Allow user to overwrite unknown shapes loaded from ONNX model (#3963)

* Allow user to specify ONNX shape

Add a command line test

Remove unused code

* Address comments

Author: wschin

src/Microsoft.ML.OnnxTransformer/OnnxCatalog.cs

@@ -3,6 +3,7 @@
 // See the LICENSE file in the project root for more information.
 
 using System;
+using System.Collections.Generic;
 using Microsoft.ML.Data;
 using Microsoft.ML.Transforms;
 using Microsoft.ML.Transforms.Onnx;
@@ -36,6 +37,34 @@ public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog
             bool fallbackToCpu = false)
         => new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), modelFile, gpuDeviceId, fallbackToCpu);
 
+        /// <summary>
+        /// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the input column.
+        /// Input/output columns are determined based on the input/output columns of the provided ONNX model.
+        /// </summary>
+        /// <remarks>
+        /// The name/type of input columns must exactly match name/type of the ONNX model inputs.
+        /// The name/type of the produced output columns will match name/type of the ONNX model outputs.
+        /// </remarks>
+        /// <param name="catalog">The transform's catalog.</param>
+        /// <param name="modelFile">The path of the file containing the ONNX model.</param>
+        /// <param name="shapeDictionary">ONNX shape should be used to over those loaded from <paramref name="modelFile"/>.</param>
+        /// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
+        /// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[ApplyOnnxModel](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
+        public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
+            string modelFile,
+            IDictionary<string, int[]> shapeDictionary,
+            int? gpuDeviceId = null,
+            bool fallbackToCpu = false)
+        => new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), modelFile, gpuDeviceId, fallbackToCpu,
+            shapeDictionary: shapeDictionary);
+
         /// <summary>
         /// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnName"/> column.
         /// </summary>
@@ -58,7 +87,53 @@ public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog
             string modelFile,
             int? gpuDeviceId = null,
             bool fallbackToCpu = false)
-        => new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), new[] { outputColumnName }, new[] { inputColumnName }, modelFile, gpuDeviceId, fallbackToCpu);
+        => new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), new[] { outputColumnName }, new[] { inputColumnName },
+            modelFile, gpuDeviceId, fallbackToCpu);
+
+        /// <summary>
+        /// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnName"/> column.
+        /// </summary>
+        /// <param name="catalog">The transform's catalog.</param>
+        /// <param name="outputColumnName">The output column resulting from the transformation.</param>
+        /// <param name="inputColumnName">The input column.</param>
+        /// <param name="modelFile">The path of the file containing the ONNX model.</param>
+        /// <param name="shapeDictionary">ONNX shape should be used to over those loaded from <paramref name="modelFile"/>.</param>
+        /// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
+        /// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[ApplyOnnxModel](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyONNXModelWithInMemoryImages.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
+        public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
+            string outputColumnName,
+            string inputColumnName,
+            string modelFile,
+            IDictionary<string, int[]> shapeDictionary,
+            int? gpuDeviceId = null,
+            bool fallbackToCpu = false)
+        => new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), new[] { outputColumnName }, new[] { inputColumnName },
+            modelFile, gpuDeviceId, fallbackToCpu, shapeDictionary: shapeDictionary);
+
+        /// <summary>
+        /// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns.
+        /// </summary>
+        /// <param name="catalog">The transform's catalog.</param>
+        /// <param name="outputColumnNames">The output columns resulting from the transformation.</param>
+        /// <param name="inputColumnNames">The input columns.</param>
+        /// <param name="modelFile">The path of the file containing the ONNX model.</param>
+        /// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
+        /// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
+        public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
+            string[] outputColumnNames,
+            string[] inputColumnNames,
+            string modelFile,
+            int? gpuDeviceId = null,
+            bool fallbackToCpu = false)
+        => new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnNames, inputColumnNames,
+            modelFile, gpuDeviceId, fallbackToCpu);
 
         /// <summary>
         /// Create a <see cref="OnnxScoringEstimator"/>, which applies a pre-trained Onnx model to the <paramref name="inputColumnNames"/> columns.
@@ -67,15 +142,18 @@ public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog
         /// <param name="outputColumnNames">The output columns resulting from the transformation.</param>
         /// <param name="inputColumnNames">The input columns.</param>
         /// <param name="modelFile">The path of the file containing the ONNX model.</param>
+        /// <param name="shapeDictionary">ONNX shape should be used to over those loaded from <paramref name="modelFile"/>.</param>
         /// <param name="gpuDeviceId">Optional GPU device ID to run execution on, <see langword="null" /> to run on CPU.</param>
         /// <param name="fallbackToCpu">If GPU error, raise exception or fallback to CPU.</param>
         public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog,
             string[] outputColumnNames,
             string[] inputColumnNames,
             string modelFile,
+            IDictionary<string, int[]> shapeDictionary,
             int? gpuDeviceId = null,
             bool fallbackToCpu = false)
-        => new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnNames, inputColumnNames, modelFile, gpuDeviceId, fallbackToCpu);
+        => new OnnxScoringEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnNames, inputColumnNames,
+            modelFile, gpuDeviceId, fallbackToCpu, shapeDictionary: shapeDictionary);
 
         /// <summary>
         /// Create <see cref="DnnImageFeaturizerEstimator"/>, which applies one of the pre-trained DNN models in