTC-BASED TEXT RECOGNITION MODEL INFERENCE
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Taking STAR-Net as an example, we introduce the recognition model inference based on CTC loss. CRNN and Rosetta are used in a similar way, by setting the recognition algorithm parameter `rec_algorithm`.
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@ -178,6 +249,7 @@ For STAR-Net text recognition model inference, execute the following commands:
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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"
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```
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<a name="ATTENTION-BASED_RECOGNITION"></a>
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### 3. ATTENTION-BASED TEXT RECOGNITION MODEL INFERENCE
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@ -196,6 +268,7 @@ self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
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dict_character = list(self.character_str)
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```
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<a name="USING_CUSTOM_CHARACTERS"></a>
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### 4. TEXT RECOGNITION MODEL INFERENCE USING CUSTOM CHARACTERS DICTIONARY
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If the chars dictionary is modified during training, you need to specify the new dictionary path by setting the parameter `rec_char_dict_path` when using your inference model to predict.
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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="en" --rec_char_dict_path="your text dict path"
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```
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<a name="CONCATENATION"></a>
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## TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION
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<a name="LIGHTWEIGHT_CHINESE_MODEL"></a>
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### 1. LIGHTWEIGHT CHINESE MODEL
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When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, and the parameter `rec_model_dir` specifies the path to identify the inference model. The visualized recognition results are saved to the `./inference_results` folder by default.
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@ -217,9 +292,14 @@ After executing the command, the recognition result image is as follows:
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<a name="OTHER_MODELS"></a>
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### 2. OTHER MODELS
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If you want to try other detection algorithms or recognition algorithms, please refer to the above text detection model inference and text recognition model inference, update the corresponding configuration and model, the following command uses the combination of the EAST text detection and STAR-Net text recognition:
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If you want to try other detection algorithms or recognition algorithms, please refer to the above text detection model inference and text recognition model inference, update the corresponding configuration and model.
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**Note: due to the limitation of rotation logic of detected box, SAST curved text detection model (using the parameter `det_sast_polygon=True`) is not supported for model combination yet.**
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The following command uses the combination of the EAST text detection and STAR-Net text recognition:
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```
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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"
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