OnnxCatalog.ApplyOnnxModel Method
Definition
Important
Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.
Overloads
| ApplyOnnxModel(TransformsCatalog, OnnxOptions) |
Create a OnnxScoringEstimator using the specified OnnxOptions. Please refer to OnnxScoringEstimator to learn more about the necessary dependencies, and how to run it on a GPU. |
| ApplyOnnxModel(TransformsCatalog, String, Nullable<Int32>, Boolean) |
Create a 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. Please refer to OnnxScoringEstimator to learn more about the necessary dependencies, and how to run it on a GPU. |
| ApplyOnnxModel(TransformsCatalog, String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean) |
Create a 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. Please refer to OnnxScoringEstimator to learn more about the necessary dependencies, and how to run it on a GPU. |
| ApplyOnnxModel(TransformsCatalog, String, String, String, Nullable<Int32>, Boolean) |
Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the |
| ApplyOnnxModel(TransformsCatalog, String[], String[], String, Nullable<Int32>, Boolean) |
Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the |
| ApplyOnnxModel(TransformsCatalog, String, String, String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean) |
Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the |
| ApplyOnnxModel(TransformsCatalog, String[], String[], String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean) |
Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the |
| ApplyOnnxModel(TransformsCatalog, String[], String[], String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean, Int32) |
Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the |
ApplyOnnxModel(TransformsCatalog, OnnxOptions)
Create a OnnxScoringEstimator using the specified OnnxOptions. Please refer to OnnxScoringEstimator to learn more about the necessary dependencies, and how to run it on a GPU.
public static Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator ApplyOnnxModel (this Microsoft.ML.TransformsCatalog catalog, Microsoft.ML.Transforms.Onnx.OnnxOptions options);static member ApplyOnnxModel : Microsoft.ML.TransformsCatalog * Microsoft.ML.Transforms.Onnx.OnnxOptions -> Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator<Extension()>
Public Function ApplyOnnxModel (catalog As TransformsCatalog, options As OnnxOptions) As OnnxScoringEstimatorParameters
- catalog
- TransformsCatalog
The transform's catalog.
- options
- OnnxOptions
Options for the OnnxScoringEstimator.
Returns
Remarks
If the options.GpuDeviceId value is null the MLContext.GpuDeviceId value will be used if it is not null.
Applies to
ApplyOnnxModel(TransformsCatalog, String, Nullable<Int32>, Boolean)
Create a 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. Please refer to OnnxScoringEstimator to learn more about the necessary dependencies, and how to run it on a GPU.
public static Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator ApplyOnnxModel (this Microsoft.ML.TransformsCatalog catalog, string modelFile, int? gpuDeviceId = default, bool fallbackToCpu = false);static member ApplyOnnxModel : Microsoft.ML.TransformsCatalog * string * Nullable<int> * bool -> Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator<Extension()>
Public Function ApplyOnnxModel (catalog As TransformsCatalog, modelFile As String, Optional gpuDeviceId As Nullable(Of Integer) = Nothing, Optional fallbackToCpu As Boolean = false) As OnnxScoringEstimatorParameters
- catalog
- TransformsCatalog
The transform's catalog.
- modelFile
- String
The path of the file containing the ONNX model.
- fallbackToCpu
- Boolean
If GPU error, raise exception or fallback to CPU.
Returns
Examples
using System;
using System.IO;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class ApplyOnnxModel
{
public static void Example()
{
// Download the squeeznet image model from ONNX model zoo, version 1.2
// https://github.com/onnx/models/tree/master/squeezenet or
// https://s3.amazonaws.com/download.onnx/models/opset_8/squeezenet.tar.gz
// or use Microsoft.ML.Onnx.TestModels nuget.
var modelPath = @"squeezenet\00000001\model.onnx";
// Create ML pipeline to score the data using OnnxScoringEstimator
var mlContext = new MLContext();
// 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);
// 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
// ----------
}
// inputSize is the overall dimensions of the model input tensor.
private const int inputSize = 224 * 224 * 3;
// A class to hold sample tensor data. Member name should match
// the inputs that the model expects (in this case, data_0)
public class TensorData
{
[VectorType(inputSize)]
public float[] data_0 { get; set; }
}
// Method to generate sample test data. Returns 2 sample rows.
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 } };
}
// Class to contain the output values from the transformation.
// This model generates a vector of 1000 floats.
class Prediction
{
[VectorType(1000)]
public float[] softmaxout_1 { get; set; }
}
}
}
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. If the gpuDeviceId value is null the MLContext.GpuDeviceId value will be used if it is not null.
Applies to
ApplyOnnxModel(TransformsCatalog, String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)
Create a 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. Please refer to OnnxScoringEstimator to learn more about the necessary dependencies, and how to run it on a GPU.
public static Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator ApplyOnnxModel (this Microsoft.ML.TransformsCatalog catalog, string modelFile, System.Collections.Generic.IDictionary<string,int[]> shapeDictionary, int? gpuDeviceId = default, bool fallbackToCpu = false);static member ApplyOnnxModel : Microsoft.ML.TransformsCatalog * string * System.Collections.Generic.IDictionary<string, int[]> * Nullable<int> * bool -> Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator<Extension()>
Public Function ApplyOnnxModel (catalog As TransformsCatalog, modelFile As String, shapeDictionary As IDictionary(Of String, Integer()), Optional gpuDeviceId As Nullable(Of Integer) = Nothing, Optional fallbackToCpu As Boolean = false) As OnnxScoringEstimatorParameters
- catalog
- TransformsCatalog
The transform's catalog.
- modelFile
- String
The path of the file containing the ONNX model.
- shapeDictionary
- IDictionary<String,Int32[]>
ONNX shapes to be used over those loaded from modelFile.
For keys use names as stated in the ONNX model, e.g. "input". Stating the shapes with this parameter
is particularly useful for working with variable dimension inputs and outputs.
- fallbackToCpu
- Boolean
If GPU error, raise exception or fallback to CPU.
Returns
Examples
using System;
using System.IO;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class ApplyOnnxModel
{
public static void Example()
{
// Download the squeeznet image model from ONNX model zoo, version 1.2
// https://github.com/onnx/models/tree/master/squeezenet or
// https://s3.amazonaws.com/download.onnx/models/opset_8/squeezenet.tar.gz
// or use Microsoft.ML.Onnx.TestModels nuget.
var modelPath = @"squeezenet\00000001\model.onnx";
// Create ML pipeline to score the data using OnnxScoringEstimator
var mlContext = new MLContext();
// 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);
// 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
// ----------
}
// inputSize is the overall dimensions of the model input tensor.
private const int inputSize = 224 * 224 * 3;
// A class to hold sample tensor data. Member name should match
// the inputs that the model expects (in this case, data_0)
public class TensorData
{
[VectorType(inputSize)]
public float[] data_0 { get; set; }
}
// Method to generate sample test data. Returns 2 sample rows.
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 } };
}
// Class to contain the output values from the transformation.
// This model generates a vector of 1000 floats.
class Prediction
{
[VectorType(1000)]
public float[] softmaxout_1 { get; set; }
}
}
}
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. If the gpuDeviceId value is null the MLContext.GpuDeviceId value will be used if it is not null.
Applies to
ApplyOnnxModel(TransformsCatalog, String, String, String, Nullable<Int32>, Boolean)
Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the inputColumnName column.
Please refer to OnnxScoringEstimator to learn more about the necessary dependencies,
and how to run it on a GPU.
public static Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator ApplyOnnxModel (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, string modelFile, int? gpuDeviceId = default, bool fallbackToCpu = false);static member ApplyOnnxModel : Microsoft.ML.TransformsCatalog * string * string * string * Nullable<int> * bool -> Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator<Extension()>
Public Function ApplyOnnxModel (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, modelFile As String, Optional gpuDeviceId As Nullable(Of Integer) = Nothing, Optional fallbackToCpu As Boolean = false) As OnnxScoringEstimatorParameters
- catalog
- TransformsCatalog
The transform's catalog.
- outputColumnName
- String
The output column resulting from the transformation.
- inputColumnName
- String
The input column.
- modelFile
- String
The path of the file containing the ONNX model.
- fallbackToCpu
- Boolean
If GPU error, raise exception or fallback to CPU.
Returns
Examples
using System;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Image;
namespace Samples.Dynamic
{
public static class ApplyOnnxModelWithInMemoryImages
{
// Example of applying ONNX transform on in-memory images.
public static void Example()
{
// Download the squeeznet image model from ONNX model zoo, version 1.2
// https://github.com/onnx/models/tree/master/vision/classification/squeezenet or use
// Microsoft.ML.Onnx.TestModels nuget.
// It's a multiclass classifier. It consumes an input "data_0" and
// produces an output "softmaxout_1".
var modelPath = @"squeezenet\00000001\model.onnx";
// Create ML pipeline to score the data using OnnxScoringEstimator
var mlContext = new MLContext();
// Create in-memory data points. Its Image/Scores field is the
// input /output of the used ONNX model.
var dataPoints = new ImageDataPoint[]
{
new ImageDataPoint(red: 255, green: 0, blue: 0), // Red color
new ImageDataPoint(red: 0, green: 128, blue: 0) // Green color
};
// Convert training data to IDataView, the general data type used in
// ML.NET.
var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);
// Create a ML.NET pipeline which contains two steps. First,
// ExtractPixle is used to convert the 224x224 image to a 3x224x224
// float tensor. Then the float tensor is fed into a ONNX model with an
// input called "data_0" and an output called "softmaxout_1". Note that
// "data_0" and "softmaxout_1" are model input and output names stored
// in the used ONNX model file. Users may need to inspect their own
// models to get the right input and output column names.
// Map column "Image" to column "data_0"
// Map column "data_0" to column "softmaxout_1"
var pipeline = mlContext.Transforms.ExtractPixels("data_0", "Image")
.Append(mlContext.Transforms.ApplyOnnxModel("softmaxout_1",
"data_0", modelPath));
var model = pipeline.Fit(dataView);
var onnx = model.Transform(dataView);
// Convert IDataView back to IEnumerable<ImageDataPoint> so that user
// can inspect the output, column "softmaxout_1", of the ONNX transform.
// Note that Column "softmaxout_1" would be stored in ImageDataPont
//.Scores because the added attributed [ColumnName("softmaxout_1")]
// tells that ImageDataPont.Scores is equivalent to column
// "softmaxout_1".
var transformedDataPoints = mlContext.Data.CreateEnumerable<
ImageDataPoint>(onnx, false).ToList();
// The scores are probabilities of all possible classes, so they should
// all be positive.
foreach (var dataPoint in transformedDataPoints)
{
var firstClassProb = dataPoint.Scores.First();
var lastClassProb = dataPoint.Scores.Last();
Console.WriteLine("The probability of being the first class is " +
(firstClassProb * 100) + "%.");
Console.WriteLine($"The probability of being the last class is " +
(lastClassProb * 100) + "%.");
}
// Expected output:
// The probability of being the first class is 0.002542659%.
// The probability of being the last class is 0.0292684%.
// The probability of being the first class is 0.02258059%.
// The probability of being the last class is 0.394428%.
}
// This class is used in Example() to describe data points which will be
// consumed by ML.NET pipeline.
private class ImageDataPoint
{
// Height of Image.
private const int height = 224;
// Width of Image.
private const int width = 224;
// Image will be consumed by ONNX image multiclass classification model.
[ImageType(height, width)]
public MLImage Image { get; set; }
// Expected output of ONNX model. It contains probabilities of all
// classes. Note that the ColumnName below should match the output name
// in the used ONNX model file.
[ColumnName("softmaxout_1")]
public float[] Scores { get; set; }
public ImageDataPoint()
{
Image = null;
}
public ImageDataPoint(byte red, byte green, byte blue)
{
byte[] imageData = new byte[width * height * 4]; // 4 for the red, green, blue and alpha colors
for (int i = 0; i < imageData.Length; i += 4)
{
// Fill the buffer with the Bgra32 format
imageData[i] = blue;
imageData[i + 1] = green;
imageData[i + 2] = red;
imageData[i + 3] = 255;
}
Image = MLImage.CreateFromPixels(width, height, MLPixelFormat.Bgra32, imageData);
}
}
}
}
Remarks
If the gpuDeviceId value is null the MLContext.GpuDeviceId value will be used if it is not null.
Applies to
ApplyOnnxModel(TransformsCatalog, String[], String[], String, Nullable<Int32>, Boolean)
Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the inputColumnNames columns.
Please refer to OnnxScoringEstimator to learn more about the necessary dependencies,
and how to run it on a GPU.
public static Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator ApplyOnnxModel (this Microsoft.ML.TransformsCatalog catalog, string[] outputColumnNames, string[] inputColumnNames, string modelFile, int? gpuDeviceId = default, bool fallbackToCpu = false);static member ApplyOnnxModel : Microsoft.ML.TransformsCatalog * string[] * string[] * string * Nullable<int> * bool -> Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator<Extension()>
Public Function ApplyOnnxModel (catalog As TransformsCatalog, outputColumnNames As String(), inputColumnNames As String(), modelFile As String, Optional gpuDeviceId As Nullable(Of Integer) = Nothing, Optional fallbackToCpu As Boolean = false) As OnnxScoringEstimatorParameters
- catalog
- TransformsCatalog
The transform's catalog.
- outputColumnNames
- String[]
The output columns resulting from the transformation.
- inputColumnNames
- String[]
The input columns.
- modelFile
- String
The path of the file containing the ONNX model.
- fallbackToCpu
- Boolean
If GPU error, raise exception or fallback to CPU.
Returns
Remarks
If the gpuDeviceId value is null the MLContext.GpuDeviceId value will be used if it is not null.
Applies to
ApplyOnnxModel(TransformsCatalog, String, String, String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)
Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the inputColumnName column.
Please refer to OnnxScoringEstimator to learn more about the necessary dependencies,
and how to run it on a GPU.
public static Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator ApplyOnnxModel (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName, string modelFile, System.Collections.Generic.IDictionary<string,int[]> shapeDictionary, int? gpuDeviceId = default, bool fallbackToCpu = false);static member ApplyOnnxModel : Microsoft.ML.TransformsCatalog * string * string * string * System.Collections.Generic.IDictionary<string, int[]> * Nullable<int> * bool -> Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator<Extension()>
Public Function ApplyOnnxModel (catalog As TransformsCatalog, outputColumnName As String, inputColumnName As String, modelFile As String, shapeDictionary As IDictionary(Of String, Integer()), Optional gpuDeviceId As Nullable(Of Integer) = Nothing, Optional fallbackToCpu As Boolean = false) As OnnxScoringEstimatorParameters
- catalog
- TransformsCatalog
The transform's catalog.
- outputColumnName
- String
The output column resulting from the transformation.
- inputColumnName
- String
The input column.
- modelFile
- String
The path of the file containing the ONNX model.
- shapeDictionary
- IDictionary<String,Int32[]>
ONNX shapes to be used over those loaded from modelFile.
For keys use names as stated in the ONNX model, e.g. "input". Stating the shapes with this parameter
is particularly useful for working with variable dimension inputs and outputs.
- fallbackToCpu
- Boolean
If GPU error, raise exception or fallback to CPU.
Returns
Examples
using System;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Image;
namespace Samples.Dynamic
{
public static class ApplyOnnxModelWithInMemoryImages
{
// Example of applying ONNX transform on in-memory images.
public static void Example()
{
// Download the squeeznet image model from ONNX model zoo, version 1.2
// https://github.com/onnx/models/tree/master/vision/classification/squeezenet or use
// Microsoft.ML.Onnx.TestModels nuget.
// It's a multiclass classifier. It consumes an input "data_0" and
// produces an output "softmaxout_1".
var modelPath = @"squeezenet\00000001\model.onnx";
// Create ML pipeline to score the data using OnnxScoringEstimator
var mlContext = new MLContext();
// Create in-memory data points. Its Image/Scores field is the
// input /output of the used ONNX model.
var dataPoints = new ImageDataPoint[]
{
new ImageDataPoint(red: 255, green: 0, blue: 0), // Red color
new ImageDataPoint(red: 0, green: 128, blue: 0) // Green color
};
// Convert training data to IDataView, the general data type used in
// ML.NET.
var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);
// Create a ML.NET pipeline which contains two steps. First,
// ExtractPixle is used to convert the 224x224 image to a 3x224x224
// float tensor. Then the float tensor is fed into a ONNX model with an
// input called "data_0" and an output called "softmaxout_1". Note that
// "data_0" and "softmaxout_1" are model input and output names stored
// in the used ONNX model file. Users may need to inspect their own
// models to get the right input and output column names.
// Map column "Image" to column "data_0"
// Map column "data_0" to column "softmaxout_1"
var pipeline = mlContext.Transforms.ExtractPixels("data_0", "Image")
.Append(mlContext.Transforms.ApplyOnnxModel("softmaxout_1",
"data_0", modelPath));
var model = pipeline.Fit(dataView);
var onnx = model.Transform(dataView);
// Convert IDataView back to IEnumerable<ImageDataPoint> so that user
// can inspect the output, column "softmaxout_1", of the ONNX transform.
// Note that Column "softmaxout_1" would be stored in ImageDataPont
//.Scores because the added attributed [ColumnName("softmaxout_1")]
// tells that ImageDataPont.Scores is equivalent to column
// "softmaxout_1".
var transformedDataPoints = mlContext.Data.CreateEnumerable<
ImageDataPoint>(onnx, false).ToList();
// The scores are probabilities of all possible classes, so they should
// all be positive.
foreach (var dataPoint in transformedDataPoints)
{
var firstClassProb = dataPoint.Scores.First();
var lastClassProb = dataPoint.Scores.Last();
Console.WriteLine("The probability of being the first class is " +
(firstClassProb * 100) + "%.");
Console.WriteLine($"The probability of being the last class is " +
(lastClassProb * 100) + "%.");
}
// Expected output:
// The probability of being the first class is 0.002542659%.
// The probability of being the last class is 0.0292684%.
// The probability of being the first class is 0.02258059%.
// The probability of being the last class is 0.394428%.
}
// This class is used in Example() to describe data points which will be
// consumed by ML.NET pipeline.
private class ImageDataPoint
{
// Height of Image.
private const int height = 224;
// Width of Image.
private const int width = 224;
// Image will be consumed by ONNX image multiclass classification model.
[ImageType(height, width)]
public MLImage Image { get; set; }
// Expected output of ONNX model. It contains probabilities of all
// classes. Note that the ColumnName below should match the output name
// in the used ONNX model file.
[ColumnName("softmaxout_1")]
public float[] Scores { get; set; }
public ImageDataPoint()
{
Image = null;
}
public ImageDataPoint(byte red, byte green, byte blue)
{
byte[] imageData = new byte[width * height * 4]; // 4 for the red, green, blue and alpha colors
for (int i = 0; i < imageData.Length; i += 4)
{
// Fill the buffer with the Bgra32 format
imageData[i] = blue;
imageData[i + 1] = green;
imageData[i + 2] = red;
imageData[i + 3] = 255;
}
Image = MLImage.CreateFromPixels(width, height, MLPixelFormat.Bgra32, imageData);
}
}
}
}
Remarks
If the gpuDeviceId value is null the MLContext.GpuDeviceId value will be used if it is not null.
Applies to
ApplyOnnxModel(TransformsCatalog, String[], String[], String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)
Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the inputColumnNames columns.
Please refer to OnnxScoringEstimator to learn more about the necessary dependencies,
and how to run it on a GPU.
public static Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator ApplyOnnxModel (this Microsoft.ML.TransformsCatalog catalog, string[] outputColumnNames, string[] inputColumnNames, string modelFile, System.Collections.Generic.IDictionary<string,int[]> shapeDictionary, int? gpuDeviceId = default, bool fallbackToCpu = false);static member ApplyOnnxModel : Microsoft.ML.TransformsCatalog * string[] * string[] * string * System.Collections.Generic.IDictionary<string, int[]> * Nullable<int> * bool -> Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator<Extension()>
Public Function ApplyOnnxModel (catalog As TransformsCatalog, outputColumnNames As String(), inputColumnNames As String(), modelFile As String, shapeDictionary As IDictionary(Of String, Integer()), Optional gpuDeviceId As Nullable(Of Integer) = Nothing, Optional fallbackToCpu As Boolean = false) As OnnxScoringEstimatorParameters
- catalog
- TransformsCatalog
The transform's catalog.
- outputColumnNames
- String[]
The output columns resulting from the transformation.
- inputColumnNames
- String[]
The input columns.
- modelFile
- String
The path of the file containing the ONNX model.
- shapeDictionary
- IDictionary<String,Int32[]>
ONNX shapes to be used over those loaded from modelFile.
For keys use names as stated in the ONNX model, e.g. "input". Stating the shapes with this parameter
is particularly useful for working with variable dimension inputs and outputs.
- fallbackToCpu
- Boolean
If GPU error, raise exception or fallback to CPU.
Returns
Remarks
If the gpuDeviceId value is null the MLContext.GpuDeviceId value will be used if it is not null.
Applies to
ApplyOnnxModel(TransformsCatalog, String[], String[], String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean, Int32)
Create a OnnxScoringEstimator, which applies a pre-trained Onnx model to the inputColumnNames columns.
Please refer to OnnxScoringEstimator to learn more about the necessary dependencies,
and how to run it on a GPU.
public static Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator ApplyOnnxModel (this Microsoft.ML.TransformsCatalog catalog, string[] outputColumnNames, string[] inputColumnNames, string modelFile, System.Collections.Generic.IDictionary<string,int[]> shapeDictionary, int? gpuDeviceId = default, bool fallbackToCpu = false, int recursionLimit = 100);static member ApplyOnnxModel : Microsoft.ML.TransformsCatalog * string[] * string[] * string * System.Collections.Generic.IDictionary<string, int[]> * Nullable<int> * bool * int -> Microsoft.ML.Transforms.Onnx.OnnxScoringEstimator<Extension()>
Public Function ApplyOnnxModel (catalog As TransformsCatalog, outputColumnNames As String(), inputColumnNames As String(), modelFile As String, shapeDictionary As IDictionary(Of String, Integer()), Optional gpuDeviceId As Nullable(Of Integer) = Nothing, Optional fallbackToCpu As Boolean = false, Optional recursionLimit As Integer = 100) As OnnxScoringEstimatorParameters
- catalog
- TransformsCatalog
The transform's catalog.
- outputColumnNames
- String[]
The output columns resulting from the transformation.
- inputColumnNames
- String[]
The input columns.
- modelFile
- String
The path of the file containing the ONNX model.
- shapeDictionary
- IDictionary<String,Int32[]>
ONNX shapes to be used over those loaded from modelFile.
For keys use names as stated in the ONNX model, e.g. "input". Stating the shapes with this parameter
is particularly useful for working with variable dimension inputs and outputs.
- fallbackToCpu
- Boolean
If GPU error, raise exception or fallback to CPU.
- recursionLimit
- Int32
Optional, specifies the Protobuf CodedInputStream recursion limit. Default value is 100.
Returns
Remarks
If the gpuDeviceId value is null the MLContext.GpuDeviceId value will be used if it is not null.