Add Quantization Function. (#256)

* Add PaddleOCR Support

* Add PaddleOCR Support

* Add PaddleOCRv3 Support

* Add PaddleOCRv3 Support

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Add PaddleOCRv3 Support

* Add PaddleOCRv3 Supports

* Add PaddleOCRv3 Suport

* Fix Rec diff

* Remove useless functions

* Remove useless comments

* Add PaddleOCRv2 Support

* Add PaddleOCRv3 & PaddleOCRv2 Support

* remove useless parameters

* Add utils of sorting det boxes

* Fix code naming convention

* Fix code naming convention

* Fix code naming convention

* Fix bug in the Classify process

* Imporve OCR Readme

* Fix diff in Cls model

* Update Model Download Link in Readme

* Fix diff in PPOCRv2

* Improve OCR readme

* Imporve OCR readme

* Improve OCR readme

* Improve OCR readme

* Imporve OCR readme

* Improve OCR readme

* Fix conflict

* Add readme for OCRResult

* Improve OCR readme

* Add OCRResult readme

* Improve OCR readme

* Improve OCR readme

* Add Model Quantization Demo

* Fix Model Quantization Readme

* Fix Model Quantization Readme

* Add the function to do PTQ quantization

* Improve quant tools readme

* Improve quant tool readme

* Improve quant tool readme

* Add PaddleInference-GPU for OCR Rec model

* Add QAT method to fastdeploy-quantization tool

* Remove examples/slim for now

* Move configs folder

* Add Quantization Support for Classification Model

* Imporve ways of importing preprocess

* Upload YOLO Benchmark on readme

* Upload YOLO Benchmark on readme

* Upload YOLO Benchmark on readme

* Improve Quantization configs and readme

* Add support for multi-inputs model

docs/model_compression/quant.md

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+# FastDeploy 支持量化模型部署
+量化是一种流行的模型压缩方法，量化后的模型拥有更小的体积和更快的推理速度. FastDeploy支持部署量化后的模型，帮助用户实现推理加速.
+
+## 1. FastDeploy 多个引擎支持量化模型部署
+
+当前，FastDeploy中多个推理后端可以在不同硬件上支持量化模型的部署. 支持情况如下:
+
+| 硬件/推理后端 | ONNXRuntime | Paddle Inference | TensorRT |
+| :-----------| :--------   | :--------------- | :------- |
+|   CPU       |  支持        |  支持            |          |  
+|   GPU       |             |                  | 支持      |
+
+
+## 2. 用户如何量化模型
+
+### 量化方式
+用户可以通过PaddleSlim来量化模型, 量化主要有量化训练和离线量化两种方式, 量化训练通过模型训练来获得量化模型, 离线量化不需要模型训练即可完成模型的量化. FastDeploy 对两种方式产出的量化模型均能部署.
+两种方法的主要对比如下表所示:
+| 量化方法 | 量化过程耗时 | 量化模型精度 | 模型体积 | 推理速度 |
+| :-----------| :--------| :-------| :------- | :------- |
+|   离线量化      |  无需训练，耗时短 |  比量化训练稍低       | 两者一致   | 两者一致   |  
+|   量化训练      |  需要训练，耗时高 |  较未量化模型有少量损失 | 两者一致   |两者一致   |  
+
+### 用户使用fastdeploy_quant命令量化模型
+Fastdeploy 为用户提供了一键模型量化的功能，请参考如下文档进行模型量化.
+- [FastDeploy 一键模型量化](../../tools/quantization/)
+当用户获得产出的量化模型之后，即可以使用FastDeploy来部署量化模型.
+
+## 3. FastDeploy 部署量化模型
+用户只需要简单地传入量化后的模型路径及相应参数，即可以使用FastDeploy进行部署.
+具体请用户参考示例文档:
+- [YOLOv5s 量化模型Python部署](../../examples/slim/yolov5s/python/)
+- [YOLOv5s 量化模型C++部署](../../examples/slim/yolov5s/cpp/)
+- [YOLOv6s 量化模型Python部署](../../examples/slim/yolov6s/python/)
+- [YOLOv6s 量化模型C++部署](../../examples/slim/yolov6s/cpp/)
+- [YOLOv7 量化模型Python部署](../../examples/slim/yolov7/python/)
+- [YOLOv7 量化模型C++部署](../../examples/slim/yolov7/cpp/)