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# Paddle2ONNX模型转化与预测
# Paddle2ONNX model transformation and prediction
本章节介绍 PaddleOCR 模型如何转化为 ONNX 模型，并基于 ONNXRuntime 引擎预测。
This chapter describes how the PaddleOCR model is converted into an ONNX model and predicted based on the ONNXRuntime engine.
## 1. 环境准备
## 1. Environment preparation
需要准备 PaddleOCR、Paddle2ONNX 模型转化环境，和 ONNXRuntime 预测环境
Need to prepare PaddleOCR, Paddle2ONNX model conversion environment, and ONNXRuntime prediction environment
### PaddleOCR
克隆PaddleOCR的仓库，使用release/2.4分支，并进行安装，由于PaddleOCR仓库比较大，git clone速度比较慢，所以本教程已下载
Clone the PaddleOCR repository, use the release/2.6 branch, and install it.
```
git clone -b release/2.4 https://github.com/PaddlePaddle/PaddleOCR.git
git clone -b release/2.6 https://github.com/PaddlePaddle/PaddleOCR.git
cd PaddleOCR && python3.7 setup.py install
```
### Paddle2ONNX
Paddle2ONNX 支持将 PaddlePaddle 模型格式转化到 ONNX 模型格式，算子目前稳定支持导出 ONNX Opset 9~11，部分Paddle算子支持更低的ONNX Opset转换。
更多细节可参考 [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX/blob/develop/README_zh.md)
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)
- 安装 Paddle2ONNX
- install Paddle2ONNX
```
python3.7 -m pip install paddle2onnx
```
- 安装 ONNXRuntime
- install ONNXRuntime
```
# 建议安装 1.9.0 版本，可根据环境更换版本号
# 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. 模型转换
## 2. Model conversion
- Paddle 模型下载
- Paddle model download
有两种方式获取Paddle静态图模型：在 [model_list](../../doc/doc_ch/models_list.md) 中下载PaddleOCR提供的预测模型；
参考[模型导出说明](../../doc/doc_ch/inference.md#训练模型转inference模型)把训练好的权重转为 inference_model。
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.
以 ppocr 中文检测、识别、分类模型为例：
Take the PP-OCRv3 detection, recognition, and classification model as an example:
```
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
cd ./inference && tar xf ch_PP-OCRv2_det_infer.tar && cd ..
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-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
cd ./inference && tar xf ch_PP-OCRv2_rec_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 ..
```
- 模型转换
- convert model
使用 Paddle2ONNX 将Paddle静态图模型转换为ONNX模型格式：
Convert Paddle inference model to ONNX model format using Paddle2ONNX:
```
paddle2onnx --model_dir ./inference/ch_PP-OCRv2_det_infer \
paddle2onnx --model_dir ./inference/en_PP-OCRv3_det_infer \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--save_file ./inference/det_onnx/model.onnx \
......@@ -65,7 +66,7 @@ paddle2onnx --model_dir ./inference/ch_PP-OCRv2_det_infer \
--input_shape_dict="{'x':[-1,3,-1,-1]}" \
--enable_onnx_checker True
paddle2onnx --model_dir ./inference/ch_PP-OCRv2_rec_infer \
paddle2onnx --model_dir ./inference/en_PP-OCRv3_rec_infer \
--model_filename inference.pdmodel \
--params_filename inference.pdiparams \
--save_file ./inference/rec_onnx/model.onnx \
......@@ -81,136 +82,89 @@ paddle2onnx --model_dir ./inference/ch_ppocr_mobile_v2.0_cls_infer \
--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
执行完毕后，ONNX 模型会被分别保存在 `./inference/det_onnx/`，`./inference/rec_onnx/`，`./inference/cls_onnx/`路径下
* 注意：对于OCR模型，转化过程中必须采用动态shape的形式，即加入选项--input_shape_dict="{'x': [-1, 3, -1, -1]}"，否则预测结果可能与直接使用Paddle预测有细微不同。
另外，以下几个模型暂不支持转换为 ONNX 模型：
NRTR、SAR、RARE、SRN
* 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. 推理预测
## 3. prediction
以中文OCR模型为例，使用 ONNXRuntime 预测可执行如下命令：
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=./deploy/lite/imgs/lite_demo.png
--image_dir=doc/imgs_en/img_12.jpg \
--rec_char_dict_path=ppocr/utils/en_dict.txt
```
以中文OCR模型为例，使用 Paddle Inference 预测可执行如下命令：
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/ch_PP-OCRv2_rec_infer \
--det_model_dir=./inference/ch_PP-OCRv2_det_infer \
--image_dir=./deploy/lite/imgs/lite_demo.png
--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
```
执行命令后在终端会打印出预测的识别信息，并在 `./inference_results/` 下保存可视化结果。
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 执行效果：
ONNXRuntime result：
<div align="center">
<img src="./images/lite_demo_onnx.png" width=800">
<img src="../../doc/imgs_results/multi_lang/img_12.jpg" width=800">
</div>
Paddle Inference 执行效果：
Paddle Inference result：
<div align="center">
<img src="./images/lite_demo_paddle.png" width=800">
<img src="../../doc/imgs_results/multi_lang/img_12.jpg" width=800">
</div>
使用 ONNXRuntime 预测，终端输出：
```
[2022/02/22 17:48:27] root DEBUG: dt_boxes num : 38, elapse : 0.043187856674194336
[2022/02/22 17:48:27] root DEBUG: rec_res num : 38, elapse : 0.592170000076294
[2022/02/22 17:48:27] root DEBUG: 0 Predict time of ./deploy/lite/imgs/lite_demo.png: 0.642s
[2022/02/22 17:48:27] root DEBUG: The, 0.984
[2022/02/22 17:48:27] root DEBUG: visualized, 0.882
[2022/02/22 17:48:27] root DEBUG: etect18片, 0.720
[2022/02/22 17:48:27] root DEBUG: image saved in./vis.jpg, 0.947
[2022/02/22 17:48:27] root DEBUG: 纯臻营养护发素0.993604, 0.996
[2022/02/22 17:48:27] root DEBUG: 产品信息/参数, 0.922
[2022/02/22 17:48:27] root DEBUG: 0.992728, 0.914
[2022/02/22 17:48:27] root DEBUG: （45元／每公斤，100公斤起订）, 0.926
[2022/02/22 17:48:27] root DEBUG: 0.97417, 0.977
[2022/02/22 17:48:27] root DEBUG: 每瓶22元，1000瓶起订）0.993976, 0.962
[2022/02/22 17:48:27] root DEBUG: 【品牌】：代加工方式/0EMODM, 0.945
[2022/02/22 17:48:27] root DEBUG: 0.985133, 0.980
[2022/02/22 17:48:27] root DEBUG: 【品名】：纯臻营养护发素, 0.921
[2022/02/22 17:48:27] root DEBUG: 0.995007, 0.883
[2022/02/22 17:48:27] root DEBUG: 【产品编号】：YM-X-30110.96899, 0.955
[2022/02/22 17:48:27] root DEBUG: 【净含量】：220ml, 0.943
[2022/02/22 17:48:27] root DEBUG: Q.996577, 0.932
[2022/02/22 17:48:27] root DEBUG: 【适用人群】：适合所有肤质, 0.913
[2022/02/22 17:48:27] root DEBUG: 0.995842, 0.969
[2022/02/22 17:48:27] root DEBUG: 【主要成分】：鲸蜡硬脂醇、燕麦B-葡聚, 0.883
[2022/02/22 17:48:27] root DEBUG: 0.961928, 0.964
[2022/02/22 17:48:27] root DEBUG: 10, 0.812
[2022/02/22 17:48:27] root DEBUG: 糖、椰油酰胺丙基甜菜碱、泛醒, 0.866
[2022/02/22 17:48:27] root DEBUG: 0.925898, 0.943
[2022/02/22 17:48:27] root DEBUG: （成品包材）, 0.974
[2022/02/22 17:48:27] root DEBUG: 0.972573, 0.961
[2022/02/22 17:48:27] root DEBUG: 【主要功能】：可紧致头发磷层，从而达到, 0.936
[2022/02/22 17:48:27] root DEBUG: 0.994448, 0.952
[2022/02/22 17:48:27] root DEBUG: 13, 0.998
[2022/02/22 17:48:27] root DEBUG: 即时持久改善头发光泽的效果，给干燥的头, 0.994
[2022/02/22 17:48:27] root DEBUG: 0.990198, 0.975
[2022/02/22 17:48:27] root DEBUG: 14, 0.977
[2022/02/22 17:48:27] root DEBUG: 发足够的滋养, 0.991
[2022/02/22 17:48:27] root DEBUG: 0.997668, 0.918
[2022/02/22 17:48:27] root DEBUG: 花费了0.457335秒, 0.901
[2022/02/22 17:48:27] root DEBUG: The visualized image saved in ./inference_results/lite_demo.png
[2022/02/22 17:48:27] root INFO: The predict total time is 0.7003889083862305
```
使用 Paddle Inference 预测，终端输出：
```
[2022/02/22 17:47:25] root DEBUG: dt_boxes num : 38, elapse : 0.11791276931762695
[2022/02/22 17:47:27] root DEBUG: rec_res num : 38, elapse : 2.6206860542297363
[2022/02/22 17:47:27] root DEBUG: 0 Predict time of ./deploy/lite/imgs/lite_demo.png: 2.746s
[2022/02/22 17:47:27] root DEBUG: The, 0.984
[2022/02/22 17:47:27] root DEBUG: visualized, 0.882
[2022/02/22 17:47:27] root DEBUG: etect18片, 0.720
[2022/02/22 17:47:27] root DEBUG: image saved in./vis.jpg, 0.947
[2022/02/22 17:47:27] root DEBUG: 纯臻营养护发素0.993604, 0.996
[2022/02/22 17:47:27] root DEBUG: 产品信息/参数, 0.922
[2022/02/22 17:47:27] root DEBUG: 0.992728, 0.914
[2022/02/22 17:47:27] root DEBUG: （45元／每公斤，100公斤起订）, 0.926
[2022/02/22 17:47:27] root DEBUG: 0.97417, 0.977
[2022/02/22 17:47:27] root DEBUG: 每瓶22元，1000瓶起订）0.993976, 0.962
[2022/02/22 17:47:27] root DEBUG: 【品牌】：代加工方式/0EMODM, 0.945
[2022/02/22 17:47:27] root DEBUG: 0.985133, 0.980
[2022/02/22 17:47:27] root DEBUG: 【品名】：纯臻营养护发素, 0.921
[2022/02/22 17:47:27] root DEBUG: 0.995007, 0.883
[2022/02/22 17:47:27] root DEBUG: 【产品编号】：YM-X-30110.96899, 0.955
[2022/02/22 17:47:27] root DEBUG: 【净含量】：220ml, 0.943
[2022/02/22 17:47:27] root DEBUG: Q.996577, 0.932
[2022/02/22 17:47:27] root DEBUG: 【适用人群】：适合所有肤质, 0.913
[2022/02/22 17:47:27] root DEBUG: 0.995842, 0.969
[2022/02/22 17:47:27] root DEBUG: 【主要成分】：鲸蜡硬脂醇、燕麦B-葡聚, 0.883
[2022/02/22 17:47:27] root DEBUG: 0.961928, 0.964
[2022/02/22 17:47:27] root DEBUG: 10, 0.812
[2022/02/22 17:47:27] root DEBUG: 糖、椰油酰胺丙基甜菜碱、泛醒, 0.866
[2022/02/22 17:47:27] root DEBUG: 0.925898, 0.943
[2022/02/22 17:47:27] root DEBUG: （成品包材）, 0.974
[2022/02/22 17:47:27] root DEBUG: 0.972573, 0.961
[2022/02/22 17:47:27] root DEBUG: 【主要功能】：可紧致头发磷层，从而达到, 0.936
[2022/02/22 17:47:27] root DEBUG: 0.994448, 0.952
[2022/02/22 17:47:27] root DEBUG: 13, 0.998
[2022/02/22 17:47:27] root DEBUG: 即时持久改善头发光泽的效果，给干燥的头, 0.994
[2022/02/22 17:47:27] root DEBUG: 0.990198, 0.975
[2022/02/22 17:47:27] root DEBUG: 14, 0.977
[2022/02/22 17:47:27] root DEBUG: 发足够的滋养, 0.991
[2022/02/22 17:47:27] root DEBUG: 0.997668, 0.918
[2022/02/22 17:47:27] root DEBUG: 花费了0.457335秒, 0.901
[2022/02/22 17:47:27] root DEBUG: The visualized image saved in ./inference_results/lite_demo.png
[2022/02/22 17:47:27] root INFO: The predict total time is 2.8338775634765625
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
```
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