Updates to Unit Test cases for the TensorFlowTransform

src/Microsoft.ML.TensorFlow/doc.xml

@@ -10,6 +10,8 @@
         The TensorflowTransform extracts the specified output from the operation computed on the graph (given the input(s)) using a pre-trained <a href="https://www.tensorflow.org">Tensorflow</a> model.
         The transform takes as input the Tensorflow model together with the names of the inputs to the model and name of the operation for which output values will be extracted from the model.
 
+        This transform requires the <a href="https://dotnet.myget.org/feed/dotnet-core/package/nuget/Microsoft.ML.TensorFlow/0.5.0-preview-26830-5">Microsoft.ML.TensorFlow</a> nuget to be installed.
+
         The TensorflowTransform has following assumptions regarding the input, output and processing of data.
         <list type="number">
           <item>
@@ -23,6 +25,9 @@
             Upon success, the transform will introduce a new column in <see cref="IDataView"/> based on the name of the output column specified.
           </item>
         </list>
+
+        The inputs and outputs of a TensorFlow model can be obtained using the <a href="https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms/README.md#inspecting-graphs"><code>summarize_graph</code> tool</a>.
+
       </remarks>
     </member>
     <example name="TensorflowTransform">
@@ -71,4 +76,4 @@
     </example>
 
   </members>
-</doc>
+</doc>

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

@@ -7,6 +7,7 @@
 using Microsoft.ML.Runtime.Data;
 using Microsoft.ML.Runtime.ImageAnalytics;
 using Microsoft.ML.Runtime.LightGBM;
+using Microsoft.ML.Trainers;
 using Microsoft.ML.Transforms;
 using System.Collections.Generic;
 using System.IO;
@@ -16,7 +17,7 @@ namespace Microsoft.ML.Scenarios
 {
     public partial class ScenariosTests
     {
-        [Fact(Skip = "Disabled due to this bug https://github.com/dotnet/machinelearning/issues/770")]
+        [Fact]
         public void TensorFlowTransformCifarLearningPipelineTest()
         {
             var imageHeight = 32;
@@ -52,23 +53,35 @@ public void TensorFlowTransformCifarLearningPipelineTest()
                 OutputColumn = "Output"
             });
 
-            using (var environment = new TlcEnvironment())
+            pipeline.Add(new ColumnConcatenator(outputColumn: "Features", "Output"));
+            pipeline.Add(new TextToKeyConverter("Label"));
+            pipeline.Add(new StochasticDualCoordinateAscentClassifier());
+
+            var model = pipeline.Train<CifarData, CifarPrediction>();
+            string[] scoreLabels;
+            model.TryGetScoreLabelNames(out scoreLabels);
+
+            Assert.NotNull(scoreLabels);
+            Assert.Equal(3, scoreLabels.Length);
+            Assert.Equal("banana", scoreLabels[0]);
+            Assert.Equal("hotdog", scoreLabels[1]);
+            Assert.Equal("tomato", scoreLabels[2]);
+
+            CifarPrediction prediction = model.Predict(new CifarData()
             {
-                IDataView trans = pipeline.Execute(environment);
-                Assert.NotNull(trans);
+                ImagePath = GetDataPath("images/banana.jpg")
+            });
+            Assert.Equal(1, prediction.PredictedLabels[0], 2);
+            Assert.Equal(0, prediction.PredictedLabels[1], 2);
+            Assert.Equal(0, prediction.PredictedLabels[2], 2);
 
-                trans.Schema.TryGetColumnIndex("Output", out int output);
-                using (var cursor = trans.GetRowCursor(col => col == output))
-                {
-                    var buffer = default(VBuffer<float>);
-                    var getter = cursor.GetGetter<VBuffer<float>>(output);
-                    while (cursor.MoveNext())
-                    {
-                        getter(ref buffer);
-                        Assert.Equal(10, buffer.Length);
-                    }
-                }
-            }
+            prediction = model.Predict(new CifarData()
+            {
+                ImagePath = GetDataPath("images/hotdog.jpg")
+            });
+            Assert.Equal(0, prediction.PredictedLabels[0], 2);
+            Assert.Equal(1, prediction.PredictedLabels[1], 2);
+            Assert.Equal(0, prediction.PredictedLabels[2], 2);
         }
     }
 
@@ -78,6 +91,12 @@ public class CifarData
         public string ImagePath;
 
         [Column("1")]
-        public string Name;
+        public string Label;
+    }
+
+    public class CifarPrediction
+    {
+        [ColumnName("Score")]
+        public float[] PredictedLabels;
     }
 }

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

@@ -119,7 +119,7 @@ public void TensorFlowTransformMNISTConvTest()
                 var metrics = Evaluate(env, testDataScorer);
 
                 Assert.Equal(0.99, metrics.AccuracyMicro, 2);
-                Assert.Equal(0.99, metrics.AccuracyMicro, 2);
+                Assert.Equal(1.0, metrics.AccuracyMacro, 2);
 
                 // Create prediction engine and test predictions
                 var model = env.CreatePredictionEngine<MNISTData, MNISTPrediction>(testDataScorer);
@@ -215,12 +215,63 @@ public void TensorFlowTransformCifar()
                 {
                     var buffer = default(VBuffer<float>);
                     var getter = cursor.GetGetter<VBuffer<float>>(output);
+                    var numRows = 0;
                     while (cursor.MoveNext())
                     {
                         getter(ref buffer);
                         Assert.Equal(10, buffer.Length);
+                        numRows += 1;
+                    }
+                    Assert.Equal(3, numRows);
+                }
+            }
+        }
+
+        [Fact]
+        public void TensorFlowTransformCifarInvalidShape()
+        {
+            var model_location = "cifar_model/frozen_model.pb";
+
+            using (var env = new TlcEnvironment())
+            {
+                var imageHeight = 28;
+                var imageWidth = 28;
+                var dataFile = GetDataPath("images/images.tsv");
+                var imageFolder = Path.GetDirectoryName(dataFile);
+                var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile));
+
+                var images = ImageLoaderTransform.Create(env, new ImageLoaderTransform.Arguments()
+                {
+                    Column = new ImageLoaderTransform.Column[1]
+                    {
+                        new ImageLoaderTransform.Column() { Source=  "ImagePath", Name="ImageReal" }
+                    },
+                    ImageFolder = imageFolder
+                }, data);
+                var cropped = ImageResizerTransform.Create(env, new ImageResizerTransform.Arguments()
+                {
+                    Column = new ImageResizerTransform.Column[1]{
+                        new ImageResizerTransform.Column() { Source = "ImageReal", Name= "ImageCropped", ImageHeight =imageHeight, ImageWidth = imageWidth, Resizing = ImageResizerTransform.ResizingKind.IsoCrop}
+                    }
+                }, images);
+
+                var pixels = ImagePixelExtractorTransform.Create(env, new ImagePixelExtractorTransform.Arguments()
+                {
+                    Column = new ImagePixelExtractorTransform.Column[1]{
+                        new ImagePixelExtractorTransform.Column() {  Source= "ImageCropped", Name = "Input", UseAlpha=false, InterleaveArgb=true}
                     }
+                }, cropped);
+
+                var thrown = false;
+                try
+                {
+                    IDataView trans = TensorFlowTransform.Create(env, pixels, model_location, "Output", "Input");
+                }
+                catch
+                {
+                    thrown = true;
                 }
+                Assert.True(thrown);
             }
         }
     }