OnnxCatalog.ApplyOnnxModel Metode

Referensi

Definisi

Ruang nama:: Microsoft.ML

Rakitan:: Microsoft.ML.OnnxTransformer.dll

Paket:: Microsoft.ML.OnnxTransformer v3.0.1

Paket:: Microsoft.ML.OnnxTransformer v1.7.0

Paket:: Microsoft.ML.OnnxTransformer v2.0.0

Paket:: Microsoft.ML.OnnxTransformer v1.2.0

Paket:: Microsoft.ML.OnnxTransformer v1.3.1

Paket:: Microsoft.ML.OnnxTransformer v1.4.0

Paket:: Microsoft.ML.OnnxTransformer v1.5.5

Paket:: Microsoft.ML.OnnxTransformer v1.6.0

Penting

Beberapa informasi terkait produk prarilis yang dapat diubah secara signifikan sebelum dirilis. Microsoft tidak memberikan jaminan, tersirat maupun tersurat, sehubungan dengan informasi yang diberikan di sini.

Overload

ApplyOnnxModel(TransformsCatalog, OnnxOptions)	OnnxScoringEstimator Buat menggunakan yang ditentukanOnnxOptions. Silakan lihat OnnxScoringEstimator untuk mempelajari lebih lanjut tentang dependensi yang diperlukan, dan cara menjalankannya pada GPU.
ApplyOnnxModel(TransformsCatalog, String, Nullable<Int32>, Boolean)	Buat OnnxScoringEstimator, yang menerapkan model Onnx yang telah dilatih sebelumnya ke kolom input. Kolom input/output ditentukan berdasarkan kolom input/output dari model ONNX yang disediakan. Silakan lihat OnnxScoringEstimator untuk mempelajari lebih lanjut tentang dependensi yang diperlukan, dan cara menjalankannya pada GPU.
ApplyOnnxModel(TransformsCatalog, String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)	Buat OnnxScoringEstimator, yang menerapkan model Onnx yang telah dilatih sebelumnya ke kolom input. Kolom input/output ditentukan berdasarkan kolom input/output dari model ONNX yang disediakan. Silakan lihat OnnxScoringEstimator untuk mempelajari lebih lanjut tentang dependensi yang diperlukan, dan cara menjalankannya pada GPU.
ApplyOnnxModel(TransformsCatalog, String, String, String, Nullable<Int32>, Boolean)	OnnxScoringEstimatorBuat , yang menerapkan model Onnx yang telah dilatih sebelumnya ke `inputColumnName` kolom. Silakan lihat OnnxScoringEstimator untuk mempelajari lebih lanjut tentang dependensi yang diperlukan, dan cara menjalankannya pada GPU.
ApplyOnnxModel(TransformsCatalog, String[], String[], String, Nullable<Int32>, Boolean)	Buat OnnxScoringEstimator, yang menerapkan model Onnx yang telah dilatih sebelumnya ke `inputColumnNames` kolom. Silakan lihat OnnxScoringEstimator untuk mempelajari lebih lanjut tentang dependensi yang diperlukan, dan cara menjalankannya pada GPU.
ApplyOnnxModel(TransformsCatalog, String, String, String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)	OnnxScoringEstimatorBuat , yang menerapkan model Onnx yang telah dilatih sebelumnya ke `inputColumnName` kolom. Silakan lihat OnnxScoringEstimator untuk mempelajari lebih lanjut tentang dependensi yang diperlukan, dan cara menjalankannya pada GPU.
ApplyOnnxModel(TransformsCatalog, String[], String[], String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)	Buat OnnxScoringEstimator, yang menerapkan model Onnx yang telah dilatih sebelumnya ke `inputColumnNames` kolom. Silakan lihat OnnxScoringEstimator untuk mempelajari lebih lanjut tentang dependensi yang diperlukan, dan cara menjalankannya pada GPU.
ApplyOnnxModel(TransformsCatalog, String[], String[], String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean, Int32)	Buat OnnxScoringEstimator, yang menerapkan model Onnx yang telah dilatih sebelumnya ke `inputColumnNames` kolom. Silakan lihat OnnxScoringEstimator untuk mempelajari lebih lanjut tentang dependensi yang diperlukan, dan cara menjalankannya pada GPU.

ApplyOnnxModel(TransformsCatalog, OnnxOptions)

OnnxScoringEstimator Buat menggunakan yang ditentukanOnnxOptions. Silakan lihat OnnxScoringEstimator untuk mempelajari lebih lanjut tentang dependensi yang diperlukan, dan cara menjalankannya pada 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 OnnxScoringEstimator

Parameter

catalog: TransformsCatalog

Katalog transformasi.

options: OnnxOptions

Opsi untuk OnnxScoringEstimator.

Mengembalikan

OnnxScoringEstimator

Keterangan

Jika opsinya. Nilai GpuDeviceId adalah nullMLContext.GpuDeviceId nilai akan digunakan jika bukan null.

Berlaku untuk

ApplyOnnxModel(TransformsCatalog, String, Nullable<Int32>, Boolean)

Buat OnnxScoringEstimator, yang menerapkan model Onnx yang telah dilatih sebelumnya ke kolom input. Kolom input/output ditentukan berdasarkan kolom input/output dari model ONNX yang disediakan. Silakan lihat OnnxScoringEstimator untuk mempelajari lebih lanjut tentang dependensi yang diperlukan, dan cara menjalankannya pada 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 OnnxScoringEstimator

Parameter

catalog: TransformsCatalog

Katalog transformasi.

modelFile: String

Jalur file yang berisi model ONNX.

gpuDeviceId: Nullable<Int32>

ID perangkat GPU opsional untuk menjalankan eksekusi, null untuk berjalan pada CPU.

fallbackToCpu: Boolean

Jika kesalahan GPU, ajukan pengecualian atau fallback ke CPU.

Mengembalikan

OnnxScoringEstimator

Contoh

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; }
        }
    }
}

Keterangan

Nama/jenis kolom input harus sama persis dengan nama/jenis input model ONNX. Nama/jenis kolom output yang dihasilkan akan cocok dengan nama/jenis output model ONNX. Jika nilai gpuDeviceId adalah nullMLContext.GpuDeviceId nilai akan digunakan jika bukan null.

Berlaku untuk

ApplyOnnxModel(TransformsCatalog, String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)

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 OnnxScoringEstimator

Parameter

catalog: TransformsCatalog

Katalog transformasi.

modelFile: String

Jalur file yang berisi model ONNX.

shapeDictionary: IDictionary<String,Int32[]>

Bentuk ONNX yang akan digunakan atas yang dimuat dari modelFile. Untuk kunci, gunakan nama seperti yang dinyatakan dalam model ONNX, misalnya "input". Menyatakan bentuk dengan parameter ini sangat berguna untuk bekerja dengan input dan output dimensi variabel.

gpuDeviceId: Nullable<Int32>

ID perangkat GPU opsional untuk menjalankan eksekusi, null untuk berjalan pada CPU.

fallbackToCpu: Boolean

Jika kesalahan GPU, ajukan pengecualian atau fallback ke CPU.

Mengembalikan

OnnxScoringEstimator

Contoh

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; }
        }
    }
}

Keterangan

Berlaku untuk

ApplyOnnxModel(TransformsCatalog, String, String, String, Nullable<Int32>, Boolean)

OnnxScoringEstimatorBuat , yang menerapkan model Onnx yang telah dilatih sebelumnya ke inputColumnName kolom. Silakan lihat OnnxScoringEstimator untuk mempelajari lebih lanjut tentang dependensi yang diperlukan, dan cara menjalankannya pada 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 OnnxScoringEstimator

Parameter

catalog: TransformsCatalog

Katalog transformasi.

outputColumnName: String

Kolom output yang dihasilkan dari transformasi.

inputColumnName: String

Kolom input.

modelFile: String

Jalur file yang berisi model ONNX.

gpuDeviceId: Nullable<Int32>

ID perangkat GPU opsional untuk menjalankan eksekusi, null untuk berjalan pada CPU.

fallbackToCpu: Boolean

Jika kesalahan GPU, ajukan pengecualian atau fallback ke CPU.

Mengembalikan

OnnxScoringEstimator

Contoh

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);
            }
        }
    }
}

Keterangan

Jika nilai gpuDeviceId adalah nullMLContext.GpuDeviceId nilai akan digunakan jika bukan null.

Berlaku untuk

ApplyOnnxModel(TransformsCatalog, String[], String[], String, Nullable<Int32>, Boolean)

Buat OnnxScoringEstimator, yang menerapkan model Onnx yang telah dilatih sebelumnya ke inputColumnNames kolom. Silakan lihat OnnxScoringEstimator untuk mempelajari lebih lanjut tentang dependensi yang diperlukan, dan cara menjalankannya pada 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 OnnxScoringEstimator

Parameter

catalog: TransformsCatalog

Katalog transformasi.

outputColumnNames: String[]

Kolom output yang dihasilkan dari transformasi.

inputColumnNames: String[]

Kolom input.

modelFile: String

Jalur file yang berisi model ONNX.

gpuDeviceId: Nullable<Int32>

ID perangkat GPU opsional untuk menjalankan eksekusi, null untuk berjalan pada CPU.

fallbackToCpu: Boolean

Jika kesalahan GPU, ajukan pengecualian atau fallback ke CPU.

Mengembalikan

OnnxScoringEstimator

Keterangan

Jika nilai gpuDeviceId adalah nullMLContext.GpuDeviceId nilai akan digunakan jika bukan null.

Berlaku untuk

ApplyOnnxModel(TransformsCatalog, String, String, String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)

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 OnnxScoringEstimator

Parameter

catalog: TransformsCatalog

Katalog transformasi.

outputColumnName: String

Kolom output yang dihasilkan dari transformasi.

inputColumnName: String

Kolom input.

modelFile: String

Jalur file yang berisi model ONNX.

shapeDictionary: IDictionary<String,Int32[]>

gpuDeviceId: Nullable<Int32>

ID perangkat GPU opsional untuk menjalankan eksekusi, null untuk berjalan pada CPU.

fallbackToCpu: Boolean

Jika kesalahan GPU, ajukan pengecualian atau fallback ke CPU.

Mengembalikan

OnnxScoringEstimator

Contoh

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);
            }
        }
    }
}

Keterangan

Jika nilai gpuDeviceId adalah nullMLContext.GpuDeviceId nilai akan digunakan jika bukan null.

Berlaku untuk

ApplyOnnxModel(TransformsCatalog, String[], String[], String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)

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 OnnxScoringEstimator

Parameter

catalog: TransformsCatalog

Katalog transformasi.

outputColumnNames: String[]

Kolom output yang dihasilkan dari transformasi.

inputColumnNames: String[]

Kolom input.

modelFile: String

Jalur file yang berisi model ONNX.

shapeDictionary: IDictionary<String,Int32[]>

Bentuk ONNX yang akan digunakan atas bentuk yang dimuat dari modelFile. Untuk kunci, gunakan nama seperti yang dinyatakan dalam model ONNX, misalnya "input". Menyatakan bentuk dengan parameter ini sangat berguna untuk bekerja dengan input dan output dimensi variabel.

gpuDeviceId: Nullable<Int32>

ID perangkat GPU opsional untuk menjalankan eksekusi, null untuk berjalan pada CPU.

fallbackToCpu: Boolean

Jika kesalahan GPU, ajukan pengecualian atau fallback ke CPU.

Mengembalikan

OnnxScoringEstimator

Keterangan

Jika nilai gpuDeviceId adalah nullMLContext.GpuDeviceId nilai akan digunakan jika bukan null.

Berlaku untuk

ApplyOnnxModel(TransformsCatalog, String[], String[], String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean, Int32)

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 OnnxScoringEstimator

Parameter

catalog: TransformsCatalog

Katalog transformasi.

outputColumnNames: String[]

Kolom output yang dihasilkan dari transformasi.

inputColumnNames: String[]

Kolom input.

modelFile: String

Jalur file yang berisi model ONNX.

shapeDictionary: IDictionary<String,Int32[]>

gpuDeviceId: Nullable<Int32>

ID perangkat GPU opsional untuk menjalankan eksekusi, null untuk berjalan pada CPU.

fallbackToCpu: Boolean

Jika kesalahan GPU, ajukan pengecualian atau fallback ke CPU.

recursionLimit: Int32

Opsional, menentukan batas rekursi Protobuf CodedInputStream. Nilai default adalah 100.

Mengembalikan

OnnxScoringEstimator

Keterangan

Jika nilai gpuDeviceId adalah nullMLContext.GpuDeviceId nilai akan digunakan jika bukan null.

Berlaku untuk

Bagikan melalui

OnnxCatalog.ApplyOnnxModel Metode

Definisi

Overload

ApplyOnnxModel(TransformsCatalog, OnnxOptions)

Parameter

Mengembalikan

Keterangan

Berlaku untuk

ApplyOnnxModel(TransformsCatalog, String, Nullable<Int32>, Boolean)

Parameter

Mengembalikan

Contoh

Keterangan

Berlaku untuk

ApplyOnnxModel(TransformsCatalog, String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)

Parameter

Mengembalikan

Contoh

Keterangan

Berlaku untuk

ApplyOnnxModel(TransformsCatalog, String, String, String, Nullable<Int32>, Boolean)

Parameter

Mengembalikan

Contoh

Keterangan

Berlaku untuk

ApplyOnnxModel(TransformsCatalog, String[], String[], String, Nullable<Int32>, Boolean)

Parameter

Mengembalikan

Keterangan

Berlaku untuk

ApplyOnnxModel(TransformsCatalog, String, String, String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)

Parameter

Mengembalikan

Contoh

Keterangan

Berlaku untuk

ApplyOnnxModel(TransformsCatalog, String[], String[], String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean)

Parameter

Mengembalikan

Keterangan

Berlaku untuk

ApplyOnnxModel(TransformsCatalog, String[], String[], String, IDictionary<String,Int32[]>, Nullable<Int32>, Boolean, Int32)

Parameter

Mengembalikan

Keterangan

Berlaku untuk

Sumber Daya Tambahan: