From 771e933df28821a608b908fd450bb74c54fedfcc Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Mon, 15 Apr 2019 21:23:20 -0700
Subject: [PATCH 1/4] Rename to ApplyOnnxModel
---
.../{OnnxTransform.cs => Transforms/ApplyOnnxModel.cs} | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
rename docs/samples/Microsoft.ML.Samples/Dynamic/{OnnxTransform.cs => Transforms/ApplyOnnxModel.cs} (97%)
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs
similarity index 97%
rename from docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs
rename to docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs
index 62a7802ec1..73e56160c6 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs
@@ -6,10 +6,10 @@
namespace Samples.Dynamic
{
- public static class OnnxTransformExample
+ public static class ApplyOnnxModel
{
/// <summary>
- /// Example use of OnnxEstimator in an ML.NET pipeline
+ /// Example use of ApplyOnnxModel in an ML.NET pipeline
/// </summary>
public static void Example()
{
From 10110131fae1590c6043ca3613c84fb3c2a29347 Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Mon, 15 Apr 2019 21:34:41 -0700
Subject: [PATCH 2/4] simplify
---
.../Dynamic/Transforms/ApplyOnnxModel.cs | 28 ++++++-------------
1 file changed, 8 insertions(+), 20 deletions(-)
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs
index 73e56160c6..deb5dfd21d 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs
@@ -18,29 +18,17 @@ public static void Example()
// Microsoft.ML.Onnx.TestModels nuget.
var modelPath = @"squeezenet\00000001\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.Data.LoadFromEnumerable(data);
- var pipeline = mlContext.Transforms.ApplyOnnxModel(new[] { outputInfo.Key }, new[] { inputInfo.Key }, modelPath);
-
- // Run the pipeline and get the transformed values
- var transformedValues = pipeline.Fit(idv).Transform(idv);
+ // Generate sample test data.
+ var samples = GetTensorData();
+ // Convert training data to IDataView, the general data type used in ML.NET.
+ var data = mlContext.Data.LoadFromEnumerable(samples);
+ // Create the pipeline to score using provided onnx model.
+ var pipeline = mlContext.Transforms.ApplyOnnxModel(modelPath);
+ // Fit the pipeline and get the transformed values
+ var transformedValues = pipeline.Fit(data).Transform(data);
// Retrieve model scores into Prediction class
var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedValues, reuseRowObject: false);
From f7b57d7a9513f333dce312cf20795f8b9e06190f Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Tue, 16 Apr 2019 10:28:48 -0700
Subject: [PATCH 3/4] fix comments
---
.../Dynamic/Transforms/ApplyOnnxModel.cs | 34 ++++++++-----------
1 file changed, 15 insertions(+), 19 deletions(-)
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs
index deb5dfd21d..edb2b073e1 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs
@@ -2,15 +2,11 @@
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
-using Microsoft.ML.OnnxRuntime;
namespace Samples.Dynamic
{
public static class ApplyOnnxModel
{
- /// <summary>
- /// Example use of ApplyOnnxModel in an ML.NET pipeline
- /// </summary>
public static void Example()
{
// Download the squeeznet image model from ONNX model zoo, version 1.2
@@ -54,25 +50,25 @@ public static void Example()
// ----------
}
- /// <summary>
- /// inputSize is the overall dimensions of the model input tensor.
- /// </summary>
+ // <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>
+ // <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>
+ // <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.
@@ -81,10 +77,10 @@ public static TensorData[] GetTensorData()
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>
+ // <summary>
+ // Class to contain the output values from the transformation.
+ // This model generates a vector of 1000 floats.
+ // </summary>
class Prediction
{
[VectorType(1000)]
From 47e7176c9cd4c51f1a368519675157ac1778db05 Mon Sep 17 00:00:00 2001
From: Gani Nazirov <ganaziro@microsoft.com>
Date: Wed, 17 Apr 2019 12:39:50 -0700
Subject: [PATCH 4/4] fix comments
---
.../Dynamic/Transforms/ApplyOnnxModel.cs | 9 ---------
1 file changed, 9 deletions(-)
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs
index edb2b073e1..abff3a339d 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs
@@ -50,25 +50,18 @@ public static void Example()
// ----------
}
- // <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.
@@ -77,10 +70,8 @@ public static TensorData[] GetTensorData()
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)]