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FastDeploy/examples/vision/segmentation/paddleseg/quantize/python
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yeliang2258 104d965b38 [Backend] Add YOLOv5、PPYOLOE and PP-Liteseg for RV1126 (#647) 
* add yolov5 and ppyoloe for rk1126

* update code, rename rk1126 to rv1126

* add PP-Liteseg

* update lite lib

* updade doc for PPYOLOE

* update doc

* fix docs

* fix doc and examples

* update code

* uodate doc

* update doc

Co-authored-by: Jason <jiangjiajun@baidu.com>
2022-12-05 16:48:00 +08:00
..
infer.py
Rename PaddleBackend to PaddleInferBackend (#728)
2022-11-28 21:29:09 +08:00
README.md
[Backend] Add YOLOv5、PPYOLOE and PP-Liteseg for RV1126 (#647)
2022-12-05 16:48:00 +08:00

README.md

PaddleSeg 量化模型 Python部署示例

本目录下提供的infer.py,可以帮助用户快速完成PaddleSeg量化模型在CPU/GPU上的部署推理加速.

部署准备

FastDeploy环境准备

量化模型准备

    1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
    1. 用户可以使用FastDeploy提供的一键模型自动化压缩工具,自行进行模型量化, 并使用产出的量化模型进行部署.(注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的deploy.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可.)

以量化后的PP_LiteSeg_T_STDC1_cityscapes模型为例, 进行部署

#下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/segmentation/paddleseg/quantize/python

#下载FastDeloy提供的PP_LiteSeg_T_STDC1_cityscapes量化模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_T_STDC1_cityscapes_without_argmax_infer_PTQ.tar
tar -xvf PP_LiteSeg_T_STDC1_cityscapes_without_argmax_infer_PTQ.tar
wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png

# 在CPU上使用Paddle-Inference推理量化模型
python infer.py --model PP_LiteSeg_T_STDC1_cityscapes_without_argmax_infer_QAT --image cityscapes_demo.png --device cpu --backend paddle