Update to use OnnxRuntime library instead of Sonoma

build/Dependencies.props

@@ -16,7 +16,7 @@
   <PropertyGroup>
     <GoogleProtobufPackageVersion>3.5.1</GoogleProtobufPackageVersion>
     <LightGBMPackageVersion>2.2.1.1</LightGBMPackageVersion>
-    <MicrosoftMLScoring>1.1.0</MicrosoftMLScoring>
+    <MicrosoftMLOnnxRuntimePackageVersion>0.1.5</MicrosoftMLOnnxRuntimePackageVersion>
     <MlNetMklDepsPackageVersion>0.0.0.7</MlNetMklDepsPackageVersion>
     <ParquetDotNetPackageVersion>2.1.3</ParquetDotNetPackageVersion>
     <SystemDrawingCommonPackageVersion>4.5.0</SystemDrawingCommonPackageVersion>

docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs

@@ -0,0 +1,110 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using Microsoft.ML.OnnxRuntime;
+using Microsoft.ML.Runtime.Api;
+using Microsoft.ML.Runtime.Data;
+using Microsoft.ML.Transforms;
+using System;
+using System.Linq;
+
+namespace Microsoft.ML.Samples.Dynamic
+{
+    class OnnxTransformExample
+    {
+        /// <summary>
+        /// Example use of OnnxEstimator in an ML.NET pipeline
+        /// </summary>
+        public static void OnnxTransformSample()
+        {
+            // Download the squeeznet image model from ONNX model zoo, version 1.2
+            // https://github.com/onnx/models/tree/master/squeezenet
+            var modelPath = @"squeezenet\model.onnx";
+
+            // Inspect the model's inputs and outputs
+            var session = new InferenceSession(modelPath);
+            var inputInfo = session.InputMetadata.First();
+            var outputInfo = session.OutputMetadata.First();
+            Console.WriteLine($"Input Name is {String.Join(",", inputInfo.Key)}");
+            Console.WriteLine($"Input Dimensions are {String.Join(",", inputInfo.Value.Dimensions)}");
+            Console.WriteLine($"Output Name is {String.Join(",", outputInfo.Key)}");
+            Console.WriteLine($"Output Dimensions are {String.Join(",", outputInfo.Value.Dimensions)}");
+            // Results..
+            // Input Name is data_0
+            // Input Dimensions are 1,3,224,224
+            // Output Name is softmaxout_1
+            // Output Dimensions are 1,1000,1,1
+
+            // Create ML pipeline to score the data using OnnxScoringEstimator
+            var mlContext = new MLContext();
+            var data = GetTensorData();
+            var idv = mlContext.CreateStreamingDataView(data);
+            var pipeline = new OnnxScoringEstimator(mlContext, modelPath, new[] { inputInfo.Key }, new[] { outputInfo.Key });
+
+            // Run the pipeline and get the transformed values
+            var transformedValues = pipeline.Fit(idv).Transform(idv);
+
+            // Retrieve model scores into Prediction class
+            var predictions = transformedValues.AsEnumerable<Prediction>(mlContext, reuseRowObject: false);
+
+            // Iterate rows
+            foreach (var prediction in predictions)
+            {
+                int numClasses = 0;
+                foreach (var classScore in prediction.softmaxout_1.Take(3))
+                {
+                    Console.WriteLine($"Class #{numClasses++} score = {classScore}");
+                }
+                Console.WriteLine(new string('-', 10));
+            }
+
+            // Results look like below...
+            // Class #0 score = 4.544065E-05
+            // Class #1 score = 0.003845858
+            // Class #2 score = 0.0001249467
+            // ----------
+            // Class #0 score = 4.491953E-05
+            // Class #1 score = 0.003848222
+            // Class #2 score = 0.0001245592
+            // ----------
+        }
+
+        /// <summary>
+        /// inputSize is the overall dimensions of the model input tensor.
+        /// </summary>
+        private const int inputSize = 224 * 224 * 3;
+
+        /// <summary>
+        /// A class to hold sample tensor data. Member name should match  
+        /// the inputs that the model expects (in this case, data_0)
+        /// </summary>
+        public class TensorData
+        {
+            [VectorType(inputSize)]
+            public float[] data_0 { get; set; }
+        }
+
+        /// <summary>
+        /// Method to generate sample test data. Returns 2 sample rows.
+        /// </summary>
+        /// <returns></returns>
+        public static TensorData[] GetTensorData()
+        {
+            // This can be any numerical data. Assume image pixel values.
+            var image1 = Enumerable.Range(0, inputSize).Select(x => (float)x / inputSize).ToArray();
+            var image2 = Enumerable.Range(0, inputSize).Select(x => (float)(x + 10000) / inputSize).ToArray();
+            return new TensorData[] { new TensorData() { data_0 = image1 }, new TensorData() { data_0 = image2 } };
+        }
+
+        /// <summary>
+        /// Class to contain the output values from the transformation.
+        /// This model generates a vector of 1000 floats.
+        /// </summary>
+        class Prediction
+        {
+            [VectorType(1000)]
+            public float[] softmaxout_1 { get; set; }
+        }
+    }
+}

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

@@ -13,6 +13,7 @@
     <ProjectReference Include="..\..\..\src\Microsoft.ML.LightGBM\Microsoft.ML.LightGBM.csproj" />
     <ProjectReference Include="..\..\..\src\Microsoft.ML.Recommender\Microsoft.ML.Recommender.csproj" />
     <ProjectReference Include="..\..\..\src\Microsoft.ML.TimeSeries\Microsoft.ML.TimeSeries.csproj" />
+    <ProjectReference Include="..\..\..\src\Microsoft.ML.OnnxTransform\Microsoft.ML.OnnxTransform.csproj" />
 
     <NativeAssemblyReference Include="CpuMathNative" />
     <NativeAssemblyReference Include="FastTreeNative" />

pkg/Microsoft.ML.OnnxTransform/Microsoft.ML.OnnxTransform.nupkgproj

@@ -7,7 +7,7 @@
 
   <ItemGroup>
     <ProjectReference Include="../Microsoft.ML/Microsoft.ML.nupkgproj" />
-    <PackageReference Include="Microsoft.ML.Scoring" Version="$(MicrosoftMLScoring)"/>
+    <PackageReference Include="Microsoft.ML.OnnxRuntime" Version="$(MicrosoftMLOnnxRuntimePackageVersion)"/>
   </ItemGroup>
 
 </Project>

src/Microsoft.ML.OnnxTransform/Microsoft.ML.OnnxTransform.csproj

@@ -9,7 +9,7 @@
   <ItemGroup>
     <ProjectReference Include="..\Microsoft.ML.Core\Microsoft.ML.Core.csproj" />
     <ProjectReference Include="..\Microsoft.ML.Data\Microsoft.ML.Data.csproj" />
-    <PackageReference Include="Microsoft.ML.Scoring" Version="$(MicrosoftMLScoring)" />
+    <PackageReference Include="Microsoft.ML.OnnxRuntime" Version="$(MicrosoftMLOnnxRuntimePackageVersion)" />
   </ItemGroup>
 
 </Project>