apps/FastDeploy
1
0
Fork 0
mirror of https://github.com/PaddlePaddle/FastDeploy.git synced 2025-12-24 13:28:13 +08:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

Add Quantization Function. (#256)

Browse Source 
* 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
This commit is contained in:
yunyaoXYY
2022-10-08 15:45:28 +08:00
committed by GitHub
parent 8d47637541
commit 1efc0fa6b0
13 changed files with 736 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

120
tools/quantization/readme.md Normal file
View File

@@ -0,0 +1,120 @@
# FastDeploy 一键模型量化
FastDeploy 给用户提供了一键量化功能, 支持离线量化和量化蒸馏训练. 本文档已Yolov5s为例, 用户可参考如何安装并执行FastDeploy的一键量化功能.
## 1.安装
### 环境依赖
1.用户参考PaddlePaddle官网, 安装develop版本
```
https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/develop/install/pip/linux-pip.html
```
2.安装paddleslim-develop版本
```bash
git clone https://github.com/PaddlePaddle/PaddleSlim.git & cd PaddleSlim
python setup.py install
```
### FastDeploy-Quantization 安装方式
用户在当前目录下，运行如下命令:
```
python setup.py install
```
## 2.使用方式
### 一键离线量化示例
#### 离线量化
##### 1. 准备模型和Calibration数据集
用户需要自行准备待量化模型与Calibration数据集.
本例中用户可执行以下命令, 下载待量化的yolov5s.onnx模型和我们为用户准备的Calibration数据集示例.
```shell
# 下载yolov5.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
# 下载数据集, 此Calibration数据集为COCO val2017中的前320张图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/COCO_val_320.tar.gz
tar -xvf COCO_val_320.tar.gz
```
##### 2.使用fastdeploy_quant命令，执行一键模型量化:
```shell
fastdeploy_quant --config_path=./configs/detection/yolov5s_quant.yaml --method='PTQ' --save_dir='./yolov5s_ptq_model/'
```
##### 3.参数说明
| 参数 | 作用 |
| -------------------- | ------------------------------------------------------------ |
| --config_path | 一键量化所需要的量化配置文件.[详解](./fdquant/configs/readme.md) |
| --method | 量化方式选择, 离线量化选PTQ，量化蒸馏训练选QAT |
| --save_dir | 产出的量化后模型路径, 该模型可直接在FastDeploy部署 |
注意：目前fastdeploy_quant暂时只支持YOLOv5,YOLOv6和YOLOv7模型的量化
#### 量化蒸馏训练
##### 1.准备待量化模型和训练数据集
FastDeploy目前的量化蒸馏训练，只支持无标注图片训练，训练过程中不支持评估模型精度.
数据集为真实预测场景下的图片，图片数量依据数据集大小来定，尽量覆盖所有部署场景. 此例中，我们为用户准备了COCO2017验证集中的前320张图片.
```shell
# 下载yolov5.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
# 下载数据集, 此Calibration数据集为COCO2017验证集中的前320张图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/COCO_val_320.tar.gz
tar -xvf COCO_val_320.tar.gz
```
##### 2.使用fastdeploy_quant命令，执行一键模型量化:
```shell
export CUDA_VISIBLE_DEVICES=0
fastdeploy_quant --config_path=./configs/detection/yolov5s_quant.yaml --method='QAT' --save_dir='./yolov5s_qat_model/'
```
##### 3.参数说明
| 参数 | 作用 |
| -------------------- | ------------------------------------------------------------ |
| --config_path | 一键量化所需要的量化配置文件.[详解](./fdquant/configs/readme.md) |
| --method | 量化方式选择, 离线量化选PTQ，量化蒸馏训练选QAT |
| --save_dir | 产出的量化后模型路径, 该模型可直接在FastDeploy部署 |
注意：目前fastdeploy_quant暂时只支持YOLOv5,YOLOv6和YOLOv7模型的量化
## 3. FastDeploy 部署量化模型
用户在获得量化模型之后，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/)
## 4.Benchmark
下表为模型量化前后，在FastDeploy部署的端到端推理性能.
- 测试图片为COCO val2017中的图片.
- 推理时延为端到端推理(包含前后处理)的平均时延, 单位是毫秒.
- CPU为Intel(R) Xeon(R) Gold 6271C, GPU为Tesla T4, TensorRT版本8.4.15, 所有测试中固定CPU线程数为1.
| 模型 |推理后端 |部署硬件 | FP32推理时延 | INT8推理时延 | 加速比 | FP32 mAP | INT8 mAP |
| ------------------- | -----------------|-----------| -------- |-------- |-------- | --------- |-------- |
| YOLOv5s | TensorRT | GPU | 14.13 | 11.22 | 1.26 | 37.6 | 36.6 |
| YOLOv5s | ONNX Runtime | CPU | 183.68 | 100.39 | 1.83 | 37.6 | 33.1 |
| YOLOv5s | Paddle Inference | CPU | 226.36 | 152.27 | 1.48 |37.6 | 36.8 |
| YOLOv6s | TensorRT | GPU | 12.89 | 8.92 | 1.45 | 42.5 | 40.6|
| YOLOv6s | ONNX Runtime | CPU | 345.85 | 131.81 | 2.60 |42.5| 36.1|
| YOLOv6s | Paddle Inference | CPU | 366.41 | 131.70 | 2.78 |42.5| 41.2|
| YOLOv7 | TensorRT | GPU | 30.43 | 15.40 | 1.98 | 51.1| 50.8|
| YOLOv7 | ONNX Runtime | CPU | 971.27 | 471.88 | 2.06 | 51.1 | 42.5|
| YOLOv7 | Paddle Inference | CPU | 1015.70 | 562.41 | 1.82 |51.1 | 46.3|