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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> * [Hackathon4 No.184] Add PaddleDetection Models Deployment Rust Examples Signed-off-by: wanziyu <ziyuwan@zju.edu.cn> * Add main and cargo files in examples 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>
54 lines
2.6 KiB
Markdown
54 lines
2.6 KiB
Markdown
English | [简体中文](README_CN.md)
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# PaddleDetection Rust Deployment Example
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This directory provides examples that `main.rs` and`build.rs` use `bindgen` to call FastDeploy C API and fast finish the deployment of PaddleDetection model YOLOv5 on CPU/GPU.
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Before deployment, three steps require confirmation
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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- 3. Download Rustup and install [Rust](https://www.rust-lang.org/tools/install)
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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.
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### Use Rust and bindgen to deploy YOLOv5 model
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Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above.
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```bash
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wget https://fastdeploy.bj.bcebos.com/dev/cpp/fastdeploy-linux-x64-0.0.0.tgz
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tar xvf fastdeploy-linux-x64-0.0.0.tgz
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```
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Download the YOLOv5 ONNX model file and test images
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```bash
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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```
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In `build.rs`, configure the`cargo:rustc-link-search`to FastDeploy dynamic library path. The FastDeploy dynamic library is located in the `/lib` directory. Configure the`cargo:rustc-link-lib` to FastDeploy dynamic library`fastdeploy` and the`headers_dir`to FastDeploy C API directory path.
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```bash
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println!("cargo:rustc-link-search=./fastdeploy-linux-x64-0.0.0/lib");
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println!("cargo:rustc-link-lib=fastdeploy");
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let headers_dir = PathBuf::from("./fastdeploy-linux-x64-0.0.0/include");
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```
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Use the following command to add Fastdeploy library path to the environment variable.
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```bash
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source /Path/to/fastdeploy-linux-x64-0.0.0/fastdeploy_init.sh
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```
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Use `Cargo` tool to compile the Rust project.
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```bash
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cargo build
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```
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After compiling, use the following command to obtain the predicted results.
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```bash
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# CPU inference
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cargo run -- --model yolov5s.onnx --image 000000014439.jpg --device 0
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# GPU inference
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cargo run -- --model yolov5s.onnx --image 000000014439.jpg --device 1
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```
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Then visualized inspection result is saved in the local image `vis_result_yolov5.jpg`.
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