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[Other] PPOCR models support model clone function (#1072)

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* Refactor PaddleSeg with preprocessor && postprocessor

* Fix bugs

* Delete redundancy code

* Modify by comments

* Refactor according to comments

* Add batch evaluation

* Add single test script

* Add ppliteseg single test script && fix eval(raise) error

* fix bug

* Fix evaluation segmentation.py batch predict

* Fix segmentation evaluation bug

* Fix evaluation segmentation bugs

* Update segmentation result docs

* Update old predict api and DisableNormalizeAndPermute

* Update resize segmentation label map with cv::INTER_NEAREST

* Add Model Clone function for PaddleClas && PaddleDet && PaddleSeg

* Add multi thread demo

* Add python model clone function

* Add multi thread python && C++ example

* Fix bug

* Update python && cpp multi_thread examples

* Add cpp && python directory

* Add README.md for examples

* Delete redundant code

* Create README_CN.md

* Rename README_CN.md to README.md

* Update README.md

* Update README.md

* Update VERSION_NUMBER

* Update requirements.txt

* Update README.md

* update version in doc:

* [Serving]Update Dockerfile (#1037)

Update Dockerfile

* Add license notice for RVM onnx model file (#1060)

* [Model] Add GPL-3.0 license (#1065)

Add GPL-3.0 license

* PPOCR model support model clone

* Update README.md

* Update PPOCRv2 && PPOCRv3 clone code

* Update PPOCR python __init__

* Add multi thread ocr example code

* Update README.md

* Update README.md

* Update ResNet50_vd_infer multi process code

* Add PPOCR multi process && thread example

* Update README.md

* Update README.md

* Update multi-thread docs

Co-authored-by: Jason <jiangjiajun@baidu.com>
Co-authored-by: leiqing <54695910+leiqing1@users.noreply.github.com>
Co-authored-by: heliqi <1101791222@qq.com>
Co-authored-by: WJJ1995 <wjjisloser@163.com>
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English | [简体中文](README_CN.md)
# Example of PaddleClas models Python multi-thread/multi-process Deployment
Before deployment, two steps require confirmation
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. Install the FastDeploy Python whl package. Please refer to [FastDeploy Python Installation](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
This directory provides example file `multi_thread_process.py` to fast deploy multi-thread/multi-process ResNet50_vd on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
```bash
# Download deployment example code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/tutorials/multi_thread/python
# Download the ResNet50_vd model file and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
tar -xvf ResNet50_vd_infer.tgz
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
# CPU multi-thread inference
python multi_thread_process.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device cpu --topk 1 --thread_num 1
# CPU multi-process inference
python multi_thread_process.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device cpu --topk 1 --use_multi_process True --process_num 1
# GPU multi-thread inference
python multi_thread_process.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device gpu --topk 1 --thread_num 1
# GPU multi-process inference
python multi_thread_process.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device gpu --topk 1 --use_multi_process True --process_num 1
# Use TensorRT multi-thread inference on GPU （Attention: It is somewhat time-consuming for the operation of model serialization when running TensorRT inference for the first time. Please be patient.）
python multi_thread_process.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1 --thread_num 1
# Use TensorRT multi-process inference on GPU （Attention: It is somewhat time-consuming for the operation of model serialization when running TensorRT inference for the first time. Please be patient.）
python multi_thread_process.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1 --use_multi_process True --process_num 1
# IPU multi-thread inference（Attention: It is somewhat time-consuming for the operation of model serialization when running IPU inference for the first time. Please be patient.）
python multi_thread_process.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device ipu --topk 1 --thread_num 1
# IPU multi-process inference（Attention: It is somewhat time-consuming for the operation of model serialization when running IPU inference for the first time. Please be patient.）
python multi_thread_process.py --model ResNet50_vd_infer --image_path ILSVRC2012_val_00000010.jpeg --device ipu --topk 1 --use_multi_process True --process_num 1
```
>> **Notice**: `--image_path` can be the path of the pictures folder
The result returned after running is as follows
```bash
ClassifyResult(
label_ids: 153,
scores: 0.686229,
)
```