Minor polishment

Author: wschin

src/Microsoft.ML.DnnImageFeaturizer.ResNet18/ResNet18Extension.cs

@@ -30,8 +30,8 @@ public static EstimatorChain<ColumnCopyingTransformer> ResNet18(this DnnImageMod
 
         /// <summary>
         /// This allows a custom model location to be specified. This is useful is a custom model is specified,
-        /// or if the model is desired to be placed or shipped separately in a different folder from the main application. Note that because Onnx models
-        /// must be in a directory all by themsleves for the OnnxTransformer to work, this method appends a ResNet18Onnx/ResNetPrepOnnx subdirectory
+        /// or if the model is desired to be placed or shipped separately in a different folder from the main application. Note that because ONNX models
+        /// must be in a directory all by themselves for the OnnxTransformer to work, this method appends a ResNet18Onnx/ResNetPrepOnnx subdirectory
         /// to the passed in directory to prevent having to make that directory manually each time.
         /// </summary>
         public static EstimatorChain<ColumnCopyingTransformer> ResNet18(this DnnImageModelSelector dnnModelContext, IHostEnvironment env, string outputColumnName, string inputColumnName, string modelDir)

src/Microsoft.ML.OnnxTransformer/OnnxTransform.cs

@@ -46,7 +46,7 @@ namespace Microsoft.ML.Transforms.Onnx
     /// | Output column data type | The same data type as the input column |
     /// | Required NuGet in addition to Microsoft.ML | Microsoft.ML.OnnxTransformer |
     ///
-    /// Supports inferencing of models in ONNX 1.2 and 1.3 format (opset 7, 8 and 9), using the
+    /// Supports inferencing of models in ONNX 1.2, 1.3, 1.4, and 1.5 format (opset 7, 8, 9, and 10), using the
     /// [Microsoft.ML.OnnxRuntime](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime/) library.
     /// Models are scored on CPU by default. If GPU execution is needed (optional), use the
     /// NuGet package available at [Microsoft.ML.OnnxRuntime.Gpu](https://www.nuget.org/packages/Microsoft.ML.OnnxRuntime.Gpu/)

src/Microsoft.ML.OnnxTransformer/OnnxUtils.cs

@@ -29,10 +29,10 @@ internal sealed class OnnxModel
         /// </summary>
         public sealed class OnnxModelInfo
         {
-            public readonly OnnxNodeInfo[] InputsInfo;
-            public readonly OnnxNodeInfo[] OutputsInfo;
+            public readonly OnnxVariableInfo[] InputsInfo;
+            public readonly OnnxVariableInfo[] OutputsInfo;
 
-            public OnnxModelInfo(IEnumerable<OnnxNodeInfo> inputsInfo, IEnumerable<OnnxNodeInfo> outputsInfo)
+            public OnnxModelInfo(IEnumerable<OnnxVariableInfo> inputsInfo, IEnumerable<OnnxVariableInfo> outputsInfo)
             {
                 InputsInfo = inputsInfo.ToArray();
                 OutputsInfo = outputsInfo.ToArray();
@@ -43,7 +43,7 @@ public OnnxModelInfo(IEnumerable<OnnxNodeInfo> inputsInfo, IEnumerable<OnnxNodeI
         /// OnnxNodeInfo contains all the information for a given node (e.g. inputs/outputs)
         /// of an Onnx model.
         /// </summary>
-        public class OnnxNodeInfo
+        public class OnnxVariableInfo
         {
             /// <summary>
             /// The Name of the node
@@ -58,7 +58,7 @@ public class OnnxNodeInfo
             /// </summary>
             public readonly System.Type Type;
 
-            public OnnxNodeInfo(string name, OnnxShape shape, System.Type type)
+            public OnnxVariableInfo(string name, OnnxShape shape, System.Type type)
             {
                 Name = name;
                 Shape = shape;
@@ -163,18 +163,18 @@ public byte[] ToByteArray()
         /// Returns input metadata of the ONNX model.
         /// </summary>
         /// <returns>OnnxNodeInfo[]</returns>
-        private IEnumerable<OnnxNodeInfo> GetInputsInfo()
+        private IEnumerable<OnnxVariableInfo> GetInputsInfo()
         {
-            return _session.InputMetadata.Select(kv => new OnnxNodeInfo(kv.Key, kv.Value.Dimensions.ToList(), kv.Value.ElementType));
+            return _session.InputMetadata.Select(kv => new OnnxVariableInfo(kv.Key, kv.Value.Dimensions.ToList(), kv.Value.ElementType));
         }
 
         /// <summary>
         /// Returns output metadata of the ONNX model.
         /// </summary>
         /// <returns></returns>
-        private IEnumerable<OnnxNodeInfo> GetOutputsInfo()
+        private IEnumerable<OnnxVariableInfo> GetOutputsInfo()
         {
-            return _session.OutputMetadata.Select(kv => new OnnxNodeInfo(kv.Key, kv.Value.Dimensions.ToList(), kv.Value.ElementType));
+            return _session.OutputMetadata.Select(kv => new OnnxVariableInfo(kv.Key, kv.Value.Dimensions.ToList(), kv.Value.ElementType));
         }
     }