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huangjianhui 294607fc4a [Serving] PaddleSeg add triton serving && simple serving example (#1171 )

* Update keypointdetection result docs

* Update im.copy() to im in examples

* Update new Api, fastdeploy::vision::Visualize to fastdeploy::vision

* Update SwapBackgroundSegmentation && SwapBackgroundMatting to SwapBackground

* Update README_CN.md

* Update README_CN.md

* Update preprocessor.h

* PaddleSeg supports triton serving

* Add PaddleSeg simple serving example

* Add PaddleSeg triton serving client code

* Update triton serving runtime config.pbtxt

* Update paddleseg grpc client

* Add paddle serving README

2023-01-30 09:34:38 +08:00

client.py

[Serving] PaddleSeg add triton serving && simple serving example (#1171 )

2023-01-30 09:34:38 +08:00

README_CN.md

[Serving] PaddleSeg add triton serving && simple serving example (#1171 )

2023-01-30 09:34:38 +08:00

README.md

[Serving] PaddleSeg add triton serving && simple serving example (#1171 )

2023-01-30 09:34:38 +08:00

server.py

[Serving] PaddleSeg add triton serving && simple serving example (#1171 )

2023-01-30 09:34:38 +08:00

README.md

English | 简体中文

PaddleSegmentation Python Simple Serving Demo

Environment

1. Prepare environment and install FastDeploy Python whl, refer to download_prebuilt_libraries

Server:

# Download demo code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/segmentation/paddleseg/python/serving

# Download PP_LiteSeg model
wget  https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer.tgz
tar -xvf PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer.tgz

# Launch server, change the configurations in server.py to select hardware, backend, etc.
# and use --host, --port to specify IP and port
fastdeploy simple_serving --app server:app

Client:

# Download demo code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/segmentation/paddleseg/python/serving

# Download test image
wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png

# Send request and get inference result (Please adapt the IP and port if necessary)
python client.py