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* 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
37 lines
1.2 KiB
Markdown
37 lines
1.2 KiB
Markdown
English | [简体中文](README_CN.md)
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# PaddleSegmentation Python Simple Serving Demo
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## Environment
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- 1. Prepare environment and install FastDeploy Python whl, refer to [download_prebuilt_libraries](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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Server:
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```bash
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# Download demo code
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/segmentation/paddleseg/python/serving
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# Download PP_LiteSeg model
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer.tgz
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tar -xvf PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer.tgz
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# Launch server, change the configurations in server.py to select hardware, backend, etc.
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# and use --host, --port to specify IP and port
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fastdeploy simple_serving --app server:app
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```
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Client:
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```bash
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# Download demo code
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/segmentation/paddleseg/python/serving
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# Download test image
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wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
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# Send request and get inference result (Please adapt the IP and port if necessary)
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python client.py
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```
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