OnnxCatalog.DnnFeaturizeImage Méthode

Définition

Espace de noms:

Microsoft.ML

Assembly:

Microsoft.ML.OnnxTransformer.dll

Paquet:

Microsoft.ML.OnnxTransformer v2.0.0

Créez DnnImageFeaturizerEstimator, qui applique l’un des modèles DNN préentraînés pour DnnImageModelSelector caractériser une image.

public static Microsoft.ML.Transforms.Onnx.DnnImageFeaturizerEstimator DnnFeaturizeImage (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, Func<Microsoft.ML.Transforms.Onnx.DnnImageFeaturizerInput,Microsoft.ML.Data.EstimatorChain<Microsoft.ML.Transforms.ColumnCopyingTransformer>> modelFactory, string inputColumnName = default);

static member DnnFeaturizeImage : Microsoft.ML.TransformsCatalog * string * Func<Microsoft.ML.Transforms.Onnx.DnnImageFeaturizerInput, Microsoft.ML.Data.EstimatorChain<Microsoft.ML.Transforms.ColumnCopyingTransformer>> * string -> Microsoft.ML.Transforms.Onnx.DnnImageFeaturizerEstimator

<Extension()>
Public Function DnnFeaturizeImage (catalog As TransformsCatalog, outputColumnName As String, modelFactory As Func(Of DnnImageFeaturizerInput, EstimatorChain(Of ColumnCopyingTransformer)), Optional inputColumnName As String = Nothing) As DnnImageFeaturizerEstimator

Paramètres

catalog

TransformsCatalog

Catalogue de la transformation.

outputColumnName

String

Nom de la colonne résultant de la transformation de inputColumnName.

modelFactory

Func<DnnImageFeaturizerInput,EstimatorChain<ColumnCopyingTransformer>>

Méthode d’extension sur la DnnImageModelSelector chaîne qui crée une chaîne de deux OnnxScoringEstimator (une pour le prétraitement et une avec un DNN d’image préentraînée) avec des modèles spécifiques inclus dans un package avec cette méthode d’extension.

inputColumnName

String

Nom de la colonne à transformer. Si la valeur est définie null, la valeur du outputColumnName fichier sera utilisée comme source.

Retours

DnnImageFeaturizerEstimator

Exemples

using System.IO;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
    public static class DnnFeaturizeImage
    {
        public static void Example()
        {
            // Create a new ML context, for ML.NET operations. It can be used for
            // exception tracking and logging, as well as the source of randomness.
            var mlContext = new MLContext();

            // Downloading a few images, and an images.tsv file, which contains a
            // list of the files from the dotnet/machinelearning/test/data/images/.
            // If you inspect the fileSystem, after running this line, an "images"
            // folder will be created, containing 4 images, and a .tsv file
            // enumerating the images. 
            var imagesDataFile = Microsoft.ML.SamplesUtils.DatasetUtils
                .GetSampleImages();

            // Preview of the content of the images.tsv file, which lists the images
            // to operate on
            //
            // imagePath    imageType
            // tomato.bmp   tomato
            // banana.jpg   banana
            // hotdog.jpg   hotdog
            // tomato.jpg   tomato

            var data = mlContext.Data.CreateTextLoader(new TextLoader.Options()
            {
                Columns = new[]
                {
                        new TextLoader.Column("ImagePath", DataKind.String, 0),
                        new TextLoader.Column("Name", DataKind.String, 1),
                }
            }).Load(imagesDataFile);

            var imagesFolder = Path.GetDirectoryName(imagesDataFile);

            // Installing the Microsoft.ML.DNNImageFeaturizer packages copies the models in the
            // `DnnImageModels` folder. 
            // Image loading pipeline. 
            var pipeline = mlContext.Transforms.LoadImages("ImageObject",
                imagesFolder, "ImagePath")
                .Append(mlContext.Transforms.ResizeImages("ImageObject", imageWidth:
                    224, imageHeight: 224))
                .Append(mlContext.Transforms.ExtractPixels("Pixels", "ImageObject"))
                .Append(mlContext.Transforms.DnnFeaturizeImage("FeaturizedImage",
                    m => m.ModelSelector.ResNet18(mlContext, m.OutputColumn, m
                    .InputColumn), "Pixels"));

            var transformedData = pipeline.Fit(data).Transform(data);

            var FeaturizedImageColumnsPerRow = transformedData.GetColumn<float[]>(
                "FeaturizedImage").ToArray();

            // Preview of FeaturizedImageColumnsPerRow for the first row,
            // FeaturizedImageColumnsPerRow[0]
            //
            // 0.696136236
            // 0.2661711
            // 0.440882325
            // 0.157903448
            // 0.0339231342
            // 0
            // 0.0936501548
            // 0.159010679
            // 0.394427955

        }
    }
}