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yolotriton

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Go (Golang) gRPC client for YOLO-NAS, YOLOv8 inference using the Triton Inference Server.

Installation

Use go get to install this package:

go get github.com/dev6699/yolotriton

Get YOLO-NAS, YOLOv8 TensorRT model

Replace yolov8m.pt with your desired model

pip install ultralytics
yolo export model=yolov8m.pt format=onnx
trtexec --onnx=yolov8m.onnx --saveEngine=model_repository/yolov8/1/model.plan

References:

  1. https://docs.nvidia.com/deeplearning/tensorrt/quick-start-guide/index.html
  2. https://docs.ultralytics.com/modes/export/
  3. https://github.com/NVIDIA/TensorRT/tree/master/samples/trtexec

Export of quantized YOLO-NAS INT8 model

  1. Export quantized onnx model
from super_gradients.conversion.conversion_enums import ExportQuantizationMode
from super_gradients.conversion import DetectionOutputFormatMode
from super_gradients.common.object_names import Models
from super_gradients.training import models

# From custom model
# model = models.get(Models.YOLO_NAS_S, num_classes=1, checkpoint_path='ckpt_best.pth')
model = models.get(Models.YOLO_NAS_S, pretrained_weights="coco")
export_result = model.export(
    "yolo_nas_s_int8.onnx",
    output_predictions_format=DetectionOutputFormatMode.BATCH_FORMAT,
    quantization_mode=ExportQuantizationMode.INT8 # or ExportQuantizationMode.FP16
)

print(export_result)
  1. Convert to TensorRT with INT8 builder
trtexec --onnx=yolo_nas_s_int8.onnx --saveEngine=yolo_nas_s_int8.plan --int8

References:

  1. https://github.com/Deci-AI/super-gradients/blob/b5eb12ccd021ca77e947bf2dde7e84a75489e7ed/documentation/source/models_export.md

Start trinton inference server

docker compose up tritonserver

References:

  1. https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/model_repository.html

Sample usage

Check cmd/main.go for more details.

  • For help
go run cmd/main.go --help
  -b    Run benchmark.
  -i string
        Inference Image. (default "images/1.jpg")
  -m string
        Name of model being served (Required) (default "yolonas")
  -n int
        Number of benchmark run. (default 1)
  -o float
        Intersection over Union (IoU) (default 0.7)
  -p float
        Minimum probability (default 0.5)
  -t string
        Type of model. Available options: [yolonas, yolonasint8, yolov8] (default "yolonas")
  -u string
        Inference Server URL. (default "tritonserver:8001")
  -x string
        Version of model. Default: Latest Version
  • Sample usage with yolonasint8 model
go run cmd/main.go -m yolonasint8 -t yolonasint8 -i images/1.jpg         
1. processing time: 123.027909ms
prediction:  0
class:  dog
confidence: 0.96
bboxes: [ 669 130 1061 563 ]
---------------------
prediction:  1
class:  person
confidence: 0.96
bboxes: [ 440 30 760 541 ]
---------------------
prediction:  2
class:  dog
confidence: 0.93
bboxes: [ 168 83 495 592 ]
---------------------
  • Sample usage to get benchmark results
go run cmd/main.go -m yolonasint8 -t yolonasint8 -i images/1.jpg  -b -n 10
1. processing time: 64.253978ms
2. processing time: 51.812457ms
3. processing time: 80.037468ms
4. processing time: 96.73738ms
5. processing time: 87.22928ms
6. processing time: 95.28627ms
7. processing time: 61.609115ms
8. processing time: 87.625844ms
9. processing time: 70.356198ms
10. processing time: 74.130759ms
Avg processing time: 76.93539ms

Results

Input Ouput