onnxruntime/csharp/tools/Microsoft.ML.OnnxRuntime.PerfTool/Program.cs
2018-11-27 19:01:28 -08:00

139 lines
4.9 KiB
C#

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