Make ImageClassification API an ITrainerEstimator and refactor code.

docs/samples/Microsoft.ML.Samples.GPU/Microsoft.ML.Samples.GPU.csproj

@@ -11,7 +11,7 @@
 
   <ItemGroup>
     <Compile Include="..\Microsoft.ML.Samples\Program.cs" Link="Program.cs" />
-    <Compile Include="..\Microsoft.ML.Samples\Dynamic\ImageClassification\*.cs">
+    <Compile Include="..\Microsoft.ML.Samples\Dynamic\Trainers\MulticlassClassification\ImageClassification\*.cs">
       <Link>Dynamic\ImageClassification\%(FileName)</Link>
     </Compile>
     <Compile Include="..\Microsoft.ML.Samples\Dynamic\TensorFlow\*.cs">

docs/samples/Microsoft.ML.Samples/Dynamic/ImageClassification/ImageClassificationDefault.cs → docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/ImageClassification/ImageClassificationDefault.cs

@@ -9,7 +9,6 @@
 using System.Threading.Tasks;
 using Microsoft.ML;
 using Microsoft.ML.Data;
-using Microsoft.ML.Transforms;
 using static Microsoft.ML.DataOperationsCatalog;
 
 namespace Samples.Dynamic
@@ -60,12 +59,11 @@ public static void Example()
                 IDataView trainDataset = trainTestData.TrainSet;
                 IDataView testDataset = trainTestData.TestSet;
 
-                var pipeline = mlContext.Model.ImageClassification("Image", "Label", validationSet: testDataset)
-                    .Append(mlContext.Transforms.Conversion.MapKeyToValue(
-                        outputColumnName: "PredictedLabel", 
+                var pipeline = mlContext.MulticlassClassification.Trainers
+                        .ImageClassification(featureColumnName:"Image", validationSet:testDataset)
+                    .Append(mlContext.Transforms.Conversion.MapKeyToValue(outputColumnName: "PredictedLabel",
                         inputColumnName: "PredictedLabel"));
 
-
                 Console.WriteLine("*** Training the image classification model " +
                     "with DNN Transfer Learning on top of the selected " +
                     "pre-trained model/architecture ***");

docs/samples/Microsoft.ML.Samples/Dynamic/ImageClassification/LearningRateSchedulingCifarResnetTransferLearning.cs → docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/ImageClassification/LearningRateSchedulingCifarResnetTransferLearning.cs

@@ -9,8 +9,8 @@
 using System.Threading.Tasks;
 using Microsoft.ML;
 using Microsoft.ML.Data;
+using Microsoft.ML.Dnn;
 using Microsoft.ML.Transforms;
-using static Microsoft.ML.DataOperationsCatalog;
 
 namespace Samples.Dynamic
 {
@@ -65,20 +65,19 @@ public static void Example()
                     .Fit(testDataset)
                     .Transform(testDataset);
 
-                var options = new ImageClassificationEstimator.Options()
+                var options = new ImageClassificationTrainer.Options()
                 {
-                    FeaturesColumnName = "Image",
+                    FeatureColumnName = "Image",
                     LabelColumnName = "Label",
                     // Just by changing/selecting InceptionV3/MobilenetV2 here instead of 
                     // ResnetV2101 you can try a different architecture/
                     // pre-trained model. 
-                    Arch = ImageClassificationEstimator.Architecture.ResnetV2101,
+                    Arch = ImageClassificationTrainer.Architecture.ResnetV2101,
                     Epoch = 182,
                     BatchSize = 128,
                     LearningRate = 0.01f,
                     MetricsCallback = (metrics) => Console.WriteLine(metrics),
                     ValidationSet = testDataset,
-                    DisableEarlyStopping = true,
                     ReuseValidationSetBottleneckCachedValues = false,
                     ReuseTrainSetBottleneckCachedValues = false,
                     // Use linear scaling rule and Learning rate decay as an option
@@ -88,7 +87,7 @@ public static void Example()
                     LearningRateScheduler = new LsrDecay()
                 };
 
-                var pipeline = mlContext.Model.ImageClassification(options)
+                var pipeline = mlContext.MulticlassClassification.Trainers.ImageClassification(options)
                     .Append(mlContext.Transforms.Conversion.MapKeyToValue(
                         outputColumnName: "PredictedLabel",
                         inputColumnName: "PredictedLabel"));

docs/samples/Microsoft.ML.Samples/Dynamic/ImageClassification/ResnetV2101TransferLearningEarlyStopping.cs → docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/ImageClassification/ResnetV2101TransferLearningEarlyStopping.cs

@@ -11,6 +11,7 @@
 using System.IO.Compression;
 using System.Threading;
 using System.Net;
+using Microsoft.ML.Dnn;
 
 namespace Samples.Dynamic
 {
@@ -62,23 +63,24 @@ public static void Example()
                 .Fit(testDataset)
                 .Transform(testDataset);
 
-                var options = new ImageClassificationEstimator.Options()
+                var options = new ImageClassificationTrainer.Options()
                 {
-                    FeaturesColumnName = "Image",
+                    FeatureColumnName = "Image",
                     LabelColumnName = "Label",
                      // Just by changing/selecting InceptionV3/MobilenetV2/ResnetV250 here instead of 
                     // ResnetV2101 you can try a different architecture/
                     // pre-trained model. 
-                    Arch = ImageClassificationEstimator.Architecture.ResnetV2101,
+                    Arch = ImageClassificationTrainer.Architecture.ResnetV2101,
                     BatchSize = 10,
                     LearningRate = 0.01f,
-                    EarlyStoppingCriteria = new ImageClassificationEstimator.EarlyStopping(minDelta: 0.001f, patience: 20, metric: ImageClassificationEstimator.EarlyStoppingMetric.Loss),
+                    EarlyStoppingCriteria = new ImageClassificationTrainer.EarlyStopping(
+                        minDelta: 0.001f, patience: 20, metric: ImageClassificationTrainer.EarlyStoppingMetric.Loss),
                     MetricsCallback = (metrics) => Console.WriteLine(metrics),
                     ValidationSet = validationSet
                 };
 
                 var pipeline = mlContext.Transforms.LoadImages("Image", fullImagesetFolderPath, false, "ImagePath") // false indicates we want the image as a VBuffer<byte>
-                    .Append(mlContext.Model.ImageClassification(options));
+                    .Append(mlContext.MulticlassClassification.Trainers.ImageClassification(options));
 
                 Console.WriteLine("*** Training the image classification model with " +
                     "DNN Transfer Learning on top of the selected pre-trained " +

docs/samples/Microsoft.ML.Samples/Dynamic/ImageClassification/ResnetV2101TransferLearningTrainTestSplit.cs → docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/ImageClassification/ResnetV2101TransferLearningTrainTestSplit.cs

@@ -9,7 +9,7 @@
 using System.Threading.Tasks;
 using Microsoft.ML;
 using Microsoft.ML.Data;
-using Microsoft.ML.Transforms;
+using Microsoft.ML.Dnn;
 using static Microsoft.ML.DataOperationsCatalog;
 
 namespace Samples.Dynamic
@@ -60,23 +60,22 @@ public static void Example()
                 IDataView trainDataset = trainTestData.TrainSet;
                 IDataView testDataset = trainTestData.TestSet;
 
-                var options = new ImageClassificationEstimator.Options()
+                var options = new ImageClassificationTrainer.Options()
                 { 
-                    FeaturesColumnName = "Image",
+                    FeatureColumnName = "Image",
                     LabelColumnName = "Label",
                     // Just by changing/selecting InceptionV3/MobilenetV2/ResnetV250 here instead of 
                     // ResnetV2101 you can try a different architecture/
                     // pre-trained model. 
-                    Arch = ImageClassificationEstimator.Architecture.ResnetV2101,
+                    Arch = ImageClassificationTrainer.Architecture.ResnetV2101,
                     Epoch = 50,
                     BatchSize = 10,
                     LearningRate = 0.01f,
                     MetricsCallback = (metrics) => Console.WriteLine(metrics),
-                    ValidationSet = testDataset,
-                    DisableEarlyStopping = true
+                    ValidationSet = testDataset
                 };
 
-                var pipeline = mlContext.Model.ImageClassification(options)
+                var pipeline = mlContext.MulticlassClassification.Trainers.ImageClassification(options)
                     .Append(mlContext.Transforms.Conversion.MapKeyToValue(
                         outputColumnName: "PredictedLabel", 
                         inputColumnName: "PredictedLabel"));

src/Microsoft.ML.Data/Scorers/MulticlassClassificationScorer.cs

@@ -6,6 +6,7 @@
 using System.Collections.Generic;
 using System.Threading;
 using Microsoft.ML;
+using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
 using Microsoft.ML.Data.IO;
 using Microsoft.ML.Internal.Utilities;
@@ -34,6 +35,11 @@ internal sealed class MulticlassClassificationScorer : PredictedLabelScorerBase
         // between scores and probabilities (using IDistributionPredictor)
         public sealed class Arguments : ScorerArgumentsBase
         {
+            [Argument(ArgumentType.AtMostOnce, HelpText = "Score Column Name.", ShortName = "scn")]
+            public string ScoreColumnName = AnnotationUtils.Const.ScoreValueKind.Score;
+
+            [Argument(ArgumentType.AtMostOnce, HelpText = "Predicted Label Column Name.", ShortName = "plcn")]
+            public string PredictedLabelColumnName = DefaultColumnNames.PredictedLabel;
         }
 
         public const string LoaderSignature = "MultiClassScoreTrans";
@@ -486,7 +492,7 @@ internal static ISchemaBoundMapper WrapCore<T>(IHostEnvironment env, ISchemaBoun
         [BestFriend]
         internal MulticlassClassificationScorer(IHostEnvironment env, Arguments args, IDataView data, ISchemaBoundMapper mapper, RoleMappedSchema trainSchema)
             : base(args, env, data, WrapIfNeeded(env, mapper, trainSchema), trainSchema, RegistrationName, AnnotationUtils.Const.ScoreColumnKind.MulticlassClassification,
-                AnnotationUtils.Const.ScoreValueKind.Score, OutputTypeMatches, GetPredColType)
+                args.ScoreColumnName, OutputTypeMatches, GetPredColType, args.PredictedLabelColumnName)
         {
         }
 

src/Microsoft.ML.Data/Scorers/PredictedLabelScorerBase.cs

@@ -42,8 +42,8 @@ private protected sealed class BindingsImpl : BindingsBase
             private readonly AnnotationUtils.AnnotationGetter<ReadOnlyMemory<char>> _getScoreValueKind;
             private readonly DataViewSchema.Annotations _predColMetadata;
             private BindingsImpl(DataViewSchema input, ISchemaBoundRowMapper mapper, string suffix, string scoreColumnKind,
-                bool user, int scoreColIndex, DataViewType predColType)
-                : base(input, mapper, suffix, user, DefaultColumnNames.PredictedLabel)
+                bool user, int scoreColIndex, DataViewType predColType, string predictedLabelColumnName = DefaultColumnNames.PredictedLabel)
+                : base(input, mapper, suffix, user, predictedLabelColumnName)
             {
                 Contracts.AssertNonEmpty(scoreColumnKind);
                 Contracts.Assert(DerivedColumnCount == 1);
@@ -82,15 +82,15 @@ private static DataViewSchema.Annotations KeyValueMetadataFromMetadata<T>(DataVi
             }
 
             public static BindingsImpl Create(DataViewSchema input, ISchemaBoundRowMapper mapper, string suffix,
-                string scoreColKind, int scoreColIndex, DataViewType predColType)
+                string scoreColKind, int scoreColIndex, DataViewType predColType, string predictedLabelColumnName = DefaultColumnNames.PredictedLabel)
             {
                 Contracts.AssertValue(input);
                 Contracts.AssertValue(mapper);
                 Contracts.AssertValueOrNull(suffix);
                 Contracts.AssertNonEmpty(scoreColKind);
 
                 return new BindingsImpl(input, mapper, suffix, scoreColKind, true,
-                    scoreColIndex, predColType);
+                    scoreColIndex, predColType, predictedLabelColumnName);
             }
 
             public BindingsImpl ApplyToSchema(DataViewSchema input, ISchemaBindableMapper bindable, IHostEnvironment env)
@@ -272,7 +272,7 @@ public override Func<int, bool> GetActiveMapperColumns(bool[] active)
         [BestFriend]
         private protected PredictedLabelScorerBase(ScorerArgumentsBase args, IHostEnvironment env, IDataView data,
             ISchemaBoundMapper mapper, RoleMappedSchema trainSchema, string registrationName, string scoreColKind, string scoreColName,
-            Func<DataViewType, bool> outputTypeMatches, Func<DataViewType, ISchemaBoundRowMapper, DataViewType> getPredColType)
+            Func<DataViewType, bool> outputTypeMatches, Func<DataViewType, ISchemaBoundRowMapper, DataViewType> getPredColType, string predictedLabelColumnName = DefaultColumnNames.PredictedLabel)
             : base(env, data, registrationName, Contracts.CheckRef(mapper, nameof(mapper)).Bindable)
         {
             Host.CheckValue(args, nameof(args));
@@ -292,7 +292,7 @@ private protected PredictedLabelScorerBase(ScorerArgumentsBase args, IHostEnviro
             Host.Check(outputTypeMatches(scoreType), "Unexpected predictor output type");
             var predColType = getPredColType(scoreType, rowMapper);
 
-            Bindings = BindingsImpl.Create(data.Schema, rowMapper, args.Suffix, scoreColKind, scoreColIndex, predColType);
+            Bindings = BindingsImpl.Create(data.Schema, rowMapper, args.Suffix, scoreColKind, scoreColIndex, predColType, predictedLabelColumnName);
             OutputSchema = Bindings.AsSchema;
         }
 

src/Microsoft.ML.Data/Scorers/PredictionTransformer.cs

@@ -5,6 +5,7 @@
 using System;
 using System.IO;
 using System.Reflection;
+using System.Runtime.CompilerServices;
 using Microsoft.ML;
 using Microsoft.ML.Data;
 using Microsoft.ML.Data.IO;
@@ -448,44 +449,60 @@ public sealed class MulticlassPredictionTransformer<TModel> : SingleFeaturePredi
         where TModel : class
     {
         private readonly string _trainLabelColumn;
+        private readonly string _scoreColumn;
+        private readonly string _predictedLabelColumn;
 
         [BestFriend]
-        internal MulticlassPredictionTransformer(IHostEnvironment env, TModel model, DataViewSchema inputSchema, string featureColumn, string labelColumn)
-            : base(Contracts.CheckRef(env, nameof(env)).Register(nameof(MulticlassPredictionTransformer<TModel>)), model, inputSchema, featureColumn)
+        internal MulticlassPredictionTransformer(IHostEnvironment env, TModel model, DataViewSchema inputSchema, string featureColumn, string labelColumn,
+            string scoreColumn = AnnotationUtils.Const.ScoreValueKind.Score, string predictedLabel = DefaultColumnNames.PredictedLabel) :
+            base(Contracts.CheckRef(env, nameof(env)).Register(nameof(MulticlassPredictionTransformer<TModel>)), model, inputSchema, featureColumn)
         {
             Host.CheckValueOrNull(labelColumn);
 
             _trainLabelColumn = labelColumn;
+            _scoreColumn = scoreColumn;
+            _predictedLabelColumn = predictedLabel;
             SetScorer();
         }
 
         internal MulticlassPredictionTransformer(IHostEnvironment env, ModelLoadContext ctx)
             : base(Contracts.CheckRef(env, nameof(env)).Register(nameof(MulticlassPredictionTransformer<TModel>)), ctx)
         {
-            InitializationLogic(ctx, out _trainLabelColumn);
+            InitializationLogic(ctx, out _trainLabelColumn, out _scoreColumn, out _predictedLabelColumn);
         }
 
         internal MulticlassPredictionTransformer(IHostEnvironment env, ModelLoadContext ctx, IHost host, TModel model)
             : base(host, ctx, model)
         {
 
-            InitializationLogic(ctx, out _trainLabelColumn);
+            InitializationLogic(ctx, out _trainLabelColumn, out _scoreColumn, out _predictedLabelColumn);
         }
 
-        private void InitializationLogic(ModelLoadContext ctx, out string trainLabelColumn)
+        private void InitializationLogic(ModelLoadContext ctx, out string trainLabelColumn, out string scoreColumn, out string predictedLabelColumn)
         {
             // *** Binary format ***
             // <base info>
             // id of string: train label column
 
             trainLabelColumn = ctx.LoadStringOrNull();
+            if (ctx.Header.ModelVerWritten >= 0x00010002)
+            {
+                scoreColumn = ctx.LoadStringOrNull();
+                predictedLabelColumn = ctx.LoadStringOrNull();
+            }
+            else
+            {
+                scoreColumn = AnnotationUtils.Const.ScoreValueKind.Score;
+                predictedLabelColumn = DefaultColumnNames.PredictedLabel;
+            }
+
             SetScorer();
         }
 
         private void SetScorer()
         {
             var schema = new RoleMappedSchema(TrainSchema, _trainLabelColumn, FeatureColumnName);
-            var args = new MulticlassClassificationScorer.Arguments();
+            var args = new MulticlassClassificationScorer.Arguments() { ScoreColumnName = _scoreColumn, PredictedLabelColumnName = _predictedLabelColumn};
             Scorer = new MulticlassClassificationScorer(Host, args, new EmptyDataView(Host, TrainSchema), BindableMapper.Bind(Host, schema), schema);
         }
 
@@ -500,13 +517,16 @@ private protected override void SaveCore(ModelSaveContext ctx)
             base.SaveCore(ctx);
 
             ctx.SaveStringOrNull(_trainLabelColumn);
+            ctx.SaveStringOrNull(_scoreColumn);
+            ctx.SaveStringOrNull(_predictedLabelColumn);
         }
 
         private static VersionInfo GetVersionInfo()
         {
             return new VersionInfo(
                 modelSignature: "MC  PRED",
-                verWrittenCur: 0x00010001, // Initial
+                //verWrittenCur: 0x00010001, // Initial
+                verWrittenCur: 0x00010002, // Score and Predicted Label column names.
                 verReadableCur: 0x00010001,
                 verWeCanReadBack: 0x00010001,
                 loaderSignature: MulticlassPredictionTransformer.LoaderSignature,