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using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML.OnnxRuntime.Tensors;
using SixLabors.ImageSharp;
using SixLabors.ImageSharp.PixelFormats;
using SixLabors.ImageSharp.Processing;
namespace Microsoft.ML.OnnxRuntime.ResNet50v2Sample
{
class Program
{
public static void Main(string[] args)
{
if (args.Length < 2) {
Console.WriteLine("Usage: prog <model.onnx> <image.jpg>");
return;
}
// Read paths
string modelFilePath = args[0];
string imageFilePath = args[1];
// Read image
using Image<Rgb24> image = Image.Load<Rgb24>(imageFilePath);
// Resize image
image.Mutate(x =>
{
x.Resize(new ResizeOptions
{
Size = new Size(224, 224),
Mode = ResizeMode.Crop
});
});
// Preprocess image
Tensor<float> input = new DenseTensor<float>(new[] { 1, 3, 224, 224 });
var mean = new[] { 0.485f, 0.456f, 0.406f };
var stddev = new[] { 0.229f, 0.224f, 0.225f };
image.ProcessPixelRows(accessor =>
{
for (int y = 0; y < accessor.Height; y++)
{
Span<Rgb24> pixelSpan = accessor.GetRowSpan(y);
for (int x = 0; x < accessor.Width; x++)
{
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>();
float sum = output.Sum(x => (float)Math.Exp(x));
IEnumerable<float> softmax = output.Select(x => (float)Math.Exp(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}");
}
}
}
}
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