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