private PredictionEngine <ImageNetData, ImageNetPrediction> LoadModel(string dataLocation, string imagesFolder, string modelLocation)
{
ConsoleHelpers.ConsoleWriteHeader("Read model");
Console.WriteLine($"Model location: {modelLocation}");
Console.WriteLine($"Images folder: {imagesFolder}");
Console.WriteLine($"Training file: {dataLocation}");
Console.WriteLine($"Default parameters: image size=({ImageNetSettings.imageWidth},{ImageNetSettings.imageHeight}), image mean: {ImageNetSettings.mean}");
var data = mlContext.Data.LoadFromTextFile <ImageNetData>(dataLocation, hasHeader: true);
var pipeline = mlContext.Transforms.LoadImages(outputColumnName: "input", imageFolder: imagesFolder, inputColumnName: nameof(ImageNetData.ImagePath))
.Append(mlContext.Transforms.ResizeImages(outputColumnName: "input", imageWidth: ImageNetSettings.imageWidth, imageHeight: ImageNetSettings.imageHeight, inputColumnName: "input"))
.Append(mlContext.Transforms.ExtractPixels(outputColumnName: "input", interleavePixelColors: ImageNetSettings.channelsLast, offsetImage: ImageNetSettings.mean))
.Append(mlContext.Model.LoadTensorFlowModel(modelLocation).
ScoreTensorFlowModel(outputColumnNames: new[] { "softmax2" },
inputColumnNames: new[] { "input" }, addBatchDimensionInput: true));
ITransformer model = pipeline.Fit(data);
var predictionEngine = mlContext.Model.CreatePredictionEngine <ImageNetData, ImageNetPrediction>(model);
return(predictionEngine);
}
