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Revert "draft regression test"
This reverts commit 1ad45c995516b9d39fc05aca855ce2abe96c407b.
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‎test/Microsoft.ML.Tests/OnnxConversionTest.cs

+14-241
Original file line numberDiff line numberDiff line change
@@ -21,52 +21,15 @@
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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")]
@@ -145,7 +108,8 @@ 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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public float[] Features;
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}
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150114
private class BreastCancerCatFeatureExample
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{
@@ -223,160 +187,7 @@ public void KmeansOnnxConversionTest()
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Done();
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}
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226-
[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);
269-
}
270-
Done();
271-
}
272-
273-
[Fact]
274-
// Conversion tests for regression
275-
public void regressionOnnxConversionTest()
276-
{
277-
/*
278-
var mlContext = new MLContext(seed: 1);
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string dataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename);
280-
281-
// Now read the file (remember though, readers are lazy, so the actual reading will happen when the data is accessed).
282-
var dataView = mlContext.Data.LoadFromTextFile<AdultData>(dataPath,
283-
separatorChar: ';',
284-
hasHeader: true);
285-
IEstimator<ITransformer>[] estimators = {
286-
//mlContext.Regression.Trainers.Ols(new OlsTrainer.Options() {
287-
// LabelColumnName = "Target",
288-
// FeatureColumnName = "FeatureVector",
289-
//}),
290-
//mlContext.Regression.Trainers.OnlineGradientDescent(new OnlineGradientDescentTrainer.Options(){
291-
// LabelColumnName = "Target",
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// FeatureColumnName = "FeatureVector",
293-
//}),
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//mlContext.Transforms.DetectAnomalyBySrCnn("Target","FeatureVector"), // needs separate data
295-
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"),
300-
};
301-
*/
302-
// Create a new context for ML.NET operations. It can be used for
303-
// exception tracking and logging, as a catalog of available operations
304-
// and as the source of randomness. Setting the seed to a fixed number
305-
// in this example to make outputs deterministic.
306-
var mlContext = new MLContext(seed: 0);
307-
308-
// Create a list of training data points.
309-
var dataPoints = GenerateRandomDataPoints(1000);
310-
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// Convert the list of data points to an IDataView object, which is
312-
// consumable by ML.NET API.
313-
var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
314-
315-
// Define the trainer.
316-
var pipeline = mlContext.Regression.Trainers.FastTreeTweedie(
317-
labelColumnName: nameof(DataPoint2.Label),
318-
featureColumnName: nameof(DataPoint2.Features));
319-
320-
// Train the model.
321-
var model = pipeline.Fit(trainingData);
322-
323-
// Create testing data. Use different random seed to make it different
324-
// from training data.
325-
var data = mlContext.Data.LoadFromEnumerable(
326-
GenerateRandomDataPoints(5, seed: 123));
327-
328-
// Run the model on test data set.
329-
var transformedTestData = model.Transform(data);
330-
// Convert IDataView object to a list.
331-
var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, data);
332-
// Convert IDataView object to a list.
333-
var predictions = mlContext.Data.CreateEnumerable<Prediction>(
334-
transformedTestData, reuseRowObject: false).ToList();
335-
foreach (var p in predictions)
336-
System.Diagnostics.Debug.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");
337-
// Compare results produced by ML.NET and ONNX's runtime.
338-
if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows) && Environment.Is64BitProcess)
339-
{
340-
var onnxFileName = "test.onnx";
341-
var onnxModelPath = GetOutputPath(onnxFileName);
342-
SaveOnnxModel(onnxModel, onnxModelPath, null);
343-
344-
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
345-
string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
346-
string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
347-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
348-
var onnxTransformer = onnxEstimator.Fit(data);
349-
var onnxResult = onnxTransformer.Transform(data);
350-
CompareSelectedR4ScalarColumns("Label", "Score0", data, onnxResult, 3);
351-
}
352-
Done();
353-
/*var initialPipeline = mlContext.Transforms.NormalizeMinMax("FeatureVector");
354-
foreach (var estimator in estimators)
355-
{
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//var pipeline = initialPipeline.Append(estimator);
357-
var pipeline = estimator;
358-
359-
var model = pipeline.Fit(dataView);
360-
var transformedData = model.Transform(dataView);
361-
var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, dataView);
362-
var onnxFileName = $"{estimator.ToString()}.onnx";
363-
var onnxModelPath = GetOutputPath(onnxFileName);
364-
SaveOnnxModel(onnxModel, onnxModelPath, null);
365-
// Compare model scores produced by ML.NET and ONNX's runtime.
366-
if (IsOnnxRuntimeSupported())
367-
{
368-
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
369-
string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
370-
string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
371-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
372-
var onnxTransformer = onnxEstimator.Fit(dataView);
373-
var onnxResult = onnxTransformer.Transform(dataView); //switched to 2 vause
374-
CompareSelectedR4ScalarColumns(transformedData.Schema[2].Name, outputNames[2], transformedData, onnxResult, 0); // compare score results
375-
}
376-
} */
377-
//Done();
378-
}
379-
private class DataPoint
190+
private class DataPoint
380191
{
381192
[VectorType(3)]
382193
public float[] Features { get; set; }
@@ -569,7 +380,8 @@ public void LogisticRegressionOnnxConversionTest()
569380
var trainDataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename);
570381
var mlContext = new MLContext(seed: 1);
571382
var data = mlContext.Data.LoadFromTextFile<AdultData>(trainDataPath,
572-
separatorChar: ';',
383+
separatorChar: ';'
384+
,
573385
hasHeader: true);
574386
var cachedTrainData = mlContext.Data.Cache(data);
575387
var dynamicPipeline =
@@ -846,21 +658,15 @@ public void WordEmbeddingsTest()
846658
var model = pipeline.Fit(data);
847659
var transformedData = model.Transform(data);
848660

661+
var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "Transforms", "Sentiment");
662+
var onnxTextName = "SmallWordEmbed.txt";
663+
var onnxFileName = "SmallWordEmbed.onnx";
664+
var onnxTextPath = GetOutputPath(subDir, onnxTextName);
665+
var onnxFilePath = GetOutputPath(subDir, onnxFileName);
849666
var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, data);
850-
if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows) && Environment.Is64BitProcess)
851-
{
852-
var onnxFileName = "WordEmbeddingEstimator.onnx";
853-
var onnxModelPath = GetOutputPath(onnxFileName);
854-
SaveOnnxModel(onnxModel, onnxModelPath, null);
667+
SaveOnnxModel(onnxModel, onnxFilePath, onnxTextPath);
855668

856-
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
857-
string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
858-
string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
859-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
860-
var onnxTransformer = onnxEstimator.Fit(data);
861-
var onnxResult = onnxTransformer.Transform(data);
862-
CompareSelectedR4VectorColumns("Embed", "Embed0", transformedData, onnxResult);
863-
}
669+
CheckEquality(subDir, onnxTextName, parseOption: NumberParseOption.UseSingle);
864670
Done();
865671
}
866672

@@ -1178,44 +984,11 @@ private void CompareSelectedR4ScalarColumns(string leftColumnName, string rightC
1178984

1179985
// Scalar such as R4 (float) is converted to [1, 1]-tensor in ONNX format for consitency of making batch prediction.
1180986
Assert.Equal(1, actual.Length);
1181-
//Assert.Equal(expected, actual.GetItemOrDefault(0), precision);
1182-
//Output.WriteLine(actual.GetItemOrDefault(0));
1183-
System.Diagnostics.Debug.WriteLine("Actual: " + actual.GetItemOrDefault(0));
1184-
System.Diagnostics.Debug.WriteLine("Expected: " + expected);
987+
Assert.Equal(expected, actual.GetItemOrDefault(0), precision);
1185988
}
1186989
}
1187990
}
1188991

1189-
private void CompareSelectedScalarColumns<T>(string leftColumnName, string rightColumnName, IDataView left, IDataView right)
1190-
{
1191-
var leftColumn = left.Schema[leftColumnName];
1192-
var rightColumn = right.Schema[rightColumnName];
1193-
1194-
using (var expectedCursor = left.GetRowCursor(leftColumn))
1195-
using (var actualCursor = right.GetRowCursor(rightColumn))
1196-
{
1197-
T expected = default;
1198-
VBuffer<T> actual = default;
1199-
var expectedGetter = expectedCursor.GetGetter<T>(leftColumn);
1200-
var actualGetter = actualCursor.GetGetter<VBuffer<T>>(rightColumn);
1201-
while (expectedCursor.MoveNext() && actualCursor.MoveNext())
1202-
{
1203-
expectedGetter(ref expected);
1204-
actualGetter(ref actual);
1205-
var actualVal = actual.GetItemOrDefault(0);
1206-
1207-
Assert.Equal(1, actual.Length);
1208-
1209-
if (typeof(T) == typeof(ReadOnlyMemory<Char>))
1210-
Assert.Equal(expected.ToString(), actualVal.ToString());
1211-
else
1212-
Assert.Equal(expected, actualVal);
1213-
}
1214-
}
1215-
}
1216-
1217-
1218-
1219992
private void SaveOnnxModel(ModelProto model, string binaryFormatPath, string textFormatPath)
1220993
{
1221994
DeleteOutputPath(binaryFormatPath); // Clean if such a file exists.

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