public void TensorFlowTransformCifarInvalidShape()
{
var model_location = "cifar_model/frozen_model.pb";
var mlContext = new MLContext(seed: 1, conc: 1);
var imageHeight = 28;
var imageWidth = 28;
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = TextLoader.Create(mlContext, new TextLoader.Arguments()
{
Column = new[]
{
new TextLoader.Column("ImagePath", DataKind.TX, 0),
new TextLoader.Column("Name", DataKind.TX, 1),
}
}, new MultiFileSource(dataFile));
var images = ImageLoaderTransform.Create(mlContext, new ImageLoaderTransform.Arguments()
{
Column = new ImageLoaderTransform.Column[1]
{
new ImageLoaderTransform.Column()
{
Source = "ImagePath", Name = "ImageReal"
}
},
ImageFolder = imageFolder
}, data);
var cropped = ImageResizerTransform.Create(mlContext, new ImageResizerTransform.Arguments()
{
Column = new ImageResizerTransform.Column[1] {
new ImageResizerTransform.Column()
{
Source = "ImageReal", Name = "ImageCropped", ImageHeight = imageHeight, ImageWidth = imageWidth, Resizing = ImageResizerTransform.ResizingKind.IsoCrop
}
}
}, images);
var pixels = ImagePixelExtractorTransform.Create(mlContext, new ImagePixelExtractorTransform.Arguments()
{
Column = new ImagePixelExtractorTransform.Column[1] {
new ImagePixelExtractorTransform.Column()
{
Source = "ImageCropped", Name = "Input", UseAlpha = false, InterleaveArgb = true
}
}
}, cropped);
var thrown = false;
try
{
IDataView trans = TensorFlowTransform.Create(mlContext, pixels, model_location, new[] { "Output" }, new[] { "Input" });
}
catch
{
thrown = true;
}
Assert.True(thrown);
}
public void TensorFlowTransformCifar()
{
var model_location = "cifar_model/frozen_model.pb";
using (var env = new TlcEnvironment())
{
var imageHeight = 32;
var imageWidth = 32;
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile));
var images = new ImageLoaderTransform(env, new ImageLoaderTransform.Arguments()
{
Column = new ImageLoaderTransform.Column[1]
{
new ImageLoaderTransform.Column()
{
Source = "ImagePath", Name = "ImageReal"
}
},
ImageFolder = imageFolder
}, data);
var cropped = new ImageResizerTransform(env, new ImageResizerTransform.Arguments()
{
Column = new ImageResizerTransform.Column[1] {
new ImageResizerTransform.Column()
{
Source = "ImageReal", Name = "ImageCropped", ImageHeight = imageHeight, ImageWidth = imageWidth, Resizing = ImageResizerTransform.ResizingKind.IsoCrop
}
}
}, images);
var pixels = new ImagePixelExtractorTransform(env, new ImagePixelExtractorTransform.Arguments()
{
Column = new ImagePixelExtractorTransform.Column[1] {
new ImagePixelExtractorTransform.Column()
{
Source = "ImageCropped", Name = "Input", UseAlpha = false, InterleaveArgb = true
}
}
}, cropped);
IDataView trans = TensorFlowTransform.Create(env, pixels, model_location, "Output", "Input");
trans.Schema.TryGetColumnIndex("Output", out int output);
using (var cursor = trans.GetRowCursor(col => col == output))
{
var buffer = default(VBuffer <float>);
var getter = cursor.GetGetter <VBuffer <float> >(output);
while (cursor.MoveNext())
{
getter(ref buffer);
Assert.Equal(10, buffer.Length);
}
}
}
}
public void TensorFlowTransformInceptionTest()
{
var model_location = @"C:\models\TensorFlow\tensorflow_inception_graph.pb";
using (var env = new ConsoleEnvironment(seed: 1, conc: 1))
{
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile));
var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments()
{
Column = new ImageLoaderTransform.Column[1]
{
new ImageLoaderTransform.Column()
{
Source = "ImagePath", Name = "ImageReal"
}
},
ImageFolder = imageFolder
}, data);
var cropped = ImageResizerTransform.Create(env, new ImageResizerTransform.Arguments()
{
Column = new ImageResizerTransform.Column[1] {
new ImageResizerTransform.Column()
{
Source = "ImageReal", Name = "ImageCropped", ImageHeight = 224, ImageWidth = 224, Resizing = ImageResizerTransform.ResizingKind.IsoCrop
}
}
}, images);
var pixels = ImagePixelExtractorTransform.Create(env, new ImagePixelExtractorTransform.Arguments()
{
Column = new ImagePixelExtractorTransform.Column[1] {
new ImagePixelExtractorTransform.Column()
{
Source = "ImageCropped", Name = "input", UseAlpha = false, InterleaveArgb = true, Convert = true
}
}
}, cropped);
var tf = TensorFlowTransform.Create(env, pixels, model_location, "softmax2_pre_activation", "input");
tf.Schema.TryGetColumnIndex("input", out int input);
tf.Schema.TryGetColumnIndex("softmax2_pre_activation", out int b);
using (var curs = tf.GetRowCursor(col => col == b || col == input))
{
var get = curs.GetGetter <VBuffer <float> >(b);
var getInput = curs.GetGetter <VBuffer <float> >(input);
var buffer = default(VBuffer <float>);
var inputBuffer = default(VBuffer <float>);
while (curs.MoveNext())
{
getInput(ref inputBuffer);
get(ref buffer);
}
}
}
}
public void TensorFlowTransformCifarInvalidShape()
{
var model_location = "cifar_model/frozen_model.pb";
using (var env = new ConsoleEnvironment())
{
var imageHeight = 28;
var imageWidth = 28;
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile));
var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments()
{
Column = new ImageLoaderTransform.Column[1]
{
new ImageLoaderTransform.Column()
{
Source = "ImagePath", Name = "ImageReal"
}
},
ImageFolder = imageFolder
}, data);
var cropped = ImageResizerTransform.Create(env, new ImageResizerTransform.Arguments()
{
Column = new ImageResizerTransform.Column[1] {
new ImageResizerTransform.Column()
{
Source = "ImageReal", Name = "ImageCropped", ImageHeight = imageHeight, ImageWidth = imageWidth, Resizing = ImageResizerTransform.ResizingKind.IsoCrop
}
}
}, images);
var pixels = ImagePixelExtractorTransform.Create(env, new ImagePixelExtractorTransform.Arguments()
{
Column = new ImagePixelExtractorTransform.Column[1] {
new ImagePixelExtractorTransform.Column()
{
Source = "ImageCropped", Name = "Input", UseAlpha = false, InterleaveArgb = true
}
}
}, cropped);
var thrown = false;
try
{
IDataView trans = TensorFlowTransform.Create(env, pixels, model_location, "Output", "Input");
}
catch
{
thrown = true;
}
Assert.True(thrown);
}
}
public void TensorFlowTransformMatrixMultiplicationTest()
{
var model_location = "model_matmul/frozen_saved_model.pb";
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline
var loader = ComponentCreation.CreateDataView(env,
new List <TestData>(new TestData[] { new TestData()
{
a = new[] { 1.0f, 2.0f,
3.0f, 4.0f },
b = new[] { 1.0f, 2.0f,
3.0f, 4.0f }
},
new TestData()
{
a = new[] { 2.0f, 2.0f,
2.0f, 2.0f },
b = new[] { 3.0f, 3.0f,
3.0f, 3.0f }
} }));
var trans = TensorFlowTransform.Create(env, loader, model_location, "c", "a", "b");
using (var cursor = trans.GetRowCursor(a => true))
{
var cgetter = cursor.GetGetter <VBuffer <float> >(2);
Assert.True(cursor.MoveNext());
VBuffer <float> c = default;
cgetter(ref c);
Assert.Equal(1.0 * 1.0 + 2.0 * 3.0, c.Values[0]);
Assert.Equal(1.0 * 2.0 + 2.0 * 4.0, c.Values[1]);
Assert.Equal(3.0 * 1.0 + 4.0 * 3.0, c.Values[2]);
Assert.Equal(3.0 * 2.0 + 4.0 * 4.0, c.Values[3]);
Assert.True(cursor.MoveNext());
c = default;
cgetter(ref c);
Assert.Equal(2.0 * 3.0 + 2.0 * 3.0, c.Values[0]);
Assert.Equal(2.0 * 3.0 + 2.0 * 3.0, c.Values[1]);
Assert.Equal(2.0 * 3.0 + 2.0 * 3.0, c.Values[2]);
Assert.Equal(2.0 * 3.0 + 2.0 * 3.0, c.Values[3]);
Assert.False(cursor.MoveNext());
}
}
}
[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // TensorFlow is 64-bit only
public void TensorFlowTransformCifar()
{
var model_location = "cifar_model/frozen_model.pb";
var mlContext = new MLContext(seed: 1, conc: 1);
var tensorFlowModel = TensorFlowUtils.LoadTensorFlowModel(mlContext, model_location);
var schema = tensorFlowModel.GetInputSchema();
Assert.True(schema.TryGetColumnIndex("Input", out int column));
var type = (VectorType)schema.GetColumnType(column);
var imageHeight = type.Dimensions[0];
var imageWidth = type.Dimensions[1];
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = TextLoader.Create(mlContext, new TextLoader.Arguments()
{
Column = new[]
{
new TextLoader.Column("ImagePath", DataKind.TX, 0),
new TextLoader.Column("Name", DataKind.TX, 1),
}
}, new MultiFileSource(dataFile));
var pipeEstimator = new ImageLoadingEstimator(mlContext, imageFolder, ("ImagePath", "ImageReal"))
.Append(new ImageResizingEstimator(mlContext, "ImageReal", "ImageCropped", imageWidth, imageHeight))
.Append(new ImagePixelExtractingEstimator(mlContext, "ImageCropped", "Input", interleave: true));
var pixels = pipeEstimator.Fit(data).Transform(data);
IDataView trans = TensorFlowTransform.Create(mlContext, pixels, tensorFlowModel, new[] { "Output" }, new[] { "Input" });
trans.Schema.TryGetColumnIndex("Output", out int output);
using (var cursor = trans.GetRowCursor(col => col == output))
{
var buffer = default(VBuffer <float>);
var getter = cursor.GetGetter <VBuffer <float> >(output);
var numRows = 0;
while (cursor.MoveNext())
{
getter(ref buffer);
Assert.Equal(10, buffer.Length);
numRows += 1;
}
Assert.Equal(3, numRows);
}
}
public void TensorFlowTransformMNISTConvTest()
{
var model_location = "mnist_model/frozen_saved_model.pb";
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
var dataPath = GetDataPath("Train-Tiny-28x28.txt");
var testDataPath = GetDataPath("MNIST.Test.tiny.txt");
// Pipeline
var loader = TextLoader.ReadFile(env,
new TextLoader.Arguments()
{
Separator = "tab",
HasHeader = true,
Column = new[]
{
new TextLoader.Column()
{
Name = "Label",
Source = new [] { new TextLoader.Range()
{
Min = 0, Max = 0
} },
Type = DataKind.Num
},
new TextLoader.Column()
{
Name = "Placeholder",
Source = new [] { new TextLoader.Range()
{
Min = 1, Max = 784
} },
Type = DataKind.Num
}
}
}, new MultiFileSource(dataPath));
IDataView trans = TensorFlowTransform.Create(env, loader, model_location, "Softmax", "Placeholder");
trans = new ConcatTransform(env, trans, "reshape_input", "Placeholder");
trans = TensorFlowTransform.Create(env, trans, model_location, "dense/Relu", "reshape_input");
trans = new ConcatTransform(env, trans, "Features", "Softmax", "dense/Relu");
var trainer = new LightGbmMulticlassTrainer(env, new LightGbmArguments());
var cached = new CacheDataView(env, trans, prefetch: null);
var trainRoles = new RoleMappedData(cached, label: "Label", feature: "Features");
var pred = trainer.Train(trainRoles);
// Get scorer and evaluate the predictions from test data
IDataScorerTransform testDataScorer = GetScorer(env, trans, pred, testDataPath);
var metrics = Evaluate(env, testDataScorer);
Assert.Equal(0.99, metrics.AccuracyMicro, 2);
Assert.Equal(0.99, metrics.AccuracyMicro, 2);
// Create prediction engine and test predictions
var model = env.CreatePredictionEngine <MNISTData, MNISTPrediction>(testDataScorer);
var sample1 = new MNISTData()
{
Placeholder = new float[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 18, 18, 18, 126, 136, 175, 26,
166, 255, 247, 127, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 36, 94, 154, 170, 253, 253, 253, 253, 253,
225, 172, 253, 242, 195, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 49, 238, 253, 253, 253, 253, 253, 253, 253,
253, 251, 93, 82, 82, 56, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 219, 253, 253, 253, 253, 253, 198,
182, 247, 241, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 80, 156, 107, 253, 253, 205, 11, 0,
43, 154, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 1, 154, 253, 90, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 253, 190, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 190, 253, 70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 241, 225, 160, 108, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 81, 240, 253, 253, 119, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 45, 186, 253, 253, 150, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 93, 252, 253, 187, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 249, 253, 249, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 46, 130, 183, 253, 253, 207, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 148, 229, 253, 253, 253, 250, 182, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 114, 221, 253, 253, 253, 253, 201, 78, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 23, 66, 213, 253, 253, 253, 253, 198, 81, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 171, 219, 253, 253, 253, 253, 195, 80, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 172, 226, 253, 253, 253, 253, 244, 133, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 136, 253, 253, 253, 212, 135, 132, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 }
};
var prediction = model.Predict(sample1);
float max = -1;
int maxIndex = -1;
for (int i = 0; i < prediction.PredictedLabels.Length; i++)
{
if (prediction.PredictedLabels[i] > max)
{
max = prediction.PredictedLabels[i];
maxIndex = i;
}
}
Assert.Equal(5, maxIndex);
}
}
public void TensorFlowTransformObjectDetectionTest()
{
var model_location = @"C:\models\TensorFlow\ssd_mobilenet_v1_coco_2018_01_28\frozen_inference_graph.pb";
var mlContext = new MLContext(seed: 1, conc: 1);
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = mlContext.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile));
var images = ImageLoaderTransform.Create(mlContext, new ImageLoaderTransform.Arguments()
{
Column = new ImageLoaderTransform.Column[1]
{
new ImageLoaderTransform.Column()
{
Source = "ImagePath", Name = "ImageReal"
}
},
ImageFolder = imageFolder
}, data);
var cropped = ImageResizerTransform.Create(mlContext, new ImageResizerTransform.Arguments()
{
Column = new ImageResizerTransform.Column[1] {
new ImageResizerTransform.Column()
{
Source = "ImageReal", Name = "ImageCropped", ImageHeight = 32, ImageWidth = 32, Resizing = ImageResizerTransform.ResizingKind.IsoCrop
}
}
}, images);
var pixels = ImagePixelExtractorTransform.Create(mlContext, new ImagePixelExtractorTransform.Arguments()
{
Column = new ImagePixelExtractorTransform.Column[1] {
new ImagePixelExtractorTransform.Column()
{
Source = "ImageCropped", Name = "image_tensor", UseAlpha = false, InterleaveArgb = true, Convert = false
}
}
}, cropped);
var tf = TensorFlowTransform.Create(mlContext, pixels, model_location,
new[] { "detection_boxes", "detection_scores", "num_detections", "detection_classes" },
new[] { "image_tensor" });
tf.Schema.TryGetColumnIndex("image_tensor", out int input);
tf.Schema.TryGetColumnIndex("detection_boxes", out int boxes);
tf.Schema.TryGetColumnIndex("detection_scores", out int scores);
tf.Schema.TryGetColumnIndex("num_detections", out int num);
tf.Schema.TryGetColumnIndex("detection_classes", out int classes);
using (var curs = tf.GetRowCursor(col => col == classes || col == num || col == scores || col == boxes || col == input))
{
var getInput = curs.GetGetter <VBuffer <byte> >(input);
var getBoxes = curs.GetGetter <VBuffer <float> >(boxes);
var getScores = curs.GetGetter <VBuffer <float> >(scores);
var getNum = curs.GetGetter <VBuffer <float> >(num);
var getClasses = curs.GetGetter <VBuffer <float> >(classes);
var buffer = default(VBuffer <float>);
var inputBuffer = default(VBuffer <byte>);
while (curs.MoveNext())
{
getInput(ref inputBuffer);
getBoxes(ref buffer);
getScores(ref buffer);
getNum(ref buffer);
getClasses(ref buffer);
}
}
}
[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // TensorFlow is 64-bit only
public void TensorFlowTransformCifarSavedModel()
{
var model_location = "cifar_saved_model";
var mlContext = new MLContext(seed: 1, conc: 1);
var tensorFlowModel = TensorFlowUtils.LoadTensorFlowModel(mlContext, model_location);
var schema = tensorFlowModel.GetInputSchema();
Assert.True(schema.TryGetColumnIndex("Input", out int column));
var type = (VectorType)schema.GetColumnType(column);
var imageHeight = type.Dimensions[0];
var imageWidth = type.Dimensions[1];
var dataFile = GetDataPath("images/images.tsv");
var imageFolder = Path.GetDirectoryName(dataFile);
var data = TextLoader.Create(mlContext, new TextLoader.Arguments()
{
Column = new[]
{
new TextLoader.Column("ImagePath", DataKind.TX, 0),
new TextLoader.Column("Name", DataKind.TX, 1),
}
}, new MultiFileSource(dataFile));
var images = ImageLoaderTransform.Create(mlContext, new ImageLoaderTransform.Arguments()
{
Column = new ImageLoaderTransform.Column[1]
{
new ImageLoaderTransform.Column()
{
Source = "ImagePath", Name = "ImageReal"
}
},
ImageFolder = imageFolder
}, data);
var cropped = ImageResizerTransform.Create(mlContext, new ImageResizerTransform.Arguments()
{
Column = new ImageResizerTransform.Column[1] {
new ImageResizerTransform.Column()
{
Source = "ImageReal", Name = "ImageCropped", ImageHeight = imageHeight, ImageWidth = imageWidth, Resizing = ImageResizerTransform.ResizingKind.IsoCrop
}
}
}, images);
var pixels = ImagePixelExtractorTransform.Create(mlContext, new ImagePixelExtractorTransform.Arguments()
{
Column = new ImagePixelExtractorTransform.Column[1] {
new ImagePixelExtractorTransform.Column()
{
Source = "ImageCropped", Name = "Input", UseAlpha = false, InterleaveArgb = true
}
}
}, cropped);
IDataView trans = TensorFlowTransform.Create(mlContext, pixels, tensorFlowModel, new[] { "Output" }, new[] { "Input" });
trans.Schema.TryGetColumnIndex("Output", out int output);
using (var cursor = trans.GetRowCursor(col => col == output))
{
var buffer = default(VBuffer <float>);
var getter = cursor.GetGetter <VBuffer <float> >(output);
var numRows = 0;
while (cursor.MoveNext())
{
getter(ref buffer);
Assert.Equal(10, buffer.Length);
numRows += 1;
}
Assert.Equal(3, numRows);
}
}
