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Reenabling MacOS tests that had an issue with libgdiplus (#4500)
* Revert "Disable tests on macOS that depend on GDI+ library. (#4492)"
1 parent a64634d commit 9af92a4

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‎test/Microsoft.ML.OnnxTransformerTest/DnnImageFeaturizerTest.cs‎

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using System.Collections.Generic;
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using System.IO;
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using System.Linq;
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using System.Runtime.InteropServices;
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using Microsoft.ML.Data;
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using Microsoft.ML.Model;
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using Microsoft.ML.RunTests;
@@ -90,11 +89,6 @@ void TestDnnImageFeaturizer()
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[OnnxFact]
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public void OnnxFeaturizerWorkout()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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var env = new MLContext(null);
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var imageHeight = 224;
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var imageWidth = 224;
@@ -203,11 +197,6 @@ internal sealed class ModelOutput
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[OnnxFact]
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public void TestLoadFromDiskAndPredictionEngine()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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var dataFile = GetDataPath("images/images.tsv");
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var imageFolder = Path.GetDirectoryName(dataFile);
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‎test/Microsoft.ML.OnnxTransformerTest/OnnxTransformTests.cs‎

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using Xunit.Abstractions;
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using Microsoft.ML.Transforms.Onnx;
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using Microsoft.ML.TestFrameworkCommon.Attributes;
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using System.Runtime.InteropServices;
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namespace Microsoft.ML.Tests
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{
@@ -202,11 +201,6 @@ void TestOldSavingAndLoading(int? gpuDeviceId, bool fallbackToCpu)
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[OnnxFact]
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public void OnnxWorkout()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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var modelFile = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet", "00000001", "model.onnx");
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var env = new MLContext();
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[OnnxFact]
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public void OnnxModelInMemoryImage()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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// Path of ONNX model. It's a multiclass classifier. It consumes an input "data_0" and produces an output "softmaxout_1".
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var modelFile = "squeezenet/00000001/model.onnx";
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‎test/Microsoft.ML.Tests/ImagesTests.cs‎

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using System.Drawing;
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using System.IO;
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using System.Linq;
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using System.Runtime.InteropServices;
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using Microsoft.ML.Data;
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using Microsoft.ML.Model;
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using Microsoft.ML.RunTests;
@@ -27,11 +26,6 @@ public ImageTests(ITestOutputHelper output) : base(output)
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[Fact]
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public void TestEstimatorChain()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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var env = new MLContext();
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var dataFile = GetDataPath("images/images.tsv");
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var imageFolder = Path.GetDirectoryName(dataFile);
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[Fact]
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public void TestSaveImages()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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var env = new MLContext();
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var dataFile = GetDataPath("images/images.tsv");
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var imageFolder = Path.GetDirectoryName(dataFile);
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[Fact]
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public void TestGreyscaleTransformImages()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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IHostEnvironment env = new MLContext();
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var imageHeight = 150;
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var imageWidth = 100;
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[Fact]
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public void TestGrayScaleInMemory()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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// Create an image list.
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var images = new List<ImageDataPoint>(){ new ImageDataPoint(10, 10, Color.Blue), new ImageDataPoint(10, 10, Color.Red) };
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@@ -291,11 +270,6 @@ public ImageDataPoint(int width, int height, Color color)
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[Fact]
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public void TestBackAndForthConversionWithAlphaInterleave()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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IHostEnvironment env = new MLContext();
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const int imageHeight = 100;
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const int imageWidth = 130;
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[Fact]
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public void TestBackAndForthConversionWithoutAlphaInterleave()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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IHostEnvironment env = new MLContext();
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const int imageHeight = 100;
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const int imageWidth = 130;
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[Fact]
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public void TestBackAndForthConversionWithDifferentOrder()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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IHostEnvironment env = new MLContext();
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const int imageHeight = 100;
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const int imageWidth = 130;
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[Fact]
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public void TestBackAndForthConversionWithAlphaNoInterleave()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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IHostEnvironment env = new MLContext();
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const int imageHeight = 100;
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const int imageWidth = 130;
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[Fact]
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public void TestBackAndForthConversionWithoutAlphaNoInterleave()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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IHostEnvironment env = new MLContext();
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const int imageHeight = 100;
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const int imageWidth = 130;
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[Fact]
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public void TestBackAndForthConversionWithAlphaInterleaveNoOffset()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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IHostEnvironment env = new MLContext();
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const int imageHeight = 100;
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const int imageWidth = 130;
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[Fact]
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public void TestBackAndForthConversionWithoutAlphaInterleaveNoOffset()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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IHostEnvironment env = new MLContext();
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const int imageHeight = 100;
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const int imageWidth = 130;
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[Fact]
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public void TestBackAndForthConversionWithAlphaNoInterleaveNoOffset()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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IHostEnvironment env = new MLContext();
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const int imageHeight = 100;
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var imageWidth = 130;
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[Fact]
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public void TestBackAndForthConversionWithoutAlphaNoInterleaveNoOffset()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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IHostEnvironment env = new MLContext();
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const int imageHeight = 100;
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const int imageWidth = 130;
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[Fact]
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public void ImageResizerTransformResizingModeFill()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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var env = new MLContext();
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var dataFile = GetDataPath("images/fillmode.tsv");
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var imageFolder = Path.GetDirectoryName(dataFile);

‎test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs‎

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[TensorFlowFact]
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public void TensorFlowTransforCifarEndToEndTest2()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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var imageHeight = 32;
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var imageWidth = 32;
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var model_location = "cifar_model/frozen_model.pb";
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[TensorFlowFact]
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public void TensorFlowTransformCifar()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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var modelLocation = "cifar_model/frozen_model.pb";
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var mlContext = new MLContext(seed: 1);
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List<string> logMessages = new List<string>();
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[TensorFlowFact]
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public void TensorFlowTransformCifarSavedModel()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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var modelLocation = "cifar_saved_model";
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var mlContext = new MLContext(seed: 1);
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var tensorFlowModel = mlContext.Model.LoadTensorFlowModel(modelLocation);
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[TensorFlowFact]
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public void TensorFlowTransformCifarInvalidShape()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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}
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var modelLocation = "cifar_model/frozen_model.pb";
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var mlContext = new MLContext(seed: 1);

‎test/Microsoft.ML.Tests/TensorFlowEstimatorTests.cs‎

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using Microsoft.ML.TensorFlow;
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using Xunit;
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using Xunit.Abstractions;
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using System.Runtime.InteropServices;
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namespace Microsoft.ML.Tests
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{
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[TensorFlowFact]
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public void TestTensorFlow()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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var modelLocation = "cifar_model/frozen_model.pb";
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var mlContext = new MLContext(seed: 1);
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public void TestTensorFlowWithSchema()
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{
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if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
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{
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return;
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}
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const string modelLocation = "cifar_model/frozen_model.pb";
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var mlContext = new MLContext(seed: 1);

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