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draft regression test
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‎test/Microsoft.ML.Tests/OnnxConversionTest.cs

+241-14
Original file line numberDiff line numberDiff line change
@@ -21,15 +21,52 @@
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using Microsoft.ML.Trainers;
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using Microsoft.ML.Transforms;
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using Microsoft.ML.Transforms.Onnx;
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using Microsoft.ML.Transforms.Text;
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using Newtonsoft.Json;
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using Xunit;
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using Xunit.Abstractions;
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using static Microsoft.ML.Model.OnnxConverter.OnnxCSharpToProtoWrapper;
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namespace Microsoft.ML.Tests
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{
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public class OnnxConversionTest : BaseTestBaseline
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public class OnnxConversionTest : BaseTestBaseline
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{
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private static IEnumerable<DataPoint2> GenerateRandomDataPoints(int count,
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int seed = 0)
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{
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var random = new Random(seed);
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for (int i = 0; i < count; i++)
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{
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float label = (float)random.NextDouble();
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yield return new DataPoint2
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{
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Label = label,
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// Create random features that are correlated with the label.
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Features = Enumerable.Repeat(label, 50).Select(
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x => x + (float)random.NextDouble()).ToArray()
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};
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}
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}
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// Example with label and 50 feature values. A data set is a collection of
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// such examples.
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private class DataPoint2
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{
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public float Label { get; set; }
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[VectorType(50)]
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public float[] Features { get; set; }
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}
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// Class used to capture predictions.
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private class Prediction
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{
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// Original label.
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public float Label { get; set; }
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// Predicted score from the trainer.
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public float Score { get; set; }
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}
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private class AdultData
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{
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[LoadColumn(0, 10), ColumnName("FeatureVector")]
@@ -108,8 +145,7 @@ public void SimpleEndToEndOnnxConversionTest()
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private class BreastCancerFeatureVector
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{
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[LoadColumn(1, 9), VectorType(9)]
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public float[] Features;
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}
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public float[] Features; }
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private class BreastCancerCatFeatureExample
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{
@@ -187,7 +223,160 @@ public void KmeansOnnxConversionTest()
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Done();
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}
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private class DataPoint
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[Fact]
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public void WordEmbeddingEstimatorOnnxConversionTest() //can't find the class - maybe
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{
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// Step 1: Create and train a ML.NET pipeline.
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var mlContext = new MLContext(seed: 1);
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string dataPath = GetDataPath(TestDatasets.Sentiment.trainFilename);
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var data = new TextLoader(ML,
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new TextLoader.Options()
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{
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Separator = "\t",
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HasHeader = true,
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Columns = new[]
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{
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new TextLoader.Column("Label", DataKind.Boolean, 0),
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new TextLoader.Column("SentimentText", DataKind.String, 1)
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}
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}).Load(GetDataPath(dataPath));
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IEstimator<ITransformer>[] estimators = { };
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var textPipeline = mlContext.Transforms.Text.NormalizeText("SentimentText")
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.Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
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"SentimentText"))
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.Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
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"Tokens", WordEmbeddingEstimator.PretrainedModelKind
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.SentimentSpecificWordEmbedding));
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var model = textPipeline.Fit(data);
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var transformedData = model.Transform(data);
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var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, data);
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// Compare results produced by ML.NET and ONNX's runtime.
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if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows) && Environment.Is64BitProcess)
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{
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var onnxFileName = "WordEmbeddingEstimator.onnx";
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var onnxModelPath = GetOutputPath(onnxFileName);
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SaveOnnxModel(onnxModel, onnxModelPath, null);
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// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
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string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
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string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
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var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
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var onnxTransformer = onnxEstimator.Fit(data);
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var onnxResult = onnxTransformer.Transform(data);
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CompareSelectedR4VectorColumns("Score", "Score0", transformedData, onnxResult, 3);
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}
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Done();
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}
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[Fact]
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// Conversion tests for regression
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public void regressionOnnxConversionTest()
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{
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/*
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var mlContext = new MLContext(seed: 1);
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string dataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename);
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// Now read the file (remember though, readers are lazy, so the actual reading will happen when the data is accessed).
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var dataView = mlContext.Data.LoadFromTextFile<AdultData>(dataPath,
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separatorChar: ';',
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hasHeader: true);
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IEstimator<ITransformer>[] estimators = {
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//mlContext.Regression.Trainers.Ols(new OlsTrainer.Options() {
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// LabelColumnName = "Target",
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// FeatureColumnName = "FeatureVector",
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//}),
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//mlContext.Regression.Trainers.OnlineGradientDescent(new OnlineGradientDescentTrainer.Options(){
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// LabelColumnName = "Target",
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// FeatureColumnName = "FeatureVector",
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//}),
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//mlContext.Transforms.DetectAnomalyBySrCnn("Target","FeatureVector"), // needs separate data
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mlContext.Regression.Trainers.FastForest("Target", "FeatureVector"),
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//mlContext.Regression.Trainers.FastTree("Target", "FeatureVector"),
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//mlContext.Regression.Trainers.FastTreeTweedie("Target", "FeatureVector"),
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//mlContext.Regression.Trainers.LightGbm("Target","FeatureVector"),
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//mlContext.Regression.Trainers.LbfgsPoissonRegression("Target", "FeatureVector"),
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};
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*/
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// Create a new context for ML.NET operations. It can be used for
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// exception tracking and logging, as a catalog of available operations
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// and as the source of randomness. Setting the seed to a fixed number
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// in this example to make outputs deterministic.
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var mlContext = new MLContext(seed: 0);
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// Create a list of training data points.
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var dataPoints = GenerateRandomDataPoints(1000);
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// Convert the list of data points to an IDataView object, which is
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// consumable by ML.NET API.
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var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
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// Define the trainer.
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var pipeline = mlContext.Regression.Trainers.FastTreeTweedie(
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labelColumnName: nameof(DataPoint2.Label),
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featureColumnName: nameof(DataPoint2.Features));
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// Train the model.
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var model = pipeline.Fit(trainingData);
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// Create testing data. Use different random seed to make it different
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// from training data.
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var data = mlContext.Data.LoadFromEnumerable(
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GenerateRandomDataPoints(5, seed: 123));
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// Run the model on test data set.
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var transformedTestData = model.Transform(data);
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// Convert IDataView object to a list.
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var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, data);
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// Convert IDataView object to a list.
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var predictions = mlContext.Data.CreateEnumerable<Prediction>(
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transformedTestData, reuseRowObject: false).ToList();
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foreach (var p in predictions)
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System.Diagnostics.Debug.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");
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// Compare results produced by ML.NET and ONNX's runtime.
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if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows) && Environment.Is64BitProcess)
339+
{
340+
var onnxFileName = "test.onnx";
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var onnxModelPath = GetOutputPath(onnxFileName);
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SaveOnnxModel(onnxModel, onnxModelPath, null);
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// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
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string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
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string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
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var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
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var onnxTransformer = onnxEstimator.Fit(data);
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var onnxResult = onnxTransformer.Transform(data);
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CompareSelectedR4ScalarColumns("Label", "Score0", data, onnxResult, 3);
351+
}
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Done();
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/*var initialPipeline = mlContext.Transforms.NormalizeMinMax("FeatureVector");
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foreach (var estimator in estimators)
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{
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//var pipeline = initialPipeline.Append(estimator);
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var pipeline = estimator;
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var model = pipeline.Fit(dataView);
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var transformedData = model.Transform(dataView);
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var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, dataView);
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var onnxFileName = $"{estimator.ToString()}.onnx";
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var onnxModelPath = GetOutputPath(onnxFileName);
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SaveOnnxModel(onnxModel, onnxModelPath, null);
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// Compare model scores produced by ML.NET and ONNX's runtime.
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if (IsOnnxRuntimeSupported())
367+
{
368+
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
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string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
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string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
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var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
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var onnxTransformer = onnxEstimator.Fit(dataView);
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var onnxResult = onnxTransformer.Transform(dataView); //switched to 2 vause
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CompareSelectedR4ScalarColumns(transformedData.Schema[2].Name, outputNames[2], transformedData, onnxResult, 0); // compare score results
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}
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} */
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//Done();
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}
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private class DataPoint
191380
{
192381
[VectorType(3)]
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public float[] Features { get; set; }
@@ -380,8 +569,7 @@ public void LogisticRegressionOnnxConversionTest()
380569
var trainDataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename);
381570
var mlContext = new MLContext(seed: 1);
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var data = mlContext.Data.LoadFromTextFile<AdultData>(trainDataPath,
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separatorChar: ';'
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,
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separatorChar: ';',
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hasHeader: true);
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var cachedTrainData = mlContext.Data.Cache(data);
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var dynamicPipeline =
@@ -658,15 +846,21 @@ public void WordEmbeddingsTest()
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var model = pipeline.Fit(data);
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var transformedData = model.Transform(data);
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var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "Transforms", "Sentiment");
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var onnxTextName = "SmallWordEmbed.txt";
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var onnxFileName = "SmallWordEmbed.onnx";
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var onnxTextPath = GetOutputPath(subDir, onnxTextName);
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var onnxFilePath = GetOutputPath(subDir, onnxFileName);
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var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, data);
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SaveOnnxModel(onnxModel, onnxFilePath, onnxTextPath);
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if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows) && Environment.Is64BitProcess)
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{
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var onnxFileName = "WordEmbeddingEstimator.onnx";
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var onnxModelPath = GetOutputPath(onnxFileName);
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SaveOnnxModel(onnxModel, onnxModelPath, null);
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669-
CheckEquality(subDir, onnxTextName, parseOption: NumberParseOption.UseSingle);
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// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
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string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
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string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
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var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
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var onnxTransformer = onnxEstimator.Fit(data);
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var onnxResult = onnxTransformer.Transform(data);
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CompareSelectedR4VectorColumns("Embed", "Embed0", transformedData, onnxResult);
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}
670864
Done();
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}
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@@ -984,11 +1178,44 @@ private void CompareSelectedR4ScalarColumns(string leftColumnName, string rightC
9841178

9851179
// Scalar such as R4 (float) is converted to [1, 1]-tensor in ONNX format for consitency of making batch prediction.
9861180
Assert.Equal(1, actual.Length);
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Assert.Equal(expected, actual.GetItemOrDefault(0), precision);
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//Assert.Equal(expected, actual.GetItemOrDefault(0), precision);
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//Output.WriteLine(actual.GetItemOrDefault(0));
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System.Diagnostics.Debug.WriteLine("Actual: " + actual.GetItemOrDefault(0));
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System.Diagnostics.Debug.WriteLine("Expected: " + expected);
9881185
}
9891186
}
9901187
}
9911188

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private void CompareSelectedScalarColumns<T>(string leftColumnName, string rightColumnName, IDataView left, IDataView right)
1190+
{
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var leftColumn = left.Schema[leftColumnName];
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var rightColumn = right.Schema[rightColumnName];
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using (var expectedCursor = left.GetRowCursor(leftColumn))
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using (var actualCursor = right.GetRowCursor(rightColumn))
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{
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T expected = default;
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VBuffer<T> actual = default;
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var expectedGetter = expectedCursor.GetGetter<T>(leftColumn);
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var actualGetter = actualCursor.GetGetter<VBuffer<T>>(rightColumn);
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while (expectedCursor.MoveNext() && actualCursor.MoveNext())
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{
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expectedGetter(ref expected);
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actualGetter(ref actual);
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var actualVal = actual.GetItemOrDefault(0);
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1207+
Assert.Equal(1, actual.Length);
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1209+
if (typeof(T) == typeof(ReadOnlyMemory<Char>))
1210+
Assert.Equal(expected.ToString(), actualVal.ToString());
1211+
else
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Assert.Equal(expected, actualVal);
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}
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}
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}
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1217+
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private void SaveOnnxModel(ModelProto model, string binaryFormatPath, string textFormatPath)
9931220
{
9941221
DeleteOutputPath(binaryFormatPath); // Clean if such a file exists.

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