Files
FastDeploy/tools
yunyaoXYY 712d7fd71b [Quantization] Improve the usage of FastDeploy tools. (#660)
* 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

* Add backends and params file for YOLOv7

* Add quantized model deployment support for YOLO series

* Fix YOLOv5 quantize readme

* Fix YOLO quantize readme

* Fix YOLO quantize readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Fix bug, change Fronted to ModelFormat

* Change Fronted to ModelFormat

* Add examples to deploy quantized paddleclas models

* Fix readme

* Add quantize Readme

* Add quantize Readme

* Add quantize Readme

* Modify readme of quantization tools

* Modify readme of quantization tools

* Improve quantization tools readme

* Improve quantization readme

* Improve PaddleClas quantized model deployment  readme

* Add PPYOLOE-l quantized deployment examples

* Improve quantization tools readme

* Improve Quantize Readme

* Fix conflicts

* Fix conflicts

* improve readme

* Improve quantization tools and readme

* Improve quantization tools and readme

* Add quantized deployment examples for PaddleSeg model

* Fix cpp readme

* Fix memory leak of reader_wrapper function

* Fix model file name in PaddleClas quantization examples

* Update Runtime and E2E benchmark

* Update Runtime and E2E benchmark

* Rename quantization tools to auto compression tools

* Remove PPYOLOE data when deployed on MKLDNN

* Fix readme

* Support PPYOLOE with OR without NMS and update readme

* Update Readme

* Update configs and readme

* Update configs and readme

* Add Paddle-TensorRT backend in quantized model deploy examples

* Support PPYOLOE+ series

* Add reused_input_tensors for PPYOLOE

* Improve fastdeploy tools usage

* improve fastdeploy tool

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* remove modify

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* Remove extra requirements for fd-auto-compress package

* Imporve fastdeploy-tools package

* Install fastdeploy-tools package when build fastdeploy-python

* Imporve quantization readme
2022-11-23 10:13:50 +08:00
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2022-11-21 13:44:34 +08:00

FastDeploy Toolkit

FastDeploy provides a series of efficient and easy-to-use tools to optimize the deployment experience and improve inference performance.

One-Click Model Auto Compression Tool

Based on PaddleSlim's Auto Compression Toolkit (ACT), FastDeploy provides users with a one-click model automation compression tool that allows users to easily compress the model with a single command. This document will take FastDeploy's one-click model automation compression tool as an example, introduce how to install the tool, and provide the corresponding documentation for usage.

Environmental Preparation

1.Install PaddlePaddle develop version

https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/develop/install/pip/linux-pip.html

2.Install PaddleSlim dev version

git clone https://github.com/PaddlePaddle/PaddleSlim.git & cd PaddleSlim
python setup.py install

3.Install fastdeploy-tools package

# Installing fastdeploy-tools via pip
# This tool is included in the python installer of FastDeploy, so you don't need to install it again.
pip install fastdeploy-tools==0.0.0

The Usage of One-Click Model Auto Compression Tool

After the above steps are successfully installed, you can use FastDeploy one-click model automation compression tool, as shown in the following example.

fastdeploy --auto_compress --config_path=./configs/detection/yolov5s_quant.yaml --method='PTQ' --save_dir='./yolov5s_ptq_model/'

For detailed documentation, please refer to FastDeploy One-Click Model Auto Compression Tool

Model Conversion Tool

Based on X2Paddle, FastDeploy provides users with a model conversion tool. Users can easily migrate external framework models to the Paddle framework with one line of commands. Currently, ONNX, TensorFlow and Caffe are supported, and most mainstream CV and NLP model conversions are supported.

Environmental Preparation

  1. Install PaddlePaddle, refer to the following documents for quick installation
https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/develop/install/pip/linux-pip.html
  1. Install X2Paddle

To use the stable version, install X2Paddle via pip:

pip install x2paddle

To experience the latest features, you can use the source installation method:

git clone https://github.com/PaddlePaddle/X2Paddle.git
cd X2Paddle
python setup.py install

How to use

After successful installation according to the above steps, you can use the FastDeploy one-click conversion tool. The example is as follows:

fastdeploy --convert --framework onnx --model yolov5s.onnx --save_dir pd_model

For more details, please refer toX2Paddle