public void trainWithBaseAlgorithm(EdgeDetectionAlgorithm algorithm, EdgeDetectionAlgorithm baseAlgorithm, int resizeFactor)
{
DateTime trainingStart = DateTime.Now;
float totalLoss = 0;
List <String> fileList = new List <string>(benchmark.getTrainingFilesPathList());
int totalNumberOfFiles = numberOfTrainingSetPasses * fileList.Count;
int totalIndex = 0;
for (int pass = 0; pass < numberOfTrainingSetPasses; pass++)
{
ListUtils.Shuffle(fileList);
int index = 1;
float totalPassLoss = 0;
DateTime trainingPassStart = DateTime.Now;
foreach (string trainingFileName in fileList)
{
DateTime start = DateTime.Now;
Console.WriteLine("Pass: "******"/" + numberOfTrainingSetPasses + ", " + index + "/" + fileList.Count + " Training file: " + Path.GetFileName(trainingFileName));
ImageDescription inputImage = ImageFileHandler.loadFromPath(trainingFileName);
ImageDescription computedImage = baseAlgorithm.test(inputImage);
ResizeFilter resizeColor = new ResizeFilter(inputImage.sizeX / resizeFactor, inputImage.sizeY / resizeFactor, ImageDescriptionUtil.colorChannels);
ImageDescription newInputImage = resizeColor.filter(inputImage);
ImageDescription inputImageGroundTruth = ImageFileHandler.loadFromPath(benchmark.getTrainingFileGroundTruth(trainingFileName));
inputImageGroundTruth.computeGrayscale();
ResizeFilter resizeGrayscale = new ResizeFilter(inputImage.sizeX / resizeFactor, inputImage.sizeY / resizeFactor, ImageDescriptionUtil.grayscaleChannel);
ImageDescription newInputImageGroundTruth = resizeGrayscale.filter(inputImageGroundTruth);
ImageDescription resizedComputed = resizeGrayscale.filter(computedImage);
newInputImage.setColorChannel(ColorChannelEnum.Layer, resizedComputed.gray);
float loss = algorithm.train(newInputImage, newInputImageGroundTruth);
totalLoss += loss;
totalPassLoss += loss;
index++;
totalIndex++;
double timeElapsed = (DateTime.Now - start).TotalSeconds;
double timeElapsedSoFar = (DateTime.Now - trainingStart).TotalSeconds;
double estimatedTime = (timeElapsedSoFar / totalIndex) * (totalNumberOfFiles - totalIndex);
Console.WriteLine("Loss: " + loss.ToString("0.00") + " Time: " + timeElapsed.ToString("0.00") + "s Time elapsed: "
+ timeElapsedSoFar.ToString("0.00") + "s ETA: " + estimatedTime.ToString("0.00") + "s");
}
double tariningPassTimeElapsed = (DateTime.Now - trainingPassStart).TotalSeconds;
Console.WriteLine("Pass took " + tariningPassTimeElapsed.ToString("0.00") + " sec. Pass loss: " + totalPassLoss.ToString("0.00")
+ " Avg loss: " + (totalPassLoss / (fileList.Count)).ToString("0.00"));
}
double totalTimeElapsed = (DateTime.Now - trainingStart).TotalSeconds;
Console.WriteLine("Training took " + totalTimeElapsed.ToString("0.00") + " sec. Total loss: " + totalLoss.ToString("0.00")
+ " Avg loss: " + (totalLoss / (totalNumberOfFiles)).ToString("0.00"));
}
public static void makeAllImagesSameSize(List <ImageDescription> inputImages, out int newSizeX, out int newSizeY, out List <ImageDescription> outputImages)
{
newSizeX = 0;
newSizeY = 0;
foreach (ImageDescription image in inputImages)
{
if (image.sizeX > newSizeX)
{
newSizeX = image.sizeX;
}
if (image.sizeY > newSizeY)
{
newSizeY = image.sizeY;
}
}
outputImages = new List <ImageDescription>(inputImages.Count);
ResizeFilter rf = new ResizeFilter(newSizeX, newSizeY, new HashSet <ColorChannelEnum> {
ColorChannelEnum.Gray
});
foreach (ImageDescription image in inputImages)
{
if (image.sizeX != newSizeX || image.sizeY != newSizeY)
{
outputImages.Add(rf.filter(image));
}
else
{
outputImages.Add(image);
}
}
}
public List <ImageDescription> computeImageForLayers(ImageDescription inputImage, int numberOfLayersToCompute)
{
List <ImageDescription> computedImages = new List <ImageDescription>(numberOfLayersToCompute);
for (int i = 0; i < numberOfLayersToCompute; i++)
{
EdgeDetectionAlgorithm algorithm = layers[i].algorithm;
int layerResizeFactor = layers[i].resizeFactor;
ImageDescription newInputImage = null;
ResizeFilter resizeGrayscale = new ResizeFilter(inputImage.sizeX / layerResizeFactor, inputImage.sizeY / layerResizeFactor, ImageDescriptionUtil.grayscaleChannel);
ResizeFilter resizeColor = new ResizeFilter(inputImage.sizeX / layerResizeFactor, inputImage.sizeY / layerResizeFactor, ImageDescriptionUtil.colorChannels);
if (layerResizeFactor == 1)
{
newInputImage = inputImage;
}
else
{
newInputImage = resizeColor.filter(inputImage);
}
if (i > 0)
{
ImageDescription resizedComputed = resizeGrayscale.filter(computedImages[i - 1]);
newInputImage.setColorChannel(ColorChannelEnum.Layer, resizedComputed.gray);
}
ImageDescription layerOutputImage = algorithm.test(newInputImage);
computedImages.Add(layerOutputImage);
}
return(computedImages);
}
public void trainNestedAlgorithm(ContextualMemoryNestedAlgorithm nestedAlgorithm)
{
BlackAndWhiteConverter blackAndWhiteConverter = new BlackAndWhiteConverter(1);
//BlackAndWhiteConverter blackAndWhiteConverter = new BlackAndWhiteConverter(63);
List <String> fileList = new List <string>(benchmark.getTrainingFilesPathList());
List <ContextualMemoryNestedAlgorithmLayer> layers = nestedAlgorithm.getLayers();
for (int layerIndex = 0; layerIndex < layers.Count; layerIndex++)
{
ContextualMemoryNestedAlgorithmLayer layer = layers[layerIndex];
layer.initialize();
Console.WriteLine("Layer: " + (layerIndex + 1) + "/" + layers.Count);
EdgeDetectionAlgorithm algorithm = layer.algorithm;
DateTime trainingStart = DateTime.Now;
float totalLoss = 0;
int totalNumberOfFiles = numberOfTrainingSetPasses * fileList.Count;
int totalIndex = 0;
for (int pass = 0; pass < numberOfTrainingSetPasses; pass++)
{
ListUtils.Shuffle(fileList);
int index = 1;
float totalPassLoss = 0;
DateTime trainingPassStart = DateTime.Now;
foreach (string trainingFileName in fileList)
{
DateTime start = DateTime.Now;
Console.WriteLine("Pass: "******"/" + numberOfTrainingSetPasses + ", " + index + "/" + fileList.Count + " Training file: " + Path.GetFileName(trainingFileName));
ImageDescription inputImage = ImageFileHandler.loadFromPath(trainingFileName);
int layerResizeFactor = layer.resizeFactor;
ImageDescription computedImage = null;
if (layerIndex > 0)
{
List <ImageDescription> computedImages = nestedAlgorithm.computeImageForLayers(inputImage, layerIndex);
computedImage = computedImages[layerIndex - 1];
}
ImageDescription inputImageGroundTruth = ImageFileHandler.loadFromPath(benchmark.getTrainingFileGroundTruth(trainingFileName));
inputImageGroundTruth = blackAndWhiteConverter.filter(inputImageGroundTruth);
ImageDescription newInputImage = null;
ImageDescription newInputImageGroundTruth = null;
ResizeFilter resizeGrayscale = new ResizeFilter(inputImage.sizeX / layerResizeFactor, inputImage.sizeY / layerResizeFactor, ImageDescriptionUtil.grayscaleChannel);
ResizeFilter resizeColor = new ResizeFilter(inputImage.sizeX / layerResizeFactor, inputImage.sizeY / layerResizeFactor, ImageDescriptionUtil.colorChannels);
if (layerResizeFactor == 1)
{
newInputImage = inputImage;
newInputImageGroundTruth = inputImageGroundTruth;
}
else
{
newInputImage = resizeColor.filter(inputImage);
newInputImageGroundTruth = resizeGrayscale.filter(inputImageGroundTruth);
}
if (layerIndex > 0)
{
ImageDescription resizedComputed = resizeGrayscale.filter(computedImage);
newInputImage.setColorChannel(ColorChannelEnum.Layer, resizedComputed.gray);
}
float loss = algorithm.train(newInputImage, newInputImageGroundTruth);
totalLoss += loss;
totalPassLoss += loss;
index++;
totalIndex++;
double timeElapsed = (DateTime.Now - start).TotalSeconds;
double timeElapsedSoFar = (DateTime.Now - trainingStart).TotalSeconds;
double estimatedTime = (timeElapsedSoFar / totalIndex) * (totalNumberOfFiles - totalIndex);
Console.WriteLine("Loss: " + loss.ToString("0.00") + " Time: " + timeElapsed.ToString("0.00") + "s Time elapsed: "
+ timeElapsedSoFar.ToString("0.00") + "s ETA: " + estimatedTime.ToString("0.00") + "s");
}
double tariningPassTimeElapsed = (DateTime.Now - trainingPassStart).TotalSeconds;
Console.WriteLine("Pass took " + tariningPassTimeElapsed.ToString("0.00") + " sec. Pass loss: " + totalPassLoss.ToString("0.00")
+ " Avg loss: " + (totalPassLoss / (fileList.Count)).ToString("0.00"));
}
double totalTimeElapsed = (DateTime.Now - trainingStart).TotalSeconds;
Console.WriteLine("Training took " + totalTimeElapsed.ToString("0.00") + " sec. Total loss: " + totalLoss.ToString("0.00")
+ " Avg loss: " + (totalLoss / (totalNumberOfFiles)).ToString("0.00"));
}
Console.WriteLine("Training blender");
DateTime blenderTrainingStart = DateTime.Now;
float blenderTotalLoss = 0;
int blenderTotalNumberOfFiles = /* numberOfTrainingSetPasses * */ fileList.Count;
int blenderTotalIndex = 0;
ImageBlender blender = nestedAlgorithm.getImageBlender();
//for (int pass = 0; pass < numberOfTrainingSetPasses; pass++)
{
ListUtils.Shuffle(fileList);
int index = 1;
float totalPassLoss = 0;
DateTime trainingPassStart = DateTime.Now;
foreach (string trainingFileName in fileList)
{
DateTime start = DateTime.Now;
//Console.Write("Pass: "******"/" + numberOfTrainingSetPasses + ", ");
Console.WriteLine(index + "/" + fileList.Count + " Training file: " + Path.GetFileName(trainingFileName));
ImageDescription inputImage = ImageFileHandler.loadFromPath(trainingFileName);
List <ImageDescription> computedImages = nestedAlgorithm.computeImageForLayers(inputImage, layers.Count);
ImageDescription inputImageGroundTruth = ImageFileHandler.loadFromPath(benchmark.getTrainingFileGroundTruth(trainingFileName));
inputImageGroundTruth = blackAndWhiteConverter.filter(inputImageGroundTruth);
float blenderLoss = blender.train(computedImages, inputImageGroundTruth);
blenderTotalLoss += blenderLoss;
totalPassLoss += blenderLoss;
index++;
blenderTotalIndex++;
double timeElapsed = (DateTime.Now - start).TotalSeconds;
double timeElapsedSoFar = (DateTime.Now - blenderTrainingStart).TotalSeconds;
double estimatedTime = (timeElapsedSoFar / blenderTotalIndex) * (blenderTotalNumberOfFiles - blenderTotalIndex);
Console.WriteLine("Loss: " + blenderLoss.ToString("0.00") + " Time: " + timeElapsed.ToString("0.00") + "s Time elapsed: "
+ timeElapsedSoFar.ToString("0.00") + "s ETA: " + estimatedTime.ToString("0.00") + "s");
}
//double tariningPassTimeElapsed = (DateTime.Now - trainingPassStart).TotalSeconds;
//Console.WriteLine("Pass took " + tariningPassTimeElapsed.ToString("0.00") + " sec. Pass loss: " + totalPassLoss.ToString("0.00")
//+ " Avg loss: " + (totalPassLoss / (fileList.Count)).ToString("0.00"));
}
double blenderTotalTimeElapsed = (DateTime.Now - blenderTrainingStart).TotalSeconds;
Console.WriteLine("Training took " + blenderTotalTimeElapsed.ToString("0.00") + " sec. Total loss: " + blenderTotalLoss.ToString("0.00")
+ " Avg loss: " + (blenderTotalLoss / (blenderTotalNumberOfFiles)).ToString("0.00"));
}