From ebd8a60c5671f930dde922c109b7fbd36bebe19b Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Thu, 3 Jan 2019 14:11:33 -0800 Subject: [PATCH 01/23] Prototype of new ONNX converter and an end-to-end test --- .../TransformerChainOnnxConverter.cs | 42 ++++++++++ .../Microsoft.ML.Tests.csproj | 1 + test/Microsoft.ML.Tests/OnnxConversionTest.cs | 80 +++++++++++++++++++ 3 files changed, 123 insertions(+) create mode 100644 src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs create mode 100644 test/Microsoft.ML.Tests/OnnxConversionTest.cs diff --git a/src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs b/src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs new file mode 100644 index 0000000000..45852d2a49 --- /dev/null +++ b/src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs @@ -0,0 +1,42 @@ +using Microsoft.ML.Core.Data; +using Microsoft.ML.Data; +using Microsoft.ML.UniversalModelFormat.Onnx; + +namespace Microsoft.ML.Model.Onnx +{ + public class TransformerChainOnnxConverter + { + public static ModelProto Convert(TransformerChain chain, Schema inputSchema) where T : class, ITransformer + { + var env = new MLContext(); + var onnxContext = new OnnxContextImpl(env, "model", "ML.NET", "0", 0, "com.test", Model.Onnx.OnnxVersion.Stable); + + for (int i = 0; i < inputSchema.Count; i++) + { + string colName = inputSchema[i].Name; + onnxContext.AddInputVariable(inputSchema[i].Type, colName); + } + + foreach (var t in chain) + { + var mapper = t.GetRowToRowMapper(inputSchema); + inputSchema = t.GetOutputSchema(inputSchema); + (mapper as ISaveAsOnnx).SaveAsOnnx(onnxContext); + } + + for (int i = 0; i < inputSchema.Count; ++i) + { + if (inputSchema[i].IsHidden) + continue; + + var idataviewColumnName = inputSchema[i].Name; + + var variableName = onnxContext.TryGetVariableName(idataviewColumnName); + var trueVariableName = onnxContext.AddIntermediateVariable(null, idataviewColumnName, true); + onnxContext.CreateNode("Identity", variableName, trueVariableName, onnxContext.GetNodeName("Identity"), ""); + onnxContext.AddOutputVariable(inputSchema[i].Type, trueVariableName); + } + return onnxContext.MakeModel(); + } + } +} diff --git a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj index 12c5547921..52b5283a52 100644 --- a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj +++ b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj @@ -14,6 +14,7 @@ + diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs new file mode 100644 index 0000000000..5f0c173bd1 --- /dev/null +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -0,0 +1,80 @@ +using System.Collections.Generic; +using System.Linq; +using Google.Protobuf; +using Microsoft.ML.Data; +using Microsoft.ML.Model.Onnx; +using Microsoft.ML.RunTests; +using Microsoft.ML.Transforms; +using Xunit; +using Xunit.Abstractions; + +namespace Microsoft.ML.Tests +{ + public class OnnxConversionTest : BaseTestBaseline + { + private class AdultData + { + [LoadColumn(0, 10), ColumnName("FeatureVector")] + public float Features { get; set; } + + [LoadColumn(11)] + public float Target { get; set; } + } + + public OnnxConversionTest(ITestOutputHelper output) : base(output) + { + } + + [Fact] + public void SimplePipelineOnnxConversionTest() + { + var trainDataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename); + var mlContext = new MLContext(); + + var trainData = mlContext.Data.ReadFromTextFile(trainDataPath, + hasHeader: true, + separatorChar: ';' + ); + + var cachedTrainData = mlContext.Data.Cache(trainData); + + var dynamicPipeline = + mlContext.Transforms.Normalize("FeatureVector") + .AppendCacheCheckpoint(mlContext) + .Append(mlContext.Regression.Trainers.StochasticDualCoordinateAscent(labelColumn: "Target", featureColumn: "FeatureVector")); + + var model = dynamicPipeline.Fit(trainData); + var transformedData = model.Transform(trainData); + + var onnxModel = TransformerChainOnnxConverter.Convert(model, trainData.Schema); + + var onnxFileName = "model.onnx"; + var onnxFilePath = GetOutputPath(onnxFileName); + using (var file = (mlContext as IHostEnvironment).CreateOutputFile(onnxFilePath)) + using (var stream = file.CreateWriteStream()) + onnxModel.WriteTo(stream); + + string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray(); + string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray(); + var onnxEstimator = new OnnxScoringEstimator(mlContext, onnxFilePath, inputNames, outputNames); + var onnxTransformer = onnxEstimator.Fit(trainData); + var onnxResult = onnxTransformer.Transform(trainData); + + using (var expectedCursor = transformedData.GetRowCursor(columnIndex => columnIndex == transformedData.Schema["Score"].Index)) + using (var actualCursor = onnxResult.GetRowCursor(columnIndex => columnIndex == onnxResult.Schema["Score0"].Index)) + { + float expected = default; + VBuffer actual = default; + var expectedGetter = expectedCursor.GetGetter(transformedData.Schema["Score"].Index); + var actualGetter = actualCursor.GetGetter>(onnxResult.Schema["Score0"].Index); + while(expectedCursor.MoveNext() && actualCursor.MoveNext()) + { + expectedGetter(ref expected); + actualGetter(ref actual); + + Assert.Equal(expected, actual.GetValues()[0], 1); + } + } + } + } +} From a3a2fe2923f5dffa0e6f671df446ad4aa0891de9 Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Thu, 3 Jan 2019 15:35:18 -0800 Subject: [PATCH 02/23] Reuse existing code to do conversion and polish a test --- src/Microsoft.ML.Onnx/SaveOnnxCommand.cs | 92 ++++++++++--------- .../TransformerChainOnnxConverter.cs | 40 +++----- test/Microsoft.ML.Tests/OnnxConversionTest.cs | 33 ++++--- 3 files changed, 80 insertions(+), 85 deletions(-) diff --git a/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs b/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs index 250969850f..a07e1b6156 100644 --- a/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs +++ b/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs @@ -12,6 +12,7 @@ using Microsoft.ML.EntryPoints; using Microsoft.ML.Internal.Utilities; using Microsoft.ML.Model.Onnx; +using Microsoft.ML.UniversalModelFormat.Onnx; using Newtonsoft.Json; [assembly: LoadableClass(SaveOnnxCommand.Summary, typeof(SaveOnnxCommand), typeof(SaveOnnxCommand.Arguments), typeof(SignatureCommand), @@ -113,9 +114,11 @@ public override void Run() } } - private void GetPipe(OnnxContextImpl ctx, IChannel ch, IDataView end, out IDataView source, out IDataView trueEnd, out LinkedList transforms) + [BestFriend] + internal static void GetPipe(OnnxContextImpl ctx, IChannel ch, IDataView end, out IDataView source, out IDataView trueEnd, out LinkedList transforms) { - Host.AssertValue(end); + Contracts.AssertValue(end); + source = trueEnd = (end as CompositeDataLoader)?.View ?? end; IDataTransform transform = source as IDataTransform; transforms = new LinkedList(); @@ -134,7 +137,51 @@ private void GetPipe(OnnxContextImpl ctx, IChannel ch, IDataView end, out IDataV transform = (source = transform.Source) as IDataTransform; } - Host.AssertValue(source); + Contracts.AssertValue(source); + } + + [BestFriend] + internal static ModelProto ConvertTransformListToOnnxModel(OnnxContextImpl ctx, IDataView inputData, IDataView outputData, + LinkedList transforms, HashSet inputColumnNamesToDrop=null, HashSet outputColumnNamesToDrop=null) + { + inputColumnNamesToDrop = inputColumnNamesToDrop ?? new HashSet(); + outputColumnNamesToDrop = outputColumnNamesToDrop ?? new HashSet(); + HashSet inputColumns = new HashSet(); + // Create graph inputs. + for (int i = 0; i < inputData.Schema.Count; i++) + { + string colName = inputData.Schema[i].Name; + if(inputColumnNamesToDrop.Contains(colName)) + continue; + + ctx.AddInputVariable(inputData.Schema[i].Type, colName); + inputColumns.Add(colName); + } + + // Create graph nodes, outputs and intermediate values. + foreach (var trans in transforms) + trans.SaveAsOnnx(ctx); + + // Add graph outputs. + for (int i = 0; i < outputData.Schema.Count; ++i) + { + if (outputData.Schema[i].IsHidden) + continue; + + var idataviewColumnName = outputData.Schema[i].Name; + + // Since the last IDataView also contains columns of the initial IDataView, last IDataView's columns found in + // _inputToDrop should be removed too. + if (inputColumnNamesToDrop.Contains(idataviewColumnName) || outputColumnNamesToDrop.Contains(idataviewColumnName)) + continue; + + var variableName = ctx.TryGetVariableName(idataviewColumnName); + var trueVariableName = ctx.AddIntermediateVariable(null, idataviewColumnName, true); + ctx.CreateNode("Identity", variableName, trueVariableName, ctx.GetNodeName("Identity"), ""); + ctx.AddOutputVariable(outputData.Schema[i].Type, trueVariableName); + } + + return ctx.MakeModel(); } private void Run(IChannel ch) @@ -210,45 +257,8 @@ private void Run(IChannel ch) nameof(Arguments.LoadPredictor), "We were explicitly told to load the predictor but one was not present."); } - HashSet inputColumns = new HashSet(); - //Create graph inputs. - for (int i = 0; i < source.Schema.Count; i++) - { - string colName = source.Schema[i].Name; - if(_inputsToDrop.Contains(colName)) - continue; - - ctx.AddInputVariable(source.Schema[i].Type, colName); - inputColumns.Add(colName); - } - - //Create graph nodes, outputs and intermediate values. - foreach (var trans in transforms) - { - Host.Assert(trans.CanSaveOnnx(ctx)); - trans.SaveAsOnnx(ctx); - } - - //Add graph outputs. - for (int i = 0; i < end.Schema.Count; ++i) - { - if (end.Schema[i].IsHidden) - continue; - - var idataviewColumnName = end.Schema[i].Name; - - // Since the last IDataView also contains columns of the initial IDataView, last IDataView's columns found in - // _inputToDrop should be removed too. - if (_inputsToDrop.Contains(idataviewColumnName) || _outputsToDrop.Contains(idataviewColumnName)) - continue; - - var variableName = ctx.TryGetVariableName(idataviewColumnName); - var trueVariableName = ctx.AddIntermediateVariable(null, idataviewColumnName, true); - ctx.CreateNode("Identity", variableName, trueVariableName, ctx.GetNodeName("Identity"), ""); - ctx.AddOutputVariable(end.Schema[i].Type, trueVariableName); - } + var model = ConvertTransformListToOnnxModel(ctx, source, end, transforms, _inputsToDrop, _outputsToDrop); - var model = ctx.MakeModel(); using (var file = Host.CreateOutputFile(_outputModelPath)) using (var stream = file.CreateWriteStream()) model.WriteTo(stream); diff --git a/src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs b/src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs index 45852d2a49..100b2dc5ed 100644 --- a/src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs +++ b/src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs @@ -1,4 +1,5 @@ -using Microsoft.ML.Core.Data; +using System.Collections.Generic; +using Microsoft.ML.Core.Data; using Microsoft.ML.Data; using Microsoft.ML.UniversalModelFormat.Onnx; @@ -6,37 +7,18 @@ namespace Microsoft.ML.Model.Onnx { public class TransformerChainOnnxConverter { - public static ModelProto Convert(TransformerChain chain, Schema inputSchema) where T : class, ITransformer + public static ModelProto Convert(TransformerChain chain, IDataView inputData, HashSet inputColumnNamesToDrop=null, HashSet outputColumnNamesToDrop=null) where T : class, ITransformer { var env = new MLContext(); - var onnxContext = new OnnxContextImpl(env, "model", "ML.NET", "0", 0, "com.test", Model.Onnx.OnnxVersion.Stable); + var ctx = new OnnxContextImpl(env, "model", "ML.NET", "0", 0, "com.test", OnnxVersion.Stable); + var outputData = chain.Transform(inputData); + IDataView source = null; + IDataView trueEnd = null; + LinkedList transforms = null; + using (var ch = (env as IChannelProvider).Start("ONNX conversion")) + SaveOnnxCommand.GetPipe(ctx, ch, outputData, out source, out trueEnd, out transforms); - for (int i = 0; i < inputSchema.Count; i++) - { - string colName = inputSchema[i].Name; - onnxContext.AddInputVariable(inputSchema[i].Type, colName); - } - - foreach (var t in chain) - { - var mapper = t.GetRowToRowMapper(inputSchema); - inputSchema = t.GetOutputSchema(inputSchema); - (mapper as ISaveAsOnnx).SaveAsOnnx(onnxContext); - } - - for (int i = 0; i < inputSchema.Count; ++i) - { - if (inputSchema[i].IsHidden) - continue; - - var idataviewColumnName = inputSchema[i].Name; - - var variableName = onnxContext.TryGetVariableName(idataviewColumnName); - var trueVariableName = onnxContext.AddIntermediateVariable(null, idataviewColumnName, true); - onnxContext.CreateNode("Identity", variableName, trueVariableName, onnxContext.GetNodeName("Identity"), ""); - onnxContext.AddOutputVariable(inputSchema[i].Type, trueVariableName); - } - return onnxContext.MakeModel(); + return SaveOnnxCommand.ConvertTransformListToOnnxModel(ctx, source, trueEnd, transforms, inputColumnNamesToDrop, outputColumnNamesToDrop); } } } diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index 5f0c173bd1..8b888990ea 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -1,5 +1,4 @@ -using System.Collections.Generic; -using System.Linq; +using System.Linq; using Google.Protobuf; using Microsoft.ML.Data; using Microsoft.ML.Model.Onnx; @@ -25,41 +24,45 @@ public OnnxConversionTest(ITestOutputHelper output) : base(output) { } + /// + /// In this test, we convert a trained into ONNX file and then + /// call to evaluate that file. The outputs of are checked against the original + /// ML.NET model's outputs. + /// [Fact] - public void SimplePipelineOnnxConversionTest() + public void SimpleEndToEndOnnxConversionTest() { + // Step 1: Create and train a ML.NET pipeline. var trainDataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename); var mlContext = new MLContext(); - - var trainData = mlContext.Data.ReadFromTextFile(trainDataPath, + var data = mlContext.Data.ReadFromTextFile(trainDataPath, hasHeader: true, separatorChar: ';' ); - - var cachedTrainData = mlContext.Data.Cache(trainData); - + var cachedTrainData = mlContext.Data.Cache(data); var dynamicPipeline = mlContext.Transforms.Normalize("FeatureVector") .AppendCacheCheckpoint(mlContext) .Append(mlContext.Regression.Trainers.StochasticDualCoordinateAscent(labelColumn: "Target", featureColumn: "FeatureVector")); + var model = dynamicPipeline.Fit(data); + var transformedData = model.Transform(data); - var model = dynamicPipeline.Fit(trainData); - var transformedData = model.Transform(trainData); - - var onnxModel = TransformerChainOnnxConverter.Convert(model, trainData.Schema); - + // Step 2: Convert ML.NET model to ONNX format and save it as a file. + var onnxModel = TransformerChainOnnxConverter.Convert(model, data); var onnxFileName = "model.onnx"; var onnxFilePath = GetOutputPath(onnxFileName); using (var file = (mlContext as IHostEnvironment).CreateOutputFile(onnxFilePath)) using (var stream = file.CreateWriteStream()) onnxModel.WriteTo(stream); + // Step 3: Evaluate the saved ONNX model using the data used to train the ML.NET pipeline. string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray(); string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray(); var onnxEstimator = new OnnxScoringEstimator(mlContext, onnxFilePath, inputNames, outputNames); - var onnxTransformer = onnxEstimator.Fit(trainData); - var onnxResult = onnxTransformer.Transform(trainData); + var onnxTransformer = onnxEstimator.Fit(data); + var onnxResult = onnxTransformer.Transform(data); + // Step 4: Compare ONNX and ML.NET results. using (var expectedCursor = transformedData.GetRowCursor(columnIndex => columnIndex == transformedData.Schema["Score"].Index)) using (var actualCursor = onnxResult.GetRowCursor(columnIndex => columnIndex == onnxResult.Schema["Score0"].Index)) { From f6d55744fef02d927f76e790702fa692e14f5b89 Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Thu, 3 Jan 2019 20:37:34 -0800 Subject: [PATCH 03/23] Test Kmeans as well --- .../TransformerChainOnnxConverter.cs | 22 ++- test/Microsoft.ML.Tests/OnnxConversionTest.cs | 149 ++++++++++++++++-- 2 files changed, 155 insertions(+), 16 deletions(-) diff --git a/src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs b/src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs index 100b2dc5ed..6e8bebf56f 100644 --- a/src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs +++ b/src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs @@ -12,13 +12,27 @@ public static ModelProto Convert(TransformerChain chain, IDataView inputDa var env = new MLContext(); var ctx = new OnnxContextImpl(env, "model", "ML.NET", "0", 0, "com.test", OnnxVersion.Stable); var outputData = chain.Transform(inputData); - IDataView source = null; - IDataView trueEnd = null; + IDataView root = null; + IDataView sink = null; LinkedList transforms = null; using (var ch = (env as IChannelProvider).Start("ONNX conversion")) - SaveOnnxCommand.GetPipe(ctx, ch, outputData, out source, out trueEnd, out transforms); + SaveOnnxCommand.GetPipe(ctx, ch, outputData, out root, out sink, out transforms); - return SaveOnnxCommand.ConvertTransformListToOnnxModel(ctx, source, trueEnd, transforms, inputColumnNamesToDrop, outputColumnNamesToDrop); + return SaveOnnxCommand.ConvertTransformListToOnnxModel(ctx, root, sink, transforms, inputColumnNamesToDrop, outputColumnNamesToDrop); + } + + public static ModelProto Convert(ITransformer transform, IDataView inputData, HashSet inputColumnNamesToDrop=null, HashSet outputColumnNamesToDrop=null) + { + var env = new MLContext(seed: 1); + var ctx = new OnnxContextImpl(env, "model", "ML.NET", "0", 0, "com.test", OnnxVersion.Stable); + var outputData = transform.Transform(inputData); + IDataView root = null; + IDataView sink = null; + LinkedList transforms = null; + using (var ch = (env as IChannelProvider).Start("ONNX conversion")) + SaveOnnxCommand.GetPipe(ctx, ch, outputData, out root, out sink, out transforms); + + return SaveOnnxCommand.ConvertTransformListToOnnxModel(ctx, root, sink, transforms, inputColumnNamesToDrop, outputColumnNamesToDrop); } } } diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index 8b888990ea..1b31321c90 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -1,9 +1,15 @@ -using System.Linq; +using System.Collections.Generic; +using System.IO; +using System.Linq; +using System.Runtime.InteropServices; +using System.Text.RegularExpressions; using Google.Protobuf; using Microsoft.ML.Data; using Microsoft.ML.Model.Onnx; using Microsoft.ML.RunTests; using Microsoft.ML.Transforms; +using Microsoft.ML.UniversalModelFormat.Onnx; +using Newtonsoft.Json; using Xunit; using Xunit.Abstractions; @@ -32,6 +38,9 @@ public OnnxConversionTest(ITestOutputHelper output) : base(output) [Fact] public void SimpleEndToEndOnnxConversionTest() { + if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) + return; + // Step 1: Create and train a ML.NET pipeline. var trainDataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename); var mlContext = new MLContext(); @@ -50,34 +59,150 @@ public void SimpleEndToEndOnnxConversionTest() // Step 2: Convert ML.NET model to ONNX format and save it as a file. var onnxModel = TransformerChainOnnxConverter.Convert(model, data); var onnxFileName = "model.onnx"; - var onnxFilePath = GetOutputPath(onnxFileName); - using (var file = (mlContext as IHostEnvironment).CreateOutputFile(onnxFilePath)) - using (var stream = file.CreateWriteStream()) - onnxModel.WriteTo(stream); + var onnxModelPath = GetOutputPath(onnxFileName); + SaveOnnxModel(onnxModel, onnxModelPath, null); // Step 3: Evaluate the saved ONNX model using the data used to train the ML.NET pipeline. string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray(); string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray(); - var onnxEstimator = new OnnxScoringEstimator(mlContext, onnxFilePath, inputNames, outputNames); + var onnxEstimator = new OnnxScoringEstimator(mlContext, onnxModelPath, inputNames, outputNames); var onnxTransformer = onnxEstimator.Fit(data); var onnxResult = onnxTransformer.Transform(data); // Step 4: Compare ONNX and ML.NET results. - using (var expectedCursor = transformedData.GetRowCursor(columnIndex => columnIndex == transformedData.Schema["Score"].Index)) - using (var actualCursor = onnxResult.GetRowCursor(columnIndex => columnIndex == onnxResult.Schema["Score0"].Index)) + CompareSelectedR4ScalarColumns("Score", "Score0", transformedData, onnxResult, 2); + Done(); + } + + private class BreastCancerFeatureVector + { + [LoadColumn(1, 9), VectorType(9)] + public float[] Features; + } + + private void CreateDummyExamplesToMakeComplierHappy() + { + var dummyExample = new BreastCancerFeatureVector() { Features = null }; + } + + [Fact] + public void KmeansOnnxConversionTest() + { + if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) + return; + + // Create a new context for ML.NET operations. It can be used for exception tracking and logging, + // as a catalog of available operations and as the source of randomness. + var mlContext = new MLContext(seed: 1); + + string dataPath = GetDataPath("breast-cancer.txt"); + // Now read the file (remember though, readers are lazy, so the actual reading will happen when the data is accessed). + var data = mlContext.Data.ReadFromTextFile(dataPath, + hasHeader: true, + separatorChar: '\t' ); + + var pipeline = mlContext.Transforms.Normalize("Features"). + Append(mlContext.Clustering.Trainers.KMeans(features: "Features", advancedSettings: settings => + { + settings.MaxIterations = 1; + settings.K = 4; + settings.NumThreads = 1; + settings.InitAlgorithm = Trainers.KMeans.KMeansPlusPlusTrainer.InitAlgorithm.KMeansPlusPlus; + })); + + var model = pipeline.Fit(data); + var transformedData = model.Transform(data); + + var onnxModel = TransformerChainOnnxConverter.Convert(model, data); + + var onnxFileName = "model.onnx"; + var onnxModelPath = GetOutputPath(onnxFileName); + SaveOnnxModel(onnxModel, onnxModelPath, null); + + // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline. + string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray(); + string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray(); + var onnxEstimator = new OnnxScoringEstimator(mlContext, onnxModelPath, inputNames, outputNames); + var onnxTransformer = onnxEstimator.Fit(data); + var onnxResult = onnxTransformer.Transform(data); + + CompareSelectedR4VectorColumns("Score", "Score0", transformedData, onnxResult, 3); + Done(); + } + + private void CompareSelectedR4VectorColumns(string leftColumnName, string rightColumnName, IDataView left, IDataView right, int precision=6) + { + var leftColumnIndex = left.Schema[leftColumnName].Index; + var rightColumnIndex = right.Schema[rightColumnName].Index; + + using (var expectedCursor = left.GetRowCursor(columnIndex => leftColumnIndex == columnIndex)) + using (var actualCursor = right.GetRowCursor(columnIndex => rightColumnIndex == columnIndex)) + { + VBuffer expected = default; + VBuffer actual = default; + var expectedGetter = expectedCursor.GetGetter>(leftColumnIndex); + var actualGetter = actualCursor.GetGetter>(rightColumnIndex); + while (expectedCursor.MoveNext() && actualCursor.MoveNext()) + { + expectedGetter(ref expected); + actualGetter(ref actual); + + Assert.Equal(expected.Length, actual.Length); + for (int i = 0; i < expected.Length; ++i) + Assert.Equal(expected.GetItemOrDefault(i), actual.GetItemOrDefault(i), precision); + } + } + } + + private void CompareSelectedR4ScalarColumns(string leftColumnName, string rightColumnName, IDataView left, IDataView right, int precision=6) + { + var leftColumnIndex = left.Schema[leftColumnName].Index; + var rightColumnIndex = right.Schema[rightColumnName].Index; + + using (var expectedCursor = left.GetRowCursor(columnIndex => leftColumnIndex == columnIndex)) + using (var actualCursor = right.GetRowCursor(columnIndex => rightColumnIndex == columnIndex)) { float expected = default; VBuffer actual = default; - var expectedGetter = expectedCursor.GetGetter(transformedData.Schema["Score"].Index); - var actualGetter = actualCursor.GetGetter>(onnxResult.Schema["Score0"].Index); - while(expectedCursor.MoveNext() && actualCursor.MoveNext()) + var expectedGetter = expectedCursor.GetGetter(leftColumnIndex); + var actualGetter = actualCursor.GetGetter>(rightColumnIndex); + while (expectedCursor.MoveNext() && actualCursor.MoveNext()) { expectedGetter(ref expected); actualGetter(ref actual); - Assert.Equal(expected, actual.GetValues()[0], 1); + // Scalar such as R4 (float) is converted to [1, 1]-tensor in ONNX format for consitency of making batch prediction. + Assert.Equal(1, actual.Length); + Assert.Equal(expected, actual.GetItemOrDefault(0), precision); } } } + + private void SaveOnnxModel(ModelProto model, string binaryFormatPath, string textFormatPath) + { + DeleteOutputPath(binaryFormatPath); // Clean if such a file exists. + DeleteOutputPath(textFormatPath); + + if (binaryFormatPath != null) + using (var file = Env.CreateOutputFile(binaryFormatPath)) + using (var stream = file.CreateWriteStream()) + model.WriteTo(stream); + + if (textFormatPath != null) + { + using (var file = Env.CreateOutputFile(textFormatPath)) + using (var stream = file.CreateWriteStream()) + using (var writer = new StreamWriter(stream)) + { + var parsedJson = JsonConvert.DeserializeObject(model.ToString()); + writer.Write(JsonConvert.SerializeObject(parsedJson, Formatting.Indented)); + } + + // Strip the version information. + var fileText = File.ReadAllText(textFormatPath); + fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\""); + File.WriteAllText(textFormatPath, fileText); + } + } } } From ee4d2016ee219487fef6f7cd0634ec4a46da03ca Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Fri, 4 Jan 2019 10:04:39 -0800 Subject: [PATCH 04/23] 1. Introduce ONNX conversion as an extention to MLContext 2. Address minor comments Remove two best friends --- .../Model/ModelOperationsCatalog.cs | 15 +++++++- src/Microsoft.ML.Onnx/ProtabilityCatalog.cs | 32 ++++++++++++++++ src/Microsoft.ML.Onnx/SaveOnnxCommand.cs | 2 - .../TransformerChainOnnxConverter.cs | 38 ------------------- test/Microsoft.ML.Tests/OnnxConversionTest.cs | 12 +++--- 5 files changed, 51 insertions(+), 48 deletions(-) create mode 100644 src/Microsoft.ML.Onnx/ProtabilityCatalog.cs delete mode 100644 src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs diff --git a/src/Microsoft.ML.Data/Model/ModelOperationsCatalog.cs b/src/Microsoft.ML.Data/Model/ModelOperationsCatalog.cs index aa8b722147..830f8ac6bf 100644 --- a/src/Microsoft.ML.Data/Model/ModelOperationsCatalog.cs +++ b/src/Microsoft.ML.Data/Model/ModelOperationsCatalog.cs @@ -17,12 +17,15 @@ public sealed class ModelOperationsCatalog public ExplainabilityTransforms Explainability { get; } + public PortabilityTransforms Portability { get; } + internal ModelOperationsCatalog(IHostEnvironment env) { Contracts.AssertValue(env); Environment = env; Explainability = new ExplainabilityTransforms(this); + Portability = new PortabilityTransforms(this); } public abstract class SubCatalogBase @@ -33,7 +36,6 @@ protected SubCatalogBase(ModelOperationsCatalog owner) { Environment = owner.Environment; } - } /// @@ -60,6 +62,17 @@ internal ExplainabilityTransforms(ModelOperationsCatalog owner) : base(owner) } } + /// + /// The catalog of model protability operations. Member function of this classes are able to convert the associated object to a protable format, + /// so that the fitted pipeline can easily be depolyed to other platforms. Currently, the only supported format is ONNX (https://github.com/onnx/onnx). + /// + public sealed class PortabilityTransforms : SubCatalogBase + { + internal PortabilityTransforms(ModelOperationsCatalog owner) : base(owner) + { + } + } + /// /// Create a prediction engine for one-time prediction. /// diff --git a/src/Microsoft.ML.Onnx/ProtabilityCatalog.cs b/src/Microsoft.ML.Onnx/ProtabilityCatalog.cs new file mode 100644 index 0000000000..65cde286c6 --- /dev/null +++ b/src/Microsoft.ML.Onnx/ProtabilityCatalog.cs @@ -0,0 +1,32 @@ +using System.Collections.Generic; +using Microsoft.ML.Core.Data; +using Microsoft.ML.Data; +using Microsoft.ML.Model.Onnx; +using Microsoft.ML.UniversalModelFormat.Onnx; + +namespace Microsoft.ML +{ + public static class ProtabilityCatalog + { + /// + /// Convert the specified to ONNX format. Note that ONNX uses Google's Protobuf so the returned value is a Protobuf object. + /// + /// A field in which this function associated with. + /// The that will be converted into ONNX format. + /// The input of the specified transform. + /// + public static ModelProto ConvertToOnnx(this ModelOperationsCatalog.PortabilityTransforms catalog, ITransformer transform, IDataView inputData) + { + var env = new MLContext(seed: 1); + var ctx = new OnnxContextImpl(env, "model", "ML.NET", "0", 0, "com.microsoft", OnnxVersion.Stable); + var outputData = transform.Transform(inputData); + IDataView root = null; + IDataView sink = null; + LinkedList transforms = null; + using (var ch = (env as IChannelProvider).Start("ONNX conversion")) + SaveOnnxCommand.GetPipe(ctx, ch, outputData, out root, out sink, out transforms); + + return SaveOnnxCommand.ConvertTransformListToOnnxModel(ctx, root, sink, transforms, null, null); + } + } +} diff --git a/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs b/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs index a07e1b6156..142dd0a8d8 100644 --- a/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs +++ b/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs @@ -114,7 +114,6 @@ public override void Run() } } - [BestFriend] internal static void GetPipe(OnnxContextImpl ctx, IChannel ch, IDataView end, out IDataView source, out IDataView trueEnd, out LinkedList transforms) { Contracts.AssertValue(end); @@ -140,7 +139,6 @@ internal static void GetPipe(OnnxContextImpl ctx, IChannel ch, IDataView end, ou Contracts.AssertValue(source); } - [BestFriend] internal static ModelProto ConvertTransformListToOnnxModel(OnnxContextImpl ctx, IDataView inputData, IDataView outputData, LinkedList transforms, HashSet inputColumnNamesToDrop=null, HashSet outputColumnNamesToDrop=null) { diff --git a/src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs b/src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs deleted file mode 100644 index 6e8bebf56f..0000000000 --- a/src/Microsoft.ML.Onnx/TransformerChainOnnxConverter.cs +++ /dev/null @@ -1,38 +0,0 @@ -using System.Collections.Generic; -using Microsoft.ML.Core.Data; -using Microsoft.ML.Data; -using Microsoft.ML.UniversalModelFormat.Onnx; - -namespace Microsoft.ML.Model.Onnx -{ - public class TransformerChainOnnxConverter - { - public static ModelProto Convert(TransformerChain chain, IDataView inputData, HashSet inputColumnNamesToDrop=null, HashSet outputColumnNamesToDrop=null) where T : class, ITransformer - { - var env = new MLContext(); - var ctx = new OnnxContextImpl(env, "model", "ML.NET", "0", 0, "com.test", OnnxVersion.Stable); - var outputData = chain.Transform(inputData); - IDataView root = null; - IDataView sink = null; - LinkedList transforms = null; - using (var ch = (env as IChannelProvider).Start("ONNX conversion")) - SaveOnnxCommand.GetPipe(ctx, ch, outputData, out root, out sink, out transforms); - - return SaveOnnxCommand.ConvertTransformListToOnnxModel(ctx, root, sink, transforms, inputColumnNamesToDrop, outputColumnNamesToDrop); - } - - public static ModelProto Convert(ITransformer transform, IDataView inputData, HashSet inputColumnNamesToDrop=null, HashSet outputColumnNamesToDrop=null) - { - var env = new MLContext(seed: 1); - var ctx = new OnnxContextImpl(env, "model", "ML.NET", "0", 0, "com.test", OnnxVersion.Stable); - var outputData = transform.Transform(inputData); - IDataView root = null; - IDataView sink = null; - LinkedList transforms = null; - using (var ch = (env as IChannelProvider).Start("ONNX conversion")) - SaveOnnxCommand.GetPipe(ctx, ch, outputData, out root, out sink, out transforms); - - return SaveOnnxCommand.ConvertTransformListToOnnxModel(ctx, root, sink, transforms, inputColumnNamesToDrop, outputColumnNamesToDrop); - } - } -} diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index 1b31321c90..27bbbe9b38 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -1,11 +1,9 @@ -using System.Collections.Generic; -using System.IO; +using System.IO; using System.Linq; using System.Runtime.InteropServices; using System.Text.RegularExpressions; using Google.Protobuf; using Microsoft.ML.Data; -using Microsoft.ML.Model.Onnx; using Microsoft.ML.RunTests; using Microsoft.ML.Transforms; using Microsoft.ML.UniversalModelFormat.Onnx; @@ -43,7 +41,7 @@ public void SimpleEndToEndOnnxConversionTest() // Step 1: Create and train a ML.NET pipeline. var trainDataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename); - var mlContext = new MLContext(); + var mlContext = new MLContext(seed: 1, conc: 1); var data = mlContext.Data.ReadFromTextFile(trainDataPath, hasHeader: true, separatorChar: ';' @@ -57,7 +55,7 @@ public void SimpleEndToEndOnnxConversionTest() var transformedData = model.Transform(data); // Step 2: Convert ML.NET model to ONNX format and save it as a file. - var onnxModel = TransformerChainOnnxConverter.Convert(model, data); + var onnxModel = mlContext.Model.Portability.ConvertToOnnx(model, data); var onnxFileName = "model.onnx"; var onnxModelPath = GetOutputPath(onnxFileName); SaveOnnxModel(onnxModel, onnxModelPath, null); @@ -93,7 +91,7 @@ public void KmeansOnnxConversionTest() // Create a new context for ML.NET operations. It can be used for exception tracking and logging, // as a catalog of available operations and as the source of randomness. - var mlContext = new MLContext(seed: 1); + var mlContext = new MLContext(seed: 1, conc: 1); string dataPath = GetDataPath("breast-cancer.txt"); // Now read the file (remember though, readers are lazy, so the actual reading will happen when the data is accessed). @@ -113,7 +111,7 @@ public void KmeansOnnxConversionTest() var model = pipeline.Fit(data); var transformedData = model.Transform(data); - var onnxModel = TransformerChainOnnxConverter.Convert(model, data); + var onnxModel = mlContext.Model.Portability.ConvertToOnnx(model, data); var onnxFileName = "model.onnx"; var onnxModelPath = GetOutputPath(onnxFileName); From 4f187b1e6f2780634b598eefc46735184778339e Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Fri, 4 Jan 2019 11:09:14 -0800 Subject: [PATCH 05/23] Add test comparison --- .../Onnx/Cluster/BreastCancer/Kmeans.txt | 338 ++++++++++++++++++ .../Onnx/Regression/Adult/SimplePipeline.txt | 276 ++++++++++++++ test/Microsoft.ML.Tests/OnnxConversionTest.cs | 64 ++-- 3 files changed, 653 insertions(+), 25 deletions(-) create mode 100644 test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt create mode 100644 test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt diff --git a/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt b/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt new file mode 100644 index 0000000000..962681c49d --- /dev/null +++ b/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt @@ -0,0 +1,338 @@ +{ + "irVersion": "3", + "producerName": "ML.NET", + "producerVersion": "##VERSION##", + "domain": "com.microsoft", + "graph": { + "node": [ + { + "input": [ + "Features" + ], + "output": [ + "Features0" + ], + "name": "Scaler", + "opType": "Scaler", + "attribute": [ + { + "name": "offset", + "floats": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0 + ], + "type": "FLOATS" + }, + { + "name": "scale", + "floats": [ + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1 + ], + "type": "FLOATS" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "Features0" + ], + "output": [ + "X2" + ], + "name": "ReduceSumSquare", + "opType": "ReduceSumSquare" + }, + { + "input": [ + "Features0", + "C", + "zero" + ], + "output": [ + "XC2" + ], + "name": "Gemm", + "opType": "Gemm", + "attribute": [ + { + "name": "alpha", + "f": -2, + "type": "FLOAT" + }, + { + "name": "transB", + "i": "1", + "type": "INT" + } + ] + }, + { + "input": [ + "X2", + "XC2" + ], + "output": [ + "Z" + ], + "name": "Add", + "opType": "Add" + }, + { + "input": [ + "Z", + "C2" + ], + "output": [ + "Score" + ], + "name": "Add0", + "opType": "Add" + }, + { + "input": [ + "Score" + ], + "output": [ + "PredictedLabel" + ], + "name": "ArgMin", + "opType": "ArgMin", + "attribute": [ + { + "name": "axis", + "i": "1", + "type": "INT" + }, + { + "name": "keepdims", + "i": "1", + "type": "INT" + } + ] + }, + { + "input": [ + "Features0" + ], + "output": [ + "Features1" + ], + "name": "Identity", + "opType": "Identity" + }, + { + "input": [ + "PredictedLabel" + ], + "output": [ + "PredictedLabel0" + ], + "name": "Identity0", + "opType": "Identity" + }, + { + "input": [ + "Score" + ], + "output": [ + "Score0" + ], + "name": "Identity1", + "opType": "Identity" + } + ], + "name": "model", + "initializer": [ + { + "dims": [ + "4", + "9" + ], + "dataType": "FLOAT", + "floatData": [ + 0.690322459, + 0.948387, + 0.835483849, + 0.9129032, + 0.7741936, + 0.7709678, + 0.729032159, + 0.606451631, + 0.6161291, + 0.473529756, + 0.15147081, + 0.1705884, + 0.144608051, + 0.220098346, + 0.13627474, + 0.206372589, + 0.140686512, + 0.1176473, + 0.7130001, + 0.6315002, + 0.646, + 0.5310001, + 0.5105002, + 0.7975002, + 0.5864999, + 0.599499941, + 0.19799982, + 0.171255022, + 0.115384832, + 0.119838275, + 0.118218832, + 0.20121488, + 0.124696605, + 0.214574754, + 0.115384884, + 0.108097412 + ], + "name": "C" + }, + { + "dims": [ + "4" + ], + "dataType": "FLOAT", + "floatData": [ + 5.380062, + 0.440423042, + 3.245642, + 0.198056549 + ], + "name": "C2" + }, + { + "dims": [ + "1" + ], + "dataType": "FLOAT", + "floatData": [ + 0 + ], + "name": "zero" + } + ], + "input": [ + { + "name": "Features", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "9" + } + ] + } + } + } + } + ], + "output": [ + { + "name": "Features1", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "9" + } + ] + } + } + } + }, + { + "name": "PredictedLabel0", + "type": { + "tensorType": { + "elemType": "INT64", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + }, + { + "name": "Score0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "4" + } + ] + } + } + } + } + ], + "valueInfo": [ + { + "name": "Features0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "9" + } + ] + } + } + } + } + ] + }, + "opsetImport": [ + { + "domain": "ai.onnx.ml", + "version": "1" + }, + { + "version": "7" + } + ] +} \ No newline at end of file diff --git a/test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt b/test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt new file mode 100644 index 0000000000..c477a806f2 --- /dev/null +++ b/test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt @@ -0,0 +1,276 @@ +{ + "irVersion": "3", + "producerName": "ML.NET", + "producerVersion": "##VERSION##", + "domain": "com.microsoft", + "graph": { + "node": [ + { + "input": [ + "FeatureVector" + ], + "output": [ + "FeatureVector0" + ], + "name": "Scaler", + "opType": "Scaler", + "attribute": [ + { + "name": "offset", + "floats": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0 + ], + "type": "FLOATS" + }, + { + "name": "scale", + "floats": [ + 0.2544529, + 0.3184713, + 0.259740263, + 0.324675322, + 0.3144654, + 0.332225919, + 0.3436426, + 0.321543425, + 0.30864197, + 0.3154574, + 0.344827563 + ], + "type": "FLOATS" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "FeatureVector0" + ], + "output": [ + "Score" + ], + "name": "LinearRegressor", + "opType": "LinearRegressor", + "attribute": [ + { + "name": "post_transform", + "s": "Tk9ORQ==", + "type": "STRING" + }, + { + "name": "targets", + "i": "1", + "type": "INT" + }, + { + "name": "coefficients", + "floats": [ + 300.146545, + -0.224054649, + 286.670166, + -0.8098665, + -0.8306167, + -0.9863483, + 55.934185, + 12.6538782, + -0.265024453, + 0.6916245, + 238.776855 + ], + "type": "FLOATS" + }, + { + "name": "intercepts", + "floats": [ + 373.62085 + ], + "type": "FLOATS" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "FeatureVector0" + ], + "output": [ + "FeatureVector1" + ], + "name": "Identity", + "opType": "Identity" + }, + { + "input": [ + "Target" + ], + "output": [ + "Target0" + ], + "name": "Identity0", + "opType": "Identity" + }, + { + "input": [ + "Score" + ], + "output": [ + "Score0" + ], + "name": "Identity1", + "opType": "Identity" + } + ], + "name": "model", + "input": [ + { + "name": "FeatureVector", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "11" + } + ] + } + } + } + }, + { + "name": "Target", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + } + ], + "output": [ + { + "name": "FeatureVector1", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "11" + } + ] + } + } + } + }, + { + "name": "Target0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + }, + { + "name": "Score0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + } + ], + "valueInfo": [ + { + "name": "FeatureVector0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "11" + } + ] + } + } + } + }, + { + "name": "Score", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + } + ] + }, + "opsetImport": [ + { + "domain": "ai.onnx.ml", + "version": "1" + }, + { + "version": "7" + } + ] +} \ No newline at end of file diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index 27bbbe9b38..bd9c28b6ec 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -36,9 +36,6 @@ public OnnxConversionTest(ITestOutputHelper output) : base(output) [Fact] public void SimpleEndToEndOnnxConversionTest() { - if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) - return; - // Step 1: Create and train a ML.NET pipeline. var trainDataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename); var mlContext = new MLContext(seed: 1, conc: 1); @@ -60,15 +57,26 @@ public void SimpleEndToEndOnnxConversionTest() var onnxModelPath = GetOutputPath(onnxFileName); SaveOnnxModel(onnxModel, onnxModelPath, null); - // Step 3: Evaluate the saved ONNX model using the data used to train the ML.NET pipeline. - string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray(); - string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray(); - var onnxEstimator = new OnnxScoringEstimator(mlContext, onnxModelPath, inputNames, outputNames); - var onnxTransformer = onnxEstimator.Fit(data); - var onnxResult = onnxTransformer.Transform(data); + if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) + { + // Step 3: Evaluate the saved ONNX model using the data used to train the ML.NET pipeline. + string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray(); + string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray(); + var onnxEstimator = new OnnxScoringEstimator(mlContext, onnxModelPath, inputNames, outputNames); + var onnxTransformer = onnxEstimator.Fit(data); + var onnxResult = onnxTransformer.Transform(data); + + // Step 4: Compare ONNX and ML.NET results. + CompareSelectedR4ScalarColumns("Score", "Score0", transformedData, onnxResult, 2); + } + + // Step 5: Check ONNX model's text format. + var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "Regression", "Adult"); + var onnxTextName = "SimplePipeline.txt"; + var onnxTextPath = GetOutputPath(subDir, onnxTextName); + SaveOnnxModel(onnxModel, null, onnxTextPath); + CheckEquality(subDir, onnxTextName); - // Step 4: Compare ONNX and ML.NET results. - CompareSelectedR4ScalarColumns("Score", "Score0", transformedData, onnxResult, 2); Done(); } @@ -86,9 +94,6 @@ private void CreateDummyExamplesToMakeComplierHappy() [Fact] public void KmeansOnnxConversionTest() { - if (!RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) - return; - // Create a new context for ML.NET operations. It can be used for exception tracking and logging, // as a catalog of available operations and as the source of randomness. var mlContext = new MLContext(seed: 1, conc: 1); @@ -113,18 +118,27 @@ public void KmeansOnnxConversionTest() var onnxModel = mlContext.Model.Portability.ConvertToOnnx(model, data); - var onnxFileName = "model.onnx"; - var onnxModelPath = GetOutputPath(onnxFileName); - SaveOnnxModel(onnxModel, onnxModelPath, null); - - // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline. - string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray(); - string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray(); - var onnxEstimator = new OnnxScoringEstimator(mlContext, onnxModelPath, inputNames, outputNames); - var onnxTransformer = onnxEstimator.Fit(data); - var onnxResult = onnxTransformer.Transform(data); + // Compare results produced by ML.NET and ONNX's runtime. + if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) + { + var onnxFileName = "model.onnx"; + var onnxModelPath = GetOutputPath(onnxFileName); + SaveOnnxModel(onnxModel, onnxModelPath, null); + + // Evaluate the saved ONNX model using the data used to train the ML.NET pipeline. + string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray(); + string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray(); + var onnxEstimator = new OnnxScoringEstimator(mlContext, onnxModelPath, inputNames, outputNames); + var onnxTransformer = onnxEstimator.Fit(data); + var onnxResult = onnxTransformer.Transform(data); + CompareSelectedR4VectorColumns("Score", "Score0", transformedData, onnxResult, 3); + } - CompareSelectedR4VectorColumns("Score", "Score0", transformedData, onnxResult, 3); + var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "Cluster", "BreastCancer"); + var onnxTextName = "Kmeans.txt"; + var onnxTextPath = GetOutputPath(subDir, onnxTextName); + SaveOnnxModel(onnxModel, null, onnxTextPath); + CheckEquality(subDir, onnxTextName); Done(); } From 061831960a2bb53fbfc9f87dcdb136ecd31cfdd7 Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Fri, 4 Jan 2019 14:52:16 -0800 Subject: [PATCH 06/23] Address comments --- .../Model/ModelOperationsCatalog.cs | 18 ++------ src/Microsoft.ML.Onnx/OnnxExportExtensions.cs | 35 +++++++++++++++ src/Microsoft.ML.Onnx/ProtabilityCatalog.cs | 32 -------------- src/Microsoft.ML.Onnx/SaveOnnxCommand.cs | 11 +++-- .../Onnx/Cluster/BreastCancer/Kmeans.txt | 2 +- .../Onnx/Regression/Adult/SimplePipeline.txt | 2 +- test/Microsoft.ML.Tests/OnnxConversionTest.cs | 43 +++++++++++-------- 7 files changed, 72 insertions(+), 71 deletions(-) create mode 100644 src/Microsoft.ML.Onnx/OnnxExportExtensions.cs delete mode 100644 src/Microsoft.ML.Onnx/ProtabilityCatalog.cs diff --git a/src/Microsoft.ML.Data/Model/ModelOperationsCatalog.cs b/src/Microsoft.ML.Data/Model/ModelOperationsCatalog.cs index 830f8ac6bf..0f03d5f8f8 100644 --- a/src/Microsoft.ML.Data/Model/ModelOperationsCatalog.cs +++ b/src/Microsoft.ML.Data/Model/ModelOperationsCatalog.cs @@ -13,19 +13,20 @@ namespace Microsoft.ML /// public sealed class ModelOperationsCatalog { + /// + /// This is a best friend because an extension method defined in another assembly needs this field. + /// + [BestFriend] internal IHostEnvironment Environment { get; } public ExplainabilityTransforms Explainability { get; } - public PortabilityTransforms Portability { get; } - internal ModelOperationsCatalog(IHostEnvironment env) { Contracts.AssertValue(env); Environment = env; Explainability = new ExplainabilityTransforms(this); - Portability = new PortabilityTransforms(this); } public abstract class SubCatalogBase @@ -62,17 +63,6 @@ internal ExplainabilityTransforms(ModelOperationsCatalog owner) : base(owner) } } - /// - /// The catalog of model protability operations. Member function of this classes are able to convert the associated object to a protable format, - /// so that the fitted pipeline can easily be depolyed to other platforms. Currently, the only supported format is ONNX (https://github.com/onnx/onnx). - /// - public sealed class PortabilityTransforms : SubCatalogBase - { - internal PortabilityTransforms(ModelOperationsCatalog owner) : base(owner) - { - } - } - /// /// Create a prediction engine for one-time prediction. /// diff --git a/src/Microsoft.ML.Onnx/OnnxExportExtensions.cs b/src/Microsoft.ML.Onnx/OnnxExportExtensions.cs new file mode 100644 index 0000000000..5ea668e28b --- /dev/null +++ b/src/Microsoft.ML.Onnx/OnnxExportExtensions.cs @@ -0,0 +1,35 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System.Collections.Generic; +using Microsoft.ML.Core.Data; +using Microsoft.ML.Data; +using Microsoft.ML.Model.Onnx; +using Microsoft.ML.UniversalModelFormat.Onnx; + +namespace Microsoft.ML +{ + public static class OnnxExportExtensions + { + /// + /// Convert the specified to ONNX format. Note that ONNX uses Google's Protobuf so the returned value is a Protobuf object. + /// + /// The class that attached to. + /// The that will be converted into ONNX format. + /// The input of the specified transform. + /// An ONNX model equivalent to the converted ML.NET model. + public static ModelProto ConvertToOnnx(this ModelOperationsCatalog catalog, ITransformer transform, IDataView inputData) + { + var env = catalog.Environment; + var ctx = new OnnxContextImpl(env, "model", "ML.NET", "0", 0, "ai.onnx.ml", OnnxVersion.Stable); + var outputData = transform.Transform(inputData); + LinkedList transforms = null; + using (var ch = env.Start("ONNX conversion")) + { + SaveOnnxCommand.GetPipe(ctx, ch, outputData, out IDataView root, out IDataView sink, out transforms); + return SaveOnnxCommand.ConvertTransformListToOnnxModel(ctx, ch, root, sink, transforms, null, null); + } + } + } +} diff --git a/src/Microsoft.ML.Onnx/ProtabilityCatalog.cs b/src/Microsoft.ML.Onnx/ProtabilityCatalog.cs deleted file mode 100644 index 65cde286c6..0000000000 --- a/src/Microsoft.ML.Onnx/ProtabilityCatalog.cs +++ /dev/null @@ -1,32 +0,0 @@ -using System.Collections.Generic; -using Microsoft.ML.Core.Data; -using Microsoft.ML.Data; -using Microsoft.ML.Model.Onnx; -using Microsoft.ML.UniversalModelFormat.Onnx; - -namespace Microsoft.ML -{ - public static class ProtabilityCatalog - { - /// - /// Convert the specified to ONNX format. Note that ONNX uses Google's Protobuf so the returned value is a Protobuf object. - /// - /// A field in which this function associated with. - /// The that will be converted into ONNX format. - /// The input of the specified transform. - /// - public static ModelProto ConvertToOnnx(this ModelOperationsCatalog.PortabilityTransforms catalog, ITransformer transform, IDataView inputData) - { - var env = new MLContext(seed: 1); - var ctx = new OnnxContextImpl(env, "model", "ML.NET", "0", 0, "com.microsoft", OnnxVersion.Stable); - var outputData = transform.Transform(inputData); - IDataView root = null; - IDataView sink = null; - LinkedList transforms = null; - using (var ch = (env as IChannelProvider).Start("ONNX conversion")) - SaveOnnxCommand.GetPipe(ctx, ch, outputData, out root, out sink, out transforms); - - return SaveOnnxCommand.ConvertTransformListToOnnxModel(ctx, root, sink, transforms, null, null); - } - } -} diff --git a/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs b/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs index 142dd0a8d8..aa4e114b58 100644 --- a/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs +++ b/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs @@ -116,7 +116,7 @@ public override void Run() internal static void GetPipe(OnnxContextImpl ctx, IChannel ch, IDataView end, out IDataView source, out IDataView trueEnd, out LinkedList transforms) { - Contracts.AssertValue(end); + ch.AssertValue(end); source = trueEnd = (end as CompositeDataLoader)?.View ?? end; IDataTransform transform = source as IDataTransform; @@ -136,10 +136,10 @@ internal static void GetPipe(OnnxContextImpl ctx, IChannel ch, IDataView end, ou transform = (source = transform.Source) as IDataTransform; } - Contracts.AssertValue(source); + ch.AssertValue(source); } - internal static ModelProto ConvertTransformListToOnnxModel(OnnxContextImpl ctx, IDataView inputData, IDataView outputData, + internal static ModelProto ConvertTransformListToOnnxModel(OnnxContextImpl ctx, IChannel ch, IDataView inputData, IDataView outputData, LinkedList transforms, HashSet inputColumnNamesToDrop=null, HashSet outputColumnNamesToDrop=null) { inputColumnNamesToDrop = inputColumnNamesToDrop ?? new HashSet(); @@ -158,7 +158,10 @@ internal static ModelProto ConvertTransformListToOnnxModel(OnnxContextImpl ctx, // Create graph nodes, outputs and intermediate values. foreach (var trans in transforms) + { + ch.Assert(trans.CanSaveOnnx(ctx)); trans.SaveAsOnnx(ctx); + } // Add graph outputs. for (int i = 0; i < outputData.Schema.Count; ++i) @@ -255,7 +258,7 @@ private void Run(IChannel ch) nameof(Arguments.LoadPredictor), "We were explicitly told to load the predictor but one was not present."); } - var model = ConvertTransformListToOnnxModel(ctx, source, end, transforms, _inputsToDrop, _outputsToDrop); + var model = ConvertTransformListToOnnxModel(ctx, ch, source, end, transforms, _inputsToDrop, _outputsToDrop); using (var file = Host.CreateOutputFile(_outputModelPath)) using (var stream = file.CreateWriteStream()) diff --git a/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt b/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt index 962681c49d..3f0829baf2 100644 --- a/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt +++ b/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt @@ -2,7 +2,7 @@ "irVersion": "3", "producerName": "ML.NET", "producerVersion": "##VERSION##", - "domain": "com.microsoft", + "domain": "ai.onnx.ml", "graph": { "node": [ { diff --git a/test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt b/test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt index c477a806f2..bf75ec55f7 100644 --- a/test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt +++ b/test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt @@ -2,7 +2,7 @@ "irVersion": "3", "producerName": "ML.NET", "producerVersion": "##VERSION##", - "domain": "com.microsoft", + "domain": "ai.onnx.ml", "graph": { "node": [ { diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index bd9c28b6ec..4edbec7adb 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -1,4 +1,5 @@ -using System.IO; +using System; +using System.IO; using System.Linq; using System.Runtime.InteropServices; using System.Text.RegularExpressions; @@ -31,7 +32,7 @@ public OnnxConversionTest(ITestOutputHelper output) : base(output) /// /// In this test, we convert a trained into ONNX file and then /// call to evaluate that file. The outputs of are checked against the original - /// ML.NET model's outputs. + /// ML.NET model's outputs. /// [Fact] public void SimpleEndToEndOnnxConversionTest() @@ -52,12 +53,12 @@ public void SimpleEndToEndOnnxConversionTest() var transformedData = model.Transform(data); // Step 2: Convert ML.NET model to ONNX format and save it as a file. - var onnxModel = mlContext.Model.Portability.ConvertToOnnx(model, data); + var onnxModel = mlContext.Model.ConvertToOnnx(model, data); var onnxFileName = "model.onnx"; var onnxModelPath = GetOutputPath(onnxFileName); SaveOnnxModel(onnxModel, onnxModelPath, null); - if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) + if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows) && Environment.Is64BitProcess) { // Step 3: Evaluate the saved ONNX model using the data used to train the ML.NET pipeline. string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray(); @@ -70,7 +71,8 @@ public void SimpleEndToEndOnnxConversionTest() CompareSelectedR4ScalarColumns("Score", "Score0", transformedData, onnxResult, 2); } - // Step 5: Check ONNX model's text format. + // Step 5: Check ONNX model's text format. This test will be not necessary if Step 3 and Step 4 can run on Linux and + // Mac to support cross-platform tests. var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "Regression", "Adult"); var onnxTextName = "SimplePipeline.txt"; var onnxTextPath = GetOutputPath(subDir, onnxTextName); @@ -86,11 +88,6 @@ private class BreastCancerFeatureVector public float[] Features; } - private void CreateDummyExamplesToMakeComplierHappy() - { - var dummyExample = new BreastCancerFeatureVector() { Features = null }; - } - [Fact] public void KmeansOnnxConversionTest() { @@ -102,24 +99,24 @@ public void KmeansOnnxConversionTest() // Now read the file (remember though, readers are lazy, so the actual reading will happen when the data is accessed). var data = mlContext.Data.ReadFromTextFile(dataPath, hasHeader: true, - separatorChar: '\t' ); + separatorChar: '\t'); var pipeline = mlContext.Transforms.Normalize("Features"). Append(mlContext.Clustering.Trainers.KMeans(features: "Features", advancedSettings: settings => { - settings.MaxIterations = 1; - settings.K = 4; - settings.NumThreads = 1; - settings.InitAlgorithm = Trainers.KMeans.KMeansPlusPlusTrainer.InitAlgorithm.KMeansPlusPlus; + settings.MaxIterations = 1; + settings.K = 4; + settings.NumThreads = 1; + settings.InitAlgorithm = Trainers.KMeans.KMeansPlusPlusTrainer.InitAlgorithm.KMeansPlusPlus; })); var model = pipeline.Fit(data); var transformedData = model.Transform(data); - var onnxModel = mlContext.Model.Portability.ConvertToOnnx(model, data); + var onnxModel = mlContext.Model.ConvertToOnnx(model, data); // Compare results produced by ML.NET and ONNX's runtime. - if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) + if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows) && Environment.Is64BitProcess) { var onnxFileName = "model.onnx"; var onnxModelPath = GetOutputPath(onnxFileName); @@ -134,6 +131,9 @@ public void KmeansOnnxConversionTest() CompareSelectedR4VectorColumns("Score", "Score0", transformedData, onnxResult, 3); } + // Check ONNX model's text format. We save the produced ONNX model as a text file and compare it against + // the associated file in ML.NET repo. Such a comparison can be retired if ONNXRuntime ported to ML.NET + // can support Linux and Mac. var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "Cluster", "BreastCancer"); var onnxTextName = "Kmeans.txt"; var onnxTextPath = GetOutputPath(subDir, onnxTextName); @@ -142,7 +142,12 @@ public void KmeansOnnxConversionTest() Done(); } - private void CompareSelectedR4VectorColumns(string leftColumnName, string rightColumnName, IDataView left, IDataView right, int precision=6) + private void CreateDummyExamplesToMakeComplierHappy() + { + var dummyExample = new BreastCancerFeatureVector() { Features = null }; + } + + private void CompareSelectedR4VectorColumns(string leftColumnName, string rightColumnName, IDataView left, IDataView right, int precision = 6) { var leftColumnIndex = left.Schema[leftColumnName].Index; var rightColumnIndex = right.Schema[rightColumnName].Index; @@ -166,7 +171,7 @@ private void CompareSelectedR4VectorColumns(string leftColumnName, string rightC } } - private void CompareSelectedR4ScalarColumns(string leftColumnName, string rightColumnName, IDataView left, IDataView right, int precision=6) + private void CompareSelectedR4ScalarColumns(string leftColumnName, string rightColumnName, IDataView left, IDataView right, int precision = 6) { var leftColumnIndex = left.Schema[leftColumnName].Index; var rightColumnIndex = right.Schema[rightColumnName].Index; From e950b285001f6f4e346a4b6f7d36386afb5d4dd4 Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Fri, 4 Jan 2019 15:08:19 -0800 Subject: [PATCH 07/23] Propose another domain name --- src/Microsoft.ML.Onnx/OnnxExportExtensions.cs | 2 +- test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt | 2 +- .../Common/Onnx/Regression/Adult/SimplePipeline.txt | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/Microsoft.ML.Onnx/OnnxExportExtensions.cs b/src/Microsoft.ML.Onnx/OnnxExportExtensions.cs index 5ea668e28b..0922fe8b0e 100644 --- a/src/Microsoft.ML.Onnx/OnnxExportExtensions.cs +++ b/src/Microsoft.ML.Onnx/OnnxExportExtensions.cs @@ -22,7 +22,7 @@ public static class OnnxExportExtensions public static ModelProto ConvertToOnnx(this ModelOperationsCatalog catalog, ITransformer transform, IDataView inputData) { var env = catalog.Environment; - var ctx = new OnnxContextImpl(env, "model", "ML.NET", "0", 0, "ai.onnx.ml", OnnxVersion.Stable); + var ctx = new OnnxContextImpl(env, "model", "ML.NET", "0", 0, "machinelearning.dotnet", OnnxVersion.Stable); var outputData = transform.Transform(inputData); LinkedList transforms = null; using (var ch = env.Start("ONNX conversion")) diff --git a/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt b/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt index 3f0829baf2..8e6951472a 100644 --- a/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt +++ b/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt @@ -2,7 +2,7 @@ "irVersion": "3", "producerName": "ML.NET", "producerVersion": "##VERSION##", - "domain": "ai.onnx.ml", + "domain": "machinelearning.dotnet", "graph": { "node": [ { diff --git a/test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt b/test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt index bf75ec55f7..30c26b8f90 100644 --- a/test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt +++ b/test/BaselineOutput/Common/Onnx/Regression/Adult/SimplePipeline.txt @@ -2,7 +2,7 @@ "irVersion": "3", "producerName": "ML.NET", "producerVersion": "##VERSION##", - "domain": "ai.onnx.ml", + "domain": "machinelearning.dotnet", "graph": { "node": [ { From dc2be3183028092c80cfe4150cc9ab9165318d7c Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Fri, 4 Jan 2019 15:18:42 -0800 Subject: [PATCH 08/23] Add missing header --- test/Microsoft.ML.Tests/OnnxConversionTest.cs | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index 4edbec7adb..d43fa4dba1 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -1,4 +1,8 @@ -using System; +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; using System.IO; using System.Linq; using System.Runtime.InteropServices; From 987dc82677ceb5f30da7903bf81272c6f51aa99c Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Fri, 4 Jan 2019 16:21:46 -0800 Subject: [PATCH 09/23] One more test for one-hot encoding's conversion --- .../BreastCancer/OneHotBagPipeline.txt | 637 ++++++++++++++++++ test/Microsoft.ML.Tests/OnnxConversionTest.cs | 43 ++ 2 files changed, 680 insertions(+) create mode 100644 test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt diff --git a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt new file mode 100644 index 0000000000..0596709963 --- /dev/null +++ b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt @@ -0,0 +1,637 @@ +{ + "irVersion": "3", + "producerName": "ML.NET", + "producerVersion": "##VERSION##", + "domain": "machinelearning.dotnet", + "graph": { + "node": [ + { + "input": [ + "F2" + ], + "output": [ + "F20" + ], + "name": "LabelEncoder", + "opType": "LabelEncoder", + "attribute": [ + { + "name": "classes_strings", + "strings": [ + "NA==", + "MQ==", + "OA==", + "MTA=", + "Mg==", + "Mw==", + "Nw==", + "NQ==", + "Ng==", + "OQ==" + ], + "type": "STRINGS" + }, + { + "name": "default_int64", + "i": "-1", + "type": "INT" + }, + { + "name": "default_string", + "s": "IA==", + "type": "STRING" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "F20" + ], + "output": [ + "encoded" + ], + "name": "OneHotEncoder", + "opType": "OneHotEncoder", + "attribute": [ + { + "name": "cats_int64s", + "ints": [ + "0", + "1", + "2", + "3", + "4", + "5", + "6", + "7", + "8", + "9" + ], + "type": "INTS" + }, + { + "name": "zeros", + "i": "1", + "type": "INT" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "encoded" + ], + "output": [ + "F21" + ], + "name": "ReduceSum", + "opType": "ReduceSum", + "attribute": [ + { + "name": "axes", + "ints": [ + "1" + ], + "type": "INTS" + }, + { + "name": "keepdims", + "type": "INT" + } + ] + }, + { + "input": [ + "F1", + "F21" + ], + "output": [ + "Features" + ], + "name": "FeatureVectorizer", + "opType": "FeatureVectorizer", + "attribute": [ + { + "name": "inputdimensions", + "ints": [ + "1", + "10" + ], + "type": "INTS" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "Features" + ], + "output": [ + "Score" + ], + "name": "TreeEnsembleRegressor", + "opType": "TreeEnsembleRegressor", + 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"nodes_missing_value_tracks_true", + "ints": [ + "0", + "0", + "0" + ], + "type": "INTS" + }, + { + "name": "target_treeids", + "ints": [ + "0", + "0" + ], + "type": "INTS" + }, + { + "name": "target_nodeids", + "ints": [ + "1", + "2" + ], + "type": "INTS" + }, + { + "name": "target_ids", + "ints": [ + "0", + "0" + ], + "type": "INTS" + }, + { + "name": "target_weights", + "floats": [ + 0.504761934, + -0.979112267 + ], + "type": "FLOATS" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "Score" + ], + "output": [ + "linearOutput" + ], + "name": "Affine", + "opType": "Affine", + "attribute": [ + { + "name": "alpha", + "f": 0.4, + "type": "FLOAT" + }, + { + "name": "beta", + "f": -1E-07, + "type": "FLOAT" + } + ] + }, + { + "input": [ + "linearOutput" + ], + "output": [ + "Probability" + ], + "name": "Sigmoid", + "opType": "Sigmoid" + }, + { + "input": [ + "Probability" + ], + "output": [ + "PredictedLabel" + ], + "name": "Binarizer", + "opType": "Binarizer", + "attribute": [ + { + "name": "threshold", + "f": 0.5, + "type": "FLOAT" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "Label" + ], + "output": [ + "Label0" + ], + "name": "Identity", + "opType": "Identity" + }, + { + "input": [ + "F1" + ], + "output": [ + "F10" + ], + "name": "Identity0", + "opType": "Identity" + }, + { + "input": [ + "F21" + ], + "output": [ + "F22" + ], + "name": "Identity1", + "opType": "Identity" + }, + { + "input": [ + "Features" + ], + "output": [ + "Features0" + ], + "name": "Identity2", + "opType": "Identity" + }, + { + "input": [ + "PredictedLabel" + ], + "output": [ + "PredictedLabel0" + ], + "name": "Identity3", + "opType": "Identity" + }, + { + "input": [ + "Score" + ], + "output": [ + "Score0" + ], + "name": "Identity4", + "opType": "Identity" + }, + { + "input": [ + "Probability" + ], + "output": [ + "Probability0" + ], + "name": "Identity5", + "opType": "Identity" + } + ], + "name": "model", + "input": [ + { + "name": "Label", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + }, + { + "name": "F1", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + }, + { + "name": "F2", + "type": { + "tensorType": { + "elemType": "STRING", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + } + ], + "output": [ + { + "name": "Label0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + }, + { + "name": "F10", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + }, + { + "name": "F22", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "10" + } + ] + } + } + } + }, + { + "name": "Features0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "11" + } + ] + } + } + } + }, + { + "name": "PredictedLabel0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + }, + { + "name": "Score0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + }, + { + "name": "Probability0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + } + ], + "valueInfo": [ + { + "name": "F20", + "type": { + "tensorType": { + "elemType": "INT64", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + }, + { + "name": "F21", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "10" + } + ] + } + } + } + }, + { + "name": "Features", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "11" + } + ] + } + } + } + } + ] + }, + "opsetImport": [ + { + "domain": "ai.onnx.ml", + "version": "1" + }, + { + "version": "7" + } + ] +} \ No newline at end of file diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index d43fa4dba1..a283ad5df8 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -92,6 +92,18 @@ private class BreastCancerFeatureVector public float[] Features; } + private class BreastCancerCatFeatureExample + { + [LoadColumn(0)] + public bool Label; + + [LoadColumn(1)] + public float F1; + + [LoadColumn(2)] + public string F2; + } + [Fact] public void KmeansOnnxConversionTest() { @@ -146,9 +158,40 @@ public void KmeansOnnxConversionTest() Done(); } + [Fact] + public void KeyToVectorWithBagOnnxConversionTest() + { + var mlContext = new MLContext(seed: 1, conc: 1); + + string dataPath = GetDataPath("breast-cancer.txt"); + var data = mlContext.Data.ReadFromTextFile(dataPath, + hasHeader: true, + separatorChar: '\t'); + + var pipeline = mlContext.Transforms.Categorical.OneHotEncoding("F2", "F2", Transforms.Categorical.OneHotEncodingTransformer.OutputKind.Bag) + .Append(mlContext.Transforms.Concatenate("Features", "F1", "F2")) + .Append(mlContext.BinaryClassification.Trainers.FastTree(labelColumn: "Label", featureColumn: "Features", numLeaves: 2, numTrees: 1, minDatapointsInLeaves: 2)); + + var model = pipeline.Fit(data); + var onnxModel = mlContext.Model.ConvertToOnnx(model, data); + + // Check ONNX model's text format. We save the produced ONNX model as a text file and compare it against + // the associated file in ML.NET repo. Such a comparison can be retired if ONNXRuntime ported to ML.NET + // can support Linux and Mac. + var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "BinaryClassification", "BreastCancer"); + var onnxTextName = "OneHotBagPipeline.txt"; + var onnxFileName = "OneHotBagPipeline.onnx"; + var onnxTextPath = GetOutputPath(subDir, onnxTextName); + var onnxFilePath = GetOutputPath(subDir, onnxFileName); + SaveOnnxModel(onnxModel, onnxFilePath, onnxTextPath); + CheckEquality(subDir, onnxTextName); + Done(); + } + private void CreateDummyExamplesToMakeComplierHappy() { var dummyExample = new BreastCancerFeatureVector() { Features = null }; + var dummyExample1 = new BreastCancerCatFeatureExample() { Label = false, F1 = 0, F2 = "Amy" }; } private void CompareSelectedR4VectorColumns(string leftColumnName, string rightColumnName, IDataView left, IDataView right, int precision = 6) From e6d42a1cea3b9179b443ab8695a5f1088c457fd1 Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Fri, 4 Jan 2019 18:05:52 -0800 Subject: [PATCH 10/23] Add one more test --- ...nLogisticRegressionSaveModelToOnnxTest.txt | 432 ++++++++++++++++++ test/Microsoft.ML.Tests/OnnxConversionTest.cs | 113 +++++ 2 files changed, 545 insertions(+) create mode 100644 test/BaselineOutput/Common/Onnx/MultiClassClassification/BreastCancer/MultiClassificationLogisticRegressionSaveModelToOnnxTest.txt diff --git a/test/BaselineOutput/Common/Onnx/MultiClassClassification/BreastCancer/MultiClassificationLogisticRegressionSaveModelToOnnxTest.txt b/test/BaselineOutput/Common/Onnx/MultiClassClassification/BreastCancer/MultiClassificationLogisticRegressionSaveModelToOnnxTest.txt new file mode 100644 index 0000000000..c614821800 --- /dev/null +++ b/test/BaselineOutput/Common/Onnx/MultiClassClassification/BreastCancer/MultiClassificationLogisticRegressionSaveModelToOnnxTest.txt @@ -0,0 +1,432 @@ +{ + "irVersion": "3", + "producerName": "ML.NET", + "producerVersion": "##VERSION##", + "domain": "machinelearning.dotnet", + "graph": { + "node": [ + { + "input": [ + "Features" + ], + "output": [ + "Features0" + ], + "name": "Scaler", + "opType": "Scaler", + "attribute": [ + { + "name": "offset", + "floats": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0 + ], + "type": "FLOATS" + }, + { + "name": "scale", + "floats": [ + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1 + ], + "type": "FLOATS" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "Label" + ], + "output": [ + "Label0" + ], + "name": "LabelEncoder", + "opType": "LabelEncoder", + "attribute": [ + { + "name": "classes_strings", + "strings": [ + "NQ==", + "Mw==", + "Ng==", + "NA==", + "OA==", + "MQ==", + "Mg==", + "Nw==", + "MTA=", + "OQ==" + ], + "type": "STRINGS" + }, + { + "name": "default_int64", + "i": "-1", + "type": "INT" + }, + { + "name": "default_string", + "s": "IA==", + "type": "STRING" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "Features0" + ], + "output": [ + "PredictedLabel", + "Score" + ], + "name": "LinearClassifier", + "opType": "LinearClassifier", + "attribute": [ + { + "name": "post_transform", + "s": "Tk9ORQ==", + "type": "STRING" + }, + { + "name": "multi_class", + "i": "1", + "type": "INT" + }, + { + "name": "coefficients", + "floats": [ + -0.0871891156, + 0.209310874, + 0.747134566, + 0.364765137, + -0.377612084, + -0.6847462, + 0, + -0.5566554, + -0.3849638, + -1.29262471, + 0, + 0, + -0.479907274, + -0.08740093, + -0.5489706, + 0, + 0.630316138, + 0, + 0, + 0, + 0.07319626, + 0.171390951, + 0.6936194, + 0, + 0, + -0.6189027, + 0, + -0.732489467, + -0.71812433, + 0.2614429, + -0.4669126, + -0.250123739, + 1.01838875, + 0.7936676, + 0, + 0, + 0.8072781, + 0, + 0.833407462, + 0, + -1.67462111, + -1.19559848, + -0.553805768, + -0.5710498, + -0.7325714, + -0.5470721, + -0.7483947, + 0, + -0.5655844, + -0.9892823, + -0.237264976, + 0, + -0.81984, + -0.0930810943, + -0.4526821, + 0, + 0, + 0, + 0.726712048, + 0, + 1.12171924, + 0.323810369, + 0.245762676, + 0.07872447, + 0.939905643, + 0.923160553, + 0, + 0, + 1.10209334, + 0.704743862, + 0, + 0.322121173, + 0.5064917, + 1.30212963, + 0, + 0.8623323, + 0.0155395176, + 0, + 0.192209348, + 0.262786478 + ], + "type": "FLOATS" + }, + { + "name": "intercepts", + "floats": [ + 1.23585367, + 1.68783426, + -0.8096311, + 1.35599542, + -1.59806383, + 2.57355452, + 1.03064489, + -1.67592752, + -1.40655541, + -2.39366078 + ], + "type": "FLOATS" + }, + { + "name": "classlabels_ints", + "ints": [ + "0", + "1", + "2", + "3", + "4", + "5", + "6", + "7", + "8", + "9" + ], + "type": "INTS" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "Label0" + ], + "output": [ + "Label1" + ], + "name": "Identity", + "opType": "Identity" + }, + { + "input": [ + "Features0" + ], + "output": [ + "Features1" + ], + "name": "Identity0", + "opType": "Identity" + }, + { + "input": [ + "PredictedLabel" + ], + "output": [ + "PredictedLabel0" + ], + "name": "Identity1", + "opType": "Identity" + }, + { + "input": [ + "Score" + ], + "output": [ + "Score0" + ], + "name": "Identity2", + "opType": "Identity" + } + ], + "name": "model", + "input": [ + { + "name": "Label", + "type": { + "tensorType": { + "elemType": "STRING", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + }, + { + "name": "Features", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "8" + } + ] + } + } + } + } + ], + "output": [ + { + "name": "Label1", + "type": { + "tensorType": { + "elemType": "INT64", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + }, + { + "name": "Features1", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "8" + } + ] + } + } + } + }, + { + "name": "PredictedLabel0", + "type": { + "tensorType": { + "elemType": "INT64", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + }, + { + "name": "Score0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "10" + } + ] + } + } + } + } + ], + "valueInfo": [ + { + "name": "Features0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "8" + } + ] + } + } + } + }, + { + "name": "Label0", + "type": { + "tensorType": { + "elemType": "INT64", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + } + ] + }, + "opsetImport": [ + { + "domain": "ai.onnx.ml", + "version": "1" + }, + { + "version": "7" + } + ] +} \ No newline at end of file diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index a283ad5df8..8bdd23f9bd 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -3,12 +3,14 @@ // See the LICENSE file in the project root for more information. using System; +using System.Collections.Generic; using System.IO; using System.Linq; using System.Runtime.InteropServices; using System.Text.RegularExpressions; using Google.Protobuf; using Microsoft.ML.Data; +using Microsoft.ML.Model.Onnx; using Microsoft.ML.RunTests; using Microsoft.ML.Transforms; using Microsoft.ML.UniversalModelFormat.Onnx; @@ -104,6 +106,15 @@ private class BreastCancerCatFeatureExample public string F2; } + private class BreastCancerMulticlassExample + { + [LoadColumn(1)] + public string Label; + + [LoadColumn(2, 9), VectorType(8)] + public float[] Features; + } + [Fact] public void KmeansOnnxConversionTest() { @@ -188,10 +199,112 @@ public void KeyToVectorWithBagOnnxConversionTest() Done(); } + [Fact] + public void InitializerCreationTest() + { + var env = new MLContext(); + // Create the actual implementation + var ctxImpl = new OnnxContextImpl(env, "model", "ML.NET", "0", 0, "com.test", Model.Onnx.OnnxVersion.Stable); + + // Use implementation as in the actual conversion code + var ctx = ctxImpl as OnnxContext; + ctx.AddInitializer(9.4f, "float"); + ctx.AddInitializer(17L, "int64"); + ctx.AddInitializer("36", "string"); + ctx.AddInitializer(new List { 9.4f, 1.7f, 3.6f }, new List { 1, 3 }, "floats"); + ctx.AddInitializer(new List { 94L, 17L, 36L }, new List { 1, 3 }, "int64s"); + ctx.AddInitializer(new List { "94", "17", "36" }, new List { 1, 3 }, "strings"); + + var model = ctxImpl.MakeModel(); + + var floatScalar = model.Graph.Initializer[0]; + Assert.True(floatScalar.Name == "float"); + Assert.True(floatScalar.Dims.Count == 0); + Assert.True(floatScalar.FloatData.Count == 1); + Assert.True(floatScalar.FloatData[0] == 9.4f); + + var int64Scalar = model.Graph.Initializer[1]; + Assert.True(int64Scalar.Name == "int64"); + Assert.True(int64Scalar.Dims.Count == 0); + Assert.True(int64Scalar.Int64Data.Count == 1); + Assert.True(int64Scalar.Int64Data[0] == 17L); + + var stringScalar = model.Graph.Initializer[2]; + Assert.True(stringScalar.Name == "string"); + Assert.True(stringScalar.Dims.Count == 0); + Assert.True(stringScalar.StringData.Count == 1); + Assert.True(stringScalar.StringData[0].ToStringUtf8() == "36"); + + var floatsTensor = model.Graph.Initializer[3]; + Assert.True(floatsTensor.Name == "floats"); + Assert.True(floatsTensor.Dims.Count == 2); + Assert.True(floatsTensor.Dims[0] == 1); + Assert.True(floatsTensor.Dims[1] == 3); + Assert.True(floatsTensor.FloatData.Count == 3); + Assert.True(floatsTensor.FloatData[0] == 9.4f); + Assert.True(floatsTensor.FloatData[1] == 1.7f); + Assert.True(floatsTensor.FloatData[2] == 3.6f); + + var int64sTensor = model.Graph.Initializer[4]; + Assert.True(int64sTensor.Name == "int64s"); + Assert.True(int64sTensor.Dims.Count == 2); + Assert.True(int64sTensor.Dims[0] == 1); + Assert.True(int64sTensor.Dims[1] == 3); + Assert.True(int64sTensor.Int64Data.Count == 3); + Assert.True(int64sTensor.Int64Data[0] == 94L); + Assert.True(int64sTensor.Int64Data[1] == 17L); + Assert.True(int64sTensor.Int64Data[2] == 36L); + + var stringsTensor = model.Graph.Initializer[5]; + Assert.True(stringsTensor.Name == "strings"); + Assert.True(stringsTensor.Dims.Count == 2); + Assert.True(stringsTensor.Dims[0] == 1); + Assert.True(stringsTensor.Dims[1] == 3); + Assert.True(stringsTensor.StringData.Count == 3); + Assert.True(stringsTensor.StringData[0].ToStringUtf8() == "94"); + Assert.True(stringsTensor.StringData[1].ToStringUtf8() == "17"); + Assert.True(stringsTensor.StringData[2].ToStringUtf8() == "36"); + } + + [Fact] + public void MulticlassClassificationLogisticRegressionSaveModelToOnnxTest() + { + var mlContext = new MLContext(seed: 1, conc: 1); + + string dataPath = GetDataPath("breast-cancer.txt"); + var data = mlContext.Data.ReadFromTextFile(dataPath, + hasHeader: true, + separatorChar: '\t'); + + var pipeline = mlContext.Transforms.Normalize("Features"). + Append(mlContext.Transforms.Conversion.MapValueToKey("Label")). + Append(mlContext.MulticlassClassification.Trainers.LogisticRegression(labelColumn: "Label", featureColumn: "Features", + advancedSettings: settings => + { + settings.UseThreads = false; + })); + + var model = pipeline.Fit(data); + var transformedData = model.Transform(data); + var onnxModel = mlContext.Model.ConvertToOnnx(model, data); + + var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "MultiClassClassification", "BreastCancer"); + var onnxFileName = "MultiClassificationLogisticRegressionSaveModelToOnnxTest.onnx"; + var onnxFilePath = GetOutputPath(subDir, onnxFileName); + var onnxTextName = "MultiClassificationLogisticRegressionSaveModelToOnnxTest.txt"; + var onnxTextPath = GetOutputPath(subDir, onnxTextName); + + SaveOnnxModel(onnxModel, onnxFilePath, onnxTextPath); + + CheckEquality(subDir, onnxTextName); + Done(); + } + private void CreateDummyExamplesToMakeComplierHappy() { var dummyExample = new BreastCancerFeatureVector() { Features = null }; var dummyExample1 = new BreastCancerCatFeatureExample() { Label = false, F1 = 0, F2 = "Amy" }; + var dummyExample2 = new BreastCancerMulticlassExample() { Label = "Amy", Features = null }; } private void CompareSelectedR4VectorColumns(string leftColumnName, string rightColumnName, IDataView left, IDataView right, int precision = 6) From 13c58db9ab8c98941b2b4f34699c95c6576a1184 Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Fri, 4 Jan 2019 23:07:39 -0800 Subject: [PATCH 11/23] Add logistic regression test --- .../LogisticRegressionSaveModelToOnnxTest.txt | 276 ++++++++++++++++++ test/Microsoft.ML.Tests/OnnxConversionTest.cs | 33 +++ 2 files changed, 309 insertions(+) create mode 100644 test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LogisticRegressionSaveModelToOnnxTest.txt diff --git a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LogisticRegressionSaveModelToOnnxTest.txt b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LogisticRegressionSaveModelToOnnxTest.txt new file mode 100644 index 0000000000..30c26b8f90 --- /dev/null +++ b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LogisticRegressionSaveModelToOnnxTest.txt @@ -0,0 +1,276 @@ +{ + "irVersion": "3", + "producerName": "ML.NET", + "producerVersion": "##VERSION##", + "domain": "machinelearning.dotnet", + "graph": { + "node": [ + { + "input": [ + "FeatureVector" + ], + "output": [ + "FeatureVector0" + ], + "name": "Scaler", + "opType": "Scaler", + "attribute": [ + { + "name": "offset", + "floats": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0 + ], + "type": "FLOATS" + }, + { + "name": "scale", + "floats": [ + 0.2544529, + 0.3184713, + 0.259740263, + 0.324675322, + 0.3144654, + 0.332225919, + 0.3436426, + 0.321543425, + 0.30864197, + 0.3154574, + 0.344827563 + ], + "type": "FLOATS" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "FeatureVector0" + ], + "output": [ + "Score" + ], + "name": "LinearRegressor", + "opType": "LinearRegressor", + "attribute": [ + { + "name": "post_transform", + "s": "Tk9ORQ==", + "type": "STRING" + }, + { + "name": "targets", + "i": "1", + "type": "INT" + }, + { + "name": "coefficients", + "floats": [ + 300.146545, + -0.224054649, + 286.670166, + -0.8098665, + -0.8306167, + -0.9863483, + 55.934185, + 12.6538782, + -0.265024453, + 0.6916245, + 238.776855 + ], + "type": "FLOATS" + }, + { + "name": "intercepts", + "floats": [ + 373.62085 + ], + "type": "FLOATS" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "FeatureVector0" + ], + "output": [ + "FeatureVector1" + ], + "name": "Identity", + "opType": "Identity" + }, + { + "input": [ + "Target" + ], + "output": [ + "Target0" + ], + "name": "Identity0", + "opType": "Identity" + }, + { + "input": [ + "Score" + ], + "output": [ + "Score0" + ], + "name": "Identity1", + "opType": "Identity" + } + ], + "name": "model", + "input": [ + { + "name": "FeatureVector", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "11" + } + ] + } + } + } + }, + { + "name": "Target", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + } + ], + "output": [ + { + "name": "FeatureVector1", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "11" + } + ] + } + } + } + }, + { + "name": "Target0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + }, + { + "name": "Score0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + } + ], + "valueInfo": [ + { + "name": "FeatureVector0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "11" + } + ] + } + } + } + }, + { + "name": "Score", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + } + ] + }, + "opsetImport": [ + { + "domain": "ai.onnx.ml", + "version": "1" + }, + { + "version": "7" + } + ] +} \ No newline at end of file diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index 8bdd23f9bd..02e803d650 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -266,6 +266,39 @@ public void InitializerCreationTest() Assert.True(stringsTensor.StringData[2].ToStringUtf8() == "36"); } + [Fact] + public void LogisticRegressionSaveModelToOnnxTest() + { + // Step 1: Create and train a ML.NET pipeline. + var trainDataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename); + var mlContext = new MLContext(seed: 1, conc: 1); + var data = mlContext.Data.ReadFromTextFile(trainDataPath, + hasHeader: true, + separatorChar: ';' + ); + var cachedTrainData = mlContext.Data.Cache(data); + var dynamicPipeline = + mlContext.Transforms.Normalize("FeatureVector") + .AppendCacheCheckpoint(mlContext) + .Append(mlContext.Regression.Trainers.StochasticDualCoordinateAscent(labelColumn: "Target", featureColumn: "FeatureVector")); + var model = dynamicPipeline.Fit(data); + + // Step 2: Convert ML.NET model to ONNX format and save it as a file. + var onnxModel = mlContext.Model.ConvertToOnnx(model, data); + + // Step 3: Save ONNX model as binary and text files. + var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "BinaryClassification", "BreastCancer"); + var onnxFileName = "LogisticRegressionSaveModelToOnnxTest.onnx"; + var onnxFilePath = GetOutputPath(subDir, onnxFileName); + var onnxTextName = "LogisticRegressionSaveModelToOnnxTest.txt"; + var onnxTextPath = GetOutputPath(subDir, onnxTextName); + SaveOnnxModel(onnxModel, onnxFilePath, onnxTextPath); + + // Step 4: Check ONNX model's text format. + CheckEquality(subDir, onnxTextName); + Done(); + } + [Fact] public void MulticlassClassificationLogisticRegressionSaveModelToOnnxTest() { From 26a064912dfb9b59d9b199376641e5709b2c55a1 Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Fri, 4 Jan 2019 23:14:56 -0800 Subject: [PATCH 12/23] Add LightGBM test --- ...BinaryClassificationOnnxConversionTest.txt | 532 ++++++++++++++++++ test/Microsoft.ML.Tests/OnnxConversionTest.cs | 33 ++ 2 files changed, 565 insertions(+) create mode 100644 test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LightGbmBinaryClassificationOnnxConversionTest.txt diff --git a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LightGbmBinaryClassificationOnnxConversionTest.txt b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LightGbmBinaryClassificationOnnxConversionTest.txt new file mode 100644 index 0000000000..a6abf86b57 --- /dev/null +++ b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/LightGbmBinaryClassificationOnnxConversionTest.txt @@ -0,0 +1,532 @@ +{ + "irVersion": "3", + "producerName": "ML.NET", + "producerVersion": "##VERSION##", + "domain": "machinelearning.dotnet", + "graph": { + "node": [ + { + "input": [ + "FeatureVector" + ], + "output": [ + "FeatureVector0" + ], + "name": "Scaler", + "opType": "Scaler", + "attribute": [ + { + "name": "offset", + "floats": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0 + ], + "type": "FLOATS" + }, + { + "name": "scale", + "floats": [ + 0.2544529, + 0.3184713, + 0.259740263, + 0.324675322, + 0.3144654, + 0.332225919, + 0.3436426, + 0.321543425, + 0.30864197, + 0.3154574, + 0.344827563 + ], + "type": "FLOATS" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "FeatureVector0" + ], + "output": [ + "Score" + ], + "name": "TreeEnsembleRegressor", + "opType": "TreeEnsembleRegressor", + "attribute": [ + { + "name": "post_transform", + "s": "Tk9ORQ==", + "type": "STRING" + }, + { + "name": "n_targets", + "i": "1", + "type": "INT" + }, + { + "name": "base_values", + "floats": [ + 0 + ], + "type": "FLOATS" + }, + { + "name": "aggregate_function", + "s": "U1VN", + "type": "STRING" + }, + { + "name": "nodes_treeids", + "ints": [ + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "1", + "1", + "1", + "1", + "1", + "1", + "1", + "2", + "2", + "2", + "2", + "2" + ], + "type": "INTS" + }, + { + "name": "nodes_nodeids", + "ints": [ + "0", + "1", + "2", + "3", + "4", + "5", + "6", + "0", + "1", + "2", + "3", + "4", + "5", + "6", + "0", + "1", + "2", + "3", + "4" + ], + "type": "INTS" + }, + { + "name": "nodes_featureids", + "ints": [ + "0", + "10", + "10", + "0", + "0", + "0", + "0", + "2", + "0", + "10", + "0", + "0", + "0", + "0", + "10", + "2", + 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+ "2", + "5", + "6", + "0", + "0", + "0", + "0", + "1", + "4", + "0", + "0", + "0" + ], + "type": "INTS" + }, + { + "name": "nodes_missing_value_tracks_true", + "ints": [ + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0" + ], + "type": "INTS" + }, + { + "name": "target_treeids", + "ints": [ + "0", + "0", + "0", + "0", + "1", + "1", + "1", + "1", + "2", + "2", + "2" + ], + "type": "INTS" + }, + { + "name": "target_nodeids", + "ints": [ + "3", + "4", + "5", + "6", + "3", + "4", + "5", + "6", + "2", + "3", + "4" + ], + "type": "INTS" + }, + { + "name": "target_ids", + "ints": [ + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0", + "0" + ], + "type": "INTS" + }, + { + "name": "target_weights", + "floats": [ + 349.8905, + 384.05722, + 378.0647, + 408.230774, + -25.229887, + 2.39983654, + -1.27583647, + 25.22761, + -13.8603878, + -3.890484, + 16.6648388 + ], + "type": "FLOATS" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "FeatureVector0" + ], + "output": [ + "FeatureVector1" + ], + "name": "Identity", + "opType": "Identity" + }, + { + "input": [ + "Target" + ], + "output": [ + "Target0" + ], + "name": "Identity0", + "opType": "Identity" + }, + { + "input": [ + "Score" + ], + "output": [ + "Score0" + ], + "name": "Identity1", + "opType": "Identity" + } + ], + "name": "model", + "input": [ + { + "name": "FeatureVector", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "11" + } + ] + } + } + } + }, + { + "name": "Target", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + } + ], + "output": [ + { + "name": "FeatureVector1", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "11" + } + ] + } + } + } + }, + { + "name": "Target0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + }, + { + "name": "Score0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + } + ], + "valueInfo": [ + { + "name": "FeatureVector0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "11" + } + ] + } + } + } + }, + { + "name": "Score", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "1" + } + ] + } + } + } + } + ] + }, + "opsetImport": [ + { + "domain": "ai.onnx.ml", + "version": "1" + }, + { + "version": "7" + } + ] +} \ No newline at end of file diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index 02e803d650..1519e5839d 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -299,6 +299,39 @@ public void LogisticRegressionSaveModelToOnnxTest() Done(); } + [Fact] + public void LightGbmBinaryClassificationOnnxConversionTest() + { + // Step 1: Create and train a ML.NET pipeline. + var trainDataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename); + var mlContext = new MLContext(seed: 1, conc: 1); + var data = mlContext.Data.ReadFromTextFile(trainDataPath, + hasHeader: true, + separatorChar: ';' + ); + var cachedTrainData = mlContext.Data.Cache(data); + var dynamicPipeline = + mlContext.Transforms.Normalize("FeatureVector") + .AppendCacheCheckpoint(mlContext) + .Append(mlContext.Regression.Trainers.LightGbm(labelColumn: "Target", featureColumn: "FeatureVector", numBoostRound: 3, numLeaves: 16, minDataPerLeaf: 100)); + var model = dynamicPipeline.Fit(data); + + // Step 2: Convert ML.NET model to ONNX format and save it as a file. + var onnxModel = mlContext.Model.ConvertToOnnx(model, data); + + // Step 3: Save ONNX model as binary and text files. + var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "BinaryClassification", "BreastCancer"); + var onnxFileName = "LightGbmBinaryClassificationOnnxConversionTest.onnx"; + var onnxFilePath = GetOutputPath(subDir, onnxFileName); + var onnxTextName = "LightGbmBinaryClassificationOnnxConversionTest.txt"; + var onnxTextPath = GetOutputPath(subDir, onnxTextName); + SaveOnnxModel(onnxModel, onnxFilePath, onnxTextPath); + + // Step 4: Check ONNX model's text format. + CheckEquality(subDir, onnxTextName); + Done(); + } + [Fact] public void MulticlassClassificationLogisticRegressionSaveModelToOnnxTest() { From 984e9d28e4e97657c892399750932ded3104985f Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Sat, 5 Jan 2019 00:19:46 -0800 Subject: [PATCH 13/23] Test NaN replacement and fix build for core30 and x86 --- .../BreastCancer/OneHotBagPipeline.txt | 51 +++++++++++++++++-- test/Microsoft.ML.Tests/OnnxConversionTest.cs | 5 +- 2 files changed, 50 insertions(+), 6 deletions(-) diff --git a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt index 0596709963..d66e9291fe 100644 --- a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt +++ b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/OneHotBagPipeline.txt @@ -103,9 +103,34 @@ }, { "input": [ - "F1", "F21" ], + "output": [ + "F22" + ], + "name": "Imputer", + "opType": "Imputer", + "attribute": [ + { + "name": "replaced_value_float", + "f": "NaN", + "type": "FLOAT" + }, + { + "name": "imputed_value_floats", + "floats": [ + 0 + ], + "type": "FLOATS" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "F1", + "F22" + ], "output": [ "Features" ], @@ -334,10 +359,10 @@ }, { "input": [ - "F21" + "F22" ], "output": [ - "F22" + "F23" ], "name": "Identity1", "opType": "Identity" @@ -478,7 +503,7 @@ } }, { - "name": "F22", + "name": "F23", "type": { "tensorType": { "elemType": "FLOAT", @@ -605,6 +630,24 @@ } } }, + { + "name": "F22", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "10" + } + ] + } + } + } + }, { "name": "Features", "type": { diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index 1519e5839d..0a60e6473d 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -74,7 +74,7 @@ public void SimpleEndToEndOnnxConversionTest() var onnxResult = onnxTransformer.Transform(data); // Step 4: Compare ONNX and ML.NET results. - CompareSelectedR4ScalarColumns("Score", "Score0", transformedData, onnxResult, 2); + CompareSelectedR4ScalarColumns("Score", "Score0", transformedData, onnxResult, 1); } // Step 5: Check ONNX model's text format. This test will be not necessary if Step 3 and Step 4 can run on Linux and @@ -180,6 +180,7 @@ public void KeyToVectorWithBagOnnxConversionTest() separatorChar: '\t'); var pipeline = mlContext.Transforms.Categorical.OneHotEncoding("F2", "F2", Transforms.Categorical.OneHotEncodingTransformer.OutputKind.Bag) + .Append(mlContext.Transforms.ReplaceMissingValues(new MissingValueReplacingTransformer.ColumnInfo("F2"))) .Append(mlContext.Transforms.Concatenate("Features", "F1", "F2")) .Append(mlContext.BinaryClassification.Trainers.FastTree(labelColumn: "Label", featureColumn: "Features", numLeaves: 2, numTrees: 1, minDatapointsInLeaves: 2)); @@ -299,7 +300,7 @@ public void LogisticRegressionSaveModelToOnnxTest() Done(); } - [Fact] + [ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // LightGBM is 64-bit only public void LightGbmBinaryClassificationOnnxConversionTest() { // Step 1: Create and train a ML.NET pipeline. From 66e63678c84a2f843bf707ebcaa2aaa2b42e158c Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Sat, 5 Jan 2019 11:51:13 -0800 Subject: [PATCH 14/23] Add one more test --- .../ExcludeVariablesInOnnxConversion.txt | 615 ++++++++++++++++++ test/Microsoft.ML.Tests/OnnxConversionTest.cs | 54 +- 2 files changed, 665 insertions(+), 4 deletions(-) create mode 100644 test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ExcludeVariablesInOnnxConversion.txt diff --git a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ExcludeVariablesInOnnxConversion.txt b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ExcludeVariablesInOnnxConversion.txt new file mode 100644 index 0000000000..0e2a01ba0d --- /dev/null +++ b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ExcludeVariablesInOnnxConversion.txt @@ -0,0 +1,615 @@ +{ + "irVersion": "3", + "producerName": "ML.NET", + "producerVersion": "##VERSION##", + "domain": "machinelearning.dotnet", + "graph": { + "node": [ + { + "input": [ + "F2" + ], + "output": [ + "F20" + ], + "name": "LabelEncoder", + "opType": "LabelEncoder", + "attribute": [ + { + "name": "classes_strings", + "strings": [ + "NA==", + "MQ==", + "OA==", + "MTA=", + "Mg==", + "Mw==", + "Nw==", + "NQ==", + "Ng==", + "OQ==" + ], + "type": "STRINGS" + }, + { + "name": "default_int64", + "i": "-1", + "type": "INT" + }, + { + "name": "default_string", + "s": "IA==", + "type": "STRING" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "F20" + ], + "output": [ + "encoded" + ], + "name": "OneHotEncoder", + "opType": "OneHotEncoder", + "attribute": [ + { + "name": "cats_int64s", + "ints": [ + "0", + "1", + "2", + "3", + "4", + "5", + "6", + "7", + "8", + "9" + ], + "type": "INTS" + }, + { + "name": "zeros", + "i": "1", + "type": "INT" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "encoded" + ], + "output": [ + "F21" + ], + "name": "ReduceSum", + "opType": "ReduceSum", + "attribute": [ + { + "name": "axes", + "ints": [ + "1" + ], + "type": "INTS" + }, + { + "name": "keepdims", + "type": "INT" + } + ] + }, + { + "input": [ + "F21" + ], + "output": [ + "F22" + ], + "name": "Imputer", + "opType": "Imputer", + "attribute": [ + { + "name": "replaced_value_float", + "f": "NaN", + "type": "FLOAT" + }, + { + "name": "imputed_value_floats", + "floats": [ + 0 + ], + "type": "FLOATS" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "F1", + "F22" + ], + "output": [ + "Features" + ], + "name": "FeatureVectorizer", + "opType": "FeatureVectorizer", + "attribute": [ + { + "name": "inputdimensions", + "ints": [ + "1", + "10" + ], + "type": "INTS" + } + ], + "domain": "ai.onnx.ml" + }, + { + "input": [ + "Features" + ], + "output": [ + "Features0" + ], + "name": "Scaler", + "opType": "Scaler", + "attribute": [ + { + "name": "offset", + "floats": [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0 + ], + "type": "FLOATS" + }, + { + "name": "scale", + 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SimpleEndToEndOnnxConversionTest() var onnxTextName = "SimplePipeline.txt"; var onnxTextPath = GetOutputPath(subDir, onnxTextName); SaveOnnxModel(onnxModel, null, onnxTextPath); - CheckEquality(subDir, onnxTextName); + CheckEquality(subDir, onnxTextName, digitsOfPrecision: 3); Done(); } @@ -134,7 +134,7 @@ public void KmeansOnnxConversionTest() settings.MaxIterations = 1; settings.K = 4; settings.NumThreads = 1; - settings.InitAlgorithm = Trainers.KMeans.KMeansPlusPlusTrainer.InitAlgorithm.KMeansPlusPlus; + settings.InitAlgorithm = Trainers.KMeans.KMeansPlusPlusTrainer.InitAlgorithm.Random; })); var model = pipeline.Fit(data); @@ -268,7 +268,7 @@ public void InitializerCreationTest() } [Fact] - public void LogisticRegressionSaveModelToOnnxTest() + public void LogisticRegressionOnnxConversionTest() { // Step 1: Create and train a ML.NET pipeline. var trainDataPath = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename); @@ -334,7 +334,7 @@ public void LightGbmBinaryClassificationOnnxConversionTest() } [Fact] - public void MulticlassClassificationLogisticRegressionSaveModelToOnnxTest() + public void MulticlassLogisticRegressionOnnxConversionTest() { var mlContext = new MLContext(seed: 1, conc: 1); @@ -367,6 +367,52 @@ public void MulticlassClassificationLogisticRegressionSaveModelToOnnxTest() Done(); } + [Fact] + public void RemoveVariablesInPipelineTest() + { + var mlContext = new MLContext(seed: 1, conc: 1); + + string dataPath = GetDataPath("breast-cancer.txt"); + var data = mlContext.Data.ReadFromTextFile(dataPath, + hasHeader: true, + separatorChar: '\t'); + + var pipeline = mlContext.Transforms.Categorical.OneHotEncoding("F2", "F2", Transforms.Categorical.OneHotEncodingTransformer.OutputKind.Bag) + .Append(mlContext.Transforms.ReplaceMissingValues(new MissingValueReplacingTransformer.ColumnInfo("F2"))) + .Append(mlContext.Transforms.Concatenate("Features", "F1", "F2")) + .Append(mlContext.Transforms.Normalize("Features")) + .Append(mlContext.BinaryClassification.Trainers.FastTree(labelColumn: "Label", featureColumn: "Features", numLeaves: 2, numTrees: 1, minDatapointsInLeaves: 2)); + + var model = pipeline.Fit(data); + var transformedData = model.Transform(data); + + var onnxConversionContext = new OnnxContextImpl(mlContext, "A Simple Pipeline", "ML.NET", "0", 0, "machinelearning.dotnet", OnnxVersion.Stable); + + LinkedList transforms = null; + using (var conversionChannel = (mlContext as IChannelProvider).Start("ONNX conversion")) + { + SaveOnnxCommand.GetPipe(onnxConversionContext, conversionChannel, transformedData, out IDataView root, out IDataView sink, out transforms); + // Input columns' names to be excluded in the resulted ONNX model. + var redundantInputColumnNames = new HashSet { "Label" }; + // Output columns' names to be excluded in the resulted ONNX model. + var redundantOutputColumnNames = new HashSet { "Label", "F1", "F2", "Features" }; + var onnxModel = SaveOnnxCommand.ConvertTransformListToOnnxModel(onnxConversionContext, conversionChannel, root, sink, transforms, + redundantInputColumnNames, redundantOutputColumnNames); + + // Check ONNX model's text format. We save the produced ONNX model as a text file and compare it against + // the associated file in ML.NET repo. Such a comparison can be retired if ONNXRuntime ported to ML.NET + // can support Linux and Mac. + var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "BinaryClassification", "BreastCancer"); + var onnxTextName = "ExcludeVariablesInOnnxConversion.txt"; + var onnxFileName = "ExcludeVariablesInOnnxConversion.onnx"; + var onnxTextPath = GetOutputPath(subDir, onnxTextName); + var onnxFilePath = GetOutputPath(subDir, onnxFileName); + SaveOnnxModel(onnxModel, onnxFilePath, onnxTextPath); + CheckEquality(subDir, onnxTextName); + } + Done(); + } + private void CreateDummyExamplesToMakeComplierHappy() { var dummyExample = new BreastCancerFeatureVector() { Features = null }; From 1a36b37c3c930847c6400ec7e470376864f29aa8 Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Sat, 5 Jan 2019 14:52:51 -0800 Subject: [PATCH 15/23] Update Kmeans file due to change of initialization --- .../Onnx/Cluster/BreastCancer/Kmeans.txt | 80 +++++++++---------- 1 file changed, 40 insertions(+), 40 deletions(-) diff --git a/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt b/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt index 8e6951472a..8c832f9db8 100644 --- a/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt +++ b/test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.txt @@ -166,42 +166,42 @@ ], "dataType": "FLOAT", "floatData": [ - 0.690322459, - 0.948387, - 0.835483849, - 0.9129032, - 0.7741936, - 0.7709678, - 0.729032159, - 0.606451631, - 0.6161291, - 0.473529756, - 0.15147081, - 0.1705884, - 0.144608051, - 0.220098346, - 0.13627474, - 0.206372589, - 0.140686512, - 0.1176473, - 0.7130001, - 0.6315002, - 0.646, - 0.5310001, - 0.5105002, - 0.7975002, - 0.5864999, - 0.599499941, - 0.19799982, - 0.171255022, - 0.115384832, - 0.119838275, - 0.118218832, - 0.20121488, - 0.124696605, - 0.214574754, - 0.115384884, - 0.108097412 + 0.625973761, + 0.424676031, + 0.4348058, + 0.378701448, + 0.395843625, + 0.489611953, 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"dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "9" + } + ] + } + } + } + }, + { + "name": "Features", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "17" + } + ] + } + } + } + } + ] + }, + "opsetImport": [ + { + "domain": "ai.onnx.ml", + "version": "1" + }, + { + "version": "7" + } + ] +} \ No newline at end of file diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index e9a0fc038f..88eb2bbec9 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -12,6 +12,7 @@ using Microsoft.ML.Data; using Microsoft.ML.Model.Onnx; using Microsoft.ML.RunTests; +using Microsoft.ML.Tools; using Microsoft.ML.Transforms; using Microsoft.ML.UniversalModelFormat.Onnx; using Newtonsoft.Json; @@ -169,12 +170,33 @@ public void KmeansOnnxConversionTest() Done(); } + [Fact] + void CommandLineOnnxConversionTest() + { + string dataPath = GetDataPath("breast-cancer.txt"); + string modelPath = GetOutputPath("ModelWithLessIO.zip"); + var trainingPathArgs = $"data={dataPath} out={modelPath}"; + var trainingArgs = " loader=text{col=Label:BL:0 col=F1:R4:1-8 col=F2:TX:9} xf=Cat{col=F2} xf=Concat{col=Features:F1,F2} tr=ft{numThreads=1 numLeaves=8 numTrees=3} seed=1"; + Assert.Equal(0, Maml.Main(new[] { "train " + trainingPathArgs + trainingArgs})); + + var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "BinaryClassification", "BreastCancer"); + var onnxTextName = "ModelWithLessIO.txt"; + var onnxFileName = "ModelWithLessIO.onnx"; + var onnxTextPath = GetOutputPath(subDir, onnxTextName); + var onnxFilePath = GetOutputPath(subDir, onnxFileName); + string conversionCommand = $"saveonnx in={modelPath} onnx={onnxFilePath} json={onnxTextPath} domain=machinelearning.dotnet name=modelWithLessIO inputsToDrop=Label outputsToDrop=F1,F2,Features,Label"; + Assert.Equal(0, Maml.Main(new[] { conversionCommand })); + CheckEquality(subDir, onnxTextName); + Done(); + } + [Fact] public void KeyToVectorWithBagOnnxConversionTest() { var mlContext = new MLContext(seed: 1, conc: 1); string dataPath = GetDataPath("breast-cancer.txt"); + var data = mlContext.Data.ReadFromTextFile(dataPath, hasHeader: true, separatorChar: '\t'); From d93f8a57b83dd9e7d06166e4bde22a35b93780a9 Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Sat, 5 Jan 2019 17:17:08 -0800 Subject: [PATCH 17/23] Add word embedding test --- .../Transforms/Sentiment/SmallWordEmbed.txt | 1122 +++++++++++++++++ test/Microsoft.ML.Tests/OnnxConversionTest.cs | 31 + 2 files changed, 1153 insertions(+) create mode 100644 test/BaselineOutput/Common/Onnx/Transforms/Sentiment/SmallWordEmbed.txt diff --git a/test/BaselineOutput/Common/Onnx/Transforms/Sentiment/SmallWordEmbed.txt b/test/BaselineOutput/Common/Onnx/Transforms/Sentiment/SmallWordEmbed.txt new file mode 100644 index 0000000000..5b1a98942b --- /dev/null +++ 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"name": "Tokens0", + "type": { + "tensorType": { + "elemType": "STRING", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "4" + } + ] + } + } + } + }, + { + "name": "Embed0", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "150" + } + ] + } + } + } + } + ], + "valueInfo": [ + { + "name": "Embed", + "type": { + "tensorType": { + "elemType": "FLOAT", + "shape": { + "dim": [ + { + "dimValue": "1" + }, + { + "dimValue": "150" + } + ] + } + } + } + } + ] + }, + "opsetImport": [ + { + "domain": "ai.onnx.ml", + "version": "1" + }, + { + "version": "7" + } + ] +} \ No newline at end of file diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index 88eb2bbec9..d3ba056051 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -435,11 +435,42 @@ public void RemoveVariablesInPipelineTest() Done(); } + private class SmallSentimentExample + { + [LoadColumn(0,3), VectorType(4)] + public string[] Tokens; + } + + [Fact] + public void WordEmbeddingsTest() + { + var mlContext = new MLContext(seed: 1, conc: 1); + var dataPath = GetDataPath(@"small-sentiment-test.tsv"); + var embedNetworkPath = GetDataPath(@"shortsentiment.emd"); + var data = mlContext.Data.ReadFromTextFile(dataPath, hasHeader: false, separatorChar: '\t'); + + var pipeline = mlContext.Transforms.Text.ExtractWordEmbeddings("Tokens", embedNetworkPath, "Embed"); + var model = pipeline.Fit(data); + var transformedData = model.Transform(data); + + var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "Transforms", "Sentiment"); + var onnxTextName = "SmallWordEmbed.txt"; + var onnxFileName = "SmallWordEmbed.onnx"; + var onnxTextPath = GetOutputPath(subDir, onnxTextName); + var onnxFilePath = GetOutputPath(subDir, onnxFileName); + var onnxModel = mlContext.Model.ConvertToOnnx(model, data); + SaveOnnxModel(onnxModel, onnxFilePath, onnxTextPath); + + CheckEquality(subDir, onnxTextName); + Done(); + } + private void CreateDummyExamplesToMakeComplierHappy() { var dummyExample = new BreastCancerFeatureVector() { Features = null }; var dummyExample1 = new BreastCancerCatFeatureExample() { Label = false, F1 = 0, F2 = "Amy" }; var dummyExample2 = new BreastCancerMulticlassExample() { Label = "Amy", Features = null }; + var dummyExample3 = new SmallSentimentExample() { Tokens = null }; } private void CompareSelectedR4VectorColumns(string leftColumnName, string rightColumnName, IDataView left, IDataView right, int precision = 6) From b683a5a684079823c95600a5e4b3951b1dea2241 Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Sat, 5 Jan 2019 17:27:04 -0800 Subject: [PATCH 18/23] Remove old tests --- ...sificationFastTreeSaveModelToOnnxTest.json | 572 --------- ...ryClassificationLRSaveModelToOnnxTest.json | 284 ----- ...sificationLightGBMSaveModelToOnnxTest.json | 315 ----- .../BreastCancer/KeyToVectorBag.json | 507 -------- .../Onnx/Cluster/BreastCancer/Kmeans.json | 318 ----- ...tiClassificationLRSaveModelToOnnxTest.json | 225 ---- .../Onnx/WordEmbeddings/WordEmbeddings.json | 1094 ----------------- test/Microsoft.ML.Tests/OnnxTests.cs | 591 --------- 8 files changed, 3906 deletions(-) delete mode 100644 test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/BinaryClassificationFastTreeSaveModelToOnnxTest.json delete mode 100644 test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/BinaryClassificationLRSaveModelToOnnxTest.json delete mode 100644 test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/BinaryClassificationLightGBMSaveModelToOnnxTest.json delete mode 100644 test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/KeyToVectorBag.json delete mode 100644 test/BaselineOutput/Common/Onnx/Cluster/BreastCancer/Kmeans.json delete mode 100644 test/BaselineOutput/Common/Onnx/MultiClassClassification/BreastCancer/MultiClassificationLRSaveModelToOnnxTest.json delete mode 100644 test/BaselineOutput/Common/Onnx/WordEmbeddings/WordEmbeddings.json delete mode 100644 test/Microsoft.ML.Tests/OnnxTests.cs diff --git a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/BinaryClassificationFastTreeSaveModelToOnnxTest.json b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/BinaryClassificationFastTreeSaveModelToOnnxTest.json deleted file mode 100644 index b032fc1aaf..0000000000 --- a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/BinaryClassificationFastTreeSaveModelToOnnxTest.json +++ /dev/null @@ -1,572 +0,0 @@ -{ - "irVersion": "3", - "producerName": "ML.NET", - "producerVersion": "##VERSION##", - "domain": "Onnx", - "graph": { - "node": [ - { - "input": [ - "F1" - ], - "output": [ - "F10" - ], - "name": "Imputer", - "opType": "Imputer", - "attribute": [ - { - "name": 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"dim": [ - { - "dimValue": "1" - }, - { - "dimValue": "4" - } - ] - } - } - } - } - ], - "output": [ - { - "name": "Cat1", - "type": { - "tensorType": { - "elemType": "FLOAT", - "shape": { - "dim": [ - { - "dimValue": "1" - }, - { - "dimValue": "150" - } - ] - } - } - } - } - ], - "valueInfo": [ - { - "name": "Cat0", - "type": { - "tensorType": { - "elemType": "FLOAT", - "shape": { - "dim": [ - { - "dimValue": "1" - }, - { - "dimValue": "150" - } - ] - } - } - } - } - ] - }, - "opsetImport": [ - { - "domain": "ai.onnx.ml", - "version": "1" - }, - { - "version": "7" - } - ] -} \ No newline at end of file diff --git a/test/Microsoft.ML.Tests/OnnxTests.cs b/test/Microsoft.ML.Tests/OnnxTests.cs deleted file mode 100644 index 117d83ffff..0000000000 --- a/test/Microsoft.ML.Tests/OnnxTests.cs +++ /dev/null @@ -1,591 +0,0 @@ -// Licensed to the .NET Foundation under one or more agreements. -// The .NET Foundation licenses this file to you under the MIT license. -// See the LICENSE file in the project root for more information. - -using System; -using System.Collections.Generic; -using System.IO; -using System.Text.RegularExpressions; -using Microsoft.ML.Data; -using Microsoft.ML.Legacy.Data; -using Microsoft.ML.Legacy.Models; -using Microsoft.ML.Legacy.Trainers; -using Microsoft.ML.Legacy.Transforms; -using Microsoft.ML.Model.Onnx; -using Microsoft.ML.RunTests; -using Xunit; -using Xunit.Abstractions; - -namespace Microsoft.ML.Tests -{ -#pragma warning disable 612, 618 - public class OnnxTests : BaseTestBaseline - { - public OnnxTests(ITestOutputHelper output) : base(output) - { - } - - public class BreastCancerData - { - public float Label; - - public float F1; - public ReadOnlyMemory F2; - } - - public class EmbeddingsData - { - [VectorType(4)] - public string[] Cat; - } - - public class EmbeddingsResult - { - [ColumnName("Cat")] - public float[] Cat; - } - - public class BreastNumericalColumns - { - [VectorType(9)] - public float[] Features; - } - - public class BreastCancerDataAllColumns - { - public float Label; - - [VectorType(9)] - public float[] Features; - } - - public class BreastCancerPrediction - { - [ColumnName("PredictedLabel")] - public bool Cancerous; - } - - public class BreastCancerMCPrediction - { - [ColumnName("Score")] - public float[] Scores; - } - - public class BreastCancerClusterPrediction - { - [ColumnName("PredictedLabel")] - public uint NearestCluster; - [ColumnName("Score")] - public float[] Distances; - } - - [Fact] - public void InitializerCreationTest() - { - var env = new MLContext(); - // Create the actual implementation - var ctxImpl = new OnnxContextImpl(env, "model", "ML.NET", "0", 0, "com.test", Model.Onnx.OnnxVersion.Stable); - - // Use implementation as in the actual conversion code - var ctx = ctxImpl as OnnxContext; - ctx.AddInitializer(9.4f, "float"); - ctx.AddInitializer(17L, "int64"); - ctx.AddInitializer("36", "string"); - ctx.AddInitializer(new List { 9.4f, 1.7f, 3.6f }, new List { 1, 3 }, "floats"); - ctx.AddInitializer(new List { 94L, 17L, 36L }, new List { 1, 3 }, "int64s"); - ctx.AddInitializer(new List { "94", "17", "36" }, new List { 1, 3 }, "strings"); - - var model = ctxImpl.MakeModel(); - - var floatScalar = model.Graph.Initializer[0]; - Assert.True(floatScalar.Name == "float"); - Assert.True(floatScalar.Dims.Count == 0); - Assert.True(floatScalar.FloatData.Count == 1); - Assert.True(floatScalar.FloatData[0] == 9.4f); - - var int64Scalar = model.Graph.Initializer[1]; - Assert.True(int64Scalar.Name == "int64"); - Assert.True(int64Scalar.Dims.Count == 0); - Assert.True(int64Scalar.Int64Data.Count == 1); - Assert.True(int64Scalar.Int64Data[0] == 17L); - - var stringScalar = model.Graph.Initializer[2]; - Assert.True(stringScalar.Name == "string"); - Assert.True(stringScalar.Dims.Count == 0); - Assert.True(stringScalar.StringData.Count == 1); - Assert.True(stringScalar.StringData[0].ToStringUtf8() == "36"); - - var floatsTensor = model.Graph.Initializer[3]; - Assert.True(floatsTensor.Name == "floats"); - Assert.True(floatsTensor.Dims.Count == 2); - Assert.True(floatsTensor.Dims[0] == 1); - Assert.True(floatsTensor.Dims[1] == 3); - Assert.True(floatsTensor.FloatData.Count == 3); - Assert.True(floatsTensor.FloatData[0] == 9.4f); - Assert.True(floatsTensor.FloatData[1] == 1.7f); - Assert.True(floatsTensor.FloatData[2] == 3.6f); - - var int64sTensor = model.Graph.Initializer[4]; - Assert.True(int64sTensor.Name == "int64s"); - Assert.True(int64sTensor.Dims.Count == 2); - Assert.True(int64sTensor.Dims[0] == 1); - Assert.True(int64sTensor.Dims[1] == 3); - Assert.True(int64sTensor.Int64Data.Count == 3); - Assert.True(int64sTensor.Int64Data[0] == 94L); - Assert.True(int64sTensor.Int64Data[1] == 17L); - Assert.True(int64sTensor.Int64Data[2] == 36L); - - var stringsTensor = model.Graph.Initializer[5]; - Assert.True(stringsTensor.Name == "strings"); - Assert.True(stringsTensor.Dims.Count == 2); - Assert.True(stringsTensor.Dims[0] == 1); - Assert.True(stringsTensor.Dims[1] == 3); - Assert.True(stringsTensor.StringData.Count == 3); - Assert.True(stringsTensor.StringData[0].ToStringUtf8() == "94"); - Assert.True(stringsTensor.StringData[1].ToStringUtf8() == "17"); - Assert.True(stringsTensor.StringData[2].ToStringUtf8() == "36"); - } - - [Fact] - public void BinaryClassificationFastTreeSaveModelToOnnxTest() - { - string dataPath = GetDataPath(@"breast-cancer.txt"); - var pipeline = new Legacy.LearningPipeline(); - - pipeline.Add(new Legacy.Data.TextLoader(dataPath) - { - Arguments = new TextLoaderArguments - { - Separator = new[] { '\t' }, - HasHeader = true, - Column = new[] - { - new TextLoaderColumn() - { - Name = "Label", - Source = new [] { new TextLoaderRange(0) }, - Type = Legacy.Data.DataKind.Num - }, - - new TextLoaderColumn() - { - Name = "F1", - Source = new [] { new TextLoaderRange(1, 1) }, - Type = Legacy.Data.DataKind.Num - }, - - new TextLoaderColumn() - { - Name = "F2", - Source = new [] { new TextLoaderRange(2, 2) }, - Type = Legacy.Data.DataKind.TX - } - } - } - }); - - pipeline.Add(new MissingValueSubstitutor("F1")); - pipeline.Add(new MinMaxNormalizer("F1")); - pipeline.Add(new CategoricalOneHotVectorizer("F2")); - pipeline.Add(new ColumnConcatenator("Features", "F1", "F2")); - pipeline.Add(new FastTreeBinaryClassifier() { NumLeaves = 2, NumTrees = 1, MinDocumentsInLeafs = 2 }); - - var model = pipeline.Train(); - var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "BinaryClassification", "BreastCancer"); - var onnxPath = GetOutputPath(subDir, "BinaryClassificationFastTreeSaveModelToOnnxTest.onnx"); - DeleteOutputPath(onnxPath); - - var onnxAsJsonPath = GetOutputPath(subDir, "BinaryClassificationFastTreeSaveModelToOnnxTest.json"); - DeleteOutputPath(onnxAsJsonPath); - - OnnxConverter converter = new OnnxConverter() - { - InputsToDrop = new[] { "Label" }, - OutputsToDrop = new[] { "Label", "F1", "F2", "Features" }, - Onnx = onnxPath, - Json = onnxAsJsonPath, - Domain = "Onnx" - }; - - converter.Convert(model); - - // Strip the version. - var fileText = File.ReadAllText(onnxAsJsonPath); - fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\""); - File.WriteAllText(onnxAsJsonPath, fileText); - - CheckEquality(subDir, "BinaryClassificationFastTreeSaveModelToOnnxTest.json"); - Done(); - } - - [Fact] - public void KeyToVectorWithBagTest() - { - string dataPath = GetDataPath(@"breast-cancer.txt"); - var pipeline = new Legacy.LearningPipeline(); - - pipeline.Add(new Legacy.Data.TextLoader(dataPath) - { - Arguments = new TextLoaderArguments - { - Separator = new[] { '\t' }, - HasHeader = true, - Column = new[] - { - new TextLoaderColumn() - { - Name = "Label", - Source = new [] { new TextLoaderRange(0) }, - Type = Legacy.Data.DataKind.Num - }, - - new TextLoaderColumn() - { - Name = "F1", - Source = new [] { new TextLoaderRange(1, 1) }, - Type = Legacy.Data.DataKind.Num - }, - - new TextLoaderColumn() - { - Name = "F2", - Source = new [] { new TextLoaderRange(2, 2) }, - Type = Legacy.Data.DataKind.TX - } - } - } - }); - - var vectorizer = new CategoricalOneHotVectorizer(); - var categoricalColumn = new OneHotEncodingTransformerColumn() - { - OutputKind = OneHotEncodingTransformerOutputKind.Bag, - Name = "F2", - Source = "F2" - }; - vectorizer.Column = new OneHotEncodingTransformerColumn[1] { categoricalColumn }; - pipeline.Add(vectorizer); - pipeline.Add(new ColumnConcatenator("Features", "F1", "F2")); - pipeline.Add(new FastTreeBinaryClassifier() { NumLeaves = 2, NumTrees = 1, MinDocumentsInLeafs = 2 }); - - var model = pipeline.Train(); - var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "BinaryClassification", "BreastCancer"); - var onnxPath = GetOutputPath(subDir, "KeyToVectorBag.onnx"); - DeleteOutputPath(onnxPath); - - var onnxAsJsonPath = GetOutputPath(subDir, "KeyToVectorBag.json"); - DeleteOutputPath(onnxAsJsonPath); - - OnnxConverter converter = new OnnxConverter() - { - InputsToDrop = new[] { "Label" }, - OutputsToDrop = new[] { "Label", "F1", "F2", "Features" }, - Onnx = onnxPath, - Json = onnxAsJsonPath, - Domain = "Onnx" - }; - - converter.Convert(model); - - // Strip the version. - var fileText = File.ReadAllText(onnxAsJsonPath); - fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\""); - File.WriteAllText(onnxAsJsonPath, fileText); - - CheckEquality(subDir, "KeyToVectorBag.json"); - Done(); - } - - [Fact] - public void WordEmbeddingsTest() - { - string dataPath = GetDataPath(@"small-sentiment-test.tsv"); - var pipeline = new Legacy.LearningPipeline(0); - - pipeline.Add(new Legacy.Data.TextLoader(dataPath) - { - Arguments = new TextLoaderArguments - { - Separator = new[] { '\t' }, - HasHeader = false, - Column = new[] - { - new TextLoaderColumn() - { - Name = "Cat", - Source = new [] { new TextLoaderRange(0, 3) }, - Type = Legacy.Data.DataKind.TX - }, - } - } - }); - - var modelPath = GetDataPath(@"shortsentiment.emd"); - var embed = new WordEmbeddings() { CustomLookupTable = modelPath }; - embed.AddColumn("Cat", "Cat"); - pipeline.Add(embed); - var model = pipeline.Train(); - - var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "WordEmbeddings"); - var onnxPath = GetOutputPath(subDir, "WordEmbeddings.onnx"); - DeleteOutputPath(onnxPath); - - var onnxAsJsonPath = GetOutputPath(subDir, "WordEmbeddings.json"); - DeleteOutputPath(onnxAsJsonPath); - - OnnxConverter converter = new OnnxConverter() - { - Onnx = onnxPath, - Json = onnxAsJsonPath, - Domain = "Onnx" - }; - - converter.Convert(model); - - var fileText = File.ReadAllText(onnxAsJsonPath); - fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\""); - File.WriteAllText(onnxAsJsonPath, fileText); - - CheckEquality(subDir, "WordEmbeddings.json"); - Done(); - } - - [ConditionalFact(typeof(BaseTestBaseline), nameof(BaseTestBaseline.LessThanNetCore30OrNotNetCore))] // netcore3.0 differs from Baseline - public void KmeansTest() - { - string dataPath = GetDataPath(@"breast-cancer.txt"); - var pipeline = new Legacy.LearningPipeline(0); - - pipeline.Add(new Legacy.Data.TextLoader(dataPath) - { - Arguments = new TextLoaderArguments - { - Separator = new[] { '\t' }, - HasHeader = true, - Column = new[] - { - new TextLoaderColumn() - { - Name = "Features", - Source = new [] { new TextLoaderRange(1, 9) }, - Type = Legacy.Data.DataKind.R4 - }, - } - } - }); - - pipeline.Add(new KMeansPlusPlusClusterer() { K = 2, MaxIterations = 1, NumThreads = 1, InitAlgorithm = KMeansPlusPlusTrainerInitAlgorithm.Random }); - var model = pipeline.Train(); - var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "Cluster", "BreastCancer"); - var onnxPath = GetOutputPath(subDir, "Kmeans.onnx"); - DeleteOutputPath(onnxPath); - - var onnxAsJsonPath = GetOutputPath(subDir, "Kmeans.json"); - DeleteOutputPath(onnxAsJsonPath); - - OnnxConverter converter = new OnnxConverter() - { - Onnx = onnxPath, - Json = onnxAsJsonPath, - Domain = "Onnx" - }; - - converter.Convert(model); - - // Strip the version. - var fileText = File.ReadAllText(onnxAsJsonPath); - fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\""); - File.WriteAllText(onnxAsJsonPath, fileText); - - CheckEquality(subDir, "Kmeans.json"); - Done(); - } - - - [ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // LightGBM is 64-bit only - public void BinaryClassificationLightGBMSaveModelToOnnxTest() - { - string dataPath = GetDataPath(@"breast-cancer.txt"); - var pipeline = new Legacy.LearningPipeline(); - - pipeline.Add(new Legacy.Data.TextLoader(dataPath) - { - Arguments = new TextLoaderArguments - { - Separator = new[] { '\t' }, - HasHeader = true, - Column = new[] - { - new TextLoaderColumn() - { - Name = "Label", - Source = new [] { new TextLoaderRange(0) }, - Type = Legacy.Data.DataKind.Num - }, - - new TextLoaderColumn() - { - Name = "Features", - Source = new [] { new TextLoaderRange(1, 9) }, - Type = Legacy.Data.DataKind.Num - } - } - } - }); - - pipeline.Add(new LightGbmBinaryClassifier() { NumLeaves = 2, NumBoostRound = 1, MinDataPerLeaf = 2 }); - - var model = pipeline.Train(); - var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "BinaryClassification", "BreastCancer"); - var onnxPath = GetOutputPath(subDir, "BinaryClassificationLightGBMSaveModelToOnnxTest.onnx"); - DeleteOutputPath(onnxPath); - - var onnxAsJsonPath = GetOutputPath(subDir, "BinaryClassificationLightGBMSaveModelToOnnxTest.json"); - DeleteOutputPath(onnxAsJsonPath); - - OnnxConverter converter = new OnnxConverter() - { - InputsToDrop = new[] { "Label" }, - OutputsToDrop = new[] { "Label", "Features" }, - Onnx = onnxPath, - Json = onnxAsJsonPath, - Domain = "Onnx" - }; - - converter.Convert(model); - - // Strip the version. - var fileText = File.ReadAllText(onnxAsJsonPath); - fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\""); - File.WriteAllText(onnxAsJsonPath, fileText); - - CheckEquality(subDir, "BinaryClassificationLightGBMSaveModelToOnnxTest.json"); - Done(); - } - - [Fact] - public void BinaryClassificationLRSaveModelToOnnxTest() - { - string dataPath = GetDataPath(@"breast-cancer.txt"); - var pipeline = new Legacy.LearningPipeline(); - - pipeline.Add(new Legacy.Data.TextLoader(dataPath) - { - Arguments = new TextLoaderArguments - { - Separator = new[] { '\t' }, - HasHeader = true, - Column = new[] - { - new TextLoaderColumn() - { - Name = "Label", - Source = new [] { new TextLoaderRange(0) }, - Type = Legacy.Data.DataKind.Num - }, - - new TextLoaderColumn() - { - Name = "Features", - Source = new [] { new TextLoaderRange(1, 9) }, - Type = Legacy.Data.DataKind.Num - } - } - } - }); - - pipeline.Add(new LogisticRegressionBinaryClassifier() { UseThreads = false }); - - var model = pipeline.Train(); - var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "BinaryClassification", "BreastCancer"); - var onnxPath = GetOutputPath(subDir, "BinaryClassificationLRSaveModelToOnnxTest.onnx"); - DeleteOutputPath(onnxPath); - - var onnxAsJsonPath = GetOutputPath(subDir, "BinaryClassificationLRSaveModelToOnnxTest.json"); - DeleteOutputPath(onnxAsJsonPath); - - OnnxConverter converter = new OnnxConverter() - { - InputsToDrop = new[] { "Label" }, - OutputsToDrop = new[] { "Label", "Features" }, - Onnx = onnxPath, - Json = onnxAsJsonPath, - Domain = "Onnx" - }; - - converter.Convert(model); - - // Strip the version. - var fileText = File.ReadAllText(onnxAsJsonPath); - fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\""); - File.WriteAllText(onnxAsJsonPath, fileText); - - CheckEquality(subDir, "BinaryClassificationLRSaveModelToOnnxTest.json", digitsOfPrecision: 3); - Done(); - } - - [Fact] - public void MultiClassificationLRSaveModelToOnnxTest() - { - string dataPath = GetDataPath(@"breast-cancer.txt"); - var pipeline = new Legacy.LearningPipeline(); - - pipeline.Add(new Legacy.Data.TextLoader(dataPath) - { - Arguments = new TextLoaderArguments - { - Separator = new[] { '\t' }, - HasHeader = true, - Column = new[] - { - new TextLoaderColumn() - { - Name = "Label", - Source = new [] { new TextLoaderRange(0) }, - Type = Legacy.Data.DataKind.Num - }, - - new TextLoaderColumn() - { - Name = "Features", - Source = new [] { new TextLoaderRange(1, 9) }, - Type = Legacy.Data.DataKind.Num - } - } - } - }); - - pipeline.Add(new Dictionarizer("Label")); - pipeline.Add(new LogisticRegressionClassifier() { UseThreads = false }); - - var model = pipeline.Train(); - var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "MultiClassClassification", "BreastCancer"); - var onnxPath = GetOutputPath(subDir, "MultiClassificationLRSaveModelToOnnxTest.onnx"); - DeleteOutputPath(onnxPath); - - var onnxAsJsonPath = GetOutputPath(subDir, "MultiClassificationLRSaveModelToOnnxTest.json"); - DeleteOutputPath(onnxAsJsonPath); - - OnnxConverter converter = new OnnxConverter() - { - InputsToDrop = new[] { "Label" }, - OutputsToDrop = new[] { "Label", "Features" }, - Onnx = onnxPath, - Json = onnxAsJsonPath, - Domain = "Onnx" - }; - - converter.Convert(model); - - // Strip the version. - var fileText = File.ReadAllText(onnxAsJsonPath); - fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\""); - File.WriteAllText(onnxAsJsonPath, fileText); - - CheckEquality(subDir, "MultiClassificationLRSaveModelToOnnxTest.json", digitsOfPrecision: 4); - Done(); - } - - } -#pragma warning restore 612, 618 -} From 0949f80cef38b2e19c0d13657dec9d24022f6a5d Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Sat, 5 Jan 2019 17:57:23 -0800 Subject: [PATCH 19/23] increase tol --- test/Microsoft.ML.Tests/OnnxConversionTest.cs | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index d3ba056051..6f288ae1b5 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -430,7 +430,7 @@ public void RemoveVariablesInPipelineTest() var onnxTextPath = GetOutputPath(subDir, onnxTextName); var onnxFilePath = GetOutputPath(subDir, onnxFileName); SaveOnnxModel(onnxModel, onnxFilePath, onnxTextPath); - CheckEquality(subDir, onnxTextName); + CheckEquality(subDir, onnxTextName, digitsOfPrecision: 3); } Done(); } @@ -543,7 +543,8 @@ private void SaveOnnxModel(ModelProto model, string binaryFormatPath, string tex // Strip the version information. var fileText = File.ReadAllText(textFormatPath); - fileText = Regex.Replace(fileText, "\"producerVersion\": \"([^\"]+)\"", "\"producerVersion\": \"##VERSION##\""); + + fileText = Regex.Replace(fileText, "\"producerVersion\": \".*\"", "\"producerVersion\": \"##VERSION##\""); File.WriteAllText(textFormatPath, fileText); } } From 1323bd91b715805b85055676b70591e136e4855a Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Sat, 5 Jan 2019 18:15:44 -0800 Subject: [PATCH 20/23] Drop version in saved baseline file --- .../BinaryClassification/BreastCancer/ModelWithLessIO.txt | 2 +- test/Microsoft.ML.Tests/OnnxConversionTest.cs | 5 +++++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ModelWithLessIO.txt b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ModelWithLessIO.txt index 642af73a8b..b52cce8e97 100644 --- a/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ModelWithLessIO.txt +++ b/test/BaselineOutput/Common/Onnx/BinaryClassification/BreastCancer/ModelWithLessIO.txt @@ -1,7 +1,7 @@ { "irVersion": "3", "producerName": "ML.NET", - "producerVersion": "0.10.27305.0", + "producerVersion": "##VERSION##", "domain": "machinelearning.dotnet", "graph": { "node": [ diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index 6f288ae1b5..37f23d4ca7 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -186,6 +186,11 @@ void CommandLineOnnxConversionTest() var onnxFilePath = GetOutputPath(subDir, onnxFileName); string conversionCommand = $"saveonnx in={modelPath} onnx={onnxFilePath} json={onnxTextPath} domain=machinelearning.dotnet name=modelWithLessIO inputsToDrop=Label outputsToDrop=F1,F2,Features,Label"; Assert.Equal(0, Maml.Main(new[] { conversionCommand })); + + var fileText = File.ReadAllText(onnxTextPath); + fileText = Regex.Replace(fileText, "\"producerVersion\": \".*\"", "\"producerVersion\": \"##VERSION##\""); + File.WriteAllText(onnxTextPath, fileText); + CheckEquality(subDir, onnxTextName); Done(); } From 8bb700fc8bb1e17fd68b8f7cdcaf21da85b2ec61 Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Sat, 5 Jan 2019 19:22:27 -0800 Subject: [PATCH 21/23] make c30 happy --- test/Microsoft.ML.Tests/OnnxConversionTest.cs | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index 37f23d4ca7..611c63358f 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -323,7 +323,7 @@ public void LogisticRegressionOnnxConversionTest() SaveOnnxModel(onnxModel, onnxFilePath, onnxTextPath); // Step 4: Check ONNX model's text format. - CheckEquality(subDir, onnxTextName); + CheckEquality(subDir, onnxTextName, digitsOfPrecision: 3); Done(); } @@ -356,7 +356,7 @@ public void LightGbmBinaryClassificationOnnxConversionTest() SaveOnnxModel(onnxModel, onnxFilePath, onnxTextPath); // Step 4: Check ONNX model's text format. - CheckEquality(subDir, onnxTextName); + CheckEquality(subDir, onnxTextName, digitsOfPrecision: 3); Done(); } @@ -390,7 +390,7 @@ public void MulticlassLogisticRegressionOnnxConversionTest() SaveOnnxModel(onnxModel, onnxFilePath, onnxTextPath); - CheckEquality(subDir, onnxTextName); + CheckEquality(subDir, onnxTextName, digitsOfPrecision: 3); Done(); } From d0c4cb90099c43324442f2aecdf742cd899eda58 Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Sat, 5 Jan 2019 19:42:46 -0800 Subject: [PATCH 22/23] Make c30 happier --- test/Microsoft.ML.Tests/OnnxConversionTest.cs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index 611c63358f..1b01c07765 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -166,7 +166,7 @@ public void KmeansOnnxConversionTest() var onnxTextName = "Kmeans.txt"; var onnxTextPath = GetOutputPath(subDir, onnxTextName); SaveOnnxModel(onnxModel, null, onnxTextPath); - CheckEquality(subDir, onnxTextName); + CheckEquality(subDir, onnxTextName, digitsOfPrecision: 2); Done(); } From 30bf2d6312109fc6ff8f2cf8d5758fdcd7f9e05f Mon Sep 17 00:00:00 2001 From: Wei-Sheng Chin Date: Sat, 5 Jan 2019 20:20:05 -0800 Subject: [PATCH 23/23] c30 needs even more --- test/Microsoft.ML.Tests/OnnxConversionTest.cs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index 1b01c07765..c03098ac28 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -390,7 +390,7 @@ public void MulticlassLogisticRegressionOnnxConversionTest() SaveOnnxModel(onnxModel, onnxFilePath, onnxTextPath); - CheckEquality(subDir, onnxTextName, digitsOfPrecision: 3); + CheckEquality(subDir, onnxTextName, digitsOfPrecision: 2); Done(); }