small cleanup refactor

Author: Sheil Kumar

Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/EncryptedModel/EncryptedModel.xaml

@@ -35,7 +35,7 @@
             </StackPanel>
         </DataTemplate>
     </Page.Resources>
-    <Grid Padding="0,20,0,0">
+    <Grid Padding="0,10,0,0">
         <StackPanel>
             <StackPanel Orientation="Horizontal">
                 <TextBlock Text="Enter the Key:" FontSize="16"
@@ -69,7 +69,7 @@
                     Padding="7, 0, 0, 7"
                        />
             </StackPanel>
-            <StackPanel Orientation="Horizontal" Padding="0,2,0,0">
+            <StackPanel Orientation="Horizontal" Padding="0,10,0,0">
 
                 <Image x:Name="InputImage" Source="ms-appx:///InputData/hummingbird.jpg"
                        Margin="0,1,0,0"

Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/ImageClassifier/ImageClassifier.xaml.cs

@@ -64,8 +64,6 @@ public sealed partial class ImageClassifier : Page
         private Dictionary<Classifier, Func<LearningModel>> _preProcessorDictionary;
 
         private static Dictionary<long, string> _labels;
-        private static Dictionary<long, string> _imagenetLabels;
-        private static Dictionary<long, string> _ilsvrc2013Labels;
 
         private BitmapDecoder CurrentImageDecoder { get; set; }
 
@@ -131,16 +129,6 @@ public ImageClassifier()
 
         private void EnsureInitialized()
         {
-            if (_imagenetLabels == null)
-            {
-                _imagenetLabels = LoadLabels("ms-appx:///InputData/sysnet.txt");
-            }
-
-            if (_ilsvrc2013Labels == null)
-            {
-                _ilsvrc2013Labels = LoadLabels("ms-appx:///InputData/ilsvrc2013.txt");
-            }
-
             if (_modelDictionary == null)
             {
                 _modelDictionary = new Dictionary<Classifier, string>{
@@ -168,10 +156,10 @@ private void EnsureInitialized()
             if (_postProcessorDictionary == null)
             {
                 _postProcessorDictionary = new Dictionary<Classifier, Func<LearningModel>>{
-                    { Classifier.DenseNet121,       () => TensorizationModels.ReshapeThenSoftmaxThenTopK(new long[] { BatchSize, _imagenetLabels.Count, 1, 1 },
+                    { Classifier.DenseNet121,       () => TensorizationModels.ReshapeThenSoftmaxThenTopK(new long[] { BatchSize, ClassificationLabels.ImageNet.Count, 1, 1 },
                                                                                                     TopK,
                                                                                                     BatchSize,
-                                                                                                    _imagenetLabels.Count) },
+                                                                                                    ClassificationLabels.ImageNet.Count) },
                     { Classifier.EfficientNetLite4, () => TensorizationModels.SoftMaxThenTopK(TopK) },
                     { Classifier.ShuffleNet_V1,     () => TensorizationModels.TopK(TopK) },
                     { Classifier.SqueezeNet,        () => TensorizationModels.SoftMaxThenTopK(TopK) },
@@ -257,11 +245,11 @@ private void InitializeWindowsMachineLearning()
 
                 if (model == Classifier.RCNN_ILSVRC13)
                 {
-                    _labels = _ilsvrc2013Labels;
+                    _labels = ClassificationLabels.ILSVRC2013;
                 }
                 else
                 {
-                    _labels = _imagenetLabels;
+                    _labels = ClassificationLabels.ImageNet;
                 }
 
                 CurrentModel = model;
@@ -396,24 +384,6 @@ private static LearningModel CreateLearningModel(string modelPath)
         }
 #pragma warning restore CA1416 // Validate platform compatibility
 
-        private static Dictionary<long, string> LoadLabels(string csvFile)
-        {
-            var file = StorageFile.GetFileFromApplicationUriAsync(new Uri(csvFile)).GetAwaiter().GetResult();
-            var text = Windows.Storage.FileIO.ReadTextAsync(file).GetAwaiter().GetResult();
-            var labels = new Dictionary<long, string>();
-            var records = text.Split(Environment.NewLine);
-            foreach (var record in records)
-            {
-                var fields = record.Split(",", 2);
-                if (fields.Length == 2)
-                {
-                    var index = long.Parse(fields[0]);
-                    labels[index] = fields[1];
-                }
-            }
-            return labels;
-        }
-
         private void TryPerformInference()
         {
             if (CurrentImageDecoder != null)

Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/ImageSharpInterop/ImageSharpInterop.xaml.cs

@@ -38,8 +38,6 @@ public sealed partial class ImageSharpInterop : Page
         private LearningModelSession _tensorizationSession;
         private LearningModelSession _postProcessingSession;
 
-        private static Dictionary<long, string> _imagenetLabels;
-
         private Image<Bgra32> CurrentImage { get; set; }
 
 #pragma warning disable CA1416 // Validate platform compatibility
@@ -58,7 +56,6 @@ public ImageSharpInterop()
         {
             this.InitializeComponent();
 
-            _imagenetLabels = LoadLabels("ms-appx:///InputData/sysnet.txt");
             var tensorizationModel = TensorizationModels.BasicTensorization(Height, Width, BatchSize, Channels, Height, Width, "nearest");
             _tensorizationSession = CreateLearningModelSession(tensorizationModel, SelectedDeviceKind);
             _inferenceSession = CreateLearningModelSession("ms-appx:///Models/squeezenet1.1-7.onnx");
@@ -104,7 +101,7 @@ public ImageSharpInterop()
             // Return results
             var probabilities = topKValues.GetAsVectorView();
             var indices = topKIndices.GetAsVectorView();
-            var labels = indices.Select((index) => _imagenetLabels[index]);
+            var labels = indices.Select((index) => ClassificationLabels.ImageNet[index]);
             stop = HighResolutionClock.UtcNow();
             var postProcessDuration = HighResolutionClock.DurationInMs(start, stop);
 
@@ -168,24 +165,6 @@ private static LearningModel CreateLearningModel(string modelPath)
         }
 #pragma warning restore CA1416 // Validate platform compatibility
 
-        private static Dictionary<long, string> LoadLabels(string csvFile)
-        {
-            var file = StorageFile.GetFileFromApplicationUriAsync(new Uri(csvFile)).GetAwaiter().GetResult();
-            var text = Windows.Storage.FileIO.ReadTextAsync(file).GetAwaiter().GetResult();
-            var labels = new Dictionary<long, string>();
-            var records = text.Split(Environment.NewLine);
-            foreach (var record in records)
-            {
-                var fields = record.Split(",", 2);
-                if (fields.Length == 2)
-                {
-                    var index = long.Parse(fields[0]);
-                    labels[index] = fields[1];
-                }
-            }
-            return labels;
-        }
-
         private void TryPerformInference(bool reloadImages = true)
         {
             if (CurrentImage != null)

Samples/WinMLSamplesGallery/WinMLSamplesGallery/Samples/OpenCVInterop/OpenCVInterop.xaml.cs

@@ -41,8 +41,6 @@ enum ClassifyChoice {
         private LearningModelSession _tensorizationSession;
         private LearningModelSession _postProcessingSession;
 
-        private static Dictionary<long, string> _imagenetLabels;
-
         private WinMLSamplesGalleryNative.OpenCVImage Original { get; set; }
         private WinMLSamplesGalleryNative.OpenCVImage Noisy { get; set; }
         private WinMLSamplesGalleryNative.OpenCVImage Denoised { get; set; }
@@ -115,7 +113,6 @@ public OpenCVInterop()
             this.InitializeComponent();
             CurrentImagePath = null;
             InferenceChoice = ClassifyChoice.Denoised;
-            _imagenetLabels = LoadLabels("ms-appx:///InputData/sysnet.txt");
             _inferenceSession = CreateLearningModelSession("ms-appx:///Models/squeezenet1.1-7.onnx");
             _postProcessingSession = CreateLearningModelSession(TensorizationModels.SoftMaxThenTopK(TopK));
 
@@ -157,7 +154,7 @@ public OpenCVInterop()
             // Return results
             var probabilities = topKValues.GetAsVectorView();
             var indices = topKIndices.GetAsVectorView();
-            var labels = indices.Select((index) => _imagenetLabels[index]);
+            var labels = indices.Select((index) => ClassificationLabels.ImageNet[index]);
             stop = HighResolutionClock.UtcNow();
             var postProcessDuration = HighResolutionClock.DurationInMs(start, stop);
 
@@ -219,24 +216,6 @@ private static LearningModel CreateLearningModel(string modelPath)
         }
 #pragma warning restore CA1416 // Validate platform compatibility
 
-        private static Dictionary<long, string> LoadLabels(string csvFile)
-        {
-            var file = StorageFile.GetFileFromApplicationUriAsync(new Uri(csvFile)).GetAwaiter().GetResult();
-            var text = Windows.Storage.FileIO.ReadTextAsync(file).GetAwaiter().GetResult();
-            var labels = new Dictionary<long, string>();
-            var records = text.Split(Environment.NewLine);
-            foreach (var record in records)
-            {
-                var fields = record.Split(",", 2);
-                if (fields.Length == 2)
-                {
-                    var index = long.Parse(fields[0]);
-                    labels[index] = fields[1];
-                }
-            }
-            return labels;
-        }
-
         private void TryPerformInference(bool reloadImages = true)
         {
             if (CurrentImagePath != null)