private PredictionFunction <ImageNetData, ImageNetPrediction> LoadModel(string dataLocation, string imagesFolder, string modelLocation)
{
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 loader = new TextLoader(mlContext,
new TextLoader.Arguments
{
Column = new[] {
new TextLoader.Column("ImagePath", DataKind.Text, 0),
}
});
var data = loader.Read(new MultiFileSource(dataLocation));
var pipeline = ImageEstimatorsCatalog.LoadImages(catalog: mlContext.Transforms, imageFolder: imagesFolder, columns: ("ImagePath", "ImageReal"))
.Append(ImageEstimatorsCatalog.Resize(mlContext.Transforms, "ImageReal", "ImageReal", ImageNetSettings.imageHeight, ImageNetSettings.imageWidth))
.Append(ImageEstimatorsCatalog.ExtractPixels(mlContext.Transforms, new[] { new ImagePixelExtractorTransform.ColumnInfo("ImageReal", "input", interleave: ImageNetSettings.channelsLast, offset: ImageNetSettings.mean) }))
.Append(new TensorFlowEstimator(mlContext, modelLocation, new[] { "input" }, new[] { "softmax2" }));
var modeld = pipeline.Fit(data);
var predictionFunction = modeld.MakePredictionFunction <ImageNetData, ImageNetPrediction>(mlContext);
return(predictionFunction);
}
private PredictionFunction <ImageInputData, ImageNetPrediction> CreatePredictionFunction()
{
try
{
var pipeline = ImageEstimatorsCatalog.LoadImages(catalog: _mlContext.Transforms, imageFolder: _imagesFolder, columns: ("ImagePath", "ImageReal"))
.Append(ImageEstimatorsCatalog.Resize(_mlContext.Transforms, "ImageReal", "ImageReal", ImageNetSettings.imageHeight, ImageNetSettings.imageWidth))
.Append(ImageEstimatorsCatalog.ExtractPixels(_mlContext.Transforms, new[] { new ImagePixelExtractorTransform.ColumnInfo("ImageReal", InceptionSettings.InputTensorName, interleave: ImageNetSettings.channelsLast, offset: ImageNetSettings.mean) }))
.Append(new TensorFlowEstimator(_mlContext, _modelLocation, new[] { InceptionSettings.InputTensorName }, new[] { InceptionSettings.OutputTensorName }));
var model = pipeline.Fit(CreateDataView());
var predictionFunction = model.MakePredictionFunction <ImageInputData, ImageNetPrediction>(_mlContext);
return(predictionFunction);
}
catch (Exception e)
{
throw e;
}
}
private PredictionFunction <ImageInputData, ImageNetPrediction> CreatePredictionFunction(string dataLocation, string imagesFolder, string modelLocation)
{
ConsoleWriteHeader("Read model");
Console.WriteLine($"Model location: {modelLocation}");
Console.WriteLine($"Images folder: {imagesFolder}");
Console.WriteLine($"Default parameters: image size=({ImageNetSettings.imageWidth},{ImageNetSettings.imageHeight}), image mean: {ImageNetSettings.mean}");
//Define pieplie for image tansformations
var pipeline = ImageEstimatorsCatalog.LoadImages(catalog: mlContext.Transforms, imageFolder: imagesFolder, columns: ("ImagePath", "ImageReal"))
.Append(ImageEstimatorsCatalog.Resize(mlContext.Transforms, "ImageReal", "ImageReal", ImageNetSettings.imageHeight, ImageNetSettings.imageWidth))
.Append(ImageEstimatorsCatalog.ExtractPixels(mlContext.Transforms, new[] { new ImagePixelExtractorTransform.ColumnInfo("ImageReal", "Placeholder", interleave: ImageNetSettings.channelsLast, offset: ImageNetSettings.mean) }))
.Append(new TensorFlowEstimator(mlContext, modelLocation, new[] { "Placeholder" }, new[] { "loss" }));
var model = pipeline.Fit(CreateDataView());
var predictionFunction = model.MakePredictionFunction <ImageInputData, ImageNetPrediction>(mlContext);
return(predictionFunction);
}
