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YOLOv5量化模型 C++部署示例
本目录下提供的infer.cc,可以帮助用户快速完成YOLOv5s量化模型在CPU/GPU上的部署推理加速.
部署准备
FastDeploy环境准备
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- 软硬件环境满足要求，参考FastDeploy环境要求
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- FastDeploy Python whl包安装，参考FastDeploy Python安装
量化模型准备
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- 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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- 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.
以量化后的YOLOv5s模型为例, 进行部署
在本目录执行如下命令即可完成编译,以及量化模型部署.支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
mkdir build
cd build
# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j
#下载FastDeloy提供的yolov5s量化模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s_quant.tar
tar -xvf yolov5s_quant.tar
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
# 在CPU上使用ONNX Runtime推理量化模型
./infer_demo yolov5s_quant 000000014439.jpg 0
# 在GPU上使用TensorRT推理量化模型
./infer_demo yolov5s_quant 000000014439.jpg 1
# 在GPU上使用Paddle-TensorRT推理量化模型
./infer_demo yolov5s_quant 000000014439.jpg 2