⏰from https://github.com/ultralytics/ultralytics
examples/YOLOv8-ONNXRuntime-Rust
This repository provides a Rust demo for performing YOLOv8 tasks like Classification
, Segmentation
, Detection
and Pose Detection
using ONNXRuntime.
- Support
Classification
,Segmentation
,Detection
,Pose(Keypoints)-Detection
tasks. - Support
FP16
&FP32
ONNX models. - Support
CPU
,CUDA
andTensorRT
execution provider to accelerate computation. - Support dynamic input shapes(
batch
,width
,height
).
Please follow the Rust official installation. (https://www.rust-lang.org/tools/install)
This repository use ort
crate, which is ONNXRuntime wrapper for Rust. (https://docs.rs/ort/latest/ort/)
You can follow the instruction with ort
doc or simply do this:
- step1: Download ONNXRuntime(https://github.com/microsoft/onnxruntime/releases)
- setp2: Set environment variable
PATH
for linking.
On ubuntu, You can do like this:
vim ~/.bashrc
# Add the path of ONNXRUntime lib
export LD_LIBRARY_PATH=/home/qweasd/Documents/onnxruntime-linux-x64-gpu-1.16.3/lib${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
source ~/.bashrc
- CUDA execution provider requires CUDA v11.6+.
- TensorRT execution provider requires CUDA v11.4+ and TensorRT v8.4+.
pip install -U ultralytics
# export onnx model with dynamic shapes
yolo export model=yolov8m.pt format=onnx simplify dynamic
yolo export model=yolov8m-cls.pt format=onnx simplify dynamic
yolo export model=yolov8m-pose.pt format=onnx simplify dynamic
yolo export model=yolov8m-seg.pt format=onnx simplify dynamic
# export onnx model with constant shapes
yolo export model=yolov8m.pt format=onnx simplify
yolo export model=yolov8m-cls.pt format=onnx simplify
yolo export model=yolov8m-pose.pt format=onnx simplify
yolo export model=yolov8m-seg.pt format=onnx simplify
It will perform inference with the ONNX model on the source image.
cargo run --release -- --model <MODEL> --source <SOURCE>
Set --cuda
to use CUDA execution provider to speed up inference.
cargo run --release -- --cuda --model <MODEL> --source <SOURCE>
Set --trt
to use TensorRT execution provider, and you can set --fp16
at the same time to use TensorRT FP16 engine.
cargo run --release -- --trt --fp16 --model <MODEL> --source <SOURCE>
Set --device_id
to select which device to run. When you have only one GPU, and you set device_id
to 1 will not cause program panic, the ort
would automatically fall back to CPU
EP.
cargo run --release -- --cuda --device_id 0 --model <MODEL> --source <SOURCE>
Set --batch
to do multi-batch-size inference.
If you're using --trt
, you can also set --batch-min
and --batch-max
to explicitly specify min/max/opt batch for dynamic batch input.(https://onnxruntime.ai/docs/execution-providers/TensorRT-ExecutionProvider.html#explicit-shape-range-for-dynamic-shape-input).(Note that the ONNX model should exported with dynamic shapes)
cargo run --release -- --cuda --batch 2 --model <MODEL> --source <SOURCE>
Set --height
and --width
to do dynamic image size inference. (Note that the ONNX model should exported with dynamic shapes)
cargo run --release -- --cuda --width 480 --height 640 --model <MODEL> --source <SOURCE>
Set --profile
to check time consumed in each stage.(Note that the model usually needs to take 1~3 times dry run to warmup. Make sure to run enough times to evaluate the result.)
cargo run --release -- --trt --fp16 --profile --model <MODEL> --source <SOURCE>
Results: (yolov8m.onnx, batch=1, 3 times, trt, fp16, RTX 3060Ti)
==> 0
[Model Preprocess]: 12.75788ms
[ORT H2D]: 237.118µs
[ORT Inference]: 507.895469ms
[ORT D2H]: 191.655µs
[Model Inference]: 508.34589ms
[Model Postprocess]: 1.061122ms
==> 1
[Model Preprocess]: 13.658655ms
[ORT H2D]: 209.975µs
[ORT Inference]: 5.12372ms
[ORT D2H]: 182.389µs
[Model Inference]: 5.530022ms
[Model Postprocess]: 1.04851ms
==> 2
[Model Preprocess]: 12.475332ms
[ORT H2D]: 246.127µs
[ORT Inference]: 5.048432ms
[ORT D2H]: 187.117µs
[Model Inference]: 5.493119ms
[Model Postprocess]: 1.040906ms
And also:
--conf
: confidence threshold [default: 0.3]
--iou
: iou threshold in NMS [default: 0.45]
--kconf
: confidence threshold of keypoint [default: 0.55]
--plot
: plot inference result with random RGB color and save
you can check out all CLI arguments by:
git clone https://github.com/ultralytics/ultralytics
cd ultralytics/examples/YOLOv8-ONNXRuntime-Rust
cargo run --release -- --help
Running dynamic shape ONNX model on CPU
with image size --height 224 --width 224
. Saving plotted image in runs
directory.
cargo run --release -- --model ../assets/weights/yolov8m-cls-dyn.onnx --source ../assets/images/dog.jpg --height 224 --width 224 --plot --profile
You will see result like:
Summary:
> Task: Classify (Ultralytics 8.0.217)
> EP: Cpu
> Dtype: Float32
> Batch: 1 (Dynamic), Height: 224 (Dynamic), Width: 224 (Dynamic)
> nc: 1000 nk: 0, nm: 0, conf: 0.3, kconf: 0.55, iou: 0.45
[Model Preprocess]: 16.363477ms
[ORT H2D]: 50.722µs
[ORT Inference]: 16.295808ms
[ORT D2H]: 8.37µs
[Model Inference]: 16.367046ms
[Model Postprocess]: 3.527µs
[
YOLOResult {
Probs(top5): Some([(208, 0.6950566), (209, 0.13823675), (178, 0.04849795), (215, 0.019029364), (212, 0.016506357)]),
Bboxes: None,
Keypoints: None,
Masks: None,
},
]
Using CUDA
EP and dynamic image size --height 640 --width 480
cargo run --release -- --cuda --model ../assets/weights/yolov8m-dynamic.onnx --source ../assets/images/bus.jpg --plot --height 640 --width 480
using TensorRT
EP
cargo run --release -- --trt --model ../assets/weights/yolov8m-pose.onnx --source ../assets/images/bus.jpg --plot
using TensorRT
EP and FP16 model --fp16
cargo run --release -- --trt --fp16 --model ../assets/weights/yolov8m-seg.onnx --source ../assets/images/0172.jpg --plot
cargo run --release -- --trt --fp16 --model models/yolov8m.onnx --source assets/bus.jpg --plot