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32 lines
1.6 KiB
Markdown
32 lines
1.6 KiB
Markdown
# YOLOv5s量化模型 Python部署示例
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本目录下提供的`infer.py`,可以帮助用户快速完成YOLOv5量化模型在CPU/GPU上的部署推理加速.
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## 部署准备
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### FastDeploy环境准备
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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### 量化模型准备
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- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
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- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
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## 以量化后的YOLOv5s模型为例, 进行部署
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```bash
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#下载部署示例代码
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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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#下载FastDeloy提供的yolov5s量化模型文件和测试图片
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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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# 在CPU上使用Paddle-Inference推理量化模型
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python infer.py --model yolov5s_quant --image 000000014439.jpg --device cpu --backend paddle
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# 在GPU上使用TensorRT推理量化模型
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python infer.py --model yolov5s_quant --image 000000014439.jpg --device gpu --backend trt
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# 在GPU上使用Paddle-TensorRT推理量化模型
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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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