图像分类-从Bitmap对象加载。Microsoft.ML

Question

我使用Visual图形助手创建了模型。工作室为我生成了一个项目和代码。一切都适合我，除了输入时，我必须以字符串的形式给出文件的路径。我能用位图格式将图像传送到神经网络吗？

我发现了许多例子，但它们都与我的代码不同，就像我有一个不同的版本。我正在尝试修改我找到的代码，但我遇到了各种错误。

请解释一下现在如何用Microsoft.ML 1.5来完成这个任务？如何调整下面生成的代码以使用位图图像(而不是路径输入)？

我的ModelInput.cs

// This file was auto-generated by ML.NET Model Builder. 

using Microsoft.ML.Data;

namespace MLTestAppML.Model
{
    public class ModelInput
    {
        [ColumnName("Label"), LoadColumn(0)]
        public string Label { get; set; }


        [ColumnName("ImageSource"), LoadColumn(1)]
        public string ImageSource { get; set; }


    }
}

我的ModelBuilder.cs

// This file was auto-generated by ML.NET Model Builder. 

using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using MLTestAppML.Model;
using Microsoft.ML.Vision;

namespace MLTestAppML.ConsoleApp
{
    public static class ModelBuilder
    {
        private static string TRAIN_DATA_FILEPATH = @"C:\Users\aaa\AppData\Local\Temp\e43005d1-d83d-4f35-ab8d-7dbc3e693583.tsv";
        private static string MODEL_FILEPATH = @"C:\Users\aaa\AppData\Local\Temp\MLVSTools\MLTestAppML\MLTestAppML.Model\MLModel.zip";
        // Create MLContext to be shared across the model creation workflow objects 
        // Set a random seed for repeatable/deterministic results across multiple trainings.
        private static MLContext mlContext = new MLContext(seed: 1);

        public static void CreateModel()
        {
            // Load Data
            IDataView trainingDataView = mlContext.Data.LoadFromTextFile<ModelInput>(
                                            path: TRAIN_DATA_FILEPATH,
                                            hasHeader: true,
                                            separatorChar: '\t',
                                            allowQuoting: true,
                                            allowSparse: false);

            // Build training pipeline
            IEstimator<ITransformer> trainingPipeline = BuildTrainingPipeline(mlContext);

            // Train Model
            ITransformer mlModel = TrainModel(mlContext, trainingDataView, trainingPipeline);

            // Evaluate quality of Model
            Evaluate(mlContext, trainingDataView, trainingPipeline);

            // Save model
            SaveModel(mlContext, mlModel, MODEL_FILEPATH, trainingDataView.Schema);
        }

        public static IEstimator<ITransformer> BuildTrainingPipeline(MLContext mlContext)
        {
            // Data process configuration with pipeline data transformations 
            var dataProcessPipeline = mlContext.Transforms.Conversion.MapValueToKey("Label", "Label")
                                      .Append(mlContext.Transforms.LoadRawImageBytes("ImageSource_featurized", null, "ImageSource"))
                                      .Append(mlContext.Transforms.CopyColumns("Features", "ImageSource_featurized"));
            // Set the training algorithm 
            var trainer = mlContext.MulticlassClassification.Trainers.ImageClassification(new ImageClassificationTrainer.Options() { LabelColumnName = "Label", FeatureColumnName = "Features" })
                                      .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel", "PredictedLabel"));

            var trainingPipeline = dataProcessPipeline.Append(trainer);

            return trainingPipeline;
        }

        public static ITransformer TrainModel(MLContext mlContext, IDataView trainingDataView, IEstimator<ITransformer> trainingPipeline)
        {
            Console.WriteLine("=============== Training  model ===============");

            ITransformer model = trainingPipeline.Fit(trainingDataView);

            Console.WriteLine("=============== End of training process ===============");
            return model;
        }

        private static void Evaluate(MLContext mlContext, IDataView trainingDataView, IEstimator<ITransformer> trainingPipeline)
        {
            // Cross-Validate with single dataset (since we don't have two datasets, one for training and for evaluate)
            // in order to evaluate and get the model's accuracy metrics
            Console.WriteLine("=============== Cross-validating to get model's accuracy metrics ===============");
            var crossValidationResults = mlContext.MulticlassClassification.CrossValidate(trainingDataView, trainingPipeline, numberOfFolds: 5, labelColumnName: "Label");
            PrintMulticlassClassificationFoldsAverageMetrics(crossValidationResults);
        }

        private static void SaveModel(MLContext mlContext, ITransformer mlModel, string modelRelativePath, DataViewSchema modelInputSchema)
        {
            // Save/persist the trained model to a .ZIP file
            Console.WriteLine($"=============== Saving the model  ===============");
            mlContext.Model.Save(mlModel, modelInputSchema, GetAbsolutePath(modelRelativePath));
            Console.WriteLine("The model is saved to {0}", GetAbsolutePath(modelRelativePath));
        }

        public static string GetAbsolutePath(string relativePath)
        {
            FileInfo _dataRoot = new FileInfo(typeof(Program).Assembly.Location);
            string assemblyFolderPath = _dataRoot.Directory.FullName;

            string fullPath = Path.Combine(assemblyFolderPath, relativePath);

            return fullPath;
        }

        public static void PrintMulticlassClassificationMetrics(MulticlassClassificationMetrics metrics)
        {
            Console.WriteLine($"************************************************************");
            Console.WriteLine($"*    Metrics for multi-class classification model   ");
            Console.WriteLine($"*-----------------------------------------------------------");
            Console.WriteLine($"    MacroAccuracy = {metrics.MacroAccuracy:0.####}, a value between 0 and 1, the closer to 1, the better");
            Console.WriteLine($"    MicroAccuracy = {metrics.MicroAccuracy:0.####}, a value between 0 and 1, the closer to 1, the better");
            Console.WriteLine($"    LogLoss = {metrics.LogLoss:0.####}, the closer to 0, the better");
            for (int i = 0; i < metrics.PerClassLogLoss.Count; i++)
            {
                Console.WriteLine($"    LogLoss for class {i + 1} = {metrics.PerClassLogLoss[i]:0.####}, the closer to 0, the better");
            }
            Console.WriteLine($"************************************************************");
        }

        public static void PrintMulticlassClassificationFoldsAverageMetrics(IEnumerable<TrainCatalogBase.CrossValidationResult<MulticlassClassificationMetrics>> crossValResults)
        {
            var metricsInMultipleFolds = crossValResults.Select(r => r.Metrics);

            var microAccuracyValues = metricsInMultipleFolds.Select(m => m.MicroAccuracy);
            var microAccuracyAverage = microAccuracyValues.Average();
            var microAccuraciesStdDeviation = CalculateStandardDeviation(microAccuracyValues);
            var microAccuraciesConfidenceInterval95 = CalculateConfidenceInterval95(microAccuracyValues);

            var macroAccuracyValues = metricsInMultipleFolds.Select(m => m.MacroAccuracy);
            var macroAccuracyAverage = macroAccuracyValues.Average();
            var macroAccuraciesStdDeviation = CalculateStandardDeviation(macroAccuracyValues);
            var macroAccuraciesConfidenceInterval95 = CalculateConfidenceInterval95(macroAccuracyValues);

            var logLossValues = metricsInMultipleFolds.Select(m => m.LogLoss);
            var logLossAverage = logLossValues.Average();
            var logLossStdDeviation = CalculateStandardDeviation(logLossValues);
            var logLossConfidenceInterval95 = CalculateConfidenceInterval95(logLossValues);

            var logLossReductionValues = metricsInMultipleFolds.Select(m => m.LogLossReduction);
            var logLossReductionAverage = logLossReductionValues.Average();
            var logLossReductionStdDeviation = CalculateStandardDeviation(logLossReductionValues);
            var logLossReductionConfidenceInterval95 = CalculateConfidenceInterval95(logLossReductionValues);

            Console.WriteLine($"*************************************************************************************************************");
            Console.WriteLine($"*       Metrics for Multi-class Classification model      ");
            Console.WriteLine($"*------------------------------------------------------------------------------------------------------------");
            Console.WriteLine($"*       Average MicroAccuracy:    {microAccuracyAverage:0.###}  - Standard deviation: ({microAccuraciesStdDeviation:#.###})  - Confidence Interval 95%: ({microAccuraciesConfidenceInterval95:#.###})");
            Console.WriteLine($"*       Average MacroAccuracy:    {macroAccuracyAverage:0.###}  - Standard deviation: ({macroAccuraciesStdDeviation:#.###})  - Confidence Interval 95%: ({macroAccuraciesConfidenceInterval95:#.###})");
            Console.WriteLine($"*       Average LogLoss:          {logLossAverage:#.###}  - Standard deviation: ({logLossStdDeviation:#.###})  - Confidence Interval 95%: ({logLossConfidenceInterval95:#.###})");
            Console.WriteLine($"*       Average LogLossReduction: {logLossReductionAverage:#.###}  - Standard deviation: ({logLossReductionStdDeviation:#.###})  - Confidence Interval 95%: ({logLossReductionConfidenceInterval95:#.###})");
            Console.WriteLine($"*************************************************************************************************************");

        }

        public static double CalculateStandardDeviation(IEnumerable<double> values)
        {
            double average = values.Average();
            double sumOfSquaresOfDifferences = values.Select(val => (val - average) * (val - average)).Sum();
            double standardDeviation = Math.Sqrt(sumOfSquaresOfDifferences / (values.Count() - 1));
            return standardDeviation;
        }

        public static double CalculateConfidenceInterval95(IEnumerable<double> values)
        {
            double confidenceInterval95 = 1.96 * CalculateStandardDeviation(values) / Math.Sqrt((values.Count() - 1));
            return confidenceInterval95;
        }
    }
}

Answer 1

更新

在较新版本的ModelBuilder上，它允许从byte[]中进行预测，确保您有16.13.10.2241902版本。

你应该重新训练你的模型，然后你就可以沿着这条路跑了。

​

//Load sample data
var imageBytes = File.ReadAllBytes(@"C:\Users\ivmendoza\Documents\MIL CoPilot Workspaces\AlignersInBag\2 Condtions DS Tiny\NonTypical\MX2-RND40K_20220725020628_PIDV_12443583U22N_12443583L22N.JPG");
AlignersCounterModel.ModelInput sampleData = new AlignersCounterModel.ModelInput()
{
    ImageSource = imageBytes,
};

//Load model and predict output
var result = AlignersCounterModel.Predict(sampleData);

​

输入/输出类

​

public class ModelInput
        {
            [ColumnName(@"Label")]
            public string Label { get; set; }

            [ColumnName(@"ImageSource")]
            public string ImageSource { get; set; }

        }

        public class ModelInputBytes
        {
            [ColumnName(@"Label")]
            public string Label { get; set; }

            [ColumnName(@"Features")]
            public byte[] ImageBytes { get; set; }

        }

        public class ModelOutput
        {
            [ColumnName("PredictedLabel")]
            public string Prediction { get; set; }

            public float[] Score { get; set; }
        }

​

消费代码

​

public static ModelOutput Predict(ModelInput input)
        {
            MLContext mlContext = new MLContext();

            // Load model & create prediction engine
            ITransformer mlModel = mlContext.Model.Load(MLNetModelPath, out var modelInputSchema);

            ITransformer dataPreProcessTransform = LoadImageFromFileTransformer(input, mlContext);

            var predEngine = mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(dataPreProcessTransform.Append(mlModel));

            ModelOutput result = predEngine.Predict(input);
            return result;
        }

        public static ITransformer LoadImageFromFileTransformer(ModelInput input, MLContext mlContext)
        {
            var dataPreProcess = mlContext.Transforms.Conversion.MapValueToKey(@"Label", @"Label")
                                    .Append(mlContext.Transforms.LoadRawImageBytes(@"ImageSource_featurized", @"ImageSource"))
                                    .Append(mlContext.Transforms.CopyColumns(@"Features", @"ImageSource_featurized"));

            var dataView = mlContext.Data.LoadFromEnumerable(new[] { input });
            var dataPreProcessTransform = dataPreProcess.Fit(dataView);
            return dataPreProcessTransform;
        }

        public static ModelOutput PredictFromBytes(ModelInputBytes input)
        {
            MLContext mlContext = new MLContext();

            // Load model & create prediction engine
            ITransformer mlModel = mlContext.Model.Load(MLNetModelPath, out var modelInputSchema);

            var predEngine = mlContext.Model.CreatePredictionEngine<ModelInputBytes, ModelOutput>(mlModel);

            ModelOutput result = predEngine.Predict(input);
            return result;
        }

​