# Paddle2ONNX model transformation and prediction This chapter describes how the PaddleOCR model is converted into an ONNX model and predicted based on the ONNXRuntime engine. ## 1. Environment preparation Need to prepare PaddleOCR, Paddle2ONNX model conversion environment, and ONNXRuntime prediction environment ### PaddleOCR Clone the PaddleOCR warehouse, use the release/2.6 branch, and install it. ``` git clone -b release/2.6 https://github.com/PaddlePaddle/PaddleOCR.git cd PaddleOCR && python3.7 setup.py install ``` ### Paddle2ONNX Paddle2ONNX supports converting the PaddlePaddle model format to the ONNX model format. The operator currently supports exporting ONNX Opset 9~11 stably, and some Paddle operators support lower ONNX Opset conversion. For more details, please refer to [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX/blob/develop/README_en.md) - install Paddle2ONNX ``` python3.7 -m pip install paddle2onnx ``` - install ONNXRuntime ``` # It is recommended to install version 1.9.0, and the version number can be changed according to the environment python3.7 -m pip install onnxruntime==1.9.0 ``` ## 2. Model conversion - Paddle model download There are two ways to obtain the Paddle model: Download the prediction model provided by PaddleOCR in [model_list](../../doc/doc_en/models_list_en.md); Refer to [Model Export Instructions](../../doc/doc_en/inference_en.md#1-convert-training-model-to-inference-model) to convert the trained weights to inference_model. Take the PP-OCRv3 detection, recognition, and classification model as an example: ``` wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar cd ./inference && tar xf en_PP-OCRv3_det_infer.tar && cd .. wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar cd ./inference && tar xf en_PP-OCRv3_rec_infer.tar && cd .. wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar cd ./inference && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar && cd .. ``` - convert model Convert Paddle inference model to ONNX model format using Paddle2ONNX: ``` paddle2onnx --model_dir ./inference/en_PP-OCRv3_det_infer \ --model_filename inference.pdmodel \ --params_filename inference.pdiparams \ --save_file ./inference/det_onnx/model.onnx \ --opset_version 10 \ --input_shape_dict="{'x':[-1,3,-1,-1]}" \ --enable_onnx_checker True paddle2onnx --model_dir ./inference/en_PP-OCRv3_rec_infer \ --model_filename inference.pdmodel \ --params_filename inference.pdiparams \ --save_file ./inference/rec_onnx/model.onnx \ --opset_version 10 \ --input_shape_dict="{'x':[-1,3,-1,-1]}" \ --enable_onnx_checker True paddle2onnx --model_dir ./inference/ch_ppocr_mobile_v2.0_cls_infer \ --model_filename ch_ppocr_mobile_v2.0_cls_infer/inference.pdmodel \ --params_filename ch_ppocr_mobile_v2.0_cls_infer/inference.pdiparams \ --save_file ./inferencecls_onnx/model.onnx \ --opset_version 10 \ --input_shape_dict="{'x':[-1,3,-1,-1]}" \ --enable_onnx_checker True ``` After execution, the ONNX model will be saved in `./inference/det_onnx/`, `./inference/rec_onnx/`, `./inference/cls_onnx/` paths respectively * Note: For the OCR model, the conversion process must be in the form of dynamic shape, that is, add the option --input_shape_dict="{'x': [-1, 3, -1, -1]}", otherwise the prediction result may be the same as Predicting directly with Paddle is slightly different. In addition, the following models do not currently support conversion to ONNX models: NRTR, SAR, RARE, SRN ## 3. prediction Take the English OCR model as an example, use **ONNXRuntime** to predict and execute the following commands: ``` python3.7 tools/infer/predict_system.py --use_gpu=False --use_onnx=True \ --det_model_dir=./inference/det_onnx/model.onnx \ --rec_model_dir=./inference/rec_onnx/model.onnx \ --cls_model_dir=./inference/cls_onnx/model.onnx \ --image_dir=doc/imgs_en/img_12.jpg \ --rec_char_dict_path=ppocr/utils/en_dict.txt ``` Taking the English OCR model as an example, use **Paddle Inference** to predict and execute the following commands: ``` python3.7 tools/infer/predict_system.py --use_gpu=False \ --cls_model_dir=./inference/ch_ppocr_mobile_v2.0_cls_infer \ --rec_model_dir=./inference/en_PP-OCRv3_rec_infer \ --det_model_dir=./inference/en_PP-OCRv3_det_infer \ --image_dir=doc/imgs_en/img_12.jpg \ --rec_char_dict_path=ppocr/utils/en_dict.txt ``` After executing the command, the predicted identification information will be printed out in the terminal, and the visualization results will be saved under `./inference_results/`. ONNXRuntime result：
Paddle Inference result：
Using ONNXRuntime to predict, terminal output: ``` [2022/10/10 12:06:28] ppocr DEBUG: dt_boxes num : 11, elapse : 0.3568880558013916 [2022/10/10 12:06:31] ppocr DEBUG: rec_res num : 11, elapse : 2.6445000171661377 [2022/10/10 12:06:31] ppocr DEBUG: 0 Predict time of doc/imgs_en/img_12.jpg: 3.021s [2022/10/10 12:06:31] ppocr DEBUG: ACKNOWLEDGEMENTS, 0.997 [2022/10/10 12:06:31] ppocr DEBUG: We would like to thank all the designers and, 0.976 [2022/10/10 12:06:31] ppocr DEBUG: contributors who have been involved in the, 0.979 [2022/10/10 12:06:31] ppocr DEBUG: production of this book; their contributions, 0.989 [2022/10/10 12:06:31] ppocr DEBUG: have been indispensable to its creation. We, 0.956 [2022/10/10 12:06:31] ppocr DEBUG: would also like to express our gratitude to all, 0.991 [2022/10/10 12:06:31] ppocr DEBUG: the producers for their invaluable opinions, 0.978 [2022/10/10 12:06:31] ppocr DEBUG: and assistance throughout this project. And to, 0.988 [2022/10/10 12:06:31] ppocr DEBUG: the many others whose names are not credited, 0.958 [2022/10/10 12:06:31] ppocr DEBUG: but have made specific input in this book, we, 0.970 [2022/10/10 12:06:31] ppocr DEBUG: thank you for your continuous support., 0.998 [2022/10/10 12:06:31] ppocr DEBUG: The visualized image saved in ./inference_results/img_12.jpg [2022/10/10 12:06:31] ppocr INFO: The predict total time is 3.2482550144195557 ``` Using Paddle Inference to predict, terminal output: ``` [2022/10/10 12:06:28] ppocr DEBUG: dt_boxes num : 11, elapse : 0.3568880558013916 [2022/10/10 12:06:31] ppocr DEBUG: rec_res num : 11, elapse : 2.6445000171661377 [2022/10/10 12:06:31] ppocr DEBUG: 0 Predict time of doc/imgs_en/img_12.jpg: 3.021s [2022/10/10 12:06:31] ppocr DEBUG: ACKNOWLEDGEMENTS, 0.997 [2022/10/10 12:06:31] ppocr DEBUG: We would like to thank all the designers and, 0.976 [2022/10/10 12:06:31] ppocr DEBUG: contributors who have been involved in the, 0.979 [2022/10/10 12:06:31] ppocr DEBUG: production of this book; their contributions, 0.989 [2022/10/10 12:06:31] ppocr DEBUG: have been indispensable to its creation. We, 0.956 [2022/10/10 12:06:31] ppocr DEBUG: would also like to express our gratitude to all, 0.991 [2022/10/10 12:06:31] ppocr DEBUG: the producers for their invaluable opinions, 0.978 [2022/10/10 12:06:31] ppocr DEBUG: and assistance throughout this project. And to, 0.988 [2022/10/10 12:06:31] ppocr DEBUG: the many others whose names are not credited, 0.958 [2022/10/10 12:06:31] ppocr DEBUG: but have made specific input in this book, we, 0.970 [2022/10/10 12:06:31] ppocr DEBUG: thank you for your continuous support., 0.998 [2022/10/10 12:06:31] ppocr DEBUG: The visualized image saved in ./inference_results/img_12.jpg [2022/10/10 12:06:31] ppocr INFO: The predict total time is 3.2482550144195557 ```