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33 lines
1.9 KiB
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33 lines
1.9 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# YOLOv5s量化模型 Python部署示例
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`infer.py` in this directory can help you quickly complete the inference acceleration of YOLOv5s quantization model deployment on CPU/GPU.
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## Deployment Preparations
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### FastDeploy Environment Preparations
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- 1. For the software and hardware requirements, please refer to [FastDeploy Environment Requirements](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).
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- 2. For the installation of FastDeploy Python whl package, please refer to [FastDeploy Python Installation](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).
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### Quantized Model Preparations
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- 1. You can directly use the quantized model provided by FastDeploy for deployment..
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- 2. You can use [one-click automatical compression tool](../../../../../../tools/common_tools/auto_compression/) provided by FastDeploy to quantize model by yourself, and use the generated quantized model for deployment.
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## Take the Quantized YOLOv5s Model as an example for Deployment
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```bash
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# Download sample deployment code.
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/vision/detection/yolov5/quantize/python
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# Download the yolov5s quantized model and test images provided by FastDeloy.
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s_quant.tar
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tar -xvf yolov5s_quant.tar
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# Use ONNX Runtime inference quantization model on CPU.
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python infer.py --model yolov5s_quant --image 000000014439.jpg --device cpu --backend ort
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# Use TensorRT inference quantization model on GPU.
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python infer.py --model yolov5s_quant --image 000000014439.jpg --device gpu --backend trt
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# Use Paddle-TensorRT inference quantization model on GPU.
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python infer.py --model yolov5s_quant --image 000000014439.jpg --device gpu --backend pptrt
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```
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