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fix test errors in unix machines #4

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ed4bbd4
fix test errors in unix machines
interesaaat Aug 6, 2019
f684e68
Update TorchTests.cs
interesaaat Aug 6, 2019
f75a966
Update TorchTests.cs
interesaaat Aug 6, 2019
013b58e
Update TorchTests.cs
interesaaat Aug 6, 2019
dd8f81c
Update TorchTests.cs
interesaaat Aug 6, 2019
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73 changes: 41 additions & 32 deletions test/Microsoft.ML.Tests/Torch/TorchTests.cs
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Original file line number Diff line number Diff line change
@@ -1,5 +1,7 @@
using System.Collections.Generic;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Runtime.InteropServices;
using Microsoft.ML.Data;
using Microsoft.ML.RunTests;
using Microsoft.ML.TestFramework.Attributes;
Expand All @@ -24,53 +26,60 @@ private class TestReLUModelData
[TorchFact]
public void TorchScoringReLUTest()
{
var mlContext = new MLContext();
var tensor = new float[] { -1, -1, 0, 1, 1 }.ToTorchTensor(dimensions: new long[] { 5 });
var data = new TestReLUModelData
if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
{
Features = tensor.Data<float>().ToArray()
};
var dataPoint = new List<TestReLUModelData>() { data };
var mlContext = new MLContext();
var tensor = new float[] { -1, -1, 0, 1, 1 }.ToTorchTensor(dimensions: new long[] { 5 });
var data = new TestReLUModelData
{
Features = tensor.Data<float>().ToArray()
};
var dataPoint = new List<TestReLUModelData>() { data };

var dataView = mlContext.Data.LoadFromEnumerable(dataPoint);
var dataView = mlContext.Data.LoadFromEnumerable(dataPoint);

var output = mlContext.Model
.LoadTorchModel(GetDataPath("Torch/relu.pt"))
.ScoreTorchModel("Features", new long[] { 5 })
.Fit(dataView)
.Transform(dataView);
var output = mlContext.Model
.LoadTorchModel(GetDataPath("Torch/relu.pt"))
.ScoreTorchModel("Features", new long[] { 5 })
.Fit(dataView)
.Transform(dataView);

var transformedData = mlContext.Data.CreateEnumerable<TestReLUModelData>(output, false).ToArray()[0].Features;
Assert.True(transformedData.Length == 5);
Assert.Equal(transformedData, new float[] { 0, 0, 0, 1, 1 });
var transformedData = mlContext.Data.CreateEnumerable<TestReLUModelData>(output, false).ToArray()[0].Features;

Assert.True(transformedData.Length == 5);
Assert.Equal(transformedData, new float[] { 0, 0, 0, 1, 1 });
}
}

[TorchFact]
public void TorchTransformerWorkoutTest()
{
var mlContext = new MLContext();
var tensorData = FloatTensor.Random(new long[] { 5 });
var datapoint = new TestReLUModelData
if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
{
Features = tensorData.Data<float>().ToArray()
};
var data = new List<TestReLUModelData>() { datapoint, datapoint, datapoint, datapoint, datapoint };
var mlContext = new MLContext();
var tensorData = FloatTensor.Random(new long[] { 5 });
var datapoint = new TestReLUModelData
{
Features = tensorData.Data<float>().ToArray()
};
var data = new List<TestReLUModelData>() { datapoint, datapoint, datapoint, datapoint, datapoint };

var dataView = mlContext.Data.LoadFromEnumerable(data);
var dataView = mlContext.Data.LoadFromEnumerable(data);

var estimator = mlContext.Model.LoadTorchModel(GetDataPath("Torch/relu.pt"))
.ScoreTorchModel("TorchOutput", new long[] { 5 }, "Features");
var estimator = mlContext.Model.LoadTorchModel(GetDataPath("Torch/relu.pt"))
.ScoreTorchModel("TorchOutput", new long[] { 5 }, "Features");

TestEstimatorCore(estimator, dataView);
TestEstimatorCore(estimator, dataView);

var output = estimator.Fit(dataView)
.Transform(dataView);
var output = estimator.Fit(dataView)
.Transform(dataView);

var transformedData = mlContext.Data.CreateEnumerable<TestReLUModelData>(output, false).ToArray()[0].Features;
Assert.True(transformedData.Length == 5);
foreach (var elt in transformedData)
Assert.True(elt >= 0);
var transformedData = mlContext.Data.CreateEnumerable<TestReLUModelData>(output, false).ToArray()[0].Features;

Assert.True(transformedData.Length == 5);
foreach (var elt in transformedData)
Assert.True(elt >= 0);
}
}
}
}