Fixing ONNX test (#3253)

Fixes #2981

* When adding an ONNX transform to an ML.NET pipeline, an exception would
occur if the input type was not a variable vector or vector type. This
is not needed as we do support converting basic types to equivalent ONNX
tensor type. Therefore the check was modified to throw if the type is a variable vector.

Author: singlis

src/Microsoft.ML.OnnxTransformer/OnnxTransform.cs

@@ -329,6 +329,7 @@ public Mapper(OnnxTransformer parent, DataViewSchema inputSchema) :
                     var col = inputSchema.GetColumnOrNull(_parent.Inputs[i]);
                     if (!col.HasValue)
                         throw Host.ExceptSchemaMismatch( nameof(inputSchema),"input", _parent.Inputs[i]);
+
                     _inputColIndices[i] = col.Value.Index;
 
                     var type = inputSchema[_inputColIndices[i]].Type;
@@ -564,7 +565,7 @@ public override SchemaShape GetOutputSchema(SchemaShape inputSchema)
                 var input = Transformer.Inputs[i];
                 if (!inputSchema.TryFindColumn(input, out var col))
                     throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", input);
-                if (!(col.Kind == SchemaShape.Column.VectorKind.VariableVector || col.Kind == SchemaShape.Column.VectorKind.Vector))
+                if (col.Kind == SchemaShape.Column.VectorKind.VariableVector)
                     throw Host.ExceptSchemaMismatch(nameof(inputSchema), "input", input, "vector", col.GetTypeString());
 
                 var inputsInfo = Transformer.Model.ModelInfo.InputsInfo;

test/Microsoft.ML.Functional.Tests/ONNX.cs

@@ -3,6 +3,7 @@
 // See the LICENSE file in the project root for more information.
 
 using System.IO;
+using Microsoft.ML.Data;
 using Microsoft.ML.Functional.Tests.Datasets;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework;
@@ -48,18 +49,21 @@ public void SaveOnnxModelLoadAndScoreFastTree()
                 mlContext.Model.ConvertToOnnx(model, data, file);
 
             // Load the model as a transform.
-            var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(modelPath);
+            // Note that when saving an ML.NET model as an ONNX model, the column types and column names will
+            // change. The name changes as ONNX doesn't not allow the same name for an input and output within the ONNX model.
+            // Therefore names maintained but have a number appended to the end of the name. In this case, Score0 is the output
+            // of the ONNX model. We are renaming Score0 to Score using Copy Columns.
+            // ONNX also uses tensors and will return an output of a tensor with the dimension of [1,1] for a single float.
+            // Therefore the VectorScoreColumn class (which contains a float [] field called Score) is used for the return
+            // type on the Prediction engine.
+            // See #2980 and #2981 for more information.
+            var onnxEstimator = mlContext.Transforms.ApplyOnnxModel(modelPath)
+                .Append(mlContext.Transforms.CopyColumns("Score", "Score0"));
             var onnxModel = onnxEstimator.Fit(data);
 
-            // TODO #2980: ONNX outputs don't match the outputs of the model, so we must hand-correct this for now.
-            // TODO #2981: ONNX models cannot be fit as part of a pipeline, so we must use a workaround like this.
-            var onnxWorkaroundPipeline = onnxModel.Append(
-                mlContext.Transforms.CopyColumns("Score", "Score0").Fit(onnxModel.Transform(data)));
-
             // Create prediction engine and test predictions.
             var originalPredictionEngine = mlContext.Model.CreatePredictionEngine<HousingRegression, ScoreColumn>(model);
-            // TODO #2982: ONNX produces vector types and not the original output type.
-            var onnxPredictionEngine = mlContext.Model.CreatePredictionEngine<HousingRegression, VectorScoreColumn>(onnxWorkaroundPipeline);
+            var onnxPredictionEngine = mlContext.Model.CreatePredictionEngine<HousingRegression, VectorScoreColumn>(onnxModel);
 
             // Take a handful of examples out of the dataset and compute predictions.
             var dataEnumerator = mlContext.Data.CreateEnumerable<HousingRegression>(mlContext.Data.TakeRows(data, 5), false);