ONNX Runtime Inference
Introduction
ONNX Runtime C++ inference example for image classification using CPU and CUDA.
Dependencies
- CMake 3.20.1
- ONNX Runtime 1.12.0
- OpenCV 4.5.2
Usages
Build Docker Image
$ docker build -f docker/onnxruntime-cuda.Dockerfile --no-cache --tag=onnxruntime-cuda:1.12.0 .Run Docker Container
$ docker run -it --rm --gpus device=0 -v $(pwd):/mnt onnxruntime-cuda:1.12.0Build Example
$ cmake -B build
$ cmake --build build --config Release --parallelRun Example
$ cd build/src/
$ ./inference --use_cpu
Inference Execution Provider: CPU
Number of Input Nodes: 1
Number of Output Nodes: 1
Input Name: data
Input Type: float
Input Dimensions: [1, 3, 224, 224]
Output Name: squeezenet0_flatten0_reshape0
Output Type: float
Output Dimensions: [1, 1000]
Predicted Label ID: 92
Predicted Label: n01828970 bee eater
Uncalibrated Confidence: 0.996137
Minimum Inference Latency: 7.45 ms$ cd build/src/
$ ./inference --use_cuda
Inference Execution Provider: CUDA
Number of Input Nodes: 1
Number of Output Nodes: 1
Input Name: data
Input Type: float
Input Dimensions: [1, 3, 224, 224]
Output Name: squeezenet0_flatten0_reshape0
Output Type: float
Output Dimensions: [1, 1000]
Predicted Label ID: 92
Predicted Label: n01828970 bee eater
Uncalibrated Confidence: 0.996137
Minimum Inference Latency: 0.98 ms