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Merged
merged 18 commits into from
Sep 6, 2019
Merged

Added onnx export functionality for LpNormNormalizingTransformer #4161

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8749a10
Merge pull request #1 from dotnet/master
harishsk Jun 20, 2019
fe25bf6
Fixed build errors resulting from upgrade to VS2019 compilers
harishsk Jun 21, 2019
cb446be
Added additional message describing the previous fix
harishsk Jun 21, 2019
b5ee220
Merge pull request #2 from dotnet/master
harishsk Jun 21, 2019
b9a7471
Merge pull request #3 from dotnet/master
harishsk Jun 24, 2019
80e238d
Merge pull request #4 from dotnet/master
harishsk Jun 27, 2019
2ef424d
Merge pull request #5 from dotnet/master
harishsk Jul 9, 2019
3958f01
Merge pull request #6 from dotnet/master
harishsk Aug 7, 2019
00bc7ef
Merge pull request #7 from dotnet/master
harishsk Aug 14, 2019
d0462f1
Merge pull request #8 from dotnet/master
harishsk Aug 16, 2019
c0a430a
Merge pull request #9 from dotnet/master
harishsk Aug 21, 2019
0b55903
Syncing upstream fork (#10)
harishsk Aug 30, 2019
798359a
Added export functionality for LpNormNormalizingTransformer
harishsk Aug 30, 2019
56983d5
Syncing upstream fork (#11)
harishsk Aug 30, 2019
fe44d4a
Merge branch 'master' into onnxexport
harishsk Aug 30, 2019
9c31984
Merge pull request #12 from dotnet/master
harishsk Aug 30, 2019
6e75d8a
Merge branch 'onnxexport' of ssh://github.com/harishsk/machinelearnin…
harishsk Aug 30, 2019
1308b4a
Replaced hardcoded Features column name
harishsk Sep 4, 2019
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137 changes: 136 additions & 1 deletion src/Microsoft.ML.Transforms/GcnTransform.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
using Microsoft.ML.EntryPoints;
using Microsoft.ML.Internal.CpuMath;
using Microsoft.ML.Internal.Utilities;
using Microsoft.ML.Model.OnnxConverter;
using Microsoft.ML.Runtime;
using Microsoft.ML.Transforms;

Expand Down Expand Up @@ -313,11 +314,13 @@ private protected override void SaveModel(ModelSaveContext ctx)

private protected override IRowMapper MakeRowMapper(DataViewSchema schema) => new Mapper(this, schema);

private sealed class Mapper : OneToOneMapperBase
private sealed class Mapper : OneToOneMapperBase, ISaveAsOnnx
{
private readonly DataViewType[] _srcTypes;
private readonly int[] _srcCols;
private readonly DataViewType[] _types;
private readonly LpNormNormalizingEstimatorBase.NormFunction[] _norms;
private readonly bool[] _ensureZeroMeans;
private readonly LpNormNormalizingTransformer _parent;

public Mapper(LpNormNormalizingTransformer parent, DataViewSchema inputSchema)
Expand All @@ -327,12 +330,16 @@ public Mapper(LpNormNormalizingTransformer parent, DataViewSchema inputSchema)
_types = new DataViewType[_parent.ColumnPairs.Length];
_srcTypes = new DataViewType[_parent.ColumnPairs.Length];
_srcCols = new int[_parent.ColumnPairs.Length];
_norms = new LpNormNormalizingEstimatorBase.NormFunction[_parent.ColumnPairs.Length];
_ensureZeroMeans = new bool[_parent.ColumnPairs.Length];
for (int i = 0; i < _parent.ColumnPairs.Length; i++)
{
inputSchema.TryGetColumnIndex(_parent.ColumnPairs[i].inputColumnName, out _srcCols[i]);
var srcCol = inputSchema[_srcCols[i]];
_srcTypes[i] = srcCol.Type;
_types[i] = srcCol.Type;
_norms[i] = _parent._columns[i].Norm;
_ensureZeroMeans[i] = _parent._columns[i].EnsureZeroMean;
}
}

Expand Down Expand Up @@ -594,6 +601,134 @@ private static float Mean(ReadOnlySpan<float> src, int length)
return 0;
return CpuMathUtils.Sum(src) / length;
}

public bool CanSaveOnnx(OnnxContext ctx) => true;

public void SaveAsOnnx(OnnxContext ctx)
{
Host.CheckValue(ctx, nameof(ctx));

for (int iinfo = 0; iinfo < _srcCols.Length; ++iinfo)
{
string inputColumnName = InputSchema[_srcCols[iinfo]].Name;
if (!ctx.ContainsColumn(inputColumnName))
{
ctx.RemoveColumn(inputColumnName, false);
continue;
}

if (!SaveAsOnnxCore(ctx, iinfo, ctx.GetVariableName(inputColumnName), ctx.AddIntermediateVariable(_srcTypes[iinfo], inputColumnName)))
{
ctx.RemoveColumn(inputColumnName, true);
}
}
}

private bool SaveAsOnnxCore(OnnxContext ctx, int iinfo, string srcVariableName, string dstVariableName)
{
string opType;

if ((_norms[iinfo] != LpNormNormalizingEstimatorBase.NormFunction.StandardDeviation) && (_ensureZeroMeans[iinfo] == false))
{
string strNorm;
if (_norms[iinfo] == LpNormNormalizingEstimatorBase.NormFunction.L1)
{
strNorm = "L1";
}
else if (_norms[iinfo] == LpNormNormalizingEstimatorBase.NormFunction.L2)
{
strNorm = "L2";
}
else
{
strNorm = "MAX";
}
opType = "Normalizer";
var node = ctx.CreateNode(opType, srcVariableName, dstVariableName, ctx.GetNodeName(opType));
node.AddAttribute("norm", strNorm);
return true;
}

opType = "ReduceMean";
string meanOfInput = ctx.AddIntermediateVariable(_types[iinfo], "MeanOfInput", true);
var meanNode = ctx.CreateNode(opType, srcVariableName, meanOfInput, ctx.GetNodeName(opType), "");
meanNode.AddAttribute("axes", new long[] { 1 });

opType = "Sub";
string inputMinusMean = ctx.AddIntermediateVariable(_types[iinfo], "InputMinusMean");
var subtractNode = ctx.CreateNode(opType, new[] { srcVariableName, meanOfInput }, new[] { inputMinusMean }, ctx.GetNodeName(opType), "");

if (_norms[iinfo] == LpNormNormalizingEstimatorBase.NormFunction.L1)
{
opType = "Abs";
string absOfInput = ctx.AddIntermediateVariable(_types[iinfo], "AbsOfInput");
var absNode = ctx.CreateNode(opType, inputMinusMean, absOfInput, ctx.GetNodeName(opType), "");

opType = "ReduceSum";
string sumOfAbsOfInput = ctx.AddIntermediateVariable(_types[iinfo], "SumOfAbsOfInput", true);
var sumOfAbsNode = ctx.CreateNode(opType, absOfInput, sumOfAbsOfInput, ctx.GetNodeName(opType), "");
sumOfAbsNode.AddAttribute("axes", new long[] { 1 });

opType = "Div";
var l1Node = ctx.CreateNode(opType, new[] { inputMinusMean, sumOfAbsOfInput }, new[] { dstVariableName }, ctx.GetNodeName(opType), "");
}
else if (_norms[iinfo] == LpNormNormalizingEstimatorBase.NormFunction.L2)
{
opType = "Pow";
string two = ctx.AddInitializer(2.0f);
string squareOfInput = ctx.AddIntermediateVariable(_types[iinfo], "SquareOfInput", true);
var squareNode = ctx.CreateNode(opType, new[] { inputMinusMean, two }, new[] { squareOfInput }, ctx.GetNodeName(opType), "");

opType = "ReduceSum";
string sumOfSquares = ctx.AddIntermediateVariable(_types[iinfo], "SumOfSquares", true);
var sumOfSquaresNode = ctx.CreateNode(opType, squareOfInput, sumOfSquares, ctx.GetNodeName(opType), "");
sumOfSquaresNode.AddAttribute("axes", new long[] { 1 });

opType = "Sqrt";
string squareRoot = ctx.AddIntermediateVariable(_types[iinfo], "SquareRoot", true);
var squareRootNode = ctx.CreateNode(opType, sumOfSquares, squareRoot, ctx.GetNodeName(opType), "");

opType = "Div";
var l2Node = ctx.CreateNode(opType, new[] { inputMinusMean, squareRoot }, new[] { dstVariableName }, ctx.GetNodeName(opType), "");
}
else if (_norms[iinfo] == LpNormNormalizingEstimatorBase.NormFunction.Infinity)
{
opType = "ReduceMax";
string maxOfInput = ctx.AddIntermediateVariable(_types[iinfo], "MaxOfInput", true);
var maxNode = ctx.CreateNode(opType, inputMinusMean, maxOfInput, ctx.GetNodeName(opType), "");
maxNode.AddAttribute("axes", new long[] { 1 });

opType = "Div";
var lMaxNode = ctx.CreateNode(opType, new[] { inputMinusMean, maxOfInput }, new[] { dstVariableName }, ctx.GetNodeName(opType), "");
}
else if (_norms[iinfo] == LpNormNormalizingEstimatorBase.NormFunction.StandardDeviation)
{
// first calculate the standard deviation
opType = "Pow";
string two = ctx.AddInitializer(2.0f);
string squareOfInputMinusMean = ctx.AddIntermediateVariable(_types[iinfo], "SquareOfInputMinusMean", true);
var squareOfInputMinusMeanNode = ctx.CreateNode(opType, new[] { inputMinusMean, two }, new[] { squareOfInputMinusMean }, ctx.GetNodeName(opType), "");

opType = "ReduceMean";
string average = ctx.AddIntermediateVariable(_types[iinfo], "SumOfSquares", true);
var sumOfSquaresNode = ctx.CreateNode(opType, squareOfInputMinusMean, average, ctx.GetNodeName(opType), "");
sumOfSquaresNode.AddAttribute("axes", new long[] { 1 });

opType = "Sqrt";
string stdDev = ctx.AddIntermediateVariable(_types[iinfo], "SquareRoot", true);
var stdDevNode = ctx.CreateNode(opType, average, stdDev, ctx.GetNodeName(opType), "");

opType = "Div";
string input = _ensureZeroMeans[iinfo] ? inputMinusMean : srcVariableName;
var lStdDevNode = ctx.CreateNode(opType, new[] {input, stdDev }, new[] { dstVariableName }, ctx.GetNodeName(opType), "");
}
else
{
Contracts.Assert(false);
return false;
}
return true;
}
}
}

Expand Down
62 changes: 62 additions & 0 deletions test/Microsoft.ML.Tests/OnnxConversionTest.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -180,6 +180,68 @@ public void KmeansOnnxConversionTest()
Done();
}

private class DataPoint
{
[VectorType(3)]
public float[] Features { get; set; }
}

[Fact]
void LpNormOnnxConversionTest()
{
var mlContext = new MLContext(seed: 1);

var samples = new List<DataPoint>()
{
new DataPoint() { Features = new float[3] {0.01f, 0.02f, 0.03f} },
new DataPoint() { Features = new float[3] {0.04f, 0.05f, 0.06f} },
new DataPoint() { Features = new float[3] {0.07f, 0.08f, 0.09f} },
new DataPoint() { Features = new float[3] {0.10f, 0.11f, 0.12f} },
new DataPoint() { Features = new float[3] {0.13f, 0.14f, 0.15f} }
};
var dataView = mlContext.Data.LoadFromEnumerable(samples);

LpNormNormalizingEstimatorBase.NormFunction[] norms =
{
LpNormNormalizingEstimatorBase.NormFunction.L1,
LpNormNormalizingEstimatorBase.NormFunction.L2,
LpNormNormalizingEstimatorBase.NormFunction.Infinity,
LpNormNormalizingEstimatorBase.NormFunction.StandardDeviation
};

bool[] ensureZeroMeans = { true, false};
foreach (var ensureZeroMean in ensureZeroMeans)
{
foreach (var norm in norms)
{
var pipe = mlContext.Transforms.NormalizeLpNorm("Features", norm:norm, ensureZeroMean: ensureZeroMean);

var model = pipe.Fit(dataView);
var transformedData = model.Transform(dataView);
var onnxModel = mlContext.Model.ConvertToOnnxProtobuf(model, dataView);

var onnxFileName = $"LpNorm-{norm.ToString()}-{ensureZeroMean}.onnx";
var onnxModelPath = GetOutputPath(onnxFileName);

SaveOnnxModel(onnxModel, onnxModelPath, null);

// Compare results produced by ML.NET and ONNX's runtime.
if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows) && Environment.Is64BitProcess)
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RuntimeInformation.IsOSPlatform(OSPlatform.Windows) [](start = 24, length = 51)

Do you need this condition? If its a linux will results match?

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I think it is that the test will run only on Windows. The results should still match. It appears that OnnxRuntime doesn't support Linux and Mac yet.

{
// 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 = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
var onnxTransformer = onnxEstimator.Fit(dataView);
var onnxResult = onnxTransformer.Transform(dataView);
CompareSelectedR4VectorColumns("Features", "Features1", transformedData, onnxResult, 3);
}
}
}

Done();
}

[Fact]
void CommandLineOnnxConversionTest()
{
Expand Down