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* [PaddlePaddle Hackathon4 No.186] Add PaddleDetection Models Deployment Go Examples Signed-off-by: wanziyu <ziyuwan@zju.edu.cn> * Fix YOLOv8 Deployment Go Example Signed-off-by: wanziyu <ziyuwan@zju.edu.cn> --------- Signed-off-by: wanziyu <ziyuwan@zju.edu.cn> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
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YOLOv5 Golang Deployment Example
This directory provides examples that infer.go uses CGO to call FastDeploy C API and finish the deployment of YOLOv5 model on CPU/GPU.
Before deployment, two steps require confirmation
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- Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
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- Download the precompiled deployment library and samples code according to your development environment. Refer to FastDeploy Precompiled Library
Taking inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 1.0.4 above (x.x.x>1.0.4) or develop version (x.x.x=0.0.0) is required to support this model.
Use Golang and CGO to deploy YOLOv5 model
Download the FastDeploy precompiled library. Users can choose your appropriate version in the FastDeploy Precompiled Library mentioned above.
wget https://fastdeploy.bj.bcebos.com/dev/cpp/fastdeploy-linux-x64-0.0.0.tgz
tar xvf fastdeploy-linux-x64-0.0.0.tgz
Copy FastDeploy C APIs from precompiled library to the current directory.
cp -r fastdeploy-linux-x64-0.0.0/include/fastdeploy_capi .
Download the YOLOv5 ONNX model file and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
Configure the cgo CFLAGS: -I to FastDeploy C API directory path and the cgo LDFLAGS: -L to FastDeploy dynamic library path. The FastDeploy dynamic library is located in the /lib directory.
cgo CFLAGS: -I./fastdeploy_capi
cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
Use the following command to add Fastdeploy library path to the environment variable.
source /Path/to/fastdeploy-linux-x64-0.0.0/fastdeploy_init.sh
Compile the Go file infer.go.
go build infer.go
After compiling, use the following command to obtain the predicted results.
# CPU inference
./infer -model yolov5s.onnx -image 000000014439.jpg -device 0
# GPU inference
./infer -model yolov5s.onnx -image 000000014439.jpg -device 1
Then visualized inspection result is saved in the local image vis_result.jpg.