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 void train(EdgeDetectionAlgorithm algorithm) { BlackAndWhiteConverter blackAndWhiteConverter = new BlackAndWhiteConverter(1); 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 inputImageGroundTruth = ImageFileHandler.loadFromPath(benchmark.getTrainingFileGroundTruth(trainingFileName)); inputImageGroundTruth.computeGrayscale(); inputImageGroundTruth = blackAndWhiteConverter.filter(inputImageGroundTruth); float loss = algorithm.train(inputImage, inputImageGroundTruth); 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 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")); }