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Revert "Add gpuDeviceId parameters to OnnxConversionTests"
This reverts commit 94e1967.
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‎test/Microsoft.ML.Tests/OnnxConversionTest.cs

Lines changed: 27 additions & 29 deletions
Original file line numberDiff line numberDiff line change
@@ -35,8 +35,6 @@ namespace Microsoft.ML.Tests
3535
{
3636
public class OnnxConversionTest : BaseTestBaseline
3737
{
38-
private int _gpuid = 0;
39-
4038
private class AdultData
4139
{
4240
[LoadColumn(0, 10), ColumnName("FeatureVector")]
@@ -91,7 +89,7 @@ public void SimpleEndToEndOnnxConversionTest()
9189
if (IsOnnxRuntimeSupported())
9290
{
9391
// Step 3: Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
94-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
92+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
9593
var onnxTransformer = onnxEstimator.Fit(data);
9694
var onnxResult = onnxTransformer.Transform(data);
9795

@@ -182,7 +180,7 @@ public void KmeansOnnxConversionTest()
182180
if (IsOnnxRuntimeSupported())
183181
{
184182
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
185-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
183+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
186184
var onnxTransformer = onnxEstimator.Fit(data);
187185
var onnxResult = onnxTransformer.Transform(data);
188186
CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
@@ -237,7 +235,7 @@ public void RegressionTrainersOnnxConversionTest()
237235
var onnxModelPath = GetOutputPath(onnxFileName);
238236
SaveOnnxModel(onnxModel, onnxModelPath, null);
239237

240-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
238+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
241239
var onnxTransformer = onnxEstimator.Fit(dataView);
242240
var onnxResult = onnxTransformer.Transform(dataView);
243241
CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
@@ -298,7 +296,7 @@ public void BinaryClassificationTrainersOnnxConversionTest()
298296
if (IsOnnxRuntimeSupported())
299297
{
300298
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
301-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
299+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
302300
var onnxTransformer = onnxEstimator.Fit(dataView);
303301
var onnxResult = onnxTransformer.Transform(dataView);
304302
CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3); //compare scores
@@ -331,7 +329,7 @@ public void TestVectorWhiteningOnnxConversionTest()
331329

332330
if (IsOnnxRuntimeSupported())
333331
{
334-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
332+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
335333
var onnxTransformer = onnxEstimator.Fit(dataView);
336334
var onnxResult = onnxTransformer.Transform(dataView);
337335
CompareSelectedColumns<float>("whitened1", "whitened1", transformedData, onnxResult);
@@ -384,7 +382,7 @@ public void PlattCalibratorOnnxConversionTest()
384382
if (IsOnnxRuntimeSupported())
385383
{
386384
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
387-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
385+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
388386
var onnxTransformer = onnxEstimator.Fit(dataView);
389387
var onnxResult = onnxTransformer.Transform(dataView);
390388
CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult, 3);
@@ -431,7 +429,7 @@ public void PlattCalibratorOnnxConversionTest2()
431429
// Compare model scores produced by ML.NET and ONNX's runtime.
432430
if (IsOnnxRuntimeSupported())
433431
{
434-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
432+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
435433
var onnxTransformer = onnxEstimator.Fit(data);
436434
var onnxResult = onnxTransformer.Transform(data);
437435
CompareSelectedColumns<float>("Probability", "Probability", transformedData, onnxResult, 3); //compare probabilities
@@ -464,7 +462,7 @@ public void TextNormalizingOnnxConversionTest()
464462
if (IsOnnxRuntimeSupported() && !RuntimeInformation.IsOSPlatform(OSPlatform.Linux))
465463
{
466464
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
467-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
465+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
468466
var onnxTransformer = onnxEstimator.Fit(dataView);
469467
var onnxResult = onnxTransformer.Transform(dataView);
470468
CompareSelectedColumns<ReadOnlyMemory<char>>("NormText", "NormText", transformedData, onnxResult);
@@ -513,7 +511,7 @@ public void LpNormOnnxConversionTest(
513511
if (IsOnnxRuntimeSupported())
514512
{
515513
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
516-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
514+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
517515
var onnxTransformer = onnxEstimator.Fit(dataView);
518516
var onnxResult = onnxTransformer.Transform(dataView);
519517
CompareSelectedColumns<float>("Features", "Features", transformedData, onnxResult, 3);
@@ -580,7 +578,7 @@ public void KeyToVectorWithBagOnnxConversionTest()
580578
if (IsOnnxRuntimeSupported())
581579
{
582580
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
583-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
581+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
584582
var onnxTransformer = onnxEstimator.Fit(data);
585583
var onnxResult = onnxTransformer.Transform(data);
586584
CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult);
@@ -901,7 +899,7 @@ public void ConcatenateOnnxConversionTest()
901899
var onnxModelPath = GetOutputPath(onnxModelName);
902900
SaveOnnxModel(onnxModel, onnxModelPath, null);
903901
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
904-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
902+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
905903
var onnxTransformer = onnxEstimator.Fit(data);
906904
var onnxResult = onnxTransformer.Transform(data);
907905
CompareSelectedColumns<double>("Features", "Features", transformedData, onnxResult);
@@ -953,7 +951,7 @@ public void RemoveVariablesInPipelineTest()
953951
if (IsOnnxRuntimeSupported())
954952
{
955953
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
956-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
954+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
957955
var onnxTransformer = onnxEstimator.Fit(data);
958956
var onnxResult = onnxTransformer.Transform(data);
959957
CompareSelectedColumns<float>("Score", "Score", transformedData, onnxResult);
@@ -1018,7 +1016,7 @@ public void TokenizingByCharactersOnnxConversionTest(bool useMarkerCharacters)
10181016
if (IsOnnxRuntimeSupported())
10191017
{
10201018
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
1021-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
1019+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
10221020
var onnxTransformer = onnxEstimator.Fit(dataView);
10231021
var onnxResult = onnxTransformer.Transform(dataView);
10241022
CompareSelectedColumns<ushort>("TokenizedText", "TokenizedText", transformedData, onnxResult);
@@ -1093,7 +1091,7 @@ public void OnnxTypeConversionTest(DataKind fromKind, DataKind toKind)
10931091

10941092
if (IsOnnxRuntimeSupported())
10951093
{
1096-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
1094+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
10971095
var onnxTransformer = onnxEstimator.Fit(dataView);
10981096
var onnxResult = onnxTransformer.Transform(dataView);
10991097

@@ -1130,7 +1128,7 @@ public void PcaOnnxConversionTest()
11301128
if (IsOnnxRuntimeSupported())
11311129
{
11321130
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
1133-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
1131+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
11341132
var onnxTransformer = onnxEstimator.Fit(dataView);
11351133
var onnxResult = onnxTransformer.Transform(dataView);
11361134
CompareSelectedColumns<float>("pca", "pca", transformedData, onnxResult);
@@ -1189,7 +1187,7 @@ public void IndicateMissingValuesOnnxConversionTest()
11891187
if (IsOnnxRuntimeSupported())
11901188
{
11911189
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
1192-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
1190+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
11931191
var onnxTransformer = onnxEstimator.Fit(dataView);
11941192
var onnxResult = onnxTransformer.Transform(dataView);
11951193
CompareSelectedColumns<int>("MissingIndicator", "MissingIndicator", transformedData, onnxResult);
@@ -1232,7 +1230,7 @@ public void ValueToKeyMappingOnnxConversionTest(DataKind valueType)
12321230

12331231
if (IsOnnxRuntimeSupported())
12341232
{
1235-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
1233+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
12361234
var onnxTransformer = onnxEstimator.Fit(dataView);
12371235
var onnxResult = onnxTransformer.Transform(dataView);
12381236
CompareSelectedColumns<uint>("Key", "Key", mlnetResult, onnxResult);
@@ -1281,7 +1279,7 @@ public void KeyToValueMappingOnnxConversionTest(DataKind valueType)
12811279

12821280
if (IsOnnxRuntimeSupported())
12831281
{
1284-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
1282+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
12851283
var onnxTransformer = onnxEstimator.Fit(dataView);
12861284
var onnxResult = onnxTransformer.Transform(dataView);
12871285
CompareResults("Value", "Value", mlnetResult, onnxResult);
@@ -1322,7 +1320,7 @@ public void WordTokenizerOnnxConversionTest()
13221320
if (IsOnnxRuntimeSupported())
13231321
{
13241322
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
1325-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath, gpuDeviceId: _gpuid);
1323+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath);
13261324
var onnxTransformer = onnxEstimator.Fit(dataView);
13271325
var onnxResult = onnxTransformer.Transform(dataView);
13281326
CompareSelectedColumns<ReadOnlyMemory<char>>("Tokens", "Tokens", transformedData, onnxResult);
@@ -1386,7 +1384,7 @@ public void NgramOnnxConversionTest(
13861384

13871385
if (IsOnnxRuntimeSupported())
13881386
{
1389-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath, gpuDeviceId: _gpuid);
1387+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxFilePath);
13901388
var onnxTransformer = onnxEstimator.Fit(dataView);
13911389
var onnxResult = onnxTransformer.Transform(dataView);
13921390
var columnName = i == pipelines.Length - 1 ? "Tokens" : "NGrams";
@@ -1454,7 +1452,7 @@ public void OptionalColumnOnnxTest(DataKind dataKind)
14541452
{
14551453
string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
14561454
string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
1457-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuid);
1455+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
14581456
var onnxTransformer = onnxEstimator.Fit(dataView);
14591457
var onnxResult = onnxTransformer.Transform(dataView);
14601458
CompareResults("Label", "Label", outputData, onnxResult);
@@ -1521,7 +1519,7 @@ public void MulticlassTrainersOnnxConversionTest()
15211519
if (IsOnnxRuntimeSupported())
15221520
{
15231521
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
1524-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
1522+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
15251523
var onnxTransformer = onnxEstimator.Fit(dataView);
15261524
var onnxResult = onnxTransformer.Transform(dataView);
15271525
CompareSelectedColumns<uint>("PredictedLabel", "PredictedLabel", transformedData, onnxResult);
@@ -1554,7 +1552,7 @@ public void CopyColumnsOnnxTest()
15541552
if (IsOnnxRuntimeSupported())
15551553
{
15561554
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
1557-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
1555+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
15581556
var onnxTransformer = onnxEstimator.Fit(dataView);
15591557
var onnxResult = onnxTransformer.Transform(dataView);
15601558
CompareSelectedColumns<float>("Target", "Target1", transformedData, onnxResult);
@@ -1615,7 +1613,7 @@ public void UseKeyDataViewTypeAsUInt32InOnnxInput()
16151613
if (IsOnnxRuntimeSupported())
16161614
{
16171615
// Step 5: Apply Onnx Model
1618-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuid);
1616+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
16191617
var onnxResult = onnxEstimator.Fit(reloadedData).Transform(reloadedData);
16201618

16211619
// Step 6: Compare results to an onnx model created using the mappedData IDataView
@@ -1627,7 +1625,7 @@ public void UseKeyDataViewTypeAsUInt32InOnnxInput()
16271625
string onnxModelPath2 = GetOutputPath("onnxmodel2-kdvt-as-uint32.onnx");
16281626
using (FileStream stream = new FileStream(onnxModelPath2, FileMode.Create))
16291627
mlContext.Model.ConvertToOnnx(model, mappedData, stream);
1630-
var onnxEstimator2 = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath2, gpuDeviceId: _gpuid);
1628+
var onnxEstimator2 = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath2);
16311629
var onnxResult2 = onnxEstimator2.Fit(originalData).Transform(originalData);
16321630

16331631
var stdSuffix = ".output";
@@ -1680,7 +1678,7 @@ public void FeatureSelectionOnnxTest()
16801678
if (IsOnnxRuntimeSupported())
16811679
{
16821680
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
1683-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath, gpuDeviceId: _gpuid);
1681+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(onnxModelPath);
16841682
var onnxTransformer = onnxEstimator.Fit(dataView);
16851683
var onnxResult = onnxTransformer.Transform(dataView);
16861684
CompareSelectedColumns<float>("FeatureSelectMIScalarFloat", "FeatureSelectMIScalarFloat", transformedData, onnxResult);
@@ -1728,7 +1726,7 @@ public void SelectColumnsOnnxTest()
17281726
// Evaluate the saved ONNX model using the data used to train the ML.NET pipeline.
17291727
string[] inputNames = onnxModel.Graph.Input.Select(valueInfoProto => valueInfoProto.Name).ToArray();
17301728
string[] outputNames = onnxModel.Graph.Output.Select(valueInfoProto => valueInfoProto.Name).ToArray();
1731-
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath, gpuDeviceId: _gpuid);
1729+
var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(outputNames, inputNames, onnxModelPath);
17321730
var onnxTransformer = onnxEstimator.Fit(dataView);
17331731
var onnxResult = onnxTransformer.Transform(dataView);
17341732

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