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Numfor Mbiziwo-Tiapo 2021-12-21 11:27:38 -08:00
Родитель 6cc69e5f4b
Коммит d033dc470c
12 изменённых файлов: 355 добавлений и 242 удалений
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3
Samples/WinMLSamplesGallery/WinMLSamplesGallery/Pages/SampleBasePage.xaml.cs
Просмотреть файл

@ -37,6 +37,9 @@ namespace WinMLSamplesGallery
case "EncryptedModel":
SampleFrame.Navigate(typeof(Samples.EncryptedModel));
break;
case "AdapterSelection":
SampleFrame.Navigate(typeof(Samples.AdapterSelection));
break;
}
if (sampleMetadata.Docs.Count > 0)
DocsHeader.Visibility = Visibility.Visible;

11
Samples/WinMLSamplesGallery/WinMLSamplesGallery/SampleMetadata/SampleMetadata.json
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@ -71,6 +71,17 @@
"CSharpGithubLink": "https://github.com/microsoft/Windows-Machine-Learning/blob/master/Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/EncryptedModel/EncryptedModel.xaml.cs",
"Docs": [],
"IsRecentlyAdded": true
},
{
"Title": "Adapter Selection",
"DescriptionShort": "Select different adapters based on your power and performance needs.",
"Description": "The sample showcases how to use Windows ML with different adapters that have tradeoffs between power and performance.",
"Icon": "\uE155",
"Tag": "AdapterSelection",
"XAMLGithubLink": "https://github.com/microsoft/Windows-Machine-Learning/blob/master/Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/EncryptedModel/EncryptedModel.xaml",
"CSharpGithubLink": "https://github.com/microsoft/Windows-Machine-Learning/blob/master/Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/EncryptedModel/EncryptedModel.xaml.cs",
"Docs": [],
"IsRecentlyAdded": true
}
]
}

22
Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/AdapterSelection/AdapterSelection.xaml Normal file
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@ -0,0 +1,22 @@
<Page
x:Class="WinMLSamplesGallery.Samples.AdapterSelection"
xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"
xmlns:d="http://schemas.microsoft.com/expression/blend/2008"
xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"
xmlns:mc="http://schemas.openxmlformats.org/markup-compatibility/2006"
xmlns:local_controls="using:WinMLSamplesGallery.Controls"
xmlns:local_samples="using:WinMLSamplesGallery.Samples"
mc:Ignorable="d"
FontFamily="Arial">
<StackPanel>
<ComboBox x:Name="DeviceBox" SelectedIndex="0">
<x:String>CPU</x:String>
<x:String>DirectX</x:String>
<x:String>DirectXHighPerformance</x:String>
<x:String>DirectXMinPower</x:String>
<x:String>Intel Iris Plus Graphics</x:String>
<x:String>NVIDIA GeForce GTX 1650</x:String>
</ComboBox>
</StackPanel>
</Page>

23
Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/AdapterSelection/AdapterSelection.xaml.cs Normal file
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@ -0,0 +1,23 @@
using Microsoft.AI.MachineLearning;
using Microsoft.UI.Xaml;
using Microsoft.UI.Xaml.Controls;
using System;
using System.Collections.Generic;
using System.IO;
using System.Threading.Tasks;
using Windows.Graphics.Imaging;
using Windows.Media;
using Windows.Storage;
namespace WinMLSamplesGallery.Samples
{
public sealed partial class AdapterSelection : Page
{
public AdapterSelection()
{
this.InitializeComponent();
System.Diagnostics.Debug.WriteLine("Initialized Adapter Selection");
}
}
}

484
Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/Batching/Batching.xaml.cs
Просмотреть файл

@ -1,263 +1,263 @@
using Microsoft.AI.MachineLearning;
using Microsoft.UI.Xaml;
using Microsoft.UI.Xaml.Controls;
using System;
using System.Collections.Generic;
using Microsoft.AI.MachineLearning;
using Microsoft.UI.Xaml;
using Microsoft.UI.Xaml.Controls;
using System;
using System.Collections.Generic;
using System.IO;
using System.Threading.Tasks;
using Windows.Graphics.Imaging;
using Windows.Media;
using Windows.Storage;
namespace WinMLSamplesGallery.Samples
{
public sealed class EvalResult
{
public string nonBatchedAvgTime { get; set; }
public string batchedAvgTime { get; set; }
public string timeRatio { get; set; }
}
public sealed partial class Batching : Page
{
const int NumInputImages = 50;
const int NumEvalIterations = 100;
private LearningModel _model = null;
private LearningModelSession _nonBatchingSession = null;
using System.Threading.Tasks;
using Windows.Graphics.Imaging;
using Windows.Media;
using Windows.Storage;
namespace WinMLSamplesGallery.Samples
{
public sealed class EvalResult
{
public string nonBatchedAvgTime { get; set; }
public string batchedAvgTime { get; set; }
public string timeRatio { get; set; }
}
public sealed partial class Batching : Page
{
const int NumInputImages = 50;
const int NumEvalIterations = 100;
private LearningModel _model = null;
private LearningModelSession _nonBatchingSession = null;
private LearningModelSession _batchingSession = null;
float _avgNonBatchedDuration = 0;
float _avgBatchDuration = 0;
// Marked volatile since it's updated across threads
static volatile bool navigatingAwayFromPage = false;
public Batching()
{
this.InitializeComponent();
// Ensure static variable is always false on page initialization
float _avgNonBatchedDuration = 0;
float _avgBatchDuration = 0;
// Marked volatile since it's updated across threads
static volatile bool navigatingAwayFromPage = false;
public Batching()
{
this.InitializeComponent();
// Ensure static variable is always false on page initialization
navigatingAwayFromPage = false;
// Load the model
var modelName = "squeezenet1.1-7-batched.onnx";
var modelPath = Path.Join(Windows.ApplicationModel.Package.Current.InstalledLocation.Path, "Models", modelName);
_model = LearningModel.LoadFromFilePath(modelPath);
}
async private void StartInference(object sender, RoutedEventArgs e)
{
ShowStatus();
ResetMetrics();
var inputImages = await GetInputImages();
int batchSize = GetBatchSizeFromBatchSizeSlider();
_nonBatchingSession = await CreateLearningModelSession(_model);
_batchingSession = await CreateLearningModelSession(_model, batchSize);
UpdateStatus(false);
await Classify(inputImages);
UpdateStatus(true);
await ClassifyBatched(inputImages, batchSize);
ShowUI();
}
private void ShowStatus()
{
StartInferenceBtn.IsEnabled = false;
BatchSizeSlider.IsEnabled = false;
DeviceComboBox.IsEnabled = false;
EvalResults.Visibility = Visibility.Collapsed;
LoadingContainer.Visibility = Visibility.Visible;
}
private void ResetMetrics()
{
_avgNonBatchedDuration = 0;
_avgBatchDuration = 0;
}
// Test input consists of 50 images (25 bird and 25 cat)
private async Task<List<VideoFrame>> GetInputImages()
{
var birdFile = await StorageFile.GetFileFromApplicationUriAsync(new Uri("ms-appx:///InputData/hummingbird.jpg"));
var catFile = await StorageFile .GetFileFromApplicationUriAsync(new Uri("ms-appx:///InputData/kitten.png"));
var birdImage = await CreateSoftwareBitmapFromStorageFile(birdFile);
var catImage = await CreateSoftwareBitmapFromStorageFile(catFile);
var inputImages = new List<VideoFrame>();
for (int i = 0; i < NumInputImages / 2; i++)
{
inputImages.Add(VideoFrame.CreateWithSoftwareBitmap(birdImage));
inputImages.Add(VideoFrame.CreateWithSoftwareBitmap(catImage));
}
return inputImages;
}
private async Task<SoftwareBitmap> CreateSoftwareBitmapFromStorageFile(StorageFile file)
{
var stream = await file.OpenAsync(FileAccessMode.Read);
var decoder = await BitmapDecoder.CreateAsync(stream);
var bitmap = await decoder.GetSoftwareBitmapAsync();
return bitmap;
}
private void UpdateStatus(bool isBatchingEval)
{
var modelName = "squeezenet1.1-7-batched.onnx";
var modelPath = Path.Join(Windows.ApplicationModel.Package.Current.InstalledLocation.Path, "Models", modelName);
_model = LearningModel.LoadFromFilePath(modelPath);
}
async private void StartInference(object sender, RoutedEventArgs e)
{
ShowStatus();
ResetMetrics();
var inputImages = await GetInputImages();
int batchSize = GetBatchSizeFromBatchSizeSlider();
_nonBatchingSession = await CreateLearningModelSession(_model);
_batchingSession = await CreateLearningModelSession(_model, batchSize);
UpdateStatus(false);
await Classify(inputImages);
UpdateStatus(true);
await ClassifyBatched(inputImages, batchSize);
ShowUI();
}
private void ShowStatus()
{
StartInferenceBtn.IsEnabled = false;
BatchSizeSlider.IsEnabled = false;
DeviceComboBox.IsEnabled = false;
EvalResults.Visibility = Visibility.Collapsed;
LoadingContainer.Visibility = Visibility.Visible;
}
private void ResetMetrics()
{
_avgNonBatchedDuration = 0;
_avgBatchDuration = 0;
}
// Test input consists of 50 images (25 bird and 25 cat)
private async Task<List<VideoFrame>> GetInputImages()
{
var birdFile = await StorageFile.GetFileFromApplicationUriAsync(new Uri("ms-appx:///InputData/hummingbird.jpg"));
var catFile = await StorageFile .GetFileFromApplicationUriAsync(new Uri("ms-appx:///InputData/kitten.png"));
var birdImage = await CreateSoftwareBitmapFromStorageFile(birdFile);
var catImage = await CreateSoftwareBitmapFromStorageFile(catFile);
var inputImages = new List<VideoFrame>();
for (int i = 0; i < NumInputImages / 2; i++)
{
inputImages.Add(VideoFrame.CreateWithSoftwareBitmap(birdImage));
inputImages.Add(VideoFrame.CreateWithSoftwareBitmap(catImage));
}
return inputImages;
}
private async Task<SoftwareBitmap> CreateSoftwareBitmapFromStorageFile(StorageFile file)
{
var stream = await file.OpenAsync(FileAccessMode.Read);
var decoder = await BitmapDecoder.CreateAsync(stream);
var bitmap = await decoder.GetSoftwareBitmapAsync();
return bitmap;
}
private void UpdateStatus(bool isBatchingEval)
{
if (isBatchingEval)
{
EvalText.Text = "Inferencing Batched Inputs:";
}
}
else
{
EvalText.Text = "Inferencing Non-Batched Inputs:";
}
}
private async Task<LearningModelSession> CreateLearningModelSession(LearningModel model, int batchSizeOverride=-1)
{
var deviceKind = DeviceComboBox.GetDeviceKind();
var device = new LearningModelDevice(deviceKind);
var options = new LearningModelSessionOptions();
if (batchSizeOverride > 0)
{
options.BatchSizeOverride = (uint)batchSizeOverride;
}
var session = new LearningModelSession(model, device, options);
return session;
}
async private Task Classify(List<VideoFrame> inputImages)
{
float totalEvalDurations = 0;
for (int i = 0; i < NumEvalIterations; i++)
{
}
}
private async Task<LearningModelSession> CreateLearningModelSession(LearningModel model, int batchSizeOverride=-1)
{
var deviceKind = DeviceComboBox.GetDeviceKind();
var device = new LearningModelDevice(deviceKind);
var options = new LearningModelSessionOptions();
if (batchSizeOverride > 0)
{
options.BatchSizeOverride = (uint)batchSizeOverride;
}
var session = new LearningModelSession(model, device, options);
return session;
}
async private Task Classify(List<VideoFrame> inputImages)
{
float totalEvalDurations = 0;
for (int i = 0; i < NumEvalIterations; i++)
{
if (navigatingAwayFromPage)
{
break;
}
UpdateProgress(i);
float evalDuration = await Task.Run(() => Evaluate(_nonBatchingSession, inputImages));
totalEvalDurations += evalDuration;
}
_avgNonBatchedDuration = totalEvalDurations / NumEvalIterations;
}
private static float Evaluate(LearningModelSession session, List<VideoFrame> input)
{
string inputName = session.Model.InputFeatures[0].Name;
float totalDuration = 0;
var binding = new LearningModelBinding(session);
for (int j = 0; j < input.Count; j++)
{
}
UpdateProgress(i);
float evalDuration = await Task.Run(() => Evaluate(_nonBatchingSession, inputImages));
totalEvalDurations += evalDuration;
}
_avgNonBatchedDuration = totalEvalDurations / NumEvalIterations;
}
private static float Evaluate(LearningModelSession session, List<VideoFrame> input)
{
string inputName = session.Model.InputFeatures[0].Name;
float totalDuration = 0;
var binding = new LearningModelBinding(session);
for (int j = 0; j < input.Count; j++)
{
if (navigatingAwayFromPage)
{
break;
}
var start = HighResolutionClock.UtcNow();
binding.Bind(inputName, input[j]);
session.Evaluate(binding, "");
var stop = HighResolutionClock.UtcNow();
var duration = HighResolutionClock.DurationInMs(start, stop);
totalDuration += duration;
}
return totalDuration;
}
async private Task ClassifyBatched(List<VideoFrame> inputImages, int batchSize)
{
float totalEvalDurations = 0;
for (int i = 0; i < NumEvalIterations; i++)
{
if (navigatingAwayFromPage)
break;
UpdateProgress(i);
float evalDuration = await Task.Run(() => EvaluateBatched(_batchingSession, inputImages, batchSize));
totalEvalDurations += evalDuration;
}
_avgBatchDuration = totalEvalDurations / NumEvalIterations;
}
private static float EvaluateBatched(LearningModelSession session, List<VideoFrame> input, int batchSize)
{
int numBatches = (int) Math.Ceiling((Decimal) input.Count / batchSize);
string inputName = session.Model.InputFeatures[0].Name;
float totalDuration = 0;
var binding = new LearningModelBinding(session);
for (int i = 0; i < numBatches; i++)
{
}
var start = HighResolutionClock.UtcNow();
binding.Bind(inputName, input[j]);
session.Evaluate(binding, "");
var stop = HighResolutionClock.UtcNow();
var duration = HighResolutionClock.DurationInMs(start, stop);
totalDuration += duration;
}
return totalDuration;
}
async private Task ClassifyBatched(List<VideoFrame> inputImages, int batchSize)
{
float totalEvalDurations = 0;
for (int i = 0; i < NumEvalIterations; i++)
{
if (navigatingAwayFromPage)
break;
UpdateProgress(i);
float evalDuration = await Task.Run(() => EvaluateBatched(_batchingSession, inputImages, batchSize));
totalEvalDurations += evalDuration;
}
_avgBatchDuration = totalEvalDurations / NumEvalIterations;
}
private static float EvaluateBatched(LearningModelSession session, List<VideoFrame> input, int batchSize)
{
int numBatches = (int) Math.Ceiling((Decimal) input.Count / batchSize);
string inputName = session.Model.InputFeatures[0].Name;
float totalDuration = 0;
var binding = new LearningModelBinding(session);
for (int i = 0; i < numBatches; i++)
{
if (navigatingAwayFromPage)
{
break;
}
int rangeStart = batchSize * i;
List<VideoFrame> batch;
// Add padding to the last batch if necessary
if (rangeStart + batchSize > input.Count)
{
int numInputsRemaining = input.Count - rangeStart;
int paddingAmount = batchSize - numInputsRemaining;
batch = input.GetRange(rangeStart, numInputsRemaining);
batch.AddRange(input.GetRange(0, paddingAmount));
}
else
{
batch = input.GetRange(rangeStart, batchSize);
}
var start = HighResolutionClock.UtcNow();
binding.Bind(inputName, batch);
session.Evaluate(binding, "");
var stop = HighResolutionClock.UtcNow();
var duration = HighResolutionClock.DurationInMs(start, stop);
totalDuration += duration;
}
return totalDuration;
}
private int GetBatchSizeFromBatchSizeSlider()
{
return int.Parse(BatchSizeSlider.Value.ToString());
}
private void UpdateProgress(int attemptNumber)
{
EvalProgressText.Text = "Attempt " + attemptNumber.ToString() + "/" + NumEvalIterations.ToString();
EvalProgressBar.Value = attemptNumber + 1;
}
private void ShowUI()
{
float ratio = (1 - (_avgBatchDuration / _avgNonBatchedDuration)) * 100;
var evalResult = new EvalResult
{
nonBatchedAvgTime = _avgNonBatchedDuration.ToString("0.00"),
batchedAvgTime = _avgBatchDuration.ToString("0.00"),
timeRatio = ratio.ToString("0.0")
};
List<EvalResult> results = new List<EvalResult>();
results.Insert(0, evalResult);
LoadingContainer.Visibility = Visibility.Collapsed;
EvalResults.Visibility = Visibility.Visible;
StartInferenceBtn.IsEnabled = true;
BatchSizeSlider.IsEnabled = true;
DeviceComboBox.IsEnabled = true;
EvalResults.ItemsSource = results;
}
private void UpdateBatchSizeText(object sender, RoutedEventArgs e)
{
BatchSizeText.Text = "Batch Size: " + BatchSizeSlider.Value.ToString();
}
public void StopAllEvents()
{
navigatingAwayFromPage = true;
}
}
}
}
int rangeStart = batchSize * i;
List<VideoFrame> batch;
// Add padding to the last batch if necessary
if (rangeStart + batchSize > input.Count)
{
int numInputsRemaining = input.Count - rangeStart;
int paddingAmount = batchSize - numInputsRemaining;
batch = input.GetRange(rangeStart, numInputsRemaining);
batch.AddRange(input.GetRange(0, paddingAmount));
}
else
{
batch = input.GetRange(rangeStart, batchSize);
}
var start = HighResolutionClock.UtcNow();
binding.Bind(inputName, batch);
session.Evaluate(binding, "");
var stop = HighResolutionClock.UtcNow();
var duration = HighResolutionClock.DurationInMs(start, stop);
totalDuration += duration;
}
return totalDuration;
}
private int GetBatchSizeFromBatchSizeSlider()
{
return int.Parse(BatchSizeSlider.Value.ToString());
}
private void UpdateProgress(int attemptNumber)
{
EvalProgressText.Text = "Attempt " + attemptNumber.ToString() + "/" + NumEvalIterations.ToString();
EvalProgressBar.Value = attemptNumber + 1;
}
private void ShowUI()
{
float ratio = (1 - (_avgBatchDuration / _avgNonBatchedDuration)) * 100;
var evalResult = new EvalResult
{
nonBatchedAvgTime = _avgNonBatchedDuration.ToString("0.00"),
batchedAvgTime = _avgBatchDuration.ToString("0.00"),
timeRatio = ratio.ToString("0.0")
};
List<EvalResult> results = new List<EvalResult>();
results.Insert(0, evalResult);
LoadingContainer.Visibility = Visibility.Collapsed;
EvalResults.Visibility = Visibility.Visible;
StartInferenceBtn.IsEnabled = true;
BatchSizeSlider.IsEnabled = true;
DeviceComboBox.IsEnabled = true;
EvalResults.ItemsSource = results;
}
private void UpdateBatchSizeText(object sender, RoutedEventArgs e)
{
BatchSizeText.Text = "Batch Size: " + BatchSizeSlider.Value.ToString();
}
public void StopAllEvents()
{
navigatingAwayFromPage = true;
}
}
}

5
Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/.editorconfig Normal file
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@ -0,0 +1,5 @@
# Rules in this file were initially inferred by Visual Studio IntelliCode from the C:\Users\numform\Windows-Machine-Learning\Samples\WinMLSamplesGallery\WinMLSamplesGalleryNative\ codebase based on best match to current usage at 12/9/2021
# You can modify the rules from these initially generated values to suit your own policies
# You can learn more about editorconfig here: https://docs.microsoft.com/en-us/visualstudio/ide/editorconfig-code-style-settings-reference
[*.cs]

9
Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/AdapterList.cpp Normal file
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@ -0,0 +1,9 @@
#include "pch.h"
#include "AdapterList.h"
namespace winrt::WinMLSamplesGalleryNative::implementation
{
void AdapterList::GetAdapters(hstring const& path) {
return;
}
}

18
Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/AdapterList.h Normal file
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@ -0,0 +1,18 @@
#pragma once
#include "AdapterList.g.h"
namespace winrt::WinMLSamplesGalleryNative::implementation
{
struct AdapterList : AdapterListT<AdapterList>
{
AdapterList() = default;
static void GetAdapters(hstring const& path);
};
}
namespace winrt::WinMLSamplesGalleryNative::factory_implementation
{
struct AdapterList : AdapterListT<AdapterList, implementation::AdapterList>
{
};
}

3
Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/Module.ixx Normal file
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@ -0,0 +1,3 @@
export module Module;
export void MyFunc();

6
Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/WinMLSamplesGalleryNative.idl
Просмотреть файл

@ -16,4 +16,10 @@ namespace WinMLSamplesGalleryNative
{
static Microsoft.AI.MachineLearning.LearningModel LoadEncryptedResource(String key);
}
[default_interface]
runtimeclass AdapterList
{
static void GetAdapters(String key);
}
}

3
Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/WinMLSamplesGalleryNative.vcxproj
Просмотреть файл

@ -142,6 +142,7 @@
</ResourceCompile>
</ItemDefinitionGroup>
<ItemGroup>
<ClInclude Include="AdapterList.h" />
<ClInclude Include="EncryptedModels.h" />
<ClInclude Include="OpenCVImage.h" />
<ClInclude Include="pch.h" />
@ -149,6 +150,7 @@
<ClInclude Include="WeakBuffer.h" />
</ItemGroup>
<ItemGroup>
<ClCompile Include="AdapterList.cpp" />
<ClCompile Include="EncryptedModels.cpp" />
<ClCompile Include="OpenCVImage.cpp" />
<ClCompile Include="pch.cpp">
@ -160,6 +162,7 @@
<Midl Include="WinMLSamplesGalleryNative.idl" />
</ItemGroup>
<ItemGroup>
<None Include=".editorconfig" />
<None Include="datafile.bin" />
<None Include="packages.config" />
<None Include="squeezenet1.1-7-batched.onnx" />

10
Samples/WinMLSamplesGallery/WinMLSamplesGalleryNative/WinMLSamplesGalleryNative.vcxproj.filters
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@ -9,6 +9,9 @@
<ClCompile Include="OpenCVImage.cpp">
<Filter>OpenCVInterop</Filter>
</ClCompile>
<ClCompile Include="AdapterList.cpp">
<Filter>AdapterList</Filter>
</ClCompile>
</ItemGroup>
<ItemGroup>
<ClInclude Include="pch.h" />
@ -24,6 +27,9 @@
<ClInclude Include="WeakBuffer.h">
<Filter>AbiHelpers</Filter>
</ClInclude>
<ClInclude Include="AdapterList.h">
<Filter>AdapterList</Filter>
</ClInclude>
</ItemGroup>
<ItemGroup>
<ResourceCompile Include="Resource.rc" />
@ -37,6 +43,7 @@
<None Include="squeezenet1.1-7-batched.onnx" />
<None Include="WinMLSamplesGalleryNative.def" />
<None Include="PropertySheet.props" />
<None Include=".editorconfig" />
</ItemGroup>
<ItemGroup>
<Text Include="readme.txt" />
@ -51,5 +58,8 @@
<Filter Include="AbiHelpers">
<UniqueIdentifier>{ed4f3871-4514-4b93-9ace-1315cad5491e}</UniqueIdentifier>
</Filter>
<Filter Include="AdapterList">
<UniqueIdentifier>{9df6a233-c2c6-4062-b738-31f7ba631acd}</UniqueIdentifier>
</Filter>
</ItemGroup>
</Project>