c# 部署onnx模型
作者:互联网
1.环境
gpu3060+cuda11.1+vs2019
+Microsoft.ML.OnnxRuntime
+SixLabors.ImageSharp
2.代码
using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML.OnnxRuntime.Tensors; // DenseTensor
using SixLabors.ImageSharp; // Image, Size
using SixLabors.ImageSharp.PixelFormats; // Rgb24
using SixLabors.ImageSharp.Processing; // image.Mutate
namespace Microsoft.ML.OnnxRuntime.ResNet50v2Sample
{
class Program
{
public static void Main(string[] args)
{
// Read paths
string modelFilePath = @"E:\code\Csharp\onnxruntime-master\csharp\sample\Microsoft.ML.OnnxRuntime.ResNet50v2Sample\resnet50-v2-7.onnx";
string imageFilePath = @"E:\code\Csharp\onnxruntime-master\csharp\sample\Microsoft.ML.OnnxRuntime.ResNet50v2Sample\dog.jpeg";
// Read image
// Rgb24:Pixel type containing three 8-bit unsigned normalized values ranging from 0 to
// 255. The color components are stored in red, green, blue order
// SixLabors.ImageSharp.Image
using Image<Rgb24> image = Image.Load<Rgb24>(imageFilePath); // 以rgb形式读取图片
// Resize image
image.Mutate(x =>
{
x.Resize(new ResizeOptions
{
Size = new Size(224, 224),
Mode = ResizeMode.Crop
});
});
//image.Mutate(x =>
// x.Resize(224, 224)
//);
// Preprocess image
Tensor<float> input = new DenseTensor<float>(new[] { 1, 3, 224, 224 }); // 声明4维变量:(b, c, h, w)
var mean = new[] { 0.485f, 0.456f, 0.406f };
var stddev = new[] { 0.229f, 0.224f, 0.225f };
for (int y = 0; y < image.Height; y++)
{
Span<Rgb24> pixelSpan = image.GetPixelRowSpan(y);
for (int x = 0; x < image.Width; x++) // 先行后列
{
input[0, 0, y, x] = ((pixelSpan[x].R / 255f) - mean[0]) / stddev[0];
input[0, 1, y, x] = ((pixelSpan[x].G / 255f) - mean[1]) / stddev[1];
input[0, 2, y, x] = ((pixelSpan[x].B / 255f) - mean[2]) / stddev[2];
}
}
// Setup inputs
var inputs = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor("data", input)
};
// Run inference
using var session = new InferenceSession(modelFilePath);
using IDisposableReadOnlyCollection<DisposableNamedOnnxValue> results = session.Run(inputs);
// Postprocess to get softmax vector
IEnumerable<float> output = results.First().AsEnumerable<float>(); // First(): The first element in the specified sequence. AsEnumerable:
float sum = output.Sum(x => (float)Math.Exp(x)); // sum(e^x)
IEnumerable<float> softmax = output.Select(x => (float)Math.Exp(x) / sum); // e^x / sum
// Extract top 10 predicted classes
IEnumerable<Prediction> top10 = softmax.Select((x, i) => new Prediction { Label = LabelMap.Labels[i], Confidence = x })
.OrderByDescending(x => x.Confidence)
.Take(10);
// Print results to console
Console.WriteLine("Top 10 predictions for ResNet50 v2...");
Console.WriteLine("--------------------------------------------------------------");
foreach (var t in top10)
{
Console.WriteLine($"Label: {t.Label}, Confidence: {t.Confidence}");
}
}
}
}
3.效果
输入图片:
网络输出:
第一个是金毛猎犬。
标签:input,c#,onnx,模型,OnnxRuntime,using,new,224,image 来源: https://blog.csdn.net/jizhidexiaoming/article/details/121036458