c# 部署onnx模型
2021/10/29 17:11:36
本文主要是介绍c# 部署onnx模型,对大家解决编程问题具有一定的参考价值,需要的程序猿们随着小编来一起学习吧!
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。
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