Files
FastDeploy/tools
yunyaoXYY 8dec2115d5 [Quantization] Remove extra requirements for fd-auto-compress package (#582)
* 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
2022-11-14 16:39:31 +08:00
..

FastDeploy Toolkit

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

FastDeploy One-Click Model Auto Compression Tool

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 fd-auto-compress package

# Installing fd-auto-compress via pip
# This tool is included in the python installer of FastDeploy, so you don't need to install it again.
pip install fd-auto-compress==0.0.1

# Execute in the current directory
python setup.py install

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