mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-16 21:00:14 +00:00
139 lines
4.9 KiB
C#
139 lines
4.9 KiB
C#
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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using System;
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using System.Collections.Generic;
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using System.Text;
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using System.IO;
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using Microsoft.ML.OnnxRuntime;
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using System.Numerics.Tensors;
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using System.Diagnostics;
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namespace Microsoft.ML.OnnxRuntime.PerfTool
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{
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public enum TimingPoint
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{
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Start = 0,
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ModelLoaded = 1,
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InputLoaded = 2,
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RunComplete = 3,
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TotalCount = 4
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}
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class Program
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{
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public static void Main(string[] args)
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{
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/*
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args[0] = model-file-name
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args[1] = input-file-name
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args[2] = iteration count
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*/
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if (args.Length < 3)
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{
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PrintUsage();
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Environment.Exit(1);
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}
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string modelPath = args[0];
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string inputPath = args[1];
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int iteration = Int32.Parse(args[2]);
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Console.WriteLine("Running model {0} in OnnxRuntime with input {1} for {2} times", modelPath, inputPath, iteration);
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DateTime[] timestamps = new DateTime[(int)TimingPoint.TotalCount];
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RunModelOnnxRuntime(modelPath, inputPath, iteration, timestamps);
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PrintReport(timestamps, iteration);
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Console.WriteLine("Done");
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Console.WriteLine("Running model {0} in Sonoma with input {1} for {2} times", modelPath, inputPath, iteration);
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RunModelOnnxRuntime(modelPath, inputPath, iteration, timestamps);
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PrintReport(timestamps, iteration);
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Console.WriteLine("Done");
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}
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public static float[] LoadTensorFromFile(string filename)
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{
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var tensorData = new List<float>();
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// read data from file
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using (var inputFile = new System.IO.StreamReader(filename))
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{
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inputFile.ReadLine(); //skip the input name
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string[] dataStr = inputFile.ReadLine().Split(new char[] { ',', '[', ']' }, StringSplitOptions.RemoveEmptyEntries);
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for (int i = 0; i < dataStr.Length; i++)
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{
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tensorData.Add(Single.Parse(dataStr[i]));
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}
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}
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return tensorData.ToArray();
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}
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static void RunModelOnnxRuntime(string modelPath, string inputPath, int iteration, DateTime[] timestamps)
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{
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if (timestamps.Length != (int)TimingPoint.TotalCount)
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{
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throw new ArgumentException("Timestamps array must have "+(int)TimingPoint.TotalCount+" size");
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}
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timestamps[(int)TimingPoint.Start] = DateTime.Now;
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using (var session = new InferenceSession(modelPath))
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{
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timestamps[(int)TimingPoint.ModelLoaded] = DateTime.Now;
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var inputMeta = session.InputMetadata;
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var container = new List<NamedOnnxValue>();
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foreach (var name in inputMeta.Keys)
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{
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float[] rawData = LoadTensorFromFile(inputPath);
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var tensor = new DenseTensor<float>(rawData, inputMeta[name].Dimensions);
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container.Add(NamedOnnxValue.CreateFromTensor<float>(name, tensor));
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}
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timestamps[(int)TimingPoint.InputLoaded] = DateTime.Now;
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// Run the inference
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for (int i=0; i < iteration; i++)
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{
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var results = session.Run(container); // results is an IReadOnlyList<NamedOnnxValue> container
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Debug.Assert(results != null);
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Debug.Assert(results.Count == 1);
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//results = null;
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//GC.Collect();
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//GC.WaitForPendingFinalizers();
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}
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timestamps[(int)TimingPoint.RunComplete] = DateTime.Now;
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}
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}
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static void PrintUsage()
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{
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Console.WriteLine("Usage:\n"
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+"dotnet Microsoft.ML.OnnxRuntime.PerfTool <onnx-model-path> <input-file-path> <iteration-count>"
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);
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}
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static void PrintReport(DateTime[] timestamps, int iterations)
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{
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Console.WriteLine("Model Load Time = " + (timestamps[(int)TimingPoint.ModelLoaded] - timestamps[(int)TimingPoint.Start]).TotalMilliseconds);
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Console.WriteLine("Input Load Time = " + (timestamps[(int)TimingPoint.InputLoaded] - timestamps[(int)TimingPoint.ModelLoaded]).TotalMilliseconds);
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double totalRuntime = (timestamps[(int)TimingPoint.RunComplete] - timestamps[(int)TimingPoint.InputLoaded]).TotalMilliseconds;
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double perIterationTime = totalRuntime / iterations;
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Console.WriteLine("Total Run time for {0} iterations = {1}", iterations, totalRuntime);
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Console.WriteLine("Per iteration time = {0}", perIterationTime);
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}
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}
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}
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