diff --git a/doc/doc_ch/inference.md b/doc/doc_ch/inference.md index 293fee2f4291a8400661de1ed08f0c6807eef977..bfab955b75ad8aecce9ef124fc6df6d62be337a5 100644 --- a/doc/doc_ch/inference.md +++ b/doc/doc_ch/inference.md @@ -11,24 +11,28 @@ inference 模型（`fluid.io.save_inference_model`保存的模型） - [一、训练模型转inference模型](#训练模型转inference模型) - [检测模型转inference模型](#检测模型转inference模型) - [识别模型转inference模型](#识别模型转inference模型) - + - [方向分类模型转inference模型](#方向模型转inference模型) + - [二、文本检测模型推理](#文本检测模型推理) - [1. 超轻量中文检测模型推理](#超轻量中文检测模型推理) - [2. DB文本检测模型推理](#DB文本检测模型推理) - [3. EAST文本检测模型推理](#EAST文本检测模型推理) - [4. SAST文本检测模型推理](#SAST文本检测模型推理) - + - [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) - -- [四、文本检测、识别串联推理](#文本检测、识别串联推理) + - [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) + +- [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) + +- [五、文本检测、方向分类和文字识别串联推理](#文本检测、方向分类和文字识别串联推理) - [1. 超轻量中文OCR模型推理](#超轻量中文OCR模型推理) - [2. 其他模型推理](#其他模型推理) - - + + ## 一、训练模型转inference模型 @@ -84,6 +88,32 @@ python3 tools/export_model.py -c configs/rec/rec_chinese_lite_train.yml -o Globa └─ params 识别inference模型的参数文件 ``` + +### 方向分类模型转inference模型 + +下载方向分类模型： +``` +wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_pre.tar && tar xf ./ch_lite/ch_ppocr_mobile-v1.1.cls_pre.tar -C ./ch_lite/ +``` + +方向分类模型转inference模型与检测的方式相同，如下： +``` +# -c后面设置训练算法的yml配置文件 +# -o配置可选参数 +# Global.checkpoints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。 +# Global.save_inference_dir参数设置转换的模型将保存的地址。 + +python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/cls_model/best_accuracy \ + Global.save_inference_dir=./inference/cls/ +``` + +转换成功后，在目录下有两个文件： +``` +/inference/cls/ + └─ model 识别inference模型的program文件 + └─ params 识别inference模型的参数文件 +``` + ## 二、文本检测模型推理 @@ -275,15 +305,36 @@ dict_character = list(self.character_str) 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" ``` - -## 四、文本检测、识别串联推理 + + +## 四、方向分类模型推理 + +下面将介绍方向分类模型推理。 + + +### 1. 方向分类模型推理 + +方向分类模型推理，可以执行如下命令： + +``` +python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/" +``` + +![](../imgs_words/ch/word_4.jpg) + +执行命令后，上面图像的预测结果（分类的方向和得分）会打印到屏幕上，示例如下： + +Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999963] + + +## 五、文本检测、方向分类和文字识别串联推理 ### 1. 超轻量中文OCR模型推理 -在执行预测时，需要通过参数image_dir指定单张图像或者图像集合的路径、参数det_model_dir指定检测inference模型的路径和参数rec_model_dir指定识别inference模型的路径。可视化识别结果默认保存到 ./inference_results 文件夹里面。 +在执行预测时，需要通过参数`image_dir`指定单张图像或者图像集合的路径、参数`det_model_dir`,`cls_model_dir`和`rec_model_dir`分别指定检测，方向分类和识别的inference模型路径。参数`use_angle_cls`用于控制是否启用方向分类模型。可视化识别结果默认保存到 ./inference_results 文件夹里面。 ``` -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls true ``` 执行命令后，识别结果图像如下： diff --git a/doc/doc_en/inference_en.md b/doc/doc_en/inference_en.md index 83ec2a90c45a320815e10e8572d894068c0b5130..db064f03e565b4fa3de3409d4a17459c636eae70 100644 --- a/doc/doc_en/inference_en.md +++ b/doc/doc_en/inference_en.md @@ -12,25 +12,28 @@ Next, we first introduce how to convert a trained model into an inference model, - [CONVERT TRAINING MODEL TO INFERENCE MODEL](#CONVERT) - [Convert detection model to inference model](#Convert_detection_model) - [Convert recognition model to inference model](#Convert_recognition_model) - - + - [Convert angle classification model to inference model](#Convert_angle_class_model) + + - [TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE) - [1. LIGHTWEIGHT CHINESE DETECTION MODEL INFERENCE](#LIGHTWEIGHT_DETECTION) - [2. DB TEXT DETECTION MODEL INFERENCE](#DB_DETECTION) - [3. EAST TEXT DETECTION MODEL INFERENCE](#EAST_DETECTION) - [4. SAST TEXT DETECTION MODEL INFERENCE](#SAST_DETECTION) - + - [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) - - -- [TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION) + +- [ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE) + - [1. ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE) + +- [TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION) - [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_CHINESE_MODEL) - [2. OTHER MODELS](#OTHER_MODELS) - + ## CONVERT TRAINING MODEL TO INFERENCE MODEL @@ -87,6 +90,33 @@ After the conversion is successful, there are two files in the directory: └─ params Identify the parameter files of the inference model ``` + +### Convert angle classification model to inference model + +Download the angle classification model: +``` +wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/20-09-22/cls/ch_ppocr_mobile-v1.1.cls_pre.tar && tar xf ./ch_lite/ch_ppocr_mobile-v1.1.cls_pre.tar -C ./ch_lite/ +``` + +The angle classification model is converted to the inference model in the same way as the detection, as follows: +``` +# -c Set the training algorithm yml configuration file +# -o Set optional parameters +# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams. +# Global.save_inference_dir Set the address where the converted model will be saved. + +python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/cls_model/best_accuracy \ + Global.save_inference_dir=./inference/cls/ +``` + +After the conversion is successful, there are two files in the directory: +``` +/inference/cls/ + └─ model Identify the saved model files + └─ params Identify the parameter files of the inference model +``` + + ## TEXT DETECTION MODEL INFERENCE @@ -276,16 +306,39 @@ If the chars dictionary is modified during training, you need to specify the new 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" ``` + + +## ANGLE CLASSIFICATION MODEL INFERENCE + +The following will introduce the angle classification model inference. + + + +### 1.ANGLE CLASSIFICATION MODEL INFERENCE + +For angle classification model inference, you can execute the following commands: + +``` +python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/" +``` + +![](../imgs_words/ch/word_4.jpg) + +After executing the command, the prediction results (classification angle and score) of the above image will be printed on the screen. + +Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999963] + + -## TEXT DETECTION AND RECOGNITION INFERENCE CONCATENATION +## TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION ### 1. LIGHTWEIGHT CHINESE MODEL -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. +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 visualized recognition results are saved to the `./inference_results` folder by default. ``` -python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" +python3 tools/infer/predict_system.py --image_dir="./doc/imgs/2.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls true ``` After executing the command, the recognition result image is as follows: