Merge pull request #3930 from Evezerest/2.3
Divide PP-OCR model inference and other model inference
This commit is contained in:
DanielYang
GitHub
commit
c00b005f59
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@ -103,13 +103,12 @@ For a new language request, please refer to [Guideline for new language_requests
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- [PP-OCR Model and Configuration](./doc/doc_en/models_and_config_en.md)
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- [PP-OCR Model Download](./doc/doc_en/models_list_en.md)
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- [Yml Configuration](./doc/doc_en/config_en.md)
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- [Python Inference](./doc/doc_en/inference_en.md)
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- [Python Inference for PP-OCR Model Library](./doc/doc_en/inference_ppocr_en.md)
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- [PP-OCR Training](./doc/doc_en/training_en.md)
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- [Text Detection](./doc/doc_en/detection_en.md)
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- [Text Recognition](./doc/doc_en/recognition_en.md)
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- [Direction Classification](./doc/doc_en/angle_class_en.md)
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- Inference and Deployment
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- [Python Inference](./doc/doc_en/inference_en.md)
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- [C++ Inference](./deploy/cpp_infer/readme_en.md)
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- [Serving](./deploy/pdserving/README.md)
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- [Mobile](./deploy/lite/readme_en.md)
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@ -120,6 +119,7 @@ For a new language request, please refer to [Guideline for new language_requests
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- Academic Circles
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- [Two-stage Algorithm](./doc/doc_en/algorithm_overview_en.md)
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- [PGNet Algorithm](./doc/doc_en/algorithm_overview_en.md)
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- [Python Inference](./doc/doc_en/inference_en.md)
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- Data Annotation and Synthesis
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- [Semi-automatic Annotation Tool: PPOCRLabel](./PPOCRLabel/README.md)
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- [Data Synthesis Tool: Style-Text](./StyleText/README.md)
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@ -95,13 +95,12 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
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- [PP-OCR模型与配置文件](./doc/doc_ch/models_and_config.md)
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- [PP-OCR模型下载](./doc/doc_ch/models_list.md)
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- [配置文件内容与生成](./doc/doc_ch/config.md)
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- [模型库快速使用](./doc/doc_ch/inference.md)
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- [PP-OCR模型库快速推理](./doc/doc_ch/inference_ppocr.md)
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- [PP-OCR模型训练](./doc/doc_ch/training.md)
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- [文本检测](./doc/doc_ch/detection.md)
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- [文本识别](./doc/doc_ch/recognition.md)
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- [方向分类器](./doc/doc_ch/angle_class.md)
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- PP-OCR模型推理部署
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- [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
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- [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
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- [服务化部署](./deploy/pdserving/README_CN.md)
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- [端侧部署](./deploy/lite/readme.md)
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@ -117,6 +116,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
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- OCR学术圈
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- [两阶段模型介绍与下载](./doc/doc_ch/algorithm_overview.md)
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- [端到端PGNet算法](./doc/doc_ch/pgnet.md)
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- [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
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- 数据集
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- [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
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- [手写中文OCR数据集](./doc/doc_ch/handwritten_datasets.md)
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@ -2,7 +2,7 @@
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# PP-OCR模型与配置文件
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PP-OCR模型与配置文件一章主要补充一些OCR模型的基本概念、配置文件的内容与作用以便对模型后续的参数调整和训练中拥有更好的体验。
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本节包含三个部分，首先在[PP-OCR模型下载](./models_list.md)中解释PP-OCR模型的类型概念，并提供所有模型的下载链接。然后在[配置文件内容与生成](./config.md)中详细说明调整PP-OCR模型所需的参数。最后的[模型库快速使用](./inference.md)是对第一节PP-OCR模型库使用方法的介绍，可以通过Python推理引擎快速利用丰富的模型库模型获得测试结果。
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本章包含三个部分，首先在[PP-OCR模型下载](./models_list.md)中解释PP-OCR模型的类型概念，并提供所有模型的下载链接。然后在[配置文件内容与生成](./config.md)中详细说明调整PP-OCR模型所需的参数。最后的[模型库快速使用](./inference_ppocr.md)是对第一节PP-OCR模型库使用方法的介绍，可以通过Python推理引擎快速利用丰富的模型库模型获得测试结果。
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------
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@ -90,7 +90,7 @@ cd /path/to/ppocr_img
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```
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更多whl包使用可参考[whl包文档](./whl.md)
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如需使用2.0模型，请指定参数`--version 2.0`，paddleocr默认使用2.1模型。更多whl包使用可参考[whl包文档](./whl.md)
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<a name="212"></a>
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@ -7,15 +7,13 @@
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- [1.2 数据下载](#数据下载)
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- [1.3 字典](#字典)
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- [1.4 支持空格](#支持空格)
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- [2 启动训练](#启动训练)
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- [2.1 数据增强](#数据增强)
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- [2.2 通用模型训练](#通用模型训练)
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- [2.3 多语言模型训练](#多语言模型训练)
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- [3 评估](#评估)
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- [4 预测](#预测)
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- [5 转Inference模型测试](#Inference)
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<a name="数据准备"></a>
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@ -424,3 +422,39 @@ python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v
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infer_img: doc/imgs_words/ch/word_1.jpg
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result: ('韩国小馆', 0.997218)
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```
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<a name="Inference"></a>
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## 5. 转Inference模型测试
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识别模型转inference模型与检测的方式相同，如下：
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```
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# -c 后面设置训练算法的yml配置文件
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# -o 配置可选参数
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# Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。
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# Global.save_inference_dir参数设置转换的模型将保存的地址。
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python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn/
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```
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**注意：**如果您是在自己的数据集上训练的模型，并且调整了中文字符的字典文件，请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。
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转换成功后，在目录下有三个文件：
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```
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/inference/rec_crnn/
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├── inference.pdiparams # 识别inference模型的参数文件
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├── inference.pdiparams.info # 识别inference模型的参数信息，可忽略
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└── inference.pdmodel # 识别inference模型的program文件
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```
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- 自定义模型推理
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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`
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```
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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="ch" --rec_char_dict_path="your text dict path"
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```
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@ -1,22 +1,22 @@
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# Reasoning based on Python prediction engine
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# Python Inference for PP-OCR Model Library
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This article introduces the use of the Python inference engine for the PP-OCR model library. The content is in order of text detection, text recognition, direction classifier and the prediction method of the three in series on the CPU and GPU.
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- [TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE)
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- [Text Detection Model Inference](#DETECTION_MODEL_INFERENCE)
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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. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE)
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- [Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE)
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- [1. Lightweight Chinese Recognition Model Inference](#LIGHTWEIGHT_RECOGNITION)
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- [2. Multilingaul Model Inference](#MULTILINGUAL_MODEL_INFERENCE)
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- [ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
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- [Angle Classification Model Inference](#ANGLE_CLASS_MODEL_INFERENCE)
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- [TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION)
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- [Text Detection Angle Classification and Recognition Inference Concatenation](#CONCATENATION)
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<a name="DETECTION_MODEL_INFERENCE"></a>
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## TEXT DETECTION MODEL INFERENCE
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## Text Detection Model Inference
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The default configuration is based on the inference setting of the DB text detection model. For lightweight Chinese detection model inference, you can execute the following commands:
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@ -52,11 +52,11 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_di
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<a name="RECOGNITION_MODEL_INFERENCE"></a>
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## TEXT RECOGNITION MODEL INFERENCE
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## Text Recognition Model Inference
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<a name="LIGHTWEIGHT_RECOGNITION"></a>
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### 1. LIGHTWEIGHT CHINESE TEXT RECOGNITION MODEL REFERENCE
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### 1. Lightweight Chinese Recognition Model Inference
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For lightweight Chinese recognition model inference, you can execute the following commands:
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@ -77,7 +77,7 @@ Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
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<a name="MULTILINGUAL_MODEL_INFERENCE"></a>
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### 2. MULTILINGAUL MODEL INFERENCE
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### 2. Multilingaul Model Inference
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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,
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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:
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@ -94,7 +94,7 @@ Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
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<a name="ANGLE_CLASS_MODEL_INFERENCE"></a>
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## ANGLE CLASSIFICATION MODEL INFERENCE
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## Angle Classification Model Inference
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For angle classification model inference, you can execute the following commands:
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@ -114,7 +114,7 @@ After executing the command, the prediction results (classification angle and sc
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```
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<a name="CONCATENATION"></a>
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## TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION
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## Text Detection Angle Classification and Recognition Inference Concatenation
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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, the parameter `cls_model_dir` specifies the path to angle classification inference model and the parameter `rec_model_dir` specifies the path to identify the inference model. The parameter `use_angle_cls` is used to control whether to enable the angle classification model. The parameter `use_mp` specifies whether to use multi-process to infer `total_process_num` specifies process number when using multi-process. The parameter . The visualized recognition results are saved to the `./inference_results` folder by default.
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# PP-OCR Model and Configuration
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The chapter on PP-OCR model and configuration file mainly adds some basic concepts of OCR model and the content and role of configuration file to have a better experience in the subsequent parameter adjustment and training of the model.
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This chapter contains three parts. Firstly, [PP-OCR Model Download](. /models_list_en.md) explains the concept of PP-OCR model types and provides links to download all models. Then in [Yml Configuration](. /config_en.md) details the parameters needed to fine-tune the PP-OCR models. The final [Python Inference for PP-OCR Model Library](. /inference_ppocr_en.md) is an introduction to the use of the PP-OCR model library in the first section, which can quickly utilize the rich model library models to obtain test results through the Python inference engine.
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------
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Let's first understand some basic concepts.
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- [INTRODUCTION ABOUT OCR](#introduction-about-ocr)
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* [BASIC CONCEPTS OF OCR DETECTION MODEL](#basic-concepts-of-ocr-detection-model)
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* [Basic concepts of OCR recognition model](#basic-concepts-of-ocr-recognition-model)
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@ -95,7 +95,7 @@ If you do not use the provided test image, you can replace the following `--imag
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['PAIN', 0.990372]
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```
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More whl package usage can be found in [whl package](./whl_en.md)
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If you need to use the 2.0 model, please specify the parameter `--version 2.0`, paddleocr uses the 2.1 model by default. More whl package usage can be found in [whl package](./whl_en.md)
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<a name="212-multi-language-model"></a>
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#### 2.1.2 Multi-language Model
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- [4 PREDICTION](#PREDICTION)
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- [4.1 Training engine prediction](#Training_engine_prediction)
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- [5 CONVERT TO INFERENCE MODEL](#Inference)
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<a name="DATA_PREPARATION"></a>
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## 1 DATA PREPARATION
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```
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<a name="EVALUATION"></a>
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## 3 EVALUATION
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The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file.
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infer_img: doc/imgs_words/ch/word_1.jpg
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result: ('韩国小馆', 0.997218)
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```
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<a name="Inference"></a>
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## 5 CONVERT TO INFERENCE MODEL
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The recognition model is converted to the inference model in the same way as the detection, as follows:
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```
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# -c Set the training algorithm yml configuration file
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# -o Set optional parameters
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# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
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# Global.save_inference_dir Set the address where the converted model will be saved.
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python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn/
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```
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If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path.
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After the conversion is successful, there are three files in the model save directory:
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```
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inference/det_db/
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├── inference.pdiparams # The parameter file of recognition inference model
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├── inference.pdiparams.info # The parameter information of recognition inference model, which can be ignored
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└── inference.pdmodel # The program file of recognition model
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```
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- Text recognition model Inference using custom characters dictionary
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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`
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```
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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="ch" --rec_char_dict_path="your text dict path"
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```
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