increment model version and add a test for recursion limit

Author: Crabzmatic

src/Microsoft.ML.OnnxTransformer/OnnxCatalog.cs

@@ -110,7 +110,7 @@ public static OnnxScoringEstimator ApplyOnnxModel(this TransformsCatalog catalog
         /// <param name="modelFile">The path of the file containing the ONNX model.</param>
         /// <param name="shapeDictionary">ONNX shapes to be used over those loaded from <paramref name="modelFile"/>.
         /// For keys use names as stated in the ONNX model, e.g. "input". Stating the shapes with this parameter
-        /// is particullarly useful for working with variable dimension inputs and outputs.
+        /// is particularly useful for working with variable dimension inputs and outputs.
         /// </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>

src/Microsoft.ML.OnnxTransformer/OnnxTransform.cs

@@ -129,7 +129,8 @@ private static VersionInfo GetVersionInfo()
                 modelSignature: "ONNXSCOR",
                 // version 10001 is single input & output.
                 // version 10002 = multiple inputs & outputs
-                verWrittenCur: 0x00010002,
+                // version 10003 = custom protobuf recursion limit
+                verWrittenCur: 0x00010003,
                 verReadableCur: 0x00010002,
                 verWeCanReadBack: 0x00010001,
                 loaderSignature: LoaderSignature,

test/Microsoft.ML.OnnxTransformerTest/OnnxTransformTests.cs

@@ -1002,5 +1002,32 @@ public void TestOnnxTransformSaveAndLoadWithCustomShapes()
             (model as IDisposable)?.Dispose();
             (loadedModel as IDisposable)?.Dispose();
         }
+
+        /// <summary>
+        /// A test to check if recursion limit works.
+        /// </summary>
+        [OnnxFact]
+        public void TestOnnxTransformSaveAndLoadWithRecursionLimit()
+        {
+            var modelFile = Path.Combine(Directory.GetCurrentDirectory(), "squeezenet", "00000001", "model.onnx");
+
+            const int imageHeight = 224;
+            const int imageWidth = 224;
+            var dataFile = GetDataPath("images/images.tsv");
+            var imageFolder = Path.GetDirectoryName(dataFile);
+
+            var data = ML.Data.LoadFromTextFile(dataFile, new[] {
+                new TextLoader.Column("imagePath", DataKind.String, 0),
+                new TextLoader.Column("name", DataKind.String, 1)
+            });
+
+            var pipe = ML.Transforms.LoadImages("data_0", imageFolder, "imagePath")
+                .Append(ML.Transforms.ResizeImages("data_0", imageHeight, imageWidth))
+                .Append(ML.Transforms.ExtractPixels("data_0", interleavePixelColors: true))
+                .Append(ML.Transforms.ApplyOnnxModel(new []{ "softmaxout_1" }, new []{ "data_0" }, modelFile, 
+                    gpuDeviceId: _gpuDeviceId, fallbackToCpu: _fallbackToCpu, shapeDictionary: null, recursionLimit: 50));
+
+            TestEstimatorCore(pipe, data);
+        }
     }
 }